LICENSE
The model is licensed with a
CreativeML Open RAIL-M license. The authors claim no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in this license. The license forbids you from sharing any content that violates any laws, produce any harm to a person, disseminate any personal information that would be meant for harm, spread misinformation and target vulnerable groups. For the full list of restrictions please
read the license
"""
)
image_blocks.launch(share=True, max_threads=1).queue()
if __name__ == "__main__":
main()
================================================
FILE: models/InstructDiffusion/edit_cli.py
================================================
# --------------------------------------------------------
# InstructDiffusion
# Based on instruct-pix2pix (https://github.com/timothybrooks/instruct-pix2pix)
# Modified by Zigang Geng (zigang@mail.ustc.edu.cn)
# --------------------------------------------------------
from __future__ import annotations
import os
import math
import random
import sys
from argparse import ArgumentParser
import einops
import k_diffusion as K
import numpy as np
import torch
import torch.nn as nn
from einops import rearrange
from omegaconf import OmegaConf
from PIL import Image, ImageOps
from torch import autocast
import requests
from stable_diffusion.ldm.util import instantiate_from_config
class CFGDenoiser(nn.Module):
def __init__(self, model):
super().__init__()
self.inner_model = model
def forward(self, z, sigma, cond, uncond, text_cfg_scale, image_cfg_scale):
cfg_z = einops.repeat(z, "b ... -> (repeat b) ...", repeat=3)
cfg_sigma = einops.repeat(sigma, "b ... -> (repeat b) ...", repeat=3)
cfg_cond = {
"c_crossattn": [torch.cat([cond["c_crossattn"][0], uncond["c_crossattn"][0], cond["c_crossattn"][0]])],
"c_concat": [torch.cat([cond["c_concat"][0], cond["c_concat"][0], uncond["c_concat"][0]])],
}
out_cond, out_img_cond, out_txt_cond \
= self.inner_model(cfg_z, cfg_sigma, cond=cfg_cond).chunk(3)
return 0.5 * (out_img_cond + out_txt_cond) + \
text_cfg_scale * (out_cond - out_img_cond) + \
image_cfg_scale * (out_cond - out_txt_cond)
def load_model_from_config(config, ckpt, vae_ckpt=None, verbose=False):
model = instantiate_from_config(config.model)
print(f"Loading model from {ckpt}")
pl_sd = torch.load(ckpt, map_location="cpu")
if 'state_dict' in pl_sd:
pl_sd = pl_sd['state_dict']
m, u = model.load_state_dict(pl_sd, strict=False)
print(m, u)
return model
def main():
parser = ArgumentParser()
parser.add_argument("--resolution", default=512, type=int)
parser.add_argument("--steps", default=100, type=int)
parser.add_argument("--config", default="configs/instruct_diffusion.yaml", type=str)
parser.add_argument("--ckpt", default="checkpoints/v1-5-pruned-emaonly-adaption-task.ckpt", type=str)
parser.add_argument("--vae-ckpt", default=None, type=str)
parser.add_argument("--input", required=True, type=str)
parser.add_argument("--outdir", default="logs", type=str)
parser.add_argument("--edit", required=True, type=str)
parser.add_argument("--cfg-text", default=5.0, type=float)
parser.add_argument("--cfg-image", default=1.25, type=float)
parser.add_argument("--seed", type=int)
args = parser.parse_args()
config = OmegaConf.load(args.config)
model = load_model_from_config(config, args.ckpt, args.vae_ckpt)
model.eval().cuda()
model_wrap = K.external.CompVisDenoiser(model)
model_wrap_cfg = CFGDenoiser(model_wrap)
null_token = model.get_learned_conditioning([""])
seed = random.randint(0, 100000) if args.seed is None else args.seed
if args.input.startswith("http"):
input_image = Image.open(requests.get(args.input, stream=True).raw).convert("RGB")
else:
input_image = Image.open(args.input).convert("RGB")
width, height = input_image.size
factor = args.resolution / max(width, height)
factor = math.ceil(min(width, height) * factor / 64) * 64 / min(width, height)
width_resize = int((width * factor) // 64) * 64
height_resize = int((height * factor) // 64) * 64
input_image = ImageOps.fit(input_image, (width_resize, height_resize), method=Image.Resampling.LANCZOS)
output_dir = args.outdir
os.makedirs(output_dir, exist_ok=True)
with torch.no_grad(), autocast("cuda"):
cond = {}
cond["c_crossattn"] = [model.get_learned_conditioning([args.edit])]
input_image = 2 * torch.tensor(np.array(input_image)).float() / 255 - 1
input_image = rearrange(input_image, "h w c -> 1 c h w").to(next(model.parameters()).device)
cond["c_concat"] = [model.encode_first_stage(input_image).mode()]
uncond = {}
uncond["c_crossattn"] = [null_token]
uncond["c_concat"] = [torch.zeros_like(cond["c_concat"][0])]
sigmas = model_wrap.get_sigmas(args.steps)
extra_args = {
"cond": cond,
"uncond": uncond,
"text_cfg_scale": args.cfg_text,
"image_cfg_scale": args.cfg_image,
}
torch.manual_seed(seed)
z = torch.randn_like(cond["c_concat"][0]) * sigmas[0]
z = K.sampling.sample_euler_ancestral(model_wrap_cfg, z, sigmas, extra_args=extra_args)
x = model.decode_first_stage(z)
x = torch.clamp((x + 1.0) / 2.0, min=0.0, max=1.0)
x = 255.0 * rearrange(x, "1 c h w -> h w c")
print(x.shape)
edited_image = Image.fromarray(x.type(torch.uint8).cpu().numpy())
edited_image = ImageOps.fit(edited_image, (width, height), method=Image.Resampling.LANCZOS)
edited_image.save(output_dir+'/output_'+args.input.split('/')[-1].split('.')[0]+'_seed'+str(seed)+'.jpg')
if __name__ == "__main__":
main()
================================================
FILE: models/InstructDiffusion/environment.yaml
================================================
# File modified by authors of InstructDiffusion from original (https://github.com/CompVis/stable-diffusion).
# See more details in LICENSE.
name: instructdiff
channels:
- pytorch
- defaults
dependencies:
- python=3.8.5
- pip=20.3
- cudatoolkit=11.3
- pytorch=1.11.0
- torchvision=0.12.0
- numpy=1.19.2
- pip:
- albumentations==0.4.3
- datasets==2.8.0
- diffusers
- opencv-python==4.1.2.30
- pudb==2019.2
- invisible-watermark
- imageio==2.9.0
- imageio-ffmpeg==0.4.2
- pytorch-lightning==1.4.2
- omegaconf==2.1.1
- test-tube>=0.7.5
- streamlit>=0.73.1
- einops==0.3.0
- torch-fidelity==0.3.0
- transformers==4.19.2
- torchmetrics==0.6.0
- kornia==0.6
- -e git+https://github.com/CompVis/taming-transformers.git@master#egg=taming-transformers
- -e git+https://github.com/openai/CLIP.git@main#egg=clip
- openai
- gradio
- seaborn
- git+https://github.com/crowsonkb/k-diffusion.git
- deepspeed
- timm
================================================
FILE: models/InstructDiffusion/main.py
================================================
# --------------------------------------------------------
# InstructDiffusion
# Based on instruct-pix2pix (https://github.com/timothybrooks/instruct-pix2pix)
# Removed Pytorch-lightning and supported deepspeed by Zigang Geng (zigang@mail.ustc.edu.cn)
# --------------------------------------------------------
import argparse, os, sys, datetime, glob
import numpy as np
import time
import json
import pickle
import wandb
import deepspeed
from packaging import version
from omegaconf import OmegaConf
from functools import partial
from PIL import Image
from timm.utils import AverageMeter
import torch
import torchvision
import torch.cuda.amp as amp
import torch.distributed as dist
import torch.backends.cudnn as cudnn
from torch.utils.data import DataLoader, Dataset, ConcatDataset
sys.path.append("./stable_diffusion")
from ldm.data.base import Txt2ImgIterableBaseDataset
from ldm.util import instantiate_from_config
from ldm.modules.ema import LitEma
from utils.logger import create_logger
from utils.utils import load_checkpoint, save_checkpoint, get_grad_norm, auto_resume_helper
from utils.deepspeed import create_ds_config
def wandb_log(*args, **kwargs):
if dist.get_rank() == 0:
wandb.log(*args, **kwargs)
def get_parser(**parser_kwargs):
def str2bool(v):
if isinstance(v, bool):
return v
if v.lower() in ("yes", "true", "t", "y", "1"):
return True
elif v.lower() in ("no", "false", "f", "n", "0"):
return False
else:
raise argparse.ArgumentTypeError("Boolean value expected.")
parser = argparse.ArgumentParser(**parser_kwargs)
parser.add_argument(
"-n",
"--name",
type=str,
const=True,
default="",
nargs="?",
help="postfix for logdir",
)
parser.add_argument(
"-r",
"--resume",
type=str,
const=True,
default="",
nargs="?",
help="resume from logdir or checkpoint in logdir",
)
parser.add_argument(
"-b",
"--base",
nargs="*",
metavar="base_config.yaml",
help="paths to base configs. Loaded from left-to-right. "
"Parameters can be overwritten or added with command-line options of the form `--key value`.",
default=list(),
)
parser.add_argument(
"-t",
"--train",
type=str2bool,
const=True,
default=False,
nargs="?",
help="train",
)
parser.add_argument(
"--no-test",
type=str2bool,
const=True,
default=False,
nargs="?",
help="disable test",
)
parser.add_argument(
"-p",
"--project",
help="name of new or path to existing project"
)
parser.add_argument(
"-d",
"--debug",
type=str2bool,
nargs="?",
const=True,
default=False,
help="enable post-mortem debugging",
)
parser.add_argument(
"-s",
"--seed",
type=int,
default=23,
help="seed for seed_everything",
)
parser.add_argument(
"-f",
"--postfix",
type=str,
default="",
help="post-postfix for default name",
)
parser.add_argument(
"-l",
"--logdir",
type=str,
default="logs",
help="directory for logging dat shit",
)
parser.add_argument(
"--scale_lr",
action="store_true",
default=False,
help="scale base-lr by ngpu * batch_size * n_accumulate",
)
parser.add_argument(
"--amd",
action="store_true",
default=False,
help="amd",
)
parser.add_argument(
"--local_rank",
type=int,
# required=False,
default=int(os.environ.get('LOCAL_RANK', 0)),
help="local rank for DistributedDataParallel",
)
return parser
class WrappedDataset(Dataset):
"""Wraps an arbitrary object with __len__ and __getitem__ into a pytorch dataset"""
def __init__(self, dataset):
self.data = dataset
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
return self.data[idx]
class DataModuleFromConfig():
def __init__(self, batch_size, train=None, validation=None, test=None, predict=None,
wrap=False, num_workers=None, shuffle_test_loader=False, use_worker_init_fn=False,
shuffle_val_dataloader=False):
super().__init__()
self.batch_size = batch_size
self.dataset_configs = dict()
self.num_workers = num_workers if num_workers is not None else batch_size * 2
self.use_worker_init_fn = use_worker_init_fn
if train is not None:
if "target" in train:
self.dataset_configs["train"] = train
self.train_dataloader = self._train_dataloader
else:
for ds in train:
ds_name = str([key for key in ds.keys()][0])
self.dataset_configs[ds_name] = ds
self.train_dataloader = self._train_concat_dataloader
if validation is not None:
self.dataset_configs["validation"] = validation
self.val_dataloader = partial(self._val_dataloader, shuffle=shuffle_val_dataloader)
if test is not None:
self.dataset_configs["test"] = test
self.test_dataloader = partial(self._test_dataloader, shuffle=shuffle_test_loader)
if predict is not None:
self.dataset_configs["predict"] = predict
self.predict_dataloader = self._predict_dataloader
self.wrap = wrap
def prepare_data(self):
for data_cfg in self.dataset_configs.values():
instantiate_from_config(data_cfg)
def setup(self, stage=None):
self.datasets = dict(
(k, instantiate_from_config(self.dataset_configs[k]))
for k in self.dataset_configs)
if self.wrap:
for k in self.datasets:
self.datasets[k] = WrappedDataset(self.datasets[k])
def _train_concat_dataloader(self):
is_iterable_dataset = isinstance(self.datasets['ds1'], Txt2ImgIterableBaseDataset)
if is_iterable_dataset or self.use_worker_init_fn:
init_fn = worker_init_fn
else:
init_fn = None
concat_dataset = []
for ds in self.datasets.keys():
concat_dataset.append(self.datasets[ds])
concat_dataset = ConcatDataset(concat_dataset)
sampler_train = torch.utils.data.DistributedSampler(
concat_dataset, num_replicas=dist.get_world_size(), rank=dist.get_rank(), shuffle=True
)
return DataLoader(concat_dataset, batch_size=self.batch_size, sampler=sampler_train,
num_workers=self.num_workers, worker_init_fn=init_fn, persistent_workers=True)
def _train_dataloader(self):
is_iterable_dataset = isinstance(self.datasets['train'], Txt2ImgIterableBaseDataset)
if is_iterable_dataset or self.use_worker_init_fn:
init_fn = worker_init_fn
else:
init_fn = None
sampler_train = torch.utils.data.DistributedSampler(
self.datasets["train"], num_replicas=dist.get_world_size(), rank=dist.get_rank(), shuffle=True
)
return DataLoader(self.datasets["train"], batch_size=self.batch_size, sampler=sampler_train,
num_workers=self.num_workers, worker_init_fn=init_fn, persistent_workers=True)
def _val_dataloader(self, shuffle=False):
if isinstance(self.datasets['validation'], Txt2ImgIterableBaseDataset) or self.use_worker_init_fn:
init_fn = worker_init_fn
else:
init_fn = None
return DataLoader(self.datasets["validation"],
batch_size=self.batch_size,
num_workers=self.num_workers,
worker_init_fn=init_fn,
shuffle=shuffle, persistent_workers=True)
def _test_dataloader(self, shuffle=False):
is_iterable_dataset = isinstance(self.datasets['train'], Txt2ImgIterableBaseDataset)
if is_iterable_dataset or self.use_worker_init_fn:
init_fn = worker_init_fn
else:
init_fn = None
# do not shuffle dataloader for iterable dataset
shuffle = shuffle and (not is_iterable_dataset)
return DataLoader(self.datasets["test"], batch_size=self.batch_size,
num_workers=self.num_workers, worker_init_fn=init_fn, shuffle=shuffle, persistent_workers=True)
def _predict_dataloader(self, shuffle=False):
if isinstance(self.datasets['predict'], Txt2ImgIterableBaseDataset) or self.use_worker_init_fn:
init_fn = worker_init_fn
else:
init_fn = None
return DataLoader(self.datasets["predict"], batch_size=self.batch_size,
num_workers=self.num_workers, worker_init_fn=init_fn, persistent_workers=True)
def train_one_epoch(config, model, model_ema, data_loader, val_data_loader, optimizer, epoch, lr_scheduler, scaler):
model.train()
optimizer.zero_grad()
num_steps = len(data_loader)
accumul_steps = config.trainer.accumulate_grad_batches
batch_time = AverageMeter()
loss_meter = AverageMeter()
val_loss_meter = AverageMeter()
norm_meter = AverageMeter()
loss_scale_meter = AverageMeter()
loss_scale_meter_min = AverageMeter()
start = time.time()
end = time.time()
for idx, batch in enumerate(data_loader):
batch_size = batch['edited'].shape[0]
if config.model.params.deepspeed != '':
loss, _ = model(batch, idx, accumul_steps)
model.backward(loss)
model.step()
loss_scale = optimizer.cur_scale
grad_norm = model.get_global_grad_norm()
with torch.no_grad():
if idx % config.trainer.accumulate_grad_batches == 0:
model_ema(model)
loss_number = loss.item()
else:
with amp.autocast(enabled=config.model.params.fp16):
loss, _ = model(batch, idx, accumul_steps)
if config.trainer.accumulate_grad_batches > 1:
loss = loss / config.trainer.accumulate_grad_batches
scaler.scale(loss).backward()
# loss.backward()
if config.trainer.clip_grad > 0.0:
scaler.unscale_(optimizer)
grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), config.trainer.clip_grad)
else:
grad_norm = get_grad_norm(model.parameters())
if (idx + 1) % config.trainer.accumulate_grad_batches == 0:
scaler.step(optimizer)
optimizer.zero_grad()
scaler.update()
# scaler.unscale_grads()
# optimizer.step()
# optimizer.zero_grad()
# lr_scheduler.step_update(epoch * num_steps + idx)
else:
optimizer.zero_grad()
scaler.scale(loss).backward()
if config.trainer.clip_grad > 0.0:
scaler.unscale_(optimizer)
grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), config.trainer.clip_grad)
else:
grad_norm = get_grad_norm(model.parameters())
scaler.step(optimizer)
scaler.update()
# lr_scheduler.step_update(epoch * num_steps + idx)
loss_scale = scaler.get_scale()
loss_number = loss.item() * config.trainer.accumulate_grad_batches
torch.cuda.synchronize()
loss_meter.update(loss_number, batch_size)
if grad_norm is not None:
norm_meter.update(grad_norm)
else:
norm_meter.update(0.0)
loss_scale_meter.update(loss_scale)
# loss_scale_meter.update(0.0)
batch_time.update(time.time() - end)
end = time.time()
if idx % 100 == 0:
lr = optimizer.param_groups[0]['lr']
memory_used = torch.cuda.max_memory_allocated() / (1024.0 * 1024.0)
etas = batch_time.avg * (num_steps - idx)
logger.info(
f'Train: [{epoch}][{idx}/{num_steps}]\t'
f'eta {datetime.timedelta(seconds=int(etas))} lr {lr:.6f}\t'
f'time {batch_time.val:.4f} ({batch_time.avg:.4f})\t'
f'loss {loss_meter.val:.4f} ({loss_meter.avg:.4f})\t'
f'grad_norm {norm_meter.val:.4f} ({norm_meter.avg:.4f})\t'
f'loss_scale {loss_scale_meter.val:.4f} ({loss_scale_meter.avg:.4f})\t'
f'mem {memory_used:.0f}MB')
if (epoch * num_steps + idx) % 100 == 0:
log_message = dict(
lr=optimizer.param_groups[0]['lr'],
time=batch_time.val,
epoch=epoch,
iter=idx,
loss=loss_meter.val,
grad_norm=norm_meter.val,
loss_scale=loss_scale_meter.val,
memory=torch.cuda.max_memory_allocated() / (1024.0 * 1024.0),
global_iter=epoch * num_steps + idx)
# log_message.update({'ref_img': wandb.Image(unnormalize(img[:8].cpu().float())), 'mask': wandb.Image(mask[:8].cpu().float().unsqueeze(1))})
# if x_rec is not None:
# log_message.update({'rec_img': wandb.Image(unnormalize(x_rec[:8].cpu().float()))})
wandb_log(
data=log_message,
step=epoch * num_steps + idx,
)
if idx == num_steps - 1:
with torch.no_grad():
model_ema.store(model.parameters())
model_ema.copy_to(model)
for val_idx, batch in enumerate(val_data_loader):
batch_size = batch['edited'].shape[0]
loss, _ = model(batch, -1, 1)
loss_number = loss.item()
val_loss_meter.update(loss_number, batch_size)
if val_idx % 10 == 0:
logger.info(
f'Val: [{val_idx}/{len(val_data_loader)}]\t'
f'loss {val_loss_meter.val:.4f} ({val_loss_meter.avg:.4f})\t')
if val_idx == 50:
break
model_ema.restore(model.parameters())
epoch_time = time.time() - start
logger.info(f"EPOCH {epoch} training takes {datetime.timedelta(seconds=int(epoch_time))}")
if __name__ == "__main__":
now = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S")
# add cwd for convenience and to make classes in this file available when
# running as `python main.py`
# (in particular `main.DataModuleFromConfig`)
sys.path.append(os.getcwd())
parser = get_parser()
opt, unknown = parser.parse_known_args()
assert opt.name
cfg_fname = os.path.split(opt.base[0])[-1]
cfg_name = os.path.splitext(cfg_fname)[0]
nowname = f"{cfg_name}_{opt.name}"
logdir = os.path.join(opt.logdir, nowname)
if 'RANK' in os.environ and 'WORLD_SIZE' in os.environ:
rank = int(os.environ["RANK"])
world_size = int(os.environ['WORLD_SIZE'])
print(f"RANK and WORLD_SIZE in environ: {rank}/{world_size}")
else:
rank = -1
world_size = -1
if opt.amd:
os.environ["CUDA_VISIBLE_DEVICES"] = str(opt.local_rank)
torch.distributed.init_process_group(backend='gloo', init_method='env://', world_size=world_size, rank=rank)
else:
torch.cuda.set_device(opt.local_rank)
torch.distributed.init_process_group(backend='nccl', init_method='env://', world_size=world_size, rank=rank)
torch.distributed.barrier()
seed = opt.seed + dist.get_rank()
torch.manual_seed(seed)
np.random.seed(seed)
cudnn.benchmark = True
ckptdir = os.path.join(logdir, "checkpoints")
cfgdir = os.path.join(logdir, "configs")
os.makedirs(logdir, exist_ok=True)
os.makedirs(ckptdir, exist_ok=True)
os.makedirs(cfgdir, exist_ok=True)
# init and save configs
# config: the configs in the config file
configs = [OmegaConf.load(cfg) for cfg in opt.base]
cli = OmegaConf.from_dotlist(unknown)
config = OmegaConf.merge(*configs, cli)
if config.model.params.deepspeed != '':
create_ds_config(opt, config, cfgdir)
if dist.get_rank() == 0:
run = wandb.init(
id=nowname,
name=nowname,
project='readoutpose',
config=OmegaConf.to_container(config, resolve=True),
)
logger = create_logger(output_dir=logdir, dist_rank=dist.get_rank(), name=f"{nowname}")
resume_file = auto_resume_helper(config, ckptdir)
if resume_file:
resume = True
logger.info(f'resume checkpoint in {resume_file}')
else:
resume = False
logger.info(f'no checkpoint found in {ckptdir}, ignoring auto resume')
# model
model = instantiate_from_config(config.model)
model_ema = LitEma(model, decay_resume=config.model.params.get('ema_resume', 0.9999))
# data
data = instantiate_from_config(config.data)
# NOTE according to https://pytorch-lightning.readthedocs.io/en/latest/datamodules.html
# calling these ourselves should not be necessary but it is.
# lightning still takes care of proper multiprocessing though
data.prepare_data()
data.setup()
data_loader_train = data.train_dataloader()
data_loader_val = data.val_dataloader()
print("#### Data #####")
for k in data.datasets:
print(f"{k}, {data.datasets[k].__class__.__name__}, {len(data.datasets[k])}")
# configure learning rate
bs, base_lr = config.data.params.batch_size, config.model.base_learning_rate
ngpu = dist.get_world_size()
if 'accumulate_grad_batches' in config.trainer:
accumulate_grad_batches = config.trainer.accumulate_grad_batches
else:
accumulate_grad_batches = 1
print(f"accumulate_grad_batches = {accumulate_grad_batches}")
if opt.scale_lr:
model.learning_rate = accumulate_grad_batches * ngpu * bs * base_lr
print(
"Setting learning rate to {:.2e} = {} (accumulate_grad_batches) * {} (num_gpus) * {} (batchsize) * {:.2e} (base_lr)".format(
model.learning_rate, accumulate_grad_batches, ngpu, bs, base_lr))
else:
model.learning_rate = base_lr
print("++++ NOT USING LR SCALING ++++")
print(f"Setting learning rate to {model.learning_rate:.2e}")
if not opt.amd:
model.cuda()
if config.model.params.fp16 and config.model.params.deepspeed == '':
scaler = amp.GradScaler()
param_groups = model.parameters()
else:
scaler = None
param_groups = model.parameters()
if config.model.params.deepspeed != '':
model, optimizer, _, _ = deepspeed.initialize(
args=config,
model=model,
model_parameters=param_groups,
dist_init_required=False,
)
for name, param in model.named_parameters():
param.global_name = name
model_without_ddp = model
lr_scheduler = None
model_ema = model_ema.to(next(model.parameters()).device)
else:
optimizer, lr_scheduler = model.configure_optimizers()
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[opt.local_rank], broadcast_buffers=False)
model_without_ddp = model.module
# print(optimizer.param_groups[1])
if opt.resume != '':
resume_file = opt.resume
if resume_file:
_, start_epoch = load_checkpoint(resume_file, config, model_without_ddp, model_ema, optimizer, lr_scheduler, scaler, logger)
else:
start_epoch = 0
logger.info("Start training")
start_time = time.time()
for epoch in range(start_epoch, config.trainer.max_epochs):
data_loader_train.sampler.set_epoch(epoch)
train_one_epoch(config, model, model_ema, data_loader_train, data_loader_val, optimizer, epoch, lr_scheduler, scaler)
if epoch % config.trainer.save_freq == 0:
save_checkpoint(ckptdir, config, epoch, model_without_ddp, model_ema, 0., optimizer, lr_scheduler, scaler, logger)
total_time = time.time() - start_time
total_time_str = str(datetime.timedelta(seconds=int(total_time)))
logger.info('Training time {}'.format(total_time_str))
================================================
FILE: models/InstructDiffusion/scripts/convert_ckpt.py
================================================
# ------------------------------------------------------------------------------
# Copyright (c) Microsoft
# Licensed under the MIT License.
# Written by Zigang Geng (zigang@mail.ustc.edu.cn)
# ------------------------------------------------------------------------------
from __future__ import annotations
import sys
import torch
from argparse import ArgumentParser
from omegaconf import OmegaConf
sys.path.append("./stable_diffusion")
from stable_diffusion.ldm.util import instantiate_from_config
if __name__ == "__main__":
parser = ArgumentParser()
parser.add_argument("--config", default="configs/instruct_diffusion.yaml", type=str)
parser.add_argument("--ema-ckpt", default="logs/instruct_diffusion/checkpoints/ckpt_epoch_200/state.pth", type=str)
parser.add_argument("--vae-ckpt", default="stable_diffusion/models/ldm/stable-diffusion-v1/v1-5-pruned-emaonly.ckpt", type=str)
parser.add_argument("--out-ckpt", default="checkpoints/v1-5-pruned-emaonly-adaption-task.ckpt", type=str)
args = parser.parse_args()
config = OmegaConf.load(args.config)
model = instantiate_from_config(config.model)
ema_ckpt = torch.load(args.ema_ckpt, map_location="cpu")
all_keys = [key for key, value in model.named_parameters()]
all_keys_rmv = [key.replace('.','') for key in all_keys]
new_ema_ckpt = {}
for k, v in ema_ckpt['model_ema'].items():
try:
k_index = all_keys_rmv.index(k)
new_ema_ckpt[all_keys[k_index]] = v
except:
print(k+' is not in the list.')
vae_ckpt = torch.load(args.vae_ckpt, map_location="cpu")
for k, v in vae_ckpt['state_dict'].items():
if k not in new_ema_ckpt and k in all_keys:
new_ema_ckpt[k] = v
checkpoint = {'state_dict': new_ema_ckpt}
with open(args.out_ckpt, 'wb') as f:
torch.save(checkpoint, f)
f.flush()
print('Converted successfully, the new checkpoint has been saved to ' + str(args.out_ckpt))
================================================
FILE: models/InstructDiffusion/scripts/download_pretrained_sd.sh
================================================
#!/bin/bash
SCRIPT_DIR=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )
mkdir -p $SCRIPT_DIR/../stable_diffusion/models/ldm/stable-diffusion-v1
curl -L https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt -o $SCRIPT_DIR/../stable_diffusion/models/ldm/stable-diffusion-v1/v1-5-pruned-emaonly.ckpt
curl -L https://huggingface.co/stabilityai/sd-vae-ft-mse-original/resolve/main/vae-ft-mse-840000-ema-pruned.ckpt -o $SCRIPT_DIR/../stable_diffusion/models/ldm/stable-diffusion-v1/vae-ft-mse-840000-ema-pruned.ckpt
================================================
FILE: models/InstructDiffusion/scripts/inference_example.sh
================================================
# Example: Image Editing
python edit_cli.py --input figure/animals.png --edit "Transform it to van Gogh, starry night style." --resolution 768 --steps 100 --config configs/instruct_diffusion.yaml --ckpt checkpoints/v1-5-pruned-emaonly-adaption-task.ckpt --cfg-text 5.0 --cfg-image 1.25 --outdir logs/ --seed 93151
python edit_cli.py --input figure/animals.png --edit "Help the elephant wear a crown and maintain the appearance of others." --resolution 512 --steps 100 --config configs/instruct_diffusion.yaml --ckpt checkpoints/v1-5-pruned-emaonly-adaption-task.ckpt --cfg-text 5.0 --cfg-image 1.25 --outdir logs/ --seed 51557
# Example: Segmentation More prompts can be found in the dataset/prompts/prompt_seg.txt
python edit_cli.py --input figure/mirrorcat.jpg --edit "Mark the pixels of the cat in the mirror to blue and leave the rest unchanged." --resolution 512 --steps 100 --config configs/instruct_diffusion.yaml --ckpt checkpoints/v1-5-pruned-emaonly-adaption-task.ckpt --cfg-text 7.5 --cfg-image 1.5 --outdir logs/ --seed 94746
# Example: Keypoint Detection More prompts can be found in the dataset/prompts/prompt_pose.txt
python edit_cli.py --input figure/people.jpg --edit "Use yellow to encircle the left knee of the people on the far left and draw a blue circle over the nose of the tallest people." --resolution 512 --steps 100 --config configs/instruct_diffusion.yaml --ckpt checkpoints/v1-5-pruned-emaonly-adaption-task.ckpt --cfg-text 6.0 --cfg-image 0.5 --outdir logs/ --seed 27775
# Example: Watermark Removal More prompts can be found in the dataset/prompts/prompt_dewatermark.txt
python edit_cli.py --input figure/watermark.png --edit "Remove watermark from this picture." --resolution 512 --steps 100 --config configs/instruct_diffusion.yaml --ckpt checkpoints/v1-5-pruned-emaonly-adaption-task.ckpt --cfg-text 1.0 --cfg-image 1.0 --outdir logs/ --seed 54763
================================================
FILE: models/InstructDiffusion/scripts/run_multinode.sh
================================================
EXP=$1
NAME=$2
GPUMUM=$3
set -x
python -m torch.distributed.launch --nnodes=${GPUMUM} --nproc_per_node=8 --node_rank=$NODE_RANK --master_addr $MASTER_ADDR --master_port $MASTER_PORT main.py --name ${NAME} --base configs/${EXP}.yaml --train --logdir /mnt/data/readout_torch_output/
================================================
FILE: models/InstructDiffusion/stable_diffusion/LICENSE
================================================
Copyright (c) 2022 Robin Rombach and Patrick Esser and contributors
CreativeML Open RAIL-M
dated August 22, 2022
Section I: PREAMBLE
Multimodal generative models are being widely adopted and used, and have the potential to transform the way artists, among other individuals, conceive and benefit from AI or ML technologies as a tool for content creation.
Notwithstanding the current and potential benefits that these artifacts can bring to society at large, there are also concerns about potential misuses of them, either due to their technical limitations or ethical considerations.
In short, this license strives for both the open and responsible downstream use of the accompanying model. When it comes to the open character, we took inspiration from open source permissive licenses regarding the grant of IP rights. Referring to the downstream responsible use, we added use-based restrictions not permitting the use of the Model in very specific scenarios, in order for the licensor to be able to enforce the license in case potential misuses of the Model may occur. At the same time, we strive to promote open and responsible research on generative models for art and content generation.
Even though downstream derivative versions of the model could be released under different licensing terms, the latter will always have to include - at minimum - the same use-based restrictions as the ones in the original license (this license). We believe in the intersection between open and responsible AI development; thus, this License aims to strike a balance between both in order to enable responsible open-science in the field of AI.
This License governs the use of the model (and its derivatives) and is informed by the model card associated with the model.
NOW THEREFORE, You and Licensor agree as follows:
1. Definitions
- "License" means the terms and conditions for use, reproduction, and Distribution as defined in this document.
- "Data" means a collection of information and/or content extracted from the dataset used with the Model, including to train, pretrain, or otherwise evaluate the Model. The Data is not licensed under this License.
- "Output" means the results of operating a Model as embodied in informational content resulting therefrom.
- "Model" means any accompanying machine-learning based assemblies (including checkpoints), consisting of learnt weights, parameters (including optimizer states), corresponding to the model architecture as embodied in the Complementary Material, that have been trained or tuned, in whole or in part on the Data, using the Complementary Material.
- "Derivatives of the Model" means all modifications to the Model, works based on the Model, or any other model which is created or initialized by transfer of patterns of the weights, parameters, activations or output of the Model, to the other model, in order to cause the other model to perform similarly to the Model, including - but not limited to - distillation methods entailing the use of intermediate data representations or methods based on the generation of synthetic data by the Model for training the other model.
- "Complementary Material" means the accompanying source code and scripts used to define, run, load, benchmark or evaluate the Model, and used to prepare data for training or evaluation, if any. This includes any accompanying documentation, tutorials, examples, etc, if any.
- "Distribution" means any transmission, reproduction, publication or other sharing of the Model or Derivatives of the Model to a third party, including providing the Model as a hosted service made available by electronic or other remote means - e.g. API-based or web access.
- "Licensor" means the copyright owner or entity authorized by the copyright owner that is granting the License, including the persons or entities that may have rights in the Model and/or distributing the Model.
- "You" (or "Your") means an individual or Legal Entity exercising permissions granted by this License and/or making use of the Model for whichever purpose and in any field of use, including usage of the Model in an end-use application - e.g. chatbot, translator, image generator.
- "Third Parties" means individuals or legal entities that are not under common control with Licensor or You.
- "Contribution" means any work of authorship, including the original version of the Model and any modifications or additions to that Model or Derivatives of the Model thereof, that is intentionally submitted to Licensor for inclusion in the Model by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, "submitted" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Model, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as "Not a Contribution."
- "Contributor" means Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Model.
Section II: INTELLECTUAL PROPERTY RIGHTS
Both copyright and patent grants apply to the Model, Derivatives of the Model and Complementary Material. The Model and Derivatives of the Model are subject to additional terms as described in Section III.
2. Grant of Copyright License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare, publicly display, publicly perform, sublicense, and distribute the Complementary Material, the Model, and Derivatives of the Model.
3. Grant of Patent License. Subject to the terms and conditions of this License and where and as applicable, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this paragraph) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Model and the Complementary Material, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Model to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Model and/or Complementary Material or a Contribution incorporated within the Model and/or Complementary Material constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for the Model and/or Work shall terminate as of the date such litigation is asserted or filed.
Section III: CONDITIONS OF USAGE, DISTRIBUTION AND REDISTRIBUTION
4. Distribution and Redistribution. You may host for Third Party remote access purposes (e.g. software-as-a-service), reproduce and distribute copies of the Model or Derivatives of the Model thereof in any medium, with or without modifications, provided that You meet the following conditions:
Use-based restrictions as referenced in paragraph 5 MUST be included as an enforceable provision by You in any type of legal agreement (e.g. a license) governing the use and/or distribution of the Model or Derivatives of the Model, and You shall give notice to subsequent users You Distribute to, that the Model or Derivatives of the Model are subject to paragraph 5. This provision does not apply to the use of Complementary Material.
You must give any Third Party recipients of the Model or Derivatives of the Model a copy of this License;
You must cause any modified files to carry prominent notices stating that You changed the files;
You must retain all copyright, patent, trademark, and attribution notices excluding those notices that do not pertain to any part of the Model, Derivatives of the Model.
You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions - respecting paragraph 4.a. - for use, reproduction, or Distribution of Your modifications, or for any such Derivatives of the Model as a whole, provided Your use, reproduction, and Distribution of the Model otherwise complies with the conditions stated in this License.
5. Use-based restrictions. The restrictions set forth in Attachment A are considered Use-based restrictions. Therefore You cannot use the Model and the Derivatives of the Model for the specified restricted uses. You may use the Model subject to this License, including only for lawful purposes and in accordance with the License. Use may include creating any content with, finetuning, updating, running, training, evaluating and/or reparametrizing the Model. You shall require all of Your users who use the Model or a Derivative of the Model to comply with the terms of this paragraph (paragraph 5).
6. The Output You Generate. Except as set forth herein, Licensor claims no rights in the Output You generate using the Model. You are accountable for the Output you generate and its subsequent uses. No use of the output can contravene any provision as stated in the License.
Section IV: OTHER PROVISIONS
7. Updates and Runtime Restrictions. To the maximum extent permitted by law, Licensor reserves the right to restrict (remotely or otherwise) usage of the Model in violation of this License, update the Model through electronic means, or modify the Output of the Model based on updates. You shall undertake reasonable efforts to use the latest version of the Model.
8. Trademarks and related. Nothing in this License permits You to make use of Licensors’ trademarks, trade names, logos or to otherwise suggest endorsement or misrepresent the relationship between the parties; and any rights not expressly granted herein are reserved by the Licensors.
9. Disclaimer of Warranty. Unless required by applicable law or agreed to in writing, Licensor provides the Model and the Complementary Material (and each Contributor provides its Contributions) on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using or redistributing the Model, Derivatives of the Model, and the Complementary Material and assume any risks associated with Your exercise of permissions under this License.
10. Limitation of Liability. In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall any Contributor be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Model and the Complementary Material (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if such Contributor has been advised of the possibility of such damages.
11. Accepting Warranty or Additional Liability. While redistributing the Model, Derivatives of the Model and the Complementary Material thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability.
12. If any provision of this License is held to be invalid, illegal or unenforceable, the remaining provisions shall be unaffected thereby and remain valid as if such provision had not been set forth herein.
END OF TERMS AND CONDITIONS
Attachment A
Use Restrictions
You agree not to use the Model or Derivatives of the Model:
- In any way that violates any applicable national, federal, state, local or international law or regulation;
- For the purpose of exploiting, harming or attempting to exploit or harm minors in any way;
- To generate or disseminate verifiably false information and/or content with the purpose of harming others;
- To generate or disseminate personal identifiable information that can be used to harm an individual;
- To defame, disparage or otherwise harass others;
- For fully automated decision making that adversely impacts an individual’s legal rights or otherwise creates or modifies a binding, enforceable obligation;
- For any use intended to or which has the effect of discriminating against or harming individuals or groups based on online or offline social behavior or known or predicted personal or personality characteristics;
- To exploit any of the vulnerabilities of a specific group of persons based on their age, social, physical or mental characteristics, in order to materially distort the behavior of a person pertaining to that group in a manner that causes or is likely to cause that person or another person physical or psychological harm;
- For any use intended to or which has the effect of discriminating against individuals or groups based on legally protected characteristics or categories;
- To provide medical advice and medical results interpretation;
- To generate or disseminate information for the purpose to be used for administration of justice, law enforcement, immigration or asylum processes, such as predicting an individual will commit fraud/crime commitment (e.g. by text profiling, drawing causal relationships between assertions made in documents, indiscriminate and arbitrarily-targeted use).
================================================
FILE: models/InstructDiffusion/stable_diffusion/README.md
================================================
# Stable Diffusion
*Stable Diffusion was made possible thanks to a collaboration with [Stability AI](https://stability.ai/) and [Runway](https://runwayml.com/) and builds upon our previous work:*
[**High-Resolution Image Synthesis with Latent Diffusion Models**](https://ommer-lab.com/research/latent-diffusion-models/)
[Robin Rombach](https://github.com/rromb)\*,
[Andreas Blattmann](https://github.com/ablattmann)\*,
[Dominik Lorenz](https://github.com/qp-qp)\,
[Patrick Esser](https://github.com/pesser),
[Björn Ommer](https://hci.iwr.uni-heidelberg.de/Staff/bommer)
_[CVPR '22 Oral](https://openaccess.thecvf.com/content/CVPR2022/html/Rombach_High-Resolution_Image_Synthesis_With_Latent_Diffusion_Models_CVPR_2022_paper.html) |
[GitHub](https://github.com/CompVis/latent-diffusion) | [arXiv](https://arxiv.org/abs/2112.10752) | [Project page](https://ommer-lab.com/research/latent-diffusion-models/)_
[Stable Diffusion](#stable-diffusion-v1) is a latent text-to-image diffusion
model.
Thanks to a generous compute donation from [Stability AI](https://stability.ai/) and support from [LAION](https://laion.ai/), we were able to train a Latent Diffusion Model on 512x512 images from a subset of the [LAION-5B](https://laion.ai/blog/laion-5b/) database.
Similar to Google's [Imagen](https://arxiv.org/abs/2205.11487),
this model uses a frozen CLIP ViT-L/14 text encoder to condition the model on text prompts.
With its 860M UNet and 123M text encoder, the model is relatively lightweight and runs on a GPU with at least 10GB VRAM.
See [this section](#stable-diffusion-v1) below and the [model card](https://huggingface.co/CompVis/stable-diffusion).
## Requirements
A suitable [conda](https://conda.io/) environment named `ldm` can be created
and activated with:
```
conda env create -f environment.yaml
conda activate ldm
```
You can also update an existing [latent diffusion](https://github.com/CompVis/latent-diffusion) environment by running
```
conda install pytorch torchvision -c pytorch
pip install transformers==4.19.2 diffusers invisible-watermark
pip install -e .
```
## Stable Diffusion v1
Stable Diffusion v1 refers to a specific configuration of the model
architecture that uses a downsampling-factor 8 autoencoder with an 860M UNet
and CLIP ViT-L/14 text encoder for the diffusion model. The model was pretrained on 256x256 images and
then finetuned on 512x512 images.
*Note: Stable Diffusion v1 is a general text-to-image diffusion model and therefore mirrors biases and (mis-)conceptions that are present
in its training data.
Details on the training procedure and data, as well as the intended use of the model can be found in the corresponding [model card](Stable_Diffusion_v1_Model_Card.md).*
The weights are available via [the CompVis organization at Hugging Face](https://huggingface.co/CompVis) under [a license which contains specific use-based restrictions to prevent misuse and harm as informed by the model card, but otherwise remains permissive](LICENSE). While commercial use is permitted under the terms of the license, **we do not recommend using the provided weights for services or products without additional safety mechanisms and considerations**, since there are [known limitations and biases](Stable_Diffusion_v1_Model_Card.md#limitations-and-bias) of the weights, and research on safe and ethical deployment of general text-to-image models is an ongoing effort. **The weights are research artifacts and should be treated as such.**
[The CreativeML OpenRAIL M license](LICENSE) is an [Open RAIL M license](https://www.licenses.ai/blog/2022/8/18/naming-convention-of-responsible-ai-licenses), adapted from the work that [BigScience](https://bigscience.huggingface.co/) and [the RAIL Initiative](https://www.licenses.ai/) are jointly carrying in the area of responsible AI licensing. See also [the article about the BLOOM Open RAIL license](https://bigscience.huggingface.co/blog/the-bigscience-rail-license) on which our license is based.
### Weights
We currently provide the following checkpoints:
- `sd-v1-1.ckpt`: 237k steps at resolution `256x256` on [laion2B-en](https://huggingface.co/datasets/laion/laion2B-en).
194k steps at resolution `512x512` on [laion-high-resolution](https://huggingface.co/datasets/laion/laion-high-resolution) (170M examples from LAION-5B with resolution `>= 1024x1024`).
- `sd-v1-2.ckpt`: Resumed from `sd-v1-1.ckpt`.
515k steps at resolution `512x512` on [laion-aesthetics v2 5+](https://laion.ai/blog/laion-aesthetics/) (a subset of laion2B-en with estimated aesthetics score `> 5.0`, and additionally
filtered to images with an original size `>= 512x512`, and an estimated watermark probability `< 0.5`. The watermark estimate is from the [LAION-5B](https://laion.ai/blog/laion-5b/) metadata, the aesthetics score is estimated using the [LAION-Aesthetics Predictor V2](https://github.com/christophschuhmann/improved-aesthetic-predictor)).
- `sd-v1-3.ckpt`: Resumed from `sd-v1-2.ckpt`. 195k steps at resolution `512x512` on "laion-aesthetics v2 5+" and 10\% dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598).
- `sd-v1-4.ckpt`: Resumed from `sd-v1-2.ckpt`. 225k steps at resolution `512x512` on "laion-aesthetics v2 5+" and 10\% dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598).
Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0,
5.0, 6.0, 7.0, 8.0) and 50 PLMS sampling
steps show the relative improvements of the checkpoints:
### Text-to-Image with Stable Diffusion
Stable Diffusion is a latent diffusion model conditioned on the (non-pooled) text embeddings of a CLIP ViT-L/14 text encoder.
We provide a [reference script for sampling](#reference-sampling-script), but
there also exists a [diffusers integration](#diffusers-integration), which we
expect to see more active community development.
#### Reference Sampling Script
We provide a reference sampling script, which incorporates
- a [Safety Checker Module](https://github.com/CompVis/stable-diffusion/pull/36),
to reduce the probability of explicit outputs,
- an [invisible watermarking](https://github.com/ShieldMnt/invisible-watermark)
of the outputs, to help viewers [identify the images as machine-generated](scripts/tests/test_watermark.py).
After [obtaining the `stable-diffusion-v1-*-original` weights](#weights), link them
```
mkdir -p models/ldm/stable-diffusion-v1/
ln -s
models/ldm/stable-diffusion-v1/model.ckpt
```
and sample with
```
python scripts/txt2img.py --prompt "a photograph of an astronaut riding a horse" --plms
```
By default, this uses a guidance scale of `--scale 7.5`, [Katherine Crowson's implementation](https://github.com/CompVis/latent-diffusion/pull/51) of the [PLMS](https://arxiv.org/abs/2202.09778) sampler,
and renders images of size 512x512 (which it was trained on) in 50 steps. All supported arguments are listed below (type `python scripts/txt2img.py --help`).
```commandline
usage: txt2img.py [-h] [--prompt [PROMPT]] [--outdir [OUTDIR]] [--skip_grid] [--skip_save] [--ddim_steps DDIM_STEPS] [--plms] [--laion400m] [--fixed_code] [--ddim_eta DDIM_ETA]
[--n_iter N_ITER] [--H H] [--W W] [--C C] [--f F] [--n_samples N_SAMPLES] [--n_rows N_ROWS] [--scale SCALE] [--from-file FROM_FILE] [--config CONFIG] [--ckpt CKPT]
[--seed SEED] [--precision {full,autocast}]
optional arguments:
-h, --help show this help message and exit
--prompt [PROMPT] the prompt to render
--outdir [OUTDIR] dir to write results to
--skip_grid do not save a grid, only individual samples. Helpful when evaluating lots of samples
--skip_save do not save individual samples. For speed measurements.
--ddim_steps DDIM_STEPS
number of ddim sampling steps
--plms use plms sampling
--laion400m uses the LAION400M model
--fixed_code if enabled, uses the same starting code across samples
--ddim_eta DDIM_ETA ddim eta (eta=0.0 corresponds to deterministic sampling
--n_iter N_ITER sample this often
--H H image height, in pixel space
--W W image width, in pixel space
--C C latent channels
--f F downsampling factor
--n_samples N_SAMPLES
how many samples to produce for each given prompt. A.k.a. batch size
--n_rows N_ROWS rows in the grid (default: n_samples)
--scale SCALE unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) - eps(x, empty))
--from-file FROM_FILE
if specified, load prompts from this file
--config CONFIG path to config which constructs model
--ckpt CKPT path to checkpoint of model
--seed SEED the seed (for reproducible sampling)
--precision {full,autocast}
evaluate at this precision
```
Note: The inference config for all v1 versions is designed to be used with EMA-only checkpoints.
For this reason `use_ema=False` is set in the configuration, otherwise the code will try to switch from
non-EMA to EMA weights. If you want to examine the effect of EMA vs no EMA, we provide "full" checkpoints
which contain both types of weights. For these, `use_ema=False` will load and use the non-EMA weights.
#### Diffusers Integration
A simple way to download and sample Stable Diffusion is by using the [diffusers library](https://github.com/huggingface/diffusers/tree/main#new--stable-diffusion-is-now-fully-compatible-with-diffusers):
```py
# make sure you're logged in with `huggingface-cli login`
from torch import autocast
from diffusers import StableDiffusionPipeline
pipe = StableDiffusionPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4",
use_auth_token=True
).to("cuda")
prompt = "a photo of an astronaut riding a horse on mars"
with autocast("cuda"):
image = pipe(prompt)["sample"][0]
image.save("astronaut_rides_horse.png")
```
### Image Modification with Stable Diffusion
By using a diffusion-denoising mechanism as first proposed by [SDEdit](https://arxiv.org/abs/2108.01073), the model can be used for different
tasks such as text-guided image-to-image translation and upscaling. Similar to the txt2img sampling script,
we provide a script to perform image modification with Stable Diffusion.
The following describes an example where a rough sketch made in [Pinta](https://www.pinta-project.com/) is converted into a detailed artwork.
```
python scripts/img2img.py --prompt "A fantasy landscape, trending on artstation" --init-img --strength 0.8
```
Here, strength is a value between 0.0 and 1.0, that controls the amount of noise that is added to the input image.
Values that approach 1.0 allow for lots of variations but will also produce images that are not semantically consistent with the input. See the following example.
**Input**
**Outputs**
This procedure can, for example, also be used to upscale samples from the base model.
## Comments
- Our codebase for the diffusion models builds heavily on [OpenAI's ADM codebase](https://github.com/openai/guided-diffusion)
and [https://github.com/lucidrains/denoising-diffusion-pytorch](https://github.com/lucidrains/denoising-diffusion-pytorch).
Thanks for open-sourcing!
- The implementation of the transformer encoder is from [x-transformers](https://github.com/lucidrains/x-transformers) by [lucidrains](https://github.com/lucidrains?tab=repositories).
## BibTeX
```
@misc{rombach2021highresolution,
title={High-Resolution Image Synthesis with Latent Diffusion Models},
author={Robin Rombach and Andreas Blattmann and Dominik Lorenz and Patrick Esser and Björn Ommer},
year={2021},
eprint={2112.10752},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
================================================
FILE: models/InstructDiffusion/stable_diffusion/Stable_Diffusion_v1_Model_Card.md
================================================
# Stable Diffusion v1 Model Card
This model card focuses on the model associated with the Stable Diffusion model, available [here](https://github.com/CompVis/stable-diffusion).
## Model Details
- **Developed by:** Robin Rombach, Patrick Esser
- **Model type:** Diffusion-based text-to-image generation model
- **Language(s):** English
- **License:** [Proprietary](LICENSE)
- **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([CLIP ViT-L/14](https://arxiv.org/abs/2103.00020)) as suggested in the [Imagen paper](https://arxiv.org/abs/2205.11487).
- **Resources for more information:** [GitHub Repository](https://github.com/CompVis/stable-diffusion), [Paper](https://arxiv.org/abs/2112.10752).
- **Cite as:**
@InProceedings{Rombach_2022_CVPR,
author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},
title = {High-Resolution Image Synthesis With Latent Diffusion Models},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {10684-10695}
}
# Uses
## Direct Use
The model is intended for research purposes only. Possible research areas and
tasks include
- Safe deployment of models which have the potential to generate harmful content.
- Probing and understanding the limitations and biases of generative models.
- Generation of artworks and use in design and other artistic processes.
- Applications in educational or creative tools.
- Research on generative models.
Excluded uses are described below.
### Misuse, Malicious Use, and Out-of-Scope Use
_Note: This section is taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), but applies in the same way to Stable Diffusion v1_.
The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.
#### Out-of-Scope Use
The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.
#### Misuse and Malicious Use
Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to:
- Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc.
- Intentionally promoting or propagating discriminatory content or harmful stereotypes.
- Impersonating individuals without their consent.
- Sexual content without consent of the people who might see it.
- Mis- and disinformation
- Representations of egregious violence and gore
- Sharing of copyrighted or licensed material in violation of its terms of use.
- Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use.
## Limitations and Bias
### Limitations
- The model does not achieve perfect photorealism
- The model cannot render legible text
- The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere”
- Faces and people in general may not be generated properly.
- The model was trained mainly with English captions and will not work as well in other languages.
- The autoencoding part of the model is lossy
- The model was trained on a large-scale dataset
[LAION-5B](https://laion.ai/blog/laion-5b/) which contains adult material
and is not fit for product use without additional safety mechanisms and
considerations.
- No additional measures were used to deduplicate the dataset. As a result, we observe some degree of memorization for images that are duplicated in the training data.
The training data can be searched at [https://rom1504.github.io/clip-retrieval/](https://rom1504.github.io/clip-retrieval/) to possibly assist in the detection of memorized images.
### Bias
While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases.
Stable Diffusion v1 was primarily trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/),
which consists of images that are limited to English descriptions.
Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for.
This affects the overall output of the model, as white and western cultures are often set as the default. Further, the
ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts.
Stable Diffusion v1 mirrors and exacerbates biases to such a degree that viewer discretion must be advised irrespective of the input or its intent.
## Training
**Training Data**
The model developers used the following dataset for training the model:
- LAION-5B and subsets thereof (see next section)
**Training Procedure**
Stable Diffusion v1 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. During training,
- Images are encoded through an encoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4
- Text prompts are encoded through a ViT-L/14 text-encoder.
- The non-pooled output of the text encoder is fed into the UNet backbone of the latent diffusion model via cross-attention.
- The loss is a reconstruction objective between the noise that was added to the latent and the prediction made by the UNet.
We currently provide the following checkpoints:
- `sd-v1-1.ckpt`: 237k steps at resolution `256x256` on [laion2B-en](https://huggingface.co/datasets/laion/laion2B-en).
194k steps at resolution `512x512` on [laion-high-resolution](https://huggingface.co/datasets/laion/laion-high-resolution) (170M examples from LAION-5B with resolution `>= 1024x1024`).
- `sd-v1-2.ckpt`: Resumed from `sd-v1-1.ckpt`.
515k steps at resolution `512x512` on [laion-aesthetics v2 5+](https://laion.ai/blog/laion-aesthetics/) (a subset of laion2B-en with estimated aesthetics score `> 5.0`, and additionally
filtered to images with an original size `>= 512x512`, and an estimated watermark probability `< 0.5`. The watermark estimate is from the [LAION-5B](https://laion.ai/blog/laion-5b/) metadata, the aesthetics score is estimated using the [LAION-Aesthetics Predictor V2](https://github.com/christophschuhmann/improved-aesthetic-predictor)).
- `sd-v1-3.ckpt`: Resumed from `sd-v1-2.ckpt`. 195k steps at resolution `512x512` on "laion-aesthetics v2 5+" and 10\% dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598).
- `sd-v1-4.ckpt`: Resumed from `sd-v1-2.ckpt`. 225k steps at resolution `512x512` on "laion-aesthetics v2 5+" and 10\% dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598).
- **Hardware:** 32 x 8 x A100 GPUs
- **Optimizer:** AdamW
- **Gradient Accumulations**: 2
- **Batch:** 32 x 8 x 2 x 4 = 2048
- **Learning rate:** warmup to 0.0001 for 10,000 steps and then kept constant
## Evaluation Results
Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0,
5.0, 6.0, 7.0, 8.0) and 50 PLMS sampling
steps show the relative improvements of the checkpoints:
Evaluated using 50 PLMS steps and 10000 random prompts from the COCO2017 validation set, evaluated at 512x512 resolution. Not optimized for FID scores.
## Environmental Impact
**Stable Diffusion v1** **Estimated Emissions**
Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact.
- **Hardware Type:** A100 PCIe 40GB
- **Hours used:** 150000
- **Cloud Provider:** AWS
- **Compute Region:** US-east
- **Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid):** 11250 kg CO2 eq.
## Citation
@InProceedings{Rombach_2022_CVPR,
author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},
title = {High-Resolution Image Synthesis With Latent Diffusion Models},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {10684-10695}
}
*This model card was written by: Robin Rombach and Patrick Esser and is based on the [DALL-E Mini model card](https://huggingface.co/dalle-mini/dalle-mini).*
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FILE: models/InstructDiffusion/stable_diffusion/configs/autoencoder/autoencoder_kl_16x16x16.yaml
================================================
model:
base_learning_rate: 4.5e-6
target: ldm.models.autoencoder.AutoencoderKL
params:
monitor: "val/rec_loss"
embed_dim: 16
lossconfig:
target: ldm.modules.losses.LPIPSWithDiscriminator
params:
disc_start: 50001
kl_weight: 0.000001
disc_weight: 0.5
ddconfig:
double_z: True
z_channels: 16
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult: [ 1,1,2,2,4] # num_down = len(ch_mult)-1
num_res_blocks: 2
attn_resolutions: [16]
dropout: 0.0
data:
target: main.DataModuleFromConfig
params:
batch_size: 12
wrap: True
train:
target: ldm.data.imagenet.ImageNetSRTrain
params:
size: 256
degradation: pil_nearest
validation:
target: ldm.data.imagenet.ImageNetSRValidation
params:
size: 256
degradation: pil_nearest
lightning:
callbacks:
image_logger:
target: main.ImageLogger
params:
batch_frequency: 1000
max_images: 8
increase_log_steps: True
trainer:
benchmark: True
accumulate_grad_batches: 2
================================================
FILE: models/InstructDiffusion/stable_diffusion/configs/autoencoder/autoencoder_kl_32x32x4.yaml
================================================
model:
base_learning_rate: 4.5e-6
target: ldm.models.autoencoder.AutoencoderKL
params:
monitor: "val/rec_loss"
embed_dim: 4
lossconfig:
target: ldm.modules.losses.LPIPSWithDiscriminator
params:
disc_start: 50001
kl_weight: 0.000001
disc_weight: 0.5
ddconfig:
double_z: True
z_channels: 4
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult: [ 1,2,4,4 ] # num_down = len(ch_mult)-1
num_res_blocks: 2
attn_resolutions: [ ]
dropout: 0.0
data:
target: main.DataModuleFromConfig
params:
batch_size: 12
wrap: True
train:
target: ldm.data.imagenet.ImageNetSRTrain
params:
size: 256
degradation: pil_nearest
validation:
target: ldm.data.imagenet.ImageNetSRValidation
params:
size: 256
degradation: pil_nearest
lightning:
callbacks:
image_logger:
target: main.ImageLogger
params:
batch_frequency: 1000
max_images: 8
increase_log_steps: True
trainer:
benchmark: True
accumulate_grad_batches: 2
================================================
FILE: models/InstructDiffusion/stable_diffusion/configs/autoencoder/autoencoder_kl_64x64x3.yaml
================================================
model:
base_learning_rate: 4.5e-6
target: ldm.models.autoencoder.AutoencoderKL
params:
monitor: "val/rec_loss"
embed_dim: 3
lossconfig:
target: ldm.modules.losses.LPIPSWithDiscriminator
params:
disc_start: 50001
kl_weight: 0.000001
disc_weight: 0.5
ddconfig:
double_z: True
z_channels: 3
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult: [ 1,2,4 ] # num_down = len(ch_mult)-1
num_res_blocks: 2
attn_resolutions: [ ]
dropout: 0.0
data:
target: main.DataModuleFromConfig
params:
batch_size: 12
wrap: True
train:
target: ldm.data.imagenet.ImageNetSRTrain
params:
size: 256
degradation: pil_nearest
validation:
target: ldm.data.imagenet.ImageNetSRValidation
params:
size: 256
degradation: pil_nearest
lightning:
callbacks:
image_logger:
target: main.ImageLogger
params:
batch_frequency: 1000
max_images: 8
increase_log_steps: True
trainer:
benchmark: True
accumulate_grad_batches: 2
================================================
FILE: models/InstructDiffusion/stable_diffusion/configs/autoencoder/autoencoder_kl_8x8x64.yaml
================================================
model:
base_learning_rate: 4.5e-6
target: ldm.models.autoencoder.AutoencoderKL
params:
monitor: "val/rec_loss"
embed_dim: 64
lossconfig:
target: ldm.modules.losses.LPIPSWithDiscriminator
params:
disc_start: 50001
kl_weight: 0.000001
disc_weight: 0.5
ddconfig:
double_z: True
z_channels: 64
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult: [ 1,1,2,2,4,4] # num_down = len(ch_mult)-1
num_res_blocks: 2
attn_resolutions: [16,8]
dropout: 0.0
data:
target: main.DataModuleFromConfig
params:
batch_size: 12
wrap: True
train:
target: ldm.data.imagenet.ImageNetSRTrain
params:
size: 256
degradation: pil_nearest
validation:
target: ldm.data.imagenet.ImageNetSRValidation
params:
size: 256
degradation: pil_nearest
lightning:
callbacks:
image_logger:
target: main.ImageLogger
params:
batch_frequency: 1000
max_images: 8
increase_log_steps: True
trainer:
benchmark: True
accumulate_grad_batches: 2
================================================
FILE: models/InstructDiffusion/stable_diffusion/configs/latent-diffusion/celebahq-ldm-vq-4.yaml
================================================
model:
base_learning_rate: 2.0e-06
target: ldm.models.diffusion.ddpm.LatentDiffusion
params:
linear_start: 0.0015
linear_end: 0.0195
num_timesteps_cond: 1
log_every_t: 200
timesteps: 1000
first_stage_key: image
image_size: 64
channels: 3
monitor: val/loss_simple_ema
unet_config:
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
params:
image_size: 64
in_channels: 3
out_channels: 3
model_channels: 224
attention_resolutions:
# note: this isn\t actually the resolution but
# the downsampling factor, i.e. this corresnponds to
# attention on spatial resolution 8,16,32, as the
# spatial reolution of the latents is 64 for f4
- 8
- 4
- 2
num_res_blocks: 2
channel_mult:
- 1
- 2
- 3
- 4
num_head_channels: 32
first_stage_config:
target: ldm.models.autoencoder.VQModelInterface
params:
embed_dim: 3
n_embed: 8192
ckpt_path: models/first_stage_models/vq-f4/model.ckpt
ddconfig:
double_z: false
z_channels: 3
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 4
num_res_blocks: 2
attn_resolutions: []
dropout: 0.0
lossconfig:
target: torch.nn.Identity
cond_stage_config: __is_unconditional__
data:
target: main.DataModuleFromConfig
params:
batch_size: 48
num_workers: 5
wrap: false
train:
target: taming.data.faceshq.CelebAHQTrain
params:
size: 256
validation:
target: taming.data.faceshq.CelebAHQValidation
params:
size: 256
lightning:
callbacks:
image_logger:
target: main.ImageLogger
params:
batch_frequency: 5000
max_images: 8
increase_log_steps: False
trainer:
benchmark: True
================================================
FILE: models/InstructDiffusion/stable_diffusion/configs/latent-diffusion/cin-ldm-vq-f8.yaml
================================================
model:
base_learning_rate: 1.0e-06
target: ldm.models.diffusion.ddpm.LatentDiffusion
params:
linear_start: 0.0015
linear_end: 0.0195
num_timesteps_cond: 1
log_every_t: 200
timesteps: 1000
first_stage_key: image
cond_stage_key: class_label
image_size: 32
channels: 4
cond_stage_trainable: true
conditioning_key: crossattn
monitor: val/loss_simple_ema
unet_config:
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
params:
image_size: 32
in_channels: 4
out_channels: 4
model_channels: 256
attention_resolutions:
#note: this isn\t actually the resolution but
# the downsampling factor, i.e. this corresnponds to
# attention on spatial resolution 8,16,32, as the
# spatial reolution of the latents is 32 for f8
- 4
- 2
- 1
num_res_blocks: 2
channel_mult:
- 1
- 2
- 4
num_head_channels: 32
use_spatial_transformer: true
transformer_depth: 1
context_dim: 512
first_stage_config:
target: ldm.models.autoencoder.VQModelInterface
params:
embed_dim: 4
n_embed: 16384
ckpt_path: configs/first_stage_models/vq-f8/model.yaml
ddconfig:
double_z: false
z_channels: 4
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 2
- 4
num_res_blocks: 2
attn_resolutions:
- 32
dropout: 0.0
lossconfig:
target: torch.nn.Identity
cond_stage_config:
target: ldm.modules.encoders.modules.ClassEmbedder
params:
embed_dim: 512
key: class_label
data:
target: main.DataModuleFromConfig
params:
batch_size: 64
num_workers: 12
wrap: false
train:
target: ldm.data.imagenet.ImageNetTrain
params:
config:
size: 256
validation:
target: ldm.data.imagenet.ImageNetValidation
params:
config:
size: 256
lightning:
callbacks:
image_logger:
target: main.ImageLogger
params:
batch_frequency: 5000
max_images: 8
increase_log_steps: False
trainer:
benchmark: True
================================================
FILE: models/InstructDiffusion/stable_diffusion/configs/latent-diffusion/cin256-v2.yaml
================================================
model:
base_learning_rate: 0.0001
target: ldm.models.diffusion.ddpm.LatentDiffusion
params:
linear_start: 0.0015
linear_end: 0.0195
num_timesteps_cond: 1
log_every_t: 200
timesteps: 1000
first_stage_key: image
cond_stage_key: class_label
image_size: 64
channels: 3
cond_stage_trainable: true
conditioning_key: crossattn
monitor: val/loss
use_ema: False
unet_config:
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
params:
image_size: 64
in_channels: 3
out_channels: 3
model_channels: 192
attention_resolutions:
- 8
- 4
- 2
num_res_blocks: 2
channel_mult:
- 1
- 2
- 3
- 5
num_heads: 1
use_spatial_transformer: true
transformer_depth: 1
context_dim: 512
first_stage_config:
target: ldm.models.autoencoder.VQModelInterface
params:
embed_dim: 3
n_embed: 8192
ddconfig:
double_z: false
z_channels: 3
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 4
num_res_blocks: 2
attn_resolutions: []
dropout: 0.0
lossconfig:
target: torch.nn.Identity
cond_stage_config:
target: ldm.modules.encoders.modules.ClassEmbedder
params:
n_classes: 1001
embed_dim: 512
key: class_label
================================================
FILE: models/InstructDiffusion/stable_diffusion/configs/latent-diffusion/ffhq-ldm-vq-4.yaml
================================================
model:
base_learning_rate: 2.0e-06
target: ldm.models.diffusion.ddpm.LatentDiffusion
params:
linear_start: 0.0015
linear_end: 0.0195
num_timesteps_cond: 1
log_every_t: 200
timesteps: 1000
first_stage_key: image
image_size: 64
channels: 3
monitor: val/loss_simple_ema
unet_config:
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
params:
image_size: 64
in_channels: 3
out_channels: 3
model_channels: 224
attention_resolutions:
# note: this isn\t actually the resolution but
# the downsampling factor, i.e. this corresnponds to
# attention on spatial resolution 8,16,32, as the
# spatial reolution of the latents is 64 for f4
- 8
- 4
- 2
num_res_blocks: 2
channel_mult:
- 1
- 2
- 3
- 4
num_head_channels: 32
first_stage_config:
target: ldm.models.autoencoder.VQModelInterface
params:
embed_dim: 3
n_embed: 8192
ckpt_path: configs/first_stage_models/vq-f4/model.yaml
ddconfig:
double_z: false
z_channels: 3
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 4
num_res_blocks: 2
attn_resolutions: []
dropout: 0.0
lossconfig:
target: torch.nn.Identity
cond_stage_config: __is_unconditional__
data:
target: main.DataModuleFromConfig
params:
batch_size: 42
num_workers: 5
wrap: false
train:
target: taming.data.faceshq.FFHQTrain
params:
size: 256
validation:
target: taming.data.faceshq.FFHQValidation
params:
size: 256
lightning:
callbacks:
image_logger:
target: main.ImageLogger
params:
batch_frequency: 5000
max_images: 8
increase_log_steps: False
trainer:
benchmark: True
================================================
FILE: models/InstructDiffusion/stable_diffusion/configs/latent-diffusion/lsun_bedrooms-ldm-vq-4.yaml
================================================
model:
base_learning_rate: 2.0e-06
target: ldm.models.diffusion.ddpm.LatentDiffusion
params:
linear_start: 0.0015
linear_end: 0.0195
num_timesteps_cond: 1
log_every_t: 200
timesteps: 1000
first_stage_key: image
image_size: 64
channels: 3
monitor: val/loss_simple_ema
unet_config:
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
params:
image_size: 64
in_channels: 3
out_channels: 3
model_channels: 224
attention_resolutions:
# note: this isn\t actually the resolution but
# the downsampling factor, i.e. this corresnponds to
# attention on spatial resolution 8,16,32, as the
# spatial reolution of the latents is 64 for f4
- 8
- 4
- 2
num_res_blocks: 2
channel_mult:
- 1
- 2
- 3
- 4
num_head_channels: 32
first_stage_config:
target: ldm.models.autoencoder.VQModelInterface
params:
ckpt_path: configs/first_stage_models/vq-f4/model.yaml
embed_dim: 3
n_embed: 8192
ddconfig:
double_z: false
z_channels: 3
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 4
num_res_blocks: 2
attn_resolutions: []
dropout: 0.0
lossconfig:
target: torch.nn.Identity
cond_stage_config: __is_unconditional__
data:
target: main.DataModuleFromConfig
params:
batch_size: 48
num_workers: 5
wrap: false
train:
target: ldm.data.lsun.LSUNBedroomsTrain
params:
size: 256
validation:
target: ldm.data.lsun.LSUNBedroomsValidation
params:
size: 256
lightning:
callbacks:
image_logger:
target: main.ImageLogger
params:
batch_frequency: 5000
max_images: 8
increase_log_steps: False
trainer:
benchmark: True
================================================
FILE: models/InstructDiffusion/stable_diffusion/configs/latent-diffusion/lsun_churches-ldm-kl-8.yaml
================================================
model:
base_learning_rate: 5.0e-5 # set to target_lr by starting main.py with '--scale_lr False'
target: ldm.models.diffusion.ddpm.LatentDiffusion
params:
linear_start: 0.0015
linear_end: 0.0155
num_timesteps_cond: 1
log_every_t: 200
timesteps: 1000
loss_type: l1
first_stage_key: "image"
cond_stage_key: "image"
image_size: 32
channels: 4
cond_stage_trainable: False
concat_mode: False
scale_by_std: True
monitor: 'val/loss_simple_ema'
scheduler_config: # 10000 warmup steps
target: ldm.lr_scheduler.LambdaLinearScheduler
params:
warm_up_steps: [10000]
cycle_lengths: [10000000000000]
f_start: [1.e-6]
f_max: [1.]
f_min: [ 1.]
unet_config:
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
params:
image_size: 32
in_channels: 4
out_channels: 4
model_channels: 192
attention_resolutions: [ 1, 2, 4, 8 ] # 32, 16, 8, 4
num_res_blocks: 2
channel_mult: [ 1,2,2,4,4 ] # 32, 16, 8, 4, 2
num_heads: 8
use_scale_shift_norm: True
resblock_updown: True
first_stage_config:
target: ldm.models.autoencoder.AutoencoderKL
params:
embed_dim: 4
monitor: "val/rec_loss"
ckpt_path: "models/first_stage_models/kl-f8/model.ckpt"
ddconfig:
double_z: True
z_channels: 4
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult: [ 1,2,4,4 ] # num_down = len(ch_mult)-1
num_res_blocks: 2
attn_resolutions: [ ]
dropout: 0.0
lossconfig:
target: torch.nn.Identity
cond_stage_config: "__is_unconditional__"
data:
target: main.DataModuleFromConfig
params:
batch_size: 96
num_workers: 5
wrap: False
train:
target: ldm.data.lsun.LSUNChurchesTrain
params:
size: 256
validation:
target: ldm.data.lsun.LSUNChurchesValidation
params:
size: 256
lightning:
callbacks:
image_logger:
target: main.ImageLogger
params:
batch_frequency: 5000
max_images: 8
increase_log_steps: False
trainer:
benchmark: True
================================================
FILE: models/InstructDiffusion/stable_diffusion/configs/latent-diffusion/txt2img-1p4B-eval.yaml
================================================
model:
base_learning_rate: 5.0e-05
target: ldm.models.diffusion.ddpm.LatentDiffusion
params:
linear_start: 0.00085
linear_end: 0.012
num_timesteps_cond: 1
log_every_t: 200
timesteps: 1000
first_stage_key: image
cond_stage_key: caption
image_size: 32
channels: 4
cond_stage_trainable: true
conditioning_key: crossattn
monitor: val/loss_simple_ema
scale_factor: 0.18215
use_ema: False
unet_config:
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
params:
image_size: 32
in_channels: 4
out_channels: 4
model_channels: 320
attention_resolutions:
- 4
- 2
- 1
num_res_blocks: 2
channel_mult:
- 1
- 2
- 4
- 4
num_heads: 8
use_spatial_transformer: true
transformer_depth: 1
context_dim: 1280
use_checkpoint: true
legacy: False
first_stage_config:
target: ldm.models.autoencoder.AutoencoderKL
params:
embed_dim: 4
monitor: val/rec_loss
ddconfig:
double_z: true
z_channels: 4
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 4
- 4
num_res_blocks: 2
attn_resolutions: []
dropout: 0.0
lossconfig:
target: torch.nn.Identity
cond_stage_config:
target: ldm.modules.encoders.modules.BERTEmbedder
params:
n_embed: 1280
n_layer: 32
================================================
FILE: models/InstructDiffusion/stable_diffusion/configs/retrieval-augmented-diffusion/768x768.yaml
================================================
model:
base_learning_rate: 0.0001
target: ldm.models.diffusion.ddpm.LatentDiffusion
params:
linear_start: 0.0015
linear_end: 0.015
num_timesteps_cond: 1
log_every_t: 200
timesteps: 1000
first_stage_key: jpg
cond_stage_key: nix
image_size: 48
channels: 16
cond_stage_trainable: false
conditioning_key: crossattn
monitor: val/loss_simple_ema
scale_by_std: false
scale_factor: 0.22765929
unet_config:
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
params:
image_size: 48
in_channels: 16
out_channels: 16
model_channels: 448
attention_resolutions:
- 4
- 2
- 1
num_res_blocks: 2
channel_mult:
- 1
- 2
- 3
- 4
use_scale_shift_norm: false
resblock_updown: false
num_head_channels: 32
use_spatial_transformer: true
transformer_depth: 1
context_dim: 768
use_checkpoint: true
first_stage_config:
target: ldm.models.autoencoder.AutoencoderKL
params:
monitor: val/rec_loss
embed_dim: 16
ddconfig:
double_z: true
z_channels: 16
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 1
- 2
- 2
- 4
num_res_blocks: 2
attn_resolutions:
- 16
dropout: 0.0
lossconfig:
target: torch.nn.Identity
cond_stage_config:
target: torch.nn.Identity
================================================
FILE: models/InstructDiffusion/stable_diffusion/configs/stable-diffusion/v1-inference.yaml
================================================
model:
base_learning_rate: 1.0e-04
target: ldm.models.diffusion.ddpm.LatentDiffusion
params:
linear_start: 0.00085
linear_end: 0.0120
num_timesteps_cond: 1
log_every_t: 200
timesteps: 1000
first_stage_key: "jpg"
cond_stage_key: "txt"
image_size: 64
channels: 4
cond_stage_trainable: false # Note: different from the one we trained before
conditioning_key: crossattn
monitor: val/loss_simple_ema
scale_factor: 0.18215
use_ema: False
scheduler_config: # 10000 warmup steps
target: ldm.lr_scheduler.LambdaLinearScheduler
params:
warm_up_steps: [ 10000 ]
cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
f_start: [ 1.e-6 ]
f_max: [ 1. ]
f_min: [ 1. ]
unet_config:
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
params:
image_size: 32 # unused
in_channels: 4
out_channels: 4
model_channels: 320
attention_resolutions: [ 4, 2, 1 ]
num_res_blocks: 2
channel_mult: [ 1, 2, 4, 4 ]
num_heads: 8
use_spatial_transformer: True
transformer_depth: 1
context_dim: 768
use_checkpoint: True
legacy: False
first_stage_config:
target: ldm.models.autoencoder.AutoencoderKL
params:
embed_dim: 4
monitor: val/rec_loss
ddconfig:
double_z: true
z_channels: 4
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 4
- 4
num_res_blocks: 2
attn_resolutions: []
dropout: 0.0
lossconfig:
target: torch.nn.Identity
cond_stage_config:
target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
================================================
FILE: models/InstructDiffusion/stable_diffusion/environment.yaml
================================================
name: ldm
channels:
- pytorch
- defaults
dependencies:
- python=3.8.5
- pip=20.3
- cudatoolkit=11.3
- pytorch=1.11.0
- torchvision=0.12.0
- numpy=1.19.2
- pip:
- albumentations==0.4.3
- diffusers
- opencv-python==4.1.2.30
- pudb==2019.2
- invisible-watermark
- imageio==2.9.0
- imageio-ffmpeg==0.4.2
- pytorch-lightning==1.4.2
- omegaconf==2.1.1
- test-tube>=0.7.5
- streamlit>=0.73.1
- einops==0.3.0
- torch-fidelity==0.3.0
- transformers==4.19.2
- torchmetrics==0.6.0
- kornia==0.6
- -e git+https://github.com/CompVis/taming-transformers.git@master#egg=taming-transformers
- -e git+https://github.com/openai/CLIP.git@main#egg=clip
- -e .
================================================
FILE: models/InstructDiffusion/stable_diffusion/ldm/lr_scheduler.py
================================================
import numpy as np
class LambdaWarmUpCosineScheduler:
"""
note: use with a base_lr of 1.0
"""
def __init__(self, warm_up_steps, lr_min, lr_max, lr_start, max_decay_steps, verbosity_interval=0):
self.lr_warm_up_steps = warm_up_steps
self.lr_start = lr_start
self.lr_min = lr_min
self.lr_max = lr_max
self.lr_max_decay_steps = max_decay_steps
self.last_lr = 0.
self.verbosity_interval = verbosity_interval
def schedule(self, n, **kwargs):
if self.verbosity_interval > 0:
if n % self.verbosity_interval == 0: print(f"current step: {n}, recent lr-multiplier: {self.last_lr}")
if n < self.lr_warm_up_steps:
lr = (self.lr_max - self.lr_start) / self.lr_warm_up_steps * n + self.lr_start
self.last_lr = lr
return lr
else:
t = (n - self.lr_warm_up_steps) / (self.lr_max_decay_steps - self.lr_warm_up_steps)
t = min(t, 1.0)
lr = self.lr_min + 0.5 * (self.lr_max - self.lr_min) * (
1 + np.cos(t * np.pi))
self.last_lr = lr
return lr
def __call__(self, n, **kwargs):
return self.schedule(n,**kwargs)
class LambdaWarmUpCosineScheduler2:
"""
supports repeated iterations, configurable via lists
note: use with a base_lr of 1.0.
"""
def __init__(self, warm_up_steps, f_min, f_max, f_start, cycle_lengths, verbosity_interval=0):
assert len(warm_up_steps) == len(f_min) == len(f_max) == len(f_start) == len(cycle_lengths)
self.lr_warm_up_steps = warm_up_steps
self.f_start = f_start
self.f_min = f_min
self.f_max = f_max
self.cycle_lengths = cycle_lengths
self.cum_cycles = np.cumsum([0] + list(self.cycle_lengths))
self.last_f = 0.
self.verbosity_interval = verbosity_interval
def find_in_interval(self, n):
interval = 0
for cl in self.cum_cycles[1:]:
if n <= cl:
return interval
interval += 1
def schedule(self, n, **kwargs):
cycle = self.find_in_interval(n)
n = n - self.cum_cycles[cycle]
if self.verbosity_interval > 0:
if n % self.verbosity_interval == 0: print(f"current step: {n}, recent lr-multiplier: {self.last_f}, "
f"current cycle {cycle}")
if n < self.lr_warm_up_steps[cycle]:
f = (self.f_max[cycle] - self.f_start[cycle]) / self.lr_warm_up_steps[cycle] * n + self.f_start[cycle]
self.last_f = f
return f
else:
t = (n - self.lr_warm_up_steps[cycle]) / (self.cycle_lengths[cycle] - self.lr_warm_up_steps[cycle])
t = min(t, 1.0)
f = self.f_min[cycle] + 0.5 * (self.f_max[cycle] - self.f_min[cycle]) * (
1 + np.cos(t * np.pi))
self.last_f = f
return f
def __call__(self, n, **kwargs):
return self.schedule(n, **kwargs)
class LambdaLinearScheduler(LambdaWarmUpCosineScheduler2):
def schedule(self, n, **kwargs):
cycle = self.find_in_interval(n)
n = n - self.cum_cycles[cycle]
if self.verbosity_interval > 0:
if n % self.verbosity_interval == 0: print(f"current step: {n}, recent lr-multiplier: {self.last_f}, "
f"current cycle {cycle}")
if n < self.lr_warm_up_steps[cycle]:
f = (self.f_max[cycle] - self.f_start[cycle]) / self.lr_warm_up_steps[cycle] * n + self.f_start[cycle]
self.last_f = f
return f
else:
f = self.f_min[cycle] + (self.f_max[cycle] - self.f_min[cycle]) * (self.cycle_lengths[cycle] - n) / (self.cycle_lengths[cycle])
self.last_f = f
return f
================================================
FILE: models/InstructDiffusion/stable_diffusion/ldm/models/autoencoder.py
================================================
# --------------------------------------------------------
# Stable-Diffusion-Torch
# Based on Stable-Diffusion (https://github.com/CompVis/stable-diffusion)
# Removed Pytorch-lightning by Zigang Geng (zigang@mail.ustc.edu.cn)
# --------------------------------------------------------
import torch
import torch.nn as nn
import torch.nn.functional as F
from contextlib import contextmanager
from taming.modules.vqvae.quantize import VectorQuantizer2 as VectorQuantizer
from ldm.modules.diffusionmodules.model import Encoder, Decoder
from ldm.modules.distributions.distributions import DiagonalGaussianDistribution
from ldm.util import instantiate_from_config
class VQModel(nn.Module):
def __init__(self,
ddconfig,
lossconfig,
n_embed,
embed_dim,
ckpt_path=None,
ignore_keys=[],
image_key="image",
colorize_nlabels=None,
monitor=None,
batch_resize_range=None,
scheduler_config=None,
lr_g_factor=1.0,
remap=None,
sane_index_shape=False, # tell vector quantizer to return indices as bhw
use_ema=False
):
super().__init__()
self.embed_dim = embed_dim
self.n_embed = n_embed
self.image_key = image_key
self.encoder = Encoder(**ddconfig)
self.decoder = Decoder(**ddconfig)
self.loss = instantiate_from_config(lossconfig)
self.quantize = VectorQuantizer(n_embed, embed_dim, beta=0.25,
remap=remap,
sane_index_shape=sane_index_shape)
self.quant_conv = torch.nn.Conv2d(ddconfig["z_channels"], embed_dim, 1)
self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
if colorize_nlabels is not None:
assert type(colorize_nlabels)==int
self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1))
if monitor is not None:
self.monitor = monitor
self.batch_resize_range = batch_resize_range
if self.batch_resize_range is not None:
print(f"{self.__class__.__name__}: Using per-batch resizing in range {batch_resize_range}.")
self.use_ema = use_ema
if self.use_ema:
self.model_ema = LitEma(self)
print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
if ckpt_path is not None:
self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
self.scheduler_config = scheduler_config
self.lr_g_factor = lr_g_factor
@contextmanager
def ema_scope(self, context=None):
if self.use_ema:
self.model_ema.store(self.parameters())
self.model_ema.copy_to(self)
if context is not None:
print(f"{context}: Switched to EMA weights")
try:
yield None
finally:
if self.use_ema:
self.model_ema.restore(self.parameters())
if context is not None:
print(f"{context}: Restored training weights")
def init_from_ckpt(self, path, ignore_keys=list()):
sd = torch.load(path, map_location="cpu")["state_dict"]
keys = list(sd.keys())
for k in keys:
for ik in ignore_keys:
if k.startswith(ik):
print("Deleting key {} from state_dict.".format(k))
del sd[k]
missing, unexpected = self.load_state_dict(sd, strict=False)
print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
if len(missing) > 0:
print(f"Missing Keys: {missing}")
print(f"Unexpected Keys: {unexpected}")
def on_train_batch_end(self, *args, **kwargs):
if self.use_ema:
self.model_ema(self)
def encode(self, x):
h = self.encoder(x)
h = self.quant_conv(h)
quant, emb_loss, info = self.quantize(h)
return quant, emb_loss, info
def encode_to_prequant(self, x):
h = self.encoder(x)
h = self.quant_conv(h)
return h
def decode(self, quant):
quant = self.post_quant_conv(quant)
dec = self.decoder(quant)
return dec
def decode_code(self, code_b):
quant_b = self.quantize.embed_code(code_b)
dec = self.decode(quant_b)
return dec
def forward(self, input, return_pred_indices=False):
quant, diff, (_,_,ind) = self.encode(input)
dec = self.decode(quant)
if return_pred_indices:
return dec, diff, ind
return dec, diff
def get_input(self, batch, k):
x = batch[k]
if len(x.shape) == 3:
x = x[..., None]
x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float()
if self.batch_resize_range is not None:
lower_size = self.batch_resize_range[0]
upper_size = self.batch_resize_range[1]
if self.global_step <= 4:
# do the first few batches with max size to avoid later oom
new_resize = upper_size
else:
new_resize = np.random.choice(np.arange(lower_size, upper_size+16, 16))
if new_resize != x.shape[2]:
x = F.interpolate(x, size=new_resize, mode="bicubic")
x = x.detach()
return x
def training_step(self, batch, batch_idx, optimizer_idx):
# https://github.com/pytorch/pytorch/issues/37142
# try not to fool the heuristics
x = self.get_input(batch, self.image_key)
xrec, qloss, ind = self(x, return_pred_indices=True)
if optimizer_idx == 0:
# autoencode
aeloss, log_dict_ae = self.loss(qloss, x, xrec, optimizer_idx, self.global_step,
last_layer=self.get_last_layer(), split="train",
predicted_indices=ind)
self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True)
return aeloss
if optimizer_idx == 1:
# discriminator
discloss, log_dict_disc = self.loss(qloss, x, xrec, optimizer_idx, self.global_step,
last_layer=self.get_last_layer(), split="train")
self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=True)
return discloss
def validation_step(self, batch, batch_idx):
log_dict = self._validation_step(batch, batch_idx)
with self.ema_scope():
log_dict_ema = self._validation_step(batch, batch_idx, suffix="_ema")
return log_dict
def _validation_step(self, batch, batch_idx, suffix=""):
x = self.get_input(batch, self.image_key)
xrec, qloss, ind = self(x, return_pred_indices=True)
aeloss, log_dict_ae = self.loss(qloss, x, xrec, 0,
self.global_step,
last_layer=self.get_last_layer(),
split="val"+suffix,
predicted_indices=ind
)
discloss, log_dict_disc = self.loss(qloss, x, xrec, 1,
self.global_step,
last_layer=self.get_last_layer(),
split="val"+suffix,
predicted_indices=ind
)
rec_loss = log_dict_ae[f"val{suffix}/rec_loss"]
self.log(f"val{suffix}/rec_loss", rec_loss,
prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True)
self.log(f"val{suffix}/aeloss", aeloss,
prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True)
# if version.parse(pl.__version__) >= version.parse('1.4.0'):
# del log_dict_ae[f"val{suffix}/rec_loss"]
self.log_dict(log_dict_ae)
self.log_dict(log_dict_disc)
return self.log_dict
def configure_optimizers(self):
lr_d = self.learning_rate
lr_g = self.lr_g_factor*self.learning_rate
print("lr_d", lr_d)
print("lr_g", lr_g)
opt_ae = torch.optim.Adam(list(self.encoder.parameters())+
list(self.decoder.parameters())+
list(self.quantize.parameters())+
list(self.quant_conv.parameters())+
list(self.post_quant_conv.parameters()),
lr=lr_g, betas=(0.5, 0.9))
opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(),
lr=lr_d, betas=(0.5, 0.9))
if self.scheduler_config is not None:
scheduler = instantiate_from_config(self.scheduler_config)
print("Setting up LambdaLR scheduler...")
scheduler = [
{
'scheduler': LambdaLR(opt_ae, lr_lambda=scheduler.schedule),
'interval': 'step',
'frequency': 1
},
{
'scheduler': LambdaLR(opt_disc, lr_lambda=scheduler.schedule),
'interval': 'step',
'frequency': 1
},
]
return [opt_ae, opt_disc], scheduler
return [opt_ae, opt_disc], []
def get_last_layer(self):
return self.decoder.conv_out.weight
def log_images(self, batch, only_inputs=False, plot_ema=False, **kwargs):
log = dict()
x = self.get_input(batch, self.image_key)
x = x.to(batch.device)
if only_inputs:
log["inputs"] = x
return log
xrec, _ = self(x)
if x.shape[1] > 3:
# colorize with random projection
assert xrec.shape[1] > 3
x = self.to_rgb(x)
xrec = self.to_rgb(xrec)
log["inputs"] = x
log["reconstructions"] = xrec
if plot_ema:
with self.ema_scope():
xrec_ema, _ = self(x)
if x.shape[1] > 3: xrec_ema = self.to_rgb(xrec_ema)
log["reconstructions_ema"] = xrec_ema
return log
def to_rgb(self, x):
assert self.image_key == "segmentation"
if not hasattr(self, "colorize"):
self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x))
x = F.conv2d(x, weight=self.colorize)
x = 2.*(x-x.min())/(x.max()-x.min()) - 1.
return x
class VQModelInterface(VQModel):
def __init__(self, embed_dim, *args, **kwargs):
super().__init__(embed_dim=embed_dim, *args, **kwargs)
self.embed_dim = embed_dim
def encode(self, x):
h = self.encoder(x)
h = self.quant_conv(h)
return h
def decode(self, h, force_not_quantize=False):
# also go through quantization layer
if not force_not_quantize:
quant, emb_loss, info = self.quantize(h)
else:
quant = h
quant = self.post_quant_conv(quant)
dec = self.decoder(quant)
return dec
class AutoencoderKL(nn.Module):
def __init__(self,
ddconfig,
lossconfig,
embed_dim,
ckpt_path=None,
ignore_keys=[],
image_key="image",
colorize_nlabels=None,
monitor=None,
):
super().__init__()
self.image_key = image_key
self.encoder = Encoder(**ddconfig)
self.decoder = Decoder(**ddconfig)
self.loss = instantiate_from_config(lossconfig)
assert ddconfig["double_z"]
self.quant_conv = torch.nn.Conv2d(2*ddconfig["z_channels"], 2*embed_dim, 1)
self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
self.embed_dim = embed_dim
if colorize_nlabels is not None:
assert type(colorize_nlabels)==int
self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1))
if monitor is not None:
self.monitor = monitor
if ckpt_path is not None:
self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
def init_from_ckpt(self, path, ignore_keys=list()):
sd = torch.load(path, map_location="cpu")["state_dict"]
keys = list(sd.keys())
for k in keys:
for ik in ignore_keys:
if k.startswith(ik):
print("Deleting key {} from state_dict.".format(k))
del sd[k]
self.load_state_dict(sd, strict=False)
print(f"Restored from {path}")
def encode(self, x):
h = self.encoder(x)
moments = self.quant_conv(h)
posterior = DiagonalGaussianDistribution(moments)
return posterior
def decode(self, z):
z = self.post_quant_conv(z.type(self.post_quant_conv.weight.dtype))
dec = self.decoder(z)
return dec
def forward(self, input, sample_posterior=True):
posterior = self.encode(input)
if sample_posterior:
z = posterior.sample()
else:
z = posterior.mode()
dec = self.decode(z)
return dec, posterior
def get_input(self, batch, k):
x = batch[k]
if len(x.shape) == 3:
x = x[..., None]
x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float()
return x
def training_step(self, batch, batch_idx, optimizer_idx):
inputs = self.get_input(batch, self.image_key)
reconstructions, posterior = self(inputs)
if optimizer_idx == 0:
# train encoder+decoder+logvar
aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step,
last_layer=self.get_last_layer(), split="train")
self.log("aeloss", aeloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=False)
return aeloss
if optimizer_idx == 1:
# train the discriminator
discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step,
last_layer=self.get_last_layer(), split="train")
self.log("discloss", discloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=False)
return discloss
def validation_step(self, batch, batch_idx):
inputs = self.get_input(batch, self.image_key)
reconstructions, posterior = self(inputs)
aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, 0, self.global_step,
last_layer=self.get_last_layer(), split="val")
discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, 1, self.global_step,
last_layer=self.get_last_layer(), split="val")
self.log("val/rec_loss", log_dict_ae["val/rec_loss"])
self.log_dict(log_dict_ae)
self.log_dict(log_dict_disc)
return self.log_dict
def configure_optimizers(self):
lr = self.learning_rate
opt_ae = torch.optim.Adam(list(self.encoder.parameters())+
list(self.decoder.parameters())+
list(self.quant_conv.parameters())+
list(self.post_quant_conv.parameters()),
lr=lr, betas=(0.5, 0.9))
opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(),
lr=lr, betas=(0.5, 0.9))
return [opt_ae, opt_disc], []
def get_last_layer(self):
return self.decoder.conv_out.weight
@torch.no_grad()
def log_images(self, batch, only_inputs=False, **kwargs):
log = dict()
x = self.get_input(batch, self.image_key)
x = x.to(batch.device)
if not only_inputs:
xrec, posterior = self(x)
if x.shape[1] > 3:
# colorize with random projection
assert xrec.shape[1] > 3
x = self.to_rgb(x)
xrec = self.to_rgb(xrec)
log["samples"] = self.decode(torch.randn_like(posterior.sample()))
log["reconstructions"] = xrec
log["inputs"] = x
return log
def to_rgb(self, x):
assert self.image_key == "segmentation"
if not hasattr(self, "colorize"):
self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x))
x = F.conv2d(x, weight=self.colorize)
x = 2.*(x-x.min())/(x.max()-x.min()) - 1.
return x
class IdentityFirstStage(torch.nn.Module):
def __init__(self, *args, vq_interface=False, **kwargs):
self.vq_interface = vq_interface # TODO: Should be true by default but check to not break older stuff
super().__init__()
def encode(self, x, *args, **kwargs):
return x
def decode(self, x, *args, **kwargs):
return x
def quantize(self, x, *args, **kwargs):
if self.vq_interface:
return x, None, [None, None, None]
return x
def forward(self, x, *args, **kwargs):
return x
================================================
FILE: models/InstructDiffusion/stable_diffusion/ldm/models/diffusion/__init__.py
================================================
================================================
FILE: models/InstructDiffusion/stable_diffusion/ldm/models/diffusion/classifier.py
================================================
import os
import torch
import pytorch_lightning as pl
from omegaconf import OmegaConf
from torch.nn import functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from copy import deepcopy
from einops import rearrange
from glob import glob
from natsort import natsorted
from ldm.modules.diffusionmodules.openaimodel import EncoderUNetModel, UNetModel
from ldm.util import log_txt_as_img, default, ismap, instantiate_from_config
__models__ = {
'class_label': EncoderUNetModel,
'segmentation': UNetModel
}
def disabled_train(self, mode=True):
"""Overwrite model.train with this function to make sure train/eval mode
does not change anymore."""
return self
class NoisyLatentImageClassifier(pl.LightningModule):
def __init__(self,
diffusion_path,
num_classes,
ckpt_path=None,
pool='attention',
label_key=None,
diffusion_ckpt_path=None,
scheduler_config=None,
weight_decay=1.e-2,
log_steps=10,
monitor='val/loss',
*args,
**kwargs):
super().__init__(*args, **kwargs)
self.num_classes = num_classes
# get latest config of diffusion model
diffusion_config = natsorted(glob(os.path.join(diffusion_path, 'configs', '*-project.yaml')))[-1]
self.diffusion_config = OmegaConf.load(diffusion_config).model
self.diffusion_config.params.ckpt_path = diffusion_ckpt_path
self.load_diffusion()
self.monitor = monitor
self.numd = self.diffusion_model.first_stage_model.encoder.num_resolutions - 1
self.log_time_interval = self.diffusion_model.num_timesteps // log_steps
self.log_steps = log_steps
self.label_key = label_key if not hasattr(self.diffusion_model, 'cond_stage_key') \
else self.diffusion_model.cond_stage_key
assert self.label_key is not None, 'label_key neither in diffusion model nor in model.params'
if self.label_key not in __models__:
raise NotImplementedError()
self.load_classifier(ckpt_path, pool)
self.scheduler_config = scheduler_config
self.use_scheduler = self.scheduler_config is not None
self.weight_decay = weight_decay
def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
sd = torch.load(path, map_location="cpu")
if "state_dict" in list(sd.keys()):
sd = sd["state_dict"]
keys = list(sd.keys())
for k in keys:
for ik in ignore_keys:
if k.startswith(ik):
print("Deleting key {} from state_dict.".format(k))
del sd[k]
missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
sd, strict=False)
print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
if len(missing) > 0:
print(f"Missing Keys: {missing}")
if len(unexpected) > 0:
print(f"Unexpected Keys: {unexpected}")
def load_diffusion(self):
model = instantiate_from_config(self.diffusion_config)
self.diffusion_model = model.eval()
self.diffusion_model.train = disabled_train
for param in self.diffusion_model.parameters():
param.requires_grad = False
def load_classifier(self, ckpt_path, pool):
model_config = deepcopy(self.diffusion_config.params.unet_config.params)
model_config.in_channels = self.diffusion_config.params.unet_config.params.out_channels
model_config.out_channels = self.num_classes
if self.label_key == 'class_label':
model_config.pool = pool
self.model = __models__[self.label_key](**model_config)
if ckpt_path is not None:
print('#####################################################################')
print(f'load from ckpt "{ckpt_path}"')
print('#####################################################################')
self.init_from_ckpt(ckpt_path)
@torch.no_grad()
def get_x_noisy(self, x, t, noise=None):
noise = default(noise, lambda: torch.randn_like(x))
continuous_sqrt_alpha_cumprod = None
if self.diffusion_model.use_continuous_noise:
continuous_sqrt_alpha_cumprod = self.diffusion_model.sample_continuous_noise_level(x.shape[0], t + 1)
# todo: make sure t+1 is correct here
return self.diffusion_model.q_sample(x_start=x, t=t, noise=noise,
continuous_sqrt_alpha_cumprod=continuous_sqrt_alpha_cumprod)
def forward(self, x_noisy, t, *args, **kwargs):
return self.model(x_noisy, t)
@torch.no_grad()
def get_input(self, batch, k):
x = batch[k]
if len(x.shape) == 3:
x = x[..., None]
x = rearrange(x, 'b h w c -> b c h w')
x = x.to(memory_format=torch.contiguous_format).float()
return x
@torch.no_grad()
def get_conditioning(self, batch, k=None):
if k is None:
k = self.label_key
assert k is not None, 'Needs to provide label key'
targets = batch[k].to(self.device)
if self.label_key == 'segmentation':
targets = rearrange(targets, 'b h w c -> b c h w')
for down in range(self.numd):
h, w = targets.shape[-2:]
targets = F.interpolate(targets, size=(h // 2, w // 2), mode='nearest')
# targets = rearrange(targets,'b c h w -> b h w c')
return targets
def compute_top_k(self, logits, labels, k, reduction="mean"):
_, top_ks = torch.topk(logits, k, dim=1)
if reduction == "mean":
return (top_ks == labels[:, None]).float().sum(dim=-1).mean().item()
elif reduction == "none":
return (top_ks == labels[:, None]).float().sum(dim=-1)
def on_train_epoch_start(self):
# save some memory
self.diffusion_model.model.to('cpu')
@torch.no_grad()
def write_logs(self, loss, logits, targets):
log_prefix = 'train' if self.training else 'val'
log = {}
log[f"{log_prefix}/loss"] = loss.mean()
log[f"{log_prefix}/acc@1"] = self.compute_top_k(
logits, targets, k=1, reduction="mean"
)
log[f"{log_prefix}/acc@5"] = self.compute_top_k(
logits, targets, k=5, reduction="mean"
)
self.log_dict(log, prog_bar=False, logger=True, on_step=self.training, on_epoch=True)
self.log('loss', log[f"{log_prefix}/loss"], prog_bar=True, logger=False)
self.log('global_step', self.global_step, logger=False, on_epoch=False, prog_bar=True)
lr = self.optimizers().param_groups[0]['lr']
self.log('lr_abs', lr, on_step=True, logger=True, on_epoch=False, prog_bar=True)
def shared_step(self, batch, t=None):
x, *_ = self.diffusion_model.get_input(batch, k=self.diffusion_model.first_stage_key)
targets = self.get_conditioning(batch)
if targets.dim() == 4:
targets = targets.argmax(dim=1)
if t is None:
t = torch.randint(0, self.diffusion_model.num_timesteps, (x.shape[0],), device=self.device).long()
else:
t = torch.full(size=(x.shape[0],), fill_value=t, device=self.device).long()
x_noisy = self.get_x_noisy(x, t)
logits = self(x_noisy, t)
loss = F.cross_entropy(logits, targets, reduction='none')
self.write_logs(loss.detach(), logits.detach(), targets.detach())
loss = loss.mean()
return loss, logits, x_noisy, targets
def training_step(self, batch, batch_idx):
loss, *_ = self.shared_step(batch)
return loss
def reset_noise_accs(self):
self.noisy_acc = {t: {'acc@1': [], 'acc@5': []} for t in
range(0, self.diffusion_model.num_timesteps, self.diffusion_model.log_every_t)}
def on_validation_start(self):
self.reset_noise_accs()
@torch.no_grad()
def validation_step(self, batch, batch_idx):
loss, *_ = self.shared_step(batch)
for t in self.noisy_acc:
_, logits, _, targets = self.shared_step(batch, t)
self.noisy_acc[t]['acc@1'].append(self.compute_top_k(logits, targets, k=1, reduction='mean'))
self.noisy_acc[t]['acc@5'].append(self.compute_top_k(logits, targets, k=5, reduction='mean'))
return loss
def configure_optimizers(self):
optimizer = AdamW(self.model.parameters(), lr=self.learning_rate, weight_decay=self.weight_decay)
if self.use_scheduler:
scheduler = instantiate_from_config(self.scheduler_config)
print("Setting up LambdaLR scheduler...")
scheduler = [
{
'scheduler': LambdaLR(optimizer, lr_lambda=scheduler.schedule),
'interval': 'step',
'frequency': 1
}]
return [optimizer], scheduler
return optimizer
@torch.no_grad()
def log_images(self, batch, N=8, *args, **kwargs):
log = dict()
x = self.get_input(batch, self.diffusion_model.first_stage_key)
log['inputs'] = x
y = self.get_conditioning(batch)
if self.label_key == 'class_label':
y = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"])
log['labels'] = y
if ismap(y):
log['labels'] = self.diffusion_model.to_rgb(y)
for step in range(self.log_steps):
current_time = step * self.log_time_interval
_, logits, x_noisy, _ = self.shared_step(batch, t=current_time)
log[f'inputs@t{current_time}'] = x_noisy
pred = F.one_hot(logits.argmax(dim=1), num_classes=self.num_classes)
pred = rearrange(pred, 'b h w c -> b c h w')
log[f'pred@t{current_time}'] = self.diffusion_model.to_rgb(pred)
for key in log:
log[key] = log[key][:N]
return log
================================================
FILE: models/InstructDiffusion/stable_diffusion/ldm/models/diffusion/ddim.py
================================================
"""SAMPLING ONLY."""
import torch
import numpy as np
from tqdm import tqdm
from functools import partial
from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like, \
extract_into_tensor
class DDIMSampler(object):
def __init__(self, model, schedule="linear", **kwargs):
super().__init__()
self.model = model
self.ddpm_num_timesteps = model.num_timesteps
self.schedule = schedule
def register_buffer(self, name, attr):
if type(attr) == torch.Tensor:
if attr.device != torch.device("cuda"):
attr = attr.to(torch.device("cuda"))
setattr(self, name, attr)
def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True):
self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps,
num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose)
alphas_cumprod = self.model.alphas_cumprod
assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep'
to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device)
self.register_buffer('betas', to_torch(self.model.betas))
self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev))
# calculations for diffusion q(x_t | x_{t-1}) and others
self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu())))
self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu())))
self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu())))
self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu())))
self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1)))
# ddim sampling parameters
ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(),
ddim_timesteps=self.ddim_timesteps,
eta=ddim_eta,verbose=verbose)
self.register_buffer('ddim_sigmas', ddim_sigmas)
self.register_buffer('ddim_alphas', ddim_alphas)
self.register_buffer('ddim_alphas_prev', ddim_alphas_prev)
self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas))
sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(
(1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * (
1 - self.alphas_cumprod / self.alphas_cumprod_prev))
self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps)
@torch.no_grad()
def sample(self,
S,
batch_size,
shape,
conditioning=None,
callback=None,
normals_sequence=None,
img_callback=None,
quantize_x0=False,
eta=0.,
mask=None,
x0=None,
temperature=1.,
noise_dropout=0.,
score_corrector=None,
corrector_kwargs=None,
verbose=True,
x_T=None,
log_every_t=100,
unconditional_guidance_scale=1.,
unconditional_conditioning=None,
# this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
**kwargs
):
if conditioning is not None:
if isinstance(conditioning, dict):
cbs = conditioning[list(conditioning.keys())[0]].shape[0]
if cbs != batch_size:
print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
else:
if conditioning.shape[0] != batch_size:
print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
# sampling
C, H, W = shape
size = (batch_size, C, H, W)
print(f'Data shape for DDIM sampling is {size}, eta {eta}')
samples, intermediates = self.ddim_sampling(conditioning, size,
callback=callback,
img_callback=img_callback,
quantize_denoised=quantize_x0,
mask=mask, x0=x0,
ddim_use_original_steps=False,
noise_dropout=noise_dropout,
temperature=temperature,
score_corrector=score_corrector,
corrector_kwargs=corrector_kwargs,
x_T=x_T,
log_every_t=log_every_t,
unconditional_guidance_scale=unconditional_guidance_scale,
unconditional_conditioning=unconditional_conditioning,
)
return samples, intermediates
@torch.no_grad()
def ddim_sampling(self, cond, shape,
x_T=None, ddim_use_original_steps=False,
callback=None, timesteps=None, quantize_denoised=False,
mask=None, x0=None, img_callback=None, log_every_t=100,
temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
unconditional_guidance_scale=1., unconditional_conditioning=None,):
device = self.model.betas.device
b = shape[0]
if x_T is None:
img = torch.randn(shape, device=device)
else:
img = x_T
if timesteps is None:
timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps
elif timesteps is not None and not ddim_use_original_steps:
subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1
timesteps = self.ddim_timesteps[:subset_end]
intermediates = {'x_inter': [img], 'pred_x0': [img]}
time_range = reversed(range(0,timesteps)) if ddim_use_original_steps else np.flip(timesteps)
total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]
print(f"Running DDIM Sampling with {total_steps} timesteps")
iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps)
for i, step in enumerate(iterator):
index = total_steps - i - 1
ts = torch.full((b,), step, device=device, dtype=torch.long)
if mask is not None:
assert x0 is not None
img_orig = self.model.q_sample(x0, ts) # TODO: deterministic forward pass?
img = img_orig * mask + (1. - mask) * img
outs = self.p_sample_ddim(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps,
quantize_denoised=quantize_denoised, temperature=temperature,
noise_dropout=noise_dropout, score_corrector=score_corrector,
corrector_kwargs=corrector_kwargs,
unconditional_guidance_scale=unconditional_guidance_scale,
unconditional_conditioning=unconditional_conditioning)
img, pred_x0 = outs
if callback: callback(i)
if img_callback: img_callback(pred_x0, i)
if index % log_every_t == 0 or index == total_steps - 1:
intermediates['x_inter'].append(img)
intermediates['pred_x0'].append(pred_x0)
return img, intermediates
@torch.no_grad()
def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
unconditional_guidance_scale=1., unconditional_conditioning=None):
b, *_, device = *x.shape, x.device
if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
e_t = self.model.apply_model(x, t, c)
else:
x_in = torch.cat([x] * 2)
t_in = torch.cat([t] * 2)
c_in = torch.cat([unconditional_conditioning, c])
e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
if score_corrector is not None:
assert self.model.parameterization == "eps"
e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
# select parameters corresponding to the currently considered timestep
a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
# current prediction for x_0
pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
if quantize_denoised:
pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
# direction pointing to x_t
dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
if noise_dropout > 0.:
noise = torch.nn.functional.dropout(noise, p=noise_dropout)
x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
return x_prev, pred_x0
@torch.no_grad()
def stochastic_encode(self, x0, t, use_original_steps=False, noise=None):
# fast, but does not allow for exact reconstruction
# t serves as an index to gather the correct alphas
if use_original_steps:
sqrt_alphas_cumprod = self.sqrt_alphas_cumprod
sqrt_one_minus_alphas_cumprod = self.sqrt_one_minus_alphas_cumprod
else:
sqrt_alphas_cumprod = torch.sqrt(self.ddim_alphas)
sqrt_one_minus_alphas_cumprod = self.ddim_sqrt_one_minus_alphas
if noise is None:
noise = torch.randn_like(x0)
return (extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0 +
extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape) * noise)
@torch.no_grad()
def decode(self, x_latent, cond, t_start, unconditional_guidance_scale=1.0, unconditional_conditioning=None,
use_original_steps=False):
timesteps = np.arange(self.ddpm_num_timesteps) if use_original_steps else self.ddim_timesteps
timesteps = timesteps[:t_start]
time_range = np.flip(timesteps)
total_steps = timesteps.shape[0]
print(f"Running DDIM Sampling with {total_steps} timesteps")
iterator = tqdm(time_range, desc='Decoding image', total=total_steps)
x_dec = x_latent
for i, step in enumerate(iterator):
index = total_steps - i - 1
ts = torch.full((x_latent.shape[0],), step, device=x_latent.device, dtype=torch.long)
x_dec, _ = self.p_sample_ddim(x_dec, cond, ts, index=index, use_original_steps=use_original_steps,
unconditional_guidance_scale=unconditional_guidance_scale,
unconditional_conditioning=unconditional_conditioning)
return x_dec
================================================
FILE: models/InstructDiffusion/stable_diffusion/ldm/models/diffusion/ddpm.py
================================================
"""
wild mixture of
https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py
https://github.com/CompVis/taming-transformers
-- merci
"""
import torch
import torch.nn as nn
import numpy as np
import pytorch_lightning as pl
from torch.optim.lr_scheduler import LambdaLR
from einops import rearrange, repeat
from contextlib import contextmanager
from functools import partial
from tqdm import tqdm
from torchvision.utils import make_grid
from pytorch_lightning.utilities.distributed import rank_zero_only
from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config
from ldm.modules.ema import LitEma
from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution
from ldm.models.autoencoder import VQModelInterface, IdentityFirstStage, AutoencoderKL
from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
from ldm.models.diffusion.ddim import DDIMSampler
__conditioning_keys__ = {'concat': 'c_concat',
'crossattn': 'c_crossattn',
'adm': 'y'}
def disabled_train(self, mode=True):
"""Overwrite model.train with this function to make sure train/eval mode
does not change anymore."""
return self
def uniform_on_device(r1, r2, shape, device):
return (r1 - r2) * torch.rand(*shape, device=device) + r2
class DDPM(pl.LightningModule):
# classic DDPM with Gaussian diffusion, in image space
def __init__(self,
unet_config,
timesteps=1000,
beta_schedule="linear",
loss_type="l2",
ckpt_path=None,
ignore_keys=[],
load_only_unet=False,
monitor="val/loss",
use_ema=True,
first_stage_key="image",
image_size=256,
channels=3,
log_every_t=100,
clip_denoised=True,
linear_start=1e-4,
linear_end=2e-2,
cosine_s=8e-3,
given_betas=None,
original_elbo_weight=0.,
v_posterior=0., # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta
l_simple_weight=1.,
conditioning_key=None,
parameterization="eps", # all assuming fixed variance schedules
scheduler_config=None,
use_positional_encodings=False,
learn_logvar=False,
logvar_init=0.,
):
super().__init__()
assert parameterization in ["eps", "x0"], 'currently only supporting "eps" and "x0"'
self.parameterization = parameterization
print(f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode")
self.cond_stage_model = None
self.clip_denoised = clip_denoised
self.log_every_t = log_every_t
self.first_stage_key = first_stage_key
self.image_size = image_size # try conv?
self.channels = channels
self.use_positional_encodings = use_positional_encodings
self.model = DiffusionWrapper(unet_config, conditioning_key)
count_params(self.model, verbose=True)
self.use_ema = use_ema
if self.use_ema:
self.model_ema = LitEma(self.model)
print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
self.use_scheduler = scheduler_config is not None
if self.use_scheduler:
self.scheduler_config = scheduler_config
self.v_posterior = v_posterior
self.original_elbo_weight = original_elbo_weight
self.l_simple_weight = l_simple_weight
if monitor is not None:
self.monitor = monitor
if ckpt_path is not None:
self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet)
self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps,
linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)
self.loss_type = loss_type
self.learn_logvar = learn_logvar
self.logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,))
if self.learn_logvar:
self.logvar = nn.Parameter(self.logvar, requires_grad=True)
def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000,
linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
if exists(given_betas):
betas = given_betas
else:
betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end,
cosine_s=cosine_s)
alphas = 1. - betas
alphas_cumprod = np.cumprod(alphas, axis=0)
alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
timesteps, = betas.shape
self.num_timesteps = int(timesteps)
self.linear_start = linear_start
self.linear_end = linear_end
assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep'
to_torch = partial(torch.tensor, dtype=torch.float32)
self.register_buffer('betas', to_torch(betas))
self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))
# calculations for diffusion q(x_t | x_{t-1}) and others
self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod)))
self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))
self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))
# calculations for posterior q(x_{t-1} | x_t, x_0)
posterior_variance = (1 - self.v_posterior) * betas * (1. - alphas_cumprod_prev) / (
1. - alphas_cumprod) + self.v_posterior * betas
# above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
self.register_buffer('posterior_variance', to_torch(posterior_variance))
# below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20))))
self.register_buffer('posterior_mean_coef1', to_torch(
betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)))
self.register_buffer('posterior_mean_coef2', to_torch(
(1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod)))
if self.parameterization == "eps":
lvlb_weights = self.betas ** 2 / (
2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod))
elif self.parameterization == "x0":
lvlb_weights = 0.5 * np.sqrt(torch.Tensor(alphas_cumprod)) / (2. * 1 - torch.Tensor(alphas_cumprod))
else:
raise NotImplementedError("mu not supported")
# TODO how to choose this term
lvlb_weights[0] = lvlb_weights[1]
self.register_buffer('lvlb_weights', lvlb_weights, persistent=False)
assert not torch.isnan(self.lvlb_weights).all()
@contextmanager
def ema_scope(self, context=None):
if self.use_ema:
self.model_ema.store(self.model.parameters())
self.model_ema.copy_to(self.model)
if context is not None:
print(f"{context}: Switched to EMA weights")
try:
yield None
finally:
if self.use_ema:
self.model_ema.restore(self.model.parameters())
if context is not None:
print(f"{context}: Restored training weights")
def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
sd = torch.load(path, map_location="cpu")
if "state_dict" in list(sd.keys()):
sd = sd["state_dict"]
keys = list(sd.keys())
for k in keys:
for ik in ignore_keys:
if k.startswith(ik):
print("Deleting key {} from state_dict.".format(k))
del sd[k]
missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
sd, strict=False)
print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
if len(missing) > 0:
print(f"Missing Keys: {missing}")
if len(unexpected) > 0:
print(f"Unexpected Keys: {unexpected}")
def q_mean_variance(self, x_start, t):
"""
Get the distribution q(x_t | x_0).
:param x_start: the [N x C x ...] tensor of noiseless inputs.
:param t: the number of diffusion steps (minus 1). Here, 0 means one step.
:return: A tuple (mean, variance, log_variance), all of x_start's shape.
"""
mean = (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start)
variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape)
log_variance = extract_into_tensor(self.log_one_minus_alphas_cumprod, t, x_start.shape)
return mean, variance, log_variance
def predict_start_from_noise(self, x_t, t, noise):
return (
extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t -
extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise
)
def q_posterior(self, x_start, x_t, t):
posterior_mean = (
extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start +
extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t
)
posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape)
posterior_log_variance_clipped = extract_into_tensor(self.posterior_log_variance_clipped, t, x_t.shape)
return posterior_mean, posterior_variance, posterior_log_variance_clipped
def p_mean_variance(self, x, t, clip_denoised: bool):
model_out = self.model(x, t)
if self.parameterization == "eps":
x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
elif self.parameterization == "x0":
x_recon = model_out
if clip_denoised:
x_recon.clamp_(-1., 1.)
model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
return model_mean, posterior_variance, posterior_log_variance
@torch.no_grad()
def p_sample(self, x, t, clip_denoised=True, repeat_noise=False):
b, *_, device = *x.shape, x.device
model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, clip_denoised=clip_denoised)
noise = noise_like(x.shape, device, repeat_noise)
# no noise when t == 0
nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
@torch.no_grad()
def p_sample_loop(self, shape, return_intermediates=False):
device = self.betas.device
b = shape[0]
img = torch.randn(shape, device=device)
intermediates = [img]
for i in tqdm(reversed(range(0, self.num_timesteps)), desc='Sampling t', total=self.num_timesteps):
img = self.p_sample(img, torch.full((b,), i, device=device, dtype=torch.long),
clip_denoised=self.clip_denoised)
if i % self.log_every_t == 0 or i == self.num_timesteps - 1:
intermediates.append(img)
if return_intermediates:
return img, intermediates
return img
@torch.no_grad()
def sample(self, batch_size=16, return_intermediates=False):
image_size = self.image_size
channels = self.channels
return self.p_sample_loop((batch_size, channels, image_size, image_size),
return_intermediates=return_intermediates)
def q_sample(self, x_start, t, noise=None):
noise = default(noise, lambda: torch.randn_like(x_start))
return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise)
def get_loss(self, pred, target, mean=True):
if self.loss_type == 'l1':
loss = (target - pred).abs()
if mean:
loss = loss.mean()
elif self.loss_type == 'l2':
if mean:
loss = torch.nn.functional.mse_loss(target, pred)
else:
loss = torch.nn.functional.mse_loss(target, pred, reduction='none')
else:
raise NotImplementedError("unknown loss type '{loss_type}'")
return loss
def p_losses(self, x_start, t, noise=None):
noise = default(noise, lambda: torch.randn_like(x_start))
x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
model_out = self.model(x_noisy, t)
loss_dict = {}
if self.parameterization == "eps":
target = noise
elif self.parameterization == "x0":
target = x_start
else:
raise NotImplementedError(f"Paramterization {self.parameterization} not yet supported")
loss = self.get_loss(model_out, target, mean=False).mean(dim=[1, 2, 3])
log_prefix = 'train' if self.training else 'val'
loss_dict.update({f'{log_prefix}/loss_simple': loss.mean()})
loss_simple = loss.mean() * self.l_simple_weight
loss_vlb = (self.lvlb_weights[t] * loss).mean()
loss_dict.update({f'{log_prefix}/loss_vlb': loss_vlb})
loss = loss_simple + self.original_elbo_weight * loss_vlb
loss_dict.update({f'{log_prefix}/loss': loss})
return loss, loss_dict
def forward(self, x, *args, **kwargs):
# b, c, h, w, device, img_size, = *x.shape, x.device, self.image_size
# assert h == img_size and w == img_size, f'height and width of image must be {img_size}'
t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
return self.p_losses(x, t, *args, **kwargs)
def get_input(self, batch, k):
x = batch[k]
if len(x.shape) == 3:
x = x[..., None]
x = rearrange(x, 'b h w c -> b c h w')
x = x.to(memory_format=torch.contiguous_format).float()
return x
def shared_step(self, batch):
x = self.get_input(batch, self.first_stage_key)
loss, loss_dict = self(x)
return loss, loss_dict
def training_step(self, batch, batch_idx):
loss, loss_dict = self.shared_step(batch)
self.log_dict(loss_dict, prog_bar=True,
logger=True, on_step=True, on_epoch=True)
self.log("global_step", self.global_step,
prog_bar=True, logger=True, on_step=True, on_epoch=False)
if self.use_scheduler:
lr = self.optimizers().param_groups[0]['lr']
self.log('lr_abs', lr, prog_bar=True, logger=True, on_step=True, on_epoch=False)
return loss
@torch.no_grad()
def validation_step(self, batch, batch_idx):
_, loss_dict_no_ema = self.shared_step(batch)
with self.ema_scope():
_, loss_dict_ema = self.shared_step(batch)
loss_dict_ema = {key + '_ema': loss_dict_ema[key] for key in loss_dict_ema}
self.log_dict(loss_dict_no_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
self.log_dict(loss_dict_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
def on_train_batch_end(self, *args, **kwargs):
if self.use_ema:
self.model_ema(self.model)
def _get_rows_from_list(self, samples):
n_imgs_per_row = len(samples)
denoise_grid = rearrange(samples, 'n b c h w -> b n c h w')
denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
return denoise_grid
@torch.no_grad()
def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs):
log = dict()
x = self.get_input(batch, self.first_stage_key)
N = min(x.shape[0], N)
n_row = min(x.shape[0], n_row)
x = x.to(self.device)[:N]
log["inputs"] = x
# get diffusion row
diffusion_row = list()
x_start = x[:n_row]
for t in range(self.num_timesteps):
if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
t = t.to(self.device).long()
noise = torch.randn_like(x_start)
x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
diffusion_row.append(x_noisy)
log["diffusion_row"] = self._get_rows_from_list(diffusion_row)
if sample:
# get denoise row
with self.ema_scope("Plotting"):
samples, denoise_row = self.sample(batch_size=N, return_intermediates=True)
log["samples"] = samples
log["denoise_row"] = self._get_rows_from_list(denoise_row)
if return_keys:
if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
return log
else:
return {key: log[key] for key in return_keys}
return log
def configure_optimizers(self):
lr = self.learning_rate
params = list(self.model.parameters())
if self.learn_logvar:
params = params + [self.logvar]
opt = torch.optim.AdamW(params, lr=lr)
return opt
class LatentDiffusion(DDPM):
"""main class"""
def __init__(self,
first_stage_config,
cond_stage_config,
num_timesteps_cond=None,
cond_stage_key="image",
cond_stage_trainable=False,
concat_mode=True,
cond_stage_forward=None,
conditioning_key=None,
scale_factor=1.0,
scale_by_std=False,
*args, **kwargs):
self.num_timesteps_cond = default(num_timesteps_cond, 1)
self.scale_by_std = scale_by_std
assert self.num_timesteps_cond <= kwargs['timesteps']
# for backwards compatibility after implementation of DiffusionWrapper
if conditioning_key is None:
conditioning_key = 'concat' if concat_mode else 'crossattn'
if cond_stage_config == '__is_unconditional__':
conditioning_key = None
ckpt_path = kwargs.pop("ckpt_path", None)
ignore_keys = kwargs.pop("ignore_keys", [])
super().__init__(conditioning_key=conditioning_key, *args, **kwargs)
self.concat_mode = concat_mode
self.cond_stage_trainable = cond_stage_trainable
self.cond_stage_key = cond_stage_key
try:
self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1
except:
self.num_downs = 0
if not scale_by_std:
self.scale_factor = scale_factor
else:
self.register_buffer('scale_factor', torch.tensor(scale_factor))
self.instantiate_first_stage(first_stage_config)
self.instantiate_cond_stage(cond_stage_config)
self.cond_stage_forward = cond_stage_forward
self.clip_denoised = False
self.bbox_tokenizer = None
self.restarted_from_ckpt = False
if ckpt_path is not None:
self.init_from_ckpt(ckpt_path, ignore_keys)
self.restarted_from_ckpt = True
def make_cond_schedule(self, ):
self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long)
ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long()
self.cond_ids[:self.num_timesteps_cond] = ids
@rank_zero_only
@torch.no_grad()
def on_train_batch_start(self, batch, batch_idx, dataloader_idx):
# only for very first batch
if self.scale_by_std and self.current_epoch == 0 and self.global_step == 0 and batch_idx == 0 and not self.restarted_from_ckpt:
assert self.scale_factor == 1., 'rather not use custom rescaling and std-rescaling simultaneously'
# set rescale weight to 1./std of encodings
print("### USING STD-RESCALING ###")
x = super().get_input(batch, self.first_stage_key)
x = x.to(self.device)
encoder_posterior = self.encode_first_stage(x)
z = self.get_first_stage_encoding(encoder_posterior).detach()
del self.scale_factor
self.register_buffer('scale_factor', 1. / z.flatten().std())
print(f"setting self.scale_factor to {self.scale_factor}")
print("### USING STD-RESCALING ###")
def register_schedule(self,
given_betas=None, beta_schedule="linear", timesteps=1000,
linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
super().register_schedule(given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s)
self.shorten_cond_schedule = self.num_timesteps_cond > 1
if self.shorten_cond_schedule:
self.make_cond_schedule()
def instantiate_first_stage(self, config):
model = instantiate_from_config(config)
self.first_stage_model = model.eval()
self.first_stage_model.train = disabled_train
for param in self.first_stage_model.parameters():
param.requires_grad = False
def instantiate_cond_stage(self, config):
if not self.cond_stage_trainable:
if config == "__is_first_stage__":
print("Using first stage also as cond stage.")
self.cond_stage_model = self.first_stage_model
elif config == "__is_unconditional__":
print(f"Training {self.__class__.__name__} as an unconditional model.")
self.cond_stage_model = None
# self.be_unconditional = True
else:
model = instantiate_from_config(config)
self.cond_stage_model = model.eval()
self.cond_stage_model.train = disabled_train
for param in self.cond_stage_model.parameters():
param.requires_grad = False
else:
assert config != '__is_first_stage__'
assert config != '__is_unconditional__'
model = instantiate_from_config(config)
self.cond_stage_model = model
def _get_denoise_row_from_list(self, samples, desc='', force_no_decoder_quantization=False):
denoise_row = []
for zd in tqdm(samples, desc=desc):
denoise_row.append(self.decode_first_stage(zd.to(self.device),
force_not_quantize=force_no_decoder_quantization))
n_imgs_per_row = len(denoise_row)
denoise_row = torch.stack(denoise_row) # n_log_step, n_row, C, H, W
denoise_grid = rearrange(denoise_row, 'n b c h w -> b n c h w')
denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
return denoise_grid
def get_first_stage_encoding(self, encoder_posterior):
if isinstance(encoder_posterior, DiagonalGaussianDistribution):
z = encoder_posterior.sample()
elif isinstance(encoder_posterior, torch.Tensor):
z = encoder_posterior
else:
raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented")
return self.scale_factor * z
def get_learned_conditioning(self, c):
if self.cond_stage_forward is None:
if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode):
c = self.cond_stage_model.encode(c)
if isinstance(c, DiagonalGaussianDistribution):
c = c.mode()
else:
c = self.cond_stage_model(c)
else:
assert hasattr(self.cond_stage_model, self.cond_stage_forward)
c = getattr(self.cond_stage_model, self.cond_stage_forward)(c)
return c
def meshgrid(self, h, w):
y = torch.arange(0, h).view(h, 1, 1).repeat(1, w, 1)
x = torch.arange(0, w).view(1, w, 1).repeat(h, 1, 1)
arr = torch.cat([y, x], dim=-1)
return arr
def delta_border(self, h, w):
"""
:param h: height
:param w: width
:return: normalized distance to image border,
wtith min distance = 0 at border and max dist = 0.5 at image center
"""
lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2)
arr = self.meshgrid(h, w) / lower_right_corner
dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0]
dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0]
edge_dist = torch.min(torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1)[0]
return edge_dist
def get_weighting(self, h, w, Ly, Lx, device):
weighting = self.delta_border(h, w)
weighting = torch.clip(weighting, self.split_input_params["clip_min_weight"],
self.split_input_params["clip_max_weight"], )
weighting = weighting.view(1, h * w, 1).repeat(1, 1, Ly * Lx).to(device)
if self.split_input_params["tie_braker"]:
L_weighting = self.delta_border(Ly, Lx)
L_weighting = torch.clip(L_weighting,
self.split_input_params["clip_min_tie_weight"],
self.split_input_params["clip_max_tie_weight"])
L_weighting = L_weighting.view(1, 1, Ly * Lx).to(device)
weighting = weighting * L_weighting
return weighting
def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1): # todo load once not every time, shorten code
"""
:param x: img of size (bs, c, h, w)
:return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1])
"""
bs, nc, h, w = x.shape
# number of crops in image
Ly = (h - kernel_size[0]) // stride[0] + 1
Lx = (w - kernel_size[1]) // stride[1] + 1
if uf == 1 and df == 1:
fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
unfold = torch.nn.Unfold(**fold_params)
fold = torch.nn.Fold(output_size=x.shape[2:], **fold_params)
weighting = self.get_weighting(kernel_size[0], kernel_size[1], Ly, Lx, x.device).to(x.dtype)
normalization = fold(weighting).view(1, 1, h, w) # normalizes the overlap
weighting = weighting.view((1, 1, kernel_size[0], kernel_size[1], Ly * Lx))
elif uf > 1 and df == 1:
fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
unfold = torch.nn.Unfold(**fold_params)
fold_params2 = dict(kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf),
dilation=1, padding=0,
stride=(stride[0] * uf, stride[1] * uf))
fold = torch.nn.Fold(output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2)
weighting = self.get_weighting(kernel_size[0] * uf, kernel_size[1] * uf, Ly, Lx, x.device).to(x.dtype)
normalization = fold(weighting).view(1, 1, h * uf, w * uf) # normalizes the overlap
weighting = weighting.view((1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx))
elif df > 1 and uf == 1:
fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
unfold = torch.nn.Unfold(**fold_params)
fold_params2 = dict(kernel_size=(kernel_size[0] // df, kernel_size[0] // df),
dilation=1, padding=0,
stride=(stride[0] // df, stride[1] // df))
fold = torch.nn.Fold(output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2)
weighting = self.get_weighting(kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device).to(x.dtype)
normalization = fold(weighting).view(1, 1, h // df, w // df) # normalizes the overlap
weighting = weighting.view((1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx))
else:
raise NotImplementedError
return fold, unfold, normalization, weighting
@torch.no_grad()
def get_input(self, batch, k, return_first_stage_outputs=False, force_c_encode=False,
cond_key=None, return_original_cond=False, bs=None):
x = super().get_input(batch, k)
if bs is not None:
x = x[:bs]
x = x.to(self.device)
encoder_posterior = self.encode_first_stage(x)
z = self.get_first_stage_encoding(encoder_posterior).detach()
if self.model.conditioning_key is not None:
if cond_key is None:
cond_key = self.cond_stage_key
if cond_key != self.first_stage_key:
if cond_key in ['caption', 'coordinates_bbox']:
xc = batch[cond_key]
elif cond_key == 'class_label':
xc = batch
else:
xc = super().get_input(batch, cond_key).to(self.device)
else:
xc = x
if not self.cond_stage_trainable or force_c_encode:
if isinstance(xc, dict) or isinstance(xc, list):
# import pudb; pudb.set_trace()
c = self.get_learned_conditioning(xc)
else:
c = self.get_learned_conditioning(xc.to(self.device))
else:
c = xc
if bs is not None:
c = c[:bs]
if self.use_positional_encodings:
pos_x, pos_y = self.compute_latent_shifts(batch)
ckey = __conditioning_keys__[self.model.conditioning_key]
c = {ckey: c, 'pos_x': pos_x, 'pos_y': pos_y}
else:
c = None
xc = None
if self.use_positional_encodings:
pos_x, pos_y = self.compute_latent_shifts(batch)
c = {'pos_x': pos_x, 'pos_y': pos_y}
out = [z, c]
if return_first_stage_outputs:
xrec = self.decode_first_stage(z)
out.extend([x, xrec])
if return_original_cond:
out.append(xc)
return out
@torch.no_grad()
def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
if predict_cids:
if z.dim() == 4:
z = torch.argmax(z.exp(), dim=1).long()
z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
z = rearrange(z, 'b h w c -> b c h w').contiguous()
z = 1. / self.scale_factor * z
if hasattr(self, "split_input_params"):
if self.split_input_params["patch_distributed_vq"]:
ks = self.split_input_params["ks"] # eg. (128, 128)
stride = self.split_input_params["stride"] # eg. (64, 64)
uf = self.split_input_params["vqf"]
bs, nc, h, w = z.shape
if ks[0] > h or ks[1] > w:
ks = (min(ks[0], h), min(ks[1], w))
print("reducing Kernel")
if stride[0] > h or stride[1] > w:
stride = (min(stride[0], h), min(stride[1], w))
print("reducing stride")
fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf)
z = unfold(z) # (bn, nc * prod(**ks), L)
# 1. Reshape to img shape
z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
# 2. apply model loop over last dim
if isinstance(self.first_stage_model, VQModelInterface):
output_list = [self.first_stage_model.decode(z[:, :, :, :, i],
force_not_quantize=predict_cids or force_not_quantize)
for i in range(z.shape[-1])]
else:
output_list = [self.first_stage_model.decode(z[:, :, :, :, i])
for i in range(z.shape[-1])]
o = torch.stack(output_list, axis=-1) # # (bn, nc, ks[0], ks[1], L)
o = o * weighting
# Reverse 1. reshape to img shape
o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
# stitch crops together
decoded = fold(o)
decoded = decoded / normalization # norm is shape (1, 1, h, w)
return decoded
else:
if isinstance(self.first_stage_model, VQModelInterface):
return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
else:
return self.first_stage_model.decode(z)
else:
if isinstance(self.first_stage_model, VQModelInterface):
return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
else:
return self.first_stage_model.decode(z)
# same as above but without decorator
def differentiable_decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
if predict_cids:
if z.dim() == 4:
z = torch.argmax(z.exp(), dim=1).long()
z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
z = rearrange(z, 'b h w c -> b c h w').contiguous()
z = 1. / self.scale_factor * z
if hasattr(self, "split_input_params"):
if self.split_input_params["patch_distributed_vq"]:
ks = self.split_input_params["ks"] # eg. (128, 128)
stride = self.split_input_params["stride"] # eg. (64, 64)
uf = self.split_input_params["vqf"]
bs, nc, h, w = z.shape
if ks[0] > h or ks[1] > w:
ks = (min(ks[0], h), min(ks[1], w))
print("reducing Kernel")
if stride[0] > h or stride[1] > w:
stride = (min(stride[0], h), min(stride[1], w))
print("reducing stride")
fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf)
z = unfold(z) # (bn, nc * prod(**ks), L)
# 1. Reshape to img shape
z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
# 2. apply model loop over last dim
if isinstance(self.first_stage_model, VQModelInterface):
output_list = [self.first_stage_model.decode(z[:, :, :, :, i],
force_not_quantize=predict_cids or force_not_quantize)
for i in range(z.shape[-1])]
else:
output_list = [self.first_stage_model.decode(z[:, :, :, :, i])
for i in range(z.shape[-1])]
o = torch.stack(output_list, axis=-1) # # (bn, nc, ks[0], ks[1], L)
o = o * weighting
# Reverse 1. reshape to img shape
o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
# stitch crops together
decoded = fold(o)
decoded = decoded / normalization # norm is shape (1, 1, h, w)
return decoded
else:
if isinstance(self.first_stage_model, VQModelInterface):
return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
else:
return self.first_stage_model.decode(z)
else:
if isinstance(self.first_stage_model, VQModelInterface):
return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
else:
return self.first_stage_model.decode(z)
@torch.no_grad()
def encode_first_stage(self, x):
if hasattr(self, "split_input_params"):
if self.split_input_params["patch_distributed_vq"]:
ks = self.split_input_params["ks"] # eg. (128, 128)
stride = self.split_input_params["stride"] # eg. (64, 64)
df = self.split_input_params["vqf"]
self.split_input_params['original_image_size'] = x.shape[-2:]
bs, nc, h, w = x.shape
if ks[0] > h or ks[1] > w:
ks = (min(ks[0], h), min(ks[1], w))
print("reducing Kernel")
if stride[0] > h or stride[1] > w:
stride = (min(stride[0], h), min(stride[1], w))
print("reducing stride")
fold, unfold, normalization, weighting = self.get_fold_unfold(x, ks, stride, df=df)
z = unfold(x) # (bn, nc * prod(**ks), L)
# Reshape to img shape
z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
output_list = [self.first_stage_model.encode(z[:, :, :, :, i])
for i in range(z.shape[-1])]
o = torch.stack(output_list, axis=-1)
o = o * weighting
# Reverse reshape to img shape
o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
# stitch crops together
decoded = fold(o)
decoded = decoded / normalization
return decoded
else:
return self.first_stage_model.encode(x)
else:
return self.first_stage_model.encode(x)
def shared_step(self, batch, **kwargs):
x, c = self.get_input(batch, self.first_stage_key)
loss = self(x, c)
return loss
def forward(self, x, c, *args, **kwargs):
t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
if self.model.conditioning_key is not None:
assert c is not None
if self.cond_stage_trainable:
c = self.get_learned_conditioning(c)
if self.shorten_cond_schedule: # TODO: drop this option
tc = self.cond_ids[t].to(self.device)
c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float()))
return self.p_losses(x, c, t, *args, **kwargs)
def _rescale_annotations(self, bboxes, crop_coordinates): # TODO: move to dataset
def rescale_bbox(bbox):
x0 = clamp((bbox[0] - crop_coordinates[0]) / crop_coordinates[2])
y0 = clamp((bbox[1] - crop_coordinates[1]) / crop_coordinates[3])
w = min(bbox[2] / crop_coordinates[2], 1 - x0)
h = min(bbox[3] / crop_coordinates[3], 1 - y0)
return x0, y0, w, h
return [rescale_bbox(b) for b in bboxes]
def apply_model(self, x_noisy, t, cond, return_ids=False):
if isinstance(cond, dict):
# hybrid case, cond is exptected to be a dict
pass
else:
if not isinstance(cond, list):
cond = [cond]
key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn'
cond = {key: cond}
if hasattr(self, "split_input_params"):
assert len(cond) == 1 # todo can only deal with one conditioning atm
assert not return_ids
ks = self.split_input_params["ks"] # eg. (128, 128)
stride = self.split_input_params["stride"] # eg. (64, 64)
h, w = x_noisy.shape[-2:]
fold, unfold, normalization, weighting = self.get_fold_unfold(x_noisy, ks, stride)
z = unfold(x_noisy) # (bn, nc * prod(**ks), L)
# Reshape to img shape
z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
z_list = [z[:, :, :, :, i] for i in range(z.shape[-1])]
if self.cond_stage_key in ["image", "LR_image", "segmentation",
'bbox_img'] and self.model.conditioning_key: # todo check for completeness
c_key = next(iter(cond.keys())) # get key
c = next(iter(cond.values())) # get value
assert (len(c) == 1) # todo extend to list with more than one elem
c = c[0] # get element
c = unfold(c)
c = c.view((c.shape[0], -1, ks[0], ks[1], c.shape[-1])) # (bn, nc, ks[0], ks[1], L )
cond_list = [{c_key: [c[:, :, :, :, i]]} for i in range(c.shape[-1])]
elif self.cond_stage_key == 'coordinates_bbox':
assert 'original_image_size' in self.split_input_params, 'BoudingBoxRescaling is missing original_image_size'
# assuming padding of unfold is always 0 and its dilation is always 1
n_patches_per_row = int((w - ks[0]) / stride[0] + 1)
full_img_h, full_img_w = self.split_input_params['original_image_size']
# as we are operating on latents, we need the factor from the original image size to the
# spatial latent size to properly rescale the crops for regenerating the bbox annotations
num_downs = self.first_stage_model.encoder.num_resolutions - 1
rescale_latent = 2 ** (num_downs)
# get top left postions of patches as conforming for the bbbox tokenizer, therefore we
# need to rescale the tl patch coordinates to be in between (0,1)
tl_patch_coordinates = [(rescale_latent * stride[0] * (patch_nr % n_patches_per_row) / full_img_w,
rescale_latent * stride[1] * (patch_nr // n_patches_per_row) / full_img_h)
for patch_nr in range(z.shape[-1])]
# patch_limits are tl_coord, width and height coordinates as (x_tl, y_tl, h, w)
patch_limits = [(x_tl, y_tl,
rescale_latent * ks[0] / full_img_w,
rescale_latent * ks[1] / full_img_h) for x_tl, y_tl in tl_patch_coordinates]
# patch_values = [(np.arange(x_tl,min(x_tl+ks, 1.)),np.arange(y_tl,min(y_tl+ks, 1.))) for x_tl, y_tl in tl_patch_coordinates]
# tokenize crop coordinates for the bounding boxes of the respective patches
patch_limits_tknzd = [torch.LongTensor(self.bbox_tokenizer._crop_encoder(bbox))[None].to(self.device)
for bbox in patch_limits] # list of length l with tensors of shape (1, 2)
print(patch_limits_tknzd[0].shape)
# cut tknzd crop position from conditioning
assert isinstance(cond, dict), 'cond must be dict to be fed into model'
cut_cond = cond['c_crossattn'][0][..., :-2].to(self.device)
print(cut_cond.shape)
adapted_cond = torch.stack([torch.cat([cut_cond, p], dim=1) for p in patch_limits_tknzd])
adapted_cond = rearrange(adapted_cond, 'l b n -> (l b) n')
print(adapted_cond.shape)
adapted_cond = self.get_learned_conditioning(adapted_cond)
print(adapted_cond.shape)
adapted_cond = rearrange(adapted_cond, '(l b) n d -> l b n d', l=z.shape[-1])
print(adapted_cond.shape)
cond_list = [{'c_crossattn': [e]} for e in adapted_cond]
else:
cond_list = [cond for i in range(z.shape[-1])] # Todo make this more efficient
# apply model by loop over crops
output_list = [self.model(z_list[i], t, **cond_list[i]) for i in range(z.shape[-1])]
assert not isinstance(output_list[0],
tuple) # todo cant deal with multiple model outputs check this never happens
o = torch.stack(output_list, axis=-1)
o = o * weighting
# Reverse reshape to img shape
o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
# stitch crops together
x_recon = fold(o) / normalization
else:
x_recon = self.model(x_noisy, t, **cond)
if isinstance(x_recon, tuple) and not return_ids:
return x_recon[0]
else:
return x_recon
def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
return (extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart) / \
extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
def _prior_bpd(self, x_start):
"""
Get the prior KL term for the variational lower-bound, measured in
bits-per-dim.
This term can't be optimized, as it only depends on the encoder.
:param x_start: the [N x C x ...] tensor of inputs.
:return: a batch of [N] KL values (in bits), one per batch element.
"""
batch_size = x_start.shape[0]
t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device)
qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t)
kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0)
return mean_flat(kl_prior) / np.log(2.0)
def p_losses(self, x_start, cond, t, noise=None):
noise = default(noise, lambda: torch.randn_like(x_start))
x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
model_output = self.apply_model(x_noisy, t, cond)
loss_dict = {}
prefix = 'train' if self.training else 'val'
if self.parameterization == "x0":
target = x_start
elif self.parameterization == "eps":
target = noise
else:
raise NotImplementedError()
loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3])
loss_dict.update({f'{prefix}/loss_simple': loss_simple.mean()})
logvar_t = self.logvar[t].to(self.device)
loss = loss_simple / torch.exp(logvar_t) + logvar_t
# loss = loss_simple / torch.exp(self.logvar) + self.logvar
if self.learn_logvar:
loss_dict.update({f'{prefix}/loss_gamma': loss.mean()})
loss_dict.update({'logvar': self.logvar.data.mean()})
loss = self.l_simple_weight * loss.mean()
loss_vlb = self.get_loss(model_output, target, mean=False).mean(dim=(1, 2, 3))
loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean()
loss_dict.update({f'{prefix}/loss_vlb': loss_vlb})
loss += (self.original_elbo_weight * loss_vlb)
loss_dict.update({f'{prefix}/loss': loss})
return loss, loss_dict
def p_mean_variance(self, x, c, t, clip_denoised: bool, return_codebook_ids=False, quantize_denoised=False,
return_x0=False, score_corrector=None, corrector_kwargs=None):
t_in = t
model_out = self.apply_model(x, t_in, c, return_ids=return_codebook_ids)
if score_corrector is not None:
assert self.parameterization == "eps"
model_out = score_corrector.modify_score(self, model_out, x, t, c, **corrector_kwargs)
if return_codebook_ids:
model_out, logits = model_out
if self.parameterization == "eps":
x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
elif self.parameterization == "x0":
x_recon = model_out
else:
raise NotImplementedError()
if clip_denoised:
x_recon.clamp_(-1., 1.)
if quantize_denoised:
x_recon, _, [_, _, indices] = self.first_stage_model.quantize(x_recon)
model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
if return_codebook_ids:
return model_mean, posterior_variance, posterior_log_variance, logits
elif return_x0:
return model_mean, posterior_variance, posterior_log_variance, x_recon
else:
return model_mean, posterior_variance, posterior_log_variance
@torch.no_grad()
def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False,
return_codebook_ids=False, quantize_denoised=False, return_x0=False,
temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None):
b, *_, device = *x.shape, x.device
outputs = self.p_mean_variance(x=x, c=c, t=t, clip_denoised=clip_denoised,
return_codebook_ids=return_codebook_ids,
quantize_denoised=quantize_denoised,
return_x0=return_x0,
score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
if return_codebook_ids:
raise DeprecationWarning("Support dropped.")
model_mean, _, model_log_variance, logits = outputs
elif return_x0:
model_mean, _, model_log_variance, x0 = outputs
else:
model_mean, _, model_log_variance = outputs
noise = noise_like(x.shape, device, repeat_noise) * temperature
if noise_dropout > 0.:
noise = torch.nn.functional.dropout(noise, p=noise_dropout)
# no noise when t == 0
nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
if return_codebook_ids:
return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, logits.argmax(dim=1)
if return_x0:
return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, x0
else:
return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
@torch.no_grad()
def progressive_denoising(self, cond, shape, verbose=True, callback=None, quantize_denoised=False,
img_callback=None, mask=None, x0=None, temperature=1., noise_dropout=0.,
score_corrector=None, corrector_kwargs=None, batch_size=None, x_T=None, start_T=None,
log_every_t=None):
if not log_every_t:
log_every_t = self.log_every_t
timesteps = self.num_timesteps
if batch_size is not None:
b = batch_size if batch_size is not None else shape[0]
shape = [batch_size] + list(shape)
else:
b = batch_size = shape[0]
if x_T is None:
img = torch.randn(shape, device=self.device)
else:
img = x_T
intermediates = []
if cond is not None:
if isinstance(cond, dict):
cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
else:
cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
if start_T is not None:
timesteps = min(timesteps, start_T)
iterator = tqdm(reversed(range(0, timesteps)), desc='Progressive Generation',
total=timesteps) if verbose else reversed(
range(0, timesteps))
if type(temperature) == float:
temperature = [temperature] * timesteps
for i in iterator:
ts = torch.full((b,), i, device=self.device, dtype=torch.long)
if self.shorten_cond_schedule:
assert self.model.conditioning_key != 'hybrid'
tc = self.cond_ids[ts].to(cond.device)
cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
img, x0_partial = self.p_sample(img, cond, ts,
clip_denoised=self.clip_denoised,
quantize_denoised=quantize_denoised, return_x0=True,
temperature=temperature[i], noise_dropout=noise_dropout,
score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
if mask is not None:
assert x0 is not None
img_orig = self.q_sample(x0, ts)
img = img_orig * mask + (1. - mask) * img
if i % log_every_t == 0 or i == timesteps - 1:
intermediates.append(x0_partial)
if callback: callback(i)
if img_callback: img_callback(img, i)
return img, intermediates
@torch.no_grad()
def p_sample_loop(self, cond, shape, return_intermediates=False,
x_T=None, verbose=True, callback=None, timesteps=None, quantize_denoised=False,
mask=None, x0=None, img_callback=None, start_T=None,
log_every_t=None):
if not log_every_t:
log_every_t = self.log_every_t
device = self.betas.device
b = shape[0]
if x_T is None:
img = torch.randn(shape, device=device)
else:
img = x_T
intermediates = [img]
if timesteps is None:
timesteps = self.num_timesteps
if start_T is not None:
timesteps = min(timesteps, start_T)
iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed(
range(0, timesteps))
if mask is not None:
assert x0 is not None
assert x0.shape[2:3] == mask.shape[2:3] # spatial size has to match
for i in iterator:
ts = torch.full((b,), i, device=device, dtype=torch.long)
if self.shorten_cond_schedule:
assert self.model.conditioning_key != 'hybrid'
tc = self.cond_ids[ts].to(cond.device)
cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
img = self.p_sample(img, cond, ts,
clip_denoised=self.clip_denoised,
quantize_denoised=quantize_denoised)
if mask is not None:
img_orig = self.q_sample(x0, ts)
img = img_orig * mask + (1. - mask) * img
if i % log_every_t == 0 or i == timesteps - 1:
intermediates.append(img)
if callback: callback(i)
if img_callback: img_callback(img, i)
if return_intermediates:
return img, intermediates
return img
@torch.no_grad()
def sample(self, cond, batch_size=16, return_intermediates=False, x_T=None,
verbose=True, timesteps=None, quantize_denoised=False,
mask=None, x0=None, shape=None,**kwargs):
if shape is None:
shape = (batch_size, self.channels, self.image_size, self.image_size)
if cond is not None:
if isinstance(cond, dict):
cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
else:
cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
return self.p_sample_loop(cond,
shape,
return_intermediates=return_intermediates, x_T=x_T,
verbose=verbose, timesteps=timesteps, quantize_denoised=quantize_denoised,
mask=mask, x0=x0)
@torch.no_grad()
def sample_log(self,cond,batch_size,ddim, ddim_steps,**kwargs):
if ddim:
ddim_sampler = DDIMSampler(self)
shape = (self.channels, self.image_size, self.image_size)
samples, intermediates =ddim_sampler.sample(ddim_steps,batch_size,
shape,cond,verbose=False,**kwargs)
else:
samples, intermediates = self.sample(cond=cond, batch_size=batch_size,
return_intermediates=True,**kwargs)
return samples, intermediates
@torch.no_grad()
def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None,
quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True,
plot_diffusion_rows=True, **kwargs):
use_ddim = ddim_steps is not None
log = dict()
z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key,
return_first_stage_outputs=True,
force_c_encode=True,
return_original_cond=True,
bs=N)
N = min(x.shape[0], N)
n_row = min(x.shape[0], n_row)
log["inputs"] = x
log["reconstruction"] = xrec
if self.model.conditioning_key is not None:
if hasattr(self.cond_stage_model, "decode"):
xc = self.cond_stage_model.decode(c)
log["conditioning"] = xc
elif self.cond_stage_key in ["caption"]:
xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["caption"])
log["conditioning"] = xc
elif self.cond_stage_key == 'class_label':
xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"])
log['conditioning'] = xc
elif isimage(xc):
log["conditioning"] = xc
if ismap(xc):
log["original_conditioning"] = self.to_rgb(xc)
if plot_diffusion_rows:
# get diffusion row
diffusion_row = list()
z_start = z[:n_row]
for t in range(self.num_timesteps):
if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
t = t.to(self.device).long()
noise = torch.randn_like(z_start)
z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)
diffusion_row.append(self.decode_first_stage(z_noisy))
diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W
diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w')
diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w')
diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0])
log["diffusion_row"] = diffusion_grid
if sample:
# get denoise row
with self.ema_scope("Plotting"):
samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim,
ddim_steps=ddim_steps,eta=ddim_eta)
# samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True)
x_samples = self.decode_first_stage(samples)
log["samples"] = x_samples
if plot_denoise_rows:
denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
log["denoise_row"] = denoise_grid
if quantize_denoised and not isinstance(self.first_stage_model, AutoencoderKL) and not isinstance(
self.first_stage_model, IdentityFirstStage):
# also display when quantizing x0 while sampling
with self.ema_scope("Plotting Quantized Denoised"):
samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim,
ddim_steps=ddim_steps,eta=ddim_eta,
quantize_denoised=True)
# samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True,
# quantize_denoised=True)
x_samples = self.decode_first_stage(samples.to(self.device))
log["samples_x0_quantized"] = x_samples
if inpaint:
# make a simple center square
b, h, w = z.shape[0], z.shape[2], z.shape[3]
mask = torch.ones(N, h, w).to(self.device)
# zeros will be filled in
mask[:, h // 4:3 * h // 4, w // 4:3 * w // 4] = 0.
mask = mask[:, None, ...]
with self.ema_scope("Plotting Inpaint"):
samples, _ = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, eta=ddim_eta,
ddim_steps=ddim_steps, x0=z[:N], mask=mask)
x_samples = self.decode_first_stage(samples.to(self.device))
log["samples_inpainting"] = x_samples
log["mask"] = mask
# outpaint
with self.ema_scope("Plotting Outpaint"):
samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,eta=ddim_eta,
ddim_steps=ddim_steps, x0=z[:N], mask=mask)
x_samples = self.decode_first_stage(samples.to(self.device))
log["samples_outpainting"] = x_samples
if plot_progressive_rows:
with self.ema_scope("Plotting Progressives"):
img, progressives = self.progressive_denoising(c,
shape=(self.channels, self.image_size, self.image_size),
batch_size=N)
prog_row = self._get_denoise_row_from_list(progressives, desc="Progressive Generation")
log["progressive_row"] = prog_row
if return_keys:
if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
return log
else:
return {key: log[key] for key in return_keys}
return log
def configure_optimizers(self):
lr = self.learning_rate
params = list(self.model.parameters())
if self.cond_stage_trainable:
print(f"{self.__class__.__name__}: Also optimizing conditioner params!")
params = params + list(self.cond_stage_model.parameters())
if self.learn_logvar:
print('Diffusion model optimizing logvar')
params.append(self.logvar)
opt = torch.optim.AdamW(params, lr=lr)
if self.use_scheduler:
assert 'target' in self.scheduler_config
scheduler = instantiate_from_config(self.scheduler_config)
print("Setting up LambdaLR scheduler...")
scheduler = [
{
'scheduler': LambdaLR(opt, lr_lambda=scheduler.schedule),
'interval': 'step',
'frequency': 1
}]
return [opt], scheduler
return opt
@torch.no_grad()
def to_rgb(self, x):
x = x.float()
if not hasattr(self, "colorize"):
self.colorize = torch.randn(3, x.shape[1], 1, 1).to(x)
x = nn.functional.conv2d(x, weight=self.colorize)
x = 2. * (x - x.min()) / (x.max() - x.min()) - 1.
return x
class DiffusionWrapper(pl.LightningModule):
def __init__(self, diff_model_config, conditioning_key):
super().__init__()
self.diffusion_model = instantiate_from_config(diff_model_config)
self.conditioning_key = conditioning_key
assert self.conditioning_key in [None, 'concat', 'crossattn', 'hybrid', 'adm']
def forward(self, x, t, c_concat: list = None, c_crossattn: list = None):
if self.conditioning_key is None:
out = self.diffusion_model(x, t)
elif self.conditioning_key == 'concat':
xc = torch.cat([x] + c_concat, dim=1)
out = self.diffusion_model(xc, t)
elif self.conditioning_key == 'crossattn':
cc = torch.cat(c_crossattn, 1)
out = self.diffusion_model(x, t, context=cc)
elif self.conditioning_key == 'hybrid':
xc = torch.cat([x] + c_concat, dim=1)
cc = torch.cat(c_crossattn, 1)
out = self.diffusion_model(xc, t, context=cc)
elif self.conditioning_key == 'adm':
cc = c_crossattn[0]
out = self.diffusion_model(x, t, y=cc)
else:
raise NotImplementedError()
return out
class Layout2ImgDiffusion(LatentDiffusion):
# TODO: move all layout-specific hacks to this class
def __init__(self, cond_stage_key, *args, **kwargs):
assert cond_stage_key == 'coordinates_bbox', 'Layout2ImgDiffusion only for cond_stage_key="coordinates_bbox"'
super().__init__(cond_stage_key=cond_stage_key, *args, **kwargs)
def log_images(self, batch, N=8, *args, **kwargs):
logs = super().log_images(batch=batch, N=N, *args, **kwargs)
key = 'train' if self.training else 'validation'
dset = self.trainer.datamodule.datasets[key]
mapper = dset.conditional_builders[self.cond_stage_key]
bbox_imgs = []
map_fn = lambda catno: dset.get_textual_label(dset.get_category_id(catno))
for tknzd_bbox in batch[self.cond_stage_key][:N]:
bboximg = mapper.plot(tknzd_bbox.detach().cpu(), map_fn, (256, 256))
bbox_imgs.append(bboximg)
cond_img = torch.stack(bbox_imgs, dim=0)
logs['bbox_image'] = cond_img
return logs
================================================
FILE: models/InstructDiffusion/stable_diffusion/ldm/models/diffusion/ddpm_edit.py
================================================
"""
wild mixture of
https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py
https://github.com/CompVis/taming-transformers
-- merci
"""
# File modified by authors of InstructPix2Pix from original (https://github.com/CompVis/stable-diffusion).
# See more details in LICENSE.
# Modified by Zigang Geng (zigang@mail.ustc.edu.cn)
import os
import warnings
import torch
import torch.nn as nn
import numpy as np
from einops import rearrange, repeat
from functools import partial
from tqdm import tqdm
from torchvision.utils import make_grid
from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config
from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution
from ldm.models.autoencoder import VQModelInterface, IdentityFirstStage, AutoencoderKL
from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
from ldm.models.diffusion.ddim import DDIMSampler
from timm.models.layers import trunc_normal_
__conditioning_keys__ = {'concat': 'c_concat',
'crossattn': 'c_crossattn',
'adm': 'y'}
def disabled_train(self, mode=True):
"""Overwrite model.train with this function to make sure train/eval mode
does not change anymore."""
return self
def uniform_on_device(r1, r2, shape, device):
return (r1 - r2) * torch.rand(*shape, device=device) + r2
class DDPM(nn.Module):
# classic DDPM with Gaussian diffusion, in image space
def __init__(self,
unet_config,
timesteps=1000,
beta_schedule="linear",
loss_type="l2",
ckpt_path=None,
ignore_keys=[],
load_only_unet=False,
monitor="val/loss",
first_stage_key="image",
image_size=256,
channels=3,
log_every_t=100,
clip_denoised=True,
linear_start=1e-4,
linear_end=2e-2,
cosine_s=8e-3,
given_betas=None,
original_elbo_weight=0.,
v_posterior=0., # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta
l_simple_weight=1.,
conditioning_key=None,
parameterization="eps", # all assuming fixed variance schedules
scheduler_config=None,
use_positional_encodings=False,
learn_logvar=False,
logvar_init=0.,
**kwargs,
):
super().__init__()
assert parameterization in ["eps", "x0"], 'currently only supporting "eps" and "x0"'
self.parameterization = parameterization
print(f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode")
self.cond_stage_model = None
self.clip_denoised = clip_denoised
self.unet_config = unet_config
self.log_every_t = log_every_t
self.first_stage_key = first_stage_key
self.image_size = image_size # try conv?
self.channels = channels
self.use_positional_encodings = use_positional_encodings
self.model = DiffusionWrapper(unet_config, conditioning_key)
count_params(self.model, verbose=True)
self.use_scheduler = scheduler_config is not None
if self.use_scheduler:
self.scheduler_config = scheduler_config
self.v_posterior = v_posterior
self.original_elbo_weight = original_elbo_weight
self.l_simple_weight = l_simple_weight
if monitor is not None:
self.monitor = monitor
if ckpt_path is not None:
self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet)
self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps,
linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)
self.loss_type = loss_type
self.learn_logvar = learn_logvar
self.logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,))
if self.learn_logvar:
self.logvar = nn.Parameter(self.logvar, requires_grad=True)
def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000,
linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
if exists(given_betas):
betas = given_betas
else:
betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end,
cosine_s=cosine_s)
alphas = 1. - betas
alphas_cumprod = np.cumprod(alphas, axis=0)
alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
timesteps, = betas.shape
self.num_timesteps = int(timesteps)
self.linear_start = linear_start
self.linear_end = linear_end
assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep'
to_torch = partial(torch.tensor, dtype=torch.float32)
self.register_buffer('betas', to_torch(betas))
self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))
# calculations for diffusion q(x_t | x_{t-1}) and others
self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod)))
self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))
self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))
# calculations for posterior q(x_{t-1} | x_t, x_0)
posterior_variance = (1 - self.v_posterior) * betas * (1. - alphas_cumprod_prev) / (
1. - alphas_cumprod) + self.v_posterior * betas
# above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
self.register_buffer('posterior_variance', to_torch(posterior_variance))
# below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20))))
self.register_buffer('posterior_mean_coef1', to_torch(
betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)))
self.register_buffer('posterior_mean_coef2', to_torch(
(1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod)))
if self.parameterization == "eps":
lvlb_weights = self.betas ** 2 / (
2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod))
elif self.parameterization == "x0":
lvlb_weights = 0.5 * np.sqrt(torch.Tensor(alphas_cumprod)) / (2. * 1 - torch.Tensor(alphas_cumprod))
else:
raise NotImplementedError("mu not supported")
# TODO how to choose this term
lvlb_weights[0] = lvlb_weights[1]
self.register_buffer('lvlb_weights', lvlb_weights, persistent=False)
assert not torch.isnan(self.lvlb_weights).all()
def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
if os.path.exists(path):
sd = torch.load(path, map_location="cpu")
if "state_dict" in list(sd.keys()):
sd = sd["state_dict"]
keys = list(sd.keys())
# Our model adds additional channels to the first layer to condition on an input image.
# For the first layer, copy existing channel weights and initialize new channel weights to zero.
input_keys = [
"model.diffusion_model.input_blocks.0.0.weight",
]
self_sd = self.state_dict()
for input_key in input_keys:
if input_key not in sd or input_key not in self_sd:
continue
input_weight = self_sd[input_key]
if input_weight.size() != sd[input_key].size():
print(f"Manual init: {input_key}")
input_weight.zero_()
input_weight[:, :4, :, :].copy_(sd[input_key])
ignore_keys.append(input_key)
for k in keys:
for ik in ignore_keys:
if k.startswith(ik):
print("Deleting key {} from state_dict.".format(k))
del sd[k]
missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
sd, strict=False)
print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
if len(missing) > 0:
print(f"Missing Keys: {missing}")
if len(unexpected) > 0:
print(f"Unexpected Keys: {unexpected}")
else:
warnings.warn("The pre-trained stable diffusion model has not been loaded. "
"If you are in the training phase, please check your code. "
"If you are in the testing phase, you can ignore this warning.")
def q_mean_variance(self, x_start, t):
"""
Get the distribution q(x_t | x_0).
:param x_start: the [N x C x ...] tensor of noiseless inputs.
:param t: the number of diffusion steps (minus 1). Here, 0 means one step.
:return: A tuple (mean, variance, log_variance), all of x_start's shape.
"""
mean = (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start)
variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape)
log_variance = extract_into_tensor(self.log_one_minus_alphas_cumprod, t, x_start.shape)
return mean, variance, log_variance
def predict_start_from_noise(self, x_t, t, noise):
return (
extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t -
extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise
)
def q_posterior(self, x_start, x_t, t):
posterior_mean = (
extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start +
extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t
)
posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape)
posterior_log_variance_clipped = extract_into_tensor(self.posterior_log_variance_clipped, t, x_t.shape)
return posterior_mean, posterior_variance, posterior_log_variance_clipped
def p_mean_variance(self, x, t, clip_denoised: bool):
model_out = self.model(x, t)
if self.parameterization == "eps":
x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
elif self.parameterization == "x0":
x_recon = model_out
if clip_denoised:
x_recon.clamp_(-1., 1.)
model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
return model_mean, posterior_variance, posterior_log_variance
@torch.no_grad()
def p_sample(self, x, t, clip_denoised=True, repeat_noise=False):
b, *_, device = *x.shape, x.device
model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, clip_denoised=clip_denoised)
noise = noise_like(x.shape, device, repeat_noise)
# no noise when t == 0
nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
@torch.no_grad()
def p_sample_loop(self, shape, return_intermediates=False):
device = self.betas.device
b = shape[0]
img = torch.randn(shape, device=device)
intermediates = [img]
for i in tqdm(reversed(range(0, self.num_timesteps)), desc='Sampling t', total=self.num_timesteps):
img = self.p_sample(img, torch.full((b,), i, device=device, dtype=torch.long),
clip_denoised=self.clip_denoised)
if i % self.log_every_t == 0 or i == self.num_timesteps - 1:
intermediates.append(img)
if return_intermediates:
return img, intermediates
return img
@torch.no_grad()
def sample(self, batch_size=16, return_intermediates=False):
image_size = self.image_size
channels = self.channels
return self.p_sample_loop((batch_size, channels, image_size, image_size),
return_intermediates=return_intermediates)
def q_sample(self, x_start, t, noise=None):
noise = default(noise, lambda: torch.randn_like(x_start))
return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise)
def get_loss(self, pred, target, mean=True):
pred = pred.float()
if self.loss_type == 'l1':
loss = (target - pred).abs()
if mean:
loss = loss.mean()
elif self.loss_type == 'l2':
if mean:
loss = torch.nn.functional.mse_loss(target, pred)
else:
loss = torch.nn.functional.mse_loss(target, pred, reduction='none')
else:
raise NotImplementedError("unknown loss type '{loss_type}'")
return loss
def p_losses(self, x_start, t, noise=None):
noise = default(noise, lambda: torch.randn_like(x_start))
x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
model_out = self.model(x_noisy, t)
loss_dict = {}
if self.parameterization == "eps":
target = noise
elif self.parameterization == "x0":
target = x_start
else:
raise NotImplementedError(f"Paramterization {self.parameterization} not yet supported")
loss = self.get_loss(model_out, target, mean=False).mean(dim=[1, 2, 3])
log_prefix = 'train' if self.training else 'val'
loss_dict.update({f'{log_prefix}/loss_simple': loss.mean()})
loss_simple = loss.mean() * self.l_simple_weight
loss_vlb = (self.lvlb_weights[t] * loss).mean()
loss_dict.update({f'{log_prefix}/loss_vlb': loss_vlb})
loss = loss_simple + self.original_elbo_weight * loss_vlb
loss_dict.update({f'{log_prefix}/loss': loss})
return loss, loss_dict
def forward(self, x, *args, **kwargs):
# b, c, h, w, device, img_size, = *x.shape, x.device, self.image_size
# assert h == img_size and w == img_size, f'height and width of image must be {img_size}'
t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=x.device).long()
return self.p_losses(x, t, *args, **kwargs)
def get_input(self, batch, k):
return batch[k]
class NNParams(nn.Module):
def __init__(self, dim):
super().__init__()
self.cls_token = nn.Parameter(torch.zeros(dim), requires_grad=True)
trunc_normal_(self.cls_token, mean=0., std=10, a=-10, b=10)
def forward(self):
return self.cls_token
class LatentDiffusion(DDPM):
"""main class"""
def __init__(self,
first_stage_config,
cond_stage_config,
num_timesteps_cond=None,
cond_stage_key="image",
cond_stage_trainable=False,
concat_mode=True,
cond_stage_forward=None,
conditioning_key=None,
scale_factor=1.0,
scale_by_std=False,
deepspeed="",
*args, **kwargs):
self.deepspeed = deepspeed
self.num_timesteps_cond = default(num_timesteps_cond, 1)
self.scale_by_std = scale_by_std
assert self.num_timesteps_cond <= kwargs['timesteps']
# for backwards compatibility after implementation of DiffusionWrapper
if conditioning_key is None:
conditioning_key = 'concat' if concat_mode else 'crossattn'
if cond_stage_config == '__is_unconditional__':
conditioning_key = None
ckpt_path = kwargs.pop("ckpt_path", None)
ignore_keys = kwargs.pop("ignore_keys", [])
super().__init__(conditioning_key=conditioning_key, *args, **kwargs)
self.concat_mode = concat_mode
self.cond_stage_trainable = cond_stage_trainable
self.cond_stage_key = cond_stage_key
try:
self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1
except:
self.num_downs = 0
if not scale_by_std:
self.scale_factor = scale_factor
else:
self.register_buffer('scale_factor', torch.tensor(scale_factor))
self.instantiate_first_stage(first_stage_config)
self.instantiate_cond_stage(cond_stage_config)
self.cond_stage_forward = cond_stage_forward
self.clip_denoised = False
self.bbox_tokenizer = None
self.restarted_from_ckpt = False
if ckpt_path is not None:
self.init_from_ckpt(ckpt_path, ignore_keys)
self.restarted_from_ckpt = True
self.additional_loss_type = kwargs.pop("additional_loss_type", None)
def make_cond_schedule(self, ):
self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long)
ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long()
self.cond_ids[:self.num_timesteps_cond] = ids
# @rank_zero_only
@torch.no_grad()
def on_train_batch_start(self, batch, batch_idx, dataloader_idx):
# only for very first batch
if self.scale_by_std and self.current_epoch == 0 and self.global_step == 0 and batch_idx == 0 and not self.restarted_from_ckpt:
assert self.scale_factor == 1., 'rather not use custom rescaling and std-rescaling simultaneously'
# set rescale weight to 1./std of encodings
print("### USING STD-RESCALING ###")
x = super().get_input(batch, self.first_stage_key)
encoder_posterior = self.encode_first_stage(x)
z = self.get_first_stage_encoding(encoder_posterior).detach()
del self.scale_factor
self.register_buffer('scale_factor', 1. / z.flatten().std())
print(f"setting self.scale_factor to {self.scale_factor}")
print("### USING STD-RESCALING ###")
def register_schedule(self,
given_betas=None, beta_schedule="linear", timesteps=1000,
linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
super().register_schedule(given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s)
self.shorten_cond_schedule = self.num_timesteps_cond > 1
if self.shorten_cond_schedule:
self.make_cond_schedule()
def instantiate_first_stage(self, config):
model = instantiate_from_config(config)
self.first_stage_model = model.eval()
self.first_stage_model.train = disabled_train
for param in self.first_stage_model.parameters():
param.requires_grad = False
def instantiate_cond_stage(self, config):
if not self.cond_stage_trainable:
if config == "__is_first_stage__":
print("Using first stage also as cond stage.")
self.cond_stage_model = self.first_stage_model
elif config == "__is_unconditional__":
print(f"Training {self.__class__.__name__} as an unconditional model.")
self.cond_stage_model = None
# self.be_unconditional = True
else:
model = instantiate_from_config(config)
self.cond_stage_model = model.eval()
self.cond_stage_model.train = disabled_train
for param in self.cond_stage_model.parameters():
param.requires_grad = False
else:
assert config != '__is_first_stage__'
assert config != '__is_unconditional__'
model = instantiate_from_config(config)
self.cond_stage_model = model
def _get_denoise_row_from_list(self, samples, desc='', force_no_decoder_quantization=False):
denoise_row = []
for zd in tqdm(samples, desc=desc):
denoise_row.append(self.decode_first_stage(zd,
force_not_quantize=force_no_decoder_quantization))
n_imgs_per_row = len(denoise_row)
denoise_row = torch.stack(denoise_row) # n_log_step, n_row, C, H, W
denoise_grid = rearrange(denoise_row, 'n b c h w -> b n c h w')
denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
return denoise_grid
def get_first_stage_encoding(self, encoder_posterior):
if isinstance(encoder_posterior, DiagonalGaussianDistribution):
z = encoder_posterior.sample()
elif isinstance(encoder_posterior, torch.Tensor):
z = encoder_posterior
else:
raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented")
return self.scale_factor * z
def get_learned_conditioning(self, c):
if self.cond_stage_forward is None:
if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode):
c = self.cond_stage_model.encode(c)
if isinstance(c, DiagonalGaussianDistribution):
c = c.mode()
else:
c = self.cond_stage_model(c)
else:
assert hasattr(self.cond_stage_model, self.cond_stage_forward)
c = getattr(self.cond_stage_model, self.cond_stage_forward)(c)
return c
def meshgrid(self, h, w):
y = torch.arange(0, h).view(h, 1, 1).repeat(1, w, 1)
x = torch.arange(0, w).view(1, w, 1).repeat(h, 1, 1)
arr = torch.cat([y, x], dim=-1)
return arr
def delta_border(self, h, w):
"""
:param h: height
:param w: width
:return: normalized distance to image border,
wtith min distance = 0 at border and max dist = 0.5 at image center
"""
lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2)
arr = self.meshgrid(h, w) / lower_right_corner
dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0]
dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0]
edge_dist = torch.min(torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1)[0]
return edge_dist
def get_weighting(self, h, w, Ly, Lx, device):
weighting = self.delta_border(h, w)
weighting = torch.clip(weighting, self.split_input_params["clip_min_weight"],
self.split_input_params["clip_max_weight"], )
weighting = weighting.view(1, h * w, 1).repeat(1, 1, Ly * Lx).to(device)
if self.split_input_params["tie_braker"]:
L_weighting = self.delta_border(Ly, Lx)
L_weighting = torch.clip(L_weighting,
self.split_input_params["clip_min_tie_weight"],
self.split_input_params["clip_max_tie_weight"])
L_weighting = L_weighting.view(1, 1, Ly * Lx).to(device)
weighting = weighting * L_weighting
return weighting
def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1): # todo load once not every time, shorten code
"""
:param x: img of size (bs, c, h, w)
:return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1])
"""
bs, nc, h, w = x.shape
# number of crops in image
Ly = (h - kernel_size[0]) // stride[0] + 1
Lx = (w - kernel_size[1]) // stride[1] + 1
if uf == 1 and df == 1:
fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
unfold = torch.nn.Unfold(**fold_params)
fold = torch.nn.Fold(output_size=x.shape[2:], **fold_params)
weighting = self.get_weighting(kernel_size[0], kernel_size[1], Ly, Lx, x.device).to(x.dtype)
normalization = fold(weighting).view(1, 1, h, w) # normalizes the overlap
weighting = weighting.view((1, 1, kernel_size[0], kernel_size[1], Ly * Lx))
elif uf > 1 and df == 1:
fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
unfold = torch.nn.Unfold(**fold_params)
fold_params2 = dict(kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf),
dilation=1, padding=0,
stride=(stride[0] * uf, stride[1] * uf))
fold = torch.nn.Fold(output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2)
weighting = self.get_weighting(kernel_size[0] * uf, kernel_size[1] * uf, Ly, Lx, x.device).to(x.dtype)
normalization = fold(weighting).view(1, 1, h * uf, w * uf) # normalizes the overlap
weighting = weighting.view((1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx))
elif df > 1 and uf == 1:
fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
unfold = torch.nn.Unfold(**fold_params)
fold_params2 = dict(kernel_size=(kernel_size[0] // df, kernel_size[0] // df),
dilation=1, padding=0,
stride=(stride[0] // df, stride[1] // df))
fold = torch.nn.Fold(output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2)
weighting = self.get_weighting(kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device).to(x.dtype)
normalization = fold(weighting).view(1, 1, h // df, w // df) # normalizes the overlap
weighting = weighting.view((1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx))
else:
raise NotImplementedError
return fold, unfold, normalization, weighting
# @torch.no_grad()
def get_input(self, batch, k, return_first_stage_outputs=False, force_c_encode=False,
cond_key=None, return_original_cond=False, bs=None, uncond=0.05):
x = super().get_input(batch, k)
if bs is not None:
x = x[:bs]
encoder_posterior = self.encode_first_stage(x)
z = self.get_first_stage_encoding(encoder_posterior).detach()
cond_key = cond_key or self.cond_stage_key
xc = super().get_input(batch, cond_key)
if bs is not None:
xc["c_crossattn"] = xc["c_crossattn"][:bs]
xc["c_concat"] = xc["c_concat"][:bs]
cond = {}
random = torch.rand(x.size(0), device=z.device)
prompt_mask = rearrange(random < 0.075, "n -> n 1 1")
input_mask = 1 - rearrange((random >= 0.075).float() * (random < 0.15).float(), "n -> n 1 1 1")
null_prompt = self.get_learned_conditioning([""])
cond["c_crossattn"] = [torch.where(prompt_mask, null_prompt, self.get_learned_conditioning(xc["c_crossattn"]).detach())]
cond["c_concat"] = [input_mask * self.encode_first_stage((xc["c_concat"])).mode().detach()]
out = [z, cond]
if return_first_stage_outputs:
xrec = self.decode_first_stage(z)
out.extend([x, xrec])
if return_original_cond:
out.append(xc)
return out
@torch.no_grad()
def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
if predict_cids:
if z.dim() == 4:
z = torch.argmax(z.exp(), dim=1).long()
z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
z = rearrange(z, 'b h w c -> b c h w').contiguous()
z = 1. / self.scale_factor * z
if hasattr(self, "split_input_params"):
if self.split_input_params["patch_distributed_vq"]:
ks = self.split_input_params["ks"] # eg. (128, 128)
stride = self.split_input_params["stride"] # eg. (64, 64)
uf = self.split_input_params["vqf"]
bs, nc, h, w = z.shape
if ks[0] > h or ks[1] > w:
ks = (min(ks[0], h), min(ks[1], w))
print("reducing Kernel")
if stride[0] > h or stride[1] > w:
stride = (min(stride[0], h), min(stride[1], w))
print("reducing stride")
fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf)
z = unfold(z) # (bn, nc * prod(**ks), L)
# 1. Reshape to img shape
z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
# 2. apply model loop over last dim
if isinstance(self.first_stage_model, VQModelInterface):
output_list = [self.first_stage_model.decode(z[:, :, :, :, i],
force_not_quantize=predict_cids or force_not_quantize)
for i in range(z.shape[-1])]
else:
output_list = [self.first_stage_model.decode(z[:, :, :, :, i])
for i in range(z.shape[-1])]
o = torch.stack(output_list, axis=-1) # # (bn, nc, ks[0], ks[1], L)
o = o * weighting
# Reverse 1. reshape to img shape
o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
# stitch crops together
decoded = fold(o)
decoded = decoded / normalization # norm is shape (1, 1, h, w)
return decoded
else:
if isinstance(self.first_stage_model, VQModelInterface):
return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
else:
return self.first_stage_model.decode(z)
else:
if isinstance(self.first_stage_model, VQModelInterface):
return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
else:
return self.first_stage_model.decode(z)
# same as above but without decorator
def differentiable_decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
if predict_cids:
if z.dim() == 4:
z = torch.argmax(z.exp(), dim=1).long()
z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
z = rearrange(z, 'b h w c -> b c h w').contiguous()
z = 1. / self.scale_factor * z
if hasattr(self, "split_input_params"):
if self.split_input_params["patch_distributed_vq"]:
ks = self.split_input_params["ks"] # eg. (128, 128)
stride = self.split_input_params["stride"] # eg. (64, 64)
uf = self.split_input_params["vqf"]
bs, nc, h, w = z.shape
if ks[0] > h or ks[1] > w:
ks = (min(ks[0], h), min(ks[1], w))
print("reducing Kernel")
if stride[0] > h or stride[1] > w:
stride = (min(stride[0], h), min(stride[1], w))
print("reducing stride")
fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf)
z = unfold(z) # (bn, nc * prod(**ks), L)
# 1. Reshape to img shape
z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
# 2. apply model loop over last dim
if isinstance(self.first_stage_model, VQModelInterface):
output_list = [self.first_stage_model.decode(z[:, :, :, :, i],
force_not_quantize=predict_cids or force_not_quantize)
for i in range(z.shape[-1])]
else:
output_list = [self.first_stage_model.decode(z[:, :, :, :, i])
for i in range(z.shape[-1])]
o = torch.stack(output_list, axis=-1) # # (bn, nc, ks[0], ks[1], L)
o = o * weighting
# Reverse 1. reshape to img shape
o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
# stitch crops together
decoded = fold(o)
decoded = decoded / normalization # norm is shape (1, 1, h, w)
return decoded
else:
if isinstance(self.first_stage_model, VQModelInterface):
return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
else:
return self.first_stage_model.decode(z)
else:
if isinstance(self.first_stage_model, VQModelInterface):
return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
else:
return self.first_stage_model.decode(z)
@torch.no_grad()
def encode_first_stage(self, x):
if hasattr(self, "split_input_params"):
if self.split_input_params["patch_distributed_vq"]:
ks = self.split_input_params["ks"] # eg. (128, 128)
stride = self.split_input_params["stride"] # eg. (64, 64)
df = self.split_input_params["vqf"]
self.split_input_params['original_image_size'] = x.shape[-2:]
bs, nc, h, w = x.shape
if ks[0] > h or ks[1] > w:
ks = (min(ks[0], h), min(ks[1], w))
print("reducing Kernel")
if stride[0] > h or stride[1] > w:
stride = (min(stride[0], h), min(stride[1], w))
print("reducing stride")
fold, unfold, normalization, weighting = self.get_fold_unfold(x, ks, stride, df=df)
z = unfold(x) # (bn, nc * prod(**ks), L)
# Reshape to img shape
z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
output_list = [self.first_stage_model.encode(z[:, :, :, :, i])
for i in range(z.shape[-1])]
o = torch.stack(output_list, axis=-1)
o = o * weighting
# Reverse reshape to img shape
o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
# stitch crops together
decoded = fold(o)
decoded = decoded / normalization
return decoded
else:
return self.first_stage_model.encode(x)
else:
return self.first_stage_model.encode(x)
def forward(self, batch, batch_idx, num_steps, *args, **kwargs):
x, c = self.get_input(batch, self.first_stage_key)
t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=x.device).long()
if self.model.conditioning_key is not None:
assert c is not None
if self.cond_stage_trainable:
c = self.get_learned_conditioning(c)
if self.shorten_cond_schedule: # TODO: drop this option
tc = self.cond_ids[t]
c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float()))
loss, loss_dict = self.p_losses(x, c, t, *args, **kwargs)
return loss, loss_dict
def _rescale_annotations(self, bboxes, crop_coordinates): # TODO: move to dataset
def rescale_bbox(bbox):
x0 = clamp((bbox[0] - crop_coordinates[0]) / crop_coordinates[2])
y0 = clamp((bbox[1] - crop_coordinates[1]) / crop_coordinates[3])
w = min(bbox[2] / crop_coordinates[2], 1 - x0)
h = min(bbox[3] / crop_coordinates[3], 1 - y0)
return x0, y0, w, h
return [rescale_bbox(b) for b in bboxes]
def apply_model(self, x_noisy, t, cond, return_ids=False):
if isinstance(cond, dict):
# hybrid case, cond is exptected to be a dict
pass
else:
if not isinstance(cond, list):
cond = [cond]
key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn'
cond = {key: cond}
if hasattr(self, "split_input_params"):
assert len(cond) == 1 # todo can only deal with one conditioning atm
assert not return_ids
ks = self.split_input_params["ks"] # eg. (128, 128)
stride = self.split_input_params["stride"] # eg. (64, 64)
h, w = x_noisy.shape[-2:]
fold, unfold, normalization, weighting = self.get_fold_unfold(x_noisy, ks, stride)
z = unfold(x_noisy) # (bn, nc * prod(**ks), L)
# Reshape to img shape
z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
z_list = [z[:, :, :, :, i] for i in range(z.shape[-1])]
if self.cond_stage_key in ["image", "LR_image", "segmentation",
'bbox_img'] and self.model.conditioning_key: # todo check for completeness
c_key = next(iter(cond.keys())) # get key
c = next(iter(cond.values())) # get value
assert (len(c) == 1) # todo extend to list with more than one elem
c = c[0] # get element
c = unfold(c)
c = c.view((c.shape[0], -1, ks[0], ks[1], c.shape[-1])) # (bn, nc, ks[0], ks[1], L )
cond_list = [{c_key: [c[:, :, :, :, i]]} for i in range(c.shape[-1])]
elif self.cond_stage_key == 'coordinates_bbox':
assert 'original_image_size' in self.split_input_params, 'BoudingBoxRescaling is missing original_image_size'
# assuming padding of unfold is always 0 and its dilation is always 1
n_patches_per_row = int((w - ks[0]) / stride[0] + 1)
full_img_h, full_img_w = self.split_input_params['original_image_size']
# as we are operating on latents, we need the factor from the original image size to the
# spatial latent size to properly rescale the crops for regenerating the bbox annotations
num_downs = self.first_stage_model.encoder.num_resolutions - 1
rescale_latent = 2 ** (num_downs)
# get top left postions of patches as conforming for the bbbox tokenizer, therefore we
# need to rescale the tl patch coordinates to be in between (0,1)
tl_patch_coordinates = [(rescale_latent * stride[0] * (patch_nr % n_patches_per_row) / full_img_w,
rescale_latent * stride[1] * (patch_nr // n_patches_per_row) / full_img_h)
for patch_nr in range(z.shape[-1])]
# patch_limits are tl_coord, width and height coordinates as (x_tl, y_tl, h, w)
patch_limits = [(x_tl, y_tl,
rescale_latent * ks[0] / full_img_w,
rescale_latent * ks[1] / full_img_h) for x_tl, y_tl in tl_patch_coordinates]
# patch_values = [(np.arange(x_tl,min(x_tl+ks, 1.)),np.arange(y_tl,min(y_tl+ks, 1.))) for x_tl, y_tl in tl_patch_coordinates]
# tokenize crop coordinates for the bounding boxes of the respective patches
patch_limits_tknzd = [torch.LongTensor(self.bbox_tokenizer._crop_encoder(bbox))[None]
for bbox in patch_limits] # list of length l with tensors of shape (1, 2)
print(patch_limits_tknzd[0].shape)
# cut tknzd crop position from conditioning
assert isinstance(cond, dict), 'cond must be dict to be fed into model'
cut_cond = cond['c_crossattn'][0][..., :-2]
print(cut_cond.shape)
adapted_cond = torch.stack([torch.cat([cut_cond, p], dim=1) for p in patch_limits_tknzd])
adapted_cond = rearrange(adapted_cond, 'l b n -> (l b) n')
print(adapted_cond.shape)
adapted_cond = self.get_learned_conditioning(adapted_cond)
print(adapted_cond.shape)
adapted_cond = rearrange(adapted_cond, '(l b) n d -> l b n d', l=z.shape[-1])
print(adapted_cond.shape)
cond_list = [{'c_crossattn': [e]} for e in adapted_cond]
else:
cond_list = [cond for i in range(z.shape[-1])] # Todo make this more efficient
# apply model by loop over crops
output_list = [self.model(z_list[i], t, **cond_list[i]) for i in range(z.shape[-1])]
assert not isinstance(output_list[0],
tuple) # todo cant deal with multiple model outputs check this never happens
o = torch.stack(output_list, axis=-1)
o = o * weighting
# Reverse reshape to img shape
o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
# stitch crops together
x_recon = fold(o) / normalization
else:
x_recon = self.model(x_noisy, t, **cond)
if isinstance(x_recon, tuple) and not return_ids:
return x_recon[0]
else:
return x_recon
def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
return (extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart) / \
extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
def _prior_bpd(self, x_start):
"""
Get the prior KL term for the variational lower-bound, measured in
bits-per-dim.
This term can't be optimized, as it only depends on the encoder.
:param x_start: the [N x C x ...] tensor of inputs.
:return: a batch of [N] KL values (in bits), one per batch element.
"""
batch_size = x_start.shape[0]
t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device)
qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t)
kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0)
return mean_flat(kl_prior) / np.log(2.0)
def p_losses(self, x_start, cond, t, noise=None):
noise = default(noise, lambda: torch.randn_like(x_start))
x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
model_output = self.apply_model(x_noisy, t, cond)
loss_dict = {}
prefix = 'train' if self.training else 'val'
if self.parameterization == "x0":
target = x_start
elif self.parameterization == "eps":
target = noise
else:
raise NotImplementedError()
loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3])
loss_dict.update({f'{prefix}/loss_simple': loss_simple.mean()})
# additional_loss_type is in the format of min_snr_k
if self.additional_loss_type is not None and isinstance(self.additional_loss_type, str) and self.additional_loss_type.startswith("min_snr_"):
k = float(self.additional_loss_type.split("_")[-1])
alpha = extract_into_tensor(self.sqrt_alphas_cumprod, t, t.shape)
sigma = extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, t.shape)
snr = (alpha / sigma) ** 2
min_snr = torch.stack([snr, k * torch.ones_like(t)], dim=1).min(dim=1)[0]
if self.parameterization == "eps":
loss_simple = loss_simple * min_snr / snr
elif self.parameterization == "x0":
loss_simple = loss_simple * min_snr
else:
raise NotImplementedError()
loss_simple = loss_simple * min_snr
logvar_t = self.logvar.to(x_start.device)[t]
loss = loss_simple / torch.exp(logvar_t) + logvar_t
# loss = loss_simple / torch.exp(self.logvar) + self.logvar
if self.learn_logvar:
loss_dict.update({f'{prefix}/loss_gamma': loss.mean()})
loss_dict.update({'logvar': self.logvar.data.mean()})
loss = self.l_simple_weight * loss.mean()
loss_vlb = self.get_loss(model_output, target, mean=False).mean(dim=(1, 2, 3))
loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean()
loss_dict.update({f'{prefix}/loss_vlb': loss_vlb})
loss += (self.original_elbo_weight * loss_vlb)
loss_dict.update({f'{prefix}/loss': loss})
return loss, loss_dict
def p_mean_variance(self, x, c, t, clip_denoised: bool, return_codebook_ids=False, quantize_denoised=False,
return_x0=False, score_corrector=None, corrector_kwargs=None):
t_in = t
model_out = self.apply_model(x, t_in, c, return_ids=return_codebook_ids)
if score_corrector is not None:
assert self.parameterization == "eps"
model_out = score_corrector.modify_score(self, model_out, x, t, c, **corrector_kwargs)
if return_codebook_ids:
model_out, logits = model_out
if self.parameterization == "eps":
x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
elif self.parameterization == "x0":
x_recon = model_out
else:
raise NotImplementedError()
if clip_denoised:
x_recon.clamp_(-1., 1.)
if quantize_denoised:
x_recon, _, [_, _, indices] = self.first_stage_model.quantize(x_recon)
model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
if return_codebook_ids:
return model_mean, posterior_variance, posterior_log_variance, logits
elif return_x0:
return model_mean, posterior_variance, posterior_log_variance, x_recon
else:
return model_mean, posterior_variance, posterior_log_variance
@torch.no_grad()
def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False,
return_codebook_ids=False, quantize_denoised=False, return_x0=False,
temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None):
b, *_, device = *x.shape, x.device
outputs = self.p_mean_variance(x=x, c=c, t=t, clip_denoised=clip_denoised,
return_codebook_ids=return_codebook_ids,
quantize_denoised=quantize_denoised,
return_x0=return_x0,
score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
if return_codebook_ids:
raise DeprecationWarning("Support dropped.")
model_mean, _, model_log_variance, logits = outputs
elif return_x0:
model_mean, _, model_log_variance, x0 = outputs
else:
model_mean, _, model_log_variance = outputs
noise = noise_like(x.shape, device, repeat_noise) * temperature
if noise_dropout > 0.:
noise = torch.nn.functional.dropout(noise, p=noise_dropout)
# no noise when t == 0
nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
if return_codebook_ids:
return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, logits.argmax(dim=1)
if return_x0:
return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, x0
else:
return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
@torch.no_grad()
def progressive_denoising(self, cond, shape, verbose=True, callback=None, quantize_denoised=False,
img_callback=None, mask=None, x0=None, temperature=1., noise_dropout=0.,
score_corrector=None, corrector_kwargs=None, batch_size=None, x_T=None, start_T=None,
log_every_t=None):
if not log_every_t:
log_every_t = self.log_every_t
timesteps = self.num_timesteps
if batch_size is not None:
b = batch_size if batch_size is not None else shape[0]
shape = [batch_size] + list(shape)
else:
b = batch_size = shape[0]
if x_T is None:
img = torch.randn(shape, device=x_T.device)
else:
img = x_T
intermediates = []
if cond is not None:
if isinstance(cond, dict):
cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
else:
cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
if start_T is not None:
timesteps = min(timesteps, start_T)
iterator = tqdm(reversed(range(0, timesteps)), desc='Progressive Generation',
total=timesteps) if verbose else reversed(
range(0, timesteps))
if type(temperature) == float:
temperature = [temperature] * timesteps
for i in iterator:
ts = torch.full((b,), i, device=cond.device, dtype=torch.long)
if self.shorten_cond_schedule:
assert self.model.conditioning_key != 'hybrid'
tc = self.cond_ids[ts].to(cond.device)
cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
img, x0_partial = self.p_sample(img, cond, ts,
clip_denoised=self.clip_denoised,
quantize_denoised=quantize_denoised, return_x0=True,
temperature=temperature[i], noise_dropout=noise_dropout,
score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
if mask is not None:
assert x0 is not None
img_orig = self.q_sample(x0, ts)
img = img_orig * mask + (1. - mask) * img
if i % log_every_t == 0 or i == timesteps - 1:
intermediates.append(x0_partial)
if callback: callback(i)
if img_callback: img_callback(img, i)
return img, intermediates
@torch.no_grad()
def p_sample_loop(self, cond, shape, return_intermediates=False,
x_T=None, verbose=True, callback=None, timesteps=None, quantize_denoised=False,
mask=None, x0=None, img_callback=None, start_T=None,
log_every_t=None):
if not log_every_t:
log_every_t = self.log_every_t
device = self.betas.device
b = shape[0]
if x_T is None:
img = torch.randn(shape, device=device)
else:
img = x_T
intermediates = [img]
if timesteps is None:
timesteps = self.num_timesteps
if start_T is not None:
timesteps = min(timesteps, start_T)
iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed(
range(0, timesteps))
if mask is not None:
assert x0 is not None
assert x0.shape[2:3] == mask.shape[2:3] # spatial size has to match
for i in iterator:
ts = torch.full((b,), i, device=device, dtype=torch.long)
if self.shorten_cond_schedule:
assert self.model.conditioning_key != 'hybrid'
tc = self.cond_ids[ts].to(cond.device)
cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
img = self.p_sample(img, cond, ts,
clip_denoised=self.clip_denoised,
quantize_denoised=quantize_denoised)
if mask is not None:
img_orig = self.q_sample(x0, ts)
img = img_orig * mask + (1. - mask) * img
if i % log_every_t == 0 or i == timesteps - 1:
intermediates.append(img)
if callback: callback(i)
if img_callback: img_callback(img, i)
if return_intermediates:
return img, intermediates
return img
@torch.no_grad()
def sample(self, cond, batch_size=16, return_intermediates=False, x_T=None,
verbose=True, timesteps=None, quantize_denoised=False,
mask=None, x0=None, shape=None,**kwargs):
if shape is None:
shape = (batch_size, self.channels, self.image_size, self.image_size)
if cond is not None:
if isinstance(cond, dict):
cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
else:
cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
return self.p_sample_loop(cond,
shape,
return_intermediates=return_intermediates, x_T=x_T,
verbose=verbose, timesteps=timesteps, quantize_denoised=quantize_denoised,
mask=mask, x0=x0)
@torch.no_grad()
def sample_log(self,cond,batch_size,ddim, ddim_steps,**kwargs):
if ddim:
ddim_sampler = DDIMSampler(self)
shape = (self.channels, self.image_size, self.image_size)
samples, intermediates =ddim_sampler.sample(ddim_steps,batch_size,
shape,cond,verbose=False,**kwargs)
else:
samples, intermediates = self.sample(cond=cond, batch_size=batch_size,
return_intermediates=True,**kwargs)
return samples, intermediates
class DiffusionWrapper(nn.Module):
def __init__(self, diff_model_config, conditioning_key):
super().__init__()
self.diffusion_model = instantiate_from_config(diff_model_config)
self.conditioning_key = conditioning_key
assert self.conditioning_key in [None, 'concat', 'crossattn', 'hybrid', 'adm']
def forward(self, x, t, c_concat: list = None, c_crossattn: list = None):
if self.conditioning_key is None:
out = self.diffusion_model(x, t)
elif self.conditioning_key == 'concat':
xc = torch.cat([x] + c_concat, dim=1)
out = self.diffusion_model(xc, t)
elif self.conditioning_key == 'crossattn':
cc = torch.cat(c_crossattn, 1)
out = self.diffusion_model(x, t, context=cc)
elif self.conditioning_key == 'hybrid':
xc = torch.cat([x] + c_concat, dim=1)
cc = torch.cat(c_crossattn, 1)
out = self.diffusion_model(xc, t, context=cc)
elif self.conditioning_key == 'adm':
cc = c_crossattn[0]
out = self.diffusion_model(x, t, y=cc)
else:
raise NotImplementedError()
return out
class Layout2ImgDiffusion(LatentDiffusion):
# TODO: move all layout-specific hacks to this class
def __init__(self, cond_stage_key, *args, **kwargs):
assert cond_stage_key == 'coordinates_bbox', 'Layout2ImgDiffusion only for cond_stage_key="coordinates_bbox"'
super().__init__(cond_stage_key=cond_stage_key, *args, **kwargs)
def log_images(self, batch, N=8, *args, **kwargs):
logs = super().log_images(batch=batch, N=N, *args, **kwargs)
key = 'train' if self.training else 'validation'
dset = self.trainer.datamodule.datasets[key]
mapper = dset.conditional_builders[self.cond_stage_key]
bbox_imgs = []
map_fn = lambda catno: dset.get_textual_label(dset.get_category_id(catno))
for tknzd_bbox in batch[self.cond_stage_key][:N]:
bboximg = mapper.plot(tknzd_bbox.detach().cpu(), map_fn, (256, 256))
bbox_imgs.append(bboximg)
cond_img = torch.stack(bbox_imgs, dim=0)
logs['bbox_image'] = cond_img
return logs
================================================
FILE: models/InstructDiffusion/stable_diffusion/ldm/models/diffusion/dpm_solver/__init__.py
================================================
from .sampler import DPMSolverSampler
================================================
FILE: models/InstructDiffusion/stable_diffusion/ldm/models/diffusion/dpm_solver/dpm_solver.py
================================================
import torch
import torch.nn.functional as F
import math
class NoiseScheduleVP:
def __init__(
self,
schedule='discrete',
betas=None,
alphas_cumprod=None,
continuous_beta_0=0.1,
continuous_beta_1=20.,
):
"""Create a wrapper class for the forward SDE (VP type).
***
Update: We support discrete-time diffusion models by implementing a picewise linear interpolation for log_alpha_t.
We recommend to use schedule='discrete' for the discrete-time diffusion models, especially for high-resolution images.
***
The forward SDE ensures that the condition distribution q_{t|0}(x_t | x_0) = N ( alpha_t * x_0, sigma_t^2 * I ).
We further define lambda_t = log(alpha_t) - log(sigma_t), which is the half-logSNR (described in the DPM-Solver paper).
Therefore, we implement the functions for computing alpha_t, sigma_t and lambda_t. For t in [0, T], we have:
log_alpha_t = self.marginal_log_mean_coeff(t)
sigma_t = self.marginal_std(t)
lambda_t = self.marginal_lambda(t)
Moreover, as lambda(t) is an invertible function, we also support its inverse function:
t = self.inverse_lambda(lambda_t)
===============================================================
We support both discrete-time DPMs (trained on n = 0, 1, ..., N-1) and continuous-time DPMs (trained on t in [t_0, T]).
1. For discrete-time DPMs:
For discrete-time DPMs trained on n = 0, 1, ..., N-1, we convert the discrete steps to continuous time steps by:
t_i = (i + 1) / N
e.g. for N = 1000, we have t_0 = 1e-3 and T = t_{N-1} = 1.
We solve the corresponding diffusion ODE from time T = 1 to time t_0 = 1e-3.
Args:
betas: A `torch.Tensor`. The beta array for the discrete-time DPM. (See the original DDPM paper for details)
alphas_cumprod: A `torch.Tensor`. The cumprod alphas for the discrete-time DPM. (See the original DDPM paper for details)
Note that we always have alphas_cumprod = cumprod(betas). Therefore, we only need to set one of `betas` and `alphas_cumprod`.
**Important**: Please pay special attention for the args for `alphas_cumprod`:
The `alphas_cumprod` is the \hat{alpha_n} arrays in the notations of DDPM. Specifically, DDPMs assume that
q_{t_n | 0}(x_{t_n} | x_0) = N ( \sqrt{\hat{alpha_n}} * x_0, (1 - \hat{alpha_n}) * I ).
Therefore, the notation \hat{alpha_n} is different from the notation alpha_t in DPM-Solver. In fact, we have
alpha_{t_n} = \sqrt{\hat{alpha_n}},
and
log(alpha_{t_n}) = 0.5 * log(\hat{alpha_n}).
2. For continuous-time DPMs:
We support two types of VPSDEs: linear (DDPM) and cosine (improved-DDPM). The hyperparameters for the noise
schedule are the default settings in DDPM and improved-DDPM:
Args:
beta_min: A `float` number. The smallest beta for the linear schedule.
beta_max: A `float` number. The largest beta for the linear schedule.
cosine_s: A `float` number. The hyperparameter in the cosine schedule.
cosine_beta_max: A `float` number. The hyperparameter in the cosine schedule.
T: A `float` number. The ending time of the forward process.
===============================================================
Args:
schedule: A `str`. The noise schedule of the forward SDE. 'discrete' for discrete-time DPMs,
'linear' or 'cosine' for continuous-time DPMs.
Returns:
A wrapper object of the forward SDE (VP type).
===============================================================
Example:
# For discrete-time DPMs, given betas (the beta array for n = 0, 1, ..., N - 1):
>>> ns = NoiseScheduleVP('discrete', betas=betas)
# For discrete-time DPMs, given alphas_cumprod (the \hat{alpha_n} array for n = 0, 1, ..., N - 1):
>>> ns = NoiseScheduleVP('discrete', alphas_cumprod=alphas_cumprod)
# For continuous-time DPMs (VPSDE), linear schedule:
>>> ns = NoiseScheduleVP('linear', continuous_beta_0=0.1, continuous_beta_1=20.)
"""
if schedule not in ['discrete', 'linear', 'cosine']:
raise ValueError("Unsupported noise schedule {}. The schedule needs to be 'discrete' or 'linear' or 'cosine'".format(schedule))
self.schedule = schedule
if schedule == 'discrete':
if betas is not None:
log_alphas = 0.5 * torch.log(1 - betas).cumsum(dim=0)
else:
assert alphas_cumprod is not None
log_alphas = 0.5 * torch.log(alphas_cumprod)
self.total_N = len(log_alphas)
self.T = 1.
self.t_array = torch.linspace(0., 1., self.total_N + 1)[1:].reshape((1, -1))
self.log_alpha_array = log_alphas.reshape((1, -1,))
else:
self.total_N = 1000
self.beta_0 = continuous_beta_0
self.beta_1 = continuous_beta_1
self.cosine_s = 0.008
self.cosine_beta_max = 999.
self.cosine_t_max = math.atan(self.cosine_beta_max * (1. + self.cosine_s) / math.pi) * 2. * (1. + self.cosine_s) / math.pi - self.cosine_s
self.cosine_log_alpha_0 = math.log(math.cos(self.cosine_s / (1. + self.cosine_s) * math.pi / 2.))
self.schedule = schedule
if schedule == 'cosine':
# For the cosine schedule, T = 1 will have numerical issues. So we manually set the ending time T.
# Note that T = 0.9946 may be not the optimal setting. However, we find it works well.
self.T = 0.9946
else:
self.T = 1.
def marginal_log_mean_coeff(self, t):
"""
Compute log(alpha_t) of a given continuous-time label t in [0, T].
"""
if self.schedule == 'discrete':
return interpolate_fn(t.reshape((-1, 1)), self.t_array.to(t.device), self.log_alpha_array.to(t.device)).reshape((-1))
elif self.schedule == 'linear':
return -0.25 * t ** 2 * (self.beta_1 - self.beta_0) - 0.5 * t * self.beta_0
elif self.schedule == 'cosine':
log_alpha_fn = lambda s: torch.log(torch.cos((s + self.cosine_s) / (1. + self.cosine_s) * math.pi / 2.))
log_alpha_t = log_alpha_fn(t) - self.cosine_log_alpha_0
return log_alpha_t
def marginal_alpha(self, t):
"""
Compute alpha_t of a given continuous-time label t in [0, T].
"""
return torch.exp(self.marginal_log_mean_coeff(t))
def marginal_std(self, t):
"""
Compute sigma_t of a given continuous-time label t in [0, T].
"""
return torch.sqrt(1. - torch.exp(2. * self.marginal_log_mean_coeff(t)))
def marginal_lambda(self, t):
"""
Compute lambda_t = log(alpha_t) - log(sigma_t) of a given continuous-time label t in [0, T].
"""
log_mean_coeff = self.marginal_log_mean_coeff(t)
log_std = 0.5 * torch.log(1. - torch.exp(2. * log_mean_coeff))
return log_mean_coeff - log_std
def inverse_lambda(self, lamb):
"""
Compute the continuous-time label t in [0, T] of a given half-logSNR lambda_t.
"""
if self.schedule == 'linear':
tmp = 2. * (self.beta_1 - self.beta_0) * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb))
Delta = self.beta_0**2 + tmp
return tmp / (torch.sqrt(Delta) + self.beta_0) / (self.beta_1 - self.beta_0)
elif self.schedule == 'discrete':
log_alpha = -0.5 * torch.logaddexp(torch.zeros((1,)).to(lamb.device), -2. * lamb)
t = interpolate_fn(log_alpha.reshape((-1, 1)), torch.flip(self.log_alpha_array.to(lamb.device), [1]), torch.flip(self.t_array.to(lamb.device), [1]))
return t.reshape((-1,))
else:
log_alpha = -0.5 * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb))
t_fn = lambda log_alpha_t: torch.arccos(torch.exp(log_alpha_t + self.cosine_log_alpha_0)) * 2. * (1. + self.cosine_s) / math.pi - self.cosine_s
t = t_fn(log_alpha)
return t
def model_wrapper(
model,
noise_schedule,
model_type="noise",
model_kwargs={},
guidance_type="uncond",
condition=None,
unconditional_condition=None,
guidance_scale=1.,
classifier_fn=None,
classifier_kwargs={},
):
"""Create a wrapper function for the noise prediction model.
DPM-Solver needs to solve the continuous-time diffusion ODEs. For DPMs trained on discrete-time labels, we need to
firstly wrap the model function to a noise prediction model that accepts the continuous time as the input.
We support four types of the diffusion model by setting `model_type`:
1. "noise": noise prediction model. (Trained by predicting noise).
2. "x_start": data prediction model. (Trained by predicting the data x_0 at time 0).
3. "v": velocity prediction model. (Trained by predicting the velocity).
The "v" prediction is derivation detailed in Appendix D of [1], and is used in Imagen-Video [2].
[1] Salimans, Tim, and Jonathan Ho. "Progressive distillation for fast sampling of diffusion models."
arXiv preprint arXiv:2202.00512 (2022).
[2] Ho, Jonathan, et al. "Imagen Video: High Definition Video Generation with Diffusion Models."
arXiv preprint arXiv:2210.02303 (2022).
4. "score": marginal score function. (Trained by denoising score matching).
Note that the score function and the noise prediction model follows a simple relationship:
```
noise(x_t, t) = -sigma_t * score(x_t, t)
```
We support three types of guided sampling by DPMs by setting `guidance_type`:
1. "uncond": unconditional sampling by DPMs.
The input `model` has the following format:
``
model(x, t_input, **model_kwargs) -> noise | x_start | v | score
``
2. "classifier": classifier guidance sampling [3] by DPMs and another classifier.
The input `model` has the following format:
``
model(x, t_input, **model_kwargs) -> noise | x_start | v | score
``
The input `classifier_fn` has the following format:
``
classifier_fn(x, t_input, cond, **classifier_kwargs) -> logits(x, t_input, cond)
``
[3] P. Dhariwal and A. Q. Nichol, "Diffusion models beat GANs on image synthesis,"
in Advances in Neural Information Processing Systems, vol. 34, 2021, pp. 8780-8794.
3. "classifier-free": classifier-free guidance sampling by conditional DPMs.
The input `model` has the following format:
``
model(x, t_input, cond, **model_kwargs) -> noise | x_start | v | score
``
And if cond == `unconditional_condition`, the model output is the unconditional DPM output.
[4] Ho, Jonathan, and Tim Salimans. "Classifier-free diffusion guidance."
arXiv preprint arXiv:2207.12598 (2022).
The `t_input` is the time label of the model, which may be discrete-time labels (i.e. 0 to 999)
or continuous-time labels (i.e. epsilon to T).
We wrap the model function to accept only `x` and `t_continuous` as inputs, and outputs the predicted noise:
``
def model_fn(x, t_continuous) -> noise:
t_input = get_model_input_time(t_continuous)
return noise_pred(model, x, t_input, **model_kwargs)
``
where `t_continuous` is the continuous time labels (i.e. epsilon to T). And we use `model_fn` for DPM-Solver.
===============================================================
Args:
model: A diffusion model with the corresponding format described above.
noise_schedule: A noise schedule object, such as NoiseScheduleVP.
model_type: A `str`. The parameterization type of the diffusion model.
"noise" or "x_start" or "v" or "score".
model_kwargs: A `dict`. A dict for the other inputs of the model function.
guidance_type: A `str`. The type of the guidance for sampling.
"uncond" or "classifier" or "classifier-free".
condition: A pytorch tensor. The condition for the guided sampling.
Only used for "classifier" or "classifier-free" guidance type.
unconditional_condition: A pytorch tensor. The condition for the unconditional sampling.
Only used for "classifier-free" guidance type.
guidance_scale: A `float`. The scale for the guided sampling.
classifier_fn: A classifier function. Only used for the classifier guidance.
classifier_kwargs: A `dict`. A dict for the other inputs of the classifier function.
Returns:
A noise prediction model that accepts the noised data and the continuous time as the inputs.
"""
def get_model_input_time(t_continuous):
"""
Convert the continuous-time `t_continuous` (in [epsilon, T]) to the model input time.
For discrete-time DPMs, we convert `t_continuous` in [1 / N, 1] to `t_input` in [0, 1000 * (N - 1) / N].
For continuous-time DPMs, we just use `t_continuous`.
"""
if noise_schedule.schedule == 'discrete':
return (t_continuous - 1. / noise_schedule.total_N) * 1000.
else:
return t_continuous
def noise_pred_fn(x, t_continuous, cond=None):
if t_continuous.reshape((-1,)).shape[0] == 1:
t_continuous = t_continuous.expand((x.shape[0]))
t_input = get_model_input_time(t_continuous)
if cond is None:
output = model(x, t_input, **model_kwargs)
else:
output = model(x, t_input, cond, **model_kwargs)
if model_type == "noise":
return output
elif model_type == "x_start":
alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous)
dims = x.dim()
return (x - expand_dims(alpha_t, dims) * output) / expand_dims(sigma_t, dims)
elif model_type == "v":
alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous)
dims = x.dim()
return expand_dims(alpha_t, dims) * output + expand_dims(sigma_t, dims) * x
elif model_type == "score":
sigma_t = noise_schedule.marginal_std(t_continuous)
dims = x.dim()
return -expand_dims(sigma_t, dims) * output
def cond_grad_fn(x, t_input):
"""
Compute the gradient of the classifier, i.e. nabla_{x} log p_t(cond | x_t).
"""
with torch.enable_grad():
x_in = x.detach().requires_grad_(True)
log_prob = classifier_fn(x_in, t_input, condition, **classifier_kwargs)
return torch.autograd.grad(log_prob.sum(), x_in)[0]
def model_fn(x, t_continuous):
"""
The noise predicition model function that is used for DPM-Solver.
"""
if t_continuous.reshape((-1,)).shape[0] == 1:
t_continuous = t_continuous.expand((x.shape[0]))
if guidance_type == "uncond":
return noise_pred_fn(x, t_continuous)
elif guidance_type == "classifier":
assert classifier_fn is not None
t_input = get_model_input_time(t_continuous)
cond_grad = cond_grad_fn(x, t_input)
sigma_t = noise_schedule.marginal_std(t_continuous)
noise = noise_pred_fn(x, t_continuous)
return noise - guidance_scale * expand_dims(sigma_t, dims=cond_grad.dim()) * cond_grad
elif guidance_type == "classifier-free":
if guidance_scale == 1. or unconditional_condition is None:
return noise_pred_fn(x, t_continuous, cond=condition)
else:
x_in = torch.cat([x] * 2)
t_in = torch.cat([t_continuous] * 2)
c_in = torch.cat([unconditional_condition, condition])
noise_uncond, noise = noise_pred_fn(x_in, t_in, cond=c_in).chunk(2)
return noise_uncond + guidance_scale * (noise - noise_uncond)
assert model_type in ["noise", "x_start", "v"]
assert guidance_type in ["uncond", "classifier", "classifier-free"]
return model_fn
class DPM_Solver:
def __init__(self, model_fn, noise_schedule, predict_x0=False, thresholding=False, max_val=1.):
"""Construct a DPM-Solver.
We support both the noise prediction model ("predicting epsilon") and the data prediction model ("predicting x0").
If `predict_x0` is False, we use the solver for the noise prediction model (DPM-Solver).
If `predict_x0` is True, we use the solver for the data prediction model (DPM-Solver++).
In such case, we further support the "dynamic thresholding" in [1] when `thresholding` is True.
The "dynamic thresholding" can greatly improve the sample quality for pixel-space DPMs with large guidance scales.
Args:
model_fn: A noise prediction model function which accepts the continuous-time input (t in [epsilon, T]):
``
def model_fn(x, t_continuous):
return noise
``
noise_schedule: A noise schedule object, such as NoiseScheduleVP.
predict_x0: A `bool`. If true, use the data prediction model; else, use the noise prediction model.
thresholding: A `bool`. Valid when `predict_x0` is True. Whether to use the "dynamic thresholding" in [1].
max_val: A `float`. Valid when both `predict_x0` and `thresholding` are True. The max value for thresholding.
[1] Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S Sara Mahdavi, Rapha Gontijo Lopes, et al. Photorealistic text-to-image diffusion models with deep language understanding. arXiv preprint arXiv:2205.11487, 2022b.
"""
self.model = model_fn
self.noise_schedule = noise_schedule
self.predict_x0 = predict_x0
self.thresholding = thresholding
self.max_val = max_val
def noise_prediction_fn(self, x, t):
"""
Return the noise prediction model.
"""
return self.model(x, t)
def data_prediction_fn(self, x, t):
"""
Return the data prediction model (with thresholding).
"""
noise = self.noise_prediction_fn(x, t)
dims = x.dim()
alpha_t, sigma_t = self.noise_schedule.marginal_alpha(t), self.noise_schedule.marginal_std(t)
x0 = (x - expand_dims(sigma_t, dims) * noise) / expand_dims(alpha_t, dims)
if self.thresholding:
p = 0.995 # A hyperparameter in the paper of "Imagen" [1].
s = torch.quantile(torch.abs(x0).reshape((x0.shape[0], -1)), p, dim=1)
s = expand_dims(torch.maximum(s, self.max_val * torch.ones_like(s).to(s.device)), dims)
x0 = torch.clamp(x0, -s, s) / s
return x0
def model_fn(self, x, t):
"""
Convert the model to the noise prediction model or the data prediction model.
"""
if self.predict_x0:
return self.data_prediction_fn(x, t)
else:
return self.noise_prediction_fn(x, t)
def get_time_steps(self, skip_type, t_T, t_0, N, device):
"""Compute the intermediate time steps for sampling.
Args:
skip_type: A `str`. The type for the spacing of the time steps. We support three types:
- 'logSNR': uniform logSNR for the time steps.
- 'time_uniform': uniform time for the time steps. (**Recommended for high-resolutional data**.)
- 'time_quadratic': quadratic time for the time steps. (Used in DDIM for low-resolutional data.)
t_T: A `float`. The starting time of the sampling (default is T).
t_0: A `float`. The ending time of the sampling (default is epsilon).
N: A `int`. The total number of the spacing of the time steps.
device: A torch device.
Returns:
A pytorch tensor of the time steps, with the shape (N + 1,).
"""
if skip_type == 'logSNR':
lambda_T = self.noise_schedule.marginal_lambda(torch.tensor(t_T).to(device))
lambda_0 = self.noise_schedule.marginal_lambda(torch.tensor(t_0).to(device))
logSNR_steps = torch.linspace(lambda_T.cpu().item(), lambda_0.cpu().item(), N + 1).to(device)
return self.noise_schedule.inverse_lambda(logSNR_steps)
elif skip_type == 'time_uniform':
return torch.linspace(t_T, t_0, N + 1).to(device)
elif skip_type == 'time_quadratic':
t_order = 2
t = torch.linspace(t_T**(1. / t_order), t_0**(1. / t_order), N + 1).pow(t_order).to(device)
return t
else:
raise ValueError("Unsupported skip_type {}, need to be 'logSNR' or 'time_uniform' or 'time_quadratic'".format(skip_type))
def get_orders_and_timesteps_for_singlestep_solver(self, steps, order, skip_type, t_T, t_0, device):
"""
Get the order of each step for sampling by the singlestep DPM-Solver.
We combine both DPM-Solver-1,2,3 to use all the function evaluations, which is named as "DPM-Solver-fast".
Given a fixed number of function evaluations by `steps`, the sampling procedure by DPM-Solver-fast is:
- If order == 1:
We take `steps` of DPM-Solver-1 (i.e. DDIM).
- If order == 2:
- Denote K = (steps // 2). We take K or (K + 1) intermediate time steps for sampling.
- If steps % 2 == 0, we use K steps of DPM-Solver-2.
- If steps % 2 == 1, we use K steps of DPM-Solver-2 and 1 step of DPM-Solver-1.
- If order == 3:
- Denote K = (steps // 3 + 1). We take K intermediate time steps for sampling.
- If steps % 3 == 0, we use (K - 2) steps of DPM-Solver-3, and 1 step of DPM-Solver-2 and 1 step of DPM-Solver-1.
- If steps % 3 == 1, we use (K - 1) steps of DPM-Solver-3 and 1 step of DPM-Solver-1.
- If steps % 3 == 2, we use (K - 1) steps of DPM-Solver-3 and 1 step of DPM-Solver-2.
============================================
Args:
order: A `int`. The max order for the solver (2 or 3).
steps: A `int`. The total number of function evaluations (NFE).
skip_type: A `str`. The type for the spacing of the time steps. We support three types:
- 'logSNR': uniform logSNR for the time steps.
- 'time_uniform': uniform time for the time steps. (**Recommended for high-resolutional data**.)
- 'time_quadratic': quadratic time for the time steps. (Used in DDIM for low-resolutional data.)
t_T: A `float`. The starting time of the sampling (default is T).
t_0: A `float`. The ending time of the sampling (default is epsilon).
device: A torch device.
Returns:
orders: A list of the solver order of each step.
"""
if order == 3:
K = steps // 3 + 1
if steps % 3 == 0:
orders = [3,] * (K - 2) + [2, 1]
elif steps % 3 == 1:
orders = [3,] * (K - 1) + [1]
else:
orders = [3,] * (K - 1) + [2]
elif order == 2:
if steps % 2 == 0:
K = steps // 2
orders = [2,] * K
else:
K = steps // 2 + 1
orders = [2,] * (K - 1) + [1]
elif order == 1:
K = 1
orders = [1,] * steps
else:
raise ValueError("'order' must be '1' or '2' or '3'.")
if skip_type == 'logSNR':
# To reproduce the results in DPM-Solver paper
timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, K, device)
else:
timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, steps, device)[torch.cumsum(torch.tensor([0,] + orders)).to(device)]
return timesteps_outer, orders
def denoise_to_zero_fn(self, x, s):
"""
Denoise at the final step, which is equivalent to solve the ODE from lambda_s to infty by first-order discretization.
"""
return self.data_prediction_fn(x, s)
def dpm_solver_first_update(self, x, s, t, model_s=None, return_intermediate=False):
"""
DPM-Solver-1 (equivalent to DDIM) from time `s` to time `t`.
Args:
x: A pytorch tensor. The initial value at time `s`.
s: A pytorch tensor. The starting time, with the shape (x.shape[0],).
t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
model_s: A pytorch tensor. The model function evaluated at time `s`.
If `model_s` is None, we evaluate the model by `x` and `s`; otherwise we directly use it.
return_intermediate: A `bool`. If true, also return the model value at time `s`.
Returns:
x_t: A pytorch tensor. The approximated solution at time `t`.
"""
ns = self.noise_schedule
dims = x.dim()
lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t)
h = lambda_t - lambda_s
log_alpha_s, log_alpha_t = ns.marginal_log_mean_coeff(s), ns.marginal_log_mean_coeff(t)
sigma_s, sigma_t = ns.marginal_std(s), ns.marginal_std(t)
alpha_t = torch.exp(log_alpha_t)
if self.predict_x0:
phi_1 = torch.expm1(-h)
if model_s is None:
model_s = self.model_fn(x, s)
x_t = (
expand_dims(sigma_t / sigma_s, dims) * x
- expand_dims(alpha_t * phi_1, dims) * model_s
)
if return_intermediate:
return x_t, {'model_s': model_s}
else:
return x_t
else:
phi_1 = torch.expm1(h)
if model_s is None:
model_s = self.model_fn(x, s)
x_t = (
expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
- expand_dims(sigma_t * phi_1, dims) * model_s
)
if return_intermediate:
return x_t, {'model_s': model_s}
else:
return x_t
def singlestep_dpm_solver_second_update(self, x, s, t, r1=0.5, model_s=None, return_intermediate=False, solver_type='dpm_solver'):
"""
Singlestep solver DPM-Solver-2 from time `s` to time `t`.
Args:
x: A pytorch tensor. The initial value at time `s`.
s: A pytorch tensor. The starting time, with the shape (x.shape[0],).
t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
r1: A `float`. The hyperparameter of the second-order solver.
model_s: A pytorch tensor. The model function evaluated at time `s`.
If `model_s` is None, we evaluate the model by `x` and `s`; otherwise we directly use it.
return_intermediate: A `bool`. If true, also return the model value at time `s` and `s1` (the intermediate time).
solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
Returns:
x_t: A pytorch tensor. The approximated solution at time `t`.
"""
if solver_type not in ['dpm_solver', 'taylor']:
raise ValueError("'solver_type' must be either 'dpm_solver' or 'taylor', got {}".format(solver_type))
if r1 is None:
r1 = 0.5
ns = self.noise_schedule
dims = x.dim()
lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t)
h = lambda_t - lambda_s
lambda_s1 = lambda_s + r1 * h
s1 = ns.inverse_lambda(lambda_s1)
log_alpha_s, log_alpha_s1, log_alpha_t = ns.marginal_log_mean_coeff(s), ns.marginal_log_mean_coeff(s1), ns.marginal_log_mean_coeff(t)
sigma_s, sigma_s1, sigma_t = ns.marginal_std(s), ns.marginal_std(s1), ns.marginal_std(t)
alpha_s1, alpha_t = torch.exp(log_alpha_s1), torch.exp(log_alpha_t)
if self.predict_x0:
phi_11 = torch.expm1(-r1 * h)
phi_1 = torch.expm1(-h)
if model_s is None:
model_s = self.model_fn(x, s)
x_s1 = (
expand_dims(sigma_s1 / sigma_s, dims) * x
- expand_dims(alpha_s1 * phi_11, dims) * model_s
)
model_s1 = self.model_fn(x_s1, s1)
if solver_type == 'dpm_solver':
x_t = (
expand_dims(sigma_t / sigma_s, dims) * x
- expand_dims(alpha_t * phi_1, dims) * model_s
- (0.5 / r1) * expand_dims(alpha_t * phi_1, dims) * (model_s1 - model_s)
)
elif solver_type == 'taylor':
x_t = (
expand_dims(sigma_t / sigma_s, dims) * x
- expand_dims(alpha_t * phi_1, dims) * model_s
+ (1. / r1) * expand_dims(alpha_t * ((torch.exp(-h) - 1.) / h + 1.), dims) * (model_s1 - model_s)
)
else:
phi_11 = torch.expm1(r1 * h)
phi_1 = torch.expm1(h)
if model_s is None:
model_s = self.model_fn(x, s)
x_s1 = (
expand_dims(torch.exp(log_alpha_s1 - log_alpha_s), dims) * x
- expand_dims(sigma_s1 * phi_11, dims) * model_s
)
model_s1 = self.model_fn(x_s1, s1)
if solver_type == 'dpm_solver':
x_t = (
expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
- expand_dims(sigma_t * phi_1, dims) * model_s
- (0.5 / r1) * expand_dims(sigma_t * phi_1, dims) * (model_s1 - model_s)
)
elif solver_type == 'taylor':
x_t = (
expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
- expand_dims(sigma_t * phi_1, dims) * model_s
- (1. / r1) * expand_dims(sigma_t * ((torch.exp(h) - 1.) / h - 1.), dims) * (model_s1 - model_s)
)
if return_intermediate:
return x_t, {'model_s': model_s, 'model_s1': model_s1}
else:
return x_t
def singlestep_dpm_solver_third_update(self, x, s, t, r1=1./3., r2=2./3., model_s=None, model_s1=None, return_intermediate=False, solver_type='dpm_solver'):
"""
Singlestep solver DPM-Solver-3 from time `s` to time `t`.
Args:
x: A pytorch tensor. The initial value at time `s`.
s: A pytorch tensor. The starting time, with the shape (x.shape[0],).
t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
r1: A `float`. The hyperparameter of the third-order solver.
r2: A `float`. The hyperparameter of the third-order solver.
model_s: A pytorch tensor. The model function evaluated at time `s`.
If `model_s` is None, we evaluate the model by `x` and `s`; otherwise we directly use it.
model_s1: A pytorch tensor. The model function evaluated at time `s1` (the intermediate time given by `r1`).
If `model_s1` is None, we evaluate the model at `s1`; otherwise we directly use it.
return_intermediate: A `bool`. If true, also return the model value at time `s`, `s1` and `s2` (the intermediate times).
solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
Returns:
x_t: A pytorch tensor. The approximated solution at time `t`.
"""
if solver_type not in ['dpm_solver', 'taylor']:
raise ValueError("'solver_type' must be either 'dpm_solver' or 'taylor', got {}".format(solver_type))
if r1 is None:
r1 = 1. / 3.
if r2 is None:
r2 = 2. / 3.
ns = self.noise_schedule
dims = x.dim()
lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t)
h = lambda_t - lambda_s
lambda_s1 = lambda_s + r1 * h
lambda_s2 = lambda_s + r2 * h
s1 = ns.inverse_lambda(lambda_s1)
s2 = ns.inverse_lambda(lambda_s2)
log_alpha_s, log_alpha_s1, log_alpha_s2, log_alpha_t = ns.marginal_log_mean_coeff(s), ns.marginal_log_mean_coeff(s1), ns.marginal_log_mean_coeff(s2), ns.marginal_log_mean_coeff(t)
sigma_s, sigma_s1, sigma_s2, sigma_t = ns.marginal_std(s), ns.marginal_std(s1), ns.marginal_std(s2), ns.marginal_std(t)
alpha_s1, alpha_s2, alpha_t = torch.exp(log_alpha_s1), torch.exp(log_alpha_s2), torch.exp(log_alpha_t)
if self.predict_x0:
phi_11 = torch.expm1(-r1 * h)
phi_12 = torch.expm1(-r2 * h)
phi_1 = torch.expm1(-h)
phi_22 = torch.expm1(-r2 * h) / (r2 * h) + 1.
phi_2 = phi_1 / h + 1.
phi_3 = phi_2 / h - 0.5
if model_s is None:
model_s = self.model_fn(x, s)
if model_s1 is None:
x_s1 = (
expand_dims(sigma_s1 / sigma_s, dims) * x
- expand_dims(alpha_s1 * phi_11, dims) * model_s
)
model_s1 = self.model_fn(x_s1, s1)
x_s2 = (
expand_dims(sigma_s2 / sigma_s, dims) * x
- expand_dims(alpha_s2 * phi_12, dims) * model_s
+ r2 / r1 * expand_dims(alpha_s2 * phi_22, dims) * (model_s1 - model_s)
)
model_s2 = self.model_fn(x_s2, s2)
if solver_type == 'dpm_solver':
x_t = (
expand_dims(sigma_t / sigma_s, dims) * x
- expand_dims(alpha_t * phi_1, dims) * model_s
+ (1. / r2) * expand_dims(alpha_t * phi_2, dims) * (model_s2 - model_s)
)
elif solver_type == 'taylor':
D1_0 = (1. / r1) * (model_s1 - model_s)
D1_1 = (1. / r2) * (model_s2 - model_s)
D1 = (r2 * D1_0 - r1 * D1_1) / (r2 - r1)
D2 = 2. * (D1_1 - D1_0) / (r2 - r1)
x_t = (
expand_dims(sigma_t / sigma_s, dims) * x
- expand_dims(alpha_t * phi_1, dims) * model_s
+ expand_dims(alpha_t * phi_2, dims) * D1
- expand_dims(alpha_t * phi_3, dims) * D2
)
else:
phi_11 = torch.expm1(r1 * h)
phi_12 = torch.expm1(r2 * h)
phi_1 = torch.expm1(h)
phi_22 = torch.expm1(r2 * h) / (r2 * h) - 1.
phi_2 = phi_1 / h - 1.
phi_3 = phi_2 / h - 0.5
if model_s is None:
model_s = self.model_fn(x, s)
if model_s1 is None:
x_s1 = (
expand_dims(torch.exp(log_alpha_s1 - log_alpha_s), dims) * x
- expand_dims(sigma_s1 * phi_11, dims) * model_s
)
model_s1 = self.model_fn(x_s1, s1)
x_s2 = (
expand_dims(torch.exp(log_alpha_s2 - log_alpha_s), dims) * x
- expand_dims(sigma_s2 * phi_12, dims) * model_s
- r2 / r1 * expand_dims(sigma_s2 * phi_22, dims) * (model_s1 - model_s)
)
model_s2 = self.model_fn(x_s2, s2)
if solver_type == 'dpm_solver':
x_t = (
expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
- expand_dims(sigma_t * phi_1, dims) * model_s
- (1. / r2) * expand_dims(sigma_t * phi_2, dims) * (model_s2 - model_s)
)
elif solver_type == 'taylor':
D1_0 = (1. / r1) * (model_s1 - model_s)
D1_1 = (1. / r2) * (model_s2 - model_s)
D1 = (r2 * D1_0 - r1 * D1_1) / (r2 - r1)
D2 = 2. * (D1_1 - D1_0) / (r2 - r1)
x_t = (
expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
- expand_dims(sigma_t * phi_1, dims) * model_s
- expand_dims(sigma_t * phi_2, dims) * D1
- expand_dims(sigma_t * phi_3, dims) * D2
)
if return_intermediate:
return x_t, {'model_s': model_s, 'model_s1': model_s1, 'model_s2': model_s2}
else:
return x_t
def multistep_dpm_solver_second_update(self, x, model_prev_list, t_prev_list, t, solver_type="dpm_solver"):
"""
Multistep solver DPM-Solver-2 from time `t_prev_list[-1]` to time `t`.
Args:
x: A pytorch tensor. The initial value at time `s`.
model_prev_list: A list of pytorch tensor. The previous computed model values.
t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (x.shape[0],)
t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
Returns:
x_t: A pytorch tensor. The approximated solution at time `t`.
"""
if solver_type not in ['dpm_solver', 'taylor']:
raise ValueError("'solver_type' must be either 'dpm_solver' or 'taylor', got {}".format(solver_type))
ns = self.noise_schedule
dims = x.dim()
model_prev_1, model_prev_0 = model_prev_list
t_prev_1, t_prev_0 = t_prev_list
lambda_prev_1, lambda_prev_0, lambda_t = ns.marginal_lambda(t_prev_1), ns.marginal_lambda(t_prev_0), ns.marginal_lambda(t)
log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t)
sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t)
alpha_t = torch.exp(log_alpha_t)
h_0 = lambda_prev_0 - lambda_prev_1
h = lambda_t - lambda_prev_0
r0 = h_0 / h
D1_0 = expand_dims(1. / r0, dims) * (model_prev_0 - model_prev_1)
if self.predict_x0:
if solver_type == 'dpm_solver':
x_t = (
expand_dims(sigma_t / sigma_prev_0, dims) * x
- expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * model_prev_0
- 0.5 * expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * D1_0
)
elif solver_type == 'taylor':
x_t = (
expand_dims(sigma_t / sigma_prev_0, dims) * x
- expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * model_prev_0
+ expand_dims(alpha_t * ((torch.exp(-h) - 1.) / h + 1.), dims) * D1_0
)
else:
if solver_type == 'dpm_solver':
x_t = (
expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x
- expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * model_prev_0
- 0.5 * expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * D1_0
)
elif solver_type == 'taylor':
x_t = (
expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x
- expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * model_prev_0
- expand_dims(sigma_t * ((torch.exp(h) - 1.) / h - 1.), dims) * D1_0
)
return x_t
def multistep_dpm_solver_third_update(self, x, model_prev_list, t_prev_list, t, solver_type='dpm_solver'):
"""
Multistep solver DPM-Solver-3 from time `t_prev_list[-1]` to time `t`.
Args:
x: A pytorch tensor. The initial value at time `s`.
model_prev_list: A list of pytorch tensor. The previous computed model values.
t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (x.shape[0],)
t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
Returns:
x_t: A pytorch tensor. The approximated solution at time `t`.
"""
ns = self.noise_schedule
dims = x.dim()
model_prev_2, model_prev_1, model_prev_0 = model_prev_list
t_prev_2, t_prev_1, t_prev_0 = t_prev_list
lambda_prev_2, lambda_prev_1, lambda_prev_0, lambda_t = ns.marginal_lambda(t_prev_2), ns.marginal_lambda(t_prev_1), ns.marginal_lambda(t_prev_0), ns.marginal_lambda(t)
log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t)
sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t)
alpha_t = torch.exp(log_alpha_t)
h_1 = lambda_prev_1 - lambda_prev_2
h_0 = lambda_prev_0 - lambda_prev_1
h = lambda_t - lambda_prev_0
r0, r1 = h_0 / h, h_1 / h
D1_0 = expand_dims(1. / r0, dims) * (model_prev_0 - model_prev_1)
D1_1 = expand_dims(1. / r1, dims) * (model_prev_1 - model_prev_2)
D1 = D1_0 + expand_dims(r0 / (r0 + r1), dims) * (D1_0 - D1_1)
D2 = expand_dims(1. / (r0 + r1), dims) * (D1_0 - D1_1)
if self.predict_x0:
x_t = (
expand_dims(sigma_t / sigma_prev_0, dims) * x
- expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * model_prev_0
+ expand_dims(alpha_t * ((torch.exp(-h) - 1.) / h + 1.), dims) * D1
- expand_dims(alpha_t * ((torch.exp(-h) - 1. + h) / h**2 - 0.5), dims) * D2
)
else:
x_t = (
expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x
- expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * model_prev_0
- expand_dims(sigma_t * ((torch.exp(h) - 1.) / h - 1.), dims) * D1
- expand_dims(sigma_t * ((torch.exp(h) - 1. - h) / h**2 - 0.5), dims) * D2
)
return x_t
def singlestep_dpm_solver_update(self, x, s, t, order, return_intermediate=False, solver_type='dpm_solver', r1=None, r2=None):
"""
Singlestep DPM-Solver with the order `order` from time `s` to time `t`.
Args:
x: A pytorch tensor. The initial value at time `s`.
s: A pytorch tensor. The starting time, with the shape (x.shape[0],).
t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
order: A `int`. The order of DPM-Solver. We only support order == 1 or 2 or 3.
return_intermediate: A `bool`. If true, also return the model value at time `s`, `s1` and `s2` (the intermediate times).
solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
r1: A `float`. The hyperparameter of the second-order or third-order solver.
r2: A `float`. The hyperparameter of the third-order solver.
Returns:
x_t: A pytorch tensor. The approximated solution at time `t`.
"""
if order == 1:
return self.dpm_solver_first_update(x, s, t, return_intermediate=return_intermediate)
elif order == 2:
return self.singlestep_dpm_solver_second_update(x, s, t, return_intermediate=return_intermediate, solver_type=solver_type, r1=r1)
elif order == 3:
return self.singlestep_dpm_solver_third_update(x, s, t, return_intermediate=return_intermediate, solver_type=solver_type, r1=r1, r2=r2)
else:
raise ValueError("Solver order must be 1 or 2 or 3, got {}".format(order))
def multistep_dpm_solver_update(self, x, model_prev_list, t_prev_list, t, order, solver_type='dpm_solver'):
"""
Multistep DPM-Solver with the order `order` from time `t_prev_list[-1]` to time `t`.
Args:
x: A pytorch tensor. The initial value at time `s`.
model_prev_list: A list of pytorch tensor. The previous computed model values.
t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (x.shape[0],)
t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
order: A `int`. The order of DPM-Solver. We only support order == 1 or 2 or 3.
solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
Returns:
x_t: A pytorch tensor. The approximated solution at time `t`.
"""
if order == 1:
return self.dpm_solver_first_update(x, t_prev_list[-1], t, model_s=model_prev_list[-1])
elif order == 2:
return self.multistep_dpm_solver_second_update(x, model_prev_list, t_prev_list, t, solver_type=solver_type)
elif order == 3:
return self.multistep_dpm_solver_third_update(x, model_prev_list, t_prev_list, t, solver_type=solver_type)
else:
raise ValueError("Solver order must be 1 or 2 or 3, got {}".format(order))
def dpm_solver_adaptive(self, x, order, t_T, t_0, h_init=0.05, atol=0.0078, rtol=0.05, theta=0.9, t_err=1e-5, solver_type='dpm_solver'):
"""
The adaptive step size solver based on singlestep DPM-Solver.
Args:
x: A pytorch tensor. The initial value at time `t_T`.
order: A `int`. The (higher) order of the solver. We only support order == 2 or 3.
t_T: A `float`. The starting time of the sampling (default is T).
t_0: A `float`. The ending time of the sampling (default is epsilon).
h_init: A `float`. The initial step size (for logSNR).
atol: A `float`. The absolute tolerance of the solver. For image data, the default setting is 0.0078, followed [1].
rtol: A `float`. The relative tolerance of the solver. The default setting is 0.05.
theta: A `float`. The safety hyperparameter for adapting the step size. The default setting is 0.9, followed [1].
t_err: A `float`. The tolerance for the time. We solve the diffusion ODE until the absolute error between the
current time and `t_0` is less than `t_err`. The default setting is 1e-5.
solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
Returns:
x_0: A pytorch tensor. The approximated solution at time `t_0`.
[1] A. Jolicoeur-Martineau, K. Li, R. Piché-Taillefer, T. Kachman, and I. Mitliagkas, "Gotta go fast when generating data with score-based models," arXiv preprint arXiv:2105.14080, 2021.
"""
ns = self.noise_schedule
s = t_T * torch.ones((x.shape[0],)).to(x)
lambda_s = ns.marginal_lambda(s)
lambda_0 = ns.marginal_lambda(t_0 * torch.ones_like(s).to(x))
h = h_init * torch.ones_like(s).to(x)
x_prev = x
nfe = 0
if order == 2:
r1 = 0.5
lower_update = lambda x, s, t: self.dpm_solver_first_update(x, s, t, return_intermediate=True)
higher_update = lambda x, s, t, **kwargs: self.singlestep_dpm_solver_second_update(x, s, t, r1=r1, solver_type=solver_type, **kwargs)
elif order == 3:
r1, r2 = 1. / 3., 2. / 3.
lower_update = lambda x, s, t: self.singlestep_dpm_solver_second_update(x, s, t, r1=r1, return_intermediate=True, solver_type=solver_type)
higher_update = lambda x, s, t, **kwargs: self.singlestep_dpm_solver_third_update(x, s, t, r1=r1, r2=r2, solver_type=solver_type, **kwargs)
else:
raise ValueError("For adaptive step size solver, order must be 2 or 3, got {}".format(order))
while torch.abs((s - t_0)).mean() > t_err:
t = ns.inverse_lambda(lambda_s + h)
x_lower, lower_noise_kwargs = lower_update(x, s, t)
x_higher = higher_update(x, s, t, **lower_noise_kwargs)
delta = torch.max(torch.ones_like(x).to(x) * atol, rtol * torch.max(torch.abs(x_lower), torch.abs(x_prev)))
norm_fn = lambda v: torch.sqrt(torch.square(v.reshape((v.shape[0], -1))).mean(dim=-1, keepdim=True))
E = norm_fn((x_higher - x_lower) / delta).max()
if torch.all(E <= 1.):
x = x_higher
s = t
x_prev = x_lower
lambda_s = ns.marginal_lambda(s)
h = torch.min(theta * h * torch.float_power(E, -1. / order).float(), lambda_0 - lambda_s)
nfe += order
print('adaptive solver nfe', nfe)
return x
def sample(self, x, steps=20, t_start=None, t_end=None, order=3, skip_type='time_uniform',
method='singlestep', lower_order_final=True, denoise_to_zero=False, solver_type='dpm_solver',
atol=0.0078, rtol=0.05,
):
"""
Compute the sample at time `t_end` by DPM-Solver, given the initial `x` at time `t_start`.
=====================================================
We support the following algorithms for both noise prediction model and data prediction model:
- 'singlestep':
Singlestep DPM-Solver (i.e. "DPM-Solver-fast" in the paper), which combines different orders of singlestep DPM-Solver.
We combine all the singlestep solvers with order <= `order` to use up all the function evaluations (steps).
The total number of function evaluations (NFE) == `steps`.
Given a fixed NFE == `steps`, the sampling procedure is:
- If `order` == 1:
- Denote K = steps. We use K steps of DPM-Solver-1 (i.e. DDIM).
- If `order` == 2:
- Denote K = (steps // 2) + (steps % 2). We take K intermediate time steps for sampling.
- If steps % 2 == 0, we use K steps of singlestep DPM-Solver-2.
- If steps % 2 == 1, we use (K - 1) steps of singlestep DPM-Solver-2 and 1 step of DPM-Solver-1.
- If `order` == 3:
- Denote K = (steps // 3 + 1). We take K intermediate time steps for sampling.
- If steps % 3 == 0, we use (K - 2) steps of singlestep DPM-Solver-3, and 1 step of singlestep DPM-Solver-2 and 1 step of DPM-Solver-1.
- If steps % 3 == 1, we use (K - 1) steps of singlestep DPM-Solver-3 and 1 step of DPM-Solver-1.
- If steps % 3 == 2, we use (K - 1) steps of singlestep DPM-Solver-3 and 1 step of singlestep DPM-Solver-2.
- 'multistep':
Multistep DPM-Solver with the order of `order`. The total number of function evaluations (NFE) == `steps`.
We initialize the first `order` values by lower order multistep solvers.
Given a fixed NFE == `steps`, the sampling procedure is:
Denote K = steps.
- If `order` == 1:
- We use K steps of DPM-Solver-1 (i.e. DDIM).
- If `order` == 2:
- We firstly use 1 step of DPM-Solver-1, then use (K - 1) step of multistep DPM-Solver-2.
- If `order` == 3:
- We firstly use 1 step of DPM-Solver-1, then 1 step of multistep DPM-Solver-2, then (K - 2) step of multistep DPM-Solver-3.
- 'singlestep_fixed':
Fixed order singlestep DPM-Solver (i.e. DPM-Solver-1 or singlestep DPM-Solver-2 or singlestep DPM-Solver-3).
We use singlestep DPM-Solver-`order` for `order`=1 or 2 or 3, with total [`steps` // `order`] * `order` NFE.
- 'adaptive':
Adaptive step size DPM-Solver (i.e. "DPM-Solver-12" and "DPM-Solver-23" in the paper).
We ignore `steps` and use adaptive step size DPM-Solver with a higher order of `order`.
You can adjust the absolute tolerance `atol` and the relative tolerance `rtol` to balance the computatation costs
(NFE) and the sample quality.
- If `order` == 2, we use DPM-Solver-12 which combines DPM-Solver-1 and singlestep DPM-Solver-2.
- If `order` == 3, we use DPM-Solver-23 which combines singlestep DPM-Solver-2 and singlestep DPM-Solver-3.
=====================================================
Some advices for choosing the algorithm:
- For **unconditional sampling** or **guided sampling with small guidance scale** by DPMs:
Use singlestep DPM-Solver ("DPM-Solver-fast" in the paper) with `order = 3`.
e.g.
>>> dpm_solver = DPM_Solver(model_fn, noise_schedule, predict_x0=False)
>>> x_sample = dpm_solver.sample(x, steps=steps, t_start=t_start, t_end=t_end, order=3,
skip_type='time_uniform', method='singlestep')
- For **guided sampling with large guidance scale** by DPMs:
Use multistep DPM-Solver with `predict_x0 = True` and `order = 2`.
e.g.
>>> dpm_solver = DPM_Solver(model_fn, noise_schedule, predict_x0=True)
>>> x_sample = dpm_solver.sample(x, steps=steps, t_start=t_start, t_end=t_end, order=2,
skip_type='time_uniform', method='multistep')
We support three types of `skip_type`:
- 'logSNR': uniform logSNR for the time steps. **Recommended for low-resolutional images**
- 'time_uniform': uniform time for the time steps. **Recommended for high-resolutional images**.
- 'time_quadratic': quadratic time for the time steps.
=====================================================
Args:
x: A pytorch tensor. The initial value at time `t_start`
e.g. if `t_start` == T, then `x` is a sample from the standard normal distribution.
steps: A `int`. The total number of function evaluations (NFE).
t_start: A `float`. The starting time of the sampling.
If `T` is None, we use self.noise_schedule.T (default is 1.0).
t_end: A `float`. The ending time of the sampling.
If `t_end` is None, we use 1. / self.noise_schedule.total_N.
e.g. if total_N == 1000, we have `t_end` == 1e-3.
For discrete-time DPMs:
- We recommend `t_end` == 1. / self.noise_schedule.total_N.
For continuous-time DPMs:
- We recommend `t_end` == 1e-3 when `steps` <= 15; and `t_end` == 1e-4 when `steps` > 15.
order: A `int`. The order of DPM-Solver.
skip_type: A `str`. The type for the spacing of the time steps. 'time_uniform' or 'logSNR' or 'time_quadratic'.
method: A `str`. The method for sampling. 'singlestep' or 'multistep' or 'singlestep_fixed' or 'adaptive'.
denoise_to_zero: A `bool`. Whether to denoise to time 0 at the final step.
Default is `False`. If `denoise_to_zero` is `True`, the total NFE is (`steps` + 1).
This trick is firstly proposed by DDPM (https://arxiv.org/abs/2006.11239) and
score_sde (https://arxiv.org/abs/2011.13456). Such trick can improve the FID
for diffusion models sampling by diffusion SDEs for low-resolutional images
(such as CIFAR-10). However, we observed that such trick does not matter for
high-resolutional images. As it needs an additional NFE, we do not recommend
it for high-resolutional images.
lower_order_final: A `bool`. Whether to use lower order solvers at the final steps.
Only valid for `method=multistep` and `steps < 15`. We empirically find that
this trick is a key to stabilizing the sampling by DPM-Solver with very few steps
(especially for steps <= 10). So we recommend to set it to be `True`.
solver_type: A `str`. The taylor expansion type for the solver. `dpm_solver` or `taylor`. We recommend `dpm_solver`.
atol: A `float`. The absolute tolerance of the adaptive step size solver. Valid when `method` == 'adaptive'.
rtol: A `float`. The relative tolerance of the adaptive step size solver. Valid when `method` == 'adaptive'.
Returns:
x_end: A pytorch tensor. The approximated solution at time `t_end`.
"""
t_0 = 1. / self.noise_schedule.total_N if t_end is None else t_end
t_T = self.noise_schedule.T if t_start is None else t_start
device = x.device
if method == 'adaptive':
with torch.no_grad():
x = self.dpm_solver_adaptive(x, order=order, t_T=t_T, t_0=t_0, atol=atol, rtol=rtol, solver_type=solver_type)
elif method == 'multistep':
assert steps >= order
timesteps = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=steps, device=device)
assert timesteps.shape[0] - 1 == steps
with torch.no_grad():
vec_t = timesteps[0].expand((x.shape[0]))
model_prev_list = [self.model_fn(x, vec_t)]
t_prev_list = [vec_t]
# Init the first `order` values by lower order multistep DPM-Solver.
for init_order in range(1, order):
vec_t = timesteps[init_order].expand(x.shape[0])
x = self.multistep_dpm_solver_update(x, model_prev_list, t_prev_list, vec_t, init_order, solver_type=solver_type)
model_prev_list.append(self.model_fn(x, vec_t))
t_prev_list.append(vec_t)
# Compute the remaining values by `order`-th order multistep DPM-Solver.
for step in range(order, steps + 1):
vec_t = timesteps[step].expand(x.shape[0])
if lower_order_final and steps < 15:
step_order = min(order, steps + 1 - step)
else:
step_order = order
x = self.multistep_dpm_solver_update(x, model_prev_list, t_prev_list, vec_t, step_order, solver_type=solver_type)
for i in range(order - 1):
t_prev_list[i] = t_prev_list[i + 1]
model_prev_list[i] = model_prev_list[i + 1]
t_prev_list[-1] = vec_t
# We do not need to evaluate the final model value.
if step < steps:
model_prev_list[-1] = self.model_fn(x, vec_t)
elif method in ['singlestep', 'singlestep_fixed']:
if method == 'singlestep':
timesteps_outer, orders = self.get_orders_and_timesteps_for_singlestep_solver(steps=steps, order=order, skip_type=skip_type, t_T=t_T, t_0=t_0, device=device)
elif method == 'singlestep_fixed':
K = steps // order
orders = [order,] * K
timesteps_outer = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=K, device=device)
for i, order in enumerate(orders):
t_T_inner, t_0_inner = timesteps_outer[i], timesteps_outer[i + 1]
timesteps_inner = self.get_time_steps(skip_type=skip_type, t_T=t_T_inner.item(), t_0=t_0_inner.item(), N=order, device=device)
lambda_inner = self.noise_schedule.marginal_lambda(timesteps_inner)
vec_s, vec_t = t_T_inner.tile(x.shape[0]), t_0_inner.tile(x.shape[0])
h = lambda_inner[-1] - lambda_inner[0]
r1 = None if order <= 1 else (lambda_inner[1] - lambda_inner[0]) / h
r2 = None if order <= 2 else (lambda_inner[2] - lambda_inner[0]) / h
x = self.singlestep_dpm_solver_update(x, vec_s, vec_t, order, solver_type=solver_type, r1=r1, r2=r2)
if denoise_to_zero:
x = self.denoise_to_zero_fn(x, torch.ones((x.shape[0],)).to(device) * t_0)
return x
#############################################################
# other utility functions
#############################################################
def interpolate_fn(x, xp, yp):
"""
A piecewise linear function y = f(x), using xp and yp as keypoints.
We implement f(x) in a differentiable way (i.e. applicable for autograd).
The function f(x) is well-defined for all x-axis. (For x beyond the bounds of xp, we use the outmost points of xp to define the linear function.)
Args:
x: PyTorch tensor with shape [N, C], where N is the batch size, C is the number of channels (we use C = 1 for DPM-Solver).
xp: PyTorch tensor with shape [C, K], where K is the number of keypoints.
yp: PyTorch tensor with shape [C, K].
Returns:
The function values f(x), with shape [N, C].
"""
N, K = x.shape[0], xp.shape[1]
all_x = torch.cat([x.unsqueeze(2), xp.unsqueeze(0).repeat((N, 1, 1))], dim=2)
sorted_all_x, x_indices = torch.sort(all_x, dim=2)
x_idx = torch.argmin(x_indices, dim=2)
cand_start_idx = x_idx - 1
start_idx = torch.where(
torch.eq(x_idx, 0),
torch.tensor(1, device=x.device),
torch.where(
torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx,
),
)
end_idx = torch.where(torch.eq(start_idx, cand_start_idx), start_idx + 2, start_idx + 1)
start_x = torch.gather(sorted_all_x, dim=2, index=start_idx.unsqueeze(2)).squeeze(2)
end_x = torch.gather(sorted_all_x, dim=2, index=end_idx.unsqueeze(2)).squeeze(2)
start_idx2 = torch.where(
torch.eq(x_idx, 0),
torch.tensor(0, device=x.device),
torch.where(
torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx,
),
)
y_positions_expanded = yp.unsqueeze(0).expand(N, -1, -1)
start_y = torch.gather(y_positions_expanded, dim=2, index=start_idx2.unsqueeze(2)).squeeze(2)
end_y = torch.gather(y_positions_expanded, dim=2, index=(start_idx2 + 1).unsqueeze(2)).squeeze(2)
cand = start_y + (x - start_x) * (end_y - start_y) / (end_x - start_x)
return cand
def expand_dims(v, dims):
"""
Expand the tensor `v` to the dim `dims`.
Args:
`v`: a PyTorch tensor with shape [N].
`dim`: a `int`.
Returns:
a PyTorch tensor with shape [N, 1, 1, ..., 1] and the total dimension is `dims`.
"""
return v[(...,) + (None,)*(dims - 1)]
================================================
FILE: models/InstructDiffusion/stable_diffusion/ldm/models/diffusion/dpm_solver/sampler.py
================================================
"""SAMPLING ONLY."""
import torch
from .dpm_solver import NoiseScheduleVP, model_wrapper, DPM_Solver
class DPMSolverSampler(object):
def __init__(self, model, **kwargs):
super().__init__()
self.model = model
to_torch = lambda x: x.clone().detach().to(torch.float32).to(model.device)
self.register_buffer('alphas_cumprod', to_torch(model.alphas_cumprod))
def register_buffer(self, name, attr):
if type(attr) == torch.Tensor:
if attr.device != torch.device("cuda"):
attr = attr.to(torch.device("cuda"))
setattr(self, name, attr)
@torch.no_grad()
def sample(self,
S,
batch_size,
shape,
conditioning=None,
callback=None,
normals_sequence=None,
img_callback=None,
quantize_x0=False,
eta=0.,
mask=None,
x0=None,
temperature=1.,
noise_dropout=0.,
score_corrector=None,
corrector_kwargs=None,
verbose=True,
x_T=None,
log_every_t=100,
unconditional_guidance_scale=1.,
unconditional_conditioning=None,
# this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
**kwargs
):
if conditioning is not None:
if isinstance(conditioning, dict):
cbs = conditioning[list(conditioning.keys())[0]].shape[0]
if cbs != batch_size:
print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
else:
if conditioning.shape[0] != batch_size:
print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
# sampling
C, H, W = shape
size = (batch_size, C, H, W)
# print(f'Data shape for DPM-Solver sampling is {size}, sampling steps {S}')
device = self.model.betas.device
if x_T is None:
img = torch.randn(size, device=device)
else:
img = x_T
ns = NoiseScheduleVP('discrete', alphas_cumprod=self.alphas_cumprod)
model_fn = model_wrapper(
lambda x, t, c: self.model.apply_model(x, t, c),
ns,
model_type="noise",
guidance_type="classifier-free",
condition=conditioning,
unconditional_condition=unconditional_conditioning,
guidance_scale=unconditional_guidance_scale,
)
dpm_solver = DPM_Solver(model_fn, ns, predict_x0=True, thresholding=False)
x = dpm_solver.sample(img, steps=S, skip_type="time_uniform", method="multistep", order=2, lower_order_final=True)
return x.to(device), None
================================================
FILE: models/InstructDiffusion/stable_diffusion/ldm/models/diffusion/plms.py
================================================
"""SAMPLING ONLY."""
import torch
import numpy as np
from tqdm import tqdm
from functools import partial
from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like
class PLMSSampler(object):
def __init__(self, model, schedule="linear", **kwargs):
super().__init__()
self.model = model
self.ddpm_num_timesteps = model.num_timesteps
self.schedule = schedule
def register_buffer(self, name, attr):
if type(attr) == torch.Tensor:
if attr.device != torch.device("cuda"):
attr = attr.to(torch.device("cuda"))
setattr(self, name, attr)
def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True):
if ddim_eta != 0:
raise ValueError('ddim_eta must be 0 for PLMS')
self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps,
num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose)
alphas_cumprod = self.model.alphas_cumprod
assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep'
to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device)
self.register_buffer('betas', to_torch(self.model.betas))
self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev))
# calculations for diffusion q(x_t | x_{t-1}) and others
self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu())))
self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu())))
self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu())))
self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu())))
self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1)))
# ddim sampling parameters
ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(),
ddim_timesteps=self.ddim_timesteps,
eta=ddim_eta,verbose=verbose)
self.register_buffer('ddim_sigmas', ddim_sigmas)
self.register_buffer('ddim_alphas', ddim_alphas)
self.register_buffer('ddim_alphas_prev', ddim_alphas_prev)
self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas))
sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(
(1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * (
1 - self.alphas_cumprod / self.alphas_cumprod_prev))
self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps)
@torch.no_grad()
def sample(self,
S,
batch_size,
shape,
conditioning=None,
callback=None,
normals_sequence=None,
img_callback=None,
quantize_x0=False,
eta=0.,
mask=None,
x0=None,
temperature=1.,
noise_dropout=0.,
score_corrector=None,
corrector_kwargs=None,
verbose=True,
x_T=None,
log_every_t=100,
unconditional_guidance_scale=1.,
unconditional_conditioning=None,
# this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
**kwargs
):
if conditioning is not None:
if isinstance(conditioning, dict):
cbs = conditioning[list(conditioning.keys())[0]].shape[0]
if cbs != batch_size:
print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
else:
if conditioning.shape[0] != batch_size:
print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
# sampling
C, H, W = shape
size = (batch_size, C, H, W)
print(f'Data shape for PLMS sampling is {size}')
samples, intermediates = self.plms_sampling(conditioning, size,
callback=callback,
img_callback=img_callback,
quantize_denoised=quantize_x0,
mask=mask, x0=x0,
ddim_use_original_steps=False,
noise_dropout=noise_dropout,
temperature=temperature,
score_corrector=score_corrector,
corrector_kwargs=corrector_kwargs,
x_T=x_T,
log_every_t=log_every_t,
unconditional_guidance_scale=unconditional_guidance_scale,
unconditional_conditioning=unconditional_conditioning,
)
return samples, intermediates
@torch.no_grad()
def plms_sampling(self, cond, shape,
x_T=None, ddim_use_original_steps=False,
callback=None, timesteps=None, quantize_denoised=False,
mask=None, x0=None, img_callback=None, log_every_t=100,
temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
unconditional_guidance_scale=1., unconditional_conditioning=None,):
device = self.model.betas.device
b = shape[0]
if x_T is None:
img = torch.randn(shape, device=device)
else:
img = x_T
if timesteps is None:
timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps
elif timesteps is not None and not ddim_use_original_steps:
subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1
timesteps = self.ddim_timesteps[:subset_end]
intermediates = {'x_inter': [img], 'pred_x0': [img]}
time_range = list(reversed(range(0,timesteps))) if ddim_use_original_steps else np.flip(timesteps)
total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]
print(f"Running PLMS Sampling with {total_steps} timesteps")
iterator = tqdm(time_range, desc='PLMS Sampler', total=total_steps)
old_eps = []
for i, step in enumerate(iterator):
index = total_steps - i - 1
ts = torch.full((b,), step, device=device, dtype=torch.long)
ts_next = torch.full((b,), time_range[min(i + 1, len(time_range) - 1)], device=device, dtype=torch.long)
if mask is not None:
assert x0 is not None
img_orig = self.model.q_sample(x0, ts) # TODO: deterministic forward pass?
img = img_orig * mask + (1. - mask) * img
outs = self.p_sample_plms(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps,
quantize_denoised=quantize_denoised, temperature=temperature,
noise_dropout=noise_dropout, score_corrector=score_corrector,
corrector_kwargs=corrector_kwargs,
unconditional_guidance_scale=unconditional_guidance_scale,
unconditional_conditioning=unconditional_conditioning,
old_eps=old_eps, t_next=ts_next)
img, pred_x0, e_t = outs
old_eps.append(e_t)
if len(old_eps) >= 4:
old_eps.pop(0)
if callback: callback(i)
if img_callback: img_callback(pred_x0, i)
if index % log_every_t == 0 or index == total_steps - 1:
intermediates['x_inter'].append(img)
intermediates['pred_x0'].append(pred_x0)
return img, intermediates
@torch.no_grad()
def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
unconditional_guidance_scale=1., unconditional_conditioning=None, old_eps=None, t_next=None):
b, *_, device = *x.shape, x.device
def get_model_output(x, t):
if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
e_t = self.model.apply_model(x, t, c)
else:
x_in = torch.cat([x] * 2)
t_in = torch.cat([t] * 2)
c_in = torch.cat([unconditional_conditioning, c])
e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
if score_corrector is not None:
assert self.model.parameterization == "eps"
e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
return e_t
alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
def get_x_prev_and_pred_x0(e_t, index):
# select parameters corresponding to the currently considered timestep
a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
# current prediction for x_0
pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
if quantize_denoised:
pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
# direction pointing to x_t
dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
if noise_dropout > 0.:
noise = torch.nn.functional.dropout(noise, p=noise_dropout)
x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
return x_prev, pred_x0
e_t = get_model_output(x, t)
if len(old_eps) == 0:
# Pseudo Improved Euler (2nd order)
x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t, index)
e_t_next = get_model_output(x_prev, t_next)
e_t_prime = (e_t + e_t_next) / 2
elif len(old_eps) == 1:
# 2nd order Pseudo Linear Multistep (Adams-Bashforth)
e_t_prime = (3 * e_t - old_eps[-1]) / 2
elif len(old_eps) == 2:
# 3nd order Pseudo Linear Multistep (Adams-Bashforth)
e_t_prime = (23 * e_t - 16 * old_eps[-1] + 5 * old_eps[-2]) / 12
elif len(old_eps) >= 3:
# 4nd order Pseudo Linear Multistep (Adams-Bashforth)
e_t_prime = (55 * e_t - 59 * old_eps[-1] + 37 * old_eps[-2] - 9 * old_eps[-3]) / 24
x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index)
return x_prev, pred_x0, e_t
================================================
FILE: models/InstructDiffusion/stable_diffusion/ldm/modules/attention.py
================================================
# File modified by authors of InstructPix2Pix from original (https://github.com/CompVis/stable-diffusion).
# See more details in LICENSE.
from inspect import isfunction
import math
import torch
import torch.nn.functional as F
from torch import nn, einsum
from einops import rearrange, repeat
from ldm.modules.diffusionmodules.util import checkpoint
def exists(val):
return val is not None
def uniq(arr):
return{el: True for el in arr}.keys()
def default(val, d):
if exists(val):
return val
return d() if isfunction(d) else d
def max_neg_value(t):
return -torch.finfo(t.dtype).max
def init_(tensor):
dim = tensor.shape[-1]
std = 1 / math.sqrt(dim)
tensor.uniform_(-std, std)
return tensor
# feedforward
class GEGLU(nn.Module):
def __init__(self, dim_in, dim_out):
super().__init__()
self.proj = nn.Linear(dim_in, dim_out * 2)
def forward(self, x):
x, gate = self.proj(x).chunk(2, dim=-1)
return x * F.gelu(gate)
class FeedForward(nn.Module):
def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
super().__init__()
inner_dim = int(dim * mult)
dim_out = default(dim_out, dim)
project_in = nn.Sequential(
nn.Linear(dim, inner_dim),
nn.GELU()
) if not glu else GEGLU(dim, inner_dim)
self.net = nn.Sequential(
project_in,
nn.Dropout(dropout),
nn.Linear(inner_dim, dim_out)
)
def forward(self, x):
return self.net(x)
def zero_module(module):
"""
Zero out the parameters of a module and return it.
"""
for p in module.parameters():
p.detach().zero_()
return module
def Normalize(in_channels, default_eps):
if default_eps:
return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, affine=True)
else:
return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
class LinearAttention(nn.Module):
def __init__(self, dim, heads=4, dim_head=32):
super().__init__()
self.heads = heads
hidden_dim = dim_head * heads
self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias = False)
self.to_out = nn.Conv2d(hidden_dim, dim, 1)
def forward(self, x):
b, c, h, w = x.shape
qkv = self.to_qkv(x)
q, k, v = rearrange(qkv, 'b (qkv heads c) h w -> qkv b heads c (h w)', heads = self.heads, qkv=3)
k = torch.softmax(k.float(), dim=-1).type(k.dtype)
# k = k.softmax(dim=-1)
context = torch.einsum('bhdn,bhen->bhde', k, v)
out = torch.einsum('bhde,bhdn->bhen', context, q)
out = rearrange(out, 'b heads c (h w) -> b (heads c) h w', heads=self.heads, h=h, w=w)
return self.to_out(out)
class SpatialSelfAttention(nn.Module):
def __init__(self, in_channels):
super().__init__()
self.in_channels = in_channels
self.norm = Normalize(in_channels)
self.q = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.k = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.v = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.proj_out = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
def forward(self, x):
h_ = x
h_ = self.norm(h_)
q = self.q(h_)
k = self.k(h_)
v = self.v(h_)
# compute attention
b,c,h,w = q.shape
q = rearrange(q, 'b c h w -> b (h w) c')
k = rearrange(k, 'b c h w -> b c (h w)')
w_ = torch.einsum('bij,bjk->bik', q, k)
w_ = w_ * (int(c)**(-0.5))
w_ = torch.softmax(w_.float(), dim=2).type(w_.dtype)
# w_ = torch.nn.functional.softmax(w_, dim=2)
# attend to values
v = rearrange(v, 'b c h w -> b c (h w)')
w_ = rearrange(w_, 'b i j -> b j i')
h_ = torch.einsum('bij,bjk->bik', v, w_)
h_ = rearrange(h_, 'b c (h w) -> b c h w', h=h)
h_ = self.proj_out(h_)
return x+h_
class CrossAttention(nn.Module):
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.):
super().__init__()
inner_dim = dim_head * heads
context_dim = default(context_dim, query_dim)
self.scale = dim_head ** -0.5
self.heads = heads
self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
self.to_out = nn.Sequential(
nn.Linear(inner_dim, query_dim),
nn.Dropout(dropout)
)
self.prompt_to_prompt = False
def forward(self, x, context=None, mask=None):
is_self_attn = context is None
h = self.heads
q = self.to_q(x)
context = default(context, x)
k = self.to_k(context)
v = self.to_v(context)
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
if self.prompt_to_prompt and is_self_attn:
# Unlike the original Prompt-to-Prompt which uses cross-attention layers, we copy attention maps for self-attention layers.
# There must be 4 elements in the batch: {conditional, unconditional} x {prompt 1, prompt 2}
assert x.size(0) == 4
sims = sim.chunk(4)
sim = torch.cat((sims[0], sims[0], sims[2], sims[2]))
if exists(mask):
mask = rearrange(mask, 'b ... -> b (...)')
max_neg_value = -torch.finfo(sim.dtype).max
mask = repeat(mask, 'b j -> (b h) () j', h=h)
sim.masked_fill_(~mask, max_neg_value)
# attention, what we cannot get enough of
# attn = sim.softmax(dim=-1)
attn = torch.softmax(sim.float(), dim=-1).type(sim.dtype)
out = einsum('b i j, b j d -> b i d', attn, v)
out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
return self.to_out(out)
# class BasicTransformerBlock(nn.Module):
# def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True):
# super().__init__()
# self.attn1 = CrossAttention(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout) # is a self-attention
# self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff)
# self.attn2 = CrossAttention(query_dim=dim, context_dim=context_dim,
# heads=n_heads, dim_head=d_head, dropout=dropout) # is self-attn if context is none
# self.norm1 = nn.LayerNorm(dim)
# self.norm2 = nn.LayerNorm(dim)
# self.norm3 = nn.LayerNorm(dim)
# self.checkpoint = checkpoint
# def forward(self, x, context=None):
# # return checkpoint(self._forward, (x, context), self.checkpoint)
# return checkpoint(self._forward, (x, context), self.parameters(), self.checkpoint)
# def _forward(self, x, context=None):
# x = x.type(self.norm1.weight.dtype)
# if context is not None:
# context = context.type(self.norm1.weight.dtype)
# x = self.attn1(self.norm1(x)) + x
# x = self.attn2(self.norm2(x), context=context) + x
# x = self.ff(self.norm3(x)) + x
# return x
class BasicTransformerBlock(nn.Module):
ATTENTION_MODES = {
"softmax": CrossAttention, # vanilla attention
}
def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True,
disable_self_attn=False):
super().__init__()
attn_mode = "softmax"
assert attn_mode in self.ATTENTION_MODES
attn_cls = self.ATTENTION_MODES[attn_mode]
self.disable_self_attn = disable_self_attn
self.attn1 = attn_cls(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout,
context_dim=context_dim if self.disable_self_attn else None) # is a self-attention if not self.disable_self_attn
self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff)
self.attn2 = attn_cls(query_dim=dim, context_dim=context_dim,
heads=n_heads, dim_head=d_head, dropout=dropout) # is self-attn if context is none
self.norm1 = nn.LayerNorm(dim)
self.norm2 = nn.LayerNorm(dim)
self.norm3 = nn.LayerNorm(dim)
self.checkpoint = checkpoint
def forward(self, x, context=None):
return checkpoint(self._forward, (x, context), self.parameters(), self.checkpoint)
def _forward(self, x, context=None):
x = x.type(self.norm1.weight.dtype)
if context is not None:
context = context.type(self.norm1.weight.dtype)
x = self.attn1(self.norm1(x)) + x
x = self.attn2(self.norm2(x), context=context) + x
x = self.ff(self.norm3(x)) + x
return x
# x = self.attn1(self.norm1(x), context=context if self.disable_self_attn else None) + x
# x = self.attn2(self.norm2(x), context=context) + x
# x = self.ff(self.norm3(x)) + x
# return x
# class SpatialTransformer(nn.Module):
# """
# Transformer block for image-like data.
# First, project the input (aka embedding)
# and reshape to b, t, d.
# Then apply standard transformer action.
# Finally, reshape to image
# """
# def __init__(self, in_channels, n_heads, d_head, default_eps, force_type_convert,
# depth=1, dropout=0., context_dim=None):
# super().__init__()
# self.in_channels = in_channels
# inner_dim = n_heads * d_head
# self.force_type_convert = force_type_convert
# self.norm = Normalize(in_channels, default_eps)
# self.proj_in = nn.Conv2d(in_channels,
# inner_dim,
# kernel_size=1,
# stride=1,
# padding=0)
# self.transformer_blocks = nn.ModuleList(
# [BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim)
# for d in range(depth)]
# )
# self.proj_out = zero_module(nn.Conv2d(inner_dim,
# in_channels,
# kernel_size=1,
# stride=1,
# padding=0))
# def forward(self, x, context=None):
# # note: if no context is given, cross-attention defaults to self-attention
# b, c, h, w = x.shape
# x_in = x
# if self.force_type_convert:
# x = self.norm.float()(x.float())
# x = x.half()
# else:
# x = self.norm(x)
# x = self.proj_in(x)
# x = rearrange(x, 'b c h w -> b (h w) c')
# for block in self.transformer_blocks:
# x = block(x, context=context)
# x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w)
# x = self.proj_out(x)
# return x + x_in
class SpatialTransformer(nn.Module):
"""
Transformer block for image-like data.
First, project the input (aka embedding)
and reshape to b, t, d.
Then apply standard transformer action.
Finally, reshape to image
NEW: use_linear for more efficiency instead of the 1x1 convs
"""
def __init__(self, in_channels, n_heads, d_head, default_eps, force_type_convert,
depth=1, dropout=0., context_dim=None,
disable_self_attn=False, use_linear=False,
use_checkpoint=True):
super().__init__()
if exists(context_dim) and not isinstance(context_dim, list):
context_dim = [context_dim]
self.in_channels = in_channels
inner_dim = n_heads * d_head
self.force_type_convert = force_type_convert
self.norm = Normalize(in_channels, default_eps)
if not use_linear:
self.proj_in = nn.Conv2d(in_channels,
inner_dim,
kernel_size=1,
stride=1,
padding=0)
else:
self.proj_in = nn.Linear(in_channels, inner_dim)
self.transformer_blocks = nn.ModuleList(
[BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim[d],
disable_self_attn=disable_self_attn, checkpoint=use_checkpoint)
for d in range(depth)]
)
if not use_linear:
self.proj_out = zero_module(nn.Conv2d(inner_dim,
in_channels,
kernel_size=1,
stride=1,
padding=0))
else:
self.proj_out = zero_module(nn.Linear(in_channels, inner_dim))
self.use_linear = use_linear
def forward(self, x, context=None):
# note: if no context is given, cross-attention defaults to self-attention
if not isinstance(context, list):
context = [context]
b, c, h, w = x.shape
x_in = x
if self.force_type_convert:
x = self.norm.float()(x.float())
x = x.half()
else:
x = self.norm(x)
if not self.use_linear:
x = self.proj_in(x)
x = rearrange(x, 'b c h w -> b (h w) c').contiguous()
if self.use_linear:
x = self.proj_in(x)
for i, block in enumerate(self.transformer_blocks):
x = block(x, context=context[i])
if self.use_linear:
x = self.proj_out(x)
x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w).contiguous()
if not self.use_linear:
x = self.proj_out(x)
return x + x_in
================================================
FILE: models/InstructDiffusion/stable_diffusion/ldm/modules/diffusionmodules/__init__.py
================================================
================================================
FILE: models/InstructDiffusion/stable_diffusion/ldm/modules/diffusionmodules/model.py
================================================
# pytorch_diffusion + derived encoder decoder
# Removed Pytorch-lightning by Zigang Geng (zigang@mail.ustc.edu.cn)
import math
import torch
import torch.nn as nn
import numpy as np
from einops import rearrange
from ldm.util import instantiate_from_config
from ldm.modules.attention import LinearAttention
def get_timestep_embedding(timesteps, embedding_dim):
"""
This matches the implementation in Denoising Diffusion Probabilistic Models:
From Fairseq.
Build sinusoidal embeddings.
This matches the implementation in tensor2tensor, but differs slightly
from the description in Section 3.5 of "Attention Is All You Need".
"""
assert len(timesteps.shape) == 1
half_dim = embedding_dim // 2
emb = math.log(10000) / (half_dim - 1)
emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb)
emb = emb.to(device=timesteps.device)
emb = timesteps.float()[:, None] * emb[None, :]
emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
if embedding_dim % 2 == 1: # zero pad
emb = torch.nn.functional.pad(emb, (0,1,0,0))
return emb
def nonlinearity(x):
# swish
return x*torch.sigmoid(x)
def Normalize(in_channels, default_eps, num_groups=32):
if default_eps:
return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, affine=True)
else:
return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True)
class Upsample(nn.Module):
def __init__(self, in_channels, with_conv):
super().__init__()
self.with_conv = with_conv
if self.with_conv:
self.conv = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=3,
stride=1,
padding=1)
def forward(self, x):
x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
if self.with_conv:
x = self.conv(x)
return x
class Downsample(nn.Module):
def __init__(self, in_channels, with_conv):
super().__init__()
self.with_conv = with_conv
if self.with_conv:
# no asymmetric padding in torch conv, must do it ourselves
self.conv = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=3,
stride=2,
padding=0)
def forward(self, x):
if self.with_conv:
pad = (0,1,0,1)
x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
x = self.conv(x)
else:
x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2)
return x
class ResnetBlock(nn.Module):
def __init__(self, *, in_channels, default_eps, force_type_convert, out_channels=None, conv_shortcut=False,
dropout, temb_channels=512):
super().__init__()
self.in_channels = in_channels
out_channels = in_channels if out_channels is None else out_channels
self.out_channels = out_channels
self.use_conv_shortcut = conv_shortcut
self.force_type_convert = force_type_convert
self.norm1 = Normalize(in_channels, default_eps)
self.conv1 = torch.nn.Conv2d(in_channels,
out_channels,
kernel_size=3,
stride=1,
padding=1)
if temb_channels > 0:
self.temb_proj = torch.nn.Linear(temb_channels,
out_channels)
self.norm2 = Normalize(out_channels, default_eps)
self.dropout = torch.nn.Dropout(dropout)
self.conv2 = torch.nn.Conv2d(out_channels,
out_channels,
kernel_size=3,
stride=1,
padding=1)
if self.in_channels != self.out_channels:
if self.use_conv_shortcut:
self.conv_shortcut = torch.nn.Conv2d(in_channels,
out_channels,
kernel_size=3,
stride=1,
padding=1)
else:
self.nin_shortcut = torch.nn.Conv2d(in_channels,
out_channels,
kernel_size=1,
stride=1,
padding=0)
def forward(self, x, temb):
h = x
if self.force_type_convert:
h = self.norm1.float()(h.float())
h = h.half()
else:
h = self.norm1(h)
h = nonlinearity(h)
h = self.conv1(h)
if temb is not None:
h = h + self.temb_proj(nonlinearity(temb))[:,:,None,None]
if self.force_type_convert:
h = self.norm2.float()(h.float())
h = h.half()
else:
h = self.norm2(h)
h = nonlinearity(h)
h = self.dropout(h)
h = self.conv2(h)
if self.in_channels != self.out_channels:
if self.use_conv_shortcut:
x = self.conv_shortcut(x)
else:
x = self.nin_shortcut(x)
return x+h
class LinAttnBlock(LinearAttention):
"""to match AttnBlock usage"""
def __init__(self, in_channels):
super().__init__(dim=in_channels, heads=1, dim_head=in_channels)
class AttnBlock(nn.Module):
def __init__(self, in_channels, default_eps, force_type_convert):
super().__init__()
self.in_channels = in_channels
self.force_type_convert = force_type_convert
self.norm = Normalize(in_channels, default_eps)
self.q = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.k = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.v = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.proj_out = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
def forward(self, x):
h_ = x
if self.force_type_convert:
h_ = self.norm.float()(h_.float())
h_ = h_.half()
else:
h_ = self.norm(h_)
q = self.q(h_)
k = self.k(h_)
v = self.v(h_)
# compute attention
b,c,h,w = q.shape
q = q.reshape(b,c,h*w)
q = q.permute(0,2,1) # b,hw,c
k = k.reshape(b,c,h*w) # b,c,hw
w_ = torch.bmm(q,k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
w_ = w_ * (int(c)**(-0.5))
w_ = torch.nn.functional.softmax(w_, dim=2)
# attend to values
v = v.reshape(b,c,h*w)
w_ = w_.permute(0,2,1) # b,hw,hw (first hw of k, second of q)
h_ = torch.bmm(v,w_) # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
h_ = h_.reshape(b,c,h,w)
h_ = self.proj_out(h_)
return x+h_
def make_attn(in_channels, default_eps, force_type_convert, attn_type="vanilla"):
assert attn_type in ["vanilla", "linear", "none"], f'attn_type {attn_type} unknown'
print(f"making attention of type '{attn_type}' with {in_channels} in_channels")
if attn_type == "vanilla":
return AttnBlock(in_channels, default_eps, force_type_convert)
elif attn_type == "none":
return nn.Identity(in_channels)
else:
return LinAttnBlock(in_channels)
class Model(nn.Module):
def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
resolution, use_timestep=True, use_linear_attn=False, attn_type="vanilla"):
super().__init__()
if use_linear_attn: attn_type = "linear"
self.ch = ch
self.temb_ch = self.ch*4
self.num_resolutions = len(ch_mult)
self.num_res_blocks = num_res_blocks
self.resolution = resolution
self.in_channels = in_channels
self.use_timestep = use_timestep
if self.use_timestep:
# timestep embedding
self.temb = nn.Module()
self.temb.dense = nn.ModuleList([
torch.nn.Linear(self.ch,
self.temb_ch),
torch.nn.Linear(self.temb_ch,
self.temb_ch),
])
# downsampling
self.conv_in = torch.nn.Conv2d(in_channels,
self.ch,
kernel_size=3,
stride=1,
padding=1)
curr_res = resolution
in_ch_mult = (1,)+tuple(ch_mult)
self.down = nn.ModuleList()
for i_level in range(self.num_resolutions):
block = nn.ModuleList()
attn = nn.ModuleList()
block_in = ch*in_ch_mult[i_level]
block_out = ch*ch_mult[i_level]
for i_block in range(self.num_res_blocks):
block.append(ResnetBlock(in_channels=block_in,
out_channels=block_out,
temb_channels=self.temb_ch,
dropout=dropout))
block_in = block_out
if curr_res in attn_resolutions:
attn.append(make_attn(block_in, attn_type=attn_type))
down = nn.Module()
down.block = block
down.attn = attn
if i_level != self.num_resolutions-1:
down.downsample = Downsample(block_in, resamp_with_conv)
curr_res = curr_res // 2
self.down.append(down)
# middle
self.mid = nn.Module()
self.mid.block_1 = ResnetBlock(in_channels=block_in,
out_channels=block_in,
temb_channels=self.temb_ch,
dropout=dropout)
self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
self.mid.block_2 = ResnetBlock(in_channels=block_in,
out_channels=block_in,
temb_channels=self.temb_ch,
dropout=dropout)
# upsampling
self.up = nn.ModuleList()
for i_level in reversed(range(self.num_resolutions)):
block = nn.ModuleList()
attn = nn.ModuleList()
block_out = ch*ch_mult[i_level]
skip_in = ch*ch_mult[i_level]
for i_block in range(self.num_res_blocks+1):
if i_block == self.num_res_blocks:
skip_in = ch*in_ch_mult[i_level]
block.append(ResnetBlock(in_channels=block_in+skip_in,
out_channels=block_out,
temb_channels=self.temb_ch,
dropout=dropout))
block_in = block_out
if curr_res in attn_resolutions:
attn.append(make_attn(block_in, attn_type=attn_type))
up = nn.Module()
up.block = block
up.attn = attn
if i_level != 0:
up.upsample = Upsample(block_in, resamp_with_conv)
curr_res = curr_res * 2
self.up.insert(0, up) # prepend to get consistent order
# end
self.norm_out = Normalize(block_in)
self.conv_out = torch.nn.Conv2d(block_in,
out_ch,
kernel_size=3,
stride=1,
padding=1)
def forward(self, x, t=None, context=None):
#assert x.shape[2] == x.shape[3] == self.resolution
if context is not None:
# assume aligned context, cat along channel axis
x = torch.cat((x, context), dim=1)
if self.use_timestep:
# timestep embedding
assert t is not None
temb = get_timestep_embedding(t, self.ch)
temb = self.temb.dense[0](temb)
temb = nonlinearity(temb)
temb = self.temb.dense[1](temb)
else:
temb = None
# downsampling
hs = [self.conv_in(x.type(self.conv_in.weight.dtype))]
for i_level in range(self.num_resolutions):
for i_block in range(self.num_res_blocks):
h = self.down[i_level].block[i_block](hs[-1], temb)
if len(self.down[i_level].attn) > 0:
h = self.down[i_level].attn[i_block](h)
hs.append(h)
if i_level != self.num_resolutions-1:
hs.append(self.down[i_level].downsample(hs[-1]))
# middle
h = hs[-1]
h = self.mid.block_1(h, temb)
h = self.mid.attn_1(h)
h = self.mid.block_2(h, temb)
# upsampling
for i_level in reversed(range(self.num_resolutions)):
for i_block in range(self.num_res_blocks+1):
h = self.up[i_level].block[i_block](
torch.cat([h, hs.pop()], dim=1), temb)
if len(self.up[i_level].attn) > 0:
h = self.up[i_level].attn[i_block](h)
if i_level != 0:
h = self.up[i_level].upsample(h)
# end
h = self.norm_out(h)
h = nonlinearity(h)
h = self.conv_out(h)
return h
def get_last_layer(self):
return self.conv_out.weight
class Encoder(nn.Module):
def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
attn_resolutions, default_eps=False, force_type_convert=False, dropout=0.0, resamp_with_conv=True, in_channels,
resolution, z_channels, double_z=True, use_linear_attn=False, attn_type="vanilla",
**ignore_kwargs):
super().__init__()
if use_linear_attn: attn_type = "linear"
self.ch = ch
self.temb_ch = 0
self.num_resolutions = len(ch_mult)
self.num_res_blocks = num_res_blocks
self.resolution = resolution
self.in_channels = in_channels
# downsampling
self.conv_in = torch.nn.Conv2d(in_channels,
self.ch,
kernel_size=3,
stride=1,
padding=1)
curr_res = resolution
in_ch_mult = (1,)+tuple(ch_mult)
self.in_ch_mult = in_ch_mult
self.down = nn.ModuleList()
for i_level in range(self.num_resolutions):
block = nn.ModuleList()
attn = nn.ModuleList()
block_in = ch*in_ch_mult[i_level]
block_out = ch*ch_mult[i_level]
for i_block in range(self.num_res_blocks):
block.append(ResnetBlock(in_channels=block_in,
out_channels=block_out,
default_eps=default_eps,
force_type_convert=force_type_convert,
temb_channels=self.temb_ch,
dropout=dropout))
block_in = block_out
if curr_res in attn_resolutions:
attn.append(make_attn(block_in, default_eps, force_type_convert, attn_type=attn_type))
down = nn.Module()
down.block = block
down.attn = attn
if i_level != self.num_resolutions-1:
down.downsample = Downsample(block_in, resamp_with_conv)
curr_res = curr_res // 2
self.down.append(down)
# middle
self.mid = nn.Module()
self.mid.block_1 = ResnetBlock(in_channels=block_in,
out_channels=block_in,
default_eps=default_eps,
force_type_convert=force_type_convert,
temb_channels=self.temb_ch,
dropout=dropout)
self.mid.attn_1 = make_attn(block_in, default_eps, force_type_convert=force_type_convert, attn_type=attn_type)
self.mid.block_2 = ResnetBlock(in_channels=block_in,
out_channels=block_in,
default_eps=default_eps,
force_type_convert=force_type_convert,
temb_channels=self.temb_ch,
dropout=dropout)
# end
self.force_type_convert = force_type_convert
self.norm_out = Normalize(block_in, default_eps)
self.conv_out = torch.nn.Conv2d(block_in,
2*z_channels if double_z else z_channels,
kernel_size=3,
stride=1,
padding=1)
def forward(self, x):
# timestep embedding
temb = None
# downsampling
hs = [self.conv_in(x.type(self.conv_in.weight.dtype).to(self.conv_in.weight.device))]
for i_level in range(self.num_resolutions):
for i_block in range(self.num_res_blocks):
h = self.down[i_level].block[i_block](hs[-1], temb)
if len(self.down[i_level].attn) > 0:
h = self.down[i_level].attn[i_block](h)
hs.append(h)
if i_level != self.num_resolutions-1:
hs.append(self.down[i_level].downsample(hs[-1]))
# middle
h = hs[-1]
h = self.mid.block_1(h, temb)
h = self.mid.attn_1(h)
h = self.mid.block_2(h, temb)
# end
if self.force_type_convert:
h = self.norm_out.float()(h.float())
h = h.half()
else:
h = self.norm_out(h)
h = nonlinearity(h)
h = self.conv_out(h)
return h
class Decoder(nn.Module):
def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
attn_resolutions, default_eps=False, force_type_convert=False, dropout=0.0, resamp_with_conv=True, in_channels,
resolution, z_channels, give_pre_end=False, tanh_out=False, use_linear_attn=False,
attn_type="vanilla", **ignorekwargs):
super().__init__()
if use_linear_attn: attn_type = "linear"
self.ch = ch
self.temb_ch = 0
self.num_resolutions = len(ch_mult)
self.num_res_blocks = num_res_blocks
self.resolution = resolution
self.in_channels = in_channels
self.give_pre_end = give_pre_end
self.tanh_out = tanh_out
# compute in_ch_mult, block_in and curr_res at lowest res
in_ch_mult = (1,)+tuple(ch_mult)
block_in = ch*ch_mult[self.num_resolutions-1]
curr_res = resolution // 2**(self.num_resolutions-1)
self.z_shape = (1,z_channels,curr_res,curr_res)
print("Working with z of shape {} = {} dimensions.".format(
self.z_shape, np.prod(self.z_shape)))
# z to block_in
self.conv_in = torch.nn.Conv2d(z_channels,
block_in,
kernel_size=3,
stride=1,
padding=1)
# middle
self.mid = nn.Module()
self.mid.block_1 = ResnetBlock(in_channels=block_in,
default_eps=default_eps,
force_type_convert=force_type_convert,
out_channels=block_in,
temb_channels=self.temb_ch,
dropout=dropout)
self.mid.attn_1 = make_attn(block_in, default_eps, force_type_convert, attn_type=attn_type)
self.mid.block_2 = ResnetBlock(in_channels=block_in,
default_eps=default_eps,
force_type_convert=force_type_convert,
out_channels=block_in,
temb_channels=self.temb_ch,
dropout=dropout)
# upsampling
self.up = nn.ModuleList()
for i_level in reversed(range(self.num_resolutions)):
block = nn.ModuleList()
attn = nn.ModuleList()
block_out = ch*ch_mult[i_level]
for i_block in range(self.num_res_blocks+1):
block.append(ResnetBlock(in_channels=block_in,
default_eps=default_eps,
force_type_convert=force_type_convert,
out_channels=block_out,
temb_channels=self.temb_ch,
dropout=dropout))
block_in = block_out
if curr_res in attn_resolutions:
attn.append(make_attn(block_in, default_eps, force_type_convert, attn_type=attn_type))
up = nn.Module()
up.block = block
up.attn = attn
if i_level != 0:
up.upsample = Upsample(block_in, resamp_with_conv)
curr_res = curr_res * 2
self.up.insert(0, up) # prepend to get consistent order
# end
self.force_type_convert = force_type_convert
self.norm_out = Normalize(block_in, default_eps)
self.conv_out = torch.nn.Conv2d(block_in,
out_ch,
kernel_size=3,
stride=1,
padding=1)
def forward(self, z):
#assert z.shape[1:] == self.z_shape[1:]
self.last_z_shape = z.shape
# timestep embedding
temb = None
# z to block_in
h = self.conv_in(z)
# middle
h = self.mid.block_1(h, temb)
h = self.mid.attn_1(h)
h = self.mid.block_2(h, temb)
# upsampling
for i_level in reversed(range(self.num_resolutions)):
for i_block in range(self.num_res_blocks+1):
h = self.up[i_level].block[i_block](h, temb)
if len(self.up[i_level].attn) > 0:
h = self.up[i_level].attn[i_block](h)
if i_level != 0:
h = self.up[i_level].upsample(h)
# end
if self.give_pre_end:
return h
if self.force_type_convert:
h = self.norm_out.float()(h.float())
h = h.half()
else:
h = self.norm_out(h)
h = nonlinearity(h)
h = self.conv_out(h)
if self.tanh_out:
h = torch.tanh(h)
return h
class SimpleDecoder(nn.Module):
def __init__(self, in_channels, out_channels, *args, **kwargs):
super().__init__()
self.model = nn.ModuleList([nn.Conv2d(in_channels, in_channels, 1),
ResnetBlock(in_channels=in_channels,
out_channels=2 * in_channels,
temb_channels=0, dropout=0.0),
ResnetBlock(in_channels=2 * in_channels,
out_channels=4 * in_channels,
temb_channels=0, dropout=0.0),
ResnetBlock(in_channels=4 * in_channels,
out_channels=2 * in_channels,
temb_channels=0, dropout=0.0),
nn.Conv2d(2*in_channels, in_channels, 1),
Upsample(in_channels, with_conv=True)])
# end
self.norm_out = Normalize(in_channels)
self.conv_out = torch.nn.Conv2d(in_channels,
out_channels,
kernel_size=3,
stride=1,
padding=1)
def forward(self, x):
for i, layer in enumerate(self.model):
if i in [1,2,3]:
x = layer(x, None)
else:
x = layer(x)
h = self.norm_out(x)
h = nonlinearity(h)
x = self.conv_out(h)
return x
class UpsampleDecoder(nn.Module):
def __init__(self, in_channels, out_channels, ch, num_res_blocks, resolution,
ch_mult=(2,2), dropout=0.0):
super().__init__()
# upsampling
self.temb_ch = 0
self.num_resolutions = len(ch_mult)
self.num_res_blocks = num_res_blocks
block_in = in_channels
curr_res = resolution // 2 ** (self.num_resolutions - 1)
self.res_blocks = nn.ModuleList()
self.upsample_blocks = nn.ModuleList()
for i_level in range(self.num_resolutions):
res_block = []
block_out = ch * ch_mult[i_level]
for i_block in range(self.num_res_blocks + 1):
res_block.append(ResnetBlock(in_channels=block_in,
out_channels=block_out,
temb_channels=self.temb_ch,
dropout=dropout))
block_in = block_out
self.res_blocks.append(nn.ModuleList(res_block))
if i_level != self.num_resolutions - 1:
self.upsample_blocks.append(Upsample(block_in, True))
curr_res = curr_res * 2
# end
self.norm_out = Normalize(block_in)
self.conv_out = torch.nn.Conv2d(block_in,
out_channels,
kernel_size=3,
stride=1,
padding=1)
def forward(self, x):
# upsampling
h = x
for k, i_level in enumerate(range(self.num_resolutions)):
for i_block in range(self.num_res_blocks + 1):
h = self.res_blocks[i_level][i_block](h, None)
if i_level != self.num_resolutions - 1:
h = self.upsample_blocks[k](h)
h = self.norm_out(h)
h = nonlinearity(h)
h = self.conv_out(h)
return h
class LatentRescaler(nn.Module):
def __init__(self, factor, in_channels, mid_channels, out_channels, depth=2):
super().__init__()
# residual block, interpolate, residual block
self.factor = factor
self.conv_in = nn.Conv2d(in_channels,
mid_channels,
kernel_size=3,
stride=1,
padding=1)
self.res_block1 = nn.ModuleList([ResnetBlock(in_channels=mid_channels,
out_channels=mid_channels,
temb_channels=0,
dropout=0.0) for _ in range(depth)])
self.attn = AttnBlock(mid_channels)
self.res_block2 = nn.ModuleList([ResnetBlock(in_channels=mid_channels,
out_channels=mid_channels,
temb_channels=0,
dropout=0.0) for _ in range(depth)])
self.conv_out = nn.Conv2d(mid_channels,
out_channels,
kernel_size=1,
)
def forward(self, x):
x = self.conv_in(x)
for block in self.res_block1:
x = block(x, None)
x = torch.nn.functional.interpolate(x, size=(int(round(x.shape[2]*self.factor)), int(round(x.shape[3]*self.factor))))
x = self.attn(x)
for block in self.res_block2:
x = block(x, None)
x = self.conv_out(x)
return x
class MergedRescaleEncoder(nn.Module):
def __init__(self, in_channels, ch, resolution, out_ch, num_res_blocks,
attn_resolutions, dropout=0.0, resamp_with_conv=True,
ch_mult=(1,2,4,8), rescale_factor=1.0, rescale_module_depth=1):
super().__init__()
intermediate_chn = ch * ch_mult[-1]
self.encoder = Encoder(in_channels=in_channels, num_res_blocks=num_res_blocks, ch=ch, ch_mult=ch_mult,
z_channels=intermediate_chn, double_z=False, resolution=resolution,
attn_resolutions=attn_resolutions, dropout=dropout, resamp_with_conv=resamp_with_conv,
out_ch=None)
self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=intermediate_chn,
mid_channels=intermediate_chn, out_channels=out_ch, depth=rescale_module_depth)
def forward(self, x):
x = self.encoder(x)
x = self.rescaler(x)
return x
class MergedRescaleDecoder(nn.Module):
def __init__(self, z_channels, out_ch, resolution, num_res_blocks, attn_resolutions, ch, ch_mult=(1,2,4,8),
dropout=0.0, resamp_with_conv=True, rescale_factor=1.0, rescale_module_depth=1):
super().__init__()
tmp_chn = z_channels*ch_mult[-1]
self.decoder = Decoder(out_ch=out_ch, z_channels=tmp_chn, attn_resolutions=attn_resolutions, dropout=dropout,
resamp_with_conv=resamp_with_conv, in_channels=None, num_res_blocks=num_res_blocks,
ch_mult=ch_mult, resolution=resolution, ch=ch)
self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=z_channels, mid_channels=tmp_chn,
out_channels=tmp_chn, depth=rescale_module_depth)
def forward(self, x):
x = self.rescaler(x)
x = self.decoder(x)
return x
class Upsampler(nn.Module):
def __init__(self, in_size, out_size, in_channels, out_channels, ch_mult=2):
super().__init__()
assert out_size >= in_size
num_blocks = int(np.log2(out_size//in_size))+1
factor_up = 1.+ (out_size % in_size)
print(f"Building {self.__class__.__name__} with in_size: {in_size} --> out_size {out_size} and factor {factor_up}")
self.rescaler = LatentRescaler(factor=factor_up, in_channels=in_channels, mid_channels=2*in_channels,
out_channels=in_channels)
self.decoder = Decoder(out_ch=out_channels, resolution=out_size, z_channels=in_channels, num_res_blocks=2,
attn_resolutions=[], in_channels=None, ch=in_channels,
ch_mult=[ch_mult for _ in range(num_blocks)])
def forward(self, x):
x = self.rescaler(x)
x = self.decoder(x)
return x
class Resize(nn.Module):
def __init__(self, in_channels=None, learned=False, mode="bilinear"):
super().__init__()
self.with_conv = learned
self.mode = mode
if self.with_conv:
print(f"Note: {self.__class__.__name} uses learned downsampling and will ignore the fixed {mode} mode")
raise NotImplementedError()
assert in_channels is not None
# no asymmetric padding in torch conv, must do it ourselves
self.conv = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=4,
stride=2,
padding=1)
def forward(self, x, scale_factor=1.0):
if scale_factor==1.0:
return x
else:
x = torch.nn.functional.interpolate(x, mode=self.mode, align_corners=False, scale_factor=scale_factor)
return x
class FirstStagePostProcessor(nn.Module):
def __init__(self, ch_mult:list, in_channels,
pretrained_model:nn.Module=None,
reshape=False,
n_channels=None,
dropout=0.,
pretrained_config=None):
super().__init__()
if pretrained_config is None:
assert pretrained_model is not None, 'Either "pretrained_model" or "pretrained_config" must not be None'
self.pretrained_model = pretrained_model
else:
assert pretrained_config is not None, 'Either "pretrained_model" or "pretrained_config" must not be None'
self.instantiate_pretrained(pretrained_config)
self.do_reshape = reshape
if n_channels is None:
n_channels = self.pretrained_model.encoder.ch
self.proj_norm = Normalize(in_channels,num_groups=in_channels//2)
self.proj = nn.Conv2d(in_channels,n_channels,kernel_size=3,
stride=1,padding=1)
blocks = []
downs = []
ch_in = n_channels
for m in ch_mult:
blocks.append(ResnetBlock(in_channels=ch_in,out_channels=m*n_channels,dropout=dropout))
ch_in = m * n_channels
downs.append(Downsample(ch_in, with_conv=False))
self.model = nn.ModuleList(blocks)
self.downsampler = nn.ModuleList(downs)
def instantiate_pretrained(self, config):
model = instantiate_from_config(config)
self.pretrained_model = model.eval()
# self.pretrained_model.train = False
for param in self.pretrained_model.parameters():
param.requires_grad = False
@torch.no_grad()
def encode_with_pretrained(self,x):
c = self.pretrained_model.encode(x)
if isinstance(c, DiagonalGaussianDistribution):
c = c.mode()
return c
def forward(self,x):
z_fs = self.encode_with_pretrained(x)
z = self.proj_norm(z_fs)
z = self.proj(z)
z = nonlinearity(z)
for submodel, downmodel in zip(self.model,self.downsampler):
z = submodel(z,temb=None)
z = downmodel(z)
if self.do_reshape:
z = rearrange(z,'b c h w -> b (h w) c')
return z
================================================
FILE: models/InstructDiffusion/stable_diffusion/ldm/modules/diffusionmodules/openaimodel.py
================================================
# --------------------------------------------------------
# Stable-Diffusion-Torch
# Based on Stable-Diffusion (https://github.com/CompVis/stable-diffusion)
# Removed Pytorch-lightning by Zigang Geng (zigang@mail.ustc.edu.cn)
# --------------------------------------------------------
from abc import abstractmethod
from functools import partial
import math
from typing import Iterable
import numpy as np
import torch as th
import torch.nn as nn
import torch.nn.functional as F
from ldm.modules.diffusionmodules.util import (
checkpoint,
conv_nd,
linear,
avg_pool_nd,
zero_module,
normalization,
timestep_embedding,
)
from ldm.modules.attention import SpatialTransformer, CrossAttention, FeedForward
# dummy replace
def convert_module_to_f16(l):
"""
Convert primitive modules to float16.
"""
if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Conv3d)):
l.weight.data = l.weight.data.half()
if l.bias is not None:
l.bias.data = l.bias.data.half()
def convert_module_to_f32(l):
"""
Convert primitive modules to float32, undoing convert_module_to_f16().
"""
if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Conv3d)):
l.weight.data = l.weight.data.float()
if l.bias is not None:
l.bias.data = l.bias.data.float()
def convert_some_linear_to_f16(l):
"""
Convert linear modules to float16.
"""
if isinstance(l, nn.Linear):
l.weight.data = l.weight.data.half()
if l.bias is not None:
l.bias.data = l.bias.data.half()
def convert_some_linear_to_f32(l):
"""
Convert linear modules to float32.
"""
if isinstance(l, nn.Linear):
l.weight.data = l.weight.data.float()
if l.bias is not None:
l.bias.data = l.bias.data.float()
class PositionEmbedding(nn.Module):
def __init__(self, embed_dim, spacial_dim):
super().__init__()
self.positional_embedding = nn.Parameter(th.randn(embed_dim, spacial_dim ** 2 + 1) / embed_dim ** 0.5)
def forward(self):
return self.positional_embedding
## go
class AttentionPool2d(nn.Module):
"""
Adapted from CLIP: https://github.com/openai/CLIP/blob/main/clip/model.py
"""
def __init__(
self,
spacial_dim: int,
embed_dim: int,
num_heads_channels: int,
output_dim: int = None,
):
super().__init__()
self.positional_embedding = PositionEmbedding(embed_dim, spacial_dim)
self.qkv_proj = conv_nd(1, embed_dim, 3 * embed_dim, 1)
self.c_proj = conv_nd(1, embed_dim, output_dim or embed_dim, 1)
self.num_heads = embed_dim // num_heads_channels
self.attention = QKVAttention(self.num_heads)
def forward(self, x):
b, c, *_spatial = x.shape
x = x.reshape(b, c, -1) # NC(HW)
x = th.cat([x.mean(dim=-1, keepdim=True), x], dim=-1) # NC(HW+1)
x = x + self.positional_embedding()[None, :, :].to(x.dtype) # NC(HW+1)
x = self.qkv_proj(x)
x = self.attention(x)
x = self.c_proj(x)
return x[:, :, 0]
class TimestepBlock(nn.Module):
"""
Any module where forward() takes timestep embeddings as a second argument.
"""
@abstractmethod
def forward(self, x, emb):
"""
Apply the module to `x` given `emb` timestep embeddings.
"""
class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
"""
A sequential module that passes timestep embeddings to the children that
support it as an extra input.
"""
def forward(self, x, emb, context=None):
for layer in self:
if isinstance(layer, TimestepBlock):
x = layer(x, emb)
elif isinstance(layer, SpatialTransformer):
x = layer(x, context)
else:
if isinstance(layer, Downsample) or isinstance(layer, Upsample):
x = layer(x)
else:
if hasattr(layer, 'weight'):
x = layer(x.type(layer.weight.dtype))
else:
x = layer(x.type(emb.dtype))
return x
class Upsample(nn.Module):
"""
An upsampling layer with an optional convolution.
:param channels: channels in the inputs and outputs.
:param use_conv: a bool determining if a convolution is applied.
:param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
upsampling occurs in the inner-two dimensions.
"""
def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1):
super().__init__()
self.channels = channels
self.out_channels = out_channels or channels
self.use_conv = use_conv
self.dims = dims
if use_conv:
self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=padding)
def forward(self, x):
assert x.shape[1] == self.channels
if self.dims == 3:
x = F.interpolate(
x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest"
)
else:
x = F.interpolate(x, scale_factor=2, mode="nearest")
if self.use_conv:
x = self.conv(x)
return x
class TransposedUpsample(nn.Module):
'Learned 2x upsampling without padding'
def __init__(self, channels, out_channels=None, ks=5):
super().__init__()
self.channels = channels
self.out_channels = out_channels or channels
self.up = nn.ConvTranspose2d(self.channels,self.out_channels,kernel_size=ks,stride=2)
def forward(self,x):
return self.up(x)
class Downsample(nn.Module):
"""
A downsampling layer with an optional convolution.
:param channels: channels in the inputs and outputs.
:param use_conv: a bool determining if a convolution is applied.
:param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
downsampling occurs in the inner-two dimensions.
"""
def __init__(self, channels, use_conv, dims=2, out_channels=None,padding=1):
super().__init__()
self.channels = channels
self.out_channels = out_channels or channels
self.use_conv = use_conv
self.dims = dims
stride = 2 if dims != 3 else (1, 2, 2)
if use_conv:
self.op = conv_nd(
dims, self.channels, self.out_channels, 3, stride=stride, padding=padding
)
else:
assert self.channels == self.out_channels
self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride)
def forward(self, x):
assert x.shape[1] == self.channels
return self.op(x)
class ResBlock(TimestepBlock):
"""
A residual block that can optionally change the number of channels.
:param channels: the number of input channels.
:param emb_channels: the number of timestep embedding channels.
:param dropout: the rate of dropout.
:param out_channels: if specified, the number of out channels.
:param use_conv: if True and out_channels is specified, use a spatial
convolution instead of a smaller 1x1 convolution to change the
channels in the skip connection.
:param dims: determines if the signal is 1D, 2D, or 3D.
:param use_checkpoint: if True, use gradient checkpointing on this module.
:param up: if True, use this block for upsampling.
:param down: if True, use this block for downsampling.
"""
def __init__(
self,
channels,
emb_channels,
dropout,
out_channels=None,
use_conv=False,
use_scale_shift_norm=False,
dims=2,
use_checkpoint=False,
up=False,
down=False,
):
super().__init__()
self.channels = channels
self.emb_channels = emb_channels
self.dropout = dropout
self.out_channels = out_channels or channels
self.use_conv = use_conv
self.use_checkpoint = use_checkpoint
self.use_scale_shift_norm = use_scale_shift_norm
self.in_layers = nn.Sequential(
normalization(channels),
nn.SiLU(),
conv_nd(dims, channels, self.out_channels, 3, padding=1),
)
self.updown = up or down
if up:
self.h_upd = Upsample(channels, False, dims)
self.x_upd = Upsample(channels, False, dims)
elif down:
self.h_upd = Downsample(channels, False, dims)
self.x_upd = Downsample(channels, False, dims)
else:
self.h_upd = self.x_upd = nn.Identity()
self.emb_layers = nn.Sequential(
nn.SiLU(),
linear(
emb_channels,
2 * self.out_channels if use_scale_shift_norm else self.out_channels,
),
)
self.out_layers = nn.Sequential(
normalization(self.out_channels),
nn.SiLU(),
nn.Dropout(p=dropout),
zero_module(
conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1)
),
)
if self.out_channels == channels:
self.skip_connection = nn.Identity()
elif use_conv:
self.skip_connection = conv_nd(
dims, channels, self.out_channels, 3, padding=1
)
else:
self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)
def forward(self, x, emb):
"""
Apply the block to a Tensor, conditioned on a timestep embedding.
:param x: an [N x C x ...] Tensor of features.
:param emb: an [N x emb_channels] Tensor of timestep embeddings.
:return: an [N x C x ...] Tensor of outputs.
"""
# return checkpoint(
# self._forward, (x, emb), self.use_checkpoint
# )
return checkpoint(
self._forward, (x, emb), self.parameters(), self.use_checkpoint
)
def _forward(self, x, emb):
x = x.type(self.emb_layers[1].weight.dtype)
if self.updown:
in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
h = in_rest(x)
h = self.h_upd(h)
x = self.x_upd(x)
h = in_conv(h)
else:
h = self.in_layers(x)
emb_out = self.emb_layers(emb.type(self.emb_layers[1].weight.dtype)).type(h.dtype)
while len(emb_out.shape) < len(h.shape):
emb_out = emb_out[..., None]
if self.use_scale_shift_norm:
out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
scale, shift = th.chunk(emb_out, 2, dim=1)
h = out_norm(h) * (1 + scale) + shift
h = out_rest(h)
else:
h = h + emb_out
h = self.out_layers(h)
return self.skip_connection(x) + h.type(self.emb_layers[1].weight.dtype)
class AttentionBlock(nn.Module):
"""
An attention block that allows spatial positions to attend to each other.
Originally ported from here, but adapted to the N-d case.
https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66.
"""
def __init__(
self,
channels,
num_heads=1,
num_head_channels=-1,
use_checkpoint=False,
use_new_attention_order=False,
):
super().__init__()
self.channels = channels
if num_head_channels == -1:
self.num_heads = num_heads
else:
assert (
channels % num_head_channels == 0
), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
self.num_heads = channels // num_head_channels
self.use_checkpoint = use_checkpoint
self.norm = normalization(channels)
self.qkv = conv_nd(1, channels, channels * 3, 1)
if use_new_attention_order:
# split qkv before split heads
self.attention = QKVAttention(self.num_heads)
else:
# split heads before split qkv
self.attention = QKVAttentionLegacy(self.num_heads)
self.proj_out = zero_module(conv_nd(1, channels, channels, 1))
def forward(self, x):
# return checkpoint(self._forward, (x,), self.use_checkpoint)
return checkpoint(self._forward, (x,), self.parameters(), self.use_checkpoint) # TODO: check checkpoint usage, is True # TODO: fix the .half call!!!
def _forward(self, x):
b, c, *spatial = x.shape
x = x.reshape(b, c, -1)
qkv = self.qkv(self.norm(x))
h = self.attention(qkv)
h = self.proj_out(h)
return (x + h).reshape(b, c, *spatial)
def count_flops_attn(model, _x, y):
"""
A counter for the `thop` package to count the operations in an
attention operation.
Meant to be used like:
macs, params = thop.profile(
model,
inputs=(inputs, timestamps),
custom_ops={QKVAttention: QKVAttention.count_flops},
)
"""
b, c, *spatial = y[0].shape
num_spatial = int(np.prod(spatial))
# We perform two matmuls with the same number of ops.
# The first computes the weight matrix, the second computes
# the combination of the value vectors.
matmul_ops = 2 * b * (num_spatial ** 2) * c
model.total_ops += th.DoubleTensor([matmul_ops])
class QKVAttentionLegacy(nn.Module):
"""
A module which performs QKV attention. Matches legacy QKVAttention + input/ouput heads shaping
"""
def __init__(self, n_heads):
super().__init__()
self.n_heads = n_heads
def forward(self, qkv):
"""
Apply QKV attention.
:param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs.
:return: an [N x (H * C) x T] tensor after attention.
"""
bs, width, length = qkv.shape
assert width % (3 * self.n_heads) == 0
ch = width // (3 * self.n_heads)
q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1)
scale = 1 / math.sqrt(math.sqrt(ch))
weight = th.einsum(
"bct,bcs->bts", q * scale, k * scale
) # More stable with f16 than dividing afterwards
weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
a = th.einsum("bts,bcs->bct", weight, v)
return a.reshape(bs, -1, length)
@staticmethod
def count_flops(model, _x, y):
return count_flops_attn(model, _x, y)
class QKVAttention(nn.Module):
"""
A module which performs QKV attention and splits in a different order.
"""
def __init__(self, n_heads):
super().__init__()
self.n_heads = n_heads
def forward(self, qkv):
"""
Apply QKV attention.
:param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs.
:return: an [N x (H * C) x T] tensor after attention.
"""
bs, width, length = qkv.shape
assert width % (3 * self.n_heads) == 0
ch = width // (3 * self.n_heads)
q, k, v = qkv.chunk(3, dim=1)
scale = 1 / math.sqrt(math.sqrt(ch))
weight = th.einsum(
"bct,bcs->bts",
(q * scale).view(bs * self.n_heads, ch, length),
(k * scale).view(bs * self.n_heads, ch, length),
) # More stable with f16 than dividing afterwards
weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
a = th.einsum("bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length))
return a.reshape(bs, -1, length)
@staticmethod
def count_flops(model, _x, y):
return count_flops_attn(model, _x, y)
class UNetModel(nn.Module):
"""
The full UNet model with attention and timestep embedding.
:param in_channels: channels in the input Tensor.
:param model_channels: base channel count for the model.
:param out_channels: channels in the output Tensor.
:param num_res_blocks: number of residual blocks per downsample.
:param attention_resolutions: a collection of downsample rates at which
attention will take place. May be a set, list, or tuple.
For example, if this contains 4, then at 4x downsampling, attention
will be used.
:param dropout: the dropout probability.
:param channel_mult: channel multiplier for each level of the UNet.
:param conv_resample: if True, use learned convolutions for upsampling and
downsampling.
:param dims: determines if the signal is 1D, 2D, or 3D.
:param num_classes: if specified (as an int), then this model will be
class-conditional with `num_classes` classes.
:param use_checkpoint: use gradient checkpointing to reduce memory usage.
:param num_heads: the number of attention heads in each attention layer.
:param num_heads_channels: if specified, ignore num_heads and instead use
a fixed channel width per attention head.
:param num_heads_upsample: works with num_heads to set a different number
of heads for upsampling. Deprecated.
:param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
:param resblock_updown: use residual blocks for up/downsampling.
:param use_new_attention_order: use a different attention pattern for potentially
increased efficiency.
"""
def __init__(
self,
image_size,
in_channels,
model_channels,
out_channels,
num_res_blocks,
attention_resolutions,
dropout=0,
channel_mult=(1, 2, 4, 8),
conv_resample=True,
dims=2,
num_classes=None,
use_checkpoint=False,
use_fp16=False,
default_eps=False,
force_type_convert=False,
num_heads=-1,
num_head_channels=-1,
num_heads_upsample=-1,
use_scale_shift_norm=False,
resblock_updown=False,
use_new_attention_order=False,
use_spatial_transformer=False, # custom transformer support
transformer_depth=1, # custom transformer support
context_dim=None, # custom transformer support
n_embed=None, # custom support for prediction of discrete ids into codebook of first stage vq model
legacy=True,
):
super().__init__()
if use_spatial_transformer:
assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...'
if context_dim is not None:
assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...'
from omegaconf.listconfig import ListConfig
if type(context_dim) == ListConfig:
context_dim = list(context_dim)
if num_heads_upsample == -1:
num_heads_upsample = num_heads
if num_heads == -1:
assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set'
if num_head_channels == -1:
assert num_heads != -1, 'Either num_heads or num_head_channels has to be set'
self.image_size = image_size
self.in_channels = in_channels
self.model_channels = model_channels
self.out_channels = out_channels
self.num_res_blocks = num_res_blocks
self.attention_resolutions = attention_resolutions
self.dropout = dropout
self.channel_mult = channel_mult
self.conv_resample = conv_resample
self.num_classes = num_classes
self.use_checkpoint = use_checkpoint
self.dtype = th.float16 if use_fp16 else th.float32
self.num_heads = num_heads
self.num_head_channels = num_head_channels
self.num_heads_upsample = num_heads_upsample
self.predict_codebook_ids = n_embed is not None
time_embed_dim = model_channels * 4
self.time_embed = nn.Sequential(
linear(model_channels, time_embed_dim),
nn.SiLU(),
linear(time_embed_dim, time_embed_dim),
)
if self.num_classes is not None:
self.label_emb = nn.Embedding(num_classes, time_embed_dim)
self.input_blocks = nn.ModuleList(
[
TimestepEmbedSequential(
conv_nd(dims, in_channels, model_channels, 3, padding=1)
)
]
)
self._feature_size = model_channels
input_block_chans = [model_channels]
ch = model_channels
ds = 1
for level, mult in enumerate(channel_mult):
for _ in range(num_res_blocks):
layers = [
ResBlock(
ch,
time_embed_dim,
dropout,
out_channels=mult * model_channels,
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
)
]
ch = mult * model_channels
if ds in attention_resolutions:
if num_head_channels == -1:
dim_head = ch // num_heads
else:
num_heads = ch // num_head_channels
dim_head = num_head_channels
if legacy:
#num_heads = 1
dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
layers.append(
AttentionBlock(
ch,
use_checkpoint=use_checkpoint,
num_heads=num_heads,
num_head_channels=dim_head,
use_new_attention_order=use_new_attention_order,
) if not use_spatial_transformer else SpatialTransformer(
ch, num_heads, dim_head, default_eps=default_eps, force_type_convert=force_type_convert, depth=transformer_depth, context_dim=context_dim
)
)
self.input_blocks.append(TimestepEmbedSequential(*layers))
self._feature_size += ch
input_block_chans.append(ch)
if level != len(channel_mult) - 1:
out_ch = ch
self.input_blocks.append(
TimestepEmbedSequential(
ResBlock(
ch,
time_embed_dim,
dropout,
out_channels=out_ch,
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
down=True,
)
if resblock_updown
else Downsample(
ch, conv_resample, dims=dims, out_channels=out_ch
)
)
)
ch = out_ch
input_block_chans.append(ch)
ds *= 2
self._feature_size += ch
if num_head_channels == -1:
dim_head = ch // num_heads
else:
num_heads = ch // num_head_channels
dim_head = num_head_channels
if legacy:
#num_heads = 1
dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
self.middle_block = TimestepEmbedSequential(
ResBlock(
ch,
time_embed_dim,
dropout,
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
),
AttentionBlock(
ch,
use_checkpoint=use_checkpoint,
num_heads=num_heads,
num_head_channels=dim_head,
use_new_attention_order=use_new_attention_order,
) if not use_spatial_transformer else SpatialTransformer(
ch, num_heads, dim_head, default_eps=default_eps, force_type_convert=force_type_convert, depth=transformer_depth, context_dim=context_dim
),
ResBlock(
ch,
time_embed_dim,
dropout,
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
),
)
self._feature_size += ch
self.output_blocks = nn.ModuleList([])
for level, mult in list(enumerate(channel_mult))[::-1]:
for i in range(num_res_blocks + 1):
ich = input_block_chans.pop()
layers = [
ResBlock(
ch + ich,
time_embed_dim,
dropout,
out_channels=model_channels * mult,
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
)
]
ch = model_channels * mult
if ds in attention_resolutions:
if num_head_channels == -1:
dim_head = ch // num_heads
else:
num_heads = ch // num_head_channels
dim_head = num_head_channels
if legacy:
#num_heads = 1
dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
layers.append(
AttentionBlock(
ch,
use_checkpoint=use_checkpoint,
num_heads=num_heads_upsample,
num_head_channels=dim_head,
use_new_attention_order=use_new_attention_order,
) if not use_spatial_transformer else SpatialTransformer(
ch, num_heads, dim_head, default_eps=default_eps, force_type_convert=force_type_convert, depth=transformer_depth, context_dim=context_dim
)
)
if level and i == num_res_blocks:
out_ch = ch
layers.append(
ResBlock(
ch,
time_embed_dim,
dropout,
out_channels=out_ch,
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
up=True,
)
if resblock_updown
else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch)
)
ds //= 2
self.output_blocks.append(TimestepEmbedSequential(*layers))
self._feature_size += ch
self.out = nn.Sequential(
normalization(ch),
nn.SiLU(),
zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)),
)
if self.predict_codebook_ids:
self.id_predictor = nn.Sequential(
normalization(ch),
conv_nd(dims, model_channels, n_embed, 1),
#nn.LogSoftmax(dim=1) # change to cross_entropy and produce non-normalized logits
)
# if use_fp16:
# self.convert_to_fp16()
# for name, m in self.named_modules():
# if 'output_blocks.11.1' in name:
# if isinstance(m, (nn.Conv2d, nn.LayerNorm)) or 'proj_out' in name:
# m.weight.data = m.weight.data.float()
# m.bias.data = m.bias.data.float()
def convert_to_fp16(self):
"""
Convert the torso of the model to float16.
"""
self.input_blocks.apply(convert_module_to_f16)
self.middle_block.apply(convert_module_to_f16)
self.output_blocks.apply(convert_module_to_f16)
for m in self.modules():
if isinstance(m, (CrossAttention, FeedForward)):
m.apply(convert_some_linear_to_f16)
def convert_to_fp32(self):
"""
Convert the torso of the model to float32.
"""
self.input_blocks.apply(convert_module_to_f32)
self.middle_block.apply(convert_module_to_f32)
self.output_blocks.apply(convert_module_to_f32)
for m in self.modules():
if isinstance(m, (CrossAttention, FeedForward)):
m.apply(convert_some_linear_to_f32)
def forward(self, x, timesteps=None, context=None, y=None,**kwargs):
"""
Apply the model to an input batch.
:param x: an [N x C x ...] Tensor of inputs.
:param timesteps: a 1-D batch of timesteps.
:param context: conditioning plugged in via crossattn
:param y: an [N] Tensor of labels, if class-conditional.
:return: an [N x C x ...] Tensor of outputs.
"""
assert (y is not None) == (
self.num_classes is not None
), "must specify y if and only if the model is class-conditional"
hs = []
t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
emb = self.time_embed(t_emb.type(self.time_embed[0].weight.dtype))
if self.num_classes is not None:
assert y.shape == (x.shape[0],)
emb = emb + self.label_emb(y)
h = x.type(self.dtype)
for module in self.input_blocks:
h = module(h, emb, context)
hs.append(h)
h = self.middle_block(h, emb, context)
for module in self.output_blocks:
h = th.cat([h, hs.pop()], dim=1)
h = module(h, emb, context)
h = h.type(x.dtype)
if self.predict_codebook_ids:
return self.id_predictor(h)
else:
return self.out(h)
class EncoderUNetModel(nn.Module):
"""
The half UNet model with attention and timestep embedding.
For usage, see UNet.
"""
def __init__(
self,
image_size,
in_channels,
model_channels,
out_channels,
num_res_blocks,
attention_resolutions,
dropout=0,
channel_mult=(1, 2, 4, 8),
conv_resample=True,
dims=2,
use_checkpoint=False,
use_fp16=False,
num_heads=1,
num_head_channels=-1,
num_heads_upsample=-1,
use_scale_shift_norm=False,
resblock_updown=False,
use_new_attention_order=False,
pool="adaptive",
*args,
**kwargs
):
super().__init__()
if num_heads_upsample == -1:
num_heads_upsample = num_heads
self.in_channels = in_channels
self.model_channels = model_channels
self.out_channels = out_channels
self.num_res_blocks = num_res_blocks
self.attention_resolutions = attention_resolutions
self.dropout = dropout
self.channel_mult = channel_mult
self.conv_resample = conv_resample
self.use_checkpoint = use_checkpoint
self.dtype = th.float16 if use_fp16 else th.float32
self.num_heads = num_heads
self.num_head_channels = num_head_channels
self.num_heads_upsample = num_heads_upsample
time_embed_dim = model_channels * 4
self.time_embed = nn.Sequential(
linear(model_channels, time_embed_dim),
nn.SiLU(),
linear(time_embed_dim, time_embed_dim),
)
self.input_blocks = nn.ModuleList(
[
TimestepEmbedSequential(
conv_nd(dims, in_channels, model_channels, 3, padding=1)
)
]
)
self._feature_size = model_channels
input_block_chans = [model_channels]
ch = model_channels
ds = 1
for level, mult in enumerate(channel_mult):
for _ in range(num_res_blocks):
layers = [
ResBlock(
ch,
time_embed_dim,
dropout,
out_channels=mult * model_channels,
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
)
]
ch = mult * model_channels
if ds in attention_resolutions:
layers.append(
AttentionBlock(
ch,
use_checkpoint=use_checkpoint,
num_heads=num_heads,
num_head_channels=num_head_channels,
use_new_attention_order=use_new_attention_order,
)
)
self.input_blocks.append(TimestepEmbedSequential(*layers))
self._feature_size += ch
input_block_chans.append(ch)
if level != len(channel_mult) - 1:
out_ch = ch
self.input_blocks.append(
TimestepEmbedSequential(
ResBlock(
ch,
time_embed_dim,
dropout,
out_channels=out_ch,
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
down=True,
)
if resblock_updown
else Downsample(
ch, conv_resample, dims=dims, out_channels=out_ch
)
)
)
ch = out_ch
input_block_chans.append(ch)
ds *= 2
self._feature_size += ch
self.middle_block = TimestepEmbedSequential(
ResBlock(
ch,
time_embed_dim,
dropout,
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
),
AttentionBlock(
ch,
use_checkpoint=use_checkpoint,
num_heads=num_heads,
num_head_channels=num_head_channels,
use_new_attention_order=use_new_attention_order,
),
ResBlock(
ch,
time_embed_dim,
dropout,
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
),
)
self._feature_size += ch
self.pool = pool
if pool == "adaptive":
self.out = nn.Sequential(
normalization(ch),
nn.SiLU(),
nn.AdaptiveAvgPool2d((1, 1)),
zero_module(conv_nd(dims, ch, out_channels, 1)),
nn.Flatten(),
)
elif pool == "attention":
assert num_head_channels != -1
self.out = nn.Sequential(
normalization(ch),
nn.SiLU(),
AttentionPool2d(
(image_size // ds), ch, num_head_channels, out_channels
),
)
elif pool == "spatial":
self.out = nn.Sequential(
nn.Linear(self._feature_size, 2048),
nn.ReLU(),
nn.Linear(2048, self.out_channels),
)
elif pool == "spatial_v2":
self.out = nn.Sequential(
nn.Linear(self._feature_size, 2048),
normalization(2048),
nn.SiLU(),
nn.Linear(2048, self.out_channels),
)
else:
raise NotImplementedError(f"Unexpected {pool} pooling")
# if use_fp16:
# self.convert_to_fp16()
def convert_to_fp16(self):
"""
Convert the torso of the model to float16.
"""
self.input_blocks.apply(convert_module_to_f16)
self.middle_block.apply(convert_module_to_f16)
for m in self.modules():
if isinstance(m, (CrossAttention, FeedForward)):
m.apply(convert_some_linear_to_f16)
def convert_to_fp32(self):
"""
Convert the torso of the model to float32.
"""
self.input_blocks.apply(convert_module_to_f32)
self.middle_block.apply(convert_module_to_f32)
for m in self.modules():
if isinstance(m, (CrossAttention, FeedForward)):
m.apply(convert_some_linear_to_f32)
def forward(self, x, timesteps):
"""
Apply the model to an input batch.
:param x: an [N x C x ...] Tensor of inputs.
:param timesteps: a 1-D batch of timesteps.
:return: an [N x K] Tensor of outputs.
"""
emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
results = []
h = x.type(self.dtype)
for module in self.input_blocks:
h = module(h, emb)
if self.pool.startswith("spatial"):
results.append(h.type(x.dtype).mean(dim=(2, 3)))
h = self.middle_block(h, emb)
if self.pool.startswith("spatial"):
results.append(h.type(x.dtype).mean(dim=(2, 3)))
h = th.cat(results, axis=-1)
return self.out(h)
else:
h = h.type(x.dtype)
return self.out(h)
================================================
FILE: models/InstructDiffusion/stable_diffusion/ldm/modules/diffusionmodules/util.py
================================================
# adopted from
# https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py
# and
# https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
# and
# https://github.com/openai/guided-diffusion/blob/0ba878e517b276c45d1195eb29f6f5f72659a05b/guided_diffusion/nn.py
#
# thanks!
import os
import math
import torch
import torch.nn as nn
import numpy as np
from einops import repeat
import deepspeed
from ldm.util import instantiate_from_config
def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
if schedule == "linear":
betas = (
torch.linspace(linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64) ** 2
)
elif schedule == "cosine":
timesteps = (
torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s
)
alphas = timesteps / (1 + cosine_s) * np.pi / 2
alphas = torch.cos(alphas).pow(2)
alphas = alphas / alphas[0]
betas = 1 - alphas[1:] / alphas[:-1]
betas = np.clip(betas, a_min=0, a_max=0.999)
elif schedule == "sqrt_linear":
betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64)
elif schedule == "sqrt":
betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) ** 0.5
else:
raise ValueError(f"schedule '{schedule}' unknown.")
return betas.numpy()
def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True):
if ddim_discr_method == 'uniform':
c = num_ddpm_timesteps // num_ddim_timesteps
ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c)))
elif ddim_discr_method == 'quad':
ddim_timesteps = ((np.linspace(0, np.sqrt(num_ddpm_timesteps * .8), num_ddim_timesteps)) ** 2).astype(int)
else:
raise NotImplementedError(f'There is no ddim discretization method called "{ddim_discr_method}"')
# assert ddim_timesteps.shape[0] == num_ddim_timesteps
# add one to get the final alpha values right (the ones from first scale to data during sampling)
steps_out = ddim_timesteps + 1
if verbose:
print(f'Selected timesteps for ddim sampler: {steps_out}')
return steps_out
def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True):
# select alphas for computing the variance schedule
alphas = alphacums[ddim_timesteps]
alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist())
# according the the formula provided in https://arxiv.org/abs/2010.02502
sigmas = eta * np.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev))
if verbose:
print(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}')
print(f'For the chosen value of eta, which is {eta}, '
f'this results in the following sigma_t schedule for ddim sampler {sigmas}')
return sigmas, alphas, alphas_prev
def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999):
"""
Create a beta schedule that discretizes the given alpha_t_bar function,
which defines the cumulative product of (1-beta) over time from t = [0,1].
:param num_diffusion_timesteps: the number of betas to produce.
:param alpha_bar: a lambda that takes an argument t from 0 to 1 and
produces the cumulative product of (1-beta) up to that
part of the diffusion process.
:param max_beta: the maximum beta to use; use values lower than 1 to
prevent singularities.
"""
betas = []
for i in range(num_diffusion_timesteps):
t1 = i / num_diffusion_timesteps
t2 = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
return np.array(betas)
def extract_into_tensor(a, t, x_shape):
b, *_ = t.shape
out = a.gather(-1, t)
return out.reshape(b, *((1,) * (len(x_shape) - 1)))
def checkpoint(func, inputs, params, flag):
"""
Evaluate a function without caching intermediate activations, allowing for
reduced memory at the expense of extra compute in the backward pass.
:param func: the function to evaluate.
:param inputs: the argument sequence to pass to `func`.
:param params: a sequence of parameters `func` depends on but does not
explicitly take as arguments.
:param flag: if False, disable gradient checkpointing.
"""
if flag:
args = tuple(inputs) + tuple(params)
return CheckpointFunction.apply(func, len(inputs), *args)
# return torch.utils.checkpoint.checkpoint(func, *inputs)
# return deepspeed.checkpointing.checkpoint(func, *inputs)
else:
return func(*inputs)
# def checkpoint(func, inputs, flag):
# """
# Evaluate a function without caching intermediate activations, allowing for
# reduced memory at the expense of extra compute in the backward pass.
# :param func: the function to evaluate.
# :param inputs: the argument sequence to pass to `func`.
# :param flag: if False, disable gradient checkpointing.
# """
# if flag:
# return torch.utils.checkpoint(self._forward, *inputs)
# else:
# return func(*inputs)
class CheckpointFunction(torch.autograd.Function):
@staticmethod
@torch.cuda.amp.custom_fwd(cast_inputs=torch.float32) # add this
def forward(ctx, run_function, length, *args):
ctx.run_function = run_function
ctx.input_tensors = list(args[:length])
ctx.input_params = list(args[length:])
with torch.no_grad():
output_tensors = ctx.run_function(*ctx.input_tensors)
return output_tensors
@staticmethod
@torch.cuda.amp.custom_bwd # add this
def backward(ctx, *output_grads):
ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors]
with torch.enable_grad():
# Fixes a bug where the first op in run_function modifies the
# Tensor storage in place, which is not allowed for detach()'d
# Tensors.
shallow_copies = [x.view_as(x) for x in ctx.input_tensors]
output_tensors = ctx.run_function(*shallow_copies)
input_grads = torch.autograd.grad(
output_tensors,
ctx.input_tensors + ctx.input_params,
output_grads,
allow_unused=True,
)
del ctx.input_tensors
del ctx.input_params
del output_tensors
return (None, None) + input_grads
def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False):
"""
Create sinusoidal timestep embeddings.
:param timesteps: a 1-D Tensor of N indices, one per batch element.
These may be fractional.
:param dim: the dimension of the output.
:param max_period: controls the minimum frequency of the embeddings.
:return: an [N x dim] Tensor of positional embeddings.
"""
if not repeat_only:
half = dim // 2
freqs = torch.exp(
-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
).to(device=timesteps.device)
args = timesteps[:, None].float() * freqs[None]
embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
if dim % 2:
embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
else:
embedding = repeat(timesteps, 'b -> b d', d=dim)
return embedding
def zero_module(module):
"""
Zero out the parameters of a module and return it.
"""
for p in module.parameters():
p.detach().zero_()
return module
def scale_module(module, scale):
"""
Scale the parameters of a module and return it.
"""
for p in module.parameters():
p.detach().mul_(scale)
return module
def mean_flat(tensor):
"""
Take the mean over all non-batch dimensions.
"""
return tensor.mean(dim=list(range(1, len(tensor.shape))))
def normalization(channels):
"""
Make a standard normalization layer.
:param channels: number of input channels.
:return: an nn.Module for normalization.
"""
return GroupNorm32(32, channels)
# PyTorch 1.7 has SiLU, but we support PyTorch 1.5.
class SiLU(nn.Module):
def forward(self, x):
return x * torch.sigmoid(x)
class GroupNorm32(nn.GroupNorm):
def forward(self, x):
return super().forward(x.type(self.weight.dtype))
def conv_nd(dims, *args, **kwargs):
"""
Create a 1D, 2D, or 3D convolution module.
"""
if dims == 1:
return nn.Conv1d(*args, **kwargs)
elif dims == 2:
return nn.Conv2d(*args, **kwargs)
elif dims == 3:
return nn.Conv3d(*args, **kwargs)
raise ValueError(f"unsupported dimensions: {dims}")
def linear(*args, **kwargs):
"""
Create a linear module.
"""
return nn.Linear(*args, **kwargs)
def avg_pool_nd(dims, *args, **kwargs):
"""
Create a 1D, 2D, or 3D average pooling module.
"""
if dims == 1:
return nn.AvgPool1d(*args, **kwargs)
elif dims == 2:
return nn.AvgPool2d(*args, **kwargs)
elif dims == 3:
return nn.AvgPool3d(*args, **kwargs)
raise ValueError(f"unsupported dimensions: {dims}")
class HybridConditioner(nn.Module):
def __init__(self, c_concat_config, c_crossattn_config):
super().__init__()
self.concat_conditioner = instantiate_from_config(c_concat_config)
self.crossattn_conditioner = instantiate_from_config(c_crossattn_config)
def forward(self, c_concat, c_crossattn):
c_concat = self.concat_conditioner(c_concat)
c_crossattn = self.crossattn_conditioner(c_crossattn)
return {'c_concat': [c_concat], 'c_crossattn': [c_crossattn]}
def noise_like(shape, device, repeat=False):
repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1)))
noise = lambda: torch.randn(shape, device=device)
return repeat_noise() if repeat else noise()
================================================
FILE: models/InstructDiffusion/stable_diffusion/ldm/modules/distributions/__init__.py
================================================
================================================
FILE: models/InstructDiffusion/stable_diffusion/ldm/modules/distributions/distributions.py
================================================
import torch
import numpy as np
class AbstractDistribution:
def sample(self):
raise NotImplementedError()
def mode(self):
raise NotImplementedError()
class DiracDistribution(AbstractDistribution):
def __init__(self, value):
self.value = value
def sample(self):
return self.value
def mode(self):
return self.value
class DiagonalGaussianDistribution(object):
def __init__(self, parameters, deterministic=False):
self.parameters = parameters
self.mean, self.logvar = torch.chunk(parameters, 2, dim=1)
self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
self.deterministic = deterministic
self.std = torch.exp(0.5 * self.logvar)
self.var = torch.exp(self.logvar)
if self.deterministic:
self.var = self.std = torch.zeros_like(self.mean).to(device=self.parameters.device)
def sample(self):
x = self.mean + self.std * torch.randn(self.mean.shape).to(device=self.parameters.device)
return x
def kl(self, other=None):
if self.deterministic:
return torch.Tensor([0.])
else:
if other is None:
return 0.5 * torch.sum(torch.pow(self.mean, 2)
+ self.var - 1.0 - self.logvar,
dim=[1, 2, 3])
else:
return 0.5 * torch.sum(
torch.pow(self.mean - other.mean, 2) / other.var
+ self.var / other.var - 1.0 - self.logvar + other.logvar,
dim=[1, 2, 3])
def nll(self, sample, dims=[1,2,3]):
if self.deterministic:
return torch.Tensor([0.])
logtwopi = np.log(2.0 * np.pi)
return 0.5 * torch.sum(
logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var,
dim=dims)
def mode(self):
return self.mean
def normal_kl(mean1, logvar1, mean2, logvar2):
"""
source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12
Compute the KL divergence between two gaussians.
Shapes are automatically broadcasted, so batches can be compared to
scalars, among other use cases.
"""
tensor = None
for obj in (mean1, logvar1, mean2, logvar2):
if isinstance(obj, torch.Tensor):
tensor = obj
break
assert tensor is not None, "at least one argument must be a Tensor"
# Force variances to be Tensors. Broadcasting helps convert scalars to
# Tensors, but it does not work for torch.exp().
logvar1, logvar2 = [
x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor)
for x in (logvar1, logvar2)
]
return 0.5 * (
-1.0
+ logvar2
- logvar1
+ torch.exp(logvar1 - logvar2)
+ ((mean1 - mean2) ** 2) * torch.exp(-logvar2)
)
================================================
FILE: models/InstructDiffusion/stable_diffusion/ldm/modules/ema.py
================================================
# --------------------------------------------------------
# Stable-Diffusion-Torch
# Based on Stable-Diffusion (https://github.com/CompVis/stable-diffusion)
# Modified by Zigang Geng (zigang@mail.ustc.edu.cn) and Tiankai Hang
# --------------------------------------------------------
import torch
from torch import nn
class LitEma(nn.Module):
def __init__(self, model, decay=0.9999, decay_resume=0.9999, use_num_upates=True):
super().__init__()
if decay < 0.0 or decay > 1.0:
raise ValueError('Decay must be between 0 and 1')
self.m_name2s_name = {}
self.decay_resume = decay_resume
self.register_buffer('decay', torch.tensor(decay, dtype=torch.float32))
self.register_buffer('num_updates', torch.tensor(0,dtype=torch.int) if use_num_upates
else torch.tensor(-1,dtype=torch.int))
for name, p in model.named_parameters():
if p.requires_grad:
#remove as '.'-character is not allowed in buffers
s_name = name.replace('.','')
self.m_name2s_name.update({name:s_name})
self.register_buffer(s_name,p.clone().float().detach().data)
self.collected_params = []
def forward(self, model):
decay = self.decay
if self.num_updates >= 0:
self.num_updates += 1
decay = min(self.decay,(1 + self.num_updates) / (10 + self.num_updates))
if self.decay_resume != 0.9999:
decay = min(self.decay_resume,(1 + self.num_updates) / (10 + self.num_updates))
one_minus_decay = 1.0 - decay
with torch.no_grad():
m_param = dict(model.named_parameters())
shadow_params = dict(self.named_buffers())
for key in m_param:
if m_param[key].requires_grad:
sname = self.m_name2s_name[key[7:]]
shadow_params[sname].sub_(one_minus_decay * (shadow_params[sname] - m_param[key]))
else:
assert not key in self.m_name2s_name
def copy_to(self, model, test=False):
m_param = dict(model.named_parameters())
shadow_params = dict(self.named_buffers())
for key in m_param:
if m_param[key].requires_grad:
if test:
m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data)
else:
m_param[key].data.copy_(shadow_params[self.m_name2s_name[key[7:]]].data)
else:
assert not key in self.m_name2s_name
def store(self, parameters):
"""
Save the current parameters for restoring later.
Args:
parameters: Iterable of `torch.nn.Parameter`; the parameters to be
temporarily stored.
"""
# self.collected_params = [param.clone() for param in parameters]
self.collected_params = [param.clone().detach().cpu() for param in parameters]
def restore(self, parameters):
"""
Restore the parameters stored with the `store` method.
Useful to validate the model with EMA parameters without affecting the
original optimization process. Store the parameters before the
`copy_to` method. After validation (or model saving), use this to
restore the former parameters.
Args:
parameters: Iterable of `torch.nn.Parameter`; the parameters to be
updated with the stored parameters.
"""
for c_param, param in zip(self.collected_params, parameters):
# param.data.copy_(c_param.data)
param.data.copy_(c_param.data.clone().to(param.device))
================================================
FILE: models/InstructDiffusion/stable_diffusion/ldm/modules/encoders/__init__.py
================================================
================================================
FILE: models/InstructDiffusion/stable_diffusion/ldm/modules/encoders/modules.py
================================================
import torch
import torch.nn as nn
from functools import partial
import clip
from einops import rearrange, repeat
from transformers import CLIPTokenizer, CLIPTextModel
import kornia
from ldm.modules.x_transformer import Encoder, TransformerWrapper # TODO: can we directly rely on lucidrains code and simply add this as a reuirement? --> test
class AbstractEncoder(nn.Module):
def __init__(self):
super().__init__()
def encode(self, *args, **kwargs):
raise NotImplementedError
class ClassEmbedder(nn.Module):
def __init__(self, embed_dim, n_classes=1000, key='class'):
super().__init__()
self.key = key
self.embedding = nn.Embedding(n_classes, embed_dim)
def forward(self, batch, key=None):
if key is None:
key = self.key
# this is for use in crossattn
c = batch[key][:, None]
c = self.embedding(c)
return c
class TransformerEmbedder(AbstractEncoder):
"""Some transformer encoder layers"""
def __init__(self, n_embed, n_layer, vocab_size, max_seq_len=77, device="cuda"):
super().__init__()
self.device = device
self.transformer = TransformerWrapper(num_tokens=vocab_size, max_seq_len=max_seq_len,
attn_layers=Encoder(dim=n_embed, depth=n_layer))
def forward(self, tokens):
tokens = tokens.to(self.device) # meh
z = self.transformer(tokens, return_embeddings=True)
return z
def encode(self, x):
return self(x)
class BERTTokenizer(AbstractEncoder):
""" Uses a pretrained BERT tokenizer by huggingface. Vocab size: 30522 (?)"""
def __init__(self, device="cuda", vq_interface=True, max_length=77):
super().__init__()
from transformers import BertTokenizerFast # TODO: add to reuquirements
self.tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased")
self.device = device
self.vq_interface = vq_interface
self.max_length = max_length
def forward(self, text):
batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
tokens = batch_encoding["input_ids"].to(self.device)
return tokens
@torch.no_grad()
def encode(self, text):
tokens = self(text)
if not self.vq_interface:
return tokens
return None, None, [None, None, tokens]
def decode(self, text):
return text
class BERTEmbedder(AbstractEncoder):
"""Uses the BERT tokenizr model and add some transformer encoder layers"""
def __init__(self, n_embed, n_layer, vocab_size=30522, max_seq_len=77,
device="cuda",use_tokenizer=True, embedding_dropout=0.0):
super().__init__()
self.use_tknz_fn = use_tokenizer
if self.use_tknz_fn:
self.tknz_fn = BERTTokenizer(vq_interface=False, max_length=max_seq_len)
self.device = device
self.transformer = TransformerWrapper(num_tokens=vocab_size, max_seq_len=max_seq_len,
attn_layers=Encoder(dim=n_embed, depth=n_layer),
emb_dropout=embedding_dropout)
def forward(self, text):
if self.use_tknz_fn:
tokens = self.tknz_fn(text)#.to(self.device)
else:
tokens = text
z = self.transformer(tokens, return_embeddings=True)
return z
def encode(self, text):
# output of length 77
return self(text)
class SpatialRescaler(nn.Module):
def __init__(self,
n_stages=1,
method='bilinear',
multiplier=0.5,
in_channels=3,
out_channels=None,
bias=False):
super().__init__()
self.n_stages = n_stages
assert self.n_stages >= 0
assert method in ['nearest','linear','bilinear','trilinear','bicubic','area']
self.multiplier = multiplier
self.interpolator = partial(torch.nn.functional.interpolate, mode=method)
self.remap_output = out_channels is not None
if self.remap_output:
print(f'Spatial Rescaler mapping from {in_channels} to {out_channels} channels after resizing.')
self.channel_mapper = nn.Conv2d(in_channels,out_channels,1,bias=bias)
def forward(self,x):
for stage in range(self.n_stages):
x = self.interpolator(x, scale_factor=self.multiplier)
if self.remap_output:
x = self.channel_mapper(x)
return x
def encode(self, x):
return self(x)
class FrozenCLIPEmbedder(AbstractEncoder):
"""Uses the CLIP transformer encoder for text (from Hugging Face)"""
def __init__(self, version="openai/clip-vit-large-patch14", device="cuda", max_length=77):
super().__init__()
self.tokenizer = CLIPTokenizer.from_pretrained(version)
self.transformer = CLIPTextModel.from_pretrained(version)
self.device = device
self.max_length = max_length
self.freeze()
def freeze(self):
self.transformer = self.transformer.eval()
for param in self.parameters():
param.requires_grad = False
def forward(self, text):
batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
tokens = batch_encoding["input_ids"].to(self.device)
outputs = self.transformer(input_ids=tokens)
z = outputs.last_hidden_state
return z
def encode(self, text):
return self(text)
class FrozenCLIPTextEmbedder(nn.Module):
"""
Uses the CLIP transformer encoder for text.
"""
def __init__(self, version='ViT-L/14', device="cuda", max_length=77, n_repeat=1, normalize=True):
super().__init__()
self.model, _ = clip.load(version, jit=False, device="cpu")
self.device = device
self.max_length = max_length
self.n_repeat = n_repeat
self.normalize = normalize
def freeze(self):
self.model = self.model.eval()
for param in self.parameters():
param.requires_grad = False
def forward(self, text):
tokens = clip.tokenize(text).to(self.device)
z = self.model.encode_text(tokens)
if self.normalize:
z = z / torch.linalg.norm(z, dim=1, keepdim=True)
return z
def encode(self, text):
z = self(text)
if z.ndim==2:
z = z[:, None, :]
z = repeat(z, 'b 1 d -> b k d', k=self.n_repeat)
return z
class FrozenClipImageEmbedder(nn.Module):
"""
Uses the CLIP image encoder.
"""
def __init__(
self,
model,
jit=False,
device='cuda' if torch.cuda.is_available() else 'cpu',
antialias=False,
):
super().__init__()
self.model, _ = clip.load(name=model, device=device, jit=jit)
self.antialias = antialias
self.register_buffer('mean', torch.Tensor([0.48145466, 0.4578275, 0.40821073]), persistent=False)
self.register_buffer('std', torch.Tensor([0.26862954, 0.26130258, 0.27577711]), persistent=False)
def preprocess(self, x):
# normalize to [0,1]
x = kornia.geometry.resize(x, (224, 224),
interpolation='bicubic',align_corners=True,
antialias=self.antialias)
x = (x + 1.) / 2.
# renormalize according to clip
x = kornia.enhance.normalize(x, self.mean, self.std)
return x
def forward(self, x):
# x is assumed to be in range [-1,1]
return self.model.encode_image(self.preprocess(x))
if __name__ == "__main__":
from ldm.util import count_params
model = FrozenCLIPEmbedder()
count_params(model, verbose=True)
================================================
FILE: models/InstructDiffusion/stable_diffusion/ldm/modules/image_degradation/__init__.py
================================================
from ldm.modules.image_degradation.bsrgan import degradation_bsrgan_variant as degradation_fn_bsr
from ldm.modules.image_degradation.bsrgan_light import degradation_bsrgan_variant as degradation_fn_bsr_light
================================================
FILE: models/InstructDiffusion/stable_diffusion/ldm/modules/image_degradation/bsrgan.py
================================================
# -*- coding: utf-8 -*-
"""
# --------------------------------------------
# Super-Resolution
# --------------------------------------------
#
# Kai Zhang (cskaizhang@gmail.com)
# https://github.com/cszn
# From 2019/03--2021/08
# --------------------------------------------
"""
import numpy as np
import cv2
import torch
from functools import partial
import random
from scipy import ndimage
import scipy
import scipy.stats as ss
from scipy.interpolate import interp2d
from scipy.linalg import orth
import albumentations
import ldm.modules.image_degradation.utils_image as util
def modcrop_np(img, sf):
'''
Args:
img: numpy image, WxH or WxHxC
sf: scale factor
Return:
cropped image
'''
w, h = img.shape[:2]
im = np.copy(img)
return im[:w - w % sf, :h - h % sf, ...]
"""
# --------------------------------------------
# anisotropic Gaussian kernels
# --------------------------------------------
"""
def analytic_kernel(k):
"""Calculate the X4 kernel from the X2 kernel (for proof see appendix in paper)"""
k_size = k.shape[0]
# Calculate the big kernels size
big_k = np.zeros((3 * k_size - 2, 3 * k_size - 2))
# Loop over the small kernel to fill the big one
for r in range(k_size):
for c in range(k_size):
big_k[2 * r:2 * r + k_size, 2 * c:2 * c + k_size] += k[r, c] * k
# Crop the edges of the big kernel to ignore very small values and increase run time of SR
crop = k_size // 2
cropped_big_k = big_k[crop:-crop, crop:-crop]
# Normalize to 1
return cropped_big_k / cropped_big_k.sum()
def anisotropic_Gaussian(ksize=15, theta=np.pi, l1=6, l2=6):
""" generate an anisotropic Gaussian kernel
Args:
ksize : e.g., 15, kernel size
theta : [0, pi], rotation angle range
l1 : [0.1,50], scaling of eigenvalues
l2 : [0.1,l1], scaling of eigenvalues
If l1 = l2, will get an isotropic Gaussian kernel.
Returns:
k : kernel
"""
v = np.dot(np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]), np.array([1., 0.]))
V = np.array([[v[0], v[1]], [v[1], -v[0]]])
D = np.array([[l1, 0], [0, l2]])
Sigma = np.dot(np.dot(V, D), np.linalg.inv(V))
k = gm_blur_kernel(mean=[0, 0], cov=Sigma, size=ksize)
return k
def gm_blur_kernel(mean, cov, size=15):
center = size / 2.0 + 0.5
k = np.zeros([size, size])
for y in range(size):
for x in range(size):
cy = y - center + 1
cx = x - center + 1
k[y, x] = ss.multivariate_normal.pdf([cx, cy], mean=mean, cov=cov)
k = k / np.sum(k)
return k
def shift_pixel(x, sf, upper_left=True):
"""shift pixel for super-resolution with different scale factors
Args:
x: WxHxC or WxH
sf: scale factor
upper_left: shift direction
"""
h, w = x.shape[:2]
shift = (sf - 1) * 0.5
xv, yv = np.arange(0, w, 1.0), np.arange(0, h, 1.0)
if upper_left:
x1 = xv + shift
y1 = yv + shift
else:
x1 = xv - shift
y1 = yv - shift
x1 = np.clip(x1, 0, w - 1)
y1 = np.clip(y1, 0, h - 1)
if x.ndim == 2:
x = interp2d(xv, yv, x)(x1, y1)
if x.ndim == 3:
for i in range(x.shape[-1]):
x[:, :, i] = interp2d(xv, yv, x[:, :, i])(x1, y1)
return x
def blur(x, k):
'''
x: image, NxcxHxW
k: kernel, Nx1xhxw
'''
n, c = x.shape[:2]
p1, p2 = (k.shape[-2] - 1) // 2, (k.shape[-1] - 1) // 2
x = torch.nn.functional.pad(x, pad=(p1, p2, p1, p2), mode='replicate')
k = k.repeat(1, c, 1, 1)
k = k.view(-1, 1, k.shape[2], k.shape[3])
x = x.view(1, -1, x.shape[2], x.shape[3])
x = torch.nn.functional.conv2d(x, k, bias=None, stride=1, padding=0, groups=n * c)
x = x.view(n, c, x.shape[2], x.shape[3])
return x
def gen_kernel(k_size=np.array([15, 15]), scale_factor=np.array([4, 4]), min_var=0.6, max_var=10., noise_level=0):
""""
# modified version of https://github.com/assafshocher/BlindSR_dataset_generator
# Kai Zhang
# min_var = 0.175 * sf # variance of the gaussian kernel will be sampled between min_var and max_var
# max_var = 2.5 * sf
"""
# Set random eigen-vals (lambdas) and angle (theta) for COV matrix
lambda_1 = min_var + np.random.rand() * (max_var - min_var)
lambda_2 = min_var + np.random.rand() * (max_var - min_var)
theta = np.random.rand() * np.pi # random theta
noise = -noise_level + np.random.rand(*k_size) * noise_level * 2
# Set COV matrix using Lambdas and Theta
LAMBDA = np.diag([lambda_1, lambda_2])
Q = np.array([[np.cos(theta), -np.sin(theta)],
[np.sin(theta), np.cos(theta)]])
SIGMA = Q @ LAMBDA @ Q.T
INV_SIGMA = np.linalg.inv(SIGMA)[None, None, :, :]
# Set expectation position (shifting kernel for aligned image)
MU = k_size // 2 - 0.5 * (scale_factor - 1) # - 0.5 * (scale_factor - k_size % 2)
MU = MU[None, None, :, None]
# Create meshgrid for Gaussian
[X, Y] = np.meshgrid(range(k_size[0]), range(k_size[1]))
Z = np.stack([X, Y], 2)[:, :, :, None]
# Calcualte Gaussian for every pixel of the kernel
ZZ = Z - MU
ZZ_t = ZZ.transpose(0, 1, 3, 2)
raw_kernel = np.exp(-0.5 * np.squeeze(ZZ_t @ INV_SIGMA @ ZZ)) * (1 + noise)
# shift the kernel so it will be centered
# raw_kernel_centered = kernel_shift(raw_kernel, scale_factor)
# Normalize the kernel and return
# kernel = raw_kernel_centered / np.sum(raw_kernel_centered)
kernel = raw_kernel / np.sum(raw_kernel)
return kernel
def fspecial_gaussian(hsize, sigma):
hsize = [hsize, hsize]
siz = [(hsize[0] - 1.0) / 2.0, (hsize[1] - 1.0) / 2.0]
std = sigma
[x, y] = np.meshgrid(np.arange(-siz[1], siz[1] + 1), np.arange(-siz[0], siz[0] + 1))
arg = -(x * x + y * y) / (2 * std * std)
h = np.exp(arg)
h[h < scipy.finfo(float).eps * h.max()] = 0
sumh = h.sum()
if sumh != 0:
h = h / sumh
return h
def fspecial_laplacian(alpha):
alpha = max([0, min([alpha, 1])])
h1 = alpha / (alpha + 1)
h2 = (1 - alpha) / (alpha + 1)
h = [[h1, h2, h1], [h2, -4 / (alpha + 1), h2], [h1, h2, h1]]
h = np.array(h)
return h
def fspecial(filter_type, *args, **kwargs):
'''
python code from:
https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py
'''
if filter_type == 'gaussian':
return fspecial_gaussian(*args, **kwargs)
if filter_type == 'laplacian':
return fspecial_laplacian(*args, **kwargs)
"""
# --------------------------------------------
# degradation models
# --------------------------------------------
"""
def bicubic_degradation(x, sf=3):
'''
Args:
x: HxWxC image, [0, 1]
sf: down-scale factor
Return:
bicubicly downsampled LR image
'''
x = util.imresize_np(x, scale=1 / sf)
return x
def srmd_degradation(x, k, sf=3):
''' blur + bicubic downsampling
Args:
x: HxWxC image, [0, 1]
k: hxw, double
sf: down-scale factor
Return:
downsampled LR image
Reference:
@inproceedings{zhang2018learning,
title={Learning a single convolutional super-resolution network for multiple degradations},
author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
pages={3262--3271},
year={2018}
}
'''
x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap') # 'nearest' | 'mirror'
x = bicubic_degradation(x, sf=sf)
return x
def dpsr_degradation(x, k, sf=3):
''' bicubic downsampling + blur
Args:
x: HxWxC image, [0, 1]
k: hxw, double
sf: down-scale factor
Return:
downsampled LR image
Reference:
@inproceedings{zhang2019deep,
title={Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels},
author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
pages={1671--1681},
year={2019}
}
'''
x = bicubic_degradation(x, sf=sf)
x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
return x
def classical_degradation(x, k, sf=3):
''' blur + downsampling
Args:
x: HxWxC image, [0, 1]/[0, 255]
k: hxw, double
sf: down-scale factor
Return:
downsampled LR image
'''
x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
# x = filters.correlate(x, np.expand_dims(np.flip(k), axis=2))
st = 0
return x[st::sf, st::sf, ...]
def add_sharpening(img, weight=0.5, radius=50, threshold=10):
"""USM sharpening. borrowed from real-ESRGAN
Input image: I; Blurry image: B.
1. K = I + weight * (I - B)
2. Mask = 1 if abs(I - B) > threshold, else: 0
3. Blur mask:
4. Out = Mask * K + (1 - Mask) * I
Args:
img (Numpy array): Input image, HWC, BGR; float32, [0, 1].
weight (float): Sharp weight. Default: 1.
radius (float): Kernel size of Gaussian blur. Default: 50.
threshold (int):
"""
if radius % 2 == 0:
radius += 1
blur = cv2.GaussianBlur(img, (radius, radius), 0)
residual = img - blur
mask = np.abs(residual) * 255 > threshold
mask = mask.astype('float32')
soft_mask = cv2.GaussianBlur(mask, (radius, radius), 0)
K = img + weight * residual
K = np.clip(K, 0, 1)
return soft_mask * K + (1 - soft_mask) * img
def add_blur(img, sf=4):
wd2 = 4.0 + sf
wd = 2.0 + 0.2 * sf
if random.random() < 0.5:
l1 = wd2 * random.random()
l2 = wd2 * random.random()
k = anisotropic_Gaussian(ksize=2 * random.randint(2, 11) + 3, theta=random.random() * np.pi, l1=l1, l2=l2)
else:
k = fspecial('gaussian', 2 * random.randint(2, 11) + 3, wd * random.random())
img = ndimage.filters.convolve(img, np.expand_dims(k, axis=2), mode='mirror')
return img
def add_resize(img, sf=4):
rnum = np.random.rand()
if rnum > 0.8: # up
sf1 = random.uniform(1, 2)
elif rnum < 0.7: # down
sf1 = random.uniform(0.5 / sf, 1)
else:
sf1 = 1.0
img = cv2.resize(img, (int(sf1 * img.shape[1]), int(sf1 * img.shape[0])), interpolation=random.choice([1, 2, 3]))
img = np.clip(img, 0.0, 1.0)
return img
# def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
# noise_level = random.randint(noise_level1, noise_level2)
# rnum = np.random.rand()
# if rnum > 0.6: # add color Gaussian noise
# img += np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
# elif rnum < 0.4: # add grayscale Gaussian noise
# img += np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
# else: # add noise
# L = noise_level2 / 255.
# D = np.diag(np.random.rand(3))
# U = orth(np.random.rand(3, 3))
# conv = np.dot(np.dot(np.transpose(U), D), U)
# img += np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
# img = np.clip(img, 0.0, 1.0)
# return img
def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
noise_level = random.randint(noise_level1, noise_level2)
rnum = np.random.rand()
if rnum > 0.6: # add color Gaussian noise
img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
elif rnum < 0.4: # add grayscale Gaussian noise
img = img + np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
else: # add noise
L = noise_level2 / 255.
D = np.diag(np.random.rand(3))
U = orth(np.random.rand(3, 3))
conv = np.dot(np.dot(np.transpose(U), D), U)
img = img + np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
img = np.clip(img, 0.0, 1.0)
return img
def add_speckle_noise(img, noise_level1=2, noise_level2=25):
noise_level = random.randint(noise_level1, noise_level2)
img = np.clip(img, 0.0, 1.0)
rnum = random.random()
if rnum > 0.6:
img += img * np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
elif rnum < 0.4:
img += img * np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
else:
L = noise_level2 / 255.
D = np.diag(np.random.rand(3))
U = orth(np.random.rand(3, 3))
conv = np.dot(np.dot(np.transpose(U), D), U)
img += img * np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
img = np.clip(img, 0.0, 1.0)
return img
def add_Poisson_noise(img):
img = np.clip((img * 255.0).round(), 0, 255) / 255.
vals = 10 ** (2 * random.random() + 2.0) # [2, 4]
if random.random() < 0.5:
img = np.random.poisson(img * vals).astype(np.float32) / vals
else:
img_gray = np.dot(img[..., :3], [0.299, 0.587, 0.114])
img_gray = np.clip((img_gray * 255.0).round(), 0, 255) / 255.
noise_gray = np.random.poisson(img_gray * vals).astype(np.float32) / vals - img_gray
img += noise_gray[:, :, np.newaxis]
img = np.clip(img, 0.0, 1.0)
return img
def add_JPEG_noise(img):
quality_factor = random.randint(30, 95)
img = cv2.cvtColor(util.single2uint(img), cv2.COLOR_RGB2BGR)
result, encimg = cv2.imencode('.jpg', img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor])
img = cv2.imdecode(encimg, 1)
img = cv2.cvtColor(util.uint2single(img), cv2.COLOR_BGR2RGB)
return img
def random_crop(lq, hq, sf=4, lq_patchsize=64):
h, w = lq.shape[:2]
rnd_h = random.randint(0, h - lq_patchsize)
rnd_w = random.randint(0, w - lq_patchsize)
lq = lq[rnd_h:rnd_h + lq_patchsize, rnd_w:rnd_w + lq_patchsize, :]
rnd_h_H, rnd_w_H = int(rnd_h * sf), int(rnd_w * sf)
hq = hq[rnd_h_H:rnd_h_H + lq_patchsize * sf, rnd_w_H:rnd_w_H + lq_patchsize * sf, :]
return lq, hq
def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None):
"""
This is the degradation model of BSRGAN from the paper
"Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
----------
img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf)
sf: scale factor
isp_model: camera ISP model
Returns
-------
img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
"""
isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
sf_ori = sf
h1, w1 = img.shape[:2]
img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop
h, w = img.shape[:2]
if h < lq_patchsize * sf or w < lq_patchsize * sf:
raise ValueError(f'img size ({h1}X{w1}) is too small!')
hq = img.copy()
if sf == 4 and random.random() < scale2_prob: # downsample1
if np.random.rand() < 0.5:
img = cv2.resize(img, (int(1 / 2 * img.shape[1]), int(1 / 2 * img.shape[0])),
interpolation=random.choice([1, 2, 3]))
else:
img = util.imresize_np(img, 1 / 2, True)
img = np.clip(img, 0.0, 1.0)
sf = 2
shuffle_order = random.sample(range(7), 7)
idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
if idx1 > idx2: # keep downsample3 last
shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]
for i in shuffle_order:
if i == 0:
img = add_blur(img, sf=sf)
elif i == 1:
img = add_blur(img, sf=sf)
elif i == 2:
a, b = img.shape[1], img.shape[0]
# downsample2
if random.random() < 0.75:
sf1 = random.uniform(1, 2 * sf)
img = cv2.resize(img, (int(1 / sf1 * img.shape[1]), int(1 / sf1 * img.shape[0])),
interpolation=random.choice([1, 2, 3]))
else:
k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
k_shifted = shift_pixel(k, sf)
k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel
img = ndimage.filters.convolve(img, np.expand_dims(k_shifted, axis=2), mode='mirror')
img = img[0::sf, 0::sf, ...] # nearest downsampling
img = np.clip(img, 0.0, 1.0)
elif i == 3:
# downsample3
img = cv2.resize(img, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
img = np.clip(img, 0.0, 1.0)
elif i == 4:
# add Gaussian noise
img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25)
elif i == 5:
# add JPEG noise
if random.random() < jpeg_prob:
img = add_JPEG_noise(img)
elif i == 6:
# add processed camera sensor noise
if random.random() < isp_prob and isp_model is not None:
with torch.no_grad():
img, hq = isp_model.forward(img.copy(), hq)
# add final JPEG compression noise
img = add_JPEG_noise(img)
# random crop
img, hq = random_crop(img, hq, sf_ori, lq_patchsize)
return img, hq
# todo no isp_model?
def degradation_bsrgan_variant(image, sf=4, isp_model=None):
"""
This is the degradation model of BSRGAN from the paper
"Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
----------
sf: scale factor
isp_model: camera ISP model
Returns
-------
img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
"""
image = util.uint2single(image)
isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
sf_ori = sf
h1, w1 = image.shape[:2]
image = image.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop
h, w = image.shape[:2]
hq = image.copy()
if sf == 4 and random.random() < scale2_prob: # downsample1
if np.random.rand() < 0.5:
image = cv2.resize(image, (int(1 / 2 * image.shape[1]), int(1 / 2 * image.shape[0])),
interpolation=random.choice([1, 2, 3]))
else:
image = util.imresize_np(image, 1 / 2, True)
image = np.clip(image, 0.0, 1.0)
sf = 2
shuffle_order = random.sample(range(7), 7)
idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
if idx1 > idx2: # keep downsample3 last
shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]
for i in shuffle_order:
if i == 0:
image = add_blur(image, sf=sf)
elif i == 1:
image = add_blur(image, sf=sf)
elif i == 2:
a, b = image.shape[1], image.shape[0]
# downsample2
if random.random() < 0.75:
sf1 = random.uniform(1, 2 * sf)
image = cv2.resize(image, (int(1 / sf1 * image.shape[1]), int(1 / sf1 * image.shape[0])),
interpolation=random.choice([1, 2, 3]))
else:
k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
k_shifted = shift_pixel(k, sf)
k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel
image = ndimage.filters.convolve(image, np.expand_dims(k_shifted, axis=2), mode='mirror')
image = image[0::sf, 0::sf, ...] # nearest downsampling
image = np.clip(image, 0.0, 1.0)
elif i == 3:
# downsample3
image = cv2.resize(image, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
image = np.clip(image, 0.0, 1.0)
elif i == 4:
# add Gaussian noise
image = add_Gaussian_noise(image, noise_level1=2, noise_level2=25)
elif i == 5:
# add JPEG noise
if random.random() < jpeg_prob:
image = add_JPEG_noise(image)
# elif i == 6:
# # add processed camera sensor noise
# if random.random() < isp_prob and isp_model is not None:
# with torch.no_grad():
# img, hq = isp_model.forward(img.copy(), hq)
# add final JPEG compression noise
image = add_JPEG_noise(image)
image = util.single2uint(image)
example = {"image":image}
return example
# TODO incase there is a pickle error one needs to replace a += x with a = a + x in add_speckle_noise etc...
def degradation_bsrgan_plus(img, sf=4, shuffle_prob=0.5, use_sharp=True, lq_patchsize=64, isp_model=None):
"""
This is an extended degradation model by combining
the degradation models of BSRGAN and Real-ESRGAN
----------
img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf)
sf: scale factor
use_shuffle: the degradation shuffle
use_sharp: sharpening the img
Returns
-------
img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
"""
h1, w1 = img.shape[:2]
img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop
h, w = img.shape[:2]
if h < lq_patchsize * sf or w < lq_patchsize * sf:
raise ValueError(f'img size ({h1}X{w1}) is too small!')
if use_sharp:
img = add_sharpening(img)
hq = img.copy()
if random.random() < shuffle_prob:
shuffle_order = random.sample(range(13), 13)
else:
shuffle_order = list(range(13))
# local shuffle for noise, JPEG is always the last one
shuffle_order[2:6] = random.sample(shuffle_order[2:6], len(range(2, 6)))
shuffle_order[9:13] = random.sample(shuffle_order[9:13], len(range(9, 13)))
poisson_prob, speckle_prob, isp_prob = 0.1, 0.1, 0.1
for i in shuffle_order:
if i == 0:
img = add_blur(img, sf=sf)
elif i == 1:
img = add_resize(img, sf=sf)
elif i == 2:
img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25)
elif i == 3:
if random.random() < poisson_prob:
img = add_Poisson_noise(img)
elif i == 4:
if random.random() < speckle_prob:
img = add_speckle_noise(img)
elif i == 5:
if random.random() < isp_prob and isp_model is not None:
with torch.no_grad():
img, hq = isp_model.forward(img.copy(), hq)
elif i == 6:
img = add_JPEG_noise(img)
elif i == 7:
img = add_blur(img, sf=sf)
elif i == 8:
img = add_resize(img, sf=sf)
elif i == 9:
img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25)
elif i == 10:
if random.random() < poisson_prob:
img = add_Poisson_noise(img)
elif i == 11:
if random.random() < speckle_prob:
img = add_speckle_noise(img)
elif i == 12:
if random.random() < isp_prob and isp_model is not None:
with torch.no_grad():
img, hq = isp_model.forward(img.copy(), hq)
else:
print('check the shuffle!')
# resize to desired size
img = cv2.resize(img, (int(1 / sf * hq.shape[1]), int(1 / sf * hq.shape[0])),
interpolation=random.choice([1, 2, 3]))
# add final JPEG compression noise
img = add_JPEG_noise(img)
# random crop
img, hq = random_crop(img, hq, sf, lq_patchsize)
return img, hq
if __name__ == '__main__':
print("hey")
img = util.imread_uint('utils/test.png', 3)
print(img)
img = util.uint2single(img)
print(img)
img = img[:448, :448]
h = img.shape[0] // 4
print("resizing to", h)
sf = 4
deg_fn = partial(degradation_bsrgan_variant, sf=sf)
for i in range(20):
print(i)
img_lq = deg_fn(img)
print(img_lq)
img_lq_bicubic = albumentations.SmallestMaxSize(max_size=h, interpolation=cv2.INTER_CUBIC)(image=img)["image"]
print(img_lq.shape)
print("bicubic", img_lq_bicubic.shape)
print(img_hq.shape)
lq_nearest = cv2.resize(util.single2uint(img_lq), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
interpolation=0)
lq_bicubic_nearest = cv2.resize(util.single2uint(img_lq_bicubic), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
interpolation=0)
img_concat = np.concatenate([lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1)
util.imsave(img_concat, str(i) + '.png')
================================================
FILE: models/InstructDiffusion/stable_diffusion/ldm/modules/image_degradation/bsrgan_light.py
================================================
# -*- coding: utf-8 -*-
import numpy as np
import cv2
import torch
from functools import partial
import random
from scipy import ndimage
import scipy
import scipy.stats as ss
from scipy.interpolate import interp2d
from scipy.linalg import orth
import albumentations
import ldm.modules.image_degradation.utils_image as util
"""
# --------------------------------------------
# Super-Resolution
# --------------------------------------------
#
# Kai Zhang (cskaizhang@gmail.com)
# https://github.com/cszn
# From 2019/03--2021/08
# --------------------------------------------
"""
def modcrop_np(img, sf):
'''
Args:
img: numpy image, WxH or WxHxC
sf: scale factor
Return:
cropped image
'''
w, h = img.shape[:2]
im = np.copy(img)
return im[:w - w % sf, :h - h % sf, ...]
"""
# --------------------------------------------
# anisotropic Gaussian kernels
# --------------------------------------------
"""
def analytic_kernel(k):
"""Calculate the X4 kernel from the X2 kernel (for proof see appendix in paper)"""
k_size = k.shape[0]
# Calculate the big kernels size
big_k = np.zeros((3 * k_size - 2, 3 * k_size - 2))
# Loop over the small kernel to fill the big one
for r in range(k_size):
for c in range(k_size):
big_k[2 * r:2 * r + k_size, 2 * c:2 * c + k_size] += k[r, c] * k
# Crop the edges of the big kernel to ignore very small values and increase run time of SR
crop = k_size // 2
cropped_big_k = big_k[crop:-crop, crop:-crop]
# Normalize to 1
return cropped_big_k / cropped_big_k.sum()
def anisotropic_Gaussian(ksize=15, theta=np.pi, l1=6, l2=6):
""" generate an anisotropic Gaussian kernel
Args:
ksize : e.g., 15, kernel size
theta : [0, pi], rotation angle range
l1 : [0.1,50], scaling of eigenvalues
l2 : [0.1,l1], scaling of eigenvalues
If l1 = l2, will get an isotropic Gaussian kernel.
Returns:
k : kernel
"""
v = np.dot(np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]), np.array([1., 0.]))
V = np.array([[v[0], v[1]], [v[1], -v[0]]])
D = np.array([[l1, 0], [0, l2]])
Sigma = np.dot(np.dot(V, D), np.linalg.inv(V))
k = gm_blur_kernel(mean=[0, 0], cov=Sigma, size=ksize)
return k
def gm_blur_kernel(mean, cov, size=15):
center = size / 2.0 + 0.5
k = np.zeros([size, size])
for y in range(size):
for x in range(size):
cy = y - center + 1
cx = x - center + 1
k[y, x] = ss.multivariate_normal.pdf([cx, cy], mean=mean, cov=cov)
k = k / np.sum(k)
return k
def shift_pixel(x, sf, upper_left=True):
"""shift pixel for super-resolution with different scale factors
Args:
x: WxHxC or WxH
sf: scale factor
upper_left: shift direction
"""
h, w = x.shape[:2]
shift = (sf - 1) * 0.5
xv, yv = np.arange(0, w, 1.0), np.arange(0, h, 1.0)
if upper_left:
x1 = xv + shift
y1 = yv + shift
else:
x1 = xv - shift
y1 = yv - shift
x1 = np.clip(x1, 0, w - 1)
y1 = np.clip(y1, 0, h - 1)
if x.ndim == 2:
x = interp2d(xv, yv, x)(x1, y1)
if x.ndim == 3:
for i in range(x.shape[-1]):
x[:, :, i] = interp2d(xv, yv, x[:, :, i])(x1, y1)
return x
def blur(x, k):
'''
x: image, NxcxHxW
k: kernel, Nx1xhxw
'''
n, c = x.shape[:2]
p1, p2 = (k.shape[-2] - 1) // 2, (k.shape[-1] - 1) // 2
x = torch.nn.functional.pad(x, pad=(p1, p2, p1, p2), mode='replicate')
k = k.repeat(1, c, 1, 1)
k = k.view(-1, 1, k.shape[2], k.shape[3])
x = x.view(1, -1, x.shape[2], x.shape[3])
x = torch.nn.functional.conv2d(x, k, bias=None, stride=1, padding=0, groups=n * c)
x = x.view(n, c, x.shape[2], x.shape[3])
return x
def gen_kernel(k_size=np.array([15, 15]), scale_factor=np.array([4, 4]), min_var=0.6, max_var=10., noise_level=0):
""""
# modified version of https://github.com/assafshocher/BlindSR_dataset_generator
# Kai Zhang
# min_var = 0.175 * sf # variance of the gaussian kernel will be sampled between min_var and max_var
# max_var = 2.5 * sf
"""
# Set random eigen-vals (lambdas) and angle (theta) for COV matrix
lambda_1 = min_var + np.random.rand() * (max_var - min_var)
lambda_2 = min_var + np.random.rand() * (max_var - min_var)
theta = np.random.rand() * np.pi # random theta
noise = -noise_level + np.random.rand(*k_size) * noise_level * 2
# Set COV matrix using Lambdas and Theta
LAMBDA = np.diag([lambda_1, lambda_2])
Q = np.array([[np.cos(theta), -np.sin(theta)],
[np.sin(theta), np.cos(theta)]])
SIGMA = Q @ LAMBDA @ Q.T
INV_SIGMA = np.linalg.inv(SIGMA)[None, None, :, :]
# Set expectation position (shifting kernel for aligned image)
MU = k_size // 2 - 0.5 * (scale_factor - 1) # - 0.5 * (scale_factor - k_size % 2)
MU = MU[None, None, :, None]
# Create meshgrid for Gaussian
[X, Y] = np.meshgrid(range(k_size[0]), range(k_size[1]))
Z = np.stack([X, Y], 2)[:, :, :, None]
# Calcualte Gaussian for every pixel of the kernel
ZZ = Z - MU
ZZ_t = ZZ.transpose(0, 1, 3, 2)
raw_kernel = np.exp(-0.5 * np.squeeze(ZZ_t @ INV_SIGMA @ ZZ)) * (1 + noise)
# shift the kernel so it will be centered
# raw_kernel_centered = kernel_shift(raw_kernel, scale_factor)
# Normalize the kernel and return
# kernel = raw_kernel_centered / np.sum(raw_kernel_centered)
kernel = raw_kernel / np.sum(raw_kernel)
return kernel
def fspecial_gaussian(hsize, sigma):
hsize = [hsize, hsize]
siz = [(hsize[0] - 1.0) / 2.0, (hsize[1] - 1.0) / 2.0]
std = sigma
[x, y] = np.meshgrid(np.arange(-siz[1], siz[1] + 1), np.arange(-siz[0], siz[0] + 1))
arg = -(x * x + y * y) / (2 * std * std)
h = np.exp(arg)
h[h < scipy.finfo(float).eps * h.max()] = 0
sumh = h.sum()
if sumh != 0:
h = h / sumh
return h
def fspecial_laplacian(alpha):
alpha = max([0, min([alpha, 1])])
h1 = alpha / (alpha + 1)
h2 = (1 - alpha) / (alpha + 1)
h = [[h1, h2, h1], [h2, -4 / (alpha + 1), h2], [h1, h2, h1]]
h = np.array(h)
return h
def fspecial(filter_type, *args, **kwargs):
'''
python code from:
https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py
'''
if filter_type == 'gaussian':
return fspecial_gaussian(*args, **kwargs)
if filter_type == 'laplacian':
return fspecial_laplacian(*args, **kwargs)
"""
# --------------------------------------------
# degradation models
# --------------------------------------------
"""
def bicubic_degradation(x, sf=3):
'''
Args:
x: HxWxC image, [0, 1]
sf: down-scale factor
Return:
bicubicly downsampled LR image
'''
x = util.imresize_np(x, scale=1 / sf)
return x
def srmd_degradation(x, k, sf=3):
''' blur + bicubic downsampling
Args:
x: HxWxC image, [0, 1]
k: hxw, double
sf: down-scale factor
Return:
downsampled LR image
Reference:
@inproceedings{zhang2018learning,
title={Learning a single convolutional super-resolution network for multiple degradations},
author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
pages={3262--3271},
year={2018}
}
'''
x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap') # 'nearest' | 'mirror'
x = bicubic_degradation(x, sf=sf)
return x
def dpsr_degradation(x, k, sf=3):
''' bicubic downsampling + blur
Args:
x: HxWxC image, [0, 1]
k: hxw, double
sf: down-scale factor
Return:
downsampled LR image
Reference:
@inproceedings{zhang2019deep,
title={Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels},
author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
pages={1671--1681},
year={2019}
}
'''
x = bicubic_degradation(x, sf=sf)
x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
return x
def classical_degradation(x, k, sf=3):
''' blur + downsampling
Args:
x: HxWxC image, [0, 1]/[0, 255]
k: hxw, double
sf: down-scale factor
Return:
downsampled LR image
'''
x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
# x = filters.correlate(x, np.expand_dims(np.flip(k), axis=2))
st = 0
return x[st::sf, st::sf, ...]
def add_sharpening(img, weight=0.5, radius=50, threshold=10):
"""USM sharpening. borrowed from real-ESRGAN
Input image: I; Blurry image: B.
1. K = I + weight * (I - B)
2. Mask = 1 if abs(I - B) > threshold, else: 0
3. Blur mask:
4. Out = Mask * K + (1 - Mask) * I
Args:
img (Numpy array): Input image, HWC, BGR; float32, [0, 1].
weight (float): Sharp weight. Default: 1.
radius (float): Kernel size of Gaussian blur. Default: 50.
threshold (int):
"""
if radius % 2 == 0:
radius += 1
blur = cv2.GaussianBlur(img, (radius, radius), 0)
residual = img - blur
mask = np.abs(residual) * 255 > threshold
mask = mask.astype('float32')
soft_mask = cv2.GaussianBlur(mask, (radius, radius), 0)
K = img + weight * residual
K = np.clip(K, 0, 1)
return soft_mask * K + (1 - soft_mask) * img
def add_blur(img, sf=4):
wd2 = 4.0 + sf
wd = 2.0 + 0.2 * sf
wd2 = wd2/4
wd = wd/4
if random.random() < 0.5:
l1 = wd2 * random.random()
l2 = wd2 * random.random()
k = anisotropic_Gaussian(ksize=random.randint(2, 11) + 3, theta=random.random() * np.pi, l1=l1, l2=l2)
else:
k = fspecial('gaussian', random.randint(2, 4) + 3, wd * random.random())
img = ndimage.filters.convolve(img, np.expand_dims(k, axis=2), mode='mirror')
return img
def add_resize(img, sf=4):
rnum = np.random.rand()
if rnum > 0.8: # up
sf1 = random.uniform(1, 2)
elif rnum < 0.7: # down
sf1 = random.uniform(0.5 / sf, 1)
else:
sf1 = 1.0
img = cv2.resize(img, (int(sf1 * img.shape[1]), int(sf1 * img.shape[0])), interpolation=random.choice([1, 2, 3]))
img = np.clip(img, 0.0, 1.0)
return img
# def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
# noise_level = random.randint(noise_level1, noise_level2)
# rnum = np.random.rand()
# if rnum > 0.6: # add color Gaussian noise
# img += np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
# elif rnum < 0.4: # add grayscale Gaussian noise
# img += np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
# else: # add noise
# L = noise_level2 / 255.
# D = np.diag(np.random.rand(3))
# U = orth(np.random.rand(3, 3))
# conv = np.dot(np.dot(np.transpose(U), D), U)
# img += np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
# img = np.clip(img, 0.0, 1.0)
# return img
def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
noise_level = random.randint(noise_level1, noise_level2)
rnum = np.random.rand()
if rnum > 0.6: # add color Gaussian noise
img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
elif rnum < 0.4: # add grayscale Gaussian noise
img = img + np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
else: # add noise
L = noise_level2 / 255.
D = np.diag(np.random.rand(3))
U = orth(np.random.rand(3, 3))
conv = np.dot(np.dot(np.transpose(U), D), U)
img = img + np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
img = np.clip(img, 0.0, 1.0)
return img
def add_speckle_noise(img, noise_level1=2, noise_level2=25):
noise_level = random.randint(noise_level1, noise_level2)
img = np.clip(img, 0.0, 1.0)
rnum = random.random()
if rnum > 0.6:
img += img * np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
elif rnum < 0.4:
img += img * np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
else:
L = noise_level2 / 255.
D = np.diag(np.random.rand(3))
U = orth(np.random.rand(3, 3))
conv = np.dot(np.dot(np.transpose(U), D), U)
img += img * np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
img = np.clip(img, 0.0, 1.0)
return img
def add_Poisson_noise(img):
img = np.clip((img * 255.0).round(), 0, 255) / 255.
vals = 10 ** (2 * random.random() + 2.0) # [2, 4]
if random.random() < 0.5:
img = np.random.poisson(img * vals).astype(np.float32) / vals
else:
img_gray = np.dot(img[..., :3], [0.299, 0.587, 0.114])
img_gray = np.clip((img_gray * 255.0).round(), 0, 255) / 255.
noise_gray = np.random.poisson(img_gray * vals).astype(np.float32) / vals - img_gray
img += noise_gray[:, :, np.newaxis]
img = np.clip(img, 0.0, 1.0)
return img
def add_JPEG_noise(img):
quality_factor = random.randint(80, 95)
img = cv2.cvtColor(util.single2uint(img), cv2.COLOR_RGB2BGR)
result, encimg = cv2.imencode('.jpg', img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor])
img = cv2.imdecode(encimg, 1)
img = cv2.cvtColor(util.uint2single(img), cv2.COLOR_BGR2RGB)
return img
def random_crop(lq, hq, sf=4, lq_patchsize=64):
h, w = lq.shape[:2]
rnd_h = random.randint(0, h - lq_patchsize)
rnd_w = random.randint(0, w - lq_patchsize)
lq = lq[rnd_h:rnd_h + lq_patchsize, rnd_w:rnd_w + lq_patchsize, :]
rnd_h_H, rnd_w_H = int(rnd_h * sf), int(rnd_w * sf)
hq = hq[rnd_h_H:rnd_h_H + lq_patchsize * sf, rnd_w_H:rnd_w_H + lq_patchsize * sf, :]
return lq, hq
def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None):
"""
This is the degradation model of BSRGAN from the paper
"Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
----------
img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf)
sf: scale factor
isp_model: camera ISP model
Returns
-------
img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
"""
isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
sf_ori = sf
h1, w1 = img.shape[:2]
img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop
h, w = img.shape[:2]
if h < lq_patchsize * sf or w < lq_patchsize * sf:
raise ValueError(f'img size ({h1}X{w1}) is too small!')
hq = img.copy()
if sf == 4 and random.random() < scale2_prob: # downsample1
if np.random.rand() < 0.5:
img = cv2.resize(img, (int(1 / 2 * img.shape[1]), int(1 / 2 * img.shape[0])),
interpolation=random.choice([1, 2, 3]))
else:
img = util.imresize_np(img, 1 / 2, True)
img = np.clip(img, 0.0, 1.0)
sf = 2
shuffle_order = random.sample(range(7), 7)
idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
if idx1 > idx2: # keep downsample3 last
shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]
for i in shuffle_order:
if i == 0:
img = add_blur(img, sf=sf)
elif i == 1:
img = add_blur(img, sf=sf)
elif i == 2:
a, b = img.shape[1], img.shape[0]
# downsample2
if random.random() < 0.75:
sf1 = random.uniform(1, 2 * sf)
img = cv2.resize(img, (int(1 / sf1 * img.shape[1]), int(1 / sf1 * img.shape[0])),
interpolation=random.choice([1, 2, 3]))
else:
k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
k_shifted = shift_pixel(k, sf)
k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel
img = ndimage.filters.convolve(img, np.expand_dims(k_shifted, axis=2), mode='mirror')
img = img[0::sf, 0::sf, ...] # nearest downsampling
img = np.clip(img, 0.0, 1.0)
elif i == 3:
# downsample3
img = cv2.resize(img, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
img = np.clip(img, 0.0, 1.0)
elif i == 4:
# add Gaussian noise
img = add_Gaussian_noise(img, noise_level1=2, noise_level2=8)
elif i == 5:
# add JPEG noise
if random.random() < jpeg_prob:
img = add_JPEG_noise(img)
elif i == 6:
# add processed camera sensor noise
if random.random() < isp_prob and isp_model is not None:
with torch.no_grad():
img, hq = isp_model.forward(img.copy(), hq)
# add final JPEG compression noise
img = add_JPEG_noise(img)
# random crop
img, hq = random_crop(img, hq, sf_ori, lq_patchsize)
return img, hq
# todo no isp_model?
def degradation_bsrgan_variant(image, sf=4, isp_model=None):
"""
This is the degradation model of BSRGAN from the paper
"Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
----------
sf: scale factor
isp_model: camera ISP model
Returns
-------
img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
"""
image = util.uint2single(image)
isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
sf_ori = sf
h1, w1 = image.shape[:2]
image = image.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop
h, w = image.shape[:2]
hq = image.copy()
if sf == 4 and random.random() < scale2_prob: # downsample1
if np.random.rand() < 0.5:
image = cv2.resize(image, (int(1 / 2 * image.shape[1]), int(1 / 2 * image.shape[0])),
interpolation=random.choice([1, 2, 3]))
else:
image = util.imresize_np(image, 1 / 2, True)
image = np.clip(image, 0.0, 1.0)
sf = 2
shuffle_order = random.sample(range(7), 7)
idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
if idx1 > idx2: # keep downsample3 last
shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]
for i in shuffle_order:
if i == 0:
image = add_blur(image, sf=sf)
# elif i == 1:
# image = add_blur(image, sf=sf)
if i == 0:
pass
elif i == 2:
a, b = image.shape[1], image.shape[0]
# downsample2
if random.random() < 0.8:
sf1 = random.uniform(1, 2 * sf)
image = cv2.resize(image, (int(1 / sf1 * image.shape[1]), int(1 / sf1 * image.shape[0])),
interpolation=random.choice([1, 2, 3]))
else:
k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
k_shifted = shift_pixel(k, sf)
k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel
image = ndimage.filters.convolve(image, np.expand_dims(k_shifted, axis=2), mode='mirror')
image = image[0::sf, 0::sf, ...] # nearest downsampling
image = np.clip(image, 0.0, 1.0)
elif i == 3:
# downsample3
image = cv2.resize(image, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
image = np.clip(image, 0.0, 1.0)
elif i == 4:
# add Gaussian noise
image = add_Gaussian_noise(image, noise_level1=1, noise_level2=2)
elif i == 5:
# add JPEG noise
if random.random() < jpeg_prob:
image = add_JPEG_noise(image)
#
# elif i == 6:
# # add processed camera sensor noise
# if random.random() < isp_prob and isp_model is not None:
# with torch.no_grad():
# img, hq = isp_model.forward(img.copy(), hq)
# add final JPEG compression noise
image = add_JPEG_noise(image)
image = util.single2uint(image)
example = {"image": image}
return example
if __name__ == '__main__':
print("hey")
img = util.imread_uint('utils/test.png', 3)
img = img[:448, :448]
h = img.shape[0] // 4
print("resizing to", h)
sf = 4
deg_fn = partial(degradation_bsrgan_variant, sf=sf)
for i in range(20):
print(i)
img_hq = img
img_lq = deg_fn(img)["image"]
img_hq, img_lq = util.uint2single(img_hq), util.uint2single(img_lq)
print(img_lq)
img_lq_bicubic = albumentations.SmallestMaxSize(max_size=h, interpolation=cv2.INTER_CUBIC)(image=img_hq)["image"]
print(img_lq.shape)
print("bicubic", img_lq_bicubic.shape)
print(img_hq.shape)
lq_nearest = cv2.resize(util.single2uint(img_lq), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
interpolation=0)
lq_bicubic_nearest = cv2.resize(util.single2uint(img_lq_bicubic),
(int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
interpolation=0)
img_concat = np.concatenate([lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1)
util.imsave(img_concat, str(i) + '.png')
================================================
FILE: models/InstructDiffusion/stable_diffusion/ldm/modules/image_degradation/utils_image.py
================================================
import os
import math
import random
import numpy as np
import torch
import cv2
from torchvision.utils import make_grid
from datetime import datetime
#import matplotlib.pyplot as plt # TODO: check with Dominik, also bsrgan.py vs bsrgan_light.py
os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
'''
# --------------------------------------------
# Kai Zhang (github: https://github.com/cszn)
# 03/Mar/2019
# --------------------------------------------
# https://github.com/twhui/SRGAN-pyTorch
# https://github.com/xinntao/BasicSR
# --------------------------------------------
'''
IMG_EXTENSIONS = ['.jpg', '.JPG', '.jpeg', '.JPEG', '.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP', '.tif']
def is_image_file(filename):
return any(filename.endswith(extension) for extension in IMG_EXTENSIONS)
def get_timestamp():
return datetime.now().strftime('%y%m%d-%H%M%S')
def imshow(x, title=None, cbar=False, figsize=None):
plt.figure(figsize=figsize)
plt.imshow(np.squeeze(x), interpolation='nearest', cmap='gray')
if title:
plt.title(title)
if cbar:
plt.colorbar()
plt.show()
def surf(Z, cmap='rainbow', figsize=None):
plt.figure(figsize=figsize)
ax3 = plt.axes(projection='3d')
w, h = Z.shape[:2]
xx = np.arange(0,w,1)
yy = np.arange(0,h,1)
X, Y = np.meshgrid(xx, yy)
ax3.plot_surface(X,Y,Z,cmap=cmap)
#ax3.contour(X,Y,Z, zdim='z',offset=-2，cmap=cmap)
plt.show()
'''
# --------------------------------------------
# get image pathes
# --------------------------------------------
'''
def get_image_paths(dataroot):
paths = None # return None if dataroot is None
if dataroot is not None:
paths = sorted(_get_paths_from_images(dataroot))
return paths
def _get_paths_from_images(path):
assert os.path.isdir(path), '{:s} is not a valid directory'.format(path)
images = []
for dirpath, _, fnames in sorted(os.walk(path)):
for fname in sorted(fnames):
if is_image_file(fname):
img_path = os.path.join(dirpath, fname)
images.append(img_path)
assert images, '{:s} has no valid image file'.format(path)
return images
'''
# --------------------------------------------
# split large images into small images
# --------------------------------------------
'''
def patches_from_image(img, p_size=512, p_overlap=64, p_max=800):
w, h = img.shape[:2]
patches = []
if w > p_max and h > p_max:
w1 = list(np.arange(0, w-p_size, p_size-p_overlap, dtype=np.int))
h1 = list(np.arange(0, h-p_size, p_size-p_overlap, dtype=np.int))
w1.append(w-p_size)
h1.append(h-p_size)
# print(w1)
# print(h1)
for i in w1:
for j in h1:
patches.append(img[i:i+p_size, j:j+p_size,:])
else:
patches.append(img)
return patches
def imssave(imgs, img_path):
"""
imgs: list, N images of size WxHxC
"""
img_name, ext = os.path.splitext(os.path.basename(img_path))
for i, img in enumerate(imgs):
if img.ndim == 3:
img = img[:, :, [2, 1, 0]]
new_path = os.path.join(os.path.dirname(img_path), img_name+str('_s{:04d}'.format(i))+'.png')
cv2.imwrite(new_path, img)
def split_imageset(original_dataroot, taget_dataroot, n_channels=3, p_size=800, p_overlap=96, p_max=1000):
"""
split the large images from original_dataroot into small overlapped images with size (p_size)x(p_size),
and save them into taget_dataroot; only the images with larger size than (p_max)x(p_max)
will be splitted.
Args:
original_dataroot:
taget_dataroot:
p_size: size of small images
p_overlap: patch size in training is a good choice
p_max: images with smaller size than (p_max)x(p_max) keep unchanged.
"""
paths = get_image_paths(original_dataroot)
for img_path in paths:
# img_name, ext = os.path.splitext(os.path.basename(img_path))
img = imread_uint(img_path, n_channels=n_channels)
patches = patches_from_image(img, p_size, p_overlap, p_max)
imssave(patches, os.path.join(taget_dataroot,os.path.basename(img_path)))
#if original_dataroot == taget_dataroot:
#del img_path
'''
# --------------------------------------------
# makedir
# --------------------------------------------
'''
def mkdir(path):
if not os.path.exists(path):
os.makedirs(path)
def mkdirs(paths):
if isinstance(paths, str):
mkdir(paths)
else:
for path in paths:
mkdir(path)
def mkdir_and_rename(path):
if os.path.exists(path):
new_name = path + '_archived_' + get_timestamp()
print('Path already exists. Rename it to [{:s}]'.format(new_name))
os.rename(path, new_name)
os.makedirs(path)
'''
# --------------------------------------------
# read image from path
# opencv is fast, but read BGR numpy image
# --------------------------------------------
'''
# --------------------------------------------
# get uint8 image of size HxWxn_channles (RGB)
# --------------------------------------------
def imread_uint(path, n_channels=3):
# input: path
# output: HxWx3(RGB or GGG), or HxWx1 (G)
if n_channels == 1:
img = cv2.imread(path, 0) # cv2.IMREAD_GRAYSCALE
img = np.expand_dims(img, axis=2) # HxWx1
elif n_channels == 3:
img = cv2.imread(path, cv2.IMREAD_UNCHANGED) # BGR or G
if img.ndim == 2:
img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB) # GGG
else:
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # RGB
return img
# --------------------------------------------
# matlab's imwrite
# --------------------------------------------
def imsave(img, img_path):
img = np.squeeze(img)
if img.ndim == 3:
img = img[:, :, [2, 1, 0]]
cv2.imwrite(img_path, img)
def imwrite(img, img_path):
img = np.squeeze(img)
if img.ndim == 3:
img = img[:, :, [2, 1, 0]]
cv2.imwrite(img_path, img)
# --------------------------------------------
# get single image of size HxWxn_channles (BGR)
# --------------------------------------------
def read_img(path):
# read image by cv2
# return: Numpy float32, HWC, BGR, [0,1]
img = cv2.imread(path, cv2.IMREAD_UNCHANGED) # cv2.IMREAD_GRAYSCALE
img = img.astype(np.float32) / 255.
if img.ndim == 2:
img = np.expand_dims(img, axis=2)
# some images have 4 channels
if img.shape[2] > 3:
img = img[:, :, :3]
return img
'''
# --------------------------------------------
# image format conversion
# --------------------------------------------
# numpy(single) <---> numpy(unit)
# numpy(single) <---> tensor
# numpy(unit) <---> tensor
# --------------------------------------------
'''
# --------------------------------------------
# numpy(single) [0, 1] <---> numpy(unit)
# --------------------------------------------
def uint2single(img):
return np.float32(img/255.)
def single2uint(img):
return np.uint8((img.clip(0, 1)*255.).round())
def uint162single(img):
return np.float32(img/65535.)
def single2uint16(img):
return np.uint16((img.clip(0, 1)*65535.).round())
# --------------------------------------------
# numpy(unit) (HxWxC or HxW) <---> tensor
# --------------------------------------------
# convert uint to 4-dimensional torch tensor
def uint2tensor4(img):
if img.ndim == 2:
img = np.expand_dims(img, axis=2)
return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.).unsqueeze(0)
# convert uint to 3-dimensional torch tensor
def uint2tensor3(img):
if img.ndim == 2:
img = np.expand_dims(img, axis=2)
return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.)
# convert 2/3/4-dimensional torch tensor to uint
def tensor2uint(img):
img = img.data.squeeze().float().clamp_(0, 1).cpu().numpy()
if img.ndim == 3:
img = np.transpose(img, (1, 2, 0))
return np.uint8((img*255.0).round())
# --------------------------------------------
# numpy(single) (HxWxC) <---> tensor
# --------------------------------------------
# convert single (HxWxC) to 3-dimensional torch tensor
def single2tensor3(img):
return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float()
# convert single (HxWxC) to 4-dimensional torch tensor
def single2tensor4(img):
return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().unsqueeze(0)
# convert torch tensor to single
def tensor2single(img):
img = img.data.squeeze().float().cpu().numpy()
if img.ndim == 3:
img = np.transpose(img, (1, 2, 0))
return img
# convert torch tensor to single
def tensor2single3(img):
img = img.data.squeeze().float().cpu().numpy()
if img.ndim == 3:
img = np.transpose(img, (1, 2, 0))
elif img.ndim == 2:
img = np.expand_dims(img, axis=2)
return img
def single2tensor5(img):
return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1, 3).float().unsqueeze(0)
def single32tensor5(img):
return torch.from_numpy(np.ascontiguousarray(img)).float().unsqueeze(0).unsqueeze(0)
def single42tensor4(img):
return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1, 3).float()
# from skimage.io import imread, imsave
def tensor2img(tensor, out_type=np.uint8, min_max=(0, 1)):
'''
Converts a torch Tensor into an image Numpy array of BGR channel order
Input: 4D(B,(3/1),H,W), 3D(C,H,W), or 2D(H,W), any range, RGB channel order
Output: 3D(H,W,C) or 2D(H,W), [0,255], np.uint8 (default)
'''
tensor = tensor.squeeze().float().cpu().clamp_(*min_max) # squeeze first, then clamp
tensor = (tensor - min_max[0]) / (min_max[1] - min_max[0]) # to range [0,1]
n_dim = tensor.dim()
if n_dim == 4:
n_img = len(tensor)
img_np = make_grid(tensor, nrow=int(math.sqrt(n_img)), normalize=False).numpy()
img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0)) # HWC, BGR
elif n_dim == 3:
img_np = tensor.numpy()
img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0)) # HWC, BGR
elif n_dim == 2:
img_np = tensor.numpy()
else:
raise TypeError(
'Only support 4D, 3D and 2D tensor. But received with dimension: {:d}'.format(n_dim))
if out_type == np.uint8:
img_np = (img_np * 255.0).round()
# Important. Unlike matlab, numpy.unit8() WILL NOT round by default.
return img_np.astype(out_type)
'''
# --------------------------------------------
# Augmentation, flipe and/or rotate
# --------------------------------------------
# The following two are enough.
# (1) augmet_img: numpy image of WxHxC or WxH
# (2) augment_img_tensor4: tensor image 1xCxWxH
# --------------------------------------------
'''
def augment_img(img, mode=0):
'''Kai Zhang (github: https://github.com/cszn)
'''
if mode == 0:
return img
elif mode == 1:
return np.flipud(np.rot90(img))
elif mode == 2:
return np.flipud(img)
elif mode == 3:
return np.rot90(img, k=3)
elif mode == 4:
return np.flipud(np.rot90(img, k=2))
elif mode == 5:
return np.rot90(img)
elif mode == 6:
return np.rot90(img, k=2)
elif mode == 7:
return np.flipud(np.rot90(img, k=3))
def augment_img_tensor4(img, mode=0):
'''Kai Zhang (github: https://github.com/cszn)
'''
if mode == 0:
return img
elif mode == 1:
return img.rot90(1, [2, 3]).flip([2])
elif mode == 2:
return img.flip([2])
elif mode == 3:
return img.rot90(3, [2, 3])
elif mode == 4:
return img.rot90(2, [2, 3]).flip([2])
elif mode == 5:
return img.rot90(1, [2, 3])
elif mode == 6:
return img.rot90(2, [2, 3])
elif mode == 7:
return img.rot90(3, [2, 3]).flip([2])
def augment_img_tensor(img, mode=0):
'''Kai Zhang (github: https://github.com/cszn)
'''
img_size = img.size()
img_np = img.data.cpu().numpy()
if len(img_size) == 3:
img_np = np.transpose(img_np, (1, 2, 0))
elif len(img_size) == 4:
img_np = np.transpose(img_np, (2, 3, 1, 0))
img_np = augment_img(img_np, mode=mode)
img_tensor = torch.from_numpy(np.ascontiguousarray(img_np))
if len(img_size) == 3:
img_tensor = img_tensor.permute(2, 0, 1)
elif len(img_size) == 4:
img_tensor = img_tensor.permute(3, 2, 0, 1)
return img_tensor.type_as(img)
def augment_img_np3(img, mode=0):
if mode == 0:
return img
elif mode == 1:
return img.transpose(1, 0, 2)
elif mode == 2:
return img[::-1, :, :]
elif mode == 3:
img = img[::-1, :, :]
img = img.transpose(1, 0, 2)
return img
elif mode == 4:
return img[:, ::-1, :]
elif mode == 5:
img = img[:, ::-1, :]
img = img.transpose(1, 0, 2)
return img
elif mode == 6:
img = img[:, ::-1, :]
img = img[::-1, :, :]
return img
elif mode == 7:
img = img[:, ::-1, :]
img = img[::-1, :, :]
img = img.transpose(1, 0, 2)
return img
def augment_imgs(img_list, hflip=True, rot=True):
# horizontal flip OR rotate
hflip = hflip and random.random() < 0.5
vflip = rot and random.random() < 0.5
rot90 = rot and random.random() < 0.5
def _augment(img):
if hflip:
img = img[:, ::-1, :]
if vflip:
img = img[::-1, :, :]
if rot90:
img = img.transpose(1, 0, 2)
return img
return [_augment(img) for img in img_list]
'''
# --------------------------------------------
# modcrop and shave
# --------------------------------------------
'''
def modcrop(img_in, scale):
# img_in: Numpy, HWC or HW
img = np.copy(img_in)
if img.ndim == 2:
H, W = img.shape
H_r, W_r = H % scale, W % scale
img = img[:H - H_r, :W - W_r]
elif img.ndim == 3:
H, W, C = img.shape
H_r, W_r = H % scale, W % scale
img = img[:H - H_r, :W - W_r, :]
else:
raise ValueError('Wrong img ndim: [{:d}].'.format(img.ndim))
return img
def shave(img_in, border=0):
# img_in: Numpy, HWC or HW
img = np.copy(img_in)
h, w = img.shape[:2]
img = img[border:h-border, border:w-border]
return img
'''
# --------------------------------------------
# image processing process on numpy image
# channel_convert(in_c, tar_type, img_list):
# rgb2ycbcr(img, only_y=True):
# bgr2ycbcr(img, only_y=True):
# ycbcr2rgb(img):
# --------------------------------------------
'''
def rgb2ycbcr(img, only_y=True):
'''same as matlab rgb2ycbcr
only_y: only return Y channel
Input:
uint8, [0, 255]
float, [0, 1]
'''
in_img_type = img.dtype
img.astype(np.float32)
if in_img_type != np.uint8:
img *= 255.
# convert
if only_y:
rlt = np.dot(img, [65.481, 128.553, 24.966]) / 255.0 + 16.0
else:
rlt = np.matmul(img, [[65.481, -37.797, 112.0], [128.553, -74.203, -93.786],
[24.966, 112.0, -18.214]]) / 255.0 + [16, 128, 128]
if in_img_type == np.uint8:
rlt = rlt.round()
else:
rlt /= 255.
return rlt.astype(in_img_type)
def ycbcr2rgb(img):
'''same as matlab ycbcr2rgb
Input:
uint8, [0, 255]
float, [0, 1]
'''
in_img_type = img.dtype
img.astype(np.float32)
if in_img_type != np.uint8:
img *= 255.
# convert
rlt = np.matmul(img, [[0.00456621, 0.00456621, 0.00456621], [0, -0.00153632, 0.00791071],
[0.00625893, -0.00318811, 0]]) * 255.0 + [-222.921, 135.576, -276.836]
if in_img_type == np.uint8:
rlt = rlt.round()
else:
rlt /= 255.
return rlt.astype(in_img_type)
def bgr2ycbcr(img, only_y=True):
'''bgr version of rgb2ycbcr
only_y: only return Y channel
Input:
uint8, [0, 255]
float, [0, 1]
'''
in_img_type = img.dtype
img.astype(np.float32)
if in_img_type != np.uint8:
img *= 255.
# convert
if only_y:
rlt = np.dot(img, [24.966, 128.553, 65.481]) / 255.0 + 16.0
else:
rlt = np.matmul(img, [[24.966, 112.0, -18.214], [128.553, -74.203, -93.786],
[65.481, -37.797, 112.0]]) / 255.0 + [16, 128, 128]
if in_img_type == np.uint8:
rlt = rlt.round()
else:
rlt /= 255.
return rlt.astype(in_img_type)
def channel_convert(in_c, tar_type, img_list):
# conversion among BGR, gray and y
if in_c == 3 and tar_type == 'gray': # BGR to gray
gray_list = [cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) for img in img_list]
return [np.expand_dims(img, axis=2) for img in gray_list]
elif in_c == 3 and tar_type == 'y': # BGR to y
y_list = [bgr2ycbcr(img, only_y=True) for img in img_list]
return [np.expand_dims(img, axis=2) for img in y_list]
elif in_c == 1 and tar_type == 'RGB': # gray/y to BGR
return [cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) for img in img_list]
else:
return img_list
'''
# --------------------------------------------
# metric, PSNR and SSIM
# --------------------------------------------
'''
# --------------------------------------------
# PSNR
# --------------------------------------------
def calculate_psnr(img1, img2, border=0):
# img1 and img2 have range [0, 255]
#img1 = img1.squeeze()
#img2 = img2.squeeze()
if not img1.shape == img2.shape:
raise ValueError('Input images must have the same dimensions.')
h, w = img1.shape[:2]
img1 = img1[border:h-border, border:w-border]
img2 = img2[border:h-border, border:w-border]
img1 = img1.astype(np.float64)
img2 = img2.astype(np.float64)
mse = np.mean((img1 - img2)**2)
if mse == 0:
return float('inf')
return 20 * math.log10(255.0 / math.sqrt(mse))
# --------------------------------------------
# SSIM
# --------------------------------------------
def calculate_ssim(img1, img2, border=0):
'''calculate SSIM
the same outputs as MATLAB's
img1, img2: [0, 255]
'''
#img1 = img1.squeeze()
#img2 = img2.squeeze()
if not img1.shape == img2.shape:
raise ValueError('Input images must have the same dimensions.')
h, w = img1.shape[:2]
img1 = img1[border:h-border, border:w-border]
img2 = img2[border:h-border, border:w-border]
if img1.ndim == 2:
return ssim(img1, img2)
elif img1.ndim == 3:
if img1.shape[2] == 3:
ssims = []
for i in range(3):
ssims.append(ssim(img1[:,:,i], img2[:,:,i]))
return np.array(ssims).mean()
elif img1.shape[2] == 1:
return ssim(np.squeeze(img1), np.squeeze(img2))
else:
raise ValueError('Wrong input image dimensions.')
def ssim(img1, img2):
C1 = (0.01 * 255)**2
C2 = (0.03 * 255)**2
img1 = img1.astype(np.float64)
img2 = img2.astype(np.float64)
kernel = cv2.getGaussianKernel(11, 1.5)
window = np.outer(kernel, kernel.transpose())
mu1 = cv2.filter2D(img1, -1, window)[5:-5, 5:-5] # valid
mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5]
mu1_sq = mu1**2
mu2_sq = mu2**2
mu1_mu2 = mu1 * mu2
sigma1_sq = cv2.filter2D(img1**2, -1, window)[5:-5, 5:-5] - mu1_sq
sigma2_sq = cv2.filter2D(img2**2, -1, window)[5:-5, 5:-5] - mu2_sq
sigma12 = cv2.filter2D(img1 * img2, -1, window)[5:-5, 5:-5] - mu1_mu2
ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) *
(sigma1_sq + sigma2_sq + C2))
return ssim_map.mean()
'''
# --------------------------------------------
# matlab's bicubic imresize (numpy and torch) [0, 1]
# --------------------------------------------
'''
# matlab 'imresize' function, now only support 'bicubic'
def cubic(x):
absx = torch.abs(x)
absx2 = absx**2
absx3 = absx**3
return (1.5*absx3 - 2.5*absx2 + 1) * ((absx <= 1).type_as(absx)) + \
(-0.5*absx3 + 2.5*absx2 - 4*absx + 2) * (((absx > 1)*(absx <= 2)).type_as(absx))
def calculate_weights_indices(in_length, out_length, scale, kernel, kernel_width, antialiasing):
if (scale < 1) and (antialiasing):
# Use a modified kernel to simultaneously interpolate and antialias- larger kernel width
kernel_width = kernel_width / scale
# Output-space coordinates
x = torch.linspace(1, out_length, out_length)
# Input-space coordinates. Calculate the inverse mapping such that 0.5
# in output space maps to 0.5 in input space, and 0.5+scale in output
# space maps to 1.5 in input space.
u = x / scale + 0.5 * (1 - 1 / scale)
# What is the left-most pixel that can be involved in the computation?
left = torch.floor(u - kernel_width / 2)
# What is the maximum number of pixels that can be involved in the
# computation? Note: it's OK to use an extra pixel here; if the
# corresponding weights are all zero, it will be eliminated at the end
# of this function.
P = math.ceil(kernel_width) + 2
# The indices of the input pixels involved in computing the k-th output
# pixel are in row k of the indices matrix.
indices = left.view(out_length, 1).expand(out_length, P) + torch.linspace(0, P - 1, P).view(
1, P).expand(out_length, P)
# The weights used to compute the k-th output pixel are in row k of the
# weights matrix.
distance_to_center = u.view(out_length, 1).expand(out_length, P) - indices
# apply cubic kernel
if (scale < 1) and (antialiasing):
weights = scale * cubic(distance_to_center * scale)
else:
weights = cubic(distance_to_center)
# Normalize the weights matrix so that each row sums to 1.
weights_sum = torch.sum(weights, 1).view(out_length, 1)
weights = weights / weights_sum.expand(out_length, P)
# If a column in weights is all zero, get rid of it. only consider the first and last column.
weights_zero_tmp = torch.sum((weights == 0), 0)
if not math.isclose(weights_zero_tmp[0], 0, rel_tol=1e-6):
indices = indices.narrow(1, 1, P - 2)
weights = weights.narrow(1, 1, P - 2)
if not math.isclose(weights_zero_tmp[-1], 0, rel_tol=1e-6):
indices = indices.narrow(1, 0, P - 2)
weights = weights.narrow(1, 0, P - 2)
weights = weights.contiguous()
indices = indices.contiguous()
sym_len_s = -indices.min() + 1
sym_len_e = indices.max() - in_length
indices = indices + sym_len_s - 1
return weights, indices, int(sym_len_s), int(sym_len_e)
# --------------------------------------------
# imresize for tensor image [0, 1]
# --------------------------------------------
def imresize(img, scale, antialiasing=True):
# Now the scale should be the same for H and W
# input: img: pytorch tensor, CHW or HW [0,1]
# output: CHW or HW [0,1] w/o round
need_squeeze = True if img.dim() == 2 else False
if need_squeeze:
img.unsqueeze_(0)
in_C, in_H, in_W = img.size()
out_C, out_H, out_W = in_C, math.ceil(in_H * scale), math.ceil(in_W * scale)
kernel_width = 4
kernel = 'cubic'
# Return the desired dimension order for performing the resize. The
# strategy is to perform the resize first along the dimension with the
# smallest scale factor.
# Now we do not support this.
# get weights and indices
weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices(
in_H, out_H, scale, kernel, kernel_width, antialiasing)
weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices(
in_W, out_W, scale, kernel, kernel_width, antialiasing)
# process H dimension
# symmetric copying
img_aug = torch.FloatTensor(in_C, in_H + sym_len_Hs + sym_len_He, in_W)
img_aug.narrow(1, sym_len_Hs, in_H).copy_(img)
sym_patch = img[:, :sym_len_Hs, :]
inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
sym_patch_inv = sym_patch.index_select(1, inv_idx)
img_aug.narrow(1, 0, sym_len_Hs).copy_(sym_patch_inv)
sym_patch = img[:, -sym_len_He:, :]
inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
sym_patch_inv = sym_patch.index_select(1, inv_idx)
img_aug.narrow(1, sym_len_Hs + in_H, sym_len_He).copy_(sym_patch_inv)
out_1 = torch.FloatTensor(in_C, out_H, in_W)
kernel_width = weights_H.size(1)
for i in range(out_H):
idx = int(indices_H[i][0])
for j in range(out_C):
out_1[j, i, :] = img_aug[j, idx:idx + kernel_width, :].transpose(0, 1).mv(weights_H[i])
# process W dimension
# symmetric copying
out_1_aug = torch.FloatTensor(in_C, out_H, in_W + sym_len_Ws + sym_len_We)
out_1_aug.narrow(2, sym_len_Ws, in_W).copy_(out_1)
sym_patch = out_1[:, :, :sym_len_Ws]
inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long()
sym_patch_inv = sym_patch.index_select(2, inv_idx)
out_1_aug.narrow(2, 0, sym_len_Ws).copy_(sym_patch_inv)
sym_patch = out_1[:, :, -sym_len_We:]
inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long()
sym_patch_inv = sym_patch.index_select(2, inv_idx)
out_1_aug.narrow(2, sym_len_Ws + in_W, sym_len_We).copy_(sym_patch_inv)
out_2 = torch.FloatTensor(in_C, out_H, out_W)
kernel_width = weights_W.size(1)
for i in range(out_W):
idx = int(indices_W[i][0])
for j in range(out_C):
out_2[j, :, i] = out_1_aug[j, :, idx:idx + kernel_width].mv(weights_W[i])
if need_squeeze:
out_2.squeeze_()
return out_2
# --------------------------------------------
# imresize for numpy image [0, 1]
# --------------------------------------------
def imresize_np(img, scale, antialiasing=True):
# Now the scale should be the same for H and W
# input: img: Numpy, HWC or HW [0,1]
# output: HWC or HW [0,1] w/o round
img = torch.from_numpy(img)
need_squeeze = True if img.dim() == 2 else False
if need_squeeze:
img.unsqueeze_(2)
in_H, in_W, in_C = img.size()
out_C, out_H, out_W = in_C, math.ceil(in_H * scale), math.ceil(in_W * scale)
kernel_width = 4
kernel = 'cubic'
# Return the desired dimension order for performing the resize. The
# strategy is to perform the resize first along the dimension with the
# smallest scale factor.
# Now we do not support this.
# get weights and indices
weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices(
in_H, out_H, scale, kernel, kernel_width, antialiasing)
weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices(
in_W, out_W, scale, kernel, kernel_width, antialiasing)
# process H dimension
# symmetric copying
img_aug = torch.FloatTensor(in_H + sym_len_Hs + sym_len_He, in_W, in_C)
img_aug.narrow(0, sym_len_Hs, in_H).copy_(img)
sym_patch = img[:sym_len_Hs, :, :]
inv_idx = torch.arange(sym_patch.size(0) - 1, -1, -1).long()
sym_patch_inv = sym_patch.index_select(0, inv_idx)
img_aug.narrow(0, 0, sym_len_Hs).copy_(sym_patch_inv)
sym_patch = img[-sym_len_He:, :, :]
inv_idx = torch.arange(sym_patch.size(0) - 1, -1, -1).long()
sym_patch_inv = sym_patch.index_select(0, inv_idx)
img_aug.narrow(0, sym_len_Hs + in_H, sym_len_He).copy_(sym_patch_inv)
out_1 = torch.FloatTensor(out_H, in_W, in_C)
kernel_width = weights_H.size(1)
for i in range(out_H):
idx = int(indices_H[i][0])
for j in range(out_C):
out_1[i, :, j] = img_aug[idx:idx + kernel_width, :, j].transpose(0, 1).mv(weights_H[i])
# process W dimension
# symmetric copying
out_1_aug = torch.FloatTensor(out_H, in_W + sym_len_Ws + sym_len_We, in_C)
out_1_aug.narrow(1, sym_len_Ws, in_W).copy_(out_1)
sym_patch = out_1[:, :sym_len_Ws, :]
inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
sym_patch_inv = sym_patch.index_select(1, inv_idx)
out_1_aug.narrow(1, 0, sym_len_Ws).copy_(sym_patch_inv)
sym_patch = out_1[:, -sym_len_We:, :]
inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
sym_patch_inv = sym_patch.index_select(1, inv_idx)
out_1_aug.narrow(1, sym_len_Ws + in_W, sym_len_We).copy_(sym_patch_inv)
out_2 = torch.FloatTensor(out_H, out_W, in_C)
kernel_width = weights_W.size(1)
for i in range(out_W):
idx = int(indices_W[i][0])
for j in range(out_C):
out_2[:, i, j] = out_1_aug[:, idx:idx + kernel_width, j].mv(weights_W[i])
if need_squeeze:
out_2.squeeze_()
return out_2.numpy()
if __name__ == '__main__':
print('---')
# img = imread_uint('test.bmp', 3)
# img = uint2single(img)
# img_bicubic = imresize_np(img, 1/4)
================================================
FILE: models/InstructDiffusion/stable_diffusion/ldm/modules/losses/__init__.py
================================================
from ldm.modules.losses.contperceptual import LPIPSWithDiscriminator
================================================
FILE: models/InstructDiffusion/stable_diffusion/ldm/modules/losses/contperceptual.py
================================================
import torch
import torch.nn as nn
from taming.modules.losses.vqperceptual import * # TODO: taming dependency yes/no?
class LPIPSWithDiscriminator(nn.Module):
def __init__(self, disc_start, logvar_init=0.0, kl_weight=1.0, pixelloss_weight=1.0,
disc_num_layers=3, disc_in_channels=3, disc_factor=1.0, disc_weight=1.0,
perceptual_weight=1.0, use_actnorm=False, disc_conditional=False,
disc_loss="hinge"):
super().__init__()
assert disc_loss in ["hinge", "vanilla"]
self.kl_weight = kl_weight
self.pixel_weight = pixelloss_weight
self.perceptual_loss = LPIPS().eval()
self.perceptual_weight = perceptual_weight
# output log variance
self.logvar = nn.Parameter(torch.ones(size=()) * logvar_init)
self.discriminator = NLayerDiscriminator(input_nc=disc_in_channels,
n_layers=disc_num_layers,
use_actnorm=use_actnorm
).apply(weights_init)
self.discriminator_iter_start = disc_start
self.disc_loss = hinge_d_loss if disc_loss == "hinge" else vanilla_d_loss
self.disc_factor = disc_factor
self.discriminator_weight = disc_weight
self.disc_conditional = disc_conditional
def calculate_adaptive_weight(self, nll_loss, g_loss, last_layer=None):
if last_layer is not None:
nll_grads = torch.autograd.grad(nll_loss, last_layer, retain_graph=True)[0]
g_grads = torch.autograd.grad(g_loss, last_layer, retain_graph=True)[0]
else:
nll_grads = torch.autograd.grad(nll_loss, self.last_layer[0], retain_graph=True)[0]
g_grads = torch.autograd.grad(g_loss, self.last_layer[0], retain_graph=True)[0]
d_weight = torch.norm(nll_grads) / (torch.norm(g_grads) + 1e-4)
d_weight = torch.clamp(d_weight, 0.0, 1e4).detach()
d_weight = d_weight * self.discriminator_weight
return d_weight
def forward(self, inputs, reconstructions, posteriors, optimizer_idx,
global_step, last_layer=None, cond=None, split="train",
weights=None):
rec_loss = torch.abs(inputs.contiguous() - reconstructions.contiguous())
if self.perceptual_weight > 0:
p_loss = self.perceptual_loss(inputs.contiguous(), reconstructions.contiguous())
rec_loss = rec_loss + self.perceptual_weight * p_loss
nll_loss = rec_loss / torch.exp(self.logvar) + self.logvar
weighted_nll_loss = nll_loss
if weights is not None:
weighted_nll_loss = weights*nll_loss
weighted_nll_loss = torch.sum(weighted_nll_loss) / weighted_nll_loss.shape[0]
nll_loss = torch.sum(nll_loss) / nll_loss.shape[0]
kl_loss = posteriors.kl()
kl_loss = torch.sum(kl_loss) / kl_loss.shape[0]
# now the GAN part
if optimizer_idx == 0:
# generator update
if cond is None:
assert not self.disc_conditional
logits_fake = self.discriminator(reconstructions.contiguous())
else:
assert self.disc_conditional
logits_fake = self.discriminator(torch.cat((reconstructions.contiguous(), cond), dim=1))
g_loss = -torch.mean(logits_fake)
if self.disc_factor > 0.0:
try:
d_weight = self.calculate_adaptive_weight(nll_loss, g_loss, last_layer=last_layer)
except RuntimeError:
assert not self.training
d_weight = torch.tensor(0.0)
else:
d_weight = torch.tensor(0.0)
disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start)
loss = weighted_nll_loss + self.kl_weight * kl_loss + d_weight * disc_factor * g_loss
log = {"{}/total_loss".format(split): loss.clone().detach().mean(), "{}/logvar".format(split): self.logvar.detach(),
"{}/kl_loss".format(split): kl_loss.detach().mean(), "{}/nll_loss".format(split): nll_loss.detach().mean(),
"{}/rec_loss".format(split): rec_loss.detach().mean(),
"{}/d_weight".format(split): d_weight.detach(),
"{}/disc_factor".format(split): torch.tensor(disc_factor),
"{}/g_loss".format(split): g_loss.detach().mean(),
}
return loss, log
if optimizer_idx == 1:
# second pass for discriminator update
if cond is None:
logits_real = self.discriminator(inputs.contiguous().detach())
logits_fake = self.discriminator(reconstructions.contiguous().detach())
else:
logits_real = self.discriminator(torch.cat((inputs.contiguous().detach(), cond), dim=1))
logits_fake = self.discriminator(torch.cat((reconstructions.contiguous().detach(), cond), dim=1))
disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start)
d_loss = disc_factor * self.disc_loss(logits_real, logits_fake)
log = {"{}/disc_loss".format(split): d_loss.clone().detach().mean(),
"{}/logits_real".format(split): logits_real.detach().mean(),
"{}/logits_fake".format(split): logits_fake.detach().mean()
}
return d_loss, log
================================================
FILE: models/InstructDiffusion/stable_diffusion/ldm/modules/losses/vqperceptual.py
================================================
import torch
from torch import nn
import torch.nn.functional as F
from einops import repeat
from taming.modules.discriminator.model import NLayerDiscriminator, weights_init
from taming.modules.losses.lpips import LPIPS
from taming.modules.losses.vqperceptual import hinge_d_loss, vanilla_d_loss
def hinge_d_loss_with_exemplar_weights(logits_real, logits_fake, weights):
assert weights.shape[0] == logits_real.shape[0] == logits_fake.shape[0]
loss_real = torch.mean(F.relu(1. - logits_real), dim=[1,2,3])
loss_fake = torch.mean(F.relu(1. + logits_fake), dim=[1,2,3])
loss_real = (weights * loss_real).sum() / weights.sum()
loss_fake = (weights * loss_fake).sum() / weights.sum()
d_loss = 0.5 * (loss_real + loss_fake)
return d_loss
def adopt_weight(weight, global_step, threshold=0, value=0.):
if global_step < threshold:
weight = value
return weight
def measure_perplexity(predicted_indices, n_embed):
# src: https://github.com/karpathy/deep-vector-quantization/blob/main/model.py
# eval cluster perplexity. when perplexity == num_embeddings then all clusters are used exactly equally
encodings = F.one_hot(predicted_indices, n_embed).float().reshape(-1, n_embed)
avg_probs = encodings.mean(0)
perplexity = (-(avg_probs * torch.log(avg_probs + 1e-10)).sum()).exp()
cluster_use = torch.sum(avg_probs > 0)
return perplexity, cluster_use
def l1(x, y):
return torch.abs(x-y)
def l2(x, y):
return torch.pow((x-y), 2)
class VQLPIPSWithDiscriminator(nn.Module):
def __init__(self, disc_start, codebook_weight=1.0, pixelloss_weight=1.0,
disc_num_layers=3, disc_in_channels=3, disc_factor=1.0, disc_weight=1.0,
perceptual_weight=1.0, use_actnorm=False, disc_conditional=False,
disc_ndf=64, disc_loss="hinge", n_classes=None, perceptual_loss="lpips",
pixel_loss="l1"):
super().__init__()
assert disc_loss in ["hinge", "vanilla"]
assert perceptual_loss in ["lpips", "clips", "dists"]
assert pixel_loss in ["l1", "l2"]
self.codebook_weight = codebook_weight
self.pixel_weight = pixelloss_weight
if perceptual_loss == "lpips":
print(f"{self.__class__.__name__}: Running with LPIPS.")
self.perceptual_loss = LPIPS().eval()
else:
raise ValueError(f"Unknown perceptual loss: >> {perceptual_loss} <<")
self.perceptual_weight = perceptual_weight
if pixel_loss == "l1":
self.pixel_loss = l1
else:
self.pixel_loss = l2
self.discriminator = NLayerDiscriminator(input_nc=disc_in_channels,
n_layers=disc_num_layers,
use_actnorm=use_actnorm,
ndf=disc_ndf
).apply(weights_init)
self.discriminator_iter_start = disc_start
if disc_loss == "hinge":
self.disc_loss = hinge_d_loss
elif disc_loss == "vanilla":
self.disc_loss = vanilla_d_loss
else:
raise ValueError(f"Unknown GAN loss '{disc_loss}'.")
print(f"VQLPIPSWithDiscriminator running with {disc_loss} loss.")
self.disc_factor = disc_factor
self.discriminator_weight = disc_weight
self.disc_conditional = disc_conditional
self.n_classes = n_classes
def calculate_adaptive_weight(self, nll_loss, g_loss, last_layer=None):
if last_layer is not None:
nll_grads = torch.autograd.grad(nll_loss, last_layer, retain_graph=True)[0]
g_grads = torch.autograd.grad(g_loss, last_layer, retain_graph=True)[0]
else:
nll_grads = torch.autograd.grad(nll_loss, self.last_layer[0], retain_graph=True)[0]
g_grads = torch.autograd.grad(g_loss, self.last_layer[0], retain_graph=True)[0]
d_weight = torch.norm(nll_grads) / (torch.norm(g_grads) + 1e-4)
d_weight = torch.clamp(d_weight, 0.0, 1e4).detach()
d_weight = d_weight * self.discriminator_weight
return d_weight
def forward(self, codebook_loss, inputs, reconstructions, optimizer_idx,
global_step, last_layer=None, cond=None, split="train", predicted_indices=None):
if not exists(codebook_loss):
codebook_loss = torch.tensor([0.]).to(inputs.device)
#rec_loss = torch.abs(inputs.contiguous() - reconstructions.contiguous())
rec_loss = self.pixel_loss(inputs.contiguous(), reconstructions.contiguous())
if self.perceptual_weight > 0:
p_loss = self.perceptual_loss(inputs.contiguous(), reconstructions.contiguous())
rec_loss = rec_loss + self.perceptual_weight * p_loss
else:
p_loss = torch.tensor([0.0])
nll_loss = rec_loss
#nll_loss = torch.sum(nll_loss) / nll_loss.shape[0]
nll_loss = torch.mean(nll_loss)
# now the GAN part
if optimizer_idx == 0:
# generator update
if cond is None:
assert not self.disc_conditional
logits_fake = self.discriminator(reconstructions.contiguous())
else:
assert self.disc_conditional
logits_fake = self.discriminator(torch.cat((reconstructions.contiguous(), cond), dim=1))
g_loss = -torch.mean(logits_fake)
try:
d_weight = self.calculate_adaptive_weight(nll_loss, g_loss, last_layer=last_layer)
except RuntimeError:
assert not self.training
d_weight = torch.tensor(0.0)
disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start)
loss = nll_loss + d_weight * disc_factor * g_loss + self.codebook_weight * codebook_loss.mean()
log = {"{}/total_loss".format(split): loss.clone().detach().mean(),
"{}/quant_loss".format(split): codebook_loss.detach().mean(),
"{}/nll_loss".format(split): nll_loss.detach().mean(),
"{}/rec_loss".format(split): rec_loss.detach().mean(),
"{}/p_loss".format(split): p_loss.detach().mean(),
"{}/d_weight".format(split): d_weight.detach(),
"{}/disc_factor".format(split): torch.tensor(disc_factor),
"{}/g_loss".format(split): g_loss.detach().mean(),
}
if predicted_indices is not None:
assert self.n_classes is not None
with torch.no_grad():
perplexity, cluster_usage = measure_perplexity(predicted_indices, self.n_classes)
log[f"{split}/perplexity"] = perplexity
log[f"{split}/cluster_usage"] = cluster_usage
return loss, log
if optimizer_idx == 1:
# second pass for discriminator update
if cond is None:
logits_real = self.discriminator(inputs.contiguous().detach())
logits_fake = self.discriminator(reconstructions.contiguous().detach())
else:
logits_real = self.discriminator(torch.cat((inputs.contiguous().detach(), cond), dim=1))
logits_fake = self.discriminator(torch.cat((reconstructions.contiguous().detach(), cond), dim=1))
disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start)
d_loss = disc_factor * self.disc_loss(logits_real, logits_fake)
log = {"{}/disc_loss".format(split): d_loss.clone().detach().mean(),
"{}/logits_real".format(split): logits_real.detach().mean(),
"{}/logits_fake".format(split): logits_fake.detach().mean()
}
return d_loss, log
================================================
FILE: models/InstructDiffusion/stable_diffusion/ldm/modules/x_transformer.py
================================================
"""shout-out to https://github.com/lucidrains/x-transformers/tree/main/x_transformers"""
import torch
from torch import nn, einsum
import torch.nn.functional as F
from functools import partial
from inspect import isfunction
from collections import namedtuple
from einops import rearrange, repeat, reduce
# constants
DEFAULT_DIM_HEAD = 64
Intermediates = namedtuple('Intermediates', [
'pre_softmax_attn',
'post_softmax_attn'
])
LayerIntermediates = namedtuple('Intermediates', [
'hiddens',
'attn_intermediates'
])
class AbsolutePositionalEmbedding(nn.Module):
def __init__(self, dim, max_seq_len):
super().__init__()
self.emb = nn.Embedding(max_seq_len, dim)
self.init_()
def init_(self):
nn.init.normal_(self.emb.weight, std=0.02)
def forward(self, x):
n = torch.arange(x.shape[1], device=x.device)
return self.emb(n)[None, :, :]
class FixedPositionalEmbedding(nn.Module):
def __init__(self, dim):
super().__init__()
inv_freq = 1. / (10000 ** (torch.arange(0, dim, 2).float() / dim))
self.register_buffer('inv_freq', inv_freq)
def forward(self, x, seq_dim=1, offset=0):
t = torch.arange(x.shape[seq_dim], device=x.device).type_as(self.inv_freq) + offset
sinusoid_inp = torch.einsum('i , j -> i j', t, self.inv_freq)
emb = torch.cat((sinusoid_inp.sin(), sinusoid_inp.cos()), dim=-1)
return emb[None, :, :]
# helpers
def exists(val):
return val is not None
def default(val, d):
if exists(val):
return val
return d() if isfunction(d) else d
def always(val):
def inner(*args, **kwargs):
return val
return inner
def not_equals(val):
def inner(x):
return x != val
return inner
def equals(val):
def inner(x):
return x == val
return inner
def max_neg_value(tensor):
return -torch.finfo(tensor.dtype).max
# keyword argument helpers
def pick_and_pop(keys, d):
values = list(map(lambda key: d.pop(key), keys))
return dict(zip(keys, values))
def group_dict_by_key(cond, d):
return_val = [dict(), dict()]
for key in d.keys():
match = bool(cond(key))
ind = int(not match)
return_val[ind][key] = d[key]
return (*return_val,)
def string_begins_with(prefix, str):
return str.startswith(prefix)
def group_by_key_prefix(prefix, d):
return group_dict_by_key(partial(string_begins_with, prefix), d)
def groupby_prefix_and_trim(prefix, d):
kwargs_with_prefix, kwargs = group_dict_by_key(partial(string_begins_with, prefix), d)
kwargs_without_prefix = dict(map(lambda x: (x[0][len(prefix):], x[1]), tuple(kwargs_with_prefix.items())))
return kwargs_without_prefix, kwargs
# classes
class Scale(nn.Module):
def __init__(self, value, fn):
super().__init__()
self.value = value
self.fn = fn
def forward(self, x, **kwargs):
x, *rest = self.fn(x, **kwargs)
return (x * self.value, *rest)
class Rezero(nn.Module):
def __init__(self, fn):
super().__init__()
self.fn = fn
self.g = nn.Parameter(torch.zeros(1))
def forward(self, x, **kwargs):
x, *rest = self.fn(x, **kwargs)
return (x * self.g, *rest)
class ScaleNorm(nn.Module):
def __init__(self, dim, eps=1e-5):
super().__init__()
self.scale = dim ** -0.5
self.eps = eps
self.g = nn.Parameter(torch.ones(1))
def forward(self, x):
norm = torch.norm(x, dim=-1, keepdim=True) * self.scale
return x / norm.clamp(min=self.eps) * self.g
class RMSNorm(nn.Module):
def __init__(self, dim, eps=1e-8):
super().__init__()
self.scale = dim ** -0.5
self.eps = eps
self.g = nn.Parameter(torch.ones(dim))
def forward(self, x):
norm = torch.norm(x, dim=-1, keepdim=True) * self.scale
return x / norm.clamp(min=self.eps) * self.g
class Residual(nn.Module):
def forward(self, x, residual):
return x + residual
class GRUGating(nn.Module):
def __init__(self, dim):
super().__init__()
self.gru = nn.GRUCell(dim, dim)
def forward(self, x, residual):
gated_output = self.gru(
rearrange(x, 'b n d -> (b n) d'),
rearrange(residual, 'b n d -> (b n) d')
)
return gated_output.reshape_as(x)
# feedforward
class GEGLU(nn.Module):
def __init__(self, dim_in, dim_out):
super().__init__()
self.proj = nn.Linear(dim_in, dim_out * 2)
def forward(self, x):
x, gate = self.proj(x).chunk(2, dim=-1)
return x * F.gelu(gate)
class FeedForward(nn.Module):
def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
super().__init__()
inner_dim = int(dim * mult)
dim_out = default(dim_out, dim)
project_in = nn.Sequential(
nn.Linear(dim, inner_dim),
nn.GELU()
) if not glu else GEGLU(dim, inner_dim)
self.net = nn.Sequential(
project_in,
nn.Dropout(dropout),
nn.Linear(inner_dim, dim_out)
)
def forward(self, x):
return self.net(x)
# attention.
class Attention(nn.Module):
def __init__(
self,
dim,
dim_head=DEFAULT_DIM_HEAD,
heads=8,
causal=False,
mask=None,
talking_heads=False,
sparse_topk=None,
use_entmax15=False,
num_mem_kv=0,
dropout=0.,
on_attn=False
):
super().__init__()
if use_entmax15:
raise NotImplementedError("Check out entmax activation instead of softmax activation!")
self.scale = dim_head ** -0.5
self.heads = heads
self.causal = causal
self.mask = mask
inner_dim = dim_head * heads
self.to_q = nn.Linear(dim, inner_dim, bias=False)
self.to_k = nn.Linear(dim, inner_dim, bias=False)
self.to_v = nn.Linear(dim, inner_dim, bias=False)
self.dropout = nn.Dropout(dropout)
# talking heads
self.talking_heads = talking_heads
if talking_heads:
self.pre_softmax_proj = nn.Parameter(torch.randn(heads, heads))
self.post_softmax_proj = nn.Parameter(torch.randn(heads, heads))
# explicit topk sparse attention
self.sparse_topk = sparse_topk
# entmax
#self.attn_fn = entmax15 if use_entmax15 else F.softmax
self.attn_fn = F.softmax
# add memory key / values
self.num_mem_kv = num_mem_kv
if num_mem_kv > 0:
self.mem_k = nn.Parameter(torch.randn(heads, num_mem_kv, dim_head))
self.mem_v = nn.Parameter(torch.randn(heads, num_mem_kv, dim_head))
# attention on attention
self.attn_on_attn = on_attn
self.to_out = nn.Sequential(nn.Linear(inner_dim, dim * 2), nn.GLU()) if on_attn else nn.Linear(inner_dim, dim)
def forward(
self,
x,
context=None,
mask=None,
context_mask=None,
rel_pos=None,
sinusoidal_emb=None,
prev_attn=None,
mem=None
):
b, n, _, h, talking_heads, device = *x.shape, self.heads, self.talking_heads, x.device
kv_input = default(context, x)
q_input = x
k_input = kv_input
v_input = kv_input
if exists(mem):
k_input = torch.cat((mem, k_input), dim=-2)
v_input = torch.cat((mem, v_input), dim=-2)
if exists(sinusoidal_emb):
# in shortformer, the query would start at a position offset depending on the past cached memory
offset = k_input.shape[-2] - q_input.shape[-2]
q_input = q_input + sinusoidal_emb(q_input, offset=offset)
k_input = k_input + sinusoidal_emb(k_input)
q = self.to_q(q_input)
k = self.to_k(k_input)
v = self.to_v(v_input)
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h=h), (q, k, v))
input_mask = None
if any(map(exists, (mask, context_mask))):
q_mask = default(mask, lambda: torch.ones((b, n), device=device).bool())
k_mask = q_mask if not exists(context) else context_mask
k_mask = default(k_mask, lambda: torch.ones((b, k.shape[-2]), device=device).bool())
q_mask = rearrange(q_mask, 'b i -> b () i ()')
k_mask = rearrange(k_mask, 'b j -> b () () j')
input_mask = q_mask * k_mask
if self.num_mem_kv > 0:
mem_k, mem_v = map(lambda t: repeat(t, 'h n d -> b h n d', b=b), (self.mem_k, self.mem_v))
k = torch.cat((mem_k, k), dim=-2)
v = torch.cat((mem_v, v), dim=-2)
if exists(input_mask):
input_mask = F.pad(input_mask, (self.num_mem_kv, 0), value=True)
dots = einsum('b h i d, b h j d -> b h i j', q, k) * self.scale
mask_value = max_neg_value(dots)
if exists(prev_attn):
dots = dots + prev_attn
pre_softmax_attn = dots
if talking_heads:
dots = einsum('b h i j, h k -> b k i j', dots, self.pre_softmax_proj).contiguous()
if exists(rel_pos):
dots = rel_pos(dots)
if exists(input_mask):
dots.masked_fill_(~input_mask, mask_value)
del input_mask
if self.causal:
i, j = dots.shape[-2:]
r = torch.arange(i, device=device)
mask = rearrange(r, 'i -> () () i ()') < rearrange(r, 'j -> () () () j')
mask = F.pad(mask, (j - i, 0), value=False)
dots.masked_fill_(mask, mask_value)
del mask
if exists(self.sparse_topk) and self.sparse_topk < dots.shape[-1]:
top, _ = dots.topk(self.sparse_topk, dim=-1)
vk = top[..., -1].unsqueeze(-1).expand_as(dots)
mask = dots < vk
dots.masked_fill_(mask, mask_value)
del mask
attn = self.attn_fn(dots, dim=-1)
post_softmax_attn = attn
attn = self.dropout(attn)
if talking_heads:
attn = einsum('b h i j, h k -> b k i j', attn, self.post_softmax_proj).contiguous()
out = einsum('b h i j, b h j d -> b h i d', attn, v)
out = rearrange(out, 'b h n d -> b n (h d)')
intermediates = Intermediates(
pre_softmax_attn=pre_softmax_attn,
post_softmax_attn=post_softmax_attn
)
return self.to_out(out), intermediates
class AttentionLayers(nn.Module):
def __init__(
self,
dim,
depth,
heads=8,
causal=False,
cross_attend=False,
only_cross=False,
use_scalenorm=False,
use_rmsnorm=False,
use_rezero=False,
rel_pos_num_buckets=32,
rel_pos_max_distance=128,
position_infused_attn=False,
custom_layers=None,
sandwich_coef=None,
par_ratio=None,
residual_attn=False,
cross_residual_attn=False,
macaron=False,
pre_norm=True,
gate_residual=False,
**kwargs
):
super().__init__()
ff_kwargs, kwargs = groupby_prefix_and_trim('ff_', kwargs)
attn_kwargs, _ = groupby_prefix_and_trim('attn_', kwargs)
dim_head = attn_kwargs.get('dim_head', DEFAULT_DIM_HEAD)
self.dim = dim
self.depth = depth
self.layers = nn.ModuleList([])
self.has_pos_emb = position_infused_attn
self.pia_pos_emb = FixedPositionalEmbedding(dim) if position_infused_attn else None
self.rotary_pos_emb = always(None)
assert rel_pos_num_buckets <= rel_pos_max_distance, 'number of relative position buckets must be less than the relative position max distance'
self.rel_pos = None
self.pre_norm = pre_norm
self.residual_attn = residual_attn
self.cross_residual_attn = cross_residual_attn
norm_class = ScaleNorm if use_scalenorm else nn.LayerNorm
norm_class = RMSNorm if use_rmsnorm else norm_class
norm_fn = partial(norm_class, dim)
norm_fn = nn.Identity if use_rezero else norm_fn
branch_fn = Rezero if use_rezero else None
if cross_attend and not only_cross:
default_block = ('a', 'c', 'f')
elif cross_attend and only_cross:
default_block = ('c', 'f')
else:
default_block = ('a', 'f')
if macaron:
default_block = ('f',) + default_block
if exists(custom_layers):
layer_types = custom_layers
elif exists(par_ratio):
par_depth = depth * len(default_block)
assert 1 < par_ratio <= par_depth, 'par ratio out of range'
default_block = tuple(filter(not_equals('f'), default_block))
par_attn = par_depth // par_ratio
depth_cut = par_depth * 2 // 3 # 2 / 3 attention layer cutoff suggested by PAR paper
par_width = (depth_cut + depth_cut // par_attn) // par_attn
assert len(default_block) <= par_width, 'default block is too large for par_ratio'
par_block = default_block + ('f',) * (par_width - len(default_block))
par_head = par_block * par_attn
layer_types = par_head + ('f',) * (par_depth - len(par_head))
elif exists(sandwich_coef):
assert sandwich_coef > 0 and sandwich_coef <= depth, 'sandwich coefficient should be less than the depth'
layer_types = ('a',) * sandwich_coef + default_block * (depth - sandwich_coef) + ('f',) * sandwich_coef
else:
layer_types = default_block * depth
self.layer_types = layer_types
self.num_attn_layers = len(list(filter(equals('a'), layer_types)))
for layer_type in self.layer_types:
if layer_type == 'a':
layer = Attention(dim, heads=heads, causal=causal, **attn_kwargs)
elif layer_type == 'c':
layer = Attention(dim, heads=heads, **attn_kwargs)
elif layer_type == 'f':
layer = FeedForward(dim, **ff_kwargs)
layer = layer if not macaron else Scale(0.5, layer)
else:
raise Exception(f'invalid layer type {layer_type}')
if isinstance(layer, Attention) and exists(branch_fn):
layer = branch_fn(layer)
if gate_residual:
residual_fn = GRUGating(dim)
else:
residual_fn = Residual()
self.layers.append(nn.ModuleList([
norm_fn(),
layer,
residual_fn
]))
def forward(
self,
x,
context=None,
mask=None,
context_mask=None,
mems=None,
return_hiddens=False
):
hiddens = []
intermediates = []
prev_attn = None
prev_cross_attn = None
mems = mems.copy() if exists(mems) else [None] * self.num_attn_layers
for ind, (layer_type, (norm, block, residual_fn)) in enumerate(zip(self.layer_types, self.layers)):
is_last = ind == (len(self.layers) - 1)
if layer_type == 'a':
hiddens.append(x)
layer_mem = mems.pop(0)
residual = x
if self.pre_norm:
x = norm(x)
if layer_type == 'a':
out, inter = block(x, mask=mask, sinusoidal_emb=self.pia_pos_emb, rel_pos=self.rel_pos,
prev_attn=prev_attn, mem=layer_mem)
elif layer_type == 'c':
out, inter = block(x, context=context, mask=mask, context_mask=context_mask, prev_attn=prev_cross_attn)
elif layer_type == 'f':
out = block(x)
x = residual_fn(out, residual)
if layer_type in ('a', 'c'):
intermediates.append(inter)
if layer_type == 'a' and self.residual_attn:
prev_attn = inter.pre_softmax_attn
elif layer_type == 'c' and self.cross_residual_attn:
prev_cross_attn = inter.pre_softmax_attn
if not self.pre_norm and not is_last:
x = norm(x)
if return_hiddens:
intermediates = LayerIntermediates(
hiddens=hiddens,
attn_intermediates=intermediates
)
return x, intermediates
return x
class Encoder(AttentionLayers):
def __init__(self, **kwargs):
assert 'causal' not in kwargs, 'cannot set causality on encoder'
super().__init__(causal=False, **kwargs)
class TransformerWrapper(nn.Module):
def __init__(
self,
*,
num_tokens,
max_seq_len,
attn_layers,
emb_dim=None,
max_mem_len=0.,
emb_dropout=0.,
num_memory_tokens=None,
tie_embedding=False,
use_pos_emb=True
):
super().__init__()
assert isinstance(attn_layers, AttentionLayers), 'attention layers must be one of Encoder or Decoder'
dim = attn_layers.dim
emb_dim = default(emb_dim, dim)
self.max_seq_len = max_seq_len
self.max_mem_len = max_mem_len
self.num_tokens = num_tokens
self.token_emb = nn.Embedding(num_tokens, emb_dim)
self.pos_emb = AbsolutePositionalEmbedding(emb_dim, max_seq_len) if (
use_pos_emb and not attn_layers.has_pos_emb) else always(0)
self.emb_dropout = nn.Dropout(emb_dropout)
self.project_emb = nn.Linear(emb_dim, dim) if emb_dim != dim else nn.Identity()
self.attn_layers = attn_layers
self.norm = nn.LayerNorm(dim)
self.init_()
self.to_logits = nn.Linear(dim, num_tokens) if not tie_embedding else lambda t: t @ self.token_emb.weight.t()
# memory tokens (like [cls]) from Memory Transformers paper
num_memory_tokens = default(num_memory_tokens, 0)
self.num_memory_tokens = num_memory_tokens
if num_memory_tokens > 0:
self.memory_tokens = nn.Parameter(torch.randn(num_memory_tokens, dim))
# let funnel encoder know number of memory tokens, if specified
if hasattr(attn_layers, 'num_memory_tokens'):
attn_layers.num_memory_tokens = num_memory_tokens
def init_(self):
nn.init.normal_(self.token_emb.weight, std=0.02)
def forward(
self,
x,
return_embeddings=False,
mask=None,
return_mems=False,
return_attn=False,
mems=None,
**kwargs
):
b, n, device, num_mem = *x.shape, x.device, self.num_memory_tokens
x = self.token_emb(x)
x += self.pos_emb(x)
x = self.emb_dropout(x)
x = self.project_emb(x)
if num_mem > 0:
mem = repeat(self.memory_tokens, 'n d -> b n d', b=b)
x = torch.cat((mem, x), dim=1)
# auto-handle masking after appending memory tokens
if exists(mask):
mask = F.pad(mask, (num_mem, 0), value=True)
x, intermediates = self.attn_layers(x, mask=mask, mems=mems, return_hiddens=True, **kwargs)
x = self.norm(x)
mem, x = x[:, :num_mem], x[:, num_mem:]
out = self.to_logits(x) if not return_embeddings else x
if return_mems:
hiddens = intermediates.hiddens
new_mems = list(map(lambda pair: torch.cat(pair, dim=-2), zip(mems, hiddens))) if exists(mems) else hiddens
new_mems = list(map(lambda t: t[..., -self.max_mem_len:, :].detach(), new_mems))
return out, new_mems
if return_attn:
attn_maps = list(map(lambda t: t.post_softmax_attn, intermediates.attn_intermediates))
return out, attn_maps
return out
================================================
FILE: models/InstructDiffusion/stable_diffusion/ldm/util.py
================================================
import importlib
import torch
import numpy as np
from collections import abc
from einops import rearrange
from functools import partial
import multiprocessing as mp
from threading import Thread
from queue import Queue
from inspect import isfunction
from PIL import Image, ImageDraw, ImageFont
def log_txt_as_img(wh, xc, size=10):
# wh a tuple of (width, height)
# xc a list of captions to plot
b = len(xc)
txts = list()
for bi in range(b):
txt = Image.new("RGB", wh, color="white")
draw = ImageDraw.Draw(txt)
font = ImageFont.truetype('data/DejaVuSans.ttf', size=size)
nc = int(40 * (wh[0] / 256))
lines = "\n".join(xc[bi][start:start + nc] for start in range(0, len(xc[bi]), nc))
try:
draw.text((0, 0), lines, fill="black", font=font)
except UnicodeEncodeError:
print("Cant encode string for logging. Skipping.")
txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0
txts.append(txt)
txts = np.stack(txts)
txts = torch.tensor(txts)
return txts
def ismap(x):
if not isinstance(x, torch.Tensor):
return False
return (len(x.shape) == 4) and (x.shape[1] > 3)
def isimage(x):
if not isinstance(x, torch.Tensor):
return False
return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1)
def exists(x):
return x is not None
def default(val, d):
if exists(val):
return val
return d() if isfunction(d) else d
def mean_flat(tensor):
"""
https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/nn.py#L86
Take the mean over all non-batch dimensions.
"""
return tensor.mean(dim=list(range(1, len(tensor.shape))))
def count_params(model, verbose=False):
total_params = sum(p.numel() for p in model.parameters())
if verbose:
print(f"{model.__class__.__name__} has {total_params * 1.e-6:.2f} M params.")
return total_params
def instantiate_from_config(config):
if not "target" in config:
if config == '__is_first_stage__':
return None
elif config == "__is_unconditional__":
return None
raise KeyError("Expected key `target` to instantiate.")
return get_obj_from_str(config["target"])(**config.get("params", dict()))
def get_obj_from_str(string, reload=False):
module, cls = string.rsplit(".", 1)
if reload:
module_imp = importlib.import_module(module)
importlib.reload(module_imp)
return getattr(importlib.import_module(module, package=None), cls)
def _do_parallel_data_prefetch(func, Q, data, idx, idx_to_fn=False):
# create dummy dataset instance
# run prefetching
if idx_to_fn:
res = func(data, worker_id=idx)
else:
res = func(data)
Q.put([idx, res])
Q.put("Done")
def parallel_data_prefetch(
func: callable, data, n_proc, target_data_type="ndarray", cpu_intensive=True, use_worker_id=False
):
# if target_data_type not in ["ndarray", "list"]:
# raise ValueError(
# "Data, which is passed to parallel_data_prefetch has to be either of type list or ndarray."
# )
if isinstance(data, np.ndarray) and target_data_type == "list":
raise ValueError("list expected but function got ndarray.")
elif isinstance(data, abc.Iterable):
if isinstance(data, dict):
print(
f'WARNING:"data" argument passed to parallel_data_prefetch is a dict: Using only its values and disregarding keys.'
)
data = list(data.values())
if target_data_type == "ndarray":
data = np.asarray(data)
else:
data = list(data)
else:
raise TypeError(
f"The data, that shall be processed parallel has to be either an np.ndarray or an Iterable, but is actually {type(data)}."
)
if cpu_intensive:
Q = mp.Queue(1000)
proc = mp.Process
else:
Q = Queue(1000)
proc = Thread
# spawn processes
if target_data_type == "ndarray":
arguments = [
[func, Q, part, i, use_worker_id]
for i, part in enumerate(np.array_split(data, n_proc))
]
else:
step = (
int(len(data) / n_proc + 1)
if len(data) % n_proc != 0
else int(len(data) / n_proc)
)
arguments = [
[func, Q, part, i, use_worker_id]
for i, part in enumerate(
[data[i: i + step] for i in range(0, len(data), step)]
)
]
processes = []
for i in range(n_proc):
p = proc(target=_do_parallel_data_prefetch, args=arguments[i])
processes += [p]
# start processes
print(f"Start prefetching...")
import time
start = time.time()
gather_res = [[] for _ in range(n_proc)]
try:
for p in processes:
p.start()
k = 0
while k < n_proc:
# get result
res = Q.get()
if res == "Done":
k += 1
else:
gather_res[res[0]] = res[1]
except Exception as e:
print("Exception: ", e)
for p in processes:
p.terminate()
raise e
finally:
for p in processes:
p.join()
print(f"Prefetching complete. [{time.time() - start} sec.]")
if target_data_type == 'ndarray':
if not isinstance(gather_res[0], np.ndarray):
return np.concatenate([np.asarray(r) for r in gather_res], axis=0)
# order outputs
return np.concatenate(gather_res, axis=0)
elif target_data_type == 'list':
out = []
for r in gather_res:
out.extend(r)
return out
else:
return gather_res
================================================
FILE: models/InstructDiffusion/stable_diffusion/main.py
================================================
import argparse, os, sys, datetime, glob, importlib, csv
import numpy as np
import time
import torch
import torchvision
import pytorch_lightning as pl
from packaging import version
from omegaconf import OmegaConf
from torch.utils.data import random_split, DataLoader, Dataset, Subset
from functools import partial
from PIL import Image
from pytorch_lightning import seed_everything
from pytorch_lightning.trainer import Trainer
from pytorch_lightning.callbacks import ModelCheckpoint, Callback, LearningRateMonitor
from pytorch_lightning.utilities.distributed import rank_zero_only
from pytorch_lightning.utilities import rank_zero_info
from ldm.data.base import Txt2ImgIterableBaseDataset
from ldm.util import instantiate_from_config
def get_parser(**parser_kwargs):
def str2bool(v):
if isinstance(v, bool):
return v
if v.lower() in ("yes", "true", "t", "y", "1"):
return True
elif v.lower() in ("no", "false", "f", "n", "0"):
return False
else:
raise argparse.ArgumentTypeError("Boolean value expected.")
parser = argparse.ArgumentParser(**parser_kwargs)
parser.add_argument(
"-n",
"--name",
type=str,
const=True,
default="",
nargs="?",
help="postfix for logdir",
)
parser.add_argument(
"-r",
"--resume",
type=str,
const=True,
default="",
nargs="?",
help="resume from logdir or checkpoint in logdir",
)
parser.add_argument(
"-b",
"--base",
nargs="*",
metavar="base_config.yaml",
help="paths to base configs. Loaded from left-to-right. "
"Parameters can be overwritten or added with command-line options of the form `--key value`.",
default=list(),
)
parser.add_argument(
"-t",
"--train",
type=str2bool,
const=True,
default=False,
nargs="?",
help="train",
)
parser.add_argument(
"--no-test",
type=str2bool,
const=True,
default=False,
nargs="?",
help="disable test",
)
parser.add_argument(
"-p",
"--project",
help="name of new or path to existing project"
)
parser.add_argument(
"-d",
"--debug",
type=str2bool,
nargs="?",
const=True,
default=False,
help="enable post-mortem debugging",
)
parser.add_argument(
"-s",
"--seed",
type=int,
default=23,
help="seed for seed_everything",
)
parser.add_argument(
"-f",
"--postfix",
type=str,
default="",
help="post-postfix for default name",
)
parser.add_argument(
"-l",
"--logdir",
type=str,
default="logs",
help="directory for logging dat shit",
)
parser.add_argument(
"--scale_lr",
type=str2bool,
nargs="?",
const=True,
default=True,
help="scale base-lr by ngpu * batch_size * n_accumulate",
)
return parser
def nondefault_trainer_args(opt):
parser = argparse.ArgumentParser()
parser = Trainer.add_argparse_args(parser)
args = parser.parse_args([])
return sorted(k for k in vars(args) if getattr(opt, k) != getattr(args, k))
class WrappedDataset(Dataset):
"""Wraps an arbitrary object with __len__ and __getitem__ into a pytorch dataset"""
def __init__(self, dataset):
self.data = dataset
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
return self.data[idx]
def worker_init_fn(_):
worker_info = torch.utils.data.get_worker_info()
dataset = worker_info.dataset
worker_id = worker_info.id
if isinstance(dataset, Txt2ImgIterableBaseDataset):
split_size = dataset.num_records // worker_info.num_workers
# reset num_records to the true number to retain reliable length information
dataset.sample_ids = dataset.valid_ids[worker_id * split_size:(worker_id + 1) * split_size]
current_id = np.random.choice(len(np.random.get_state()[1]), 1)
return np.random.seed(np.random.get_state()[1][current_id] + worker_id)
else:
return np.random.seed(np.random.get_state()[1][0] + worker_id)
class DataModuleFromConfig(pl.LightningDataModule):
def __init__(self, batch_size, train=None, validation=None, test=None, predict=None,
wrap=False, num_workers=None, shuffle_test_loader=False, use_worker_init_fn=False,
shuffle_val_dataloader=False):
super().__init__()
self.batch_size = batch_size
self.dataset_configs = dict()
self.num_workers = num_workers if num_workers is not None else batch_size * 2
self.use_worker_init_fn = use_worker_init_fn
if train is not None:
self.dataset_configs["train"] = train
self.train_dataloader = self._train_dataloader
if validation is not None:
self.dataset_configs["validation"] = validation
self.val_dataloader = partial(self._val_dataloader, shuffle=shuffle_val_dataloader)
if test is not None:
self.dataset_configs["test"] = test
self.test_dataloader = partial(self._test_dataloader, shuffle=shuffle_test_loader)
if predict is not None:
self.dataset_configs["predict"] = predict
self.predict_dataloader = self._predict_dataloader
self.wrap = wrap
def prepare_data(self):
for data_cfg in self.dataset_configs.values():
instantiate_from_config(data_cfg)
def setup(self, stage=None):
self.datasets = dict(
(k, instantiate_from_config(self.dataset_configs[k]))
for k in self.dataset_configs)
if self.wrap:
for k in self.datasets:
self.datasets[k] = WrappedDataset(self.datasets[k])
def _train_dataloader(self):
is_iterable_dataset = isinstance(self.datasets['train'], Txt2ImgIterableBaseDataset)
if is_iterable_dataset or self.use_worker_init_fn:
init_fn = worker_init_fn
else:
init_fn = None
return DataLoader(self.datasets["train"], batch_size=self.batch_size,
num_workers=self.num_workers, shuffle=False if is_iterable_dataset else True,
worker_init_fn=init_fn)
def _val_dataloader(self, shuffle=False):
if isinstance(self.datasets['validation'], Txt2ImgIterableBaseDataset) or self.use_worker_init_fn:
init_fn = worker_init_fn
else:
init_fn = None
return DataLoader(self.datasets["validation"],
batch_size=self.batch_size,
num_workers=self.num_workers,
worker_init_fn=init_fn,
shuffle=shuffle)
def _test_dataloader(self, shuffle=False):
is_iterable_dataset = isinstance(self.datasets['train'], Txt2ImgIterableBaseDataset)
if is_iterable_dataset or self.use_worker_init_fn:
init_fn = worker_init_fn
else:
init_fn = None
# do not shuffle dataloader for iterable dataset
shuffle = shuffle and (not is_iterable_dataset)
return DataLoader(self.datasets["test"], batch_size=self.batch_size,
num_workers=self.num_workers, worker_init_fn=init_fn, shuffle=shuffle)
def _predict_dataloader(self, shuffle=False):
if isinstance(self.datasets['predict'], Txt2ImgIterableBaseDataset) or self.use_worker_init_fn:
init_fn = worker_init_fn
else:
init_fn = None
return DataLoader(self.datasets["predict"], batch_size=self.batch_size,
num_workers=self.num_workers, worker_init_fn=init_fn)
class SetupCallback(Callback):
def __init__(self, resume, now, logdir, ckptdir, cfgdir, config, lightning_config):
super().__init__()
self.resume = resume
self.now = now
self.logdir = logdir
self.ckptdir = ckptdir
self.cfgdir = cfgdir
self.config = config
self.lightning_config = lightning_config
def on_keyboard_interrupt(self, trainer, pl_module):
if trainer.global_rank == 0:
print("Summoning checkpoint.")
ckpt_path = os.path.join(self.ckptdir, "last.ckpt")
trainer.save_checkpoint(ckpt_path)
def on_pretrain_routine_start(self, trainer, pl_module):
if trainer.global_rank == 0:
# Create logdirs and save configs
os.makedirs(self.logdir, exist_ok=True)
os.makedirs(self.ckptdir, exist_ok=True)
os.makedirs(self.cfgdir, exist_ok=True)
if "callbacks" in self.lightning_config:
if 'metrics_over_trainsteps_checkpoint' in self.lightning_config['callbacks']:
os.makedirs(os.path.join(self.ckptdir, 'trainstep_checkpoints'), exist_ok=True)
print("Project config")
print(OmegaConf.to_yaml(self.config))
OmegaConf.save(self.config,
os.path.join(self.cfgdir, "{}-project.yaml".format(self.now)))
print("Lightning config")
print(OmegaConf.to_yaml(self.lightning_config))
OmegaConf.save(OmegaConf.create({"lightning": self.lightning_config}),
os.path.join(self.cfgdir, "{}-lightning.yaml".format(self.now)))
else:
# ModelCheckpoint callback created log directory --- remove it
if not self.resume and os.path.exists(self.logdir):
dst, name = os.path.split(self.logdir)
dst = os.path.join(dst, "child_runs", name)
os.makedirs(os.path.split(dst)[0], exist_ok=True)
try:
os.rename(self.logdir, dst)
except FileNotFoundError:
pass
class ImageLogger(Callback):
def __init__(self, batch_frequency, max_images, clamp=True, increase_log_steps=True,
rescale=True, disabled=False, log_on_batch_idx=False, log_first_step=False,
log_images_kwargs=None):
super().__init__()
self.rescale = rescale
self.batch_freq = batch_frequency
self.max_images = max_images
self.logger_log_images = {
pl.loggers.TestTubeLogger: self._testtube,
}
self.log_steps = [2 ** n for n in range(int(np.log2(self.batch_freq)) + 1)]
if not increase_log_steps:
self.log_steps = [self.batch_freq]
self.clamp = clamp
self.disabled = disabled
self.log_on_batch_idx = log_on_batch_idx
self.log_images_kwargs = log_images_kwargs if log_images_kwargs else {}
self.log_first_step = log_first_step
@rank_zero_only
def _testtube(self, pl_module, images, batch_idx, split):
for k in images:
grid = torchvision.utils.make_grid(images[k])
grid = (grid + 1.0) / 2.0 # -1,1 -> 0,1; c,h,w
tag = f"{split}/{k}"
pl_module.logger.experiment.add_image(
tag, grid,
global_step=pl_module.global_step)
@rank_zero_only
def log_local(self, save_dir, split, images,
global_step, current_epoch, batch_idx):
root = os.path.join(save_dir, "images", split)
for k in images:
grid = torchvision.utils.make_grid(images[k], nrow=4)
if self.rescale:
grid = (grid + 1.0) / 2.0 # -1,1 -> 0,1; c,h,w
grid = grid.transpose(0, 1).transpose(1, 2).squeeze(-1)
grid = grid.numpy()
grid = (grid * 255).astype(np.uint8)
filename = "{}_gs-{:06}_e-{:06}_b-{:06}.png".format(
k,
global_step,
current_epoch,
batch_idx)
path = os.path.join(root, filename)
os.makedirs(os.path.split(path)[0], exist_ok=True)
Image.fromarray(grid).save(path)
def log_img(self, pl_module, batch, batch_idx, split="train"):
check_idx = batch_idx if self.log_on_batch_idx else pl_module.global_step
if (self.check_frequency(check_idx) and # batch_idx % self.batch_freq == 0
hasattr(pl_module, "log_images") and
callable(pl_module.log_images) and
self.max_images > 0):
logger = type(pl_module.logger)
is_train = pl_module.training
if is_train:
pl_module.eval()
with torch.no_grad():
images = pl_module.log_images(batch, split=split, **self.log_images_kwargs)
for k in images:
N = min(images[k].shape[0], self.max_images)
images[k] = images[k][:N]
if isinstance(images[k], torch.Tensor):
images[k] = images[k].detach().cpu()
if self.clamp:
images[k] = torch.clamp(images[k], -1., 1.)
self.log_local(pl_module.logger.save_dir, split, images,
pl_module.global_step, pl_module.current_epoch, batch_idx)
logger_log_images = self.logger_log_images.get(logger, lambda *args, **kwargs: None)
logger_log_images(pl_module, images, pl_module.global_step, split)
if is_train:
pl_module.train()
def check_frequency(self, check_idx):
if ((check_idx % self.batch_freq) == 0 or (check_idx in self.log_steps)) and (
check_idx > 0 or self.log_first_step):
try:
self.log_steps.pop(0)
except IndexError as e:
print(e)
pass
return True
return False
def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
if not self.disabled and (pl_module.global_step > 0 or self.log_first_step):
self.log_img(pl_module, batch, batch_idx, split="train")
def on_validation_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
if not self.disabled and pl_module.global_step > 0:
self.log_img(pl_module, batch, batch_idx, split="val")
if hasattr(pl_module, 'calibrate_grad_norm'):
if (pl_module.calibrate_grad_norm and batch_idx % 25 == 0) and batch_idx > 0:
self.log_gradients(trainer, pl_module, batch_idx=batch_idx)
class CUDACallback(Callback):
# see https://github.com/SeanNaren/minGPT/blob/master/mingpt/callback.py
def on_train_epoch_start(self, trainer, pl_module):
# Reset the memory use counter
torch.cuda.reset_peak_memory_stats(trainer.root_gpu)
torch.cuda.synchronize(trainer.root_gpu)
self.start_time = time.time()
def on_train_epoch_end(self, trainer, pl_module, outputs):
torch.cuda.synchronize(trainer.root_gpu)
max_memory = torch.cuda.max_memory_allocated(trainer.root_gpu) / 2 ** 20
epoch_time = time.time() - self.start_time
try:
max_memory = trainer.training_type_plugin.reduce(max_memory)
epoch_time = trainer.training_type_plugin.reduce(epoch_time)
rank_zero_info(f"Average Epoch time: {epoch_time:.2f} seconds")
rank_zero_info(f"Average Peak memory {max_memory:.2f}MiB")
except AttributeError:
pass
if __name__ == "__main__":
# custom parser to specify config files, train, test and debug mode,
# postfix, resume.
# `--key value` arguments are interpreted as arguments to the trainer.
# `nested.key=value` arguments are interpreted as config parameters.
# configs are merged from left-to-right followed by command line parameters.
# model:
# base_learning_rate: float
# target: path to lightning module
# params:
# key: value
# data:
# target: main.DataModuleFromConfig
# params:
# batch_size: int
# wrap: bool
# train:
# target: path to train dataset
# params:
# key: value
# validation:
# target: path to validation dataset
# params:
# key: value
# test:
# target: path to test dataset
# params:
# key: value
# lightning: (optional, has sane defaults and can be specified on cmdline)
# trainer:
# additional arguments to trainer
# logger:
# logger to instantiate
# modelcheckpoint:
# modelcheckpoint to instantiate
# callbacks:
# callback1:
# target: importpath
# params:
# key: value
now = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S")
# add cwd for convenience and to make classes in this file available when
# running as `python main.py`
# (in particular `main.DataModuleFromConfig`)
sys.path.append(os.getcwd())
parser = get_parser()
parser = Trainer.add_argparse_args(parser)
opt, unknown = parser.parse_known_args()
if opt.name and opt.resume:
raise ValueError(
"-n/--name and -r/--resume cannot be specified both."
"If you want to resume training in a new log folder, "
"use -n/--name in combination with --resume_from_checkpoint"
)
if opt.resume:
if not os.path.exists(opt.resume):
raise ValueError("Cannot find {}".format(opt.resume))
if os.path.isfile(opt.resume):
paths = opt.resume.split("/")
# idx = len(paths)-paths[::-1].index("logs")+1
# logdir = "/".join(paths[:idx])
logdir = "/".join(paths[:-2])
ckpt = opt.resume
else:
assert os.path.isdir(opt.resume), opt.resume
logdir = opt.resume.rstrip("/")
ckpt = os.path.join(logdir, "checkpoints", "last.ckpt")
opt.resume_from_checkpoint = ckpt
base_configs = sorted(glob.glob(os.path.join(logdir, "configs/*.yaml")))
opt.base = base_configs + opt.base
_tmp = logdir.split("/")
nowname = _tmp[-1]
else:
if opt.name:
name = "_" + opt.name
elif opt.base:
cfg_fname = os.path.split(opt.base[0])[-1]
cfg_name = os.path.splitext(cfg_fname)[0]
name = "_" + cfg_name
else:
name = ""
nowname = now + name + opt.postfix
logdir = os.path.join(opt.logdir, nowname)
ckptdir = os.path.join(logdir, "checkpoints")
cfgdir = os.path.join(logdir, "configs")
seed_everything(opt.seed)
try:
# init and save configs
configs = [OmegaConf.load(cfg) for cfg in opt.base]
cli = OmegaConf.from_dotlist(unknown)
config = OmegaConf.merge(*configs, cli)
lightning_config = config.pop("lightning", OmegaConf.create())
# merge trainer cli with config
trainer_config = lightning_config.get("trainer", OmegaConf.create())
# default to ddp
trainer_config["accelerator"] = "ddp"
for k in nondefault_trainer_args(opt):
trainer_config[k] = getattr(opt, k)
if not "gpus" in trainer_config:
del trainer_config["accelerator"]
cpu = True
else:
gpuinfo = trainer_config["gpus"]
print(f"Running on GPUs {gpuinfo}")
cpu = False
trainer_opt = argparse.Namespace(**trainer_config)
lightning_config.trainer = trainer_config
# model
model = instantiate_from_config(config.model)
# trainer and callbacks
trainer_kwargs = dict()
# default logger configs
default_logger_cfgs = {
"wandb": {
"target": "pytorch_lightning.loggers.WandbLogger",
"params": {
"name": nowname,
"save_dir": logdir,
"offline": opt.debug,
"id": nowname,
}
},
"testtube": {
"target": "pytorch_lightning.loggers.TestTubeLogger",
"params": {
"name": "testtube",
"save_dir": logdir,
}
},
}
default_logger_cfg = default_logger_cfgs["testtube"]
if "logger" in lightning_config:
logger_cfg = lightning_config.logger
else:
logger_cfg = OmegaConf.create()
logger_cfg = OmegaConf.merge(default_logger_cfg, logger_cfg)
trainer_kwargs["logger"] = instantiate_from_config(logger_cfg)
# modelcheckpoint - use TrainResult/EvalResult(checkpoint_on=metric) to
# specify which metric is used to determine best models
default_modelckpt_cfg = {
"target": "pytorch_lightning.callbacks.ModelCheckpoint",
"params": {
"dirpath": ckptdir,
"filename": "{epoch:06}",
"verbose": True,
"save_last": True,
}
}
if hasattr(model, "monitor"):
print(f"Monitoring {model.monitor} as checkpoint metric.")
default_modelckpt_cfg["params"]["monitor"] = model.monitor
default_modelckpt_cfg["params"]["save_top_k"] = 3
if "modelcheckpoint" in lightning_config:
modelckpt_cfg = lightning_config.modelcheckpoint
else:
modelckpt_cfg = OmegaConf.create()
modelckpt_cfg = OmegaConf.merge(default_modelckpt_cfg, modelckpt_cfg)
print(f"Merged modelckpt-cfg: \n{modelckpt_cfg}")
if version.parse(pl.__version__) < version.parse('1.4.0'):
trainer_kwargs["checkpoint_callback"] = instantiate_from_config(modelckpt_cfg)
# add callback which sets up log directory
default_callbacks_cfg = {
"setup_callback": {
"target": "main.SetupCallback",
"params": {
"resume": opt.resume,
"now": now,
"logdir": logdir,
"ckptdir": ckptdir,
"cfgdir": cfgdir,
"config": config,
"lightning_config": lightning_config,
}
},
"image_logger": {
"target": "main.ImageLogger",
"params": {
"batch_frequency": 750,
"max_images": 4,
"clamp": True
}
},
"learning_rate_logger": {
"target": "main.LearningRateMonitor",
"params": {
"logging_interval": "step",
# "log_momentum": True
}
},
"cuda_callback": {
"target": "main.CUDACallback"
},
}
if version.parse(pl.__version__) >= version.parse('1.4.0'):
default_callbacks_cfg.update({'checkpoint_callback': modelckpt_cfg})
if "callbacks" in lightning_config:
callbacks_cfg = lightning_config.callbacks
else:
callbacks_cfg = OmegaConf.create()
if 'metrics_over_trainsteps_checkpoint' in callbacks_cfg:
print(
'Caution: Saving checkpoints every n train steps without deleting. This might require some free space.')
default_metrics_over_trainsteps_ckpt_dict = {
'metrics_over_trainsteps_checkpoint':
{"target": 'pytorch_lightning.callbacks.ModelCheckpoint',
'params': {
"dirpath": os.path.join(ckptdir, 'trainstep_checkpoints'),
"filename": "{epoch:06}-{step:09}",
"verbose": True,
'save_top_k': -1,
'every_n_train_steps': 10000,
'save_weights_only': True
}
}
}
default_callbacks_cfg.update(default_metrics_over_trainsteps_ckpt_dict)
callbacks_cfg = OmegaConf.merge(default_callbacks_cfg, callbacks_cfg)
if 'ignore_keys_callback' in callbacks_cfg and hasattr(trainer_opt, 'resume_from_checkpoint'):
callbacks_cfg.ignore_keys_callback.params['ckpt_path'] = trainer_opt.resume_from_checkpoint
elif 'ignore_keys_callback' in callbacks_cfg:
del callbacks_cfg['ignore_keys_callback']
trainer_kwargs["callbacks"] = [instantiate_from_config(callbacks_cfg[k]) for k in callbacks_cfg]
trainer = Trainer.from_argparse_args(trainer_opt, **trainer_kwargs)
trainer.logdir = logdir ###
# data
data = instantiate_from_config(config.data)
# NOTE according to https://pytorch-lightning.readthedocs.io/en/latest/datamodules.html
# calling these ourselves should not be necessary but it is.
# lightning still takes care of proper multiprocessing though
data.prepare_data()
data.setup()
print("#### Data #####")
for k in data.datasets:
print(f"{k}, {data.datasets[k].__class__.__name__}, {len(data.datasets[k])}")
# configure learning rate
bs, base_lr = config.data.params.batch_size, config.model.base_learning_rate
if not cpu:
ngpu = len(lightning_config.trainer.gpus.strip(",").split(','))
else:
ngpu = 1
if 'accumulate_grad_batches' in lightning_config.trainer:
accumulate_grad_batches = lightning_config.trainer.accumulate_grad_batches
else:
accumulate_grad_batches = 1
print(f"accumulate_grad_batches = {accumulate_grad_batches}")
lightning_config.trainer.accumulate_grad_batches = accumulate_grad_batches
if opt.scale_lr:
model.learning_rate = accumulate_grad_batches * ngpu * bs * base_lr
print(
"Setting learning rate to {:.2e} = {} (accumulate_grad_batches) * {} (num_gpus) * {} (batchsize) * {:.2e} (base_lr)".format(
model.learning_rate, accumulate_grad_batches, ngpu, bs, base_lr))
else:
model.learning_rate = base_lr
print("++++ NOT USING LR SCALING ++++")
print(f"Setting learning rate to {model.learning_rate:.2e}")
# allow checkpointing via USR1
def melk(*args, **kwargs):
# run all checkpoint hooks
if trainer.global_rank == 0:
print("Summoning checkpoint.")
ckpt_path = os.path.join(ckptdir, "last.ckpt")
trainer.save_checkpoint(ckpt_path)
def divein(*args, **kwargs):
if trainer.global_rank == 0:
import pudb;
pudb.set_trace()
import signal
signal.signal(signal.SIGUSR1, melk)
signal.signal(signal.SIGUSR2, divein)
# run
if opt.train:
try:
trainer.fit(model, data)
except Exception:
melk()
raise
if not opt.no_test and not trainer.interrupted:
trainer.test(model, data)
except Exception:
if opt.debug and trainer.global_rank == 0:
try:
import pudb as debugger
except ImportError:
import pdb as debugger
debugger.post_mortem()
raise
finally:
# move newly created debug project to debug_runs
if opt.debug and not opt.resume and trainer.global_rank == 0:
dst, name = os.path.split(logdir)
dst = os.path.join(dst, "debug_runs", name)
os.makedirs(os.path.split(dst)[0], exist_ok=True)
os.rename(logdir, dst)
try:
if trainer.global_rank == 0:
print(trainer.profiler.summary())
except:
pass
================================================
FILE: models/InstructDiffusion/stable_diffusion/models/first_stage_models/kl-f16/config.yaml
================================================
model:
base_learning_rate: 4.5e-06
target: ldm.models.autoencoder.AutoencoderKL
params:
monitor: val/rec_loss
embed_dim: 16
lossconfig:
target: ldm.modules.losses.LPIPSWithDiscriminator
params:
disc_start: 50001
kl_weight: 1.0e-06
disc_weight: 0.5
ddconfig:
double_z: true
z_channels: 16
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 1
- 2
- 2
- 4
num_res_blocks: 2
attn_resolutions:
- 16
dropout: 0.0
data:
target: main.DataModuleFromConfig
params:
batch_size: 6
wrap: true
train:
target: ldm.data.openimages.FullOpenImagesTrain
params:
size: 384
crop_size: 256
validation:
target: ldm.data.openimages.FullOpenImagesValidation
params:
size: 384
crop_size: 256
================================================
FILE: models/InstructDiffusion/stable_diffusion/models/first_stage_models/kl-f32/config.yaml
================================================
model:
base_learning_rate: 4.5e-06
target: ldm.models.autoencoder.AutoencoderKL
params:
monitor: val/rec_loss
embed_dim: 64
lossconfig:
target: ldm.modules.losses.LPIPSWithDiscriminator
params:
disc_start: 50001
kl_weight: 1.0e-06
disc_weight: 0.5
ddconfig:
double_z: true
z_channels: 64
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 1
- 2
- 2
- 4
- 4
num_res_blocks: 2
attn_resolutions:
- 16
- 8
dropout: 0.0
data:
target: main.DataModuleFromConfig
params:
batch_size: 6
wrap: true
train:
target: ldm.data.openimages.FullOpenImagesTrain
params:
size: 384
crop_size: 256
validation:
target: ldm.data.openimages.FullOpenImagesValidation
params:
size: 384
crop_size: 256
================================================
FILE: models/InstructDiffusion/stable_diffusion/models/first_stage_models/kl-f4/config.yaml
================================================
model:
base_learning_rate: 4.5e-06
target: ldm.models.autoencoder.AutoencoderKL
params:
monitor: val/rec_loss
embed_dim: 3
lossconfig:
target: ldm.modules.losses.LPIPSWithDiscriminator
params:
disc_start: 50001
kl_weight: 1.0e-06
disc_weight: 0.5
ddconfig:
double_z: true
z_channels: 3
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 4
num_res_blocks: 2
attn_resolutions: []
dropout: 0.0
data:
target: main.DataModuleFromConfig
params:
batch_size: 10
wrap: true
train:
target: ldm.data.openimages.FullOpenImagesTrain
params:
size: 384
crop_size: 256
validation:
target: ldm.data.openimages.FullOpenImagesValidation
params:
size: 384
crop_size: 256
================================================
FILE: models/InstructDiffusion/stable_diffusion/models/first_stage_models/kl-f8/config.yaml
================================================
model:
base_learning_rate: 4.5e-06
target: ldm.models.autoencoder.AutoencoderKL
params:
monitor: val/rec_loss
embed_dim: 4
lossconfig:
target: ldm.modules.losses.LPIPSWithDiscriminator
params:
disc_start: 50001
kl_weight: 1.0e-06
disc_weight: 0.5
ddconfig:
double_z: true
z_channels: 4
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 4
- 4
num_res_blocks: 2
attn_resolutions: []
dropout: 0.0
data:
target: main.DataModuleFromConfig
params:
batch_size: 4
wrap: true
train:
target: ldm.data.openimages.FullOpenImagesTrain
params:
size: 384
crop_size: 256
validation:
target: ldm.data.openimages.FullOpenImagesValidation
params:
size: 384
crop_size: 256
================================================
FILE: models/InstructDiffusion/stable_diffusion/models/first_stage_models/vq-f16/config.yaml
================================================
model:
base_learning_rate: 4.5e-06
target: ldm.models.autoencoder.VQModel
params:
embed_dim: 8
n_embed: 16384
ddconfig:
double_z: false
z_channels: 8
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 1
- 2
- 2
- 4
num_res_blocks: 2
attn_resolutions:
- 16
dropout: 0.0
lossconfig:
target: taming.modules.losses.vqperceptual.VQLPIPSWithDiscriminator
params:
disc_conditional: false
disc_in_channels: 3
disc_start: 250001
disc_weight: 0.75
disc_num_layers: 2
codebook_weight: 1.0
data:
target: main.DataModuleFromConfig
params:
batch_size: 14
num_workers: 20
wrap: true
train:
target: ldm.data.openimages.FullOpenImagesTrain
params:
size: 384
crop_size: 256
validation:
target: ldm.data.openimages.FullOpenImagesValidation
params:
size: 384
crop_size: 256
================================================
FILE: models/InstructDiffusion/stable_diffusion/models/first_stage_models/vq-f4/config.yaml
================================================
model:
base_learning_rate: 4.5e-06
target: ldm.models.autoencoder.VQModel
params:
embed_dim: 3
n_embed: 8192
monitor: val/rec_loss
ddconfig:
double_z: false
z_channels: 3
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 4
num_res_blocks: 2
attn_resolutions: []
dropout: 0.0
lossconfig:
target: taming.modules.losses.vqperceptual.VQLPIPSWithDiscriminator
params:
disc_conditional: false
disc_in_channels: 3
disc_start: 0
disc_weight: 0.75
codebook_weight: 1.0
data:
target: main.DataModuleFromConfig
params:
batch_size: 8
num_workers: 16
wrap: true
train:
target: ldm.data.openimages.FullOpenImagesTrain
params:
crop_size: 256
validation:
target: ldm.data.openimages.FullOpenImagesValidation
params:
crop_size: 256
================================================
FILE: models/InstructDiffusion/stable_diffusion/models/first_stage_models/vq-f4-noattn/config.yaml
================================================
model:
base_learning_rate: 4.5e-06
target: ldm.models.autoencoder.VQModel
params:
embed_dim: 3
n_embed: 8192
monitor: val/rec_loss
ddconfig:
attn_type: none
double_z: false
z_channels: 3
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 4
num_res_blocks: 2
attn_resolutions: []
dropout: 0.0
lossconfig:
target: taming.modules.losses.vqperceptual.VQLPIPSWithDiscriminator
params:
disc_conditional: false
disc_in_channels: 3
disc_start: 11
disc_weight: 0.75
codebook_weight: 1.0
data:
target: main.DataModuleFromConfig
params:
batch_size: 8
num_workers: 12
wrap: true
train:
target: ldm.data.openimages.FullOpenImagesTrain
params:
crop_size: 256
validation:
target: ldm.data.openimages.FullOpenImagesValidation
params:
crop_size: 256
================================================
FILE: models/InstructDiffusion/stable_diffusion/models/first_stage_models/vq-f8/config.yaml
================================================
model:
base_learning_rate: 4.5e-06
target: ldm.models.autoencoder.VQModel
params:
embed_dim: 4
n_embed: 16384
monitor: val/rec_loss
ddconfig:
double_z: false
z_channels: 4
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 2
- 4
num_res_blocks: 2
attn_resolutions:
- 32
dropout: 0.0
lossconfig:
target: taming.modules.losses.vqperceptual.VQLPIPSWithDiscriminator
params:
disc_conditional: false
disc_in_channels: 3
disc_num_layers: 2
disc_start: 1
disc_weight: 0.6
codebook_weight: 1.0
data:
target: main.DataModuleFromConfig
params:
batch_size: 10
num_workers: 20
wrap: true
train:
target: ldm.data.openimages.FullOpenImagesTrain
params:
size: 384
crop_size: 256
validation:
target: ldm.data.openimages.FullOpenImagesValidation
params:
size: 384
crop_size: 256
================================================
FILE: models/InstructDiffusion/stable_diffusion/models/first_stage_models/vq-f8-n256/config.yaml
================================================
model:
base_learning_rate: 4.5e-06
target: ldm.models.autoencoder.VQModel
params:
embed_dim: 4
n_embed: 256
monitor: val/rec_loss
ddconfig:
double_z: false
z_channels: 4
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 2
- 4
num_res_blocks: 2
attn_resolutions:
- 32
dropout: 0.0
lossconfig:
target: taming.modules.losses.vqperceptual.VQLPIPSWithDiscriminator
params:
disc_conditional: false
disc_in_channels: 3
disc_start: 250001
disc_weight: 0.75
codebook_weight: 1.0
data:
target: main.DataModuleFromConfig
params:
batch_size: 10
num_workers: 20
wrap: true
train:
target: ldm.data.openimages.FullOpenImagesTrain
params:
size: 384
crop_size: 256
validation:
target: ldm.data.openimages.FullOpenImagesValidation
params:
size: 384
crop_size: 256
================================================
FILE: models/InstructDiffusion/stable_diffusion/models/ldm/bsr_sr/config.yaml
================================================
model:
base_learning_rate: 1.0e-06
target: ldm.models.diffusion.ddpm.LatentDiffusion
params:
linear_start: 0.0015
linear_end: 0.0155
log_every_t: 100
timesteps: 1000
loss_type: l2
first_stage_key: image
cond_stage_key: LR_image
image_size: 64
channels: 3
concat_mode: true
cond_stage_trainable: false
unet_config:
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
params:
image_size: 64
in_channels: 6
out_channels: 3
model_channels: 160
attention_resolutions:
- 16
- 8
num_res_blocks: 2
channel_mult:
- 1
- 2
- 2
- 4
num_head_channels: 32
first_stage_config:
target: ldm.models.autoencoder.VQModelInterface
params:
embed_dim: 3
n_embed: 8192
monitor: val/rec_loss
ddconfig:
double_z: false
z_channels: 3
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 4
num_res_blocks: 2
attn_resolutions: []
dropout: 0.0
lossconfig:
target: torch.nn.Identity
cond_stage_config:
target: torch.nn.Identity
data:
target: main.DataModuleFromConfig
params:
batch_size: 64
wrap: false
num_workers: 12
train:
target: ldm.data.openimages.SuperresOpenImagesAdvancedTrain
params:
size: 256
degradation: bsrgan_light
downscale_f: 4
min_crop_f: 0.5
max_crop_f: 1.0
random_crop: true
validation:
target: ldm.data.openimages.SuperresOpenImagesAdvancedValidation
params:
size: 256
degradation: bsrgan_light
downscale_f: 4
min_crop_f: 0.5
max_crop_f: 1.0
random_crop: true
================================================
FILE: models/InstructDiffusion/stable_diffusion/models/ldm/celeba256/config.yaml
================================================
model:
base_learning_rate: 2.0e-06
target: ldm.models.diffusion.ddpm.LatentDiffusion
params:
linear_start: 0.0015
linear_end: 0.0195
num_timesteps_cond: 1
log_every_t: 200
timesteps: 1000
first_stage_key: image
cond_stage_key: class_label
image_size: 64
channels: 3
cond_stage_trainable: false
concat_mode: false
monitor: val/loss
unet_config:
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
params:
image_size: 64
in_channels: 3
out_channels: 3
model_channels: 224
attention_resolutions:
- 8
- 4
- 2
num_res_blocks: 2
channel_mult:
- 1
- 2
- 3
- 4
num_head_channels: 32
first_stage_config:
target: ldm.models.autoencoder.VQModelInterface
params:
embed_dim: 3
n_embed: 8192
ddconfig:
double_z: false
z_channels: 3
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 4
num_res_blocks: 2
attn_resolutions: []
dropout: 0.0
lossconfig:
target: torch.nn.Identity
cond_stage_config: __is_unconditional__
data:
target: main.DataModuleFromConfig
params:
batch_size: 48
num_workers: 5
wrap: false
train:
target: ldm.data.faceshq.CelebAHQTrain
params:
size: 256
validation:
target: ldm.data.faceshq.CelebAHQValidation
params:
size: 256
================================================
FILE: models/InstructDiffusion/stable_diffusion/models/ldm/cin256/config.yaml
================================================
model:
base_learning_rate: 1.0e-06
target: ldm.models.diffusion.ddpm.LatentDiffusion
params:
linear_start: 0.0015
linear_end: 0.0195
num_timesteps_cond: 1
log_every_t: 200
timesteps: 1000
first_stage_key: image
cond_stage_key: class_label
image_size: 32
channels: 4
cond_stage_trainable: true
conditioning_key: crossattn
monitor: val/loss_simple_ema
unet_config:
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
params:
image_size: 32
in_channels: 4
out_channels: 4
model_channels: 256
attention_resolutions:
- 4
- 2
- 1
num_res_blocks: 2
channel_mult:
- 1
- 2
- 4
num_head_channels: 32
use_spatial_transformer: true
transformer_depth: 1
context_dim: 512
first_stage_config:
target: ldm.models.autoencoder.VQModelInterface
params:
embed_dim: 4
n_embed: 16384
ddconfig:
double_z: false
z_channels: 4
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 2
- 4
num_res_blocks: 2
attn_resolutions:
- 32
dropout: 0.0
lossconfig:
target: torch.nn.Identity
cond_stage_config:
target: ldm.modules.encoders.modules.ClassEmbedder
params:
embed_dim: 512
key: class_label
data:
target: main.DataModuleFromConfig
params:
batch_size: 64
num_workers: 12
wrap: false
train:
target: ldm.data.imagenet.ImageNetTrain
params:
config:
size: 256
validation:
target: ldm.data.imagenet.ImageNetValidation
params:
config:
size: 256
================================================
FILE: models/InstructDiffusion/stable_diffusion/models/ldm/ffhq256/config.yaml
================================================
model:
base_learning_rate: 2.0e-06
target: ldm.models.diffusion.ddpm.LatentDiffusion
params:
linear_start: 0.0015
linear_end: 0.0195
num_timesteps_cond: 1
log_every_t: 200
timesteps: 1000
first_stage_key: image
cond_stage_key: class_label
image_size: 64
channels: 3
cond_stage_trainable: false
concat_mode: false
monitor: val/loss
unet_config:
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
params:
image_size: 64
in_channels: 3
out_channels: 3
model_channels: 224
attention_resolutions:
- 8
- 4
- 2
num_res_blocks: 2
channel_mult:
- 1
- 2
- 3
- 4
num_head_channels: 32
first_stage_config:
target: ldm.models.autoencoder.VQModelInterface
params:
embed_dim: 3
n_embed: 8192
ddconfig:
double_z: false
z_channels: 3
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 4
num_res_blocks: 2
attn_resolutions: []
dropout: 0.0
lossconfig:
target: torch.nn.Identity
cond_stage_config: __is_unconditional__
data:
target: main.DataModuleFromConfig
params:
batch_size: 42
num_workers: 5
wrap: false
train:
target: ldm.data.faceshq.FFHQTrain
params:
size: 256
validation:
target: ldm.data.faceshq.FFHQValidation
params:
size: 256
================================================
FILE: models/InstructDiffusion/stable_diffusion/models/ldm/inpainting_big/config.yaml
================================================
model:
base_learning_rate: 1.0e-06
target: ldm.models.diffusion.ddpm.LatentDiffusion
params:
linear_start: 0.0015
linear_end: 0.0205
log_every_t: 100
timesteps: 1000
loss_type: l1
first_stage_key: image
cond_stage_key: masked_image
image_size: 64
channels: 3
concat_mode: true
monitor: val/loss
scheduler_config:
target: ldm.lr_scheduler.LambdaWarmUpCosineScheduler
params:
verbosity_interval: 0
warm_up_steps: 1000
max_decay_steps: 50000
lr_start: 0.001
lr_max: 0.1
lr_min: 0.0001
unet_config:
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
params:
image_size: 64
in_channels: 7
out_channels: 3
model_channels: 256
attention_resolutions:
- 8
- 4
- 2
num_res_blocks: 2
channel_mult:
- 1
- 2
- 3
- 4
num_heads: 8
resblock_updown: true
first_stage_config:
target: ldm.models.autoencoder.VQModelInterface
params:
embed_dim: 3
n_embed: 8192
monitor: val/rec_loss
ddconfig:
attn_type: none
double_z: false
z_channels: 3
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 4
num_res_blocks: 2
attn_resolutions: []
dropout: 0.0
lossconfig:
target: ldm.modules.losses.contperceptual.DummyLoss
cond_stage_config: __is_first_stage__
================================================
FILE: models/InstructDiffusion/stable_diffusion/models/ldm/layout2img-openimages256/config.yaml
================================================
model:
base_learning_rate: 2.0e-06
target: ldm.models.diffusion.ddpm.LatentDiffusion
params:
linear_start: 0.0015
linear_end: 0.0205
log_every_t: 100
timesteps: 1000
loss_type: l1
first_stage_key: image
cond_stage_key: coordinates_bbox
image_size: 64
channels: 3
conditioning_key: crossattn
cond_stage_trainable: true
unet_config:
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
params:
image_size: 64
in_channels: 3
out_channels: 3
model_channels: 128
attention_resolutions:
- 8
- 4
- 2
num_res_blocks: 2
channel_mult:
- 1
- 2
- 3
- 4
num_head_channels: 32
use_spatial_transformer: true
transformer_depth: 3
context_dim: 512
first_stage_config:
target: ldm.models.autoencoder.VQModelInterface
params:
embed_dim: 3
n_embed: 8192
monitor: val/rec_loss
ddconfig:
double_z: false
z_channels: 3
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 4
num_res_blocks: 2
attn_resolutions: []
dropout: 0.0
lossconfig:
target: torch.nn.Identity
cond_stage_config:
target: ldm.modules.encoders.modules.BERTEmbedder
params:
n_embed: 512
n_layer: 16
vocab_size: 8192
max_seq_len: 92
use_tokenizer: false
monitor: val/loss_simple_ema
data:
target: main.DataModuleFromConfig
params:
batch_size: 24
wrap: false
num_workers: 10
train:
target: ldm.data.openimages.OpenImagesBBoxTrain
params:
size: 256
validation:
target: ldm.data.openimages.OpenImagesBBoxValidation
params:
size: 256
================================================
FILE: models/InstructDiffusion/stable_diffusion/models/ldm/lsun_beds256/config.yaml
================================================
model:
base_learning_rate: 2.0e-06
target: ldm.models.diffusion.ddpm.LatentDiffusion
params:
linear_start: 0.0015
linear_end: 0.0195
num_timesteps_cond: 1
log_every_t: 200
timesteps: 1000
first_stage_key: image
cond_stage_key: class_label
image_size: 64
channels: 3
cond_stage_trainable: false
concat_mode: false
monitor: val/loss
unet_config:
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
params:
image_size: 64
in_channels: 3
out_channels: 3
model_channels: 224
attention_resolutions:
- 8
- 4
- 2
num_res_blocks: 2
channel_mult:
- 1
- 2
- 3
- 4
num_head_channels: 32
first_stage_config:
target: ldm.models.autoencoder.VQModelInterface
params:
embed_dim: 3
n_embed: 8192
ddconfig:
double_z: false
z_channels: 3
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 4
num_res_blocks: 2
attn_resolutions: []
dropout: 0.0
lossconfig:
target: torch.nn.Identity
cond_stage_config: __is_unconditional__
data:
target: main.DataModuleFromConfig
params:
batch_size: 48
num_workers: 5
wrap: false
train:
target: ldm.data.lsun.LSUNBedroomsTrain
params:
size: 256
validation:
target: ldm.data.lsun.LSUNBedroomsValidation
params:
size: 256
================================================
FILE: models/InstructDiffusion/stable_diffusion/models/ldm/lsun_churches256/config.yaml
================================================
model:
base_learning_rate: 5.0e-05
target: ldm.models.diffusion.ddpm.LatentDiffusion
params:
linear_start: 0.0015
linear_end: 0.0155
num_timesteps_cond: 1
log_every_t: 200
timesteps: 1000
loss_type: l1
first_stage_key: image
cond_stage_key: image
image_size: 32
channels: 4
cond_stage_trainable: false
concat_mode: false
scale_by_std: true
monitor: val/loss_simple_ema
scheduler_config:
target: ldm.lr_scheduler.LambdaLinearScheduler
params:
warm_up_steps:
- 10000
cycle_lengths:
- 10000000000000
f_start:
- 1.0e-06
f_max:
- 1.0
f_min:
- 1.0
unet_config:
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
params:
image_size: 32
in_channels: 4
out_channels: 4
model_channels: 192
attention_resolutions:
- 1
- 2
- 4
- 8
num_res_blocks: 2
channel_mult:
- 1
- 2
- 2
- 4
- 4
num_heads: 8
use_scale_shift_norm: true
resblock_updown: true
first_stage_config:
target: ldm.models.autoencoder.AutoencoderKL
params:
embed_dim: 4
monitor: val/rec_loss
ddconfig:
double_z: true
z_channels: 4
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 4
- 4
num_res_blocks: 2
attn_resolutions: []
dropout: 0.0
lossconfig:
target: torch.nn.Identity
cond_stage_config: '__is_unconditional__'
data:
target: main.DataModuleFromConfig
params:
batch_size: 96
num_workers: 5
wrap: false
train:
target: ldm.data.lsun.LSUNChurchesTrain
params:
size: 256
validation:
target: ldm.data.lsun.LSUNChurchesValidation
params:
size: 256
================================================
FILE: models/InstructDiffusion/stable_diffusion/models/ldm/semantic_synthesis256/config.yaml
================================================
model:
base_learning_rate: 1.0e-06
target: ldm.models.diffusion.ddpm.LatentDiffusion
params:
linear_start: 0.0015
linear_end: 0.0205
log_every_t: 100
timesteps: 1000
loss_type: l1
first_stage_key: image
cond_stage_key: segmentation
image_size: 64
channels: 3
concat_mode: true
cond_stage_trainable: true
unet_config:
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
params:
image_size: 64
in_channels: 6
out_channels: 3
model_channels: 128
attention_resolutions:
- 32
- 16
- 8
num_res_blocks: 2
channel_mult:
- 1
- 4
- 8
num_heads: 8
first_stage_config:
target: ldm.models.autoencoder.VQModelInterface
params:
embed_dim: 3
n_embed: 8192
ddconfig:
double_z: false
z_channels: 3
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 4
num_res_blocks: 2
attn_resolutions: []
dropout: 0.0
lossconfig:
target: torch.nn.Identity
cond_stage_config:
target: ldm.modules.encoders.modules.SpatialRescaler
params:
n_stages: 2
in_channels: 182
out_channels: 3
================================================
FILE: models/InstructDiffusion/stable_diffusion/models/ldm/semantic_synthesis512/config.yaml
================================================
model:
base_learning_rate: 1.0e-06
target: ldm.models.diffusion.ddpm.LatentDiffusion
params:
linear_start: 0.0015
linear_end: 0.0205
log_every_t: 100
timesteps: 1000
loss_type: l1
first_stage_key: image
cond_stage_key: segmentation
image_size: 128
channels: 3
concat_mode: true
cond_stage_trainable: true
unet_config:
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
params:
image_size: 128
in_channels: 6
out_channels: 3
model_channels: 128
attention_resolutions:
- 32
- 16
- 8
num_res_blocks: 2
channel_mult:
- 1
- 4
- 8
num_heads: 8
first_stage_config:
target: ldm.models.autoencoder.VQModelInterface
params:
embed_dim: 3
n_embed: 8192
monitor: val/rec_loss
ddconfig:
double_z: false
z_channels: 3
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 4
num_res_blocks: 2
attn_resolutions: []
dropout: 0.0
lossconfig:
target: torch.nn.Identity
cond_stage_config:
target: ldm.modules.encoders.modules.SpatialRescaler
params:
n_stages: 2
in_channels: 182
out_channels: 3
data:
target: main.DataModuleFromConfig
params:
batch_size: 8
wrap: false
num_workers: 10
train:
target: ldm.data.landscapes.RFWTrain
params:
size: 768
crop_size: 512
segmentation_to_float32: true
validation:
target: ldm.data.landscapes.RFWValidation
params:
size: 768
crop_size: 512
segmentation_to_float32: true
================================================
FILE: models/InstructDiffusion/stable_diffusion/models/ldm/text2img256/config.yaml
================================================
model:
base_learning_rate: 2.0e-06
target: ldm.models.diffusion.ddpm.LatentDiffusion
params:
linear_start: 0.0015
linear_end: 0.0195
num_timesteps_cond: 1
log_every_t: 200
timesteps: 1000
first_stage_key: image
cond_stage_key: caption
image_size: 64
channels: 3
cond_stage_trainable: true
conditioning_key: crossattn
monitor: val/loss_simple_ema
unet_config:
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
params:
image_size: 64
in_channels: 3
out_channels: 3
model_channels: 192
attention_resolutions:
- 8
- 4
- 2
num_res_blocks: 2
channel_mult:
- 1
- 2
- 3
- 5
num_head_channels: 32
use_spatial_transformer: true
transformer_depth: 1
context_dim: 640
first_stage_config:
target: ldm.models.autoencoder.VQModelInterface
params:
embed_dim: 3
n_embed: 8192
ddconfig:
double_z: false
z_channels: 3
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 4
num_res_blocks: 2
attn_resolutions: []
dropout: 0.0
lossconfig:
target: torch.nn.Identity
cond_stage_config:
target: ldm.modules.encoders.modules.BERTEmbedder
params:
n_embed: 640
n_layer: 32
data:
target: main.DataModuleFromConfig
params:
batch_size: 28
num_workers: 10
wrap: false
train:
target: ldm.data.previews.pytorch_dataset.PreviewsTrain
params:
size: 256
validation:
target: ldm.data.previews.pytorch_dataset.PreviewsValidation
params:
size: 256
================================================
FILE: models/InstructDiffusion/stable_diffusion/notebook_helpers.py
================================================
from torchvision.datasets.utils import download_url
from ldm.util import instantiate_from_config
import torch
import os
# todo ?
from google.colab import files
from IPython.display import Image as ipyimg
import ipywidgets as widgets
from PIL import Image
from numpy import asarray
from einops import rearrange, repeat
import torch, torchvision
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.util import ismap
import time
from omegaconf import OmegaConf
def download_models(mode):
if mode == "superresolution":
# this is the small bsr light model
url_conf = 'https://heibox.uni-heidelberg.de/f/31a76b13ea27482981b4/?dl=1'
url_ckpt = 'https://heibox.uni-heidelberg.de/f/578df07c8fc04ffbadf3/?dl=1'
path_conf = 'logs/diffusion/superresolution_bsr/configs/project.yaml'
path_ckpt = 'logs/diffusion/superresolution_bsr/checkpoints/last.ckpt'
download_url(url_conf, path_conf)
download_url(url_ckpt, path_ckpt)
path_conf = path_conf + '/?dl=1' # fix it
path_ckpt = path_ckpt + '/?dl=1' # fix it
return path_conf, path_ckpt
else:
raise NotImplementedError
def load_model_from_config(config, ckpt):
print(f"Loading model from {ckpt}")
pl_sd = torch.load(ckpt, map_location="cpu")
global_step = pl_sd["global_step"]
sd = pl_sd["state_dict"]
model = instantiate_from_config(config.model)
m, u = model.load_state_dict(sd, strict=False)
model.cuda()
model.eval()
return {"model": model}, global_step
def get_model(mode):
path_conf, path_ckpt = download_models(mode)
config = OmegaConf.load(path_conf)
model, step = load_model_from_config(config, path_ckpt)
return model
def get_custom_cond(mode):
dest = "data/example_conditioning"
if mode == "superresolution":
uploaded_img = files.upload()
filename = next(iter(uploaded_img))
name, filetype = filename.split(".") # todo assumes just one dot in name !
os.rename(f"{filename}", f"{dest}/{mode}/custom_{name}.{filetype}")
elif mode == "text_conditional":
w = widgets.Text(value='A cake with cream!', disabled=True)
display(w)
with open(f"{dest}/{mode}/custom_{w.value[:20]}.txt", 'w') as f:
f.write(w.value)
elif mode == "class_conditional":
w = widgets.IntSlider(min=0, max=1000)
display(w)
with open(f"{dest}/{mode}/custom.txt", 'w') as f:
f.write(w.value)
else:
raise NotImplementedError(f"cond not implemented for mode{mode}")
def get_cond_options(mode):
path = "data/example_conditioning"
path = os.path.join(path, mode)
onlyfiles = [f for f in sorted(os.listdir(path))]
return path, onlyfiles
def select_cond_path(mode):
path = "data/example_conditioning" # todo
path = os.path.join(path, mode)
onlyfiles = [f for f in sorted(os.listdir(path))]
selected = widgets.RadioButtons(
options=onlyfiles,
description='Select conditioning:',
disabled=False
)
display(selected)
selected_path = os.path.join(path, selected.value)
return selected_path
def get_cond(mode, selected_path):
example = dict()
if mode == "superresolution":
up_f = 4
visualize_cond_img(selected_path)
c = Image.open(selected_path)
c = torch.unsqueeze(torchvision.transforms.ToTensor()(c), 0)
c_up = torchvision.transforms.functional.resize(c, size=[up_f * c.shape[2], up_f * c.shape[3]], antialias=True)
c_up = rearrange(c_up, '1 c h w -> 1 h w c')
c = rearrange(c, '1 c h w -> 1 h w c')
c = 2. * c - 1.
c = c.to(torch.device("cuda"))
example["LR_image"] = c
example["image"] = c_up
return example
def visualize_cond_img(path):
display(ipyimg(filename=path))
def run(model, selected_path, task, custom_steps, resize_enabled=False, classifier_ckpt=None, global_step=None):
example = get_cond(task, selected_path)
save_intermediate_vid = False
n_runs = 1
masked = False
guider = None
ckwargs = None
mode = 'ddim'
ddim_use_x0_pred = False
temperature = 1.
eta = 1.
make_progrow = True
custom_shape = None
height, width = example["image"].shape[1:3]
split_input = height >= 128 and width >= 128
if split_input:
ks = 128
stride = 64
vqf = 4 #
model.split_input_params = {"ks": (ks, ks), "stride": (stride, stride),
"vqf": vqf,
"patch_distributed_vq": True,
"tie_braker": False,
"clip_max_weight": 0.5,
"clip_min_weight": 0.01,
"clip_max_tie_weight": 0.5,
"clip_min_tie_weight": 0.01}
else:
if hasattr(model, "split_input_params"):
delattr(model, "split_input_params")
invert_mask = False
x_T = None
for n in range(n_runs):
if custom_shape is not None:
x_T = torch.randn(1, custom_shape[1], custom_shape[2], custom_shape[3]).to(model.device)
x_T = repeat(x_T, '1 c h w -> b c h w', b=custom_shape[0])
logs = make_convolutional_sample(example, model,
mode=mode, custom_steps=custom_steps,
eta=eta, swap_mode=False , masked=masked,
invert_mask=invert_mask, quantize_x0=False,
custom_schedule=None, decode_interval=10,
resize_enabled=resize_enabled, custom_shape=custom_shape,
temperature=temperature, noise_dropout=0.,
corrector=guider, corrector_kwargs=ckwargs, x_T=x_T, save_intermediate_vid=save_intermediate_vid,
make_progrow=make_progrow,ddim_use_x0_pred=ddim_use_x0_pred
)
return logs
@torch.no_grad()
def convsample_ddim(model, cond, steps, shape, eta=1.0, callback=None, normals_sequence=None,
mask=None, x0=None, quantize_x0=False, img_callback=None,
temperature=1., noise_dropout=0., score_corrector=None,
corrector_kwargs=None, x_T=None, log_every_t=None
):
ddim = DDIMSampler(model)
bs = shape[0] # dont know where this comes from but wayne
shape = shape[1:] # cut batch dim
print(f"Sampling with eta = {eta}; steps: {steps}")
samples, intermediates = ddim.sample(steps, batch_size=bs, shape=shape, conditioning=cond, callback=callback,
normals_sequence=normals_sequence, quantize_x0=quantize_x0, eta=eta,
mask=mask, x0=x0, temperature=temperature, verbose=False,
score_corrector=score_corrector,
corrector_kwargs=corrector_kwargs, x_T=x_T)
return samples, intermediates
@torch.no_grad()
def make_convolutional_sample(batch, model, mode="vanilla", custom_steps=None, eta=1.0, swap_mode=False, masked=False,
invert_mask=True, quantize_x0=False, custom_schedule=None, decode_interval=1000,
resize_enabled=False, custom_shape=None, temperature=1., noise_dropout=0., corrector=None,
corrector_kwargs=None, x_T=None, save_intermediate_vid=False, make_progrow=True,ddim_use_x0_pred=False):
log = dict()
z, c, x, xrec, xc = model.get_input(batch, model.first_stage_key,
return_first_stage_outputs=True,
force_c_encode=not (hasattr(model, 'split_input_params')
and model.cond_stage_key == 'coordinates_bbox'),
return_original_cond=True)
log_every_t = 1 if save_intermediate_vid else None
if custom_shape is not None:
z = torch.randn(custom_shape)
print(f"Generating {custom_shape[0]} samples of shape {custom_shape[1:]}")
z0 = None
log["input"] = x
log["reconstruction"] = xrec
if ismap(xc):
log["original_conditioning"] = model.to_rgb(xc)
if hasattr(model, 'cond_stage_key'):
log[model.cond_stage_key] = model.to_rgb(xc)
else:
log["original_conditioning"] = xc if xc is not None else torch.zeros_like(x)
if model.cond_stage_model:
log[model.cond_stage_key] = xc if xc is not None else torch.zeros_like(x)
if model.cond_stage_key =='class_label':
log[model.cond_stage_key] = xc[model.cond_stage_key]
with model.ema_scope("Plotting"):
t0 = time.time()
img_cb = None
sample, intermediates = convsample_ddim(model, c, steps=custom_steps, shape=z.shape,
eta=eta,
quantize_x0=quantize_x0, img_callback=img_cb, mask=None, x0=z0,
temperature=temperature, noise_dropout=noise_dropout,
score_corrector=corrector, corrector_kwargs=corrector_kwargs,
x_T=x_T, log_every_t=log_every_t)
t1 = time.time()
if ddim_use_x0_pred:
sample = intermediates['pred_x0'][-1]
x_sample = model.decode_first_stage(sample)
try:
x_sample_noquant = model.decode_first_stage(sample, force_not_quantize=True)
log["sample_noquant"] = x_sample_noquant
log["sample_diff"] = torch.abs(x_sample_noquant - x_sample)
except:
pass
log["sample"] = x_sample
log["time"] = t1 - t0
return log
================================================
FILE: models/InstructDiffusion/stable_diffusion/scripts/download_first_stages.sh
================================================
#!/bin/bash
wget -O models/first_stage_models/kl-f4/model.zip https://ommer-lab.com/files/latent-diffusion/kl-f4.zip
wget -O models/first_stage_models/kl-f8/model.zip https://ommer-lab.com/files/latent-diffusion/kl-f8.zip
wget -O models/first_stage_models/kl-f16/model.zip https://ommer-lab.com/files/latent-diffusion/kl-f16.zip
wget -O models/first_stage_models/kl-f32/model.zip https://ommer-lab.com/files/latent-diffusion/kl-f32.zip
wget -O models/first_stage_models/vq-f4/model.zip https://ommer-lab.com/files/latent-diffusion/vq-f4.zip
wget -O models/first_stage_models/vq-f4-noattn/model.zip https://ommer-lab.com/files/latent-diffusion/vq-f4-noattn.zip
wget -O models/first_stage_models/vq-f8/model.zip https://ommer-lab.com/files/latent-diffusion/vq-f8.zip
wget -O models/first_stage_models/vq-f8-n256/model.zip https://ommer-lab.com/files/latent-diffusion/vq-f8-n256.zip
wget -O models/first_stage_models/vq-f16/model.zip https://ommer-lab.com/files/latent-diffusion/vq-f16.zip
cd models/first_stage_models/kl-f4
unzip -o model.zip
cd ../kl-f8
unzip -o model.zip
cd ../kl-f16
unzip -o model.zip
cd ../kl-f32
unzip -o model.zip
cd ../vq-f4
unzip -o model.zip
cd ../vq-f4-noattn
unzip -o model.zip
cd ../vq-f8
unzip -o model.zip
cd ../vq-f8-n256
unzip -o model.zip
cd ../vq-f16
unzip -o model.zip
cd ../..
================================================
FILE: models/InstructDiffusion/stable_diffusion/scripts/download_models.sh
================================================
#!/bin/bash
wget -O models/ldm/celeba256/celeba-256.zip https://ommer-lab.com/files/latent-diffusion/celeba.zip
wget -O models/ldm/ffhq256/ffhq-256.zip https://ommer-lab.com/files/latent-diffusion/ffhq.zip
wget -O models/ldm/lsun_churches256/lsun_churches-256.zip https://ommer-lab.com/files/latent-diffusion/lsun_churches.zip
wget -O models/ldm/lsun_beds256/lsun_beds-256.zip https://ommer-lab.com/files/latent-diffusion/lsun_bedrooms.zip
wget -O models/ldm/text2img256/model.zip https://ommer-lab.com/files/latent-diffusion/text2img.zip
wget -O models/ldm/cin256/model.zip https://ommer-lab.com/files/latent-diffusion/cin.zip
wget -O models/ldm/semantic_synthesis512/model.zip https://ommer-lab.com/files/latent-diffusion/semantic_synthesis.zip
wget -O models/ldm/semantic_synthesis256/model.zip https://ommer-lab.com/files/latent-diffusion/semantic_synthesis256.zip
wget -O models/ldm/bsr_sr/model.zip https://ommer-lab.com/files/latent-diffusion/sr_bsr.zip
wget -O models/ldm/layout2img-openimages256/model.zip https://ommer-lab.com/files/latent-diffusion/layout2img_model.zip
wget -O models/ldm/inpainting_big/model.zip https://ommer-lab.com/files/latent-diffusion/inpainting_big.zip
cd models/ldm/celeba256
unzip -o celeba-256.zip
cd ../ffhq256
unzip -o ffhq-256.zip
cd ../lsun_churches256
unzip -o lsun_churches-256.zip
cd ../lsun_beds256
unzip -o lsun_beds-256.zip
cd ../text2img256
unzip -o model.zip
cd ../cin256
unzip -o model.zip
cd ../semantic_synthesis512
unzip -o model.zip
cd ../semantic_synthesis256
unzip -o model.zip
cd ../bsr_sr
unzip -o model.zip
cd ../layout2img-openimages256
unzip -o model.zip
cd ../inpainting_big
unzip -o model.zip
cd ../..
================================================
FILE: models/InstructDiffusion/stable_diffusion/scripts/img2img.py
================================================
"""make variations of input image"""
import argparse, os, sys, glob
import PIL
import torch
import numpy as np
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from itertools import islice
from einops import rearrange, repeat
from torchvision.utils import make_grid
from torch import autocast
from contextlib import nullcontext
import time
from pytorch_lightning import seed_everything
from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.plms import PLMSSampler
def chunk(it, size):
it = iter(it)
return iter(lambda: tuple(islice(it, size)), ())
def load_model_from_config(config, ckpt, verbose=False):
print(f"Loading model from {ckpt}")
pl_sd = torch.load(ckpt, map_location="cpu")
if "global_step" in pl_sd:
print(f"Global Step: {pl_sd['global_step']}")
sd = pl_sd["state_dict"]
model = instantiate_from_config(config.model)
m, u = model.load_state_dict(sd, strict=False)
if len(m) > 0 and verbose:
print("missing keys:")
print(m)
if len(u) > 0 and verbose:
print("unexpected keys:")
print(u)
model.cuda()
model.eval()
return model
def load_img(path):
image = Image.open(path).convert("RGB")
w, h = image.size
print(f"loaded input image of size ({w}, {h}) from {path}")
w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32
image = image.resize((w, h), resample=PIL.Image.LANCZOS)
image = np.array(image).astype(np.float32) / 255.0
image = image[None].transpose(0, 3, 1, 2)
image = torch.from_numpy(image)
return 2.*image - 1.
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"--prompt",
type=str,
nargs="?",
default="a painting of a virus monster playing guitar",
help="the prompt to render"
)
parser.add_argument(
"--init-img",
type=str,
nargs="?",
help="path to the input image"
)
parser.add_argument(
"--outdir",
type=str,
nargs="?",
help="dir to write results to",
default="outputs/img2img-samples"
)
parser.add_argument(
"--skip_grid",
action='store_true',
help="do not save a grid, only individual samples. Helpful when evaluating lots of samples",
)
parser.add_argument(
"--skip_save",
action='store_true',
help="do not save indiviual samples. For speed measurements.",
)
parser.add_argument(
"--ddim_steps",
type=int,
default=50,
help="number of ddim sampling steps",
)
parser.add_argument(
"--plms",
action='store_true',
help="use plms sampling",
)
parser.add_argument(
"--fixed_code",
action='store_true',
help="if enabled, uses the same starting code across all samples ",
)
parser.add_argument(
"--ddim_eta",
type=float,
default=0.0,
help="ddim eta (eta=0.0 corresponds to deterministic sampling",
)
parser.add_argument(
"--n_iter",
type=int,
default=1,
help="sample this often",
)
parser.add_argument(
"--C",
type=int,
default=4,
help="latent channels",
)
parser.add_argument(
"--f",
type=int,
default=8,
help="downsampling factor, most often 8 or 16",
)
parser.add_argument(
"--n_samples",
type=int,
default=2,
help="how many samples to produce for each given prompt. A.k.a batch size",
)
parser.add_argument(
"--n_rows",
type=int,
default=0,
help="rows in the grid (default: n_samples)",
)
parser.add_argument(
"--scale",
type=float,
default=5.0,
help="unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) - eps(x, empty))",
)
parser.add_argument(
"--strength",
type=float,
default=0.75,
help="strength for noising/unnoising. 1.0 corresponds to full destruction of information in init image",
)
parser.add_argument(
"--from-file",
type=str,
help="if specified, load prompts from this file",
)
parser.add_argument(
"--config",
type=str,
default="configs/stable-diffusion/v1-inference.yaml",
help="path to config which constructs model",
)
parser.add_argument(
"--ckpt",
type=str,
default="models/ldm/stable-diffusion-v1/model.ckpt",
help="path to checkpoint of model",
)
parser.add_argument(
"--seed",
type=int,
default=42,
help="the seed (for reproducible sampling)",
)
parser.add_argument(
"--precision",
type=str,
help="evaluate at this precision",
choices=["full", "autocast"],
default="autocast"
)
opt = parser.parse_args()
seed_everything(opt.seed)
config = OmegaConf.load(f"{opt.config}")
model = load_model_from_config(config, f"{opt.ckpt}")
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
model = model.to(device)
if opt.plms:
raise NotImplementedError("PLMS sampler not (yet) supported")
sampler = PLMSSampler(model)
else:
sampler = DDIMSampler(model)
os.makedirs(opt.outdir, exist_ok=True)
outpath = opt.outdir
batch_size = opt.n_samples
n_rows = opt.n_rows if opt.n_rows > 0 else batch_size
if not opt.from_file:
prompt = opt.prompt
assert prompt is not None
data = [batch_size * [prompt]]
else:
print(f"reading prompts from {opt.from_file}")
with open(opt.from_file, "r") as f:
data = f.read().splitlines()
data = list(chunk(data, batch_size))
sample_path = os.path.join(outpath, "samples")
os.makedirs(sample_path, exist_ok=True)
base_count = len(os.listdir(sample_path))
grid_count = len(os.listdir(outpath)) - 1
assert os.path.isfile(opt.init_img)
init_image = load_img(opt.init_img).to(device)
init_image = repeat(init_image, '1 ... -> b ...', b=batch_size)
init_latent = model.get_first_stage_encoding(model.encode_first_stage(init_image)) # move to latent space
sampler.make_schedule(ddim_num_steps=opt.ddim_steps, ddim_eta=opt.ddim_eta, verbose=False)
assert 0. <= opt.strength <= 1., 'can only work with strength in [0.0, 1.0]'
t_enc = int(opt.strength * opt.ddim_steps)
print(f"target t_enc is {t_enc} steps")
precision_scope = autocast if opt.precision == "autocast" else nullcontext
with torch.no_grad():
with precision_scope("cuda"):
with model.ema_scope():
tic = time.time()
all_samples = list()
for n in trange(opt.n_iter, desc="Sampling"):
for prompts in tqdm(data, desc="data"):
uc = None
if opt.scale != 1.0:
uc = model.get_learned_conditioning(batch_size * [""])
if isinstance(prompts, tuple):
prompts = list(prompts)
c = model.get_learned_conditioning(prompts)
# encode (scaled latent)
z_enc = sampler.stochastic_encode(init_latent, torch.tensor([t_enc]*batch_size).to(device))
# decode it
samples = sampler.decode(z_enc, c, t_enc, unconditional_guidance_scale=opt.scale,
unconditional_conditioning=uc,)
x_samples = model.decode_first_stage(samples)
x_samples = torch.clamp((x_samples + 1.0) / 2.0, min=0.0, max=1.0)
if not opt.skip_save:
for x_sample in x_samples:
x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
Image.fromarray(x_sample.astype(np.uint8)).save(
os.path.join(sample_path, f"{base_count:05}.png"))
base_count += 1
all_samples.append(x_samples)
if not opt.skip_grid:
# additionally, save as grid
grid = torch.stack(all_samples, 0)
grid = rearrange(grid, 'n b c h w -> (n b) c h w')
grid = make_grid(grid, nrow=n_rows)
# to image
grid = 255. * rearrange(grid, 'c h w -> h w c').cpu().numpy()
Image.fromarray(grid.astype(np.uint8)).save(os.path.join(outpath, f'grid-{grid_count:04}.png'))
grid_count += 1
toc = time.time()
print(f"Your samples are ready and waiting for you here: \n{outpath} \n"
f" \nEnjoy.")
if __name__ == "__main__":
main()
================================================
FILE: models/InstructDiffusion/stable_diffusion/scripts/inpaint.py
================================================
import argparse, os, sys, glob
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm
import numpy as np
import torch
from main import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
def make_batch(image, mask, device):
image = np.array(Image.open(image).convert("RGB"))
image = image.astype(np.float32)/255.0
image = image[None].transpose(0,3,1,2)
image = torch.from_numpy(image)
mask = np.array(Image.open(mask).convert("L"))
mask = mask.astype(np.float32)/255.0
mask = mask[None,None]
mask[mask < 0.5] = 0
mask[mask >= 0.5] = 1
mask = torch.from_numpy(mask)
masked_image = (1-mask)*image
batch = {"image": image, "mask": mask, "masked_image": masked_image}
for k in batch:
batch[k] = batch[k].to(device=device)
batch[k] = batch[k]*2.0-1.0
return batch
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--indir",
type=str,
nargs="?",
help="dir containing image-mask pairs (`example.png` and `example_mask.png`)",
)
parser.add_argument(
"--outdir",
type=str,
nargs="?",
help="dir to write results to",
)
parser.add_argument(
"--steps",
type=int,
default=50,
help="number of ddim sampling steps",
)
opt = parser.parse_args()
masks = sorted(glob.glob(os.path.join(opt.indir, "*_mask.png")))
images = [x.replace("_mask.png", ".png") for x in masks]
print(f"Found {len(masks)} inputs.")
config = OmegaConf.load("models/ldm/inpainting_big/config.yaml")
model = instantiate_from_config(config.model)
model.load_state_dict(torch.load("models/ldm/inpainting_big/last.ckpt")["state_dict"],
strict=False)
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
model = model.to(device)
sampler = DDIMSampler(model)
os.makedirs(opt.outdir, exist_ok=True)
with torch.no_grad():
with model.ema_scope():
for image, mask in tqdm(zip(images, masks)):
outpath = os.path.join(opt.outdir, os.path.split(image)[1])
batch = make_batch(image, mask, device=device)
# encode masked image and concat downsampled mask
c = model.cond_stage_model.encode(batch["masked_image"])
cc = torch.nn.functional.interpolate(batch["mask"],
size=c.shape[-2:])
c = torch.cat((c, cc), dim=1)
shape = (c.shape[1]-1,)+c.shape[2:]
samples_ddim, _ = sampler.sample(S=opt.steps,
conditioning=c,
batch_size=c.shape[0],
shape=shape,
verbose=False)
x_samples_ddim = model.decode_first_stage(samples_ddim)
image = torch.clamp((batch["image"]+1.0)/2.0,
min=0.0, max=1.0)
mask = torch.clamp((batch["mask"]+1.0)/2.0,
min=0.0, max=1.0)
predicted_image = torch.clamp((x_samples_ddim+1.0)/2.0,
min=0.0, max=1.0)
inpainted = (1-mask)*image+mask*predicted_image
inpainted = inpainted.cpu().numpy().transpose(0,2,3,1)[0]*255
Image.fromarray(inpainted.astype(np.uint8)).save(outpath)
================================================
FILE: models/InstructDiffusion/stable_diffusion/scripts/knn2img.py
================================================
import argparse, os, sys, glob
import clip
import torch
import torch.nn as nn
import numpy as np
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from itertools import islice
from einops import rearrange, repeat
from torchvision.utils import make_grid
import scann
import time
from multiprocessing import cpu_count
from ldm.util import instantiate_from_config, parallel_data_prefetch
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.plms import PLMSSampler
from ldm.modules.encoders.modules import FrozenClipImageEmbedder, FrozenCLIPTextEmbedder
DATABASES = [
"openimages",
"artbench-art_nouveau",
"artbench-baroque",
"artbench-expressionism",
"artbench-impressionism",
"artbench-post_impressionism",
"artbench-realism",
"artbench-romanticism",
"artbench-renaissance",
"artbench-surrealism",
"artbench-ukiyo_e",
]
def chunk(it, size):
it = iter(it)
return iter(lambda: tuple(islice(it, size)), ())
def load_model_from_config(config, ckpt, verbose=False):
print(f"Loading model from {ckpt}")
pl_sd = torch.load(ckpt, map_location="cpu")
if "global_step" in pl_sd:
print(f"Global Step: {pl_sd['global_step']}")
sd = pl_sd["state_dict"]
model = instantiate_from_config(config.model)
m, u = model.load_state_dict(sd, strict=False)
if len(m) > 0 and verbose:
print("missing keys:")
print(m)
if len(u) > 0 and verbose:
print("unexpected keys:")
print(u)
model.cuda()
model.eval()
return model
class Searcher(object):
def __init__(self, database, retriever_version='ViT-L/14'):
assert database in DATABASES
# self.database = self.load_database(database)
self.database_name = database
self.searcher_savedir = f'data/rdm/searchers/{self.database_name}'
self.database_path = f'data/rdm/retrieval_databases/{self.database_name}'
self.retriever = self.load_retriever(version=retriever_version)
self.database = {'embedding': [],
'img_id': [],
'patch_coords': []}
self.load_database()
self.load_searcher()
def train_searcher(self, k,
metric='dot_product',
searcher_savedir=None):
print('Start training searcher')
searcher = scann.scann_ops_pybind.builder(self.database['embedding'] /
np.linalg.norm(self.database['embedding'], axis=1)[:, np.newaxis],
k, metric)
self.searcher = searcher.score_brute_force().build()
print('Finish training searcher')
if searcher_savedir is not None:
print(f'Save trained searcher under "{searcher_savedir}"')
os.makedirs(searcher_savedir, exist_ok=True)
self.searcher.serialize(searcher_savedir)
def load_single_file(self, saved_embeddings):
compressed = np.load(saved_embeddings)
self.database = {key: compressed[key] for key in compressed.files}
print('Finished loading of clip embeddings.')
def load_multi_files(self, data_archive):
out_data = {key: [] for key in self.database}
for d in tqdm(data_archive, desc=f'Loading datapool from {len(data_archive)} individual files.'):
for key in d.files:
out_data[key].append(d[key])
return out_data
def load_database(self):
print(f'Load saved patch embedding from "{self.database_path}"')
file_content = glob.glob(os.path.join(self.database_path, '*.npz'))
if len(file_content) == 1:
self.load_single_file(file_content[0])
elif len(file_content) > 1:
data = [np.load(f) for f in file_content]
prefetched_data = parallel_data_prefetch(self.load_multi_files, data,
n_proc=min(len(data), cpu_count()), target_data_type='dict')
self.database = {key: np.concatenate([od[key] for od in prefetched_data], axis=1)[0] for key in
self.database}
else:
raise ValueError(f'No npz-files in specified path "{self.database_path}" is this directory existing?')
print(f'Finished loading of retrieval database of length {self.database["embedding"].shape[0]}.')
def load_retriever(self, version='ViT-L/14', ):
model = FrozenClipImageEmbedder(model=version)
if torch.cuda.is_available():
model.cuda()
model.eval()
return model
def load_searcher(self):
print(f'load searcher for database {self.database_name} from {self.searcher_savedir}')
self.searcher = scann.scann_ops_pybind.load_searcher(self.searcher_savedir)
print('Finished loading searcher.')
def search(self, x, k):
if self.searcher is None and self.database['embedding'].shape[0] < 2e4:
self.train_searcher(k) # quickly fit searcher on the fly for small databases
assert self.searcher is not None, 'Cannot search with uninitialized searcher'
if isinstance(x, torch.Tensor):
x = x.detach().cpu().numpy()
if len(x.shape) == 3:
x = x[:, 0]
query_embeddings = x / np.linalg.norm(x, axis=1)[:, np.newaxis]
start = time.time()
nns, distances = self.searcher.search_batched(query_embeddings, final_num_neighbors=k)
end = time.time()
out_embeddings = self.database['embedding'][nns]
out_img_ids = self.database['img_id'][nns]
out_pc = self.database['patch_coords'][nns]
out = {'nn_embeddings': out_embeddings / np.linalg.norm(out_embeddings, axis=-1)[..., np.newaxis],
'img_ids': out_img_ids,
'patch_coords': out_pc,
'queries': x,
'exec_time': end - start,
'nns': nns,
'q_embeddings': query_embeddings}
return out
def __call__(self, x, n):
return self.search(x, n)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# TODO: add n_neighbors and modes (text-only, text-image-retrieval, image-image retrieval etc)
# TODO: add 'image variation' mode when knn=0 but a single image is given instead of a text prompt?
parser.add_argument(
"--prompt",
type=str,
nargs="?",
default="a painting of a virus monster playing guitar",
help="the prompt to render"
)
parser.add_argument(
"--outdir",
type=str,
nargs="?",
help="dir to write results to",
default="outputs/txt2img-samples"
)
parser.add_argument(
"--skip_grid",
action='store_true',
help="do not save a grid, only individual samples. Helpful when evaluating lots of samples",
)
parser.add_argument(
"--ddim_steps",
type=int,
default=50,
help="number of ddim sampling steps",
)
parser.add_argument(
"--n_repeat",
type=int,
default=1,
help="number of repeats in CLIP latent space",
)
parser.add_argument(
"--plms",
action='store_true',
help="use plms sampling",
)
parser.add_argument(
"--ddim_eta",
type=float,
default=0.0,
help="ddim eta (eta=0.0 corresponds to deterministic sampling",
)
parser.add_argument(
"--n_iter",
type=int,
default=1,
help="sample this often",
)
parser.add_argument(
"--H",
type=int,
default=768,
help="image height, in pixel space",
)
parser.add_argument(
"--W",
type=int,
default=768,
help="image width, in pixel space",
)
parser.add_argument(
"--n_samples",
type=int,
default=3,
help="how many samples to produce for each given prompt. A.k.a batch size",
)
parser.add_argument(
"--n_rows",
type=int,
default=0,
help="rows in the grid (default: n_samples)",
)
parser.add_argument(
"--scale",
type=float,
default=5.0,
help="unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) - eps(x, empty))",
)
parser.add_argument(
"--from-file",
type=str,
help="if specified, load prompts from this file",
)
parser.add_argument(
"--config",
type=str,
default="configs/retrieval-augmented-diffusion/768x768.yaml",
help="path to config which constructs model",
)
parser.add_argument(
"--ckpt",
type=str,
default="models/rdm/rdm768x768/model.ckpt",
help="path to checkpoint of model",
)
parser.add_argument(
"--clip_type",
type=str,
default="ViT-L/14",
help="which CLIP model to use for retrieval and NN encoding",
)
parser.add_argument(
"--database",
type=str,
default='artbench-surrealism',
choices=DATABASES,
help="The database used for the search, only applied when --use_neighbors=True",
)
parser.add_argument(
"--use_neighbors",
default=False,
action='store_true',
help="Include neighbors in addition to text prompt for conditioning",
)
parser.add_argument(
"--knn",
default=10,
type=int,
help="The number of included neighbors, only applied when --use_neighbors=True",
)
opt = parser.parse_args()
config = OmegaConf.load(f"{opt.config}")
model = load_model_from_config(config, f"{opt.ckpt}")
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
model = model.to(device)
clip_text_encoder = FrozenCLIPTextEmbedder(opt.clip_type).to(device)
if opt.plms:
sampler = PLMSSampler(model)
else:
sampler = DDIMSampler(model)
os.makedirs(opt.outdir, exist_ok=True)
outpath = opt.outdir
batch_size = opt.n_samples
n_rows = opt.n_rows if opt.n_rows > 0 else batch_size
if not opt.from_file:
prompt = opt.prompt
assert prompt is not None
data = [batch_size * [prompt]]
else:
print(f"reading prompts from {opt.from_file}")
with open(opt.from_file, "r") as f:
data = f.read().splitlines()
data = list(chunk(data, batch_size))
sample_path = os.path.join(outpath, "samples")
os.makedirs(sample_path, exist_ok=True)
base_count = len(os.listdir(sample_path))
grid_count = len(os.listdir(outpath)) - 1
print(f"sampling scale for cfg is {opt.scale:.2f}")
searcher = None
if opt.use_neighbors:
searcher = Searcher(opt.database)
with torch.no_grad():
with model.ema_scope():
for n in trange(opt.n_iter, desc="Sampling"):
all_samples = list()
for prompts in tqdm(data, desc="data"):
print("sampling prompts:", prompts)
if isinstance(prompts, tuple):
prompts = list(prompts)
c = clip_text_encoder.encode(prompts)
uc = None
if searcher is not None:
nn_dict = searcher(c, opt.knn)
c = torch.cat([c, torch.from_numpy(nn_dict['nn_embeddings']).cuda()], dim=1)
if opt.scale != 1.0:
uc = torch.zeros_like(c)
if isinstance(prompts, tuple):
prompts = list(prompts)
shape = [16, opt.H // 16, opt.W // 16] # note: currently hardcoded for f16 model
samples_ddim, _ = sampler.sample(S=opt.ddim_steps,
conditioning=c,
batch_size=c.shape[0],
shape=shape,
verbose=False,
unconditional_guidance_scale=opt.scale,
unconditional_conditioning=uc,
eta=opt.ddim_eta,
)
x_samples_ddim = model.decode_first_stage(samples_ddim)
x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
for x_sample in x_samples_ddim:
x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
Image.fromarray(x_sample.astype(np.uint8)).save(
os.path.join(sample_path, f"{base_count:05}.png"))
base_count += 1
all_samples.append(x_samples_ddim)
if not opt.skip_grid:
# additionally, save as grid
grid = torch.stack(all_samples, 0)
grid = rearrange(grid, 'n b c h w -> (n b) c h w')
grid = make_grid(grid, nrow=n_rows)
# to image
grid = 255. * rearrange(grid, 'c h w -> h w c').cpu().numpy()
Image.fromarray(grid.astype(np.uint8)).save(os.path.join(outpath, f'grid-{grid_count:04}.png'))
grid_count += 1
print(f"Your samples are ready and waiting for you here: \n{outpath} \nEnjoy.")
================================================
FILE: models/InstructDiffusion/stable_diffusion/scripts/latent_imagenet_diffusion.ipynb.REMOVED.git-id
================================================
607f94fc7d3ef6d8d1627017215476d9dfc7ddc4
================================================
FILE: models/InstructDiffusion/stable_diffusion/scripts/sample_diffusion.py
================================================
import argparse, os, sys, glob, datetime, yaml
import torch
import time
import numpy as np
from tqdm import trange
from omegaconf import OmegaConf
from PIL import Image
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.util import instantiate_from_config
rescale = lambda x: (x + 1.) / 2.
def custom_to_pil(x):
x = x.detach().cpu()
x = torch.clamp(x, -1., 1.)
x = (x + 1.) / 2.
x = x.permute(1, 2, 0).numpy()
x = (255 * x).astype(np.uint8)
x = Image.fromarray(x)
if not x.mode == "RGB":
x = x.convert("RGB")
return x
def custom_to_np(x):
# saves the batch in adm style as in https://github.com/openai/guided-diffusion/blob/main/scripts/image_sample.py
sample = x.detach().cpu()
sample = ((sample + 1) * 127.5).clamp(0, 255).to(torch.uint8)
sample = sample.permute(0, 2, 3, 1)
sample = sample.contiguous()
return sample
def logs2pil(logs, keys=["sample"]):
imgs = dict()
for k in logs:
try:
if len(logs[k].shape) == 4:
img = custom_to_pil(logs[k][0, ...])
elif len(logs[k].shape) == 3:
img = custom_to_pil(logs[k])
else:
print(f"Unknown format for key {k}. ")
img = None
except:
img = None
imgs[k] = img
return imgs
@torch.no_grad()
def convsample(model, shape, return_intermediates=True,
verbose=True,
make_prog_row=False):
if not make_prog_row:
return model.p_sample_loop(None, shape,
return_intermediates=return_intermediates, verbose=verbose)
else:
return model.progressive_denoising(
None, shape, verbose=True
)
@torch.no_grad()
def convsample_ddim(model, steps, shape, eta=1.0
):
ddim = DDIMSampler(model)
bs = shape[0]
shape = shape[1:]
samples, intermediates = ddim.sample(steps, batch_size=bs, shape=shape, eta=eta, verbose=False,)
return samples, intermediates
@torch.no_grad()
def make_convolutional_sample(model, batch_size, vanilla=False, custom_steps=None, eta=1.0,):
log = dict()
shape = [batch_size,
model.model.diffusion_model.in_channels,
model.model.diffusion_model.image_size,
model.model.diffusion_model.image_size]
with model.ema_scope("Plotting"):
t0 = time.time()
if vanilla:
sample, progrow = convsample(model, shape,
make_prog_row=True)
else:
sample, intermediates = convsample_ddim(model, steps=custom_steps, shape=shape,
eta=eta)
t1 = time.time()
x_sample = model.decode_first_stage(sample)
log["sample"] = x_sample
log["time"] = t1 - t0
log['throughput'] = sample.shape[0] / (t1 - t0)
print(f'Throughput for this batch: {log["throughput"]}')
return log
def run(model, logdir, batch_size=50, vanilla=False, custom_steps=None, eta=None, n_samples=50000, nplog=None):
if vanilla:
print(f'Using Vanilla DDPM sampling with {model.num_timesteps} sampling steps.')
else:
print(f'Using DDIM sampling with {custom_steps} sampling steps and eta={eta}')
tstart = time.time()
n_saved = len(glob.glob(os.path.join(logdir,'*.png')))-1
# path = logdir
if model.cond_stage_model is None:
all_images = []
print(f"Running unconditional sampling for {n_samples} samples")
for _ in trange(n_samples // batch_size, desc="Sampling Batches (unconditional)"):
logs = make_convolutional_sample(model, batch_size=batch_size,
vanilla=vanilla, custom_steps=custom_steps,
eta=eta)
n_saved = save_logs(logs, logdir, n_saved=n_saved, key="sample")
all_images.extend([custom_to_np(logs["sample"])])
if n_saved >= n_samples:
print(f'Finish after generating {n_saved} samples')
break
all_img = np.concatenate(all_images, axis=0)
all_img = all_img[:n_samples]
shape_str = "x".join([str(x) for x in all_img.shape])
nppath = os.path.join(nplog, f"{shape_str}-samples.npz")
np.savez(nppath, all_img)
else:
raise NotImplementedError('Currently only sampling for unconditional models supported.')
print(f"sampling of {n_saved} images finished in {(time.time() - tstart) / 60.:.2f} minutes.")
def save_logs(logs, path, n_saved=0, key="sample", np_path=None):
for k in logs:
if k == key:
batch = logs[key]
if np_path is None:
for x in batch:
img = custom_to_pil(x)
imgpath = os.path.join(path, f"{key}_{n_saved:06}.png")
img.save(imgpath)
n_saved += 1
else:
npbatch = custom_to_np(batch)
shape_str = "x".join([str(x) for x in npbatch.shape])
nppath = os.path.join(np_path, f"{n_saved}-{shape_str}-samples.npz")
np.savez(nppath, npbatch)
n_saved += npbatch.shape[0]
return n_saved
def get_parser():
parser = argparse.ArgumentParser()
parser.add_argument(
"-r",
"--resume",
type=str,
nargs="?",
help="load from logdir or checkpoint in logdir",
)
parser.add_argument(
"-n",
"--n_samples",
type=int,
nargs="?",
help="number of samples to draw",
default=50000
)
parser.add_argument(
"-e",
"--eta",
type=float,
nargs="?",
help="eta for ddim sampling (0.0 yields deterministic sampling)",
default=1.0
)
parser.add_argument(
"-v",
"--vanilla_sample",
default=False,
action='store_true',
help="vanilla sampling (default option is DDIM sampling)?",
)
parser.add_argument(
"-l",
"--logdir",
type=str,
nargs="?",
help="extra logdir",
default="none"
)
parser.add_argument(
"-c",
"--custom_steps",
type=int,
nargs="?",
help="number of steps for ddim and fastdpm sampling",
default=50
)
parser.add_argument(
"--batch_size",
type=int,
nargs="?",
help="the bs",
default=10
)
return parser
def load_model_from_config(config, sd):
model = instantiate_from_config(config)
model.load_state_dict(sd,strict=False)
model.cuda()
model.eval()
return model
def load_model(config, ckpt, gpu, eval_mode):
if ckpt:
print(f"Loading model from {ckpt}")
pl_sd = torch.load(ckpt, map_location="cpu")
global_step = pl_sd["global_step"]
else:
pl_sd = {"state_dict": None}
global_step = None
model = load_model_from_config(config.model,
pl_sd["state_dict"])
return model, global_step
if __name__ == "__main__":
now = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
sys.path.append(os.getcwd())
command = " ".join(sys.argv)
parser = get_parser()
opt, unknown = parser.parse_known_args()
ckpt = None
if not os.path.exists(opt.resume):
raise ValueError("Cannot find {}".format(opt.resume))
if os.path.isfile(opt.resume):
# paths = opt.resume.split("/")
try:
logdir = '/'.join(opt.resume.split('/')[:-1])
# idx = len(paths)-paths[::-1].index("logs")+1
print(f'Logdir is {logdir}')
except ValueError:
paths = opt.resume.split("/")
idx = -2 # take a guess: path/to/logdir/checkpoints/model.ckpt
logdir = "/".join(paths[:idx])
ckpt = opt.resume
else:
assert os.path.isdir(opt.resume), f"{opt.resume} is not a directory"
logdir = opt.resume.rstrip("/")
ckpt = os.path.join(logdir, "model.ckpt")
base_configs = sorted(glob.glob(os.path.join(logdir, "config.yaml")))
opt.base = base_configs
configs = [OmegaConf.load(cfg) for cfg in opt.base]
cli = OmegaConf.from_dotlist(unknown)
config = OmegaConf.merge(*configs, cli)
gpu = True
eval_mode = True
if opt.logdir != "none":
locallog = logdir.split(os.sep)[-1]
if locallog == "": locallog = logdir.split(os.sep)[-2]
print(f"Switching logdir from '{logdir}' to '{os.path.join(opt.logdir, locallog)}'")
logdir = os.path.join(opt.logdir, locallog)
print(config)
model, global_step = load_model(config, ckpt, gpu, eval_mode)
print(f"global step: {global_step}")
print(75 * "=")
print("logging to:")
logdir = os.path.join(logdir, "samples", f"{global_step:08}", now)
imglogdir = os.path.join(logdir, "img")
numpylogdir = os.path.join(logdir, "numpy")
os.makedirs(imglogdir)
os.makedirs(numpylogdir)
print(logdir)
print(75 * "=")
# write config out
sampling_file = os.path.join(logdir, "sampling_config.yaml")
sampling_conf = vars(opt)
with open(sampling_file, 'w') as f:
yaml.dump(sampling_conf, f, default_flow_style=False)
print(sampling_conf)
run(model, imglogdir, eta=opt.eta,
vanilla=opt.vanilla_sample, n_samples=opt.n_samples, custom_steps=opt.custom_steps,
batch_size=opt.batch_size, nplog=numpylogdir)
print("done.")
================================================
FILE: models/InstructDiffusion/stable_diffusion/scripts/tests/test_watermark.py
================================================
import cv2
import fire
from imwatermark import WatermarkDecoder
def testit(img_path):
bgr = cv2.imread(img_path)
decoder = WatermarkDecoder('bytes', 136)
watermark = decoder.decode(bgr, 'dwtDct')
try:
dec = watermark.decode('utf-8')
except:
dec = "null"
print(dec)
if __name__ == "__main__":
fire.Fire(testit)
================================================
FILE: models/InstructDiffusion/stable_diffusion/scripts/train_searcher.py
================================================
import os, sys
import numpy as np
import scann
import argparse
import glob
from multiprocessing import cpu_count
from tqdm import tqdm
from ldm.util import parallel_data_prefetch
def search_bruteforce(searcher):
return searcher.score_brute_force().build()
def search_partioned_ah(searcher, dims_per_block, aiq_threshold, reorder_k,
partioning_trainsize, num_leaves, num_leaves_to_search):
return searcher.tree(num_leaves=num_leaves,
num_leaves_to_search=num_leaves_to_search,
training_sample_size=partioning_trainsize). \
score_ah(dims_per_block, anisotropic_quantization_threshold=aiq_threshold).reorder(reorder_k).build()
def search_ah(searcher, dims_per_block, aiq_threshold, reorder_k):
return searcher.score_ah(dims_per_block, anisotropic_quantization_threshold=aiq_threshold).reorder(
reorder_k).build()
def load_datapool(dpath):
def load_single_file(saved_embeddings):
compressed = np.load(saved_embeddings)
database = {key: compressed[key] for key in compressed.files}
return database
def load_multi_files(data_archive):
database = {key: [] for key in data_archive[0].files}
for d in tqdm(data_archive, desc=f'Loading datapool from {len(data_archive)} individual files.'):
for key in d.files:
database[key].append(d[key])
return database
print(f'Load saved patch embedding from "{dpath}"')
file_content = glob.glob(os.path.join(dpath, '*.npz'))
if len(file_content) == 1:
data_pool = load_single_file(file_content[0])
elif len(file_content) > 1:
data = [np.load(f) for f in file_content]
prefetched_data = parallel_data_prefetch(load_multi_files, data,
n_proc=min(len(data), cpu_count()), target_data_type='dict')
data_pool = {key: np.concatenate([od[key] for od in prefetched_data], axis=1)[0] for key in prefetched_data[0].keys()}
else:
raise ValueError(f'No npz-files in specified path "{dpath}" is this directory existing?')
print(f'Finished loading of retrieval database of length {data_pool["embedding"].shape[0]}.')
return data_pool
def train_searcher(opt,
metric='dot_product',
partioning_trainsize=None,
reorder_k=None,
# todo tune
aiq_thld=0.2,
dims_per_block=2,
num_leaves=None,
num_leaves_to_search=None,):
data_pool = load_datapool(opt.database)
k = opt.knn
if not reorder_k:
reorder_k = 2 * k
# normalize
# embeddings =
searcher = scann.scann_ops_pybind.builder(data_pool['embedding'] / np.linalg.norm(data_pool['embedding'], axis=1)[:, np.newaxis], k, metric)
pool_size = data_pool['embedding'].shape[0]
print(*(['#'] * 100))
print('Initializing scaNN searcher with the following values:')
print(f'k: {k}')
print(f'metric: {metric}')
print(f'reorder_k: {reorder_k}')
print(f'anisotropic_quantization_threshold: {aiq_thld}')
print(f'dims_per_block: {dims_per_block}')
print(*(['#'] * 100))
print('Start training searcher....')
print(f'N samples in pool is {pool_size}')
# this reflects the recommended design choices proposed at
# https://github.com/google-research/google-research/blob/aca5f2e44e301af172590bb8e65711f0c9ee0cfd/scann/docs/algorithms.md
if pool_size < 2e4:
print('Using brute force search.')
searcher = search_bruteforce(searcher)
elif 2e4 <= pool_size and pool_size < 1e5:
print('Using asymmetric hashing search and reordering.')
searcher = search_ah(searcher, dims_per_block, aiq_thld, reorder_k)
else:
print('Using using partioning, asymmetric hashing search and reordering.')
if not partioning_trainsize:
partioning_trainsize = data_pool['embedding'].shape[0] // 10
if not num_leaves:
num_leaves = int(np.sqrt(pool_size))
if not num_leaves_to_search:
num_leaves_to_search = max(num_leaves // 20, 1)
print('Partitioning params:')
print(f'num_leaves: {num_leaves}')
print(f'num_leaves_to_search: {num_leaves_to_search}')
# self.searcher = self.search_ah(searcher, dims_per_block, aiq_thld, reorder_k)
searcher = search_partioned_ah(searcher, dims_per_block, aiq_thld, reorder_k,
partioning_trainsize, num_leaves, num_leaves_to_search)
print('Finish training searcher')
searcher_savedir = opt.target_path
os.makedirs(searcher_savedir, exist_ok=True)
searcher.serialize(searcher_savedir)
print(f'Saved trained searcher under "{searcher_savedir}"')
if __name__ == '__main__':
sys.path.append(os.getcwd())
parser = argparse.ArgumentParser()
parser.add_argument('--database',
'-d',
default='data/rdm/retrieval_databases/openimages',
type=str,
help='path to folder containing the clip feature of the database')
parser.add_argument('--target_path',
'-t',
default='data/rdm/searchers/openimages',
type=str,
help='path to the target folder where the searcher shall be stored.')
parser.add_argument('--knn',
'-k',
default=20,
type=int,
help='number of nearest neighbors, for which the searcher shall be optimized')
opt, _ = parser.parse_known_args()
train_searcher(opt,)
================================================
FILE: models/InstructDiffusion/stable_diffusion/scripts/txt2img.py
================================================
import argparse, os, sys, glob
import cv2
import torch
import numpy as np
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from imwatermark import WatermarkEncoder
from itertools import islice
from einops import rearrange
from torchvision.utils import make_grid
import time
from pytorch_lightning import seed_everything
from torch import autocast
from contextlib import contextmanager, nullcontext
from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.plms import PLMSSampler
from ldm.models.diffusion.dpm_solver import DPMSolverSampler
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from transformers import AutoFeatureExtractor
# load safety model
safety_model_id = "CompVis/stable-diffusion-safety-checker"
safety_feature_extractor = AutoFeatureExtractor.from_pretrained(safety_model_id)
safety_checker = StableDiffusionSafetyChecker.from_pretrained(safety_model_id)
def chunk(it, size):
it = iter(it)
return iter(lambda: tuple(islice(it, size)), ())
def numpy_to_pil(images):
"""
Convert a numpy image or a batch of images to a PIL image.
"""
if images.ndim == 3:
images = images[None, ...]
images = (images * 255).round().astype("uint8")
pil_images = [Image.fromarray(image) for image in images]
return pil_images
def load_model_from_config(config, ckpt, verbose=False):
print(f"Loading model from {ckpt}")
pl_sd = torch.load(ckpt, map_location="cpu")
if "global_step" in pl_sd:
print(f"Global Step: {pl_sd['global_step']}")
sd = pl_sd["state_dict"]
model = instantiate_from_config(config.model)
m, u = model.load_state_dict(sd, strict=False)
if len(m) > 0 and verbose:
print("missing keys:")
print(m)
if len(u) > 0 and verbose:
print("unexpected keys:")
print(u)
model.cuda()
model.eval()
return model
def put_watermark(img, wm_encoder=None):
if wm_encoder is not None:
img = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
img = wm_encoder.encode(img, 'dwtDct')
img = Image.fromarray(img[:, :, ::-1])
return img
def load_replacement(x):
try:
hwc = x.shape
y = Image.open("assets/rick.jpeg").convert("RGB").resize((hwc[1], hwc[0]))
y = (np.array(y)/255.0).astype(x.dtype)
assert y.shape == x.shape
return y
except Exception:
return x
def check_safety(x_image):
safety_checker_input = safety_feature_extractor(numpy_to_pil(x_image), return_tensors="pt")
x_checked_image, has_nsfw_concept = safety_checker(images=x_image, clip_input=safety_checker_input.pixel_values)
assert x_checked_image.shape[0] == len(has_nsfw_concept)
for i in range(len(has_nsfw_concept)):
if has_nsfw_concept[i]:
x_checked_image[i] = load_replacement(x_checked_image[i])
return x_checked_image, has_nsfw_concept
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"--prompt",
type=str,
nargs="?",
default="a painting of a virus monster playing guitar",
help="the prompt to render"
)
parser.add_argument(
"--outdir",
type=str,
nargs="?",
help="dir to write results to",
default="outputs/txt2img-samples"
)
parser.add_argument(
"--skip_grid",
action='store_true',
help="do not save a grid, only individual samples. Helpful when evaluating lots of samples",
)
parser.add_argument(
"--skip_save",
action='store_true',
help="do not save individual samples. For speed measurements.",
)
parser.add_argument(
"--ddim_steps",
type=int,
default=50,
help="number of ddim sampling steps",
)
parser.add_argument(
"--plms",
action='store_true',
help="use plms sampling",
)
parser.add_argument(
"--dpm_solver",
action='store_true',
help="use dpm_solver sampling",
)
parser.add_argument(
"--laion400m",
action='store_true',
help="uses the LAION400M model",
)
parser.add_argument(
"--fixed_code",
action='store_true',
help="if enabled, uses the same starting code across samples ",
)
parser.add_argument(
"--ddim_eta",
type=float,
default=0.0,
help="ddim eta (eta=0.0 corresponds to deterministic sampling",
)
parser.add_argument(
"--n_iter",
type=int,
default=2,
help="sample this often",
)
parser.add_argument(
"--H",
type=int,
default=512,
help="image height, in pixel space",
)
parser.add_argument(
"--W",
type=int,
default=512,
help="image width, in pixel space",
)
parser.add_argument(
"--C",
type=int,
default=4,
help="latent channels",
)
parser.add_argument(
"--f",
type=int,
default=8,
help="downsampling factor",
)
parser.add_argument(
"--n_samples",
type=int,
default=3,
help="how many samples to produce for each given prompt. A.k.a. batch size",
)
parser.add_argument(
"--n_rows",
type=int,
default=0,
help="rows in the grid (default: n_samples)",
)
parser.add_argument(
"--scale",
type=float,
default=7.5,
help="unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) - eps(x, empty))",
)
parser.add_argument(
"--from-file",
type=str,
help="if specified, load prompts from this file",
)
parser.add_argument(
"--config",
type=str,
default="configs/stable-diffusion/v1-inference.yaml",
help="path to config which constructs model",
)
parser.add_argument(
"--ckpt",
type=str,
default="models/ldm/stable-diffusion-v1/model.ckpt",
help="path to checkpoint of model",
)
parser.add_argument(
"--seed",
type=int,
default=42,
help="the seed (for reproducible sampling)",
)
parser.add_argument(
"--precision",
type=str,
help="evaluate at this precision",
choices=["full", "autocast"],
default="autocast"
)
opt = parser.parse_args()
if opt.laion400m:
print("Falling back to LAION 400M model...")
opt.config = "configs/latent-diffusion/txt2img-1p4B-eval.yaml"
opt.ckpt = "models/ldm/text2img-large/model.ckpt"
opt.outdir = "outputs/txt2img-samples-laion400m"
seed_everything(opt.seed)
config = OmegaConf.load(f"{opt.config}")
model = load_model_from_config(config, f"{opt.ckpt}")
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
model = model.to(device)
if opt.dpm_solver:
sampler = DPMSolverSampler(model)
elif opt.plms:
sampler = PLMSSampler(model)
else:
sampler = DDIMSampler(model)
os.makedirs(opt.outdir, exist_ok=True)
outpath = opt.outdir
print("Creating invisible watermark encoder (see https://github.com/ShieldMnt/invisible-watermark)...")
wm = "StableDiffusionV1"
wm_encoder = WatermarkEncoder()
wm_encoder.set_watermark('bytes', wm.encode('utf-8'))
batch_size = opt.n_samples
n_rows = opt.n_rows if opt.n_rows > 0 else batch_size
if not opt.from_file:
prompt = opt.prompt
assert prompt is not None
data = [batch_size * [prompt]]
else:
print(f"reading prompts from {opt.from_file}")
with open(opt.from_file, "r") as f:
data = f.read().splitlines()
data = list(chunk(data, batch_size))
sample_path = os.path.join(outpath, "samples")
os.makedirs(sample_path, exist_ok=True)
base_count = len(os.listdir(sample_path))
grid_count = len(os.listdir(outpath)) - 1
start_code = None
if opt.fixed_code:
start_code = torch.randn([opt.n_samples, opt.C, opt.H // opt.f, opt.W // opt.f], device=device)
precision_scope = autocast if opt.precision=="autocast" else nullcontext
with torch.no_grad():
with precision_scope("cuda"):
with model.ema_scope():
tic = time.time()
all_samples = list()
for n in trange(opt.n_iter, desc="Sampling"):
for prompts in tqdm(data, desc="data"):
uc = None
if opt.scale != 1.0:
uc = model.get_learned_conditioning(batch_size * [""])
if isinstance(prompts, tuple):
prompts = list(prompts)
c = model.get_learned_conditioning(prompts)
shape = [opt.C, opt.H // opt.f, opt.W // opt.f]
samples_ddim, _ = sampler.sample(S=opt.ddim_steps,
conditioning=c,
batch_size=opt.n_samples,
shape=shape,
verbose=False,
unconditional_guidance_scale=opt.scale,
unconditional_conditioning=uc,
eta=opt.ddim_eta,
x_T=start_code)
x_samples_ddim = model.decode_first_stage(samples_ddim)
x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
x_samples_ddim = x_samples_ddim.cpu().permute(0, 2, 3, 1).numpy()
x_checked_image, has_nsfw_concept = check_safety(x_samples_ddim)
x_checked_image_torch = torch.from_numpy(x_checked_image).permute(0, 3, 1, 2)
if not opt.skip_save:
for x_sample in x_checked_image_torch:
x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
img = Image.fromarray(x_sample.astype(np.uint8))
img = put_watermark(img, wm_encoder)
img.save(os.path.join(sample_path, f"{base_count:05}.png"))
base_count += 1
if not opt.skip_grid:
all_samples.append(x_checked_image_torch)
if not opt.skip_grid:
# additionally, save as grid
grid = torch.stack(all_samples, 0)
grid = rearrange(grid, 'n b c h w -> (n b) c h w')
grid = make_grid(grid, nrow=n_rows)
# to image
grid = 255. * rearrange(grid, 'c h w -> h w c').cpu().numpy()
img = Image.fromarray(grid.astype(np.uint8))
img = put_watermark(img, wm_encoder)
img.save(os.path.join(outpath, f'grid-{grid_count:04}.png'))
grid_count += 1
toc = time.time()
print(f"Your samples are ready and waiting for you here: \n{outpath} \n"
f" \nEnjoy.")
if __name__ == "__main__":
main()
================================================
FILE: models/InstructDiffusion/stable_diffusion/setup.py
================================================
from setuptools import setup, find_packages
setup(
name='latent-diffusion',
version='0.0.1',
description='',
packages=find_packages(),
install_requires=[
'torch',
'numpy',
'tqdm',
],
)
================================================
FILE: models/InstructDiffusion/utils/deepspeed.py
================================================
import os
import torch
import torch.distributed as dist
import json
def create_ds_config(args, config, cfgdir):
config.deepspeed_config = os.path.join(cfgdir, f"deepspeed_config_{dist.get_rank()}.json")
opt_lower = config.trainer.optimizer.lower()
assert opt_lower == 'adamw', "deepspeed only support adamw"
with open(config.deepspeed_config, mode="w") as writer:
ds_config = {
"train_batch_size": config.data.params.batch_size * config.trainer.accumulate_grad_batches * dist.get_world_size(),
"train_micro_batch_size_per_gpu": config.data.params.batch_size,
"steps_per_print": 10,
"optimizer": {
"type": "Adam",
"adam_w_mode": True,
"params": {
"lr": config.model.base_learning_rate,
"weight_decay": config.model.weight_decay,
"bias_correction": True,
"betas": [
0.9, 0.999
],
"eps": 1e-8
}
},
}
if 'fp32' in config.model.params.deepspeed:
ds_config["fp16"] = {
"enabled": False}
else:
ds_config["fp16"] = {
"enabled": True,
"loss_scale": 0,
"initial_scale_power": config.trainer.initial_scale,
"loss_scale_window": 128}
if config.trainer.clip_grad > 0.0:
ds_config["gradient_clipping"] = config.trainer.clip_grad
zero_opt = int(config.model.params.deepspeed.split('_')[-1])
if zero_opt == 1:
ds_config["zero_optimization"] = {"stage": zero_opt}
elif zero_opt == 2:
ds_config["zero_optimization"] = {
"stage": 2,
"offload_optimizer": {
"device": "cpu",
},
"contiguous_gradients": True,
"overlap_comm": True
}
writer.write(json.dumps(ds_config, indent=2))
================================================
FILE: models/InstructDiffusion/utils/logger.py
================================================
# --------------------------------------------------------
# Swin Transformer
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ze Liu
# --------------------------------------------------------
import os
import sys
import logging
import functools
from termcolor import colored
@functools.lru_cache()
def create_logger(output_dir, dist_rank=0, name=''):
# create logger
logger = logging.getLogger(name)
logger.setLevel(logging.DEBUG)
logger.propagate = False
# create formatter
fmt = '[%(asctime)s %(name)s] (%(filename)s %(lineno)d): %(levelname)s %(message)s'
color_fmt = colored('[%(asctime)s %(name)s]', 'green') + \
colored('(%(filename)s %(lineno)d)', 'yellow') + ': %(levelname)s %(message)s'
# create console handlers for master process
if dist_rank == 0:
console_handler = logging.StreamHandler(sys.stdout)
console_handler.setLevel(logging.DEBUG)
console_handler.setFormatter(
logging.Formatter(fmt=color_fmt, datefmt='%Y-%m-%d %H:%M:%S'))
logger.addHandler(console_handler)
# create file handlers
file_handler = logging.FileHandler(os.path.join(output_dir, f'log_rank{dist_rank}.txt'), mode='a')
file_handler.setLevel(logging.DEBUG)
file_handler.setFormatter(logging.Formatter(fmt=fmt, datefmt='%Y-%m-%d %H:%M:%S'))
logger.addHandler(file_handler)
return logger
================================================
FILE: models/InstructDiffusion/utils/utils.py
================================================
# --------------------------------------------------------
# Swin Transformer
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ze Liu
# --------------------------------------------------------
import os
import torch
import torch.distributed as dist
from torch._six import inf
def load_checkpoint(file_name, config, model, model_ema, optimizer, lr_scheduler, loss_scaler, logger):
if config.model.params.deepspeed != '':
file_name = file_name.split('/')
ckptdir = '/'.join(file_name[:-1])
tag = file_name[-1]
_, client_states = model.load_checkpoint(ckptdir, tag=tag)
print(client_states)
logger.info("Resume checkpoint %s" % file_name)
checkpoint = torch.load(
os.path.join(ckptdir, tag, "state.pth"), map_location="cpu"
)
msg = model_ema.load_state_dict(checkpoint['model_ema'])
logger.info(msg)
start_epoch = checkpoint["epoch"] + 1
max_accuracy = 0.0
if loss_scaler and "grad_scale_manager" in checkpoint:
loss_scaler.load_state_dict(checkpoint["grad_scale_manager"])
if 'max_accuracy' in checkpoint:
max_accuracy = checkpoint['max_accuracy']
else:
logger.info(f"==============> Resuming form {file_name}....................")
checkpoint = torch.load(file_name, map_location='cpu')
msg = model.load_state_dict(checkpoint['model'], strict=False)
logger.info(msg)
max_accuracy = 0.0
if 'optimizer' in checkpoint and 'epoch' in checkpoint:
optimizer.load_state_dict(checkpoint['optimizer'])
if 'lr_scheduler' in checkpoint:
lr_scheduler.load_state_dict(checkpoint['lr_scheduler'])
start_epoch = checkpoint['epoch'] + 1
if 'scaler' in checkpoint:
loss_scaler.load_state_dict(checkpoint['scaler'])
logger.info(f"=> loaded successfully '{file_name}' (epoch {checkpoint['epoch']})")
if 'max_accuracy' in checkpoint:
max_accuracy = checkpoint['max_accuracy']
del checkpoint
torch.cuda.empty_cache()
return max_accuracy, start_epoch
def save_checkpoint(ckptdir, config, epoch, model, model_ema, max_accuracy, optimizer, lr_scheduler, loss_scaler, logger):
if config.model.params.deepspeed != '':
if dist.get_rank() == 0:
os.makedirs(os.path.join(ckptdir, f'ckpt_epoch_{epoch}'), exist_ok=True)
checkpoint_path = os.path.join(ckptdir, f'ckpt_epoch_{epoch}', f'state.pth')
to_save = {
'epoch': epoch,
'config': config,
'max_accuracy': max_accuracy,
'model_ema': model_ema.state_dict(),
}
if loss_scaler is not None:
to_save["grad_scale_manager"] = loss_scaler.state_dict()
logger.info(f"Saving checkpoint to {checkpoint_path}")
torch.save(to_save, checkpoint_path)
model.save_checkpoint(save_dir=ckptdir, tag=f'ckpt_epoch_{epoch}')
print(f"rank[{dist.get_rank()}]: {ckptdir}/{f'ckpt_epoch_{epoch}'} saved")
dist.barrier()
else:
if dist.get_rank() == 0:
save_state = {'model': model.state_dict(),
'optimizer': optimizer.state_dict(),
# 'lr_scheduler': lr_scheduler.state_dict(),
'max_accuracy': max_accuracy,
'scaler': loss_scaler.state_dict(),
'epoch': epoch,
'config': config}
save_path = os.path.join(ckptdir, f'ckpt_epoch_{epoch}.pth')
logger.info(f"{save_path} saving......")
torch.save(save_state, save_path)
logger.info(f"{save_path} saved !!!")
def get_grad_norm(parameters, norm_type=2):
if isinstance(parameters, torch.Tensor):
parameters = [parameters]
parameters = list(filter(lambda p: p.grad is not None, parameters))
norm_type = float(norm_type)
total_norm = 0
for p in parameters:
param_norm = p.grad.data.norm(norm_type)
total_norm += param_norm.item() ** norm_type
total_norm = total_norm ** (1. / norm_type)
return total_norm
def auto_resume_helper(config, output_dir):
if config.model.params.deepspeed != '':
dirs = os.listdir(output_dir)
dirs = [d for d in dirs if d.startswith('ckpt_epoch')]
print(f"All checkpoints founded in {output_dir}: {dirs}")
if len(dirs) > 0:
dirs = max([int(d.split('_')[-1]) for d in dirs])
latest_checkpoint = os.path.join(output_dir, 'ckpt_epoch_{}'.format(dirs))
print(f"The latest checkpoint founded: {latest_checkpoint}")
resume_file = latest_checkpoint
else:
resume_file = None
else:
checkpoints = os.listdir(output_dir)
checkpoints = [ckpt for ckpt in checkpoints if ckpt.endswith('pth')]
print(f"All checkpoints founded in {output_dir}: {checkpoints}")
if len(checkpoints) > 0:
latest_checkpoint = max([os.path.join(output_dir, d) for d in checkpoints], key=os.path.getmtime)
print(f"The latest checkpoint founded: {latest_checkpoint}")
resume_file = latest_checkpoint
else:
resume_file = None
return resume_file
def reduce_tensor(tensor):
rt = tensor.clone()
dist.all_reduce(rt, op=dist.ReduceOp.SUM)
rt /= dist.get_world_size()
return rt
def ampscaler_get_grad_norm(parameters, norm_type: float = 2.0) -> torch.Tensor:
if isinstance(parameters, torch.Tensor):
parameters = [parameters]
parameters = [p for p in parameters if p.grad is not None]
norm_type = float(norm_type)
if len(parameters) == 0:
return torch.tensor(0.)
device = parameters[0].grad.device
if norm_type == inf:
total_norm = max(p.grad.detach().abs().max().to(device) for p in parameters)
else:
total_norm = torch.norm(torch.stack([torch.norm(p.grad.detach(),
norm_type).to(device) for p in parameters]), norm_type)
return total_norm
class NativeScalerWithGradNormCount:
state_dict_key = "amp_scaler"
def __init__(self):
self._scaler = torch.cuda.amp.GradScaler()
def __call__(self, loss, optimizer, clip_grad=None, parameters=None, create_graph=False, update_grad=True):
self._scaler.scale(loss).backward(create_graph=create_graph)
if update_grad:
if clip_grad is not None:
assert parameters is not None
self._scaler.unscale_(optimizer) # unscale the gradients of optimizer's assigned params in-place
norm = torch.nn.utils.clip_grad_norm_(parameters, clip_grad)
else:
self._scaler.unscale_(optimizer)
norm = ampscaler_get_grad_norm(parameters)
self._scaler.step(optimizer)
self._scaler.update()
else:
norm = None
return norm
def state_dict(self):
return self._scaler.state_dict()
def load_state_dict(self, state_dict):
self._scaler.load_state_dict(state_dict)
================================================
FILE: models/edict/edict_functions.py
================================================
import torch
from transformers import CLIPModel, CLIPTextModel, CLIPTokenizer
from omegaconf import OmegaConf
import matplotlib.pyplot as plt
import math
import imageio
from PIL import Image
import torchvision
import torch.nn.functional as F
import torch
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
import time
import datetime
import torch
import sys
import os
from torchvision import datasets
import pickle
# StableDiffusion P2P implementation originally from https://github.com/bloc97/CrossAttentionControl
# Have diffusers with hardcoded double-casting instead of float
from models.edict.my_diffusers import AutoencoderKL, UNet2DConditionModel
from models.edict.my_diffusers.schedulers.scheduling_utils import SchedulerOutput
from models.edict.my_diffusers import LMSDiscreteScheduler, PNDMScheduler, DDPMScheduler, DDIMScheduler
import random
from tqdm.auto import tqdm
from torch import autocast
from difflib import SequenceMatcher
# Build our CLIP model
model_path_clip = "openai/clip-vit-large-patch14"
clip_tokenizer = CLIPTokenizer.from_pretrained(model_path_clip)
clip_model = CLIPModel.from_pretrained(model_path_clip, torch_dtype=torch.float16)
clip = clip_model.text_model
model_path_diffusion = "CompVis/stable-diffusion-v1-4"
# Build our SD model
unet = UNet2DConditionModel.from_pretrained(model_path_diffusion, subfolder="unet", revision="fp16", torch_dtype=torch.float16)
vae = AutoencoderKL.from_pretrained(model_path_diffusion, subfolder="vae", revision="fp16", torch_dtype=torch.float16)
# Push to devices w/ double precision
device = 'cuda'
unet.double().to(device)
vae.double().to(device)
clip.double().to(device)
print("Loaded all models")
def EDICT_editing(im_path,
base_prompt,
edit_prompt,
use_p2p=False,
steps=50,
mix_weight=0.93,
init_image_strength=0.8,
guidance_scale=3,
run_baseline=False):
"""
Main call of our research, performs editing with either EDICT or DDIM
Args:
im_path: path to image to run on
base_prompt: conditional prompt to deterministically noise with
edit_prompt: desired text conditoining
steps: ddim steps
mix_weight: Weight of mixing layers.
Higher means more consistent generations but divergence in inversion
Lower means opposite
This is fairly tuned and can get good results
init_image_strength: Editing strength. Higher = more dramatic edit.
Typically [0.6, 0.9] is good range.
Definitely tunable per-image/maybe best results are at a different value
guidance_scale: classifier-free guidance scale
3 I've found is the best for both our method and basic DDIM inversion
Higher can result in more distorted results
run_baseline:
VERY IMPORTANT
True is EDICT, False is DDIM
Output:
PAIR of Images (tuple)
If run_baseline=True then [0] will be edit and [1] will be original
This is to maintain consistently structured outputs across function calls
The functions below will never operate on [1], leaving it unchanged
If run_baseline=False then they will be two nearly identical edited versions
"""
# Resize/center crop to 512x512 (Can do higher res. if desired)
orig_im = load_im_into_format_from_path(im_path) if isinstance(im_path, str) else im_path # trust OK
# compute latent pair (second one will be original latent if run_baseline=True)
latents = coupled_stablediffusion(base_prompt,
reverse=True,
init_image=orig_im,
init_image_strength=init_image_strength,
steps=steps,
mix_weight=mix_weight,
guidance_scale=guidance_scale,
run_baseline=run_baseline)
# Denoise intermediate state with new conditioning
gen = coupled_stablediffusion(edit_prompt if (not use_p2p) else base_prompt,
None if (not use_p2p) else edit_prompt,
fixed_starting_latent=latents,
init_image_strength=init_image_strength,
steps=steps,
mix_weight=mix_weight,
guidance_scale=guidance_scale,
run_baseline=run_baseline)
return gen
def recon_test(im, steps=50, strength=1.0,
run_baseline=False,
back_and_forth=False,
prompt='',
guidance_scale=7,
plot=False,
mix_weight=1):
"""
Compute MSE Loss for images that are fully backed into latent space then decoded
MSE computed on pixels normalized to [-1, 1]
Args:
im: a PIL Image or image path
strength: How far to decode to
run_baseline: DDIM if True, EDICT if False
prompt: Default is unconditional, this should work with baseline too. Conditional is differentiator
guidance_scale: classifier-free guidance scale. Default to widely accepted value for generation
mix_weight: Weight of mixing layers in EDICT
Output:
Single float of MSE for image and method
"""
if isinstance(im, str): im = load_im_into_format_from_path(im)
latents = coupled_stablediffusion(prompt,
reverse=True,
init_image=im,
steps=steps,
init_image_strength=strength,
run_baseline=run_baseline,
back_and_forth=back_and_forth,
guidance_scale=guidance_scale,
mix_weight=mix_weight
)
if run_baseline:
latents = latents[0]
recon = coupled_stablediffusion(prompt,
reverse=False,
fixed_starting_latent=latents,
steps=steps,
init_image_strength=strength,
run_baseline=run_baseline,
back_and_forth=back_and_forth,
guidance_scale=guidance_scale,
mix_weight=mix_weight
)
recon = recon[0] if isinstance(recon, list) else recon
orig_im_arr = im_to_np(im)
recon_im_arr = im_to_np(recon)
return np.square(orig_im_arr - recon_im_arr).mean()
def im_to_np(im):
return np.array(im).astype(np.float64) / 255.0 * 2.0 - 1.0
def img2img_editing(im_path,
edit_prompt,
steps=50,
init_image_strength=0.7,
guidance_scale=3):
"""
Basic SDEdit/img2img, given an image add some noise and denoise with prompt
"""
orig_im = load_im_into_format_from_path(im_path)
return baseline_stablediffusion(edit_prompt,
init_image_strength=init_image_strength,
steps=steps,
init_image=orig_im,
guidance_scale=guidance_scale)
def center_crop(im):
width, height = im.size # Get dimensions
min_dim = min(width, height)
left = (width - min_dim)/2
top = (height - min_dim)/2
right = (width + min_dim)/2
bottom = (height + min_dim)/2
# Crop the center of the image
im = im.crop((left, top, right, bottom))
return im
def load_im_into_format_from_path(im_path):
return center_crop(Image.open(im_path)).resize((512,512))
#### P2P STUFF ####
def init_attention_weights(weight_tuples):
tokens_length = clip_tokenizer.model_max_length
weights = torch.ones(tokens_length)
for i, w in weight_tuples:
if i < tokens_length and i >= 0:
weights[i] = w
for name, module in unet.named_modules():
module_name = type(module).__name__
if module_name == "CrossAttention" and "attn2" in name:
module.last_attn_slice_weights = weights.to(device)
if module_name == "CrossAttention" and "attn1" in name:
module.last_attn_slice_weights = None
def init_attention_edit(tokens, tokens_edit):
tokens_length = clip_tokenizer.model_max_length
mask = torch.zeros(tokens_length)
indices_target = torch.arange(tokens_length, dtype=torch.long)
indices = torch.zeros(tokens_length, dtype=torch.long)
tokens = tokens.input_ids.numpy()[0]
tokens_edit = tokens_edit.input_ids.numpy()[0]
for name, a0, a1, b0, b1 in SequenceMatcher(None, tokens, tokens_edit).get_opcodes():
if b0 < tokens_length:
if name == "equal" or (name == "replace" and a1-a0 == b1-b0):
mask[b0:b1] = 1
indices[b0:b1] = indices_target[a0:a1]
for name, module in unet.named_modules():
module_name = type(module).__name__
if module_name == "CrossAttention" and "attn2" in name:
module.last_attn_slice_mask = mask.to(device)
module.last_attn_slice_indices = indices.to(device)
if module_name == "CrossAttention" and "attn1" in name:
module.last_attn_slice_mask = None
module.last_attn_slice_indices = None
def init_attention_func():
def new_attention(self, query, key, value, sequence_length, dim):
batch_size_attention = query.shape[0]
hidden_states = torch.zeros(
(batch_size_attention, sequence_length, dim // self.heads), device=query.device, dtype=query.dtype
)
slice_size = self._slice_size if self._slice_size is not None else hidden_states.shape[0]
for i in range(hidden_states.shape[0] // slice_size):
start_idx = i * slice_size
end_idx = (i + 1) * slice_size
attn_slice = (
torch.einsum("b i d, b j d -> b i j", query[start_idx:end_idx], key[start_idx:end_idx]) * self.scale
)
attn_slice = attn_slice.softmax(dim=-1)
if self.use_last_attn_slice:
if self.last_attn_slice_mask is not None:
new_attn_slice = torch.index_select(self.last_attn_slice, -1, self.last_attn_slice_indices)
attn_slice = attn_slice * (1 - self.last_attn_slice_mask) + new_attn_slice * self.last_attn_slice_mask
else:
attn_slice = self.last_attn_slice
self.use_last_attn_slice = False
if self.save_last_attn_slice:
self.last_attn_slice = attn_slice
self.save_last_attn_slice = False
if self.use_last_attn_weights and self.last_attn_slice_weights is not None:
attn_slice = attn_slice * self.last_attn_slice_weights
self.use_last_attn_weights = False
attn_slice = torch.einsum("b i j, b j d -> b i d", attn_slice, value[start_idx:end_idx])
hidden_states[start_idx:end_idx] = attn_slice
# reshape hidden_states
hidden_states = self.reshape_batch_dim_to_heads(hidden_states)
return hidden_states
for name, module in unet.named_modules():
module_name = type(module).__name__
if module_name == "CrossAttention":
module.last_attn_slice = None
module.use_last_attn_slice = False
module.use_last_attn_weights = False
module.save_last_attn_slice = False
module._attention = new_attention.__get__(module, type(module))
def use_last_tokens_attention(use=True):
for name, module in unet.named_modules():
module_name = type(module).__name__
if module_name == "CrossAttention" and "attn2" in name:
module.use_last_attn_slice = use
def use_last_tokens_attention_weights(use=True):
for name, module in unet.named_modules():
module_name = type(module).__name__
if module_name == "CrossAttention" and "attn2" in name:
module.use_last_attn_weights = use
def use_last_self_attention(use=True):
for name, module in unet.named_modules():
module_name = type(module).__name__
if module_name == "CrossAttention" and "attn1" in name:
module.use_last_attn_slice = use
def save_last_tokens_attention(save=True):
for name, module in unet.named_modules():
module_name = type(module).__name__
if module_name == "CrossAttention" and "attn2" in name:
module.save_last_attn_slice = save
def save_last_self_attention(save=True):
for name, module in unet.named_modules():
module_name = type(module).__name__
if module_name == "CrossAttention" and "attn1" in name:
module.save_last_attn_slice = save
####################################
##### BASELINE ALGORITHM, ONLY USED NOW FOR SDEDIT ####3
@torch.no_grad()
def baseline_stablediffusion(prompt="",
prompt_edit=None,
null_prompt='',
prompt_edit_token_weights=[],
prompt_edit_tokens_start=0.0,
prompt_edit_tokens_end=1.0,
prompt_edit_spatial_start=0.0,
prompt_edit_spatial_end=1.0,
clip_start=0.0,
clip_end=1.0,
guidance_scale=7,
steps=50,
seed=1,
width=512, height=512,
init_image=None, init_image_strength=0.5,
fixed_starting_latent = None,
prev_image= None,
grid=None,
clip_guidance=None,
clip_guidance_scale=1,
num_cutouts=4,
cut_power=1,
scheduler_str='lms',
return_latent=False,
one_pass=False,
normalize_noise_pred=False):
width = width - width % 64
height = height - height % 64
#If seed is None, randomly select seed from 0 to 2^32-1
if seed is None: seed = random.randrange(2**32 - 1)
generator = torch.cuda.manual_seed(seed)
#Set inference timesteps to scheduler
scheduler_dict = {'ddim':DDIMScheduler,
'lms':LMSDiscreteScheduler,
'pndm':PNDMScheduler,
'ddpm':DDPMScheduler}
scheduler_call = scheduler_dict[scheduler_str]
if scheduler_str == 'ddim':
scheduler = DDIMScheduler(beta_start=0.00085, beta_end=0.012,
beta_schedule="scaled_linear",
clip_sample=False, set_alpha_to_one=False)
else:
scheduler = scheduler_call(beta_schedule="scaled_linear",
num_train_timesteps=1000)
scheduler.set_timesteps(steps)
if prev_image is not None:
prev_scheduler = LMSDiscreteScheduler(beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
num_train_timesteps=1000)
prev_scheduler.set_timesteps(steps)
#Preprocess image if it exists (img2img)
if init_image is not None:
init_image = init_image.resize((width, height), resample=Image.Resampling.LANCZOS)
init_image = np.array(init_image).astype(np.float64) / 255.0 * 2.0 - 1.0
init_image = torch.from_numpy(init_image[np.newaxis, ...].transpose(0, 3, 1, 2))
#If there is alpha channel, composite alpha for white, as the diffusion model does not support alpha channel
if init_image.shape[1] > 3:
init_image = init_image[:, :3] * init_image[:, 3:] + (1 - init_image[:, 3:])
#Move image to GPU
init_image = init_image.to(device)
#Encode image
with autocast(device):
init_latent = vae.encode(init_image).latent_dist.sample(generator=generator) * 0.18215
t_start = steps - int(steps * init_image_strength)
else:
init_latent = torch.zeros((1, unet.in_channels, height // 8, width // 8), device=device)
t_start = 0
#Generate random normal noise
if fixed_starting_latent is None:
noise = torch.randn(init_latent.shape, generator=generator, device=device, dtype=unet.dtype)
if scheduler_str == 'ddim':
if init_image is not None:
raise notImplementedError
latent = scheduler.add_noise(init_latent, noise,
1000 - int(1000 * init_image_strength)).to(device)
else:
latent = noise
else:
latent = scheduler.add_noise(init_latent, noise,
t_start).to(device)
else:
latent = fixed_starting_latent
t_start = steps - int(steps * init_image_strength)
if prev_image is not None:
#Resize and prev_image for numpy b h w c -> torch b c h w
prev_image = prev_image.resize((width, height), resample=Image.Resampling.LANCZOS)
prev_image = np.array(prev_image).astype(np.float64) / 255.0 * 2.0 - 1.0
prev_image = torch.from_numpy(prev_image[np.newaxis, ...].transpose(0, 3, 1, 2))
#If there is alpha channel, composite alpha for white, as the diffusion model does not support alpha channel
if prev_image.shape[1] > 3:
prev_image = prev_image[:, :3] * prev_image[:, 3:] + (1 - prev_image[:, 3:])
#Move image to GPU
prev_image = prev_image.to(device)
#Encode image
with autocast(device):
prev_init_latent = vae.encode(prev_image).latent_dist.sample(generator=generator) * 0.18215
t_start = steps - int(steps * init_image_strength)
prev_latent = prev_scheduler.add_noise(prev_init_latent, noise, t_start).to(device)
else:
prev_latent = None
#Process clip
with autocast(device):
tokens_unconditional = clip_tokenizer(null_prompt, padding="max_length", max_length=clip_tokenizer.model_max_length, truncation=True, return_tensors="pt", return_overflowing_tokens=True)
embedding_unconditional = clip(tokens_unconditional.input_ids.to(device)).last_hidden_state
tokens_conditional = clip_tokenizer(prompt, padding="max_length", max_length=clip_tokenizer.model_max_length, truncation=True, return_tensors="pt", return_overflowing_tokens=True)
embedding_conditional = clip(tokens_conditional.input_ids.to(device)).last_hidden_state
#Process prompt editing
assert not ((prompt_edit is not None) and (prev_image is not None))
if prompt_edit is not None:
tokens_conditional_edit = clip_tokenizer(prompt_edit, padding="max_length", max_length=clip_tokenizer.model_max_length, truncation=True, return_tensors="pt", return_overflowing_tokens=True)
embedding_conditional_edit = clip(tokens_conditional_edit.input_ids.to(device)).last_hidden_state
init_attention_edit(tokens_conditional, tokens_conditional_edit)
elif prev_image is not None:
init_attention_edit(tokens_conditional, tokens_conditional)
init_attention_func()
init_attention_weights(prompt_edit_token_weights)
timesteps = scheduler.timesteps[t_start:]
# print(timesteps)
assert isinstance(guidance_scale, int)
num_cycles = 1 # guidance_scale + 1
last_noise_preds = None
for i, t in tqdm(enumerate(timesteps), total=len(timesteps)):
t_index = t_start + i
latent_model_input = latent
if scheduler_str=='lms':
sigma = scheduler.sigmas[t_index] # last is first and first is last
latent_model_input = (latent_model_input / ((sigma**2 + 1) ** 0.5)).to(unet.dtype)
else:
assert scheduler_str in ['ddim', 'pndm', 'ddpm']
#Predict the unconditional noise residual
if len(t.shape) == 0:
t = t[None].to(unet.device)
noise_pred_uncond = unet(latent_model_input, t, encoder_hidden_states=embedding_unconditional,
).sample
if prev_latent is not None:
prev_latent_model_input = prev_latent
prev_latent_model_input = (prev_latent_model_input / ((sigma**2 + 1) ** 0.5)).to(unet.dtype)
prev_noise_pred_uncond = unet(prev_latent_model_input, t,
encoder_hidden_states=embedding_unconditional,
).sample
# noise_pred_uncond = unet(latent_model_input, t,
# encoder_hidden_states=embedding_unconditional)['sample']
#Prepare the Cross-Attention layers
if prompt_edit is not None or prev_latent is not None:
save_last_tokens_attention()
save_last_self_attention()
else:
#Use weights on non-edited prompt when edit is None
use_last_tokens_attention_weights()
#Predict the conditional noise residual and save the cross-attention layer activations
if prev_latent is not None:
raise NotImplementedError # I totally lost track of what this is
prev_noise_pred_cond = unet(prev_latent_model_input, t, encoder_hidden_states=embedding_conditional,
).sample
else:
noise_pred_cond = unet(latent_model_input, t, encoder_hidden_states=embedding_conditional,
).sample
#Edit the Cross-Attention layer activations
t_scale = t / scheduler.num_train_timesteps
if prompt_edit is not None or prev_latent is not None:
if t_scale >= prompt_edit_tokens_start and t_scale <= prompt_edit_tokens_end:
use_last_tokens_attention()
if t_scale >= prompt_edit_spatial_start and t_scale <= prompt_edit_spatial_end:
use_last_self_attention()
#Use weights on edited prompt
use_last_tokens_attention_weights()
#Predict the edited conditional noise residual using the cross-attention masks
if prompt_edit is not None:
noise_pred_cond = unet(latent_model_input, t,
encoder_hidden_states=embedding_conditional_edit).sample
#Perform guidance
# if i%(num_cycles)==0: # cycle_i+1==num_cycles:
"""
if cycle_i+1==num_cycles:
noise_pred = noise_pred_uncond
else:
noise_pred = noise_pred_cond - noise_pred_uncond
"""
if last_noise_preds is not None:
# print( (last_noise_preds[0]*noise_pred_uncond).sum(), (last_noise_preds[1]*noise_pred_cond).sum())
# print(F.cosine_similarity(last_noise_preds[0].flatten(), noise_pred_uncond.flatten(), dim=0),
# F.cosine_similarity(last_noise_preds[1].flatten(), noise_pred_cond.flatten(), dim=0))
last_grad= last_noise_preds[1] - last_noise_preds[0]
new_grad = noise_pred_cond - noise_pred_uncond
# print( F.cosine_similarity(last_grad.flatten(), new_grad.flatten(), dim=0))
last_noise_preds = (noise_pred_uncond, noise_pred_cond)
use_cond_guidance = True
if use_cond_guidance:
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_cond - noise_pred_uncond)
else:
noise_pred = noise_pred_uncond
if clip_guidance is not None and t_scale >= clip_start and t_scale <= clip_end:
noise_pred, latent = new_cond_fn(latent, t, t_index,
embedding_conditional, noise_pred,clip_guidance,
clip_guidance_scale,
num_cutouts,
scheduler, unet,use_cutouts=True,
cut_power=cut_power)
if normalize_noise_pred:
noise_pred = noise_pred * noise_pred_uncond.norm() / noise_pred.norm()
if scheduler_str == 'ddim':
latent = forward_step(scheduler, noise_pred,
t,
latent).prev_sample
else:
latent = scheduler.step(noise_pred,
t_index,
latent).prev_sample
if prev_latent is not None:
prev_noise_pred = prev_noise_pred_uncond + guidance_scale * (prev_noise_pred_cond - prev_noise_pred_uncond)
prev_latent = prev_scheduler.step(prev_noise_pred, t_index, prev_latent).prev_sample
if one_pass: break
#scale and decode the image latents with vae
if return_latent: return latent
latent = latent / 0.18215
image = vae.decode(latent.to(vae.dtype)).sample
image = (image / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).numpy()
image = (image[0] * 255).round().astype("uint8")
return Image.fromarray(image)
####################################
#### HELPER FUNCTIONS FOR OUR METHOD #####
def get_alpha_and_beta(t, scheduler):
# want to run this for both current and previous timnestep
if t.dtype==torch.long:
alpha = scheduler.alphas_cumprod[t]
return alpha, 1-alpha
if t<0:
return scheduler.final_alpha_cumprod, 1 - scheduler.final_alpha_cumprod
low = t.floor().long()
high = t.ceil().long()
rem = t - low
low_alpha = scheduler.alphas_cumprod[low]
high_alpha = scheduler.alphas_cumprod[high]
interpolated_alpha = low_alpha * rem + high_alpha * (1-rem)
interpolated_beta = 1 - interpolated_alpha
return interpolated_alpha, interpolated_beta
# A DDIM forward step function
def forward_step(
self,
model_output,
timestep: int,
sample,
eta: float = 0.0,
use_clipped_model_output: bool = False,
generator=None,
return_dict: bool = True,
use_double=False,
) :
if self.num_inference_steps is None:
raise ValueError(
"Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"
)
prev_timestep = timestep - self.config.num_train_timesteps / self.num_inference_steps
if timestep > self.timesteps.max():
raise NotImplementedError("Need to double check what the overflow is")
alpha_prod_t, beta_prod_t = get_alpha_and_beta(timestep, self)
alpha_prod_t_prev, _ = get_alpha_and_beta(prev_timestep, self)
alpha_quotient = ((alpha_prod_t / alpha_prod_t_prev)**0.5)
first_term = (1./alpha_quotient) * sample
second_term = (1./alpha_quotient) * (beta_prod_t ** 0.5) * model_output
third_term = ((1 - alpha_prod_t_prev)**0.5) * model_output
return first_term - second_term + third_term
# A DDIM reverse step function, the inverse of above
def reverse_step(
self,
model_output,
timestep: int,
sample,
eta: float = 0.0,
use_clipped_model_output: bool = False,
generator=None,
return_dict: bool = True,
use_double=False,
) :
if self.num_inference_steps is None:
raise ValueError(
"Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"
)
prev_timestep = timestep - self.config.num_train_timesteps / self.num_inference_steps
if timestep > self.timesteps.max():
raise NotImplementedError
else:
alpha_prod_t = self.alphas_cumprod[timestep]
alpha_prod_t, beta_prod_t = get_alpha_and_beta(timestep, self)
alpha_prod_t_prev, _ = get_alpha_and_beta(prev_timestep, self)
alpha_quotient = ((alpha_prod_t / alpha_prod_t_prev)**0.5)
first_term = alpha_quotient * sample
second_term = ((beta_prod_t)**0.5) * model_output
third_term = alpha_quotient * ((1 - alpha_prod_t_prev)**0.5) * model_output
return first_term + second_term - third_term
@torch.no_grad()
def latent_to_image(latent):
image = vae.decode(latent.to(vae.dtype)/0.18215).sample
image = prep_image_for_return(image)
return image
def prep_image_for_return(image):
image = (image / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).numpy()
image = (image[0] * 255).round().astype("uint8")
image = Image.fromarray(image)
return image
#############################
##### MAIN EDICT FUNCTION #######
# Use EDICT_editing to perform calls
@torch.no_grad()
def coupled_stablediffusion(prompt="",
prompt_edit=None,
null_prompt='',
prompt_edit_token_weights=[],
prompt_edit_tokens_start=0.0,
prompt_edit_tokens_end=1.0,
prompt_edit_spatial_start=0.0,
prompt_edit_spatial_end=1.0,
guidance_scale=7.0, steps=50,
seed=1, width=512, height=512,
init_image=None, init_image_strength=1.0,
run_baseline=False,
use_lms=False,
leapfrog_steps=True,
reverse=False,
return_latents=False,
fixed_starting_latent=None,
beta_schedule='scaled_linear',
mix_weight=0.93):
#If seed is None, randomly select seed from 0 to 2^32-1
if seed is None: seed = random.randrange(2**32 - 1)
generator = torch.cuda.manual_seed(seed)
def image_to_latent(im):
if isinstance(im, torch.Tensor):
# assume it's the latent
# used to avoid clipping new generation before inversion
init_latent = im.to(device)
else:
#Resize and transpose for numpy b h w c -> torch b c h w
im = im.resize((width, height), resample=Image.Resampling.LANCZOS)
im = np.array(im).astype(np.float64) / 255.0 * 2.0 - 1.0
# check if black and white
if len(im.shape) < 3:
im = np.stack([im for _ in range(3)], axis=2) # putting at end b/c channels
im = torch.from_numpy(im[np.newaxis, ...].transpose(0, 3, 1, 2))
#If there is alpha channel, composite alpha for white, as the diffusion model does not support alpha channel
if im.shape[1] > 3:
im = im[:, :3] * im[:, 3:] + (1 - im[:, 3:])
#Move image to GPU
im = im.to(device)
#Encode image
init_latent = vae.encode(im).latent_dist.sample(generator=generator) * 0.18215
return init_latent
assert not use_lms, "Can't invert LMS the same as DDIM"
if run_baseline: leapfrog_steps=False
#Change size to multiple of 64 to prevent size mismatches inside model
width = width - width % 64
height = height - height % 64
#Preprocess image if it exists (img2img)
if init_image is not None:
assert reverse # want to be performing deterministic noising
# can take either pair (output of generative process) or single image
if isinstance(init_image, list):
if isinstance(init_image[0], torch.Tensor):
init_latent = [t.clone() for t in init_image]
else:
init_latent = [image_to_latent(im) for im in init_image]
else:
init_latent = image_to_latent(init_image)
# this is t_start for forward, t_end for reverse
t_limit = steps - int(steps * init_image_strength)
else:
assert not reverse, 'Need image to reverse from'
init_latent = torch.zeros((1, unet.in_channels, height // 8, width // 8), device=device)
t_limit = 0
if reverse:
latent = init_latent
else:
#Generate random normal noise
noise = torch.randn(init_latent.shape,
generator=generator,
device=device,
dtype=torch.float64)
if fixed_starting_latent is None:
latent = noise
else:
if isinstance(fixed_starting_latent, list):
latent = [l.clone() for l in fixed_starting_latent]
else:
latent = fixed_starting_latent.clone()
t_limit = steps - int(steps * init_image_strength)
if isinstance(latent, list): # initializing from pair of images
latent_pair = latent
else: # initializing from noise
latent_pair = [latent.clone(), latent.clone()]
if steps==0:
if init_image is not None:
return image_to_latent(init_image)
else:
image = vae.decode(latent.to(vae.dtype) / 0.18215).sample
return prep_image_for_return(image)
#Set inference timesteps to scheduler
schedulers = []
for i in range(2):
# num_raw_timesteps = max(1000, steps)
scheduler = DDIMScheduler(beta_start=0.00085, beta_end=0.012,
beta_schedule=beta_schedule,
num_train_timesteps=1000,
clip_sample=False,
set_alpha_to_one=False)
scheduler.set_timesteps(steps)
schedulers.append(scheduler)
# CLIP Text Embeddings
tokens_unconditional = clip_tokenizer(null_prompt, padding="max_length",
max_length=clip_tokenizer.model_max_length,
truncation=True, return_tensors="pt",
return_overflowing_tokens=True)
embedding_unconditional = clip(tokens_unconditional.input_ids.to(device)).last_hidden_state
tokens_conditional = clip_tokenizer(prompt, padding="max_length",
max_length=clip_tokenizer.model_max_length,
truncation=True, return_tensors="pt",
return_overflowing_tokens=True)
embedding_conditional = clip(tokens_conditional.input_ids.to(device)).last_hidden_state
#Process prompt editing (if running Prompt-to-Prompt)
if prompt_edit is not None:
tokens_conditional_edit = clip_tokenizer(prompt_edit, padding="max_length",
max_length=clip_tokenizer.model_max_length,
truncation=True, return_tensors="pt",
return_overflowing_tokens=True)
embedding_conditional_edit = clip(tokens_conditional_edit.input_ids.to(device)).last_hidden_state
init_attention_edit(tokens_conditional, tokens_conditional_edit)
init_attention_func()
init_attention_weights(prompt_edit_token_weights)
timesteps = schedulers[0].timesteps[t_limit:]
if reverse: timesteps = timesteps.flip(0)
for i, t in tqdm(enumerate(timesteps), total=len(timesteps)):
t_scale = t / schedulers[0].num_train_timesteps
if (reverse) and (not run_baseline):
# Reverse mixing layer
new_latents = [l.clone() for l in latent_pair]
new_latents[1] = (new_latents[1].clone() - (1-mix_weight)*new_latents[0].clone()) / mix_weight
new_latents[0] = (new_latents[0].clone() - (1-mix_weight)*new_latents[1].clone()) / mix_weight
latent_pair = new_latents
# alternate EDICT steps
for latent_i in range(2):
if run_baseline and latent_i==1: continue # just have one sequence for baseline
# this modifies latent_pair[i] while using
# latent_pair[(i+1)%2]
if reverse and (not run_baseline):
if leapfrog_steps:
# what i would be from going other way
orig_i = len(timesteps) - (i+1)
offset = (orig_i+1) % 2
latent_i = (latent_i + offset) % 2
else:
# Do 1 then 0
latent_i = (latent_i+1)%2
else:
if leapfrog_steps:
offset = i%2
latent_i = (latent_i + offset) % 2
latent_j = ((latent_i+1) % 2) if not run_baseline else latent_i
latent_model_input = latent_pair[latent_j]
latent_base = latent_pair[latent_i]
#Predict the unconditional noise residual
noise_pred_uncond = unet(latent_model_input, t,
encoder_hidden_states=embedding_unconditional).sample
#Prepare the Cross-Attention layers
if prompt_edit is not None:
save_last_tokens_attention()
save_last_self_attention()
else:
#Use weights on non-edited prompt when edit is None
use_last_tokens_attention_weights()
#Predict the conditional noise residual and save the cross-attention layer activations
noise_pred_cond = unet(latent_model_input, t,
encoder_hidden_states=embedding_conditional).sample
#Edit the Cross-Attention layer activations
if prompt_edit is not None:
t_scale = t / schedulers[0].num_train_timesteps
if t_scale >= prompt_edit_tokens_start and t_scale <= prompt_edit_tokens_end:
use_last_tokens_attention()
if t_scale >= prompt_edit_spatial_start and t_scale <= prompt_edit_spatial_end:
use_last_self_attention()
#Use weights on edited prompt
use_last_tokens_attention_weights()
#Predict the edited conditional noise residual using the cross-attention masks
noise_pred_cond = unet(latent_model_input,
t,
encoder_hidden_states=embedding_conditional_edit).sample
#Perform guidance
grad = (noise_pred_cond - noise_pred_uncond)
noise_pred = noise_pred_uncond + guidance_scale * grad
step_call = reverse_step if reverse else forward_step
new_latent = step_call(schedulers[latent_i],
noise_pred,
t,
latent_base)# .prev_sample
new_latent = new_latent.to(latent_base.dtype)
latent_pair[latent_i] = new_latent
if (not reverse) and (not run_baseline):
# Mixing layer (contraction) during generative process
new_latents = [l.clone() for l in latent_pair]
new_latents[0] = (mix_weight*new_latents[0] + (1-mix_weight)*new_latents[1]).clone()
new_latents[1] = ((1-mix_weight)*new_latents[0] + (mix_weight)*new_latents[1]).clone()
latent_pair = new_latents
#scale and decode the image latents with vae, can return latents instead of images
if reverse or return_latents:
results = [latent_pair]
return results if len(results)>1 else results[0]
# decode latents to iamges
images = []
for latent_i in range(2):
latent = latent_pair[latent_i] / 0.18215
image = vae.decode(latent.to(vae.dtype)).sample
images.append(image)
# Return images
return_arr = []
for image in images:
image = prep_image_for_return(image)
return_arr.append(image)
results = [return_arr]
return results if len(results)>1 else results[0]
================================================
FILE: models/edict/my_diffusers/__init__.py
================================================
from .utils import (
is_inflect_available,
is_onnx_available,
is_scipy_available,
is_transformers_available,
is_unidecode_available,
)
__version__ = "0.3.0"
from .configuration_utils import ConfigMixin
from .modeling_utils import ModelMixin
from .models import AutoencoderKL, UNet2DConditionModel, UNet2DModel, VQModel
from .onnx_utils import OnnxRuntimeModel
from .optimization import (
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
get_scheduler,
)
from .pipeline_utils import DiffusionPipeline
from .pipelines import DDIMPipeline, DDPMPipeline, KarrasVePipeline, LDMPipeline, PNDMPipeline, ScoreSdeVePipeline
from .schedulers import (
DDIMScheduler,
DDPMScheduler,
KarrasVeScheduler,
PNDMScheduler,
SchedulerMixin,
ScoreSdeVeScheduler,
)
from .utils import logging
if is_scipy_available():
from .schedulers import LMSDiscreteScheduler
else:
from .utils.dummy_scipy_objects import * # noqa F403
from .training_utils import EMAModel
if is_transformers_available():
from .pipelines import (
LDMTextToImagePipeline,
StableDiffusionImg2ImgPipeline,
StableDiffusionInpaintPipeline,
StableDiffusionPipeline,
)
else:
from .utils.dummy_transformers_objects import * # noqa F403
if is_transformers_available() and is_onnx_available():
from .pipelines import StableDiffusionOnnxPipeline
else:
from .utils.dummy_transformers_and_onnx_objects import * # noqa F403
================================================
FILE: models/edict/my_diffusers/commands/__init__.py
================================================
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class BaseDiffusersCLICommand(ABC):
@staticmethod
@abstractmethod
def register_subcommand(parser: ArgumentParser):
raise NotImplementedError()
@abstractmethod
def run(self):
raise NotImplementedError()
================================================
FILE: models/edict/my_diffusers/commands/diffusers_cli.py
================================================
#!/usr/bin/env python
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from argparse import ArgumentParser
from .env import EnvironmentCommand
def main():
parser = ArgumentParser("Diffusers CLI tool", usage="diffusers-cli []")
commands_parser = parser.add_subparsers(help="diffusers-cli command helpers")
# Register commands
EnvironmentCommand.register_subcommand(commands_parser)
# Let's go
args = parser.parse_args()
if not hasattr(args, "func"):
parser.print_help()
exit(1)
# Run
service = args.func(args)
service.run()
if __name__ == "__main__":
main()
================================================
FILE: models/edict/my_diffusers/commands/env.py
================================================
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import is_torch_available, is_transformers_available
from . import BaseDiffusersCLICommand
def info_command_factory(_):
return EnvironmentCommand()
class EnvironmentCommand(BaseDiffusersCLICommand):
@staticmethod
def register_subcommand(parser: ArgumentParser):
download_parser = parser.add_parser("env")
download_parser.set_defaults(func=info_command_factory)
def run(self):
hub_version = huggingface_hub.__version__
pt_version = "not installed"
pt_cuda_available = "NA"
if is_torch_available():
import torch
pt_version = torch.__version__
pt_cuda_available = torch.cuda.is_available()
transformers_version = "not installed"
if is_transformers_available:
import transformers
transformers_version = transformers.__version__
info = {
"`diffusers` version": version,
"Platform": platform.platform(),
"Python version": platform.python_version(),
"PyTorch version (GPU?)": f"{pt_version} ({pt_cuda_available})",
"Huggingface_hub version": hub_version,
"Transformers version": transformers_version,
"Using GPU in script?": "",
"Using distributed or parallel set-up in script?": "",
}
print("\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n")
print(self.format_dict(info))
return info
@staticmethod
def format_dict(d):
return "\n".join([f"- {prop}: {val}" for prop, val in d.items()]) + "\n"
================================================
FILE: models/edict/my_diffusers/configuration_utils.py
================================================
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" ConfigMixinuration base class and utilities."""
import functools
import inspect
import json
import os
import re
from collections import OrderedDict
from typing import Any, Dict, Tuple, Union
from huggingface_hub import hf_hub_download
from huggingface_hub.utils import EntryNotFoundError, RepositoryNotFoundError, RevisionNotFoundError
from requests import HTTPError
from . import __version__
from .utils import DIFFUSERS_CACHE, HUGGINGFACE_CO_RESOLVE_ENDPOINT, logging
logger = logging.get_logger(__name__)
_re_configuration_file = re.compile(r"config\.(.*)\.json")
class ConfigMixin:
r"""
Base class for all configuration classes. Stores all configuration parameters under `self.config` Also handles all
methods for loading/downloading/saving classes inheriting from [`ConfigMixin`] with
- [`~ConfigMixin.from_config`]
- [`~ConfigMixin.save_config`]
Class attributes:
- **config_name** (`str`) -- A filename under which the config should stored when calling
[`~ConfigMixin.save_config`] (should be overridden by parent class).
- **ignore_for_config** (`List[str]`) -- A list of attributes that should not be saved in the config (should be
overridden by parent class).
"""
config_name = None
ignore_for_config = []
def register_to_config(self, **kwargs):
if self.config_name is None:
raise NotImplementedError(f"Make sure that {self.__class__} has defined a class name `config_name`")
kwargs["_class_name"] = self.__class__.__name__
kwargs["_diffusers_version"] = __version__
for key, value in kwargs.items():
try:
setattr(self, key, value)
except AttributeError as err:
logger.error(f"Can't set {key} with value {value} for {self}")
raise err
if not hasattr(self, "_internal_dict"):
internal_dict = kwargs
else:
previous_dict = dict(self._internal_dict)
internal_dict = {**self._internal_dict, **kwargs}
logger.debug(f"Updating config from {previous_dict} to {internal_dict}")
self._internal_dict = FrozenDict(internal_dict)
def save_config(self, save_directory: Union[str, os.PathLike], push_to_hub: bool = False, **kwargs):
"""
Save a configuration object to the directory `save_directory`, so that it can be re-loaded using the
[`~ConfigMixin.from_config`] class method.
Args:
save_directory (`str` or `os.PathLike`):
Directory where the configuration JSON file will be saved (will be created if it does not exist).
"""
if os.path.isfile(save_directory):
raise AssertionError(f"Provided path ({save_directory}) should be a directory, not a file")
os.makedirs(save_directory, exist_ok=True)
# If we save using the predefined names, we can load using `from_config`
output_config_file = os.path.join(save_directory, self.config_name)
self.to_json_file(output_config_file)
logger.info(f"ConfigMixinuration saved in {output_config_file}")
@classmethod
def from_config(cls, pretrained_model_name_or_path: Union[str, os.PathLike], return_unused_kwargs=False, **kwargs):
r"""
Instantiate a Python class from a pre-defined JSON-file.
Parameters:
pretrained_model_name_or_path (`str` or `os.PathLike`, *optional*):
Can be either:
- A string, the *model id* of a model repo on huggingface.co. Valid model ids should have an
organization name, like `google/ddpm-celebahq-256`.
- A path to a *directory* containing model weights saved using [`~ConfigMixin.save_config`], e.g.,
`./my_model_directory/`.
cache_dir (`Union[str, os.PathLike]`, *optional*):
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.
ignore_mismatched_sizes (`bool`, *optional*, defaults to `False`):
Whether or not to raise an error if some of the weights from the checkpoint do not have the same size
as the weights of the model (if for instance, you are instantiating a model with 10 labels from a
checkpoint with 3 labels).
force_download (`bool`, *optional*, defaults to `False`):
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download (`bool`, *optional*, defaults to `False`):
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.
proxies (`Dict[str, str]`, *optional*):
A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
output_loading_info(`bool`, *optional*, defaults to `False`):
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
local_files_only(`bool`, *optional*, defaults to `False`):
Whether or not to only look at local files (i.e., do not try to download the model).
use_auth_token (`str` or *bool*, *optional*):
The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated
when running `transformers-cli login` (stored in `~/.huggingface`).
revision (`str`, *optional*, defaults to `"main"`):
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
identifier allowed by git.
mirror (`str`, *optional*):
Mirror source to accelerate downloads in China. If you are from China and have an accessibility
problem, you can set this option to resolve it. Note that we do not guarantee the timeliness or safety.
Please refer to the mirror site for more information.
Passing `use_auth_token=True`` is required when you want to use a private model.
Activate the special ["offline-mode"](https://huggingface.co/transformers/installation.html#offline-mode) to
use this method in a firewalled environment.
"""
config_dict = cls.get_config_dict(pretrained_model_name_or_path=pretrained_model_name_or_path, **kwargs)
init_dict, unused_kwargs = cls.extract_init_dict(config_dict, **kwargs)
model = cls(**init_dict)
if return_unused_kwargs:
return model, unused_kwargs
else:
return model
@classmethod
def get_config_dict(
cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs
) -> Tuple[Dict[str, Any], Dict[str, Any]]:
cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE)
force_download = kwargs.pop("force_download", False)
resume_download = kwargs.pop("resume_download", False)
proxies = kwargs.pop("proxies", None)
use_auth_token = kwargs.pop("use_auth_token", None)
local_files_only = kwargs.pop("local_files_only", False)
revision = kwargs.pop("revision", None)
subfolder = kwargs.pop("subfolder", None)
user_agent = {"file_type": "config"}
pretrained_model_name_or_path = str(pretrained_model_name_or_path)
if cls.config_name is None:
raise ValueError(
"`self.config_name` is not defined. Note that one should not load a config from "
"`ConfigMixin`. Please make sure to define `config_name` in a class inheriting from `ConfigMixin`"
)
if os.path.isfile(pretrained_model_name_or_path):
config_file = pretrained_model_name_or_path
elif os.path.isdir(pretrained_model_name_or_path):
if os.path.isfile(os.path.join(pretrained_model_name_or_path, cls.config_name)):
# Load from a PyTorch checkpoint
config_file = os.path.join(pretrained_model_name_or_path, cls.config_name)
elif subfolder is not None and os.path.isfile(
os.path.join(pretrained_model_name_or_path, subfolder, cls.config_name)
):
config_file = os.path.join(pretrained_model_name_or_path, subfolder, cls.config_name)
else:
raise EnvironmentError(
f"Error no file named {cls.config_name} found in directory {pretrained_model_name_or_path}."
)
else:
try:
# Load from URL or cache if already cached
config_file = hf_hub_download(
pretrained_model_name_or_path,
filename=cls.config_name,
cache_dir=cache_dir,
force_download=force_download,
proxies=proxies,
resume_download=resume_download,
local_files_only=local_files_only,
use_auth_token=use_auth_token,
user_agent=user_agent,
subfolder=subfolder,
revision=revision,
)
except RepositoryNotFoundError:
raise EnvironmentError(
f"{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier"
" listed on 'https://huggingface.co/models'\nIf this is a private repository, make sure to pass a"
" token having permission to this repo with `use_auth_token` or log in with `huggingface-cli"
" login` and pass `use_auth_token=True`."
)
except RevisionNotFoundError:
raise EnvironmentError(
f"{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for"
" this model name. Check the model page at"
f" 'https://huggingface.co/{pretrained_model_name_or_path}' for available revisions."
)
except EntryNotFoundError:
raise EnvironmentError(
f"{pretrained_model_name_or_path} does not appear to have a file named {cls.config_name}."
)
except HTTPError as err:
raise EnvironmentError(
"There was a specific connection error when trying to load"
f" {pretrained_model_name_or_path}:\n{err}"
)
except ValueError:
raise EnvironmentError(
f"We couldn't connect to '{HUGGINGFACE_CO_RESOLVE_ENDPOINT}' to load this model, couldn't find it"
f" in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a"
f" directory containing a {cls.config_name} file.\nCheckout your internet connection or see how to"
" run the library in offline mode at"
" 'https://huggingface.co/docs/diffusers/installation#offline-mode'."
)
except EnvironmentError:
raise EnvironmentError(
f"Can't load config for '{pretrained_model_name_or_path}'. If you were trying to load it from "
"'https://huggingface.co/models', make sure you don't have a local directory with the same name. "
f"Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a directory "
f"containing a {cls.config_name} file"
)
try:
# Load config dict
config_dict = cls._dict_from_json_file(config_file)
except (json.JSONDecodeError, UnicodeDecodeError):
raise EnvironmentError(f"It looks like the config file at '{config_file}' is not a valid JSON file.")
return config_dict
@classmethod
def extract_init_dict(cls, config_dict, **kwargs):
expected_keys = set(dict(inspect.signature(cls.__init__).parameters).keys())
expected_keys.remove("self")
# remove general kwargs if present in dict
if "kwargs" in expected_keys:
expected_keys.remove("kwargs")
# remove keys to be ignored
if len(cls.ignore_for_config) > 0:
expected_keys = expected_keys - set(cls.ignore_for_config)
init_dict = {}
for key in expected_keys:
if key in kwargs:
# overwrite key
init_dict[key] = kwargs.pop(key)
elif key in config_dict:
# use value from config dict
init_dict[key] = config_dict.pop(key)
unused_kwargs = config_dict.update(kwargs)
passed_keys = set(init_dict.keys())
if len(expected_keys - passed_keys) > 0:
logger.warning(
f"{expected_keys - passed_keys} was not found in config. Values will be initialized to default values."
)
return init_dict, unused_kwargs
@classmethod
def _dict_from_json_file(cls, json_file: Union[str, os.PathLike]):
with open(json_file, "r", encoding="utf-8") as reader:
text = reader.read()
return json.loads(text)
def __repr__(self):
return f"{self.__class__.__name__} {self.to_json_string()}"
@property
def config(self) -> Dict[str, Any]:
return self._internal_dict
def to_json_string(self) -> str:
"""
Serializes this instance to a JSON string.
Returns:
`str`: String containing all the attributes that make up this configuration instance in JSON format.
"""
config_dict = self._internal_dict if hasattr(self, "_internal_dict") else {}
return json.dumps(config_dict, indent=2, sort_keys=True) + "\n"
def to_json_file(self, json_file_path: Union[str, os.PathLike]):
"""
Save this instance to a JSON file.
Args:
json_file_path (`str` or `os.PathLike`):
Path to the JSON file in which this configuration instance's parameters will be saved.
"""
with open(json_file_path, "w", encoding="utf-8") as writer:
writer.write(self.to_json_string())
class FrozenDict(OrderedDict):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
for key, value in self.items():
setattr(self, key, value)
self.__frozen = True
def __delitem__(self, *args, **kwargs):
raise Exception(f"You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.")
def setdefault(self, *args, **kwargs):
raise Exception(f"You cannot use ``setdefault`` on a {self.__class__.__name__} instance.")
def pop(self, *args, **kwargs):
raise Exception(f"You cannot use ``pop`` on a {self.__class__.__name__} instance.")
def update(self, *args, **kwargs):
raise Exception(f"You cannot use ``update`` on a {self.__class__.__name__} instance.")
def __setattr__(self, name, value):
if hasattr(self, "__frozen") and self.__frozen:
raise Exception(f"You cannot use ``__setattr__`` on a {self.__class__.__name__} instance.")
super().__setattr__(name, value)
def __setitem__(self, name, value):
if hasattr(self, "__frozen") and self.__frozen:
raise Exception(f"You cannot use ``__setattr__`` on a {self.__class__.__name__} instance.")
super().__setitem__(name, value)
def register_to_config(init):
r"""
Decorator to apply on the init of classes inheriting from [`ConfigMixin`] so that all the arguments are
automatically sent to `self.register_for_config`. To ignore a specific argument accepted by the init but that
shouldn't be registered in the config, use the `ignore_for_config` class variable
Warning: Once decorated, all private arguments (beginning with an underscore) are trashed and not sent to the init!
"""
@functools.wraps(init)
def inner_init(self, *args, **kwargs):
# Ignore private kwargs in the init.
init_kwargs = {k: v for k, v in kwargs.items() if not k.startswith("_")}
init(self, *args, **init_kwargs)
if not isinstance(self, ConfigMixin):
raise RuntimeError(
f"`@register_for_config` was applied to {self.__class__.__name__} init method, but this class does "
"not inherit from `ConfigMixin`."
)
ignore = getattr(self, "ignore_for_config", [])
# Get positional arguments aligned with kwargs
new_kwargs = {}
signature = inspect.signature(init)
parameters = {
name: p.default for i, (name, p) in enumerate(signature.parameters.items()) if i > 0 and name not in ignore
}
for arg, name in zip(args, parameters.keys()):
new_kwargs[name] = arg
# Then add all kwargs
new_kwargs.update(
{
k: init_kwargs.get(k, default)
for k, default in parameters.items()
if k not in ignore and k not in new_kwargs
}
)
getattr(self, "register_to_config")(**new_kwargs)
return inner_init
================================================
FILE: models/edict/my_diffusers/dependency_versions_check.py
================================================
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers
pkgs_to_check_at_runtime = "python tqdm regex requests packaging filelock numpy tokenizers".split()
if sys.version_info < (3, 7):
pkgs_to_check_at_runtime.append("dataclasses")
if sys.version_info < (3, 8):
pkgs_to_check_at_runtime.append("importlib_metadata")
for pkg in pkgs_to_check_at_runtime:
if pkg in deps:
if pkg == "tokenizers":
# must be loaded here, or else tqdm check may fail
from .utils import is_tokenizers_available
if not is_tokenizers_available():
continue # not required, check version only if installed
require_version_core(deps[pkg])
else:
raise ValueError(f"can't find {pkg} in {deps.keys()}, check dependency_versions_table.py")
def dep_version_check(pkg, hint=None):
require_version(deps[pkg], hint)
================================================
FILE: models/edict/my_diffusers/dependency_versions_table.py
================================================
# THIS FILE HAS BEEN AUTOGENERATED. To update:
# 1. modify the `_deps` dict in setup.py
# 2. run `make deps_table_update``
deps = {
"Pillow": "Pillow",
"accelerate": "accelerate>=0.11.0",
"black": "black==22.3",
"datasets": "datasets",
"filelock": "filelock",
"flake8": "flake8>=3.8.3",
"hf-doc-builder": "hf-doc-builder>=0.3.0",
"huggingface-hub": "huggingface-hub>=0.8.1",
"importlib_metadata": "importlib_metadata",
"isort": "isort>=5.5.4",
"modelcards": "modelcards==0.1.4",
"numpy": "numpy",
"pytest": "pytest",
"pytest-timeout": "pytest-timeout",
"pytest-xdist": "pytest-xdist",
"scipy": "scipy",
"regex": "regex!=2019.12.17",
"requests": "requests",
"tensorboard": "tensorboard",
"torch": "torch>=1.4",
"transformers": "transformers>=4.21.0",
}
================================================
FILE: models/edict/my_diffusers/dynamic_modules_utils.py
================================================
# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Utilities to dynamically load objects from the Hub."""
import importlib
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from huggingface_hub import cached_download
from .utils import DIFFUSERS_DYNAMIC_MODULE_NAME, HF_MODULES_CACHE, logging
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
def init_hf_modules():
"""
Creates the cache directory for modules with an init, and adds it to the Python path.
"""
# This function has already been executed if HF_MODULES_CACHE already is in the Python path.
if HF_MODULES_CACHE in sys.path:
return
sys.path.append(HF_MODULES_CACHE)
os.makedirs(HF_MODULES_CACHE, exist_ok=True)
init_path = Path(HF_MODULES_CACHE) / "__init__.py"
if not init_path.exists():
init_path.touch()
def create_dynamic_module(name: Union[str, os.PathLike]):
"""
Creates a dynamic module in the cache directory for modules.
"""
init_hf_modules()
dynamic_module_path = Path(HF_MODULES_CACHE) / name
# If the parent module does not exist yet, recursively create it.
if not dynamic_module_path.parent.exists():
create_dynamic_module(dynamic_module_path.parent)
os.makedirs(dynamic_module_path, exist_ok=True)
init_path = dynamic_module_path / "__init__.py"
if not init_path.exists():
init_path.touch()
def get_relative_imports(module_file):
"""
Get the list of modules that are relatively imported in a module file.
Args:
module_file (`str` or `os.PathLike`): The module file to inspect.
"""
with open(module_file, "r", encoding="utf-8") as f:
content = f.read()
# Imports of the form `import .xxx`
relative_imports = re.findall("^\s*import\s+\.(\S+)\s*$", content, flags=re.MULTILINE)
# Imports of the form `from .xxx import yyy`
relative_imports += re.findall("^\s*from\s+\.(\S+)\s+import", content, flags=re.MULTILINE)
# Unique-ify
return list(set(relative_imports))
def get_relative_import_files(module_file):
"""
Get the list of all files that are needed for a given module. Note that this function recurses through the relative
imports (if a imports b and b imports c, it will return module files for b and c).
Args:
module_file (`str` or `os.PathLike`): The module file to inspect.
"""
no_change = False
files_to_check = [module_file]
all_relative_imports = []
# Let's recurse through all relative imports
while not no_change:
new_imports = []
for f in files_to_check:
new_imports.extend(get_relative_imports(f))
module_path = Path(module_file).parent
new_import_files = [str(module_path / m) for m in new_imports]
new_import_files = [f for f in new_import_files if f not in all_relative_imports]
files_to_check = [f"{f}.py" for f in new_import_files]
no_change = len(new_import_files) == 0
all_relative_imports.extend(files_to_check)
return all_relative_imports
def check_imports(filename):
"""
Check if the current Python environment contains all the libraries that are imported in a file.
"""
with open(filename, "r", encoding="utf-8") as f:
content = f.read()
# Imports of the form `import xxx`
imports = re.findall("^\s*import\s+(\S+)\s*$", content, flags=re.MULTILINE)
# Imports of the form `from xxx import yyy`
imports += re.findall("^\s*from\s+(\S+)\s+import", content, flags=re.MULTILINE)
# Only keep the top-level module
imports = [imp.split(".")[0] for imp in imports if not imp.startswith(".")]
# Unique-ify and test we got them all
imports = list(set(imports))
missing_packages = []
for imp in imports:
try:
importlib.import_module(imp)
except ImportError:
missing_packages.append(imp)
if len(missing_packages) > 0:
raise ImportError(
"This modeling file requires the following packages that were not found in your environment: "
f"{', '.join(missing_packages)}. Run `pip install {' '.join(missing_packages)}`"
)
return get_relative_imports(filename)
def get_class_in_module(class_name, module_path):
"""
Import a module on the cache directory for modules and extract a class from it.
"""
module_path = module_path.replace(os.path.sep, ".")
module = importlib.import_module(module_path)
return getattr(module, class_name)
def get_cached_module_file(
pretrained_model_name_or_path: Union[str, os.PathLike],
module_file: str,
cache_dir: Optional[Union[str, os.PathLike]] = None,
force_download: bool = False,
resume_download: bool = False,
proxies: Optional[Dict[str, str]] = None,
use_auth_token: Optional[Union[bool, str]] = None,
revision: Optional[str] = None,
local_files_only: bool = False,
):
"""
Prepares Downloads a module from a local folder or a distant repo and returns its path inside the cached
Transformers module.
Args:
pretrained_model_name_or_path (`str` or `os.PathLike`):
This can be either:
- a string, the *model id* of a pretrained model configuration hosted inside a model repo on
huggingface.co. Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced
under a user or organization name, like `dbmdz/bert-base-german-cased`.
- a path to a *directory* containing a configuration file saved using the
[`~PreTrainedTokenizer.save_pretrained`] method, e.g., `./my_model_directory/`.
module_file (`str`):
The name of the module file containing the class to look for.
cache_dir (`str` or `os.PathLike`, *optional*):
Path to a directory in which a downloaded pretrained model configuration should be cached if the standard
cache should not be used.
force_download (`bool`, *optional*, defaults to `False`):
Whether or not to force to (re-)download the configuration files and override the cached versions if they
exist.
resume_download (`bool`, *optional*, defaults to `False`):
Whether or not to delete incompletely received file. Attempts to resume the download if such a file exists.
proxies (`Dict[str, str]`, *optional*):
A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
'http://hostname': 'foo.bar:4012'}.` The proxies are used on each request.
use_auth_token (`str` or *bool*, *optional*):
The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated
when running `transformers-cli login` (stored in `~/.huggingface`).
revision (`str`, *optional*, defaults to `"main"`):
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
identifier allowed by git.
local_files_only (`bool`, *optional*, defaults to `False`):
If `True`, will only try to load the tokenizer configuration from local files.
Passing `use_auth_token=True` is required when you want to use a private model.
Returns:
`str`: The path to the module inside the cache.
"""
# Download and cache module_file from the repo `pretrained_model_name_or_path` of grab it if it's a local file.
pretrained_model_name_or_path = str(pretrained_model_name_or_path)
module_file_or_url = os.path.join(pretrained_model_name_or_path, module_file)
submodule = "local"
if os.path.isfile(module_file_or_url):
resolved_module_file = module_file_or_url
else:
try:
# Load from URL or cache if already cached
resolved_module_file = cached_download(
module_file_or_url,
cache_dir=cache_dir,
force_download=force_download,
proxies=proxies,
resume_download=resume_download,
local_files_only=local_files_only,
use_auth_token=use_auth_token,
)
except EnvironmentError:
logger.error(f"Could not locate the {module_file} inside {pretrained_model_name_or_path}.")
raise
# Check we have all the requirements in our environment
modules_needed = check_imports(resolved_module_file)
# Now we move the module inside our cached dynamic modules.
full_submodule = DIFFUSERS_DYNAMIC_MODULE_NAME + os.path.sep + submodule
create_dynamic_module(full_submodule)
submodule_path = Path(HF_MODULES_CACHE) / full_submodule
# We always copy local files (we could hash the file to see if there was a change, and give them the name of
# that hash, to only copy when there is a modification but it seems overkill for now).
# The only reason we do the copy is to avoid putting too many folders in sys.path.
shutil.copy(resolved_module_file, submodule_path / module_file)
for module_needed in modules_needed:
module_needed = f"{module_needed}.py"
shutil.copy(os.path.join(pretrained_model_name_or_path, module_needed), submodule_path / module_needed)
return os.path.join(full_submodule, module_file)
def get_class_from_dynamic_module(
pretrained_model_name_or_path: Union[str, os.PathLike],
module_file: str,
class_name: str,
cache_dir: Optional[Union[str, os.PathLike]] = None,
force_download: bool = False,
resume_download: bool = False,
proxies: Optional[Dict[str, str]] = None,
use_auth_token: Optional[Union[bool, str]] = None,
revision: Optional[str] = None,
local_files_only: bool = False,
**kwargs,
):
"""
Extracts a class from a module file, present in the local folder or repository of a model.
Calling this function will execute the code in the module file found locally or downloaded from the Hub. It should
therefore only be called on trusted repos.
Args:
pretrained_model_name_or_path (`str` or `os.PathLike`):
This can be either:
- a string, the *model id* of a pretrained model configuration hosted inside a model repo on
huggingface.co. Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced
under a user or organization name, like `dbmdz/bert-base-german-cased`.
- a path to a *directory* containing a configuration file saved using the
[`~PreTrainedTokenizer.save_pretrained`] method, e.g., `./my_model_directory/`.
module_file (`str`):
The name of the module file containing the class to look for.
class_name (`str`):
The name of the class to import in the module.
cache_dir (`str` or `os.PathLike`, *optional*):
Path to a directory in which a downloaded pretrained model configuration should be cached if the standard
cache should not be used.
force_download (`bool`, *optional*, defaults to `False`):
Whether or not to force to (re-)download the configuration files and override the cached versions if they
exist.
resume_download (`bool`, *optional*, defaults to `False`):
Whether or not to delete incompletely received file. Attempts to resume the download if such a file exists.
proxies (`Dict[str, str]`, *optional*):
A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
'http://hostname': 'foo.bar:4012'}.` The proxies are used on each request.
use_auth_token (`str` or `bool`, *optional*):
The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated
when running `transformers-cli login` (stored in `~/.huggingface`).
revision (`str`, *optional*, defaults to `"main"`):
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
identifier allowed by git.
local_files_only (`bool`, *optional*, defaults to `False`):
If `True`, will only try to load the tokenizer configuration from local files.
Passing `use_auth_token=True` is required when you want to use a private model.
Returns:
`type`: The class, dynamically imported from the module.
Examples:
```python
# Download module `modeling.py` from huggingface.co and cache then extract the class `MyBertModel` from this
# module.
cls = get_class_from_dynamic_module("sgugger/my-bert-model", "modeling.py", "MyBertModel")
```"""
# And lastly we get the class inside our newly created module
final_module = get_cached_module_file(
pretrained_model_name_or_path,
module_file,
cache_dir=cache_dir,
force_download=force_download,
resume_download=resume_download,
proxies=proxies,
use_auth_token=use_auth_token,
revision=revision,
local_files_only=local_files_only,
)
return get_class_in_module(class_name, final_module.replace(".py", ""))
================================================
FILE: models/edict/my_diffusers/hub_utils.py
================================================
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import shutil
from pathlib import Path
from typing import Optional
from huggingface_hub import HfFolder, Repository, whoami
from .pipeline_utils import DiffusionPipeline
from .utils import is_modelcards_available, logging
if is_modelcards_available():
from modelcards import CardData, ModelCard
logger = logging.get_logger(__name__)
MODEL_CARD_TEMPLATE_PATH = Path(__file__).parent / "utils" / "model_card_template.md"
def get_full_repo_name(model_id: str, organization: Optional[str] = None, token: Optional[str] = None):
if token is None:
token = HfFolder.get_token()
if organization is None:
username = whoami(token)["name"]
return f"{username}/{model_id}"
else:
return f"{organization}/{model_id}"
def init_git_repo(args, at_init: bool = False):
"""
Args:
Initializes a git repo in `args.hub_model_id`.
at_init (`bool`, *optional*, defaults to `False`):
Whether this function is called before any training or not. If `self.args.overwrite_output_dir` is `True`
and `at_init` is `True`, the path to the repo (which is `self.args.output_dir`) might be wiped out.
"""
if hasattr(args, "local_rank") and args.local_rank not in [-1, 0]:
return
hub_token = args.hub_token if hasattr(args, "hub_token") else None
use_auth_token = True if hub_token is None else hub_token
if not hasattr(args, "hub_model_id") or args.hub_model_id is None:
repo_name = Path(args.output_dir).absolute().name
else:
repo_name = args.hub_model_id
if "/" not in repo_name:
repo_name = get_full_repo_name(repo_name, token=hub_token)
try:
repo = Repository(
args.output_dir,
clone_from=repo_name,
use_auth_token=use_auth_token,
private=args.hub_private_repo,
)
except EnvironmentError:
if args.overwrite_output_dir and at_init:
# Try again after wiping output_dir
shutil.rmtree(args.output_dir)
repo = Repository(
args.output_dir,
clone_from=repo_name,
use_auth_token=use_auth_token,
)
else:
raise
repo.git_pull()
# By default, ignore the checkpoint folders
if not os.path.exists(os.path.join(args.output_dir, ".gitignore")):
with open(os.path.join(args.output_dir, ".gitignore"), "w", encoding="utf-8") as writer:
writer.writelines(["checkpoint-*/"])
return repo
def push_to_hub(
args,
pipeline: DiffusionPipeline,
repo: Repository,
commit_message: Optional[str] = "End of training",
blocking: bool = True,
**kwargs,
) -> str:
"""
Parameters:
Upload *self.model* and *self.tokenizer* to the 🤗 model hub on the repo *self.args.hub_model_id*.
commit_message (`str`, *optional*, defaults to `"End of training"`):
Message to commit while pushing.
blocking (`bool`, *optional*, defaults to `True`):
Whether the function should return only when the `git push` has finished.
kwargs:
Additional keyword arguments passed along to [`create_model_card`].
Returns:
The url of the commit of your model in the given repository if `blocking=False`, a tuple with the url of the
commit and an object to track the progress of the commit if `blocking=True`
"""
if not hasattr(args, "hub_model_id") or args.hub_model_id is None:
model_name = Path(args.output_dir).name
else:
model_name = args.hub_model_id.split("/")[-1]
output_dir = args.output_dir
os.makedirs(output_dir, exist_ok=True)
logger.info(f"Saving pipeline checkpoint to {output_dir}")
pipeline.save_pretrained(output_dir)
# Only push from one node.
if hasattr(args, "local_rank") and args.local_rank not in [-1, 0]:
return
# Cancel any async push in progress if blocking=True. The commits will all be pushed together.
if (
blocking
and len(repo.command_queue) > 0
and repo.command_queue[-1] is not None
and not repo.command_queue[-1].is_done
):
repo.command_queue[-1]._process.kill()
git_head_commit_url = repo.push_to_hub(commit_message=commit_message, blocking=blocking, auto_lfs_prune=True)
# push separately the model card to be independent from the rest of the model
create_model_card(args, model_name=model_name)
try:
repo.push_to_hub(commit_message="update model card README.md", blocking=blocking, auto_lfs_prune=True)
except EnvironmentError as exc:
logger.error(f"Error pushing update to the model card. Please read logs and retry.\n${exc}")
return git_head_commit_url
def create_model_card(args, model_name):
if not is_modelcards_available:
raise ValueError(
"Please make sure to have `modelcards` installed when using the `create_model_card` function. You can"
" install the package with `pip install modelcards`."
)
if hasattr(args, "local_rank") and args.local_rank not in [-1, 0]:
return
hub_token = args.hub_token if hasattr(args, "hub_token") else None
repo_name = get_full_repo_name(model_name, token=hub_token)
model_card = ModelCard.from_template(
card_data=CardData( # Card metadata object that will be converted to YAML block
language="en",
license="apache-2.0",
library_name="diffusers",
tags=[],
datasets=args.dataset_name,
metrics=[],
),
template_path=MODEL_CARD_TEMPLATE_PATH,
model_name=model_name,
repo_name=repo_name,
dataset_name=args.dataset_name if hasattr(args, "dataset_name") else None,
learning_rate=args.learning_rate,
train_batch_size=args.train_batch_size,
eval_batch_size=args.eval_batch_size,
gradient_accumulation_steps=args.gradient_accumulation_steps
if hasattr(args, "gradient_accumulation_steps")
else None,
adam_beta1=args.adam_beta1 if hasattr(args, "adam_beta1") else None,
adam_beta2=args.adam_beta2 if hasattr(args, "adam_beta2") else None,
adam_weight_decay=args.adam_weight_decay if hasattr(args, "adam_weight_decay") else None,
adam_epsilon=args.adam_epsilon if hasattr(args, "adam_epsilon") else None,
lr_scheduler=args.lr_scheduler if hasattr(args, "lr_scheduler") else None,
lr_warmup_steps=args.lr_warmup_steps if hasattr(args, "lr_warmup_steps") else None,
ema_inv_gamma=args.ema_inv_gamma if hasattr(args, "ema_inv_gamma") else None,
ema_power=args.ema_power if hasattr(args, "ema_power") else None,
ema_max_decay=args.ema_max_decay if hasattr(args, "ema_max_decay") else None,
mixed_precision=args.mixed_precision,
)
card_path = os.path.join(args.output_dir, "README.md")
model_card.save(card_path)
================================================
FILE: models/edict/my_diffusers/modeling_utils.py
================================================
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from typing import Callable, List, Optional, Tuple, Union
import torch
from torch import Tensor, device
from huggingface_hub import hf_hub_download
from huggingface_hub.utils import EntryNotFoundError, RepositoryNotFoundError, RevisionNotFoundError
from requests import HTTPError
from .utils import CONFIG_NAME, DIFFUSERS_CACHE, HUGGINGFACE_CO_RESOLVE_ENDPOINT, logging
WEIGHTS_NAME = "diffusion_pytorch_model.bin"
logger = logging.get_logger(__name__)
def get_parameter_device(parameter: torch.nn.Module):
try:
return next(parameter.parameters()).device
except StopIteration:
# For torch.nn.DataParallel compatibility in PyTorch 1.5
def find_tensor_attributes(module: torch.nn.Module) -> List[Tuple[str, Tensor]]:
tuples = [(k, v) for k, v in module.__dict__.items() if torch.is_tensor(v)]
return tuples
gen = parameter._named_members(get_members_fn=find_tensor_attributes)
first_tuple = next(gen)
return first_tuple[1].device
def get_parameter_dtype(parameter: torch.nn.Module):
try:
return next(parameter.parameters()).dtype
except StopIteration:
# For torch.nn.DataParallel compatibility in PyTorch 1.5
def find_tensor_attributes(module: torch.nn.Module) -> List[Tuple[str, Tensor]]:
tuples = [(k, v) for k, v in module.__dict__.items() if torch.is_tensor(v)]
return tuples
gen = parameter._named_members(get_members_fn=find_tensor_attributes)
first_tuple = next(gen)
return first_tuple[1].dtype
def load_state_dict(checkpoint_file: Union[str, os.PathLike]):
"""
Reads a PyTorch checkpoint file, returning properly formatted errors if they arise.
"""
try:
return torch.load(checkpoint_file, map_location="cpu")
except Exception as e:
try:
with open(checkpoint_file) as f:
if f.read().startswith("version"):
raise OSError(
"You seem to have cloned a repository without having git-lfs installed. Please install "
"git-lfs and run `git lfs install` followed by `git lfs pull` in the folder "
"you cloned."
)
else:
raise ValueError(
f"Unable to locate the file {checkpoint_file} which is necessary to load this pretrained "
"model. Make sure you have saved the model properly."
) from e
except (UnicodeDecodeError, ValueError):
raise OSError(
f"Unable to load weights from pytorch checkpoint file for '{checkpoint_file}' "
f"at '{checkpoint_file}'. "
"If you tried to load a PyTorch model from a TF 2.0 checkpoint, please set from_tf=True."
)
def _load_state_dict_into_model(model_to_load, state_dict):
# Convert old format to new format if needed from a PyTorch state_dict
# copy state_dict so _load_from_state_dict can modify it
state_dict = state_dict.copy()
error_msgs = []
# PyTorch's `_load_from_state_dict` does not copy parameters in a module's descendants
# so we need to apply the function recursively.
def load(module: torch.nn.Module, prefix=""):
args = (state_dict, prefix, {}, True, [], [], error_msgs)
module._load_from_state_dict(*args)
for name, child in module._modules.items():
if child is not None:
load(child, prefix + name + ".")
load(model_to_load)
return error_msgs
class ModelMixin(torch.nn.Module):
r"""
Base class for all models.
[`ModelMixin`] takes care of storing the configuration of the models and handles methods for loading, downloading
and saving models.
- **config_name** ([`str`]) -- A filename under which the model should be stored when calling
[`~modeling_utils.ModelMixin.save_pretrained`].
"""
config_name = CONFIG_NAME
_automatically_saved_args = ["_diffusers_version", "_class_name", "_name_or_path"]
def __init__(self):
super().__init__()
def save_pretrained(
self,
save_directory: Union[str, os.PathLike],
is_main_process: bool = True,
save_function: Callable = torch.save,
):
"""
Save a model and its configuration file to a directory, so that it can be re-loaded using the
`[`~modeling_utils.ModelMixin.from_pretrained`]` class method.
Arguments:
save_directory (`str` or `os.PathLike`):
Directory to which to save. Will be created if it doesn't exist.
is_main_process (`bool`, *optional*, defaults to `True`):
Whether the process calling this is the main process or not. Useful when in distributed training like
TPUs and need to call this function on all processes. In this case, set `is_main_process=True` only on
the main process to avoid race conditions.
save_function (`Callable`):
The function to use to save the state dictionary. Useful on distributed training like TPUs when one
need to replace `torch.save` by another method.
"""
if os.path.isfile(save_directory):
logger.error(f"Provided path ({save_directory}) should be a directory, not a file")
return
os.makedirs(save_directory, exist_ok=True)
model_to_save = self
# Attach architecture to the config
# Save the config
if is_main_process:
model_to_save.save_config(save_directory)
# Save the model
state_dict = model_to_save.state_dict()
# Clean the folder from a previous save
for filename in os.listdir(save_directory):
full_filename = os.path.join(save_directory, filename)
# If we have a shard file that is not going to be replaced, we delete it, but only from the main process
# in distributed settings to avoid race conditions.
if filename.startswith(WEIGHTS_NAME[:-4]) and os.path.isfile(full_filename) and is_main_process:
os.remove(full_filename)
# Save the model
save_function(state_dict, os.path.join(save_directory, WEIGHTS_NAME))
logger.info(f"Model weights saved in {os.path.join(save_directory, WEIGHTS_NAME)}")
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], **kwargs):
r"""
Instantiate a pretrained pytorch model from a pre-trained model configuration.
The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated). To train
the model, you should first set it back in training mode with `model.train()`.
The warning *Weights from XXX not initialized from pretrained model* means that the weights of XXX do not come
pretrained with the rest of the model. It is up to you to train those weights with a downstream fine-tuning
task.
The warning *Weights from XXX not used in YYY* means that the layer XXX is not used by YYY, therefore those
weights are discarded.
Parameters:
pretrained_model_name_or_path (`str` or `os.PathLike`, *optional*):
Can be either:
- A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids should have an organization name, like `google/ddpm-celebahq-256`.
- A path to a *directory* containing model weights saved using [`~ModelMixin.save_config`], e.g.,
`./my_model_directory/`.
cache_dir (`Union[str, os.PathLike]`, *optional*):
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.
torch_dtype (`str` or `torch.dtype`, *optional*):
Override the default `torch.dtype` and load the model under this dtype. If `"auto"` is passed the dtype
will be automatically derived from the model's weights.
force_download (`bool`, *optional*, defaults to `False`):
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download (`bool`, *optional*, defaults to `False`):
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.
proxies (`Dict[str, str]`, *optional*):
A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
output_loading_info(`bool`, *optional*, defaults to `False`):
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
local_files_only(`bool`, *optional*, defaults to `False`):
Whether or not to only look at local files (i.e., do not try to download the model).
use_auth_token (`str` or *bool*, *optional*):
The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated
when running `diffusers-cli login` (stored in `~/.huggingface`).
revision (`str`, *optional*, defaults to `"main"`):
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
identifier allowed by git.
mirror (`str`, *optional*):
Mirror source to accelerate downloads in China. If you are from China and have an accessibility
problem, you can set this option to resolve it. Note that we do not guarantee the timeliness or safety.
Please refer to the mirror site for more information.
Passing `use_auth_token=True`` is required when you want to use a private model.
Activate the special ["offline-mode"](https://huggingface.co/diffusers/installation.html#offline-mode) to use
this method in a firewalled environment.
"""
cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE)
ignore_mismatched_sizes = kwargs.pop("ignore_mismatched_sizes", False)
force_download = kwargs.pop("force_download", False)
resume_download = kwargs.pop("resume_download", False)
proxies = kwargs.pop("proxies", None)
output_loading_info = kwargs.pop("output_loading_info", False)
local_files_only = kwargs.pop("local_files_only", False)
use_auth_token = kwargs.pop("use_auth_token", None)
revision = kwargs.pop("revision", None)
from_auto_class = kwargs.pop("_from_auto", False)
torch_dtype = kwargs.pop("torch_dtype", None)
subfolder = kwargs.pop("subfolder", None)
user_agent = {"file_type": "model", "framework": "pytorch", "from_auto_class": from_auto_class}
# Load config if we don't provide a configuration
config_path = pretrained_model_name_or_path
model, unused_kwargs = cls.from_config(
config_path,
cache_dir=cache_dir,
return_unused_kwargs=True,
force_download=force_download,
resume_download=resume_download,
proxies=proxies,
local_files_only=local_files_only,
use_auth_token=use_auth_token,
revision=revision,
subfolder=subfolder,
**kwargs,
)
if torch_dtype is not None and not isinstance(torch_dtype, torch.dtype):
raise ValueError(
f"{torch_dtype} needs to be of type `torch.dtype`, e.g. `torch.float16`, but is {type(torch_dtype)}."
)
elif torch_dtype is not None:
model = model.to(torch_dtype)
model.register_to_config(_name_or_path=pretrained_model_name_or_path)
# This variable will flag if we're loading a sharded checkpoint. In this case the archive file is just the
# Load model
pretrained_model_name_or_path = str(pretrained_model_name_or_path)
if os.path.isdir(pretrained_model_name_or_path):
if os.path.isfile(os.path.join(pretrained_model_name_or_path, WEIGHTS_NAME)):
# Load from a PyTorch checkpoint
model_file = os.path.join(pretrained_model_name_or_path, WEIGHTS_NAME)
elif subfolder is not None and os.path.isfile(
os.path.join(pretrained_model_name_or_path, subfolder, WEIGHTS_NAME)
):
model_file = os.path.join(pretrained_model_name_or_path, subfolder, WEIGHTS_NAME)
else:
raise EnvironmentError(
f"Error no file named {WEIGHTS_NAME} found in directory {pretrained_model_name_or_path}."
)
else:
try:
# Load from URL or cache if already cached
model_file = hf_hub_download(
pretrained_model_name_or_path,
filename=WEIGHTS_NAME,
cache_dir=cache_dir,
force_download=force_download,
proxies=proxies,
resume_download=resume_download,
local_files_only=local_files_only,
use_auth_token=use_auth_token,
user_agent=user_agent,
subfolder=subfolder,
revision=revision,
)
except RepositoryNotFoundError:
raise EnvironmentError(
f"{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier "
"listed on 'https://huggingface.co/models'\nIf this is a private repository, make sure to pass a "
"token having permission to this repo with `use_auth_token` or log in with `huggingface-cli "
"login` and pass `use_auth_token=True`."
)
except RevisionNotFoundError:
raise EnvironmentError(
f"{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for "
"this model name. Check the model page at "
f"'https://huggingface.co/{pretrained_model_name_or_path}' for available revisions."
)
except EntryNotFoundError:
raise EnvironmentError(
f"{pretrained_model_name_or_path} does not appear to have a file named {WEIGHTS_NAME}."
)
except HTTPError as err:
raise EnvironmentError(
"There was a specific connection error when trying to load"
f" {pretrained_model_name_or_path}:\n{err}"
)
except ValueError:
raise EnvironmentError(
f"We couldn't connect to '{HUGGINGFACE_CO_RESOLVE_ENDPOINT}' to load this model, couldn't find it"
f" in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a"
f" directory containing a file named {WEIGHTS_NAME} or"
" \nCheckout your internet connection or see how to run the library in"
" offline mode at 'https://huggingface.co/docs/diffusers/installation#offline-mode'."
)
except EnvironmentError:
raise EnvironmentError(
f"Can't load the model for '{pretrained_model_name_or_path}'. If you were trying to load it from "
"'https://huggingface.co/models', make sure you don't have a local directory with the same name. "
f"Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a directory "
f"containing a file named {WEIGHTS_NAME}"
)
# restore default dtype
state_dict = load_state_dict(model_file)
model, missing_keys, unexpected_keys, mismatched_keys, error_msgs = cls._load_pretrained_model(
model,
state_dict,
model_file,
pretrained_model_name_or_path,
ignore_mismatched_sizes=ignore_mismatched_sizes,
)
# Set model in evaluation mode to deactivate DropOut modules by default
model.eval()
if output_loading_info:
loading_info = {
"missing_keys": missing_keys,
"unexpected_keys": unexpected_keys,
"mismatched_keys": mismatched_keys,
"error_msgs": error_msgs,
}
return model, loading_info
return model
@classmethod
def _load_pretrained_model(
cls,
model,
state_dict,
resolved_archive_file,
pretrained_model_name_or_path,
ignore_mismatched_sizes=False,
):
# Retrieve missing & unexpected_keys
model_state_dict = model.state_dict()
loaded_keys = [k for k in state_dict.keys()]
expected_keys = list(model_state_dict.keys())
original_loaded_keys = loaded_keys
missing_keys = list(set(expected_keys) - set(loaded_keys))
unexpected_keys = list(set(loaded_keys) - set(expected_keys))
# Make sure we are able to load base models as well as derived models (with heads)
model_to_load = model
def _find_mismatched_keys(
state_dict,
model_state_dict,
loaded_keys,
ignore_mismatched_sizes,
):
mismatched_keys = []
if ignore_mismatched_sizes:
for checkpoint_key in loaded_keys:
model_key = checkpoint_key
if (
model_key in model_state_dict
and state_dict[checkpoint_key].shape != model_state_dict[model_key].shape
):
mismatched_keys.append(
(checkpoint_key, state_dict[checkpoint_key].shape, model_state_dict[model_key].shape)
)
del state_dict[checkpoint_key]
return mismatched_keys
if state_dict is not None:
# Whole checkpoint
mismatched_keys = _find_mismatched_keys(
state_dict,
model_state_dict,
original_loaded_keys,
ignore_mismatched_sizes,
)
error_msgs = _load_state_dict_into_model(model_to_load, state_dict)
if len(error_msgs) > 0:
error_msg = "\n\t".join(error_msgs)
if "size mismatch" in error_msg:
error_msg += (
"\n\tYou may consider adding `ignore_mismatched_sizes=True` in the model `from_pretrained` method."
)
raise RuntimeError(f"Error(s) in loading state_dict for {model.__class__.__name__}:\n\t{error_msg}")
if len(unexpected_keys) > 0:
logger.warning(
f"Some weights of the model checkpoint at {pretrained_model_name_or_path} were not used when"
f" initializing {model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are"
f" initializing {model.__class__.__name__} from the checkpoint of a model trained on another task"
" or with another architecture (e.g. initializing a BertForSequenceClassification model from a"
" BertForPreTraining model).\n- This IS NOT expected if you are initializing"
f" {model.__class__.__name__} from the checkpoint of a model that you expect to be exactly"
" identical (initializing a BertForSequenceClassification model from a"
" BertForSequenceClassification model)."
)
else:
logger.info(f"All model checkpoint weights were used when initializing {model.__class__.__name__}.\n")
if len(missing_keys) > 0:
logger.warning(
f"Some weights of {model.__class__.__name__} were not initialized from the model checkpoint at"
f" {pretrained_model_name_or_path} and are newly initialized: {missing_keys}\nYou should probably"
" TRAIN this model on a down-stream task to be able to use it for predictions and inference."
)
elif len(mismatched_keys) == 0:
logger.info(
f"All the weights of {model.__class__.__name__} were initialized from the model checkpoint at"
f" {pretrained_model_name_or_path}.\nIf your task is similar to the task the model of the"
f" checkpoint was trained on, you can already use {model.__class__.__name__} for predictions"
" without further training."
)
if len(mismatched_keys) > 0:
mismatched_warning = "\n".join(
[
f"- {key}: found shape {shape1} in the checkpoint and {shape2} in the model instantiated"
for key, shape1, shape2 in mismatched_keys
]
)
logger.warning(
f"Some weights of {model.__class__.__name__} were not initialized from the model checkpoint at"
f" {pretrained_model_name_or_path} and are newly initialized because the shapes did not"
f" match:\n{mismatched_warning}\nYou should probably TRAIN this model on a down-stream task to be"
" able to use it for predictions and inference."
)
return model, missing_keys, unexpected_keys, mismatched_keys, error_msgs
@property
def device(self) -> device:
"""
`torch.device`: The device on which the module is (assuming that all the module parameters are on the same
device).
"""
return get_parameter_device(self)
@property
def dtype(self) -> torch.dtype:
"""
`torch.dtype`: The dtype of the module (assuming that all the module parameters have the same dtype).
"""
return get_parameter_dtype(self)
def num_parameters(self, only_trainable: bool = False, exclude_embeddings: bool = False) -> int:
"""
Get number of (optionally, trainable or non-embeddings) parameters in the module.
Args:
only_trainable (`bool`, *optional*, defaults to `False`):
Whether or not to return only the number of trainable parameters
exclude_embeddings (`bool`, *optional*, defaults to `False`):
Whether or not to return only the number of non-embeddings parameters
Returns:
`int`: The number of parameters.
"""
if exclude_embeddings:
embedding_param_names = [
f"{name}.weight"
for name, module_type in self.named_modules()
if isinstance(module_type, torch.nn.Embedding)
]
non_embedding_parameters = [
parameter for name, parameter in self.named_parameters() if name not in embedding_param_names
]
return sum(p.numel() for p in non_embedding_parameters if p.requires_grad or not only_trainable)
else:
return sum(p.numel() for p in self.parameters() if p.requires_grad or not only_trainable)
def unwrap_model(model: torch.nn.Module) -> torch.nn.Module:
"""
Recursively unwraps a model from potential containers (as used in distributed training).
Args:
model (`torch.nn.Module`): The model to unwrap.
"""
# since there could be multiple levels of wrapping, unwrap recursively
if hasattr(model, "module"):
return unwrap_model(model.module)
else:
return model
================================================
FILE: models/edict/my_diffusers/models/__init__.py
================================================
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from .unet_2d import UNet2DModel
from .unet_2d_condition import UNet2DConditionModel
from .vae import AutoencoderKL, VQModel
================================================
FILE: models/edict/my_diffusers/models/attention.py
================================================
import math
from typing import Optional
import torch
import torch.nn.functional as F
from torch import nn
class AttentionBlock(nn.Module):
"""
An attention block that allows spatial positions to attend to each other. Originally ported from here, but adapted
to the N-d case.
https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66.
Uses three q, k, v linear layers to compute attention.
Parameters:
channels (:obj:`int`): The number of channels in the input and output.
num_head_channels (:obj:`int`, *optional*):
The number of channels in each head. If None, then `num_heads` = 1.
num_groups (:obj:`int`, *optional*, defaults to 32): The number of groups to use for group norm.
rescale_output_factor (:obj:`float`, *optional*, defaults to 1.0): The factor to rescale the output by.
eps (:obj:`float`, *optional*, defaults to 1e-5): The epsilon value to use for group norm.
"""
def __init__(
self,
channels: int,
num_head_channels: Optional[int] = None,
num_groups: int = 32,
rescale_output_factor = 1.0,
eps = 1e-5,
):
super().__init__()
self.channels = channels
self.num_heads = channels // num_head_channels if num_head_channels is not None else 1
self.num_head_size = num_head_channels
self.group_norm = nn.GroupNorm(num_channels=channels, num_groups=num_groups, eps=eps, affine=True)
# define q,k,v as linear layers
self.query = nn.Linear(channels, channels)
self.key = nn.Linear(channels, channels)
self.value = nn.Linear(channels, channels)
self.rescale_output_factor = rescale_output_factor
self.proj_attn = nn.Linear(channels, channels, 1)
def transpose_for_scores(self, projection: torch.Tensor) -> torch.Tensor:
new_projection_shape = projection.size()[:-1] + (self.num_heads, -1)
# move heads to 2nd position (B, T, H * D) -> (B, T, H, D) -> (B, H, T, D)
new_projection = projection.view(new_projection_shape).permute(0, 2, 1, 3)
return new_projection
def forward(self, hidden_states):
residual = hidden_states
batch, channel, height, width = hidden_states.shape
# norm
hidden_states = self.group_norm(hidden_states)
hidden_states = hidden_states.view(batch, channel, height * width).transpose(1, 2)
# proj to q, k, v
query_proj = self.query(hidden_states)
key_proj = self.key(hidden_states)
value_proj = self.value(hidden_states)
# transpose
query_states = self.transpose_for_scores(query_proj)
key_states = self.transpose_for_scores(key_proj)
value_states = self.transpose_for_scores(value_proj)
# get scores
scale = 1 / math.sqrt(math.sqrt(self.channels / self.num_heads))
attention_scores = torch.matmul(query_states * scale, key_states.transpose(-1, -2) * scale)
attention_probs = torch.softmax(attention_scores.double(), dim=-1).type(attention_scores.dtype)
# compute attention output
hidden_states = torch.matmul(attention_probs, value_states)
hidden_states = hidden_states.permute(0, 2, 1, 3).contiguous()
new_hidden_states_shape = hidden_states.size()[:-2] + (self.channels,)
hidden_states = hidden_states.view(new_hidden_states_shape)
# compute next hidden_states
hidden_states = self.proj_attn(hidden_states)
hidden_states = hidden_states.transpose(-1, -2).reshape(batch, channel, height, width)
# res connect and rescale
hidden_states = (hidden_states + residual) / self.rescale_output_factor
return hidden_states
class SpatialTransformer(nn.Module):
"""
Transformer block for image-like data. First, project the input (aka embedding) and reshape to b, t, d. Then apply
standard transformer action. Finally, reshape to image.
Parameters:
in_channels (:obj:`int`): The number of channels in the input and output.
n_heads (:obj:`int`): The number of heads to use for multi-head attention.
d_head (:obj:`int`): The number of channels in each head.
depth (:obj:`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use.
dropout (:obj:`float`, *optional*, defaults to 0.1): The dropout probability to use.
context_dim (:obj:`int`, *optional*): The number of context dimensions to use.
"""
def __init__(
self,
in_channels: int,
n_heads: int,
d_head: int,
depth: int = 1,
dropout = 0.0,
context_dim: Optional[int] = None,
):
super().__init__()
self.n_heads = n_heads
self.d_head = d_head
self.in_channels = in_channels
inner_dim = n_heads * d_head
self.norm = torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
self.proj_in = nn.Conv2d(in_channels, inner_dim, kernel_size=1, stride=1, padding=0)
self.transformer_blocks = nn.ModuleList(
[
BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim)
for d in range(depth)
]
)
self.proj_out = nn.Conv2d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0)
def _set_attention_slice(self, slice_size):
for block in self.transformer_blocks:
block._set_attention_slice(slice_size)
def forward(self, x, context=None):
# note: if no context is given, cross-attention defaults to self-attention
b, c, h, w = x.shape
x_in = x
x = self.norm(x)
x = self.proj_in(x)
x = x.permute(0, 2, 3, 1).reshape(b, h * w, c)
for block in self.transformer_blocks:
x = block(x, context=context)
x = x.reshape(b, h, w, c).permute(0, 3, 1, 2)
x = self.proj_out(x)
return x + x_in
class BasicTransformerBlock(nn.Module):
r"""
A basic Transformer block.
Parameters:
dim (:obj:`int`): The number of channels in the input and output.
n_heads (:obj:`int`): The number of heads to use for multi-head attention.
d_head (:obj:`int`): The number of channels in each head.
dropout (:obj:`float`, *optional*, defaults to 0.0): The dropout probability to use.
context_dim (:obj:`int`, *optional*): The size of the context vector for cross attention.
gated_ff (:obj:`bool`, *optional*, defaults to :obj:`False`): Whether to use a gated feed-forward network.
checkpoint (:obj:`bool`, *optional*, defaults to :obj:`False`): Whether to use checkpointing.
"""
def __init__(
self,
dim: int,
n_heads: int,
d_head: int,
dropout=0.0,
context_dim: Optional[int] = None,
gated_ff: bool = True,
checkpoint: bool = True,
):
super().__init__()
self.attn1 = CrossAttention(
query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout
) # is a self-attention
self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff)
self.attn2 = CrossAttention(
query_dim=dim, context_dim=context_dim, heads=n_heads, dim_head=d_head, dropout=dropout
) # is self-attn if context is none
self.norm1 = nn.LayerNorm(dim)
self.norm2 = nn.LayerNorm(dim)
self.norm3 = nn.LayerNorm(dim)
self.checkpoint = checkpoint
def _set_attention_slice(self, slice_size):
self.attn1._slice_size = slice_size
self.attn2._slice_size = slice_size
def forward(self, x, context=None):
x = x.contiguous() if x.device.type == "mps" else x
x = self.attn1(self.norm1(x)) + x
x = self.attn2(self.norm2(x), context=context) + x
x = self.ff(self.norm3(x)) + x
return x
class CrossAttention(nn.Module):
r"""
A cross attention layer.
Parameters:
query_dim (:obj:`int`): The number of channels in the query.
context_dim (:obj:`int`, *optional*):
The number of channels in the context. If not given, defaults to `query_dim`.
heads (:obj:`int`, *optional*, defaults to 8): The number of heads to use for multi-head attention.
dim_head (:obj:`int`, *optional*, defaults to 64): The number of channels in each head.
dropout (:obj:`float`, *optional*, defaults to 0.0): The dropout probability to use.
"""
def __init__(
self, query_dim: int, context_dim: Optional[int] = None, heads: int = 8, dim_head: int = 64, dropout: int = 0.0
):
super().__init__()
inner_dim = dim_head * heads
context_dim = context_dim if context_dim is not None else query_dim
self.scale = dim_head**-0.5
self.heads = heads
# for slice_size > 0 the attention score computation
# is split across the batch axis to save memory
# You can set slice_size with `set_attention_slice`
self._slice_size = None
self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
self.to_out = nn.Sequential(nn.Linear(inner_dim, query_dim), nn.Dropout(dropout))
def reshape_heads_to_batch_dim(self, tensor):
batch_size, seq_len, dim = tensor.shape
head_size = self.heads
tensor = tensor.reshape(batch_size, seq_len, head_size, dim // head_size)
tensor = tensor.permute(0, 2, 1, 3).reshape(batch_size * head_size, seq_len, dim // head_size)
return tensor
def reshape_batch_dim_to_heads(self, tensor):
batch_size, seq_len, dim = tensor.shape
head_size = self.heads
tensor = tensor.reshape(batch_size // head_size, head_size, seq_len, dim)
tensor = tensor.permute(0, 2, 1, 3).reshape(batch_size // head_size, seq_len, dim * head_size)
return tensor
def forward(self, x, context=None, mask=None):
batch_size, sequence_length, dim = x.shape
q = self.to_q(x)
context = context if context is not None else x
k = self.to_k(context)
v = self.to_v(context)
q = self.reshape_heads_to_batch_dim(q)
k = self.reshape_heads_to_batch_dim(k)
v = self.reshape_heads_to_batch_dim(v)
# TODO(PVP) - mask is currently never used. Remember to re-implement when used
# attention, what we cannot get enough of
hidden_states = self._attention(q, k, v, sequence_length, dim)
return self.to_out(hidden_states)
def _attention(self, query, key, value, sequence_length, dim):
batch_size_attention = query.shape[0]
hidden_states = torch.zeros(
(batch_size_attention, sequence_length, dim // self.heads), device=query.device, dtype=query.dtype
)
slice_size = self._slice_size if self._slice_size is not None else hidden_states.shape[0]
for i in range(hidden_states.shape[0] // slice_size):
start_idx = i * slice_size
end_idx = (i + 1) * slice_size
attn_slice = (
torch.einsum("b i d, b j d -> b i j", query[start_idx:end_idx], key[start_idx:end_idx]) * self.scale
)
attn_slice = attn_slice.softmax(dim=-1)
attn_slice = torch.einsum("b i j, b j d -> b i d", attn_slice, value[start_idx:end_idx])
hidden_states[start_idx:end_idx] = attn_slice
# reshape hidden_states
hidden_states = self.reshape_batch_dim_to_heads(hidden_states)
return hidden_states
class FeedForward(nn.Module):
r"""
A feed-forward layer.
Parameters:
dim (:obj:`int`): The number of channels in the input.
dim_out (:obj:`int`, *optional*): The number of channels in the output. If not given, defaults to `dim`.
mult (:obj:`int`, *optional*, defaults to 4): The multiplier to use for the hidden dimension.
glu (:obj:`bool`, *optional*, defaults to :obj:`False`): Whether to use GLU activation.
dropout (:obj:`float`, *optional*, defaults to 0.0): The dropout probability to use.
"""
def __init__(
self, dim: int, dim_out: Optional[int] = None, mult: int = 4, glu: bool = False, dropout = 0.0
):
super().__init__()
inner_dim = int(dim * mult)
dim_out = dim_out if dim_out is not None else dim
project_in = GEGLU(dim, inner_dim)
self.net = nn.Sequential(project_in, nn.Dropout(dropout), nn.Linear(inner_dim, dim_out))
def forward(self, x):
return self.net(x)
# feedforward
class GEGLU(nn.Module):
r"""
A variant of the gated linear unit activation function from https://arxiv.org/abs/2002.05202.
Parameters:
dim_in (:obj:`int`): The number of channels in the input.
dim_out (:obj:`int`): The number of channels in the output.
"""
def __init__(self, dim_in: int, dim_out: int):
super().__init__()
self.proj = nn.Linear(dim_in, dim_out * 2)
def forward(self, x):
x, gate = self.proj(x).chunk(2, dim=-1)
return x * F.gelu(gate)
================================================
FILE: models/edict/my_diffusers/models/embeddings.py
================================================
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import math
import numpy as np
import torch
from torch import nn
def get_timestep_embedding(
timesteps: torch.Tensor,
embedding_dim: int,
flip_sin_to_cos: bool = False,
downscale_freq_shift: float = 1,
scale: float = 1,
max_period: int = 10000,
):
# print(timesteps)
"""
This matches the implementation in Denoising Diffusion Probabilistic Models: Create sinusoidal timestep embeddings.
:param timesteps: a 1-D Tensor of N indices, one per batch element.
These may be fractional.
:param embedding_dim: the dimension of the output. :param max_period: controls the minimum frequency of the
embeddings. :return: an [N x dim] Tensor of positional embeddings.
"""
assert len(timesteps.shape) == 1, "Timesteps should be a 1d-array"
half_dim = embedding_dim // 2
exponent = -math.log(max_period) * torch.arange(start=0, end=half_dim, dtype=torch.float64)
exponent = exponent / (half_dim - downscale_freq_shift)
emb = torch.exp(exponent).to(device=timesteps.device)
emb = timesteps[:, None].double() * emb[None, :]
# scale embeddings
emb = scale * emb
# concat sine and cosine embeddings
emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=-1)
# flip sine and cosine embeddings
if flip_sin_to_cos:
emb = torch.cat([emb[:, half_dim:], emb[:, :half_dim]], dim=-1)
# zero pad
if embedding_dim % 2 == 1:
emb = torch.nn.functional.pad(emb, (0, 1, 0, 0))
return emb
class TimestepEmbedding(nn.Module):
def __init__(self, channel: int, time_embed_dim: int, act_fn: str = "silu"):
super().__init__()
self.linear_1 = nn.Linear(channel, time_embed_dim)
self.act = None
if act_fn == "silu":
self.act = nn.SiLU()
self.linear_2 = nn.Linear(time_embed_dim, time_embed_dim)
def forward(self, sample):
sample = self.linear_1(sample)
if self.act is not None:
sample = self.act(sample)
sample = self.linear_2(sample)
return sample
class Timesteps(nn.Module):
def __init__(self, num_channels: int, flip_sin_to_cos: bool, downscale_freq_shift: float):
super().__init__()
self.num_channels = num_channels
self.flip_sin_to_cos = flip_sin_to_cos
self.downscale_freq_shift = downscale_freq_shift
def forward(self, timesteps):
t_emb = get_timestep_embedding(
timesteps,
self.num_channels,
flip_sin_to_cos=self.flip_sin_to_cos,
downscale_freq_shift=self.downscale_freq_shift,
)
return t_emb
class GaussianFourierProjection(nn.Module):
"""Gaussian Fourier embeddings for noise levels."""
def __init__(self, embedding_size: int = 256, scale: float = 1.0):
super().__init__()
self.weight = nn.Parameter(torch.randn(embedding_size) * scale, requires_grad=False)
# to delete later
self.W = nn.Parameter(torch.randn(embedding_size) * scale, requires_grad=False)
self.weight = self.W
def forward(self, x):
x = torch.log(x)
x_proj = x[:, None] * self.weight[None, :] * 2 * np.pi
out = torch.cat([torch.sin(x_proj), torch.cos(x_proj)], dim=-1)
return out
================================================
FILE: models/edict/my_diffusers/models/resnet.py
================================================
from functools import partial
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
class Upsample2D(nn.Module):
"""
An upsampling layer with an optional convolution.
:param channels: channels in the inputs and outputs. :param use_conv: a bool determining if a convolution is
applied. :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
upsampling occurs in the inner-two dimensions.
"""
def __init__(self, channels, use_conv=False, use_conv_transpose=False, out_channels=None, name="conv"):
super().__init__()
self.channels = channels
self.out_channels = out_channels or channels
self.use_conv = use_conv
self.use_conv_transpose = use_conv_transpose
self.name = name
conv = None
if use_conv_transpose:
conv = nn.ConvTranspose2d(channels, self.out_channels, 4, 2, 1)
elif use_conv:
conv = nn.Conv2d(self.channels, self.out_channels, 3, padding=1)
# TODO(Suraj, Patrick) - clean up after weight dicts are correctly renamed
if name == "conv":
self.conv = conv
else:
self.Conv2d_0 = conv
def forward(self, x):
assert x.shape[1] == self.channels
if self.use_conv_transpose:
return self.conv(x)
x = F.interpolate(x, scale_factor=2.0, mode="nearest")
# TODO(Suraj, Patrick) - clean up after weight dicts are correctly renamed
if self.use_conv:
if self.name == "conv":
x = self.conv(x)
else:
x = self.Conv2d_0(x)
return x
class Downsample2D(nn.Module):
"""
A downsampling layer with an optional convolution.
:param channels: channels in the inputs and outputs. :param use_conv: a bool determining if a convolution is
applied. :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
downsampling occurs in the inner-two dimensions.
"""
def __init__(self, channels, use_conv=False, out_channels=None, padding=1, name="conv"):
super().__init__()
self.channels = channels
self.out_channels = out_channels or channels
self.use_conv = use_conv
self.padding = padding
stride = 2
self.name = name
if use_conv:
conv = nn.Conv2d(self.channels, self.out_channels, 3, stride=stride, padding=padding)
else:
assert self.channels == self.out_channels
conv = nn.AvgPool2d(kernel_size=stride, stride=stride)
# TODO(Suraj, Patrick) - clean up after weight dicts are correctly renamed
if name == "conv":
self.Conv2d_0 = conv
self.conv = conv
elif name == "Conv2d_0":
self.conv = conv
else:
self.conv = conv
def forward(self, x):
assert x.shape[1] == self.channels
if self.use_conv and self.padding == 0:
pad = (0, 1, 0, 1)
x = F.pad(x, pad, mode="constant", value=0)
assert x.shape[1] == self.channels
x = self.conv(x)
return x
class FirUpsample2D(nn.Module):
def __init__(self, channels=None, out_channels=None, use_conv=False, fir_kernel=(1, 3, 3, 1)):
super().__init__()
out_channels = out_channels if out_channels else channels
if use_conv:
self.Conv2d_0 = nn.Conv2d(channels, out_channels, kernel_size=3, stride=1, padding=1)
self.use_conv = use_conv
self.fir_kernel = fir_kernel
self.out_channels = out_channels
def _upsample_2d(self, x, weight=None, kernel=None, factor=2, gain=1):
"""Fused `upsample_2d()` followed by `Conv2d()`.
Args:
Padding is performed only once at the beginning, not between the operations. The fused op is considerably more
efficient than performing the same calculation using standard TensorFlow ops. It supports gradients of arbitrary:
order.
x: Input tensor of the shape `[N, C, H, W]` or `[N, H, W,
C]`.
weight: Weight tensor of the shape `[filterH, filterW, inChannels,
outChannels]`. Grouped convolution can be performed by `inChannels = x.shape[0] // numGroups`.
kernel: FIR filter of the shape `[firH, firW]` or `[firN]`
(separable). The default is `[1] * factor`, which corresponds to nearest-neighbor upsampling.
factor: Integer upsampling factor (default: 2). gain: Scaling factor for signal magnitude (default: 1.0).
Returns:
Tensor of the shape `[N, C, H * factor, W * factor]` or `[N, H * factor, W * factor, C]`, and same datatype as
`x`.
"""
assert isinstance(factor, int) and factor >= 1
# Setup filter kernel.
if kernel is None:
kernel = [1] * factor
# setup kernel
kernel = np.asarray(kernel, dtype=np.float64)
if kernel.ndim == 1:
kernel = np.outer(kernel, kernel)
kernel /= np.sum(kernel)
kernel = kernel * (gain * (factor**2))
if self.use_conv:
convH = weight.shape[2]
convW = weight.shape[3]
inC = weight.shape[1]
p = (kernel.shape[0] - factor) - (convW - 1)
stride = (factor, factor)
# Determine data dimensions.
stride = [1, 1, factor, factor]
output_shape = ((x.shape[2] - 1) * factor + convH, (x.shape[3] - 1) * factor + convW)
output_padding = (
output_shape[0] - (x.shape[2] - 1) * stride[0] - convH,
output_shape[1] - (x.shape[3] - 1) * stride[1] - convW,
)
assert output_padding[0] >= 0 and output_padding[1] >= 0
inC = weight.shape[1]
num_groups = x.shape[1] // inC
# Transpose weights.
weight = torch.reshape(weight, (num_groups, -1, inC, convH, convW))
weight = weight[..., ::-1, ::-1].permute(0, 2, 1, 3, 4)
weight = torch.reshape(weight, (num_groups * inC, -1, convH, convW))
x = F.conv_transpose2d(x, weight, stride=stride, output_padding=output_padding, padding=0)
x = upfirdn2d_native(x, torch.tensor(kernel, device=x.device), pad=((p + 1) // 2 + factor - 1, p // 2 + 1))
else:
p = kernel.shape[0] - factor
x = upfirdn2d_native(
x, torch.tensor(kernel, device=x.device), up=factor, pad=((p + 1) // 2 + factor - 1, p // 2)
)
return x
def forward(self, x):
if self.use_conv:
height = self._upsample_2d(x, self.Conv2d_0.weight, kernel=self.fir_kernel)
height = height + self.Conv2d_0.bias.reshape(1, -1, 1, 1)
else:
height = self._upsample_2d(x, kernel=self.fir_kernel, factor=2)
return height
class FirDownsample2D(nn.Module):
def __init__(self, channels=None, out_channels=None, use_conv=False, fir_kernel=(1, 3, 3, 1)):
super().__init__()
out_channels = out_channels if out_channels else channels
if use_conv:
self.Conv2d_0 = nn.Conv2d(channels, out_channels, kernel_size=3, stride=1, padding=1)
self.fir_kernel = fir_kernel
self.use_conv = use_conv
self.out_channels = out_channels
def _downsample_2d(self, x, weight=None, kernel=None, factor=2, gain=1):
"""Fused `Conv2d()` followed by `downsample_2d()`.
Args:
Padding is performed only once at the beginning, not between the operations. The fused op is considerably more
efficient than performing the same calculation using standard TensorFlow ops. It supports gradients of arbitrary:
order.
x: Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`. w: Weight tensor of the shape `[filterH,
filterW, inChannels, outChannels]`. Grouped convolution can be performed by `inChannels = x.shape[0] //
numGroups`. k: FIR filter of the shape `[firH, firW]` or `[firN]` (separable). The default is `[1] *
factor`, which corresponds to average pooling. factor: Integer downsampling factor (default: 2). gain:
Scaling factor for signal magnitude (default: 1.0).
Returns:
Tensor of the shape `[N, C, H // factor, W // factor]` or `[N, H // factor, W // factor, C]`, and same
datatype as `x`.
"""
assert isinstance(factor, int) and factor >= 1
if kernel is None:
kernel = [1] * factor
# setup kernel
kernel = np.asarray(kernel, dtype=np.float64)
if kernel.ndim == 1:
kernel = np.outer(kernel, kernel)
kernel /= np.sum(kernel)
kernel = kernel * gain
if self.use_conv:
_, _, convH, convW = weight.shape
p = (kernel.shape[0] - factor) + (convW - 1)
s = [factor, factor]
x = upfirdn2d_native(x, torch.tensor(kernel, device=x.device), pad=((p + 1) // 2, p // 2))
x = F.conv2d(x, weight, stride=s, padding=0)
else:
p = kernel.shape[0] - factor
x = upfirdn2d_native(x, torch.tensor(kernel, device=x.device), down=factor, pad=((p + 1) // 2, p // 2))
return x
def forward(self, x):
if self.use_conv:
x = self._downsample_2d(x, weight=self.Conv2d_0.weight, kernel=self.fir_kernel)
x = x + self.Conv2d_0.bias.reshape(1, -1, 1, 1)
else:
x = self._downsample_2d(x, kernel=self.fir_kernel, factor=2)
return x
class ResnetBlock2D(nn.Module):
def __init__(
self,
*,
in_channels,
out_channels=None,
conv_shortcut=False,
dropout=0.0,
temb_channels=512,
groups=32,
groups_out=None,
pre_norm=True,
eps=1e-6,
non_linearity="swish",
time_embedding_norm="default",
kernel=None,
output_scale_factor=1.0,
use_nin_shortcut=None,
up=False,
down=False,
):
super().__init__()
self.pre_norm = pre_norm
self.pre_norm = True
self.in_channels = in_channels
out_channels = in_channels if out_channels is None else out_channels
self.out_channels = out_channels
self.use_conv_shortcut = conv_shortcut
self.time_embedding_norm = time_embedding_norm
self.up = up
self.down = down
self.output_scale_factor = output_scale_factor
if groups_out is None:
groups_out = groups
self.norm1 = torch.nn.GroupNorm(num_groups=groups, num_channels=in_channels, eps=eps, affine=True)
self.conv1 = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
if temb_channels is not None:
self.time_emb_proj = torch.nn.Linear(temb_channels, out_channels)
else:
self.time_emb_proj = None
self.norm2 = torch.nn.GroupNorm(num_groups=groups_out, num_channels=out_channels, eps=eps, affine=True)
self.dropout = torch.nn.Dropout(dropout)
self.conv2 = torch.nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
if non_linearity == "swish":
self.nonlinearity = lambda x: F.silu(x)
elif non_linearity == "mish":
self.nonlinearity = Mish()
elif non_linearity == "silu":
self.nonlinearity = nn.SiLU()
self.upsample = self.downsample = None
if self.up:
if kernel == "fir":
fir_kernel = (1, 3, 3, 1)
self.upsample = lambda x: upsample_2d(x, kernel=fir_kernel)
elif kernel == "sde_vp":
self.upsample = partial(F.interpolate, scale_factor=2.0, mode="nearest")
else:
self.upsample = Upsample2D(in_channels, use_conv=False)
elif self.down:
if kernel == "fir":
fir_kernel = (1, 3, 3, 1)
self.downsample = lambda x: downsample_2d(x, kernel=fir_kernel)
elif kernel == "sde_vp":
self.downsample = partial(F.avg_pool2d, kernel_size=2, stride=2)
else:
self.downsample = Downsample2D(in_channels, use_conv=False, padding=1, name="op")
self.use_nin_shortcut = self.in_channels != self.out_channels if use_nin_shortcut is None else use_nin_shortcut
self.conv_shortcut = None
if self.use_nin_shortcut:
self.conv_shortcut = torch.nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
def forward(self, x, temb):
hidden_states = x
# make sure hidden states is in float32
# when running in half-precision
hidden_states = self.norm1(hidden_states).type(hidden_states.dtype)
hidden_states = self.nonlinearity(hidden_states)
if self.upsample is not None:
x = self.upsample(x)
hidden_states = self.upsample(hidden_states)
elif self.downsample is not None:
x = self.downsample(x)
hidden_states = self.downsample(hidden_states)
hidden_states = self.conv1(hidden_states)
if temb is not None:
temb = self.time_emb_proj(self.nonlinearity(temb))[:, :, None, None]
hidden_states = hidden_states + temb
# make sure hidden states is in float32
# when running in half-precision
hidden_states = self.norm2(hidden_states).type(hidden_states.dtype)
hidden_states = self.nonlinearity(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.conv2(hidden_states)
if self.conv_shortcut is not None:
x = self.conv_shortcut(x)
out = (x + hidden_states) / self.output_scale_factor
return out
class Mish(torch.nn.Module):
def forward(self, x):
return x * torch.tanh(torch.nn.functional.softplus(x))
def upsample_2d(x, kernel=None, factor=2, gain=1):
r"""Upsample2D a batch of 2D images with the given filter.
Args:
Accepts a batch of 2D images of the shape `[N, C, H, W]` or `[N, H, W, C]` and upsamples each image with the given
filter. The filter is normalized so that if the input pixels are constant, they will be scaled by the specified
`gain`. Pixels outside the image are assumed to be zero, and the filter is padded with zeros so that its shape is a:
multiple of the upsampling factor.
x: Input tensor of the shape `[N, C, H, W]` or `[N, H, W,
C]`.
k: FIR filter of the shape `[firH, firW]` or `[firN]`
(separable). The default is `[1] * factor`, which corresponds to nearest-neighbor upsampling.
factor: Integer upsampling factor (default: 2). gain: Scaling factor for signal magnitude (default: 1.0).
Returns:
Tensor of the shape `[N, C, H * factor, W * factor]`
"""
assert isinstance(factor, int) and factor >= 1
if kernel is None:
kernel = [1] * factor
kernel = np.asarray(kernel, dtype=np.float64)
if kernel.ndim == 1:
kernel = np.outer(kernel, kernel)
kernel /= np.sum(kernel)
kernel = kernel * (gain * (factor**2))
p = kernel.shape[0] - factor
return upfirdn2d_native(
x, torch.tensor(kernel, device=x.device), up=factor, pad=((p + 1) // 2 + factor - 1, p // 2)
)
def downsample_2d(x, kernel=None, factor=2, gain=1):
r"""Downsample2D a batch of 2D images with the given filter.
Args:
Accepts a batch of 2D images of the shape `[N, C, H, W]` or `[N, H, W, C]` and downsamples each image with the
given filter. The filter is normalized so that if the input pixels are constant, they will be scaled by the
specified `gain`. Pixels outside the image are assumed to be zero, and the filter is padded with zeros so that its
shape is a multiple of the downsampling factor.
x: Input tensor of the shape `[N, C, H, W]` or `[N, H, W,
C]`.
kernel: FIR filter of the shape `[firH, firW]` or `[firN]`
(separable). The default is `[1] * factor`, which corresponds to average pooling.
factor: Integer downsampling factor (default: 2). gain: Scaling factor for signal magnitude (default: 1.0).
Returns:
Tensor of the shape `[N, C, H // factor, W // factor]`
"""
assert isinstance(factor, int) and factor >= 1
if kernel is None:
kernel = [1] * factor
kernel = np.asarray(kernel, dtype=np.float64)
if kernel.ndim == 1:
kernel = np.outer(kernel, kernel)
kernel /= np.sum(kernel)
kernel = kernel * gain
p = kernel.shape[0] - factor
return upfirdn2d_native(x, torch.tensor(kernel, device=x.device), down=factor, pad=((p + 1) // 2, p // 2))
def upfirdn2d_native(input, kernel, up=1, down=1, pad=(0, 0)):
up_x = up_y = up
down_x = down_y = down
pad_x0 = pad_y0 = pad[0]
pad_x1 = pad_y1 = pad[1]
_, channel, in_h, in_w = input.shape
input = input.reshape(-1, in_h, in_w, 1)
_, in_h, in_w, minor = input.shape
kernel_h, kernel_w = kernel.shape
out = input.view(-1, in_h, 1, in_w, 1, minor)
# Temporary workaround for mps specific issue: https://github.com/pytorch/pytorch/issues/84535
if input.device.type == "mps":
out = out.to("cpu")
out = F.pad(out, [0, 0, 0, up_x - 1, 0, 0, 0, up_y - 1])
out = out.view(-1, in_h * up_y, in_w * up_x, minor)
out = F.pad(out, [0, 0, max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)])
out = out.to(input.device) # Move back to mps if necessary
out = out[
:,
max(-pad_y0, 0) : out.shape[1] - max(-pad_y1, 0),
max(-pad_x0, 0) : out.shape[2] - max(-pad_x1, 0),
:,
]
out = out.permute(0, 3, 1, 2)
out = out.reshape([-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1])
w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w)
out = F.conv2d(out, w)
out = out.reshape(
-1,
minor,
in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1,
in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1,
)
out = out.permute(0, 2, 3, 1)
out = out[:, ::down_y, ::down_x, :]
out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1
out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1
return out.view(-1, channel, out_h, out_w)
================================================
FILE: models/edict/my_diffusers/models/unet_2d.py
================================================
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..modeling_utils import ModelMixin
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .unet_blocks import UNetMidBlock2D, get_down_block, get_up_block
@dataclass
class UNet2DOutput(BaseOutput):
"""
Args:
sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Hidden states output. Output of last layer of model.
"""
sample: torch.DoubleTensor
class UNet2DModel(ModelMixin, ConfigMixin):
r"""
UNet2DModel is a 2D UNet model that takes in a noisy sample and a timestep and returns sample shaped output.
This model inherits from [`ModelMixin`]. Check the superclass documentation for the generic methods the library
implements for all the model (such as downloading or saving, etc.)
Parameters:
sample_size (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`, *optional*):
Input sample size.
in_channels (`int`, *optional*, defaults to 3): Number of channels in the input image.
out_channels (`int`, *optional*, defaults to 3): Number of channels in the output.
center_input_sample (`bool`, *optional*, defaults to `False`): Whether to center the input sample.
time_embedding_type (`str`, *optional*, defaults to `"positional"`): Type of time embedding to use.
freq_shift (`int`, *optional*, defaults to 0): Frequency shift for fourier time embedding.
flip_sin_to_cos (`bool`, *optional*, defaults to :
obj:`False`): Whether to flip sin to cos for fourier time embedding.
down_block_types (`Tuple[str]`, *optional*, defaults to :
obj:`("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")`): Tuple of downsample block
types.
up_block_types (`Tuple[str]`, *optional*, defaults to :
obj:`("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")`): Tuple of upsample block types.
block_out_channels (`Tuple[int]`, *optional*, defaults to :
obj:`(224, 448, 672, 896)`): Tuple of block output channels.
layers_per_block (`int`, *optional*, defaults to `2`): The number of layers per block.
mid_block_scale_factor (`float`, *optional*, defaults to `1`): The scale factor for the mid block.
downsample_padding (`int`, *optional*, defaults to `1`): The padding for the downsample convolution.
act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
attention_head_dim (`int`, *optional*, defaults to `8`): The attention head dimension.
norm_num_groups (`int`, *optional*, defaults to `32`): The number of groups for the normalization.
norm_eps (`float`, *optional*, defaults to `1e-5`): The epsilon for the normalization.
"""
@register_to_config
def __init__(
self,
sample_size: Optional[int] = None,
in_channels: int = 3,
out_channels: int = 3,
center_input_sample: bool = False,
time_embedding_type: str = "positional",
freq_shift: int = 0,
flip_sin_to_cos: bool = True,
down_block_types: Tuple[str] = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D"),
up_block_types: Tuple[str] = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D"),
block_out_channels: Tuple[int] = (224, 448, 672, 896),
layers_per_block: int = 2,
mid_block_scale_factor = 1,
downsample_padding: int = 1,
act_fn: str = "silu",
attention_head_dim: int = 8,
norm_num_groups: int = 32,
norm_eps = 1e-5,
):
super().__init__()
self.sample_size = sample_size
time_embed_dim = block_out_channels[0] * 4
# input
self.conv_in = nn.Conv2d(in_channels, block_out_channels[0], kernel_size=3, padding=(1, 1))
# time
if time_embedding_type == "fourier":
self.time_proj = GaussianFourierProjection(embedding_size=block_out_channels[0], scale=16)
timestep_input_dim = 2 * block_out_channels[0]
elif time_embedding_type == "positional":
self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)
timestep_input_dim = block_out_channels[0]
self.time_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)
self.down_blocks = nn.ModuleList([])
self.mid_block = None
self.up_blocks = nn.ModuleList([])
# down
output_channel = block_out_channels[0]
for i, down_block_type in enumerate(down_block_types):
input_channel = output_channel
output_channel = block_out_channels[i]
is_final_block = i == len(block_out_channels) - 1
down_block = get_down_block(
down_block_type,
num_layers=layers_per_block,
in_channels=input_channel,
out_channels=output_channel,
temb_channels=time_embed_dim,
add_downsample=not is_final_block,
resnet_eps=norm_eps,
resnet_act_fn=act_fn,
attn_num_head_channels=attention_head_dim,
downsample_padding=downsample_padding,
)
self.down_blocks.append(down_block)
# mid
self.mid_block = UNetMidBlock2D(
in_channels=block_out_channels[-1],
temb_channels=time_embed_dim,
resnet_eps=norm_eps,
resnet_act_fn=act_fn,
output_scale_factor=mid_block_scale_factor,
resnet_time_scale_shift="default",
attn_num_head_channels=attention_head_dim,
resnet_groups=norm_num_groups,
)
# up
reversed_block_out_channels = list(reversed(block_out_channels))
output_channel = reversed_block_out_channels[0]
for i, up_block_type in enumerate(up_block_types):
prev_output_channel = output_channel
output_channel = reversed_block_out_channels[i]
input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]
is_final_block = i == len(block_out_channels) - 1
up_block = get_up_block(
up_block_type,
num_layers=layers_per_block + 1,
in_channels=input_channel,
out_channels=output_channel,
prev_output_channel=prev_output_channel,
temb_channels=time_embed_dim,
add_upsample=not is_final_block,
resnet_eps=norm_eps,
resnet_act_fn=act_fn,
attn_num_head_channels=attention_head_dim,
)
self.up_blocks.append(up_block)
prev_output_channel = output_channel
# out
num_groups_out = norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4, 32)
self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=num_groups_out, eps=norm_eps)
self.conv_act = nn.SiLU()
self.conv_out = nn.Conv2d(block_out_channels[0], out_channels, 3, padding=1)
def forward(
self,
sample: torch.DoubleTensor,
timestep: Union[torch.Tensor, float, int],
return_dict: bool = True,
) -> Union[UNet2DOutput, Tuple]:
"""r
Args:
sample (`torch.FloatTensor`): (batch, channel, height, width) noisy inputs tensor
timestep (`torch.FloatTensor` or `float` or `int): (batch) timesteps
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~models.unet_2d.UNet2DOutput`] instead of a plain tuple.
Returns:
[`~models.unet_2d.UNet2DOutput`] or `tuple`: [`~models.unet_2d.UNet2DOutput`] if `return_dict` is True,
otherwise a `tuple`. When returning a tuple, the first element is the sample tensor.
"""
# 0. center input if necessary
if self.config.center_input_sample:
sample = 2 * sample - 1.0
# 1. time
timesteps = timestep
if not torch.is_tensor(timesteps):
timesteps = torch.tensor([timesteps], dtype=torch.long, device=sample.device)
elif torch.is_tensor(timesteps) and len(timesteps.shape) == 0:
timesteps = timesteps[None].to(sample.device)
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
timesteps = timesteps * torch.ones(sample.shape[0], dtype=timesteps.dtype, device=timesteps.device)
t_emb = self.time_proj(timesteps)
emb = self.time_embedding(t_emb)
# 2. pre-process
skip_sample = sample
sample = self.conv_in(sample)
# 3. down
down_block_res_samples = (sample,)
for downsample_block in self.down_blocks:
if hasattr(downsample_block, "skip_conv"):
sample, res_samples, skip_sample = downsample_block(
hidden_states=sample, temb=emb, skip_sample=skip_sample
)
else:
sample, res_samples = downsample_block(hidden_states=sample, temb=emb)
down_block_res_samples += res_samples
# 4. mid
sample = self.mid_block(sample, emb)
# 5. up
skip_sample = None
for upsample_block in self.up_blocks:
res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]
if hasattr(upsample_block, "skip_conv"):
sample, skip_sample = upsample_block(sample, res_samples, emb, skip_sample)
else:
sample = upsample_block(sample, res_samples, emb)
# 6. post-process
# make sure hidden states is in float32
# when running in half-precision
sample = self.conv_norm_out(sample.double()).type(sample.dtype)
sample = self.conv_act(sample)
sample = self.conv_out(sample)
if skip_sample is not None:
sample += skip_sample
if self.config.time_embedding_type == "fourier":
timesteps = timesteps.reshape((sample.shape[0], *([1] * len(sample.shape[1:]))))
sample = sample / timesteps
if not return_dict:
return (sample,)
return UNet2DOutput(sample=sample)
================================================
FILE: models/edict/my_diffusers/models/unet_2d_condition.py
================================================
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..modeling_utils import ModelMixin
from ..utils import BaseOutput
from .embeddings import TimestepEmbedding, Timesteps
from .unet_blocks import UNetMidBlock2DCrossAttn, get_down_block, get_up_block
@dataclass
class UNet2DConditionOutput(BaseOutput):
"""
Args:
sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Hidden states conditioned on `encoder_hidden_states` input. Output of last layer of model.
"""
sample: torch.FloatTensor
class UNet2DConditionModel(ModelMixin, ConfigMixin):
r"""
UNet2DConditionModel is a conditional 2D UNet model that takes in a noisy sample, conditional state, and a timestep
and returns sample shaped output.
This model inherits from [`ModelMixin`]. Check the superclass documentation for the generic methods the library
implements for all the model (such as downloading or saving, etc.)
Parameters:
sample_size (`int`, *optional*): The size of the input sample.
in_channels (`int`, *optional*, defaults to 4): The number of channels in the input sample.
out_channels (`int`, *optional*, defaults to 4): The number of channels in the output.
center_input_sample (`bool`, *optional*, defaults to `False`): Whether to center the input sample.
flip_sin_to_cos (`bool`, *optional*, defaults to `False`):
Whether to flip the sin to cos in the time embedding.
freq_shift (`int`, *optional*, defaults to 0): The frequency shift to apply to the time embedding.
down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`):
The tuple of downsample blocks to use.
up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D",)`):
The tuple of upsample blocks to use.
block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):
The tuple of output channels for each block.
layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block.
downsample_padding (`int`, *optional*, defaults to 1): The padding to use for the downsampling convolution.
mid_block_scale_factor (`float`, *optional*, defaults to 1.0): The scale factor to use for the mid block.
act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
norm_num_groups (`int`, *optional*, defaults to 32): The number of groups to use for the normalization.
norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon to use for the normalization.
cross_attention_dim (`int`, *optional*, defaults to 1280): The dimension of the cross attention features.
attention_head_dim (`int`, *optional*, defaults to 8): The dimension of the attention heads.
"""
@register_to_config
def __init__(
self,
sample_size: Optional[int] = None,
in_channels: int = 4,
out_channels: int = 4,
center_input_sample: bool = False,
flip_sin_to_cos: bool = True,
freq_shift: int = 0,
down_block_types: Tuple[str] = (
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"DownBlock2D",
),
up_block_types: Tuple[str] = ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D"),
block_out_channels: Tuple[int] = (320, 640, 1280, 1280),
layers_per_block: int = 2,
downsample_padding: int = 1,
mid_block_scale_factor: float = 1,
act_fn: str = "silu",
norm_num_groups: int = 32,
norm_eps: float = 1e-5,
cross_attention_dim: int = 1280,
attention_head_dim: int = 8,
):
super().__init__()
self.sample_size = sample_size
time_embed_dim = block_out_channels[0] * 4
# input
self.conv_in = nn.Conv2d(in_channels, block_out_channels[0], kernel_size=3, padding=(1, 1))
# time
self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)
timestep_input_dim = block_out_channels[0]
self.time_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)
self.down_blocks = nn.ModuleList([])
self.mid_block = None
self.up_blocks = nn.ModuleList([])
# down
output_channel = block_out_channels[0]
for i, down_block_type in enumerate(down_block_types):
input_channel = output_channel
output_channel = block_out_channels[i]
is_final_block = i == len(block_out_channels) - 1
down_block = get_down_block(
down_block_type,
num_layers=layers_per_block,
in_channels=input_channel,
out_channels=output_channel,
temb_channels=time_embed_dim,
add_downsample=not is_final_block,
resnet_eps=norm_eps,
resnet_act_fn=act_fn,
cross_attention_dim=cross_attention_dim,
attn_num_head_channels=attention_head_dim,
downsample_padding=downsample_padding,
)
self.down_blocks.append(down_block)
# mid
self.mid_block = UNetMidBlock2DCrossAttn(
in_channels=block_out_channels[-1],
temb_channels=time_embed_dim,
resnet_eps=norm_eps,
resnet_act_fn=act_fn,
output_scale_factor=mid_block_scale_factor,
resnet_time_scale_shift="default",
cross_attention_dim=cross_attention_dim,
attn_num_head_channels=attention_head_dim,
resnet_groups=norm_num_groups,
)
# up
reversed_block_out_channels = list(reversed(block_out_channels))
output_channel = reversed_block_out_channels[0]
for i, up_block_type in enumerate(up_block_types):
prev_output_channel = output_channel
output_channel = reversed_block_out_channels[i]
input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]
is_final_block = i == len(block_out_channels) - 1
up_block = get_up_block(
up_block_type,
num_layers=layers_per_block + 1,
in_channels=input_channel,
out_channels=output_channel,
prev_output_channel=prev_output_channel,
temb_channels=time_embed_dim,
add_upsample=not is_final_block,
resnet_eps=norm_eps,
resnet_act_fn=act_fn,
cross_attention_dim=cross_attention_dim,
attn_num_head_channels=attention_head_dim,
)
self.up_blocks.append(up_block)
prev_output_channel = output_channel
# out
self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps)
self.conv_act = nn.SiLU()
self.conv_out = nn.Conv2d(block_out_channels[0], out_channels, 3, padding=1)
def set_attention_slice(self, slice_size):
if slice_size is not None and self.config.attention_head_dim % slice_size != 0:
raise ValueError(
f"Make sure slice_size {slice_size} is a divisor of "
f"the number of heads used in cross_attention {self.config.attention_head_dim}"
)
if slice_size is not None and slice_size > self.config.attention_head_dim:
raise ValueError(
f"Chunk_size {slice_size} has to be smaller or equal to "
f"the number of heads used in cross_attention {self.config.attention_head_dim}"
)
for block in self.down_blocks:
if hasattr(block, "attentions") and block.attentions is not None:
block.set_attention_slice(slice_size)
self.mid_block.set_attention_slice(slice_size)
for block in self.up_blocks:
if hasattr(block, "attentions") and block.attentions is not None:
block.set_attention_slice(slice_size)
def forward(
self,
sample: torch.FloatTensor,
timestep: Union[torch.Tensor, float, int],
encoder_hidden_states: torch.Tensor,
return_dict: bool = True,
) -> Union[UNet2DConditionOutput, Tuple]:
"""r
Args:
sample (`torch.FloatTensor`): (batch, channel, height, width) noisy inputs tensor
timestep (`torch.FloatTensor` or `float` or `int): (batch) timesteps
encoder_hidden_states (`torch.FloatTensor`): (batch, channel, height, width) encoder hidden states
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain tuple.
Returns:
[`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`:
[`~models.unet_2d_condition.UNet2DConditionOutput`] if `return_dict` is True, otherwise a `tuple`. When
returning a tuple, the first element is the sample tensor.
"""
# 0. center input if necessary
if self.config.center_input_sample:
sample = 2 * sample - 1.0
# 1. time
timesteps = timestep
if not torch.is_tensor(timesteps):
timesteps = torch.tensor([timesteps], dtype=torch.long, device=sample.device)
elif torch.is_tensor(timesteps) and len(timesteps.shape) == 0:
timesteps = timesteps.to(dtype=torch.float64)
timesteps = timesteps[None].to(device=sample.device)
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
timesteps = timesteps.expand(sample.shape[0])
t_emb = self.time_proj(timesteps)
# print(t_emb.dtype)
t_emb = t_emb.to(sample.dtype).to(sample.device)
emb = self.time_embedding(t_emb)
# 2. pre-process
sample = self.conv_in(sample)
# 3. down
down_block_res_samples = (sample,)
for downsample_block in self.down_blocks:
if hasattr(downsample_block, "attentions") and downsample_block.attentions is not None:
# print(sample.dtype, emb.dtype, encoder_hidden_states.dtype)
sample, res_samples = downsample_block(
hidden_states=sample, temb=emb, encoder_hidden_states=encoder_hidden_states
)
else:
sample, res_samples = downsample_block(hidden_states=sample, temb=emb)
down_block_res_samples += res_samples
# 4. mid
sample = self.mid_block(sample, emb, encoder_hidden_states=encoder_hidden_states)
# 5. up
for upsample_block in self.up_blocks:
res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]
if hasattr(upsample_block, "attentions") and upsample_block.attentions is not None:
sample = upsample_block(
hidden_states=sample,
temb=emb,
res_hidden_states_tuple=res_samples,
encoder_hidden_states=encoder_hidden_states,
)
else:
sample = upsample_block(hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples)
# 6. post-process
# make sure hidden states is in float32
# when running in half-precision
sample = self.conv_norm_out(sample.double()).type(sample.dtype)
sample = self.conv_act(sample)
sample = self.conv_out(sample)
if not return_dict:
return (sample,)
return UNet2DConditionOutput(sample=sample)
================================================
FILE: models/edict/my_diffusers/models/unet_blocks.py
================================================
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
import numpy as np
# limitations under the License.
import torch
from torch import nn
from .attention import AttentionBlock, SpatialTransformer
from .resnet import Downsample2D, FirDownsample2D, FirUpsample2D, ResnetBlock2D, Upsample2D
def get_down_block(
down_block_type,
num_layers,
in_channels,
out_channels,
temb_channels,
add_downsample,
resnet_eps,
resnet_act_fn,
attn_num_head_channels,
cross_attention_dim=None,
downsample_padding=None,
):
down_block_type = down_block_type[7:] if down_block_type.startswith("UNetRes") else down_block_type
if down_block_type == "DownBlock2D":
return DownBlock2D(
num_layers=num_layers,
in_channels=in_channels,
out_channels=out_channels,
temb_channels=temb_channels,
add_downsample=add_downsample,
resnet_eps=resnet_eps,
resnet_act_fn=resnet_act_fn,
downsample_padding=downsample_padding,
)
elif down_block_type == "AttnDownBlock2D":
return AttnDownBlock2D(
num_layers=num_layers,
in_channels=in_channels,
out_channels=out_channels,
temb_channels=temb_channels,
add_downsample=add_downsample,
resnet_eps=resnet_eps,
resnet_act_fn=resnet_act_fn,
downsample_padding=downsample_padding,
attn_num_head_channels=attn_num_head_channels,
)
elif down_block_type == "CrossAttnDownBlock2D":
if cross_attention_dim is None:
raise ValueError("cross_attention_dim must be specified for CrossAttnDownBlock2D")
return CrossAttnDownBlock2D(
num_layers=num_layers,
in_channels=in_channels,
out_channels=out_channels,
temb_channels=temb_channels,
add_downsample=add_downsample,
resnet_eps=resnet_eps,
resnet_act_fn=resnet_act_fn,
downsample_padding=downsample_padding,
cross_attention_dim=cross_attention_dim,
attn_num_head_channels=attn_num_head_channels,
)
elif down_block_type == "SkipDownBlock2D":
return SkipDownBlock2D(
num_layers=num_layers,
in_channels=in_channels,
out_channels=out_channels,
temb_channels=temb_channels,
add_downsample=add_downsample,
resnet_eps=resnet_eps,
resnet_act_fn=resnet_act_fn,
downsample_padding=downsample_padding,
)
elif down_block_type == "AttnSkipDownBlock2D":
return AttnSkipDownBlock2D(
num_layers=num_layers,
in_channels=in_channels,
out_channels=out_channels,
temb_channels=temb_channels,
add_downsample=add_downsample,
resnet_eps=resnet_eps,
resnet_act_fn=resnet_act_fn,
downsample_padding=downsample_padding,
attn_num_head_channels=attn_num_head_channels,
)
elif down_block_type == "DownEncoderBlock2D":
return DownEncoderBlock2D(
num_layers=num_layers,
in_channels=in_channels,
out_channels=out_channels,
add_downsample=add_downsample,
resnet_eps=resnet_eps,
resnet_act_fn=resnet_act_fn,
downsample_padding=downsample_padding,
)
def get_up_block(
up_block_type,
num_layers,
in_channels,
out_channels,
prev_output_channel,
temb_channels,
add_upsample,
resnet_eps,
resnet_act_fn,
attn_num_head_channels,
cross_attention_dim=None,
):
up_block_type = up_block_type[7:] if up_block_type.startswith("UNetRes") else up_block_type
if up_block_type == "UpBlock2D":
return UpBlock2D(
num_layers=num_layers,
in_channels=in_channels,
out_channels=out_channels,
prev_output_channel=prev_output_channel,
temb_channels=temb_channels,
add_upsample=add_upsample,
resnet_eps=resnet_eps,
resnet_act_fn=resnet_act_fn,
)
elif up_block_type == "CrossAttnUpBlock2D":
if cross_attention_dim is None:
raise ValueError("cross_attention_dim must be specified for CrossAttnUpBlock2D")
return CrossAttnUpBlock2D(
num_layers=num_layers,
in_channels=in_channels,
out_channels=out_channels,
prev_output_channel=prev_output_channel,
temb_channels=temb_channels,
add_upsample=add_upsample,
resnet_eps=resnet_eps,
resnet_act_fn=resnet_act_fn,
cross_attention_dim=cross_attention_dim,
attn_num_head_channels=attn_num_head_channels,
)
elif up_block_type == "AttnUpBlock2D":
return AttnUpBlock2D(
num_layers=num_layers,
in_channels=in_channels,
out_channels=out_channels,
prev_output_channel=prev_output_channel,
temb_channels=temb_channels,
add_upsample=add_upsample,
resnet_eps=resnet_eps,
resnet_act_fn=resnet_act_fn,
attn_num_head_channels=attn_num_head_channels,
)
elif up_block_type == "SkipUpBlock2D":
return SkipUpBlock2D(
num_layers=num_layers,
in_channels=in_channels,
out_channels=out_channels,
prev_output_channel=prev_output_channel,
temb_channels=temb_channels,
add_upsample=add_upsample,
resnet_eps=resnet_eps,
resnet_act_fn=resnet_act_fn,
)
elif up_block_type == "AttnSkipUpBlock2D":
return AttnSkipUpBlock2D(
num_layers=num_layers,
in_channels=in_channels,
out_channels=out_channels,
prev_output_channel=prev_output_channel,
temb_channels=temb_channels,
add_upsample=add_upsample,
resnet_eps=resnet_eps,
resnet_act_fn=resnet_act_fn,
attn_num_head_channels=attn_num_head_channels,
)
elif up_block_type == "UpDecoderBlock2D":
return UpDecoderBlock2D(
num_layers=num_layers,
in_channels=in_channels,
out_channels=out_channels,
add_upsample=add_upsample,
resnet_eps=resnet_eps,
resnet_act_fn=resnet_act_fn,
)
raise ValueError(f"{up_block_type} does not exist.")
class UNetMidBlock2D(nn.Module):
def __init__(
self,
in_channels: int,
temb_channels: int,
dropout: float = 0.0,
num_layers: int = 1,
resnet_eps: float = 1e-6,
resnet_time_scale_shift: str = "default",
resnet_act_fn: str = "swish",
resnet_groups: int = 32,
resnet_pre_norm: bool = True,
attn_num_head_channels=1,
attention_type="default",
output_scale_factor=1.0,
**kwargs,
):
super().__init__()
self.attention_type = attention_type
resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)
# there is always at least one resnet
resnets = [
ResnetBlock2D(
in_channels=in_channels,
out_channels=in_channels,
temb_channels=temb_channels,
eps=resnet_eps,
groups=resnet_groups,
dropout=dropout,
time_embedding_norm=resnet_time_scale_shift,
non_linearity=resnet_act_fn,
output_scale_factor=output_scale_factor,
pre_norm=resnet_pre_norm,
)
]
attentions = []
for _ in range(num_layers):
attentions.append(
AttentionBlock(
in_channels,
num_head_channels=attn_num_head_channels,
rescale_output_factor=output_scale_factor,
eps=resnet_eps,
num_groups=resnet_groups,
)
)
resnets.append(
ResnetBlock2D(
in_channels=in_channels,
out_channels=in_channels,
temb_channels=temb_channels,
eps=resnet_eps,
groups=resnet_groups,
dropout=dropout,
time_embedding_norm=resnet_time_scale_shift,
non_linearity=resnet_act_fn,
output_scale_factor=output_scale_factor,
pre_norm=resnet_pre_norm,
)
)
self.attentions = nn.ModuleList(attentions)
self.resnets = nn.ModuleList(resnets)
def forward(self, hidden_states, temb=None, encoder_states=None):
hidden_states = self.resnets[0](hidden_states, temb)
for attn, resnet in zip(self.attentions, self.resnets[1:]):
if self.attention_type == "default":
hidden_states = attn(hidden_states)
else:
hidden_states = attn(hidden_states, encoder_states)
hidden_states = resnet(hidden_states, temb)
return hidden_states
class UNetMidBlock2DCrossAttn(nn.Module):
def __init__(
self,
in_channels: int,
temb_channels: int,
dropout: float = 0.0,
num_layers: int = 1,
resnet_eps: float = 1e-6,
resnet_time_scale_shift: str = "default",
resnet_act_fn: str = "swish",
resnet_groups: int = 32,
resnet_pre_norm: bool = True,
attn_num_head_channels=1,
attention_type="default",
output_scale_factor=1.0,
cross_attention_dim=1280,
**kwargs,
):
super().__init__()
self.attention_type = attention_type
self.attn_num_head_channels = attn_num_head_channels
resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)
# there is always at least one resnet
resnets = [
ResnetBlock2D(
in_channels=in_channels,
out_channels=in_channels,
temb_channels=temb_channels,
eps=resnet_eps,
groups=resnet_groups,
dropout=dropout,
time_embedding_norm=resnet_time_scale_shift,
non_linearity=resnet_act_fn,
output_scale_factor=output_scale_factor,
pre_norm=resnet_pre_norm,
)
]
attentions = []
for _ in range(num_layers):
attentions.append(
SpatialTransformer(
in_channels,
attn_num_head_channels,
in_channels // attn_num_head_channels,
depth=1,
context_dim=cross_attention_dim,
)
)
resnets.append(
ResnetBlock2D(
in_channels=in_channels,
out_channels=in_channels,
temb_channels=temb_channels,
eps=resnet_eps,
groups=resnet_groups,
dropout=dropout,
time_embedding_norm=resnet_time_scale_shift,
non_linearity=resnet_act_fn,
output_scale_factor=output_scale_factor,
pre_norm=resnet_pre_norm,
)
)
self.attentions = nn.ModuleList(attentions)
self.resnets = nn.ModuleList(resnets)
def set_attention_slice(self, slice_size):
if slice_size is not None and self.attn_num_head_channels % slice_size != 0:
raise ValueError(
f"Make sure slice_size {slice_size} is a divisor of "
f"the number of heads used in cross_attention {self.attn_num_head_channels}"
)
if slice_size is not None and slice_size > self.attn_num_head_channels:
raise ValueError(
f"Chunk_size {slice_size} has to be smaller or equal to "
f"the number of heads used in cross_attention {self.attn_num_head_channels}"
)
for attn in self.attentions:
attn._set_attention_slice(slice_size)
def forward(self, hidden_states, temb=None, encoder_hidden_states=None):
hidden_states = self.resnets[0](hidden_states, temb)
for attn, resnet in zip(self.attentions, self.resnets[1:]):
hidden_states = attn(hidden_states, encoder_hidden_states)
hidden_states = resnet(hidden_states, temb)
return hidden_states
class AttnDownBlock2D(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
temb_channels: int,
dropout: float = 0.0,
num_layers: int = 1,
resnet_eps: float = 1e-6,
resnet_time_scale_shift: str = "default",
resnet_act_fn: str = "swish",
resnet_groups: int = 32,
resnet_pre_norm: bool = True,
attn_num_head_channels=1,
attention_type="default",
output_scale_factor=1.0,
downsample_padding=1,
add_downsample=True,
):
super().__init__()
resnets = []
attentions = []
self.attention_type = attention_type
for i in range(num_layers):
in_channels = in_channels if i == 0 else out_channels
resnets.append(
ResnetBlock2D(
in_channels=in_channels,
out_channels=out_channels,
temb_channels=temb_channels,
eps=resnet_eps,
groups=resnet_groups,
dropout=dropout,
time_embedding_norm=resnet_time_scale_shift,
non_linearity=resnet_act_fn,
output_scale_factor=output_scale_factor,
pre_norm=resnet_pre_norm,
)
)
attentions.append(
AttentionBlock(
out_channels,
num_head_channels=attn_num_head_channels,
rescale_output_factor=output_scale_factor,
eps=resnet_eps,
)
)
self.attentions = nn.ModuleList(attentions)
self.resnets = nn.ModuleList(resnets)
if add_downsample:
self.downsamplers = nn.ModuleList(
[
Downsample2D(
in_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op"
)
]
)
else:
self.downsamplers = None
def forward(self, hidden_states, temb=None):
output_states = ()
for resnet, attn in zip(self.resnets, self.attentions):
hidden_states = resnet(hidden_states, temb)
hidden_states = attn(hidden_states)
output_states += (hidden_states,)
if self.downsamplers is not None:
for downsampler in self.downsamplers:
hidden_states = downsampler(hidden_states)
output_states += (hidden_states,)
return hidden_states, output_states
class CrossAttnDownBlock2D(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
temb_channels: int,
dropout: float = 0.0,
num_layers: int = 1,
resnet_eps: float = 1e-6,
resnet_time_scale_shift: str = "default",
resnet_act_fn: str = "swish",
resnet_groups: int = 32,
resnet_pre_norm: bool = True,
attn_num_head_channels=1,
cross_attention_dim=1280,
attention_type="default",
output_scale_factor=1.0,
downsample_padding=1,
add_downsample=True,
):
super().__init__()
resnets = []
attentions = []
self.attention_type = attention_type
self.attn_num_head_channels = attn_num_head_channels
for i in range(num_layers):
in_channels = in_channels if i == 0 else out_channels
resnets.append(
ResnetBlock2D(
in_channels=in_channels,
out_channels=out_channels,
temb_channels=temb_channels,
eps=resnet_eps,
groups=resnet_groups,
dropout=dropout,
time_embedding_norm=resnet_time_scale_shift,
non_linearity=resnet_act_fn,
output_scale_factor=output_scale_factor,
pre_norm=resnet_pre_norm,
)
)
attentions.append(
SpatialTransformer(
out_channels,
attn_num_head_channels,
out_channels // attn_num_head_channels,
depth=1,
context_dim=cross_attention_dim,
)
)
self.attentions = nn.ModuleList(attentions)
self.resnets = nn.ModuleList(resnets)
if add_downsample:
self.downsamplers = nn.ModuleList(
[
Downsample2D(
in_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op"
)
]
)
else:
self.downsamplers = None
def set_attention_slice(self, slice_size):
if slice_size is not None and self.attn_num_head_channels % slice_size != 0:
raise ValueError(
f"Make sure slice_size {slice_size} is a divisor of "
f"the number of heads used in cross_attention {self.attn_num_head_channels}"
)
if slice_size is not None and slice_size > self.attn_num_head_channels:
raise ValueError(
f"Chunk_size {slice_size} has to be smaller or equal to "
f"the number of heads used in cross_attention {self.attn_num_head_channels}"
)
for attn in self.attentions:
attn._set_attention_slice(slice_size)
def forward(self, hidden_states, temb=None, encoder_hidden_states=None):
output_states = ()
for resnet, attn in zip(self.resnets, self.attentions):
hidden_states = resnet(hidden_states, temb)
hidden_states = attn(hidden_states, context=encoder_hidden_states)
output_states += (hidden_states,)
if self.downsamplers is not None:
for downsampler in self.downsamplers:
hidden_states = downsampler(hidden_states)
output_states += (hidden_states,)
return hidden_states, output_states
class DownBlock2D(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
temb_channels: int,
dropout: float = 0.0,
num_layers: int = 1,
resnet_eps: float = 1e-6,
resnet_time_scale_shift: str = "default",
resnet_act_fn: str = "swish",
resnet_groups: int = 32,
resnet_pre_norm: bool = True,
output_scale_factor=1.0,
add_downsample=True,
downsample_padding=1,
):
super().__init__()
resnets = []
for i in range(num_layers):
in_channels = in_channels if i == 0 else out_channels
resnets.append(
ResnetBlock2D(
in_channels=in_channels,
out_channels=out_channels,
temb_channels=temb_channels,
eps=resnet_eps,
groups=resnet_groups,
dropout=dropout,
time_embedding_norm=resnet_time_scale_shift,
non_linearity=resnet_act_fn,
output_scale_factor=output_scale_factor,
pre_norm=resnet_pre_norm,
)
)
self.resnets = nn.ModuleList(resnets)
if add_downsample:
self.downsamplers = nn.ModuleList(
[
Downsample2D(
in_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op"
)
]
)
else:
self.downsamplers = None
def forward(self, hidden_states, temb=None):
output_states = ()
for resnet in self.resnets:
hidden_states = resnet(hidden_states, temb)
output_states += (hidden_states,)
if self.downsamplers is not None:
for downsampler in self.downsamplers:
hidden_states = downsampler(hidden_states)
output_states += (hidden_states,)
return hidden_states, output_states
class DownEncoderBlock2D(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
dropout: float = 0.0,
num_layers: int = 1,
resnet_eps: float = 1e-6,
resnet_time_scale_shift: str = "default",
resnet_act_fn: str = "swish",
resnet_groups: int = 32,
resnet_pre_norm: bool = True,
output_scale_factor=1.0,
add_downsample=True,
downsample_padding=1,
):
super().__init__()
resnets = []
for i in range(num_layers):
in_channels = in_channels if i == 0 else out_channels
resnets.append(
ResnetBlock2D(
in_channels=in_channels,
out_channels=out_channels,
temb_channels=None,
eps=resnet_eps,
groups=resnet_groups,
dropout=dropout,
time_embedding_norm=resnet_time_scale_shift,
non_linearity=resnet_act_fn,
output_scale_factor=output_scale_factor,
pre_norm=resnet_pre_norm,
)
)
self.resnets = nn.ModuleList(resnets)
if add_downsample:
self.downsamplers = nn.ModuleList(
[
Downsample2D(
in_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op"
)
]
)
else:
self.downsamplers = None
def forward(self, hidden_states):
for resnet in self.resnets:
hidden_states = resnet(hidden_states, temb=None)
if self.downsamplers is not None:
for downsampler in self.downsamplers:
hidden_states = downsampler(hidden_states)
return hidden_states
class AttnDownEncoderBlock2D(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
dropout: float = 0.0,
num_layers: int = 1,
resnet_eps: float = 1e-6,
resnet_time_scale_shift: str = "default",
resnet_act_fn: str = "swish",
resnet_groups: int = 32,
resnet_pre_norm: bool = True,
attn_num_head_channels=1,
output_scale_factor=1.0,
add_downsample=True,
downsample_padding=1,
):
super().__init__()
resnets = []
attentions = []
for i in range(num_layers):
in_channels = in_channels if i == 0 else out_channels
resnets.append(
ResnetBlock2D(
in_channels=in_channels,
out_channels=out_channels,
temb_channels=None,
eps=resnet_eps,
groups=resnet_groups,
dropout=dropout,
time_embedding_norm=resnet_time_scale_shift,
non_linearity=resnet_act_fn,
output_scale_factor=output_scale_factor,
pre_norm=resnet_pre_norm,
)
)
attentions.append(
AttentionBlock(
out_channels,
num_head_channels=attn_num_head_channels,
rescale_output_factor=output_scale_factor,
eps=resnet_eps,
num_groups=resnet_groups,
)
)
self.attentions = nn.ModuleList(attentions)
self.resnets = nn.ModuleList(resnets)
if add_downsample:
self.downsamplers = nn.ModuleList(
[
Downsample2D(
in_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op"
)
]
)
else:
self.downsamplers = None
def forward(self, hidden_states):
for resnet, attn in zip(self.resnets, self.attentions):
hidden_states = resnet(hidden_states, temb=None)
hidden_states = attn(hidden_states)
if self.downsamplers is not None:
for downsampler in self.downsamplers:
hidden_states = downsampler(hidden_states)
return hidden_states
class AttnSkipDownBlock2D(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
temb_channels: int,
dropout: float = 0.0,
num_layers: int = 1,
resnet_eps: float = 1e-6,
resnet_time_scale_shift: str = "default",
resnet_act_fn: str = "swish",
resnet_pre_norm: bool = True,
attn_num_head_channels=1,
attention_type="default",
output_scale_factor=np.sqrt(2.0),
downsample_padding=1,
add_downsample=True,
):
super().__init__()
self.attentions = nn.ModuleList([])
self.resnets = nn.ModuleList([])
self.attention_type = attention_type
for i in range(num_layers):
in_channels = in_channels if i == 0 else out_channels
self.resnets.append(
ResnetBlock2D(
in_channels=in_channels,
out_channels=out_channels,
temb_channels=temb_channels,
eps=resnet_eps,
groups=min(in_channels // 4, 32),
groups_out=min(out_channels // 4, 32),
dropout=dropout,
time_embedding_norm=resnet_time_scale_shift,
non_linearity=resnet_act_fn,
output_scale_factor=output_scale_factor,
pre_norm=resnet_pre_norm,
)
)
self.attentions.append(
AttentionBlock(
out_channels,
num_head_channels=attn_num_head_channels,
rescale_output_factor=output_scale_factor,
eps=resnet_eps,
)
)
if add_downsample:
self.resnet_down = ResnetBlock2D(
in_channels=out_channels,
out_channels=out_channels,
temb_channels=temb_channels,
eps=resnet_eps,
groups=min(out_channels // 4, 32),
dropout=dropout,
time_embedding_norm=resnet_time_scale_shift,
non_linearity=resnet_act_fn,
output_scale_factor=output_scale_factor,
pre_norm=resnet_pre_norm,
use_nin_shortcut=True,
down=True,
kernel="fir",
)
self.downsamplers = nn.ModuleList([FirDownsample2D(in_channels, out_channels=out_channels)])
self.skip_conv = nn.Conv2d(3, out_channels, kernel_size=(1, 1), stride=(1, 1))
else:
self.resnet_down = None
self.downsamplers = None
self.skip_conv = None
def forward(self, hidden_states, temb=None, skip_sample=None):
output_states = ()
for resnet, attn in zip(self.resnets, self.attentions):
hidden_states = resnet(hidden_states, temb)
hidden_states = attn(hidden_states)
output_states += (hidden_states,)
if self.downsamplers is not None:
hidden_states = self.resnet_down(hidden_states, temb)
for downsampler in self.downsamplers:
skip_sample = downsampler(skip_sample)
hidden_states = self.skip_conv(skip_sample) + hidden_states
output_states += (hidden_states,)
return hidden_states, output_states, skip_sample
class SkipDownBlock2D(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
temb_channels: int,
dropout: float = 0.0,
num_layers: int = 1,
resnet_eps: float = 1e-6,
resnet_time_scale_shift: str = "default",
resnet_act_fn: str = "swish",
resnet_pre_norm: bool = True,
output_scale_factor=np.sqrt(2.0),
add_downsample=True,
downsample_padding=1,
):
super().__init__()
self.resnets = nn.ModuleList([])
for i in range(num_layers):
in_channels = in_channels if i == 0 else out_channels
self.resnets.append(
ResnetBlock2D(
in_channels=in_channels,
out_channels=out_channels,
temb_channels=temb_channels,
eps=resnet_eps,
groups=min(in_channels // 4, 32),
groups_out=min(out_channels // 4, 32),
dropout=dropout,
time_embedding_norm=resnet_time_scale_shift,
non_linearity=resnet_act_fn,
output_scale_factor=output_scale_factor,
pre_norm=resnet_pre_norm,
)
)
if add_downsample:
self.resnet_down = ResnetBlock2D(
in_channels=out_channels,
out_channels=out_channels,
temb_channels=temb_channels,
eps=resnet_eps,
groups=min(out_channels // 4, 32),
dropout=dropout,
time_embedding_norm=resnet_time_scale_shift,
non_linearity=resnet_act_fn,
output_scale_factor=output_scale_factor,
pre_norm=resnet_pre_norm,
use_nin_shortcut=True,
down=True,
kernel="fir",
)
self.downsamplers = nn.ModuleList([FirDownsample2D(in_channels, out_channels=out_channels)])
self.skip_conv = nn.Conv2d(3, out_channels, kernel_size=(1, 1), stride=(1, 1))
else:
self.resnet_down = None
self.downsamplers = None
self.skip_conv = None
def forward(self, hidden_states, temb=None, skip_sample=None):
output_states = ()
for resnet in self.resnets:
hidden_states = resnet(hidden_states, temb)
output_states += (hidden_states,)
if self.downsamplers is not None:
hidden_states = self.resnet_down(hidden_states, temb)
for downsampler in self.downsamplers:
skip_sample = downsampler(skip_sample)
hidden_states = self.skip_conv(skip_sample) + hidden_states
output_states += (hidden_states,)
return hidden_states, output_states, skip_sample
class AttnUpBlock2D(nn.Module):
def __init__(
self,
in_channels: int,
prev_output_channel: int,
out_channels: int,
temb_channels: int,
dropout: float = 0.0,
num_layers: int = 1,
resnet_eps: float = 1e-6,
resnet_time_scale_shift: str = "default",
resnet_act_fn: str = "swish",
resnet_groups: int = 32,
resnet_pre_norm: bool = True,
attention_type="default",
attn_num_head_channels=1,
output_scale_factor=1.0,
add_upsample=True,
):
super().__init__()
resnets = []
attentions = []
self.attention_type = attention_type
for i in range(num_layers):
res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
resnet_in_channels = prev_output_channel if i == 0 else out_channels
resnets.append(
ResnetBlock2D(
in_channels=resnet_in_channels + res_skip_channels,
out_channels=out_channels,
temb_channels=temb_channels,
eps=resnet_eps,
groups=resnet_groups,
dropout=dropout,
time_embedding_norm=resnet_time_scale_shift,
non_linearity=resnet_act_fn,
output_scale_factor=output_scale_factor,
pre_norm=resnet_pre_norm,
)
)
attentions.append(
AttentionBlock(
out_channels,
num_head_channels=attn_num_head_channels,
rescale_output_factor=output_scale_factor,
eps=resnet_eps,
)
)
self.attentions = nn.ModuleList(attentions)
self.resnets = nn.ModuleList(resnets)
if add_upsample:
self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)])
else:
self.upsamplers = None
def forward(self, hidden_states, res_hidden_states_tuple, temb=None):
for resnet, attn in zip(self.resnets, self.attentions):
# pop res hidden states
res_hidden_states = res_hidden_states_tuple[-1]
res_hidden_states_tuple = res_hidden_states_tuple[:-1]
hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
hidden_states = resnet(hidden_states, temb)
hidden_states = attn(hidden_states)
if self.upsamplers is not None:
for upsampler in self.upsamplers:
hidden_states = upsampler(hidden_states)
return hidden_states
class CrossAttnUpBlock2D(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
prev_output_channel: int,
temb_channels: int,
dropout: float = 0.0,
num_layers: int = 1,
resnet_eps: float = 1e-6,
resnet_time_scale_shift: str = "default",
resnet_act_fn: str = "swish",
resnet_groups: int = 32,
resnet_pre_norm: bool = True,
attn_num_head_channels=1,
cross_attention_dim=1280,
attention_type="default",
output_scale_factor=1.0,
downsample_padding=1,
add_upsample=True,
):
super().__init__()
resnets = []
attentions = []
self.attention_type = attention_type
self.attn_num_head_channels = attn_num_head_channels
for i in range(num_layers):
res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
resnet_in_channels = prev_output_channel if i == 0 else out_channels
resnets.append(
ResnetBlock2D(
in_channels=resnet_in_channels + res_skip_channels,
out_channels=out_channels,
temb_channels=temb_channels,
eps=resnet_eps,
groups=resnet_groups,
dropout=dropout,
time_embedding_norm=resnet_time_scale_shift,
non_linearity=resnet_act_fn,
output_scale_factor=output_scale_factor,
pre_norm=resnet_pre_norm,
)
)
attentions.append(
SpatialTransformer(
out_channels,
attn_num_head_channels,
out_channels // attn_num_head_channels,
depth=1,
context_dim=cross_attention_dim,
)
)
self.attentions = nn.ModuleList(attentions)
self.resnets = nn.ModuleList(resnets)
if add_upsample:
self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)])
else:
self.upsamplers = None
def set_attention_slice(self, slice_size):
if slice_size is not None and self.attn_num_head_channels % slice_size != 0:
raise ValueError(
f"Make sure slice_size {slice_size} is a divisor of "
f"the number of heads used in cross_attention {self.attn_num_head_channels}"
)
if slice_size is not None and slice_size > self.attn_num_head_channels:
raise ValueError(
f"Chunk_size {slice_size} has to be smaller or equal to "
f"the number of heads used in cross_attention {self.attn_num_head_channels}"
)
for attn in self.attentions:
attn._set_attention_slice(slice_size)
def forward(self, hidden_states, res_hidden_states_tuple, temb=None, encoder_hidden_states=None):
for resnet, attn in zip(self.resnets, self.attentions):
# pop res hidden states
res_hidden_states = res_hidden_states_tuple[-1]
res_hidden_states_tuple = res_hidden_states_tuple[:-1]
hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
hidden_states = resnet(hidden_states, temb)
hidden_states = attn(hidden_states, context=encoder_hidden_states)
if self.upsamplers is not None:
for upsampler in self.upsamplers:
hidden_states = upsampler(hidden_states)
return hidden_states
class UpBlock2D(nn.Module):
def __init__(
self,
in_channels: int,
prev_output_channel: int,
out_channels: int,
temb_channels: int,
dropout: float = 0.0,
num_layers: int = 1,
resnet_eps: float = 1e-6,
resnet_time_scale_shift: str = "default",
resnet_act_fn: str = "swish",
resnet_groups: int = 32,
resnet_pre_norm: bool = True,
output_scale_factor=1.0,
add_upsample=True,
):
super().__init__()
resnets = []
for i in range(num_layers):
res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
resnet_in_channels = prev_output_channel if i == 0 else out_channels
resnets.append(
ResnetBlock2D(
in_channels=resnet_in_channels + res_skip_channels,
out_channels=out_channels,
temb_channels=temb_channels,
eps=resnet_eps,
groups=resnet_groups,
dropout=dropout,
time_embedding_norm=resnet_time_scale_shift,
non_linearity=resnet_act_fn,
output_scale_factor=output_scale_factor,
pre_norm=resnet_pre_norm,
)
)
self.resnets = nn.ModuleList(resnets)
if add_upsample:
self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)])
else:
self.upsamplers = None
def forward(self, hidden_states, res_hidden_states_tuple, temb=None):
for resnet in self.resnets:
# pop res hidden states
res_hidden_states = res_hidden_states_tuple[-1]
res_hidden_states_tuple = res_hidden_states_tuple[:-1]
hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
hidden_states = resnet(hidden_states, temb)
if self.upsamplers is not None:
for upsampler in self.upsamplers:
hidden_states = upsampler(hidden_states)
return hidden_states
class UpDecoderBlock2D(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
dropout: float = 0.0,
num_layers: int = 1,
resnet_eps: float = 1e-6,
resnet_time_scale_shift: str = "default",
resnet_act_fn: str = "swish",
resnet_groups: int = 32,
resnet_pre_norm: bool = True,
output_scale_factor=1.0,
add_upsample=True,
):
super().__init__()
resnets = []
for i in range(num_layers):
input_channels = in_channels if i == 0 else out_channels
resnets.append(
ResnetBlock2D(
in_channels=input_channels,
out_channels=out_channels,
temb_channels=None,
eps=resnet_eps,
groups=resnet_groups,
dropout=dropout,
time_embedding_norm=resnet_time_scale_shift,
non_linearity=resnet_act_fn,
output_scale_factor=output_scale_factor,
pre_norm=resnet_pre_norm,
)
)
self.resnets = nn.ModuleList(resnets)
if add_upsample:
self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)])
else:
self.upsamplers = None
def forward(self, hidden_states):
for resnet in self.resnets:
hidden_states = resnet(hidden_states, temb=None)
if self.upsamplers is not None:
for upsampler in self.upsamplers:
hidden_states = upsampler(hidden_states)
return hidden_states
class AttnUpDecoderBlock2D(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
dropout: float = 0.0,
num_layers: int = 1,
resnet_eps: float = 1e-6,
resnet_time_scale_shift: str = "default",
resnet_act_fn: str = "swish",
resnet_groups: int = 32,
resnet_pre_norm: bool = True,
attn_num_head_channels=1,
output_scale_factor=1.0,
add_upsample=True,
):
super().__init__()
resnets = []
attentions = []
for i in range(num_layers):
input_channels = in_channels if i == 0 else out_channels
resnets.append(
ResnetBlock2D(
in_channels=input_channels,
out_channels=out_channels,
temb_channels=None,
eps=resnet_eps,
groups=resnet_groups,
dropout=dropout,
time_embedding_norm=resnet_time_scale_shift,
non_linearity=resnet_act_fn,
output_scale_factor=output_scale_factor,
pre_norm=resnet_pre_norm,
)
)
attentions.append(
AttentionBlock(
out_channels,
num_head_channels=attn_num_head_channels,
rescale_output_factor=output_scale_factor,
eps=resnet_eps,
num_groups=resnet_groups,
)
)
self.attentions = nn.ModuleList(attentions)
self.resnets = nn.ModuleList(resnets)
if add_upsample:
self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)])
else:
self.upsamplers = None
def forward(self, hidden_states):
for resnet, attn in zip(self.resnets, self.attentions):
hidden_states = resnet(hidden_states, temb=None)
hidden_states = attn(hidden_states)
if self.upsamplers is not None:
for upsampler in self.upsamplers:
hidden_states = upsampler(hidden_states)
return hidden_states
class AttnSkipUpBlock2D(nn.Module):
def __init__(
self,
in_channels: int,
prev_output_channel: int,
out_channels: int,
temb_channels: int,
dropout: float = 0.0,
num_layers: int = 1,
resnet_eps: float = 1e-6,
resnet_time_scale_shift: str = "default",
resnet_act_fn: str = "swish",
resnet_pre_norm: bool = True,
attn_num_head_channels=1,
attention_type="default",
output_scale_factor=np.sqrt(2.0),
upsample_padding=1,
add_upsample=True,
):
super().__init__()
self.attentions = nn.ModuleList([])
self.resnets = nn.ModuleList([])
self.attention_type = attention_type
for i in range(num_layers):
res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
resnet_in_channels = prev_output_channel if i == 0 else out_channels
self.resnets.append(
ResnetBlock2D(
in_channels=resnet_in_channels + res_skip_channels,
out_channels=out_channels,
temb_channels=temb_channels,
eps=resnet_eps,
groups=min(resnet_in_channels + res_skip_channels // 4, 32),
groups_out=min(out_channels // 4, 32),
dropout=dropout,
time_embedding_norm=resnet_time_scale_shift,
non_linearity=resnet_act_fn,
output_scale_factor=output_scale_factor,
pre_norm=resnet_pre_norm,
)
)
self.attentions.append(
AttentionBlock(
out_channels,
num_head_channels=attn_num_head_channels,
rescale_output_factor=output_scale_factor,
eps=resnet_eps,
)
)
self.upsampler = FirUpsample2D(in_channels, out_channels=out_channels)
if add_upsample:
self.resnet_up = ResnetBlock2D(
in_channels=out_channels,
out_channels=out_channels,
temb_channels=temb_channels,
eps=resnet_eps,
groups=min(out_channels // 4, 32),
groups_out=min(out_channels // 4, 32),
dropout=dropout,
time_embedding_norm=resnet_time_scale_shift,
non_linearity=resnet_act_fn,
output_scale_factor=output_scale_factor,
pre_norm=resnet_pre_norm,
use_nin_shortcut=True,
up=True,
kernel="fir",
)
self.skip_conv = nn.Conv2d(out_channels, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
self.skip_norm = torch.nn.GroupNorm(
num_groups=min(out_channels // 4, 32), num_channels=out_channels, eps=resnet_eps, affine=True
)
self.act = nn.SiLU()
else:
self.resnet_up = None
self.skip_conv = None
self.skip_norm = None
self.act = None
def forward(self, hidden_states, res_hidden_states_tuple, temb=None, skip_sample=None):
for resnet in self.resnets:
# pop res hidden states
res_hidden_states = res_hidden_states_tuple[-1]
res_hidden_states_tuple = res_hidden_states_tuple[:-1]
hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
hidden_states = resnet(hidden_states, temb)
hidden_states = self.attentions[0](hidden_states)
if skip_sample is not None:
skip_sample = self.upsampler(skip_sample)
else:
skip_sample = 0
if self.resnet_up is not None:
skip_sample_states = self.skip_norm(hidden_states)
skip_sample_states = self.act(skip_sample_states)
skip_sample_states = self.skip_conv(skip_sample_states)
skip_sample = skip_sample + skip_sample_states
hidden_states = self.resnet_up(hidden_states, temb)
return hidden_states, skip_sample
class SkipUpBlock2D(nn.Module):
def __init__(
self,
in_channels: int,
prev_output_channel: int,
out_channels: int,
temb_channels: int,
dropout: float = 0.0,
num_layers: int = 1,
resnet_eps: float = 1e-6,
resnet_time_scale_shift: str = "default",
resnet_act_fn: str = "swish",
resnet_pre_norm: bool = True,
output_scale_factor=np.sqrt(2.0),
add_upsample=True,
upsample_padding=1,
):
super().__init__()
self.resnets = nn.ModuleList([])
for i in range(num_layers):
res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
resnet_in_channels = prev_output_channel if i == 0 else out_channels
self.resnets.append(
ResnetBlock2D(
in_channels=resnet_in_channels + res_skip_channels,
out_channels=out_channels,
temb_channels=temb_channels,
eps=resnet_eps,
groups=min((resnet_in_channels + res_skip_channels) // 4, 32),
groups_out=min(out_channels // 4, 32),
dropout=dropout,
time_embedding_norm=resnet_time_scale_shift,
non_linearity=resnet_act_fn,
output_scale_factor=output_scale_factor,
pre_norm=resnet_pre_norm,
)
)
self.upsampler = FirUpsample2D(in_channels, out_channels=out_channels)
if add_upsample:
self.resnet_up = ResnetBlock2D(
in_channels=out_channels,
out_channels=out_channels,
temb_channels=temb_channels,
eps=resnet_eps,
groups=min(out_channels // 4, 32),
groups_out=min(out_channels // 4, 32),
dropout=dropout,
time_embedding_norm=resnet_time_scale_shift,
non_linearity=resnet_act_fn,
output_scale_factor=output_scale_factor,
pre_norm=resnet_pre_norm,
use_nin_shortcut=True,
up=True,
kernel="fir",
)
self.skip_conv = nn.Conv2d(out_channels, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
self.skip_norm = torch.nn.GroupNorm(
num_groups=min(out_channels // 4, 32), num_channels=out_channels, eps=resnet_eps, affine=True
)
self.act = nn.SiLU()
else:
self.resnet_up = None
self.skip_conv = None
self.skip_norm = None
self.act = None
def forward(self, hidden_states, res_hidden_states_tuple, temb=None, skip_sample=None):
for resnet in self.resnets:
# pop res hidden states
res_hidden_states = res_hidden_states_tuple[-1]
res_hidden_states_tuple = res_hidden_states_tuple[:-1]
hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
hidden_states = resnet(hidden_states, temb)
if skip_sample is not None:
skip_sample = self.upsampler(skip_sample)
else:
skip_sample = 0
if self.resnet_up is not None:
skip_sample_states = self.skip_norm(hidden_states)
skip_sample_states = self.act(skip_sample_states)
skip_sample_states = self.skip_conv(skip_sample_states)
skip_sample = skip_sample + skip_sample_states
hidden_states = self.resnet_up(hidden_states, temb)
return hidden_states, skip_sample
================================================
FILE: models/edict/my_diffusers/models/vae.py
================================================
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..modeling_utils import ModelMixin
from ..utils import BaseOutput
from .unet_blocks import UNetMidBlock2D, get_down_block, get_up_block
@dataclass
class DecoderOutput(BaseOutput):
"""
Output of decoding method.
Args:
sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Decoded output sample of the model. Output of the last layer of the model.
"""
sample: torch.FloatTensor
@dataclass
class VQEncoderOutput(BaseOutput):
"""
Output of VQModel encoding method.
Args:
latents (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Encoded output sample of the model. Output of the last layer of the model.
"""
latents: torch.FloatTensor
@dataclass
class AutoencoderKLOutput(BaseOutput):
"""
Output of AutoencoderKL encoding method.
Args:
latent_dist (`DiagonalGaussianDistribution`):
Encoded outputs of `Encoder` represented as the mean and logvar of `DiagonalGaussianDistribution`.
`DiagonalGaussianDistribution` allows for sampling latents from the distribution.
"""
latent_dist: "DiagonalGaussianDistribution"
class Encoder(nn.Module):
def __init__(
self,
in_channels=3,
out_channels=3,
down_block_types=("DownEncoderBlock2D",),
block_out_channels=(64,),
layers_per_block=2,
act_fn="silu",
double_z=True,
):
super().__init__()
self.layers_per_block = layers_per_block
self.conv_in = torch.nn.Conv2d(in_channels, block_out_channels[0], kernel_size=3, stride=1, padding=1)
self.mid_block = None
self.down_blocks = nn.ModuleList([])
# down
output_channel = block_out_channels[0]
for i, down_block_type in enumerate(down_block_types):
input_channel = output_channel
output_channel = block_out_channels[i]
is_final_block = i == len(block_out_channels) - 1
down_block = get_down_block(
down_block_type,
num_layers=self.layers_per_block,
in_channels=input_channel,
out_channels=output_channel,
add_downsample=not is_final_block,
resnet_eps=1e-6,
downsample_padding=0,
resnet_act_fn=act_fn,
attn_num_head_channels=None,
temb_channels=None,
)
self.down_blocks.append(down_block)
# mid
self.mid_block = UNetMidBlock2D(
in_channels=block_out_channels[-1],
resnet_eps=1e-6,
resnet_act_fn=act_fn,
output_scale_factor=1,
resnet_time_scale_shift="default",
attn_num_head_channels=None,
resnet_groups=32,
temb_channels=None,
)
# out
num_groups_out = 32
self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[-1], num_groups=num_groups_out, eps=1e-6)
self.conv_act = nn.SiLU()
conv_out_channels = 2 * out_channels if double_z else out_channels
self.conv_out = nn.Conv2d(block_out_channels[-1], conv_out_channels, 3, padding=1)
def forward(self, x):
sample = x
sample = self.conv_in(sample)
# down
for down_block in self.down_blocks:
sample = down_block(sample)
# middle
sample = self.mid_block(sample)
# post-process
sample = self.conv_norm_out(sample)
sample = self.conv_act(sample)
sample = self.conv_out(sample)
return sample
class Decoder(nn.Module):
def __init__(
self,
in_channels=3,
out_channels=3,
up_block_types=("UpDecoderBlock2D",),
block_out_channels=(64,),
layers_per_block=2,
act_fn="silu",
):
super().__init__()
self.layers_per_block = layers_per_block
self.conv_in = nn.Conv2d(in_channels, block_out_channels[-1], kernel_size=3, stride=1, padding=1)
self.mid_block = None
self.up_blocks = nn.ModuleList([])
# mid
self.mid_block = UNetMidBlock2D(
in_channels=block_out_channels[-1],
resnet_eps=1e-6,
resnet_act_fn=act_fn,
output_scale_factor=1,
resnet_time_scale_shift="default",
attn_num_head_channels=None,
resnet_groups=32,
temb_channels=None,
)
# up
reversed_block_out_channels = list(reversed(block_out_channels))
output_channel = reversed_block_out_channels[0]
for i, up_block_type in enumerate(up_block_types):
prev_output_channel = output_channel
output_channel = reversed_block_out_channels[i]
is_final_block = i == len(block_out_channels) - 1
up_block = get_up_block(
up_block_type,
num_layers=self.layers_per_block + 1,
in_channels=prev_output_channel,
out_channels=output_channel,
prev_output_channel=None,
add_upsample=not is_final_block,
resnet_eps=1e-6,
resnet_act_fn=act_fn,
attn_num_head_channels=None,
temb_channels=None,
)
self.up_blocks.append(up_block)
prev_output_channel = output_channel
# out
num_groups_out = 32
self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=num_groups_out, eps=1e-6)
self.conv_act = nn.SiLU()
self.conv_out = nn.Conv2d(block_out_channels[0], out_channels, 3, padding=1)
def forward(self, z):
sample = z
sample = self.conv_in(sample)
# middle
sample = self.mid_block(sample)
# up
for up_block in self.up_blocks:
sample = up_block(sample)
# post-process
sample = self.conv_norm_out(sample)
sample = self.conv_act(sample)
sample = self.conv_out(sample)
return sample
class VectorQuantizer(nn.Module):
"""
Improved version over VectorQuantizer, can be used as a drop-in replacement. Mostly avoids costly matrix
multiplications and allows for post-hoc remapping of indices.
"""
# NOTE: due to a bug the beta term was applied to the wrong term. for
# backwards compatibility we use the buggy version by default, but you can
# specify legacy=False to fix it.
def __init__(self, n_e, e_dim, beta, remap=None, unknown_index="random", sane_index_shape=False, legacy=True):
super().__init__()
self.n_e = n_e
self.e_dim = e_dim
self.beta = beta
self.legacy = legacy
self.embedding = nn.Embedding(self.n_e, self.e_dim)
self.embedding.weight.data.uniform_(-1.0 / self.n_e, 1.0 / self.n_e)
self.remap = remap
if self.remap is not None:
self.register_buffer("used", torch.tensor(np.load(self.remap)))
self.re_embed = self.used.shape[0]
self.unknown_index = unknown_index # "random" or "extra" or integer
if self.unknown_index == "extra":
self.unknown_index = self.re_embed
self.re_embed = self.re_embed + 1
print(
f"Remapping {self.n_e} indices to {self.re_embed} indices. "
f"Using {self.unknown_index} for unknown indices."
)
else:
self.re_embed = n_e
self.sane_index_shape = sane_index_shape
def remap_to_used(self, inds):
ishape = inds.shape
assert len(ishape) > 1
inds = inds.reshape(ishape[0], -1)
used = self.used.to(inds)
match = (inds[:, :, None] == used[None, None, ...]).long()
new = match.argmax(-1)
unknown = match.sum(2) < 1
if self.unknown_index == "random":
new[unknown] = torch.randint(0, self.re_embed, size=new[unknown].shape).to(device=new.device)
else:
new[unknown] = self.unknown_index
return new.reshape(ishape)
def unmap_to_all(self, inds):
ishape = inds.shape
assert len(ishape) > 1
inds = inds.reshape(ishape[0], -1)
used = self.used.to(inds)
if self.re_embed > self.used.shape[0]: # extra token
inds[inds >= self.used.shape[0]] = 0 # simply set to zero
back = torch.gather(used[None, :][inds.shape[0] * [0], :], 1, inds)
return back.reshape(ishape)
def forward(self, z):
# reshape z -> (batch, height, width, channel) and flatten
z = z.permute(0, 2, 3, 1).contiguous()
z_flattened = z.view(-1, self.e_dim)
# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
d = (
torch.sum(z_flattened**2, dim=1, keepdim=True)
+ torch.sum(self.embedding.weight**2, dim=1)
- 2 * torch.einsum("bd,dn->bn", z_flattened, self.embedding.weight.t())
)
min_encoding_indices = torch.argmin(d, dim=1)
z_q = self.embedding(min_encoding_indices).view(z.shape)
perplexity = None
min_encodings = None
# compute loss for embedding
if not self.legacy:
loss = self.beta * torch.mean((z_q.detach() - z) ** 2) + torch.mean((z_q - z.detach()) ** 2)
else:
loss = torch.mean((z_q.detach() - z) ** 2) + self.beta * torch.mean((z_q - z.detach()) ** 2)
# preserve gradients
z_q = z + (z_q - z).detach()
# reshape back to match original input shape
z_q = z_q.permute(0, 3, 1, 2).contiguous()
if self.remap is not None:
min_encoding_indices = min_encoding_indices.reshape(z.shape[0], -1) # add batch axis
min_encoding_indices = self.remap_to_used(min_encoding_indices)
min_encoding_indices = min_encoding_indices.reshape(-1, 1) # flatten
if self.sane_index_shape:
min_encoding_indices = min_encoding_indices.reshape(z_q.shape[0], z_q.shape[2], z_q.shape[3])
return z_q, loss, (perplexity, min_encodings, min_encoding_indices)
def get_codebook_entry(self, indices, shape):
# shape specifying (batch, height, width, channel)
if self.remap is not None:
indices = indices.reshape(shape[0], -1) # add batch axis
indices = self.unmap_to_all(indices)
indices = indices.reshape(-1) # flatten again
# get quantized latent vectors
z_q = self.embedding(indices)
if shape is not None:
z_q = z_q.view(shape)
# reshape back to match original input shape
z_q = z_q.permute(0, 3, 1, 2).contiguous()
return z_q
class DiagonalGaussianDistribution(object):
def __init__(self, parameters, deterministic=False):
self.parameters = parameters
self.mean, self.logvar = torch.chunk(parameters, 2, dim=1)
self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
self.deterministic = deterministic
self.std = torch.exp(0.5 * self.logvar)
self.var = torch.exp(self.logvar)
if self.deterministic:
self.var = self.std = torch.zeros_like(self.mean).to(device=self.parameters.device)
def sample(self, generator: Optional[torch.Generator] = None) -> torch.FloatTensor:
device = self.parameters.device
sample_device = "cpu" if device.type == "mps" else device
sample = torch.randn(self.mean.shape, generator=generator, device=sample_device).to(device)
x = self.mean + self.std * sample
return x
def kl(self, other=None):
if self.deterministic:
return torch.Tensor([0.0])
else:
if other is None:
return 0.5 * torch.sum(torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar, dim=[1, 2, 3])
else:
return 0.5 * torch.sum(
torch.pow(self.mean - other.mean, 2) / other.var
+ self.var / other.var
- 1.0
- self.logvar
+ other.logvar,
dim=[1, 2, 3],
)
def nll(self, sample, dims=[1, 2, 3]):
if self.deterministic:
return torch.Tensor([0.0])
logtwopi = np.log(2.0 * np.pi)
return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var, dim=dims)
def mode(self):
return self.mean
class VQModel(ModelMixin, ConfigMixin):
r"""VQ-VAE model from the paper Neural Discrete Representation Learning by Aaron van den Oord, Oriol Vinyals and Koray
Kavukcuoglu.
This model inherits from [`ModelMixin`]. Check the superclass documentation for the generic methods the library
implements for all the model (such as downloading or saving, etc.)
Parameters:
in_channels (int, *optional*, defaults to 3): Number of channels in the input image.
out_channels (int, *optional*, defaults to 3): Number of channels in the output.
down_block_types (`Tuple[str]`, *optional*, defaults to :
obj:`("DownEncoderBlock2D",)`): Tuple of downsample block types.
up_block_types (`Tuple[str]`, *optional*, defaults to :
obj:`("UpDecoderBlock2D",)`): Tuple of upsample block types.
block_out_channels (`Tuple[int]`, *optional*, defaults to :
obj:`(64,)`): Tuple of block output channels.
act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
latent_channels (`int`, *optional*, defaults to `3`): Number of channels in the latent space.
sample_size (`int`, *optional*, defaults to `32`): TODO
num_vq_embeddings (`int`, *optional*, defaults to `256`): Number of codebook vectors in the VQ-VAE.
"""
@register_to_config
def __init__(
self,
in_channels: int = 3,
out_channels: int = 3,
down_block_types: Tuple[str] = ("DownEncoderBlock2D",),
up_block_types: Tuple[str] = ("UpDecoderBlock2D",),
block_out_channels: Tuple[int] = (64,),
layers_per_block: int = 1,
act_fn: str = "silu",
latent_channels: int = 3,
sample_size: int = 32,
num_vq_embeddings: int = 256,
):
super().__init__()
# pass init params to Encoder
self.encoder = Encoder(
in_channels=in_channels,
out_channels=latent_channels,
down_block_types=down_block_types,
block_out_channels=block_out_channels,
layers_per_block=layers_per_block,
act_fn=act_fn,
double_z=False,
)
self.quant_conv = torch.nn.Conv2d(latent_channels, latent_channels, 1)
self.quantize = VectorQuantizer(
num_vq_embeddings, latent_channels, beta=0.25, remap=None, sane_index_shape=False
)
self.post_quant_conv = torch.nn.Conv2d(latent_channels, latent_channels, 1)
# pass init params to Decoder
self.decoder = Decoder(
in_channels=latent_channels,
out_channels=out_channels,
up_block_types=up_block_types,
block_out_channels=block_out_channels,
layers_per_block=layers_per_block,
act_fn=act_fn,
)
def encode(self, x: torch.FloatTensor, return_dict: bool = True) -> VQEncoderOutput:
h = self.encoder(x)
h = self.quant_conv(h)
if not return_dict:
return (h,)
return VQEncoderOutput(latents=h)
def decode(
self, h: torch.FloatTensor, force_not_quantize: bool = False, return_dict: bool = True
) -> Union[DecoderOutput, torch.FloatTensor]:
# also go through quantization layer
if not force_not_quantize:
quant, emb_loss, info = self.quantize(h)
else:
quant = h
quant = self.post_quant_conv(quant)
dec = self.decoder(quant)
if not return_dict:
return (dec,)
return DecoderOutput(sample=dec)
def forward(self, sample: torch.FloatTensor, return_dict: bool = True) -> Union[DecoderOutput, torch.FloatTensor]:
r"""
Args:
sample (`torch.FloatTensor`): Input sample.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`DecoderOutput`] instead of a plain tuple.
"""
x = sample
h = self.encode(x).latents
dec = self.decode(h).sample
if not return_dict:
return (dec,)
return DecoderOutput(sample=dec)
class AutoencoderKL(ModelMixin, ConfigMixin):
r"""Variational Autoencoder (VAE) model with KL loss from the paper Auto-Encoding Variational Bayes by Diederik P. Kingma
and Max Welling.
This model inherits from [`ModelMixin`]. Check the superclass documentation for the generic methods the library
implements for all the model (such as downloading or saving, etc.)
Parameters:
in_channels (int, *optional*, defaults to 3): Number of channels in the input image.
out_channels (int, *optional*, defaults to 3): Number of channels in the output.
down_block_types (`Tuple[str]`, *optional*, defaults to :
obj:`("DownEncoderBlock2D",)`): Tuple of downsample block types.
up_block_types (`Tuple[str]`, *optional*, defaults to :
obj:`("UpDecoderBlock2D",)`): Tuple of upsample block types.
block_out_channels (`Tuple[int]`, *optional*, defaults to :
obj:`(64,)`): Tuple of block output channels.
act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
latent_channels (`int`, *optional*, defaults to `4`): Number of channels in the latent space.
sample_size (`int`, *optional*, defaults to `32`): TODO
"""
@register_to_config
def __init__(
self,
in_channels: int = 3,
out_channels: int = 3,
down_block_types: Tuple[str] = ("DownEncoderBlock2D",),
up_block_types: Tuple[str] = ("UpDecoderBlock2D",),
block_out_channels: Tuple[int] = (64,),
layers_per_block: int = 1,
act_fn: str = "silu",
latent_channels: int = 4,
sample_size: int = 32,
):
super().__init__()
# pass init params to Encoder
self.encoder = Encoder(
in_channels=in_channels,
out_channels=latent_channels,
down_block_types=down_block_types,
block_out_channels=block_out_channels,
layers_per_block=layers_per_block,
act_fn=act_fn,
double_z=True,
)
# pass init params to Decoder
self.decoder = Decoder(
in_channels=latent_channels,
out_channels=out_channels,
up_block_types=up_block_types,
block_out_channels=block_out_channels,
layers_per_block=layers_per_block,
act_fn=act_fn,
)
self.quant_conv = torch.nn.Conv2d(2 * latent_channels, 2 * latent_channels, 1)
self.post_quant_conv = torch.nn.Conv2d(latent_channels, latent_channels, 1)
def encode(self, x: torch.FloatTensor, return_dict: bool = True) -> AutoencoderKLOutput:
h = self.encoder(x)
moments = self.quant_conv(h)
posterior = DiagonalGaussianDistribution(moments)
if not return_dict:
return (posterior,)
return AutoencoderKLOutput(latent_dist=posterior)
def decode(self, z: torch.FloatTensor, return_dict: bool = True) -> Union[DecoderOutput, torch.FloatTensor]:
z = self.post_quant_conv(z)
dec = self.decoder(z)
if not return_dict:
return (dec,)
return DecoderOutput(sample=dec)
def forward(
self, sample: torch.FloatTensor, sample_posterior: bool = False, return_dict: bool = True
) -> Union[DecoderOutput, torch.FloatTensor]:
r"""
Args:
sample (`torch.FloatTensor`): Input sample.
sample_posterior (`bool`, *optional*, defaults to `False`):
Whether to sample from the posterior.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`DecoderOutput`] instead of a plain tuple.
"""
x = sample
posterior = self.encode(x).latent_dist
if sample_posterior:
z = posterior.sample()
else:
z = posterior.mode()
dec = self.decode(z).sample
if not return_dict:
return (dec,)
return DecoderOutput(sample=dec)
================================================
FILE: models/edict/my_diffusers/onnx_utils.py
================================================
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import shutil
from pathlib import Path
from typing import Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from .utils import is_onnx_available, logging
if is_onnx_available():
import onnxruntime as ort
ONNX_WEIGHTS_NAME = "model.onnx"
logger = logging.get_logger(__name__)
class OnnxRuntimeModel:
base_model_prefix = "onnx_model"
def __init__(self, model=None, **kwargs):
logger.info("`diffusers.OnnxRuntimeModel` is experimental and might change in the future.")
self.model = model
self.model_save_dir = kwargs.get("model_save_dir", None)
self.latest_model_name = kwargs.get("latest_model_name", "model.onnx")
def __call__(self, **kwargs):
inputs = {k: np.array(v) for k, v in kwargs.items()}
return self.model.run(None, inputs)
@staticmethod
def load_model(path: Union[str, Path], provider=None):
"""
Loads an ONNX Inference session with an ExecutionProvider. Default provider is `CPUExecutionProvider`
Arguments:
path (`str` or `Path`):
Directory from which to load
provider(`str`, *optional*):
Onnxruntime execution provider to use for loading the model, defaults to `CPUExecutionProvider`
"""
if provider is None:
logger.info("No onnxruntime provider specified, using CPUExecutionProvider")
provider = "CPUExecutionProvider"
return ort.InferenceSession(path, providers=[provider])
def _save_pretrained(self, save_directory: Union[str, Path], file_name: Optional[str] = None, **kwargs):
"""
Save a model and its configuration file to a directory, so that it can be re-loaded using the
[`~optimum.onnxruntime.modeling_ort.ORTModel.from_pretrained`] class method. It will always save the
latest_model_name.
Arguments:
save_directory (`str` or `Path`):
Directory where to save the model file.
file_name(`str`, *optional*):
Overwrites the default model file name from `"model.onnx"` to `file_name`. This allows you to save the
model with a different name.
"""
model_file_name = file_name if file_name is not None else ONNX_WEIGHTS_NAME
src_path = self.model_save_dir.joinpath(self.latest_model_name)
dst_path = Path(save_directory).joinpath(model_file_name)
if not src_path.samefile(dst_path):
shutil.copyfile(src_path, dst_path)
def save_pretrained(
self,
save_directory: Union[str, os.PathLike],
**kwargs,
):
"""
Save a model to a directory, so that it can be re-loaded using the [`~OnnxModel.from_pretrained`] class
method.:
Arguments:
save_directory (`str` or `os.PathLike`):
Directory to which to save. Will be created if it doesn't exist.
"""
if os.path.isfile(save_directory):
logger.error(f"Provided path ({save_directory}) should be a directory, not a file")
return
os.makedirs(save_directory, exist_ok=True)
# saving model weights/files
self._save_pretrained(save_directory, **kwargs)
@classmethod
def _from_pretrained(
cls,
model_id: Union[str, Path],
use_auth_token: Optional[Union[bool, str, None]] = None,
revision: Optional[Union[str, None]] = None,
force_download: bool = False,
cache_dir: Optional[str] = None,
file_name: Optional[str] = None,
provider: Optional[str] = None,
**kwargs,
):
"""
Load a model from a directory or the HF Hub.
Arguments:
model_id (`str` or `Path`):
Directory from which to load
use_auth_token (`str` or `bool`):
Is needed to load models from a private or gated repository
revision (`str`):
Revision is the specific model version to use. It can be a branch name, a tag name, or a commit id
cache_dir (`Union[str, Path]`, *optional*):
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.
force_download (`bool`, *optional*, defaults to `False`):
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
file_name(`str`):
Overwrites the default model file name from `"model.onnx"` to `file_name`. This allows you to load
different model files from the same repository or directory.
provider(`str`):
The ONNX runtime provider, e.g. `CPUExecutionProvider` or `CUDAExecutionProvider`.
kwargs (`Dict`, *optional*):
kwargs will be passed to the model during initialization
"""
model_file_name = file_name if file_name is not None else ONNX_WEIGHTS_NAME
# load model from local directory
if os.path.isdir(model_id):
model = OnnxRuntimeModel.load_model(os.path.join(model_id, model_file_name), provider=provider)
kwargs["model_save_dir"] = Path(model_id)
# load model from hub
else:
# download model
model_cache_path = hf_hub_download(
repo_id=model_id,
filename=model_file_name,
use_auth_token=use_auth_token,
revision=revision,
cache_dir=cache_dir,
force_download=force_download,
)
kwargs["model_save_dir"] = Path(model_cache_path).parent
kwargs["latest_model_name"] = Path(model_cache_path).name
model = OnnxRuntimeModel.load_model(model_cache_path, provider=provider)
return cls(model=model, **kwargs)
@classmethod
def from_pretrained(
cls,
model_id: Union[str, Path],
force_download: bool = True,
use_auth_token: Optional[str] = None,
cache_dir: Optional[str] = None,
**model_kwargs,
):
revision = None
if len(str(model_id).split("@")) == 2:
model_id, revision = model_id.split("@")
return cls._from_pretrained(
model_id=model_id,
revision=revision,
cache_dir=cache_dir,
force_download=force_download,
use_auth_token=use_auth_token,
**model_kwargs,
)
================================================
FILE: models/edict/my_diffusers/optimization.py
================================================
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""PyTorch optimization for diffusion models."""
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
logger = logging.get_logger(__name__)
class SchedulerType(Enum):
LINEAR = "linear"
COSINE = "cosine"
COSINE_WITH_RESTARTS = "cosine_with_restarts"
POLYNOMIAL = "polynomial"
CONSTANT = "constant"
CONSTANT_WITH_WARMUP = "constant_with_warmup"
def get_constant_schedule(optimizer: Optimizer, last_epoch: int = -1):
"""
Create a schedule with a constant learning rate, using the learning rate set in optimizer.
Args:
optimizer ([`~torch.optim.Optimizer`]):
The optimizer for which to schedule the learning rate.
last_epoch (`int`, *optional*, defaults to -1):
The index of the last epoch when resuming training.
Return:
`torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.
"""
return LambdaLR(optimizer, lambda _: 1, last_epoch=last_epoch)
def get_constant_schedule_with_warmup(optimizer: Optimizer, num_warmup_steps: int, last_epoch: int = -1):
"""
Create a schedule with a constant learning rate preceded by a warmup period during which the learning rate
increases linearly between 0 and the initial lr set in the optimizer.
Args:
optimizer ([`~torch.optim.Optimizer`]):
The optimizer for which to schedule the learning rate.
num_warmup_steps (`int`):
The number of steps for the warmup phase.
last_epoch (`int`, *optional*, defaults to -1):
The index of the last epoch when resuming training.
Return:
`torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.
"""
def lr_lambda(current_step: int):
if current_step < num_warmup_steps:
return float(current_step) / float(max(1.0, num_warmup_steps))
return 1.0
return LambdaLR(optimizer, lr_lambda, last_epoch=last_epoch)
def get_linear_schedule_with_warmup(optimizer, num_warmup_steps, num_training_steps, last_epoch=-1):
"""
Create a schedule with a learning rate that decreases linearly from the initial lr set in the optimizer to 0, after
a warmup period during which it increases linearly from 0 to the initial lr set in the optimizer.
Args:
optimizer ([`~torch.optim.Optimizer`]):
The optimizer for which to schedule the learning rate.
num_warmup_steps (`int`):
The number of steps for the warmup phase.
num_training_steps (`int`):
The total number of training steps.
last_epoch (`int`, *optional*, defaults to -1):
The index of the last epoch when resuming training.
Return:
`torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.
"""
def lr_lambda(current_step: int):
if current_step < num_warmup_steps:
return float(current_step) / float(max(1, num_warmup_steps))
return max(
0.0, float(num_training_steps - current_step) / float(max(1, num_training_steps - num_warmup_steps))
)
return LambdaLR(optimizer, lr_lambda, last_epoch)
def get_cosine_schedule_with_warmup(
optimizer: Optimizer, num_warmup_steps: int, num_training_steps: int, num_cycles: float = 0.5, last_epoch: int = -1
):
"""
Create a schedule with a learning rate that decreases following the values of the cosine function between the
initial lr set in the optimizer to 0, after a warmup period during which it increases linearly between 0 and the
initial lr set in the optimizer.
Args:
optimizer ([`~torch.optim.Optimizer`]):
The optimizer for which to schedule the learning rate.
num_warmup_steps (`int`):
The number of steps for the warmup phase.
num_training_steps (`int`):
The total number of training steps.
num_cycles (`float`, *optional*, defaults to 0.5):
The number of waves in the cosine schedule (the defaults is to just decrease from the max value to 0
following a half-cosine).
last_epoch (`int`, *optional*, defaults to -1):
The index of the last epoch when resuming training.
Return:
`torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.
"""
def lr_lambda(current_step):
if current_step < num_warmup_steps:
return float(current_step) / float(max(1, num_warmup_steps))
progress = float(current_step - num_warmup_steps) / float(max(1, num_training_steps - num_warmup_steps))
return max(0.0, 0.5 * (1.0 + math.cos(math.pi * float(num_cycles) * 2.0 * progress)))
return LambdaLR(optimizer, lr_lambda, last_epoch)
def get_cosine_with_hard_restarts_schedule_with_warmup(
optimizer: Optimizer, num_warmup_steps: int, num_training_steps: int, num_cycles: int = 1, last_epoch: int = -1
):
"""
Create a schedule with a learning rate that decreases following the values of the cosine function between the
initial lr set in the optimizer to 0, with several hard restarts, after a warmup period during which it increases
linearly between 0 and the initial lr set in the optimizer.
Args:
optimizer ([`~torch.optim.Optimizer`]):
The optimizer for which to schedule the learning rate.
num_warmup_steps (`int`):
The number of steps for the warmup phase.
num_training_steps (`int`):
The total number of training steps.
num_cycles (`int`, *optional*, defaults to 1):
The number of hard restarts to use.
last_epoch (`int`, *optional*, defaults to -1):
The index of the last epoch when resuming training.
Return:
`torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.
"""
def lr_lambda(current_step):
if current_step < num_warmup_steps:
return float(current_step) / float(max(1, num_warmup_steps))
progress = float(current_step - num_warmup_steps) / float(max(1, num_training_steps - num_warmup_steps))
if progress >= 1.0:
return 0.0
return max(0.0, 0.5 * (1.0 + math.cos(math.pi * ((float(num_cycles) * progress) % 1.0))))
return LambdaLR(optimizer, lr_lambda, last_epoch)
def get_polynomial_decay_schedule_with_warmup(
optimizer, num_warmup_steps, num_training_steps, lr_end=1e-7, power=1.0, last_epoch=-1
):
"""
Create a schedule with a learning rate that decreases as a polynomial decay from the initial lr set in the
optimizer to end lr defined by *lr_end*, after a warmup period during which it increases linearly from 0 to the
initial lr set in the optimizer.
Args:
optimizer ([`~torch.optim.Optimizer`]):
The optimizer for which to schedule the learning rate.
num_warmup_steps (`int`):
The number of steps for the warmup phase.
num_training_steps (`int`):
The total number of training steps.
lr_end (`float`, *optional*, defaults to 1e-7):
The end LR.
power (`float`, *optional*, defaults to 1.0):
Power factor.
last_epoch (`int`, *optional*, defaults to -1):
The index of the last epoch when resuming training.
Note: *power* defaults to 1.0 as in the fairseq implementation, which in turn is based on the original BERT
implementation at
https://github.com/google-research/bert/blob/f39e881b169b9d53bea03d2d341b31707a6c052b/optimization.py#L37
Return:
`torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.
"""
lr_init = optimizer.defaults["lr"]
if not (lr_init > lr_end):
raise ValueError(f"lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})")
def lr_lambda(current_step: int):
if current_step < num_warmup_steps:
return float(current_step) / float(max(1, num_warmup_steps))
elif current_step > num_training_steps:
return lr_end / lr_init # as LambdaLR multiplies by lr_init
else:
lr_range = lr_init - lr_end
decay_steps = num_training_steps - num_warmup_steps
pct_remaining = 1 - (current_step - num_warmup_steps) / decay_steps
decay = lr_range * pct_remaining**power + lr_end
return decay / lr_init # as LambdaLR multiplies by lr_init
return LambdaLR(optimizer, lr_lambda, last_epoch)
TYPE_TO_SCHEDULER_FUNCTION = {
SchedulerType.LINEAR: get_linear_schedule_with_warmup,
SchedulerType.COSINE: get_cosine_schedule_with_warmup,
SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
SchedulerType.CONSTANT: get_constant_schedule,
SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
}
def get_scheduler(
name: Union[str, SchedulerType],
optimizer: Optimizer,
num_warmup_steps: Optional[int] = None,
num_training_steps: Optional[int] = None,
):
"""
Unified API to get any scheduler from its name.
Args:
name (`str` or `SchedulerType`):
The name of the scheduler to use.
optimizer (`torch.optim.Optimizer`):
The optimizer that will be used during training.
num_warmup_steps (`int`, *optional*):
The number of warmup steps to do. This is not required by all schedulers (hence the argument being
optional), the function will raise an error if it's unset and the scheduler type requires it.
num_training_steps (`int``, *optional*):
The number of training steps to do. This is not required by all schedulers (hence the argument being
optional), the function will raise an error if it's unset and the scheduler type requires it.
"""
name = SchedulerType(name)
schedule_func = TYPE_TO_SCHEDULER_FUNCTION[name]
if name == SchedulerType.CONSTANT:
return schedule_func(optimizer)
# All other schedulers require `num_warmup_steps`
if num_warmup_steps is None:
raise ValueError(f"{name} requires `num_warmup_steps`, please provide that argument.")
if name == SchedulerType.CONSTANT_WITH_WARMUP:
return schedule_func(optimizer, num_warmup_steps=num_warmup_steps)
# All other schedulers require `num_training_steps`
if num_training_steps is None:
raise ValueError(f"{name} requires `num_training_steps`, please provide that argument.")
return schedule_func(optimizer, num_warmup_steps=num_warmup_steps, num_training_steps=num_training_steps)
================================================
FILE: models/edict/my_diffusers/pipeline_utils.py
================================================
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import importlib
import inspect
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import torch
import diffusers
import PIL
from huggingface_hub import snapshot_download
from PIL import Image
from tqdm.auto import tqdm
from .configuration_utils import ConfigMixin
from .utils import DIFFUSERS_CACHE, BaseOutput, logging
INDEX_FILE = "diffusion_pytorch_model.bin"
logger = logging.get_logger(__name__)
LOADABLE_CLASSES = {
"diffusers": {
"ModelMixin": ["save_pretrained", "from_pretrained"],
"SchedulerMixin": ["save_config", "from_config"],
"DiffusionPipeline": ["save_pretrained", "from_pretrained"],
"OnnxRuntimeModel": ["save_pretrained", "from_pretrained"],
},
"transformers": {
"PreTrainedTokenizer": ["save_pretrained", "from_pretrained"],
"PreTrainedTokenizerFast": ["save_pretrained", "from_pretrained"],
"PreTrainedModel": ["save_pretrained", "from_pretrained"],
"FeatureExtractionMixin": ["save_pretrained", "from_pretrained"],
},
}
ALL_IMPORTABLE_CLASSES = {}
for library in LOADABLE_CLASSES:
ALL_IMPORTABLE_CLASSES.update(LOADABLE_CLASSES[library])
@dataclass
class ImagePipelineOutput(BaseOutput):
"""
Output class for image pipelines.
Args:
images (`List[PIL.Image.Image]` or `np.ndarray`)
List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width,
num_channels)`. PIL images or numpy array present the denoised images of the diffusion pipeline.
"""
images: Union[List[PIL.Image.Image], np.ndarray]
class DiffusionPipeline(ConfigMixin):
r"""
Base class for all models.
[`DiffusionPipeline`] takes care of storing all components (models, schedulers, processors) for diffusion pipelines
and handles methods for loading, downloading and saving models as well as a few methods common to all pipelines to:
- move all PyTorch modules to the device of your choice
- enabling/disabling the progress bar for the denoising iteration
Class attributes:
- **config_name** ([`str`]) -- name of the config file that will store the class and module names of all
compenents of the diffusion pipeline.
"""
config_name = "model_index.json"
def register_modules(self, **kwargs):
# import it here to avoid circular import
from diffusers import pipelines
for name, module in kwargs.items():
# retrive library
library = module.__module__.split(".")[0]
# check if the module is a pipeline module
pipeline_dir = module.__module__.split(".")[-2]
path = module.__module__.split(".")
is_pipeline_module = pipeline_dir in path and hasattr(pipelines, pipeline_dir)
# if library is not in LOADABLE_CLASSES, then it is a custom module.
# Or if it's a pipeline module, then the module is inside the pipeline
# folder so we set the library to module name.
if library not in LOADABLE_CLASSES or is_pipeline_module:
library = pipeline_dir
# retrive class_name
class_name = module.__class__.__name__
register_dict = {name: (library, class_name)}
# save model index config
self.register_to_config(**register_dict)
# set models
setattr(self, name, module)
def save_pretrained(self, save_directory: Union[str, os.PathLike]):
"""
Save all variables of the pipeline that can be saved and loaded as well as the pipelines configuration file to
a directory. A pipeline variable can be saved and loaded if its class implements both a save and loading
method. The pipeline can easily be re-loaded using the `[`~DiffusionPipeline.from_pretrained`]` class method.
Arguments:
save_directory (`str` or `os.PathLike`):
Directory to which to save. Will be created if it doesn't exist.
"""
self.save_config(save_directory)
model_index_dict = dict(self.config)
model_index_dict.pop("_class_name")
model_index_dict.pop("_diffusers_version")
model_index_dict.pop("_module", None)
for pipeline_component_name in model_index_dict.keys():
sub_model = getattr(self, pipeline_component_name)
model_cls = sub_model.__class__
save_method_name = None
# search for the model's base class in LOADABLE_CLASSES
for library_name, library_classes in LOADABLE_CLASSES.items():
library = importlib.import_module(library_name)
for base_class, save_load_methods in library_classes.items():
class_candidate = getattr(library, base_class)
if issubclass(model_cls, class_candidate):
# if we found a suitable base class in LOADABLE_CLASSES then grab its save method
save_method_name = save_load_methods[0]
break
if save_method_name is not None:
break
save_method = getattr(sub_model, save_method_name)
save_method(os.path.join(save_directory, pipeline_component_name))
def to(self, torch_device: Optional[Union[str, torch.device]] = None):
if torch_device is None:
return self
module_names, _ = self.extract_init_dict(dict(self.config))
for name in module_names.keys():
module = getattr(self, name)
if isinstance(module, torch.nn.Module):
module.to(torch_device)
return self
@property
def device(self) -> torch.device:
r"""
Returns:
`torch.device`: The torch device on which the pipeline is located.
"""
module_names, _ = self.extract_init_dict(dict(self.config))
for name in module_names.keys():
module = getattr(self, name)
if isinstance(module, torch.nn.Module):
return module.device
return torch.device("cpu")
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], **kwargs):
r"""
Instantiate a PyTorch diffusion pipeline from pre-trained pipeline weights.
The pipeline is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated).
The warning *Weights from XXX not initialized from pretrained model* means that the weights of XXX do not come
pretrained with the rest of the model. It is up to you to train those weights with a downstream fine-tuning
task.
The warning *Weights from XXX not used in YYY* means that the layer XXX is not used by YYY, therefore those
weights are discarded.
Parameters:
pretrained_model_name_or_path (`str` or `os.PathLike`, *optional*):
Can be either:
- A string, the *repo id* of a pretrained pipeline hosted inside a model repo on
https://huggingface.co/ Valid repo ids have to be located under a user or organization name, like
`CompVis/ldm-text2im-large-256`.
- A path to a *directory* containing pipeline weights saved using
[`~DiffusionPipeline.save_pretrained`], e.g., `./my_pipeline_directory/`.
torch_dtype (`str` or `torch.dtype`, *optional*):
Override the default `torch.dtype` and load the model under this dtype. If `"auto"` is passed the dtype
will be automatically derived from the model's weights.
force_download (`bool`, *optional*, defaults to `False`):
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download (`bool`, *optional*, defaults to `False`):
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.
proxies (`Dict[str, str]`, *optional*):
A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
output_loading_info(`bool`, *optional*, defaults to `False`):
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
local_files_only(`bool`, *optional*, defaults to `False`):
Whether or not to only look at local files (i.e., do not try to download the model).
use_auth_token (`str` or *bool*, *optional*):
The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated
when running `huggingface-cli login` (stored in `~/.huggingface`).
revision (`str`, *optional*, defaults to `"main"`):
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
identifier allowed by git.
mirror (`str`, *optional*):
Mirror source to accelerate downloads in China. If you are from China and have an accessibility
problem, you can set this option to resolve it. Note that we do not guarantee the timeliness or safety.
Please refer to the mirror site for more information. specify the folder name here.
kwargs (remaining dictionary of keyword arguments, *optional*):
Can be used to overwrite load - and saveable variables - *i.e.* the pipeline components - of the
speficic pipeline class. The overritten components are then directly passed to the pipelines `__init__`
method. See example below for more information.
Passing `use_auth_token=True`` is required when you want to use a private model, *e.g.*
`"CompVis/stable-diffusion-v1-4"`
Activate the special ["offline-mode"](https://huggingface.co/diffusers/installation.html#offline-mode) to use
this method in a firewalled environment.
Examples:
```py
>>> from diffusers import DiffusionPipeline
>>> # Download pipeline from huggingface.co and cache.
>>> pipeline = DiffusionPipeline.from_pretrained("CompVis/ldm-text2im-large-256")
>>> # Download pipeline that requires an authorization token
>>> # For more information on access tokens, please refer to this section
>>> # of the documentation](https://huggingface.co/docs/hub/security-tokens)
>>> pipeline = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", use_auth_token=True)
>>> # Download pipeline, but overwrite scheduler
>>> from diffusers import LMSDiscreteScheduler
>>> scheduler = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear")
>>> pipeline = DiffusionPipeline.from_pretrained(
... "CompVis/stable-diffusion-v1-4", scheduler=scheduler, use_auth_token=True
... )
```
"""
cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE)
resume_download = kwargs.pop("resume_download", False)
proxies = kwargs.pop("proxies", None)
local_files_only = kwargs.pop("local_files_only", False)
use_auth_token = kwargs.pop("use_auth_token", None)
revision = kwargs.pop("revision", None)
torch_dtype = kwargs.pop("torch_dtype", None)
provider = kwargs.pop("provider", None)
# 1. Download the checkpoints and configs
# use snapshot download here to get it working from from_pretrained
if not os.path.isdir(pretrained_model_name_or_path):
cached_folder = snapshot_download(
pretrained_model_name_or_path,
cache_dir=cache_dir,
resume_download=resume_download,
proxies=proxies,
local_files_only=local_files_only,
use_auth_token=use_auth_token,
revision=revision,
)
else:
cached_folder = pretrained_model_name_or_path
config_dict = cls.get_config_dict(cached_folder)
# 2. Load the pipeline class, if using custom module then load it from the hub
# if we load from explicit class, let's use it
if cls != DiffusionPipeline:
pipeline_class = cls
else:
diffusers_module = importlib.import_module(cls.__module__.split(".")[0])
pipeline_class = getattr(diffusers_module, config_dict["_class_name"])
# some modules can be passed directly to the init
# in this case they are already instantiated in `kwargs`
# extract them here
expected_modules = set(inspect.signature(pipeline_class.__init__).parameters.keys())
passed_class_obj = {k: kwargs.pop(k) for k in expected_modules if k in kwargs}
init_dict, _ = pipeline_class.extract_init_dict(config_dict, **kwargs)
init_kwargs = {}
# import it here to avoid circular import
from diffusers import pipelines
# 3. Load each module in the pipeline
for name, (library_name, class_name) in init_dict.items():
is_pipeline_module = hasattr(pipelines, library_name)
loaded_sub_model = None
# if the model is in a pipeline module, then we load it from the pipeline
if name in passed_class_obj:
# 1. check that passed_class_obj has correct parent class
if not is_pipeline_module:
library = importlib.import_module(library_name)
class_obj = getattr(library, class_name)
importable_classes = LOADABLE_CLASSES[library_name]
class_candidates = {c: getattr(library, c) for c in importable_classes.keys()}
expected_class_obj = None
for class_name, class_candidate in class_candidates.items():
if issubclass(class_obj, class_candidate):
expected_class_obj = class_candidate
if not issubclass(passed_class_obj[name].__class__, expected_class_obj):
raise ValueError(
f"{passed_class_obj[name]} is of type: {type(passed_class_obj[name])}, but should be"
f" {expected_class_obj}"
)
else:
logger.warn(
f"You have passed a non-standard module {passed_class_obj[name]}. We cannot verify whether it"
" has the correct type"
)
# set passed class object
loaded_sub_model = passed_class_obj[name]
elif is_pipeline_module:
pipeline_module = getattr(pipelines, library_name)
class_obj = getattr(pipeline_module, class_name)
importable_classes = ALL_IMPORTABLE_CLASSES
class_candidates = {c: class_obj for c in importable_classes.keys()}
else:
# else we just import it from the library.
library = importlib.import_module(library_name)
class_obj = getattr(library, class_name)
importable_classes = LOADABLE_CLASSES[library_name]
class_candidates = {c: getattr(library, c) for c in importable_classes.keys()}
if loaded_sub_model is None:
load_method_name = None
for class_name, class_candidate in class_candidates.items():
if issubclass(class_obj, class_candidate):
load_method_name = importable_classes[class_name][1]
load_method = getattr(class_obj, load_method_name)
loading_kwargs = {}
if issubclass(class_obj, torch.nn.Module):
loading_kwargs["torch_dtype"] = torch_dtype
if issubclass(class_obj, diffusers.OnnxRuntimeModel):
loading_kwargs["provider"] = provider
# check if the module is in a subdirectory
if os.path.isdir(os.path.join(cached_folder, name)):
loaded_sub_model = load_method(os.path.join(cached_folder, name), **loading_kwargs)
else:
# else load from the root directory
loaded_sub_model = load_method(cached_folder, **loading_kwargs)
init_kwargs[name] = loaded_sub_model # UNet(...), # DiffusionSchedule(...)
# 4. Instantiate the pipeline
model = pipeline_class(**init_kwargs)
return model
@staticmethod
def numpy_to_pil(images):
"""
Convert a numpy image or a batch of images to a PIL image.
"""
if images.ndim == 3:
images = images[None, ...]
images = (images * 255).round().astype("uint8")
pil_images = [Image.fromarray(image) for image in images]
return pil_images
def progress_bar(self, iterable):
if not hasattr(self, "_progress_bar_config"):
self._progress_bar_config = {}
elif not isinstance(self._progress_bar_config, dict):
raise ValueError(
f"`self._progress_bar_config` should be of type `dict`, but is {type(self._progress_bar_config)}."
)
return tqdm(iterable, **self._progress_bar_config)
def set_progress_bar_config(self, **kwargs):
self._progress_bar_config = kwargs
================================================
FILE: models/edict/my_diffusers/pipelines/__init__.py
================================================
from ..utils import is_onnx_available, is_transformers_available
from .ddim import DDIMPipeline
from .ddpm import DDPMPipeline
from .latent_diffusion_uncond import LDMPipeline
from .pndm import PNDMPipeline
from .score_sde_ve import ScoreSdeVePipeline
from .stochastic_karras_ve import KarrasVePipeline
if is_transformers_available():
from .latent_diffusion import LDMTextToImagePipeline
from .stable_diffusion import (
StableDiffusionImg2ImgPipeline,
StableDiffusionInpaintPipeline,
StableDiffusionPipeline,
)
if is_transformers_available() and is_onnx_available():
from .stable_diffusion import StableDiffusionOnnxPipeline
================================================
FILE: models/edict/my_diffusers/pipelines/ddim/__init__.py
================================================
# flake8: noqa
from .pipeline_ddim import DDIMPipeline
================================================
FILE: models/edict/my_diffusers/pipelines/ddim/pipeline_ddim.py
================================================
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import warnings
from typing import Optional, Tuple, Union
import torch
from ...pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class DDIMPipeline(DiffusionPipeline):
r"""
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
Parameters:
unet ([`UNet2DModel`]): U-Net architecture to denoise the encoded image.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image. Can be one of
[`DDPMScheduler`], or [`DDIMScheduler`].
"""
def __init__(self, unet, scheduler):
super().__init__()
scheduler = scheduler.set_format("pt")
self.register_modules(unet=unet, scheduler=scheduler)
@torch.no_grad()
def __call__(
self,
batch_size: int = 1,
generator: Optional[torch.Generator] = None,
eta: float = 0.0,
num_inference_steps: int = 50,
output_type: Optional[str] = "pil",
return_dict: bool = True,
**kwargs,
) -> Union[ImagePipelineOutput, Tuple]:
r"""
Args:
batch_size (`int`, *optional*, defaults to 1):
The number of images to generate.
generator (`torch.Generator`, *optional*):
A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
deterministic.
eta (`float`, *optional*, defaults to 0.0):
The eta parameter which controls the scale of the variance (0 is DDIM and 1 is one type of DDPM).
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `nd.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipeline_utils.ImagePipelineOutput`] instead of a plain tuple.
Returns:
[`~pipeline_utils.ImagePipelineOutput`] or `tuple`: [`~pipelines.utils.ImagePipelineOutput`] if
`return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the
generated images.
"""
if "torch_device" in kwargs:
device = kwargs.pop("torch_device")
warnings.warn(
"`torch_device` is deprecated as an input argument to `__call__` and will be removed in v0.3.0."
" Consider using `pipe.to(torch_device)` instead."
)
# Set device as before (to be removed in 0.3.0)
if device is None:
device = "cuda" if torch.cuda.is_available() else "cpu"
self.to(device)
# eta corresponds to η in paper and should be between [0, 1]
# Sample gaussian noise to begin loop
image = torch.randn(
(batch_size, self.unet.in_channels, self.unet.sample_size, self.unet.sample_size),
generator=generator,
)
image = image.to(self.device)
# set step values
self.scheduler.set_timesteps(num_inference_steps)
for t in self.progress_bar(self.scheduler.timesteps):
# 1. predict noise model_output
model_output = self.unet(image, t).sample
# 2. predict previous mean of image x_t-1 and add variance depending on eta
# do x_t -> x_t-1
image = self.scheduler.step(model_output, t, image, eta).prev_sample
image = (image / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).numpy()
if output_type == "pil":
image = self.numpy_to_pil(image)
if not return_dict:
return (image,)
return ImagePipelineOutput(images=image)
================================================
FILE: models/edict/my_diffusers/pipelines/ddpm/__init__.py
================================================
# flake8: noqa
from .pipeline_ddpm import DDPMPipeline
================================================
FILE: models/edict/my_diffusers/pipelines/ddpm/pipeline_ddpm.py
================================================
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import warnings
from typing import Optional, Tuple, Union
import torch
from ...pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class DDPMPipeline(DiffusionPipeline):
r"""
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
Parameters:
unet ([`UNet2DModel`]): U-Net architecture to denoise the encoded image.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image. Can be one of
[`DDPMScheduler`], or [`DDIMScheduler`].
"""
def __init__(self, unet, scheduler):
super().__init__()
scheduler = scheduler.set_format("pt")
self.register_modules(unet=unet, scheduler=scheduler)
@torch.no_grad()
def __call__(
self,
batch_size: int = 1,
generator: Optional[torch.Generator] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
**kwargs,
) -> Union[ImagePipelineOutput, Tuple]:
r"""
Args:
batch_size (`int`, *optional*, defaults to 1):
The number of images to generate.
generator (`torch.Generator`, *optional*):
A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
deterministic.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `nd.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipeline_utils.ImagePipelineOutput`] instead of a plain tuple.
Returns:
[`~pipeline_utils.ImagePipelineOutput`] or `tuple`: [`~pipelines.utils.ImagePipelineOutput`] if
`return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the
generated images.
"""
if "torch_device" in kwargs:
device = kwargs.pop("torch_device")
warnings.warn(
"`torch_device` is deprecated as an input argument to `__call__` and will be removed in v0.3.0."
" Consider using `pipe.to(torch_device)` instead."
)
# Set device as before (to be removed in 0.3.0)
if device is None:
device = "cuda" if torch.cuda.is_available() else "cpu"
self.to(device)
# Sample gaussian noise to begin loop
image = torch.randn(
(batch_size, self.unet.in_channels, self.unet.sample_size, self.unet.sample_size),
generator=generator,
)
image = image.to(self.device)
# set step values
self.scheduler.set_timesteps(1000)
for t in self.progress_bar(self.scheduler.timesteps):
# 1. predict noise model_output
model_output = self.unet(image, t).sample
# 2. compute previous image: x_t -> t_t-1
image = self.scheduler.step(model_output, t, image, generator=generator).prev_sample
image = (image / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).numpy()
if output_type == "pil":
image = self.numpy_to_pil(image)
if not return_dict:
return (image,)
return ImagePipelineOutput(images=image)
================================================
FILE: models/edict/my_diffusers/pipelines/latent_diffusion/__init__.py
================================================
# flake8: noqa
from ...utils import is_transformers_available
if is_transformers_available():
from .pipeline_latent_diffusion import LDMBertModel, LDMTextToImagePipeline
================================================
FILE: models/edict/my_diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion.py
================================================
import inspect
import warnings
from typing import List, Optional, Tuple, Union
import torch
import torch.nn as nn
import torch.utils.checkpoint
from transformers.activations import ACT2FN
from transformers.configuration_utils import PretrainedConfig
from transformers.modeling_outputs import BaseModelOutput
from transformers.modeling_utils import PreTrainedModel
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.utils import logging
from ...models import AutoencoderKL, UNet2DConditionModel, UNet2DModel, VQModel
from ...pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
class LDMTextToImagePipeline(DiffusionPipeline):
r"""
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
Parameters:
vqvae ([`VQModel`]):
Vector-quantized (VQ) Model to encode and decode images to and from latent representations.
bert ([`LDMBertModel`]):
Text-encoder model based on [BERT](ttps://huggingface.co/docs/transformers/model_doc/bert) architecture.
tokenizer (`transformers.BertTokenizer`):
Tokenizer of class
[BertTokenizer](https://huggingface.co/docs/transformers/model_doc/bert#transformers.BertTokenizer).
unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latens. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
"""
def __init__(
self,
vqvae: Union[VQModel, AutoencoderKL],
bert: PreTrainedModel,
tokenizer: PreTrainedTokenizer,
unet: Union[UNet2DModel, UNet2DConditionModel],
scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
):
super().__init__()
scheduler = scheduler.set_format("pt")
self.register_modules(vqvae=vqvae, bert=bert, tokenizer=tokenizer, unet=unet, scheduler=scheduler)
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]],
height: Optional[int] = 256,
width: Optional[int] = 256,
num_inference_steps: Optional[int] = 50,
guidance_scale: Optional[float] = 1.0,
eta: Optional[float] = 0.0,
generator: Optional[torch.Generator] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
**kwargs,
) -> Union[Tuple, ImagePipelineOutput]:
r"""
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
height (`int`, *optional*, defaults to 256):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to 256):
The width in pixels of the generated image.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 1.0):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt` at
the, usually at the expense of lower image quality.
generator (`torch.Generator`, *optional*):
A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
deterministic.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `nd.array`.
return_dict (`bool`, *optional*):
Whether or not to return a [`~pipeline_utils.ImagePipelineOutput`] instead of a plain tuple.
Returns:
[`~pipeline_utils.ImagePipelineOutput`] or `tuple`: [`~pipelines.utils.ImagePipelineOutput`] if
`return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the
generated images.
"""
if "torch_device" in kwargs:
device = kwargs.pop("torch_device")
warnings.warn(
"`torch_device` is deprecated as an input argument to `__call__` and will be removed in v0.3.0."
" Consider using `pipe.to(torch_device)` instead."
)
# Set device as before (to be removed in 0.3.0)
if device is None:
device = "cuda" if torch.cuda.is_available() else "cpu"
self.to(device)
if isinstance(prompt, str):
batch_size = 1
elif isinstance(prompt, list):
batch_size = len(prompt)
else:
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
# get unconditional embeddings for classifier free guidance
if guidance_scale != 1.0:
uncond_input = self.tokenizer([""] * batch_size, padding="max_length", max_length=77, return_tensors="pt")
uncond_embeddings = self.bert(uncond_input.input_ids.to(self.device))[0]
# get prompt text embeddings
text_input = self.tokenizer(prompt, padding="max_length", max_length=77, return_tensors="pt")
text_embeddings = self.bert(text_input.input_ids.to(self.device))[0]
latents = torch.randn(
(batch_size, self.unet.in_channels, height // 8, width // 8),
generator=generator,
)
latents = latents.to(self.device)
self.scheduler.set_timesteps(num_inference_steps)
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_kwargs = {}
if accepts_eta:
extra_kwargs["eta"] = eta
for t in self.progress_bar(self.scheduler.timesteps):
if guidance_scale == 1.0:
# guidance_scale of 1 means no guidance
latents_input = latents
context = text_embeddings
else:
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
latents_input = torch.cat([latents] * 2)
context = torch.cat([uncond_embeddings, text_embeddings])
# predict the noise residual
noise_pred = self.unet(latents_input, t, encoder_hidden_states=context).sample
# perform guidance
if guidance_scale != 1.0:
noise_pred_uncond, noise_prediction_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_prediction_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_kwargs).prev_sample
# scale and decode the image latents with vae
latents = 1 / 0.18215 * latents
image = self.vqvae.decode(latents).sample
image = (image / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).numpy()
if output_type == "pil":
image = self.numpy_to_pil(image)
if not return_dict:
return (image,)
return ImagePipelineOutput(images=image)
################################################################################
# Code for the text transformer model
################################################################################
""" PyTorch LDMBERT model."""
logger = logging.get_logger(__name__)
LDMBERT_PRETRAINED_MODEL_ARCHIVE_LIST = [
"ldm-bert",
# See all LDMBert models at https://huggingface.co/models?filter=ldmbert
]
LDMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"ldm-bert": "https://huggingface.co/ldm-bert/resolve/main/config.json",
}
""" LDMBERT model configuration"""
class LDMBertConfig(PretrainedConfig):
model_type = "ldmbert"
keys_to_ignore_at_inference = ["past_key_values"]
attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
def __init__(
self,
vocab_size=30522,
max_position_embeddings=77,
encoder_layers=32,
encoder_ffn_dim=5120,
encoder_attention_heads=8,
head_dim=64,
encoder_layerdrop=0.0,
activation_function="gelu",
d_model=1280,
dropout=0.1,
attention_dropout=0.0,
activation_dropout=0.0,
init_std=0.02,
classifier_dropout=0.0,
scale_embedding=False,
use_cache=True,
pad_token_id=0,
**kwargs,
):
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.d_model = d_model
self.encoder_ffn_dim = encoder_ffn_dim
self.encoder_layers = encoder_layers
self.encoder_attention_heads = encoder_attention_heads
self.head_dim = head_dim
self.dropout = dropout
self.attention_dropout = attention_dropout
self.activation_dropout = activation_dropout
self.activation_function = activation_function
self.init_std = init_std
self.encoder_layerdrop = encoder_layerdrop
self.classifier_dropout = classifier_dropout
self.use_cache = use_cache
self.num_hidden_layers = encoder_layers
self.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True
super().__init__(pad_token_id=pad_token_id, **kwargs)
def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
"""
Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
"""
bsz, src_len = mask.size()
tgt_len = tgt_len if tgt_len is not None else src_len
expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
inverted_mask = 1.0 - expanded_mask
return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
# Copied from transformers.models.bart.modeling_bart.BartAttention with Bart->LDMBert
class LDMBertAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(
self,
embed_dim: int,
num_heads: int,
head_dim: int,
dropout: float = 0.0,
is_decoder: bool = False,
bias: bool = False,
):
super().__init__()
self.embed_dim = embed_dim
self.num_heads = num_heads
self.dropout = dropout
self.head_dim = head_dim
self.inner_dim = head_dim * num_heads
self.scaling = self.head_dim**-0.5
self.is_decoder = is_decoder
self.k_proj = nn.Linear(embed_dim, self.inner_dim, bias=bias)
self.v_proj = nn.Linear(embed_dim, self.inner_dim, bias=bias)
self.q_proj = nn.Linear(embed_dim, self.inner_dim, bias=bias)
self.out_proj = nn.Linear(self.inner_dim, embed_dim)
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
def forward(
self,
hidden_states: torch.Tensor,
key_value_states: Optional[torch.Tensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
attention_mask: Optional[torch.Tensor] = None,
layer_head_mask: Optional[torch.Tensor] = None,
output_attentions: bool = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
"""Input shape: Batch x Time x Channel"""
# if key_value_states are provided this layer is used as a cross-attention layer
# for the decoder
is_cross_attention = key_value_states is not None
bsz, tgt_len, _ = hidden_states.size()
# get query proj
query_states = self.q_proj(hidden_states) * self.scaling
# get key, value proj
if is_cross_attention and past_key_value is not None:
# reuse k,v, cross_attentions
key_states = past_key_value[0]
value_states = past_key_value[1]
elif is_cross_attention:
# cross_attentions
key_states = self._shape(self.k_proj(key_value_states), -1, bsz)
value_states = self._shape(self.v_proj(key_value_states), -1, bsz)
elif past_key_value is not None:
# reuse k, v, self_attention
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
key_states = torch.cat([past_key_value[0], key_states], dim=2)
value_states = torch.cat([past_key_value[1], value_states], dim=2)
else:
# self_attention
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
if self.is_decoder:
# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
# Further calls to cross_attention layer can then reuse all cross-attention
# key/value_states (first "if" case)
# if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
# all previous decoder key/value_states. Further calls to uni-directional self-attention
# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
# if encoder bi-directional self-attention `past_key_value` is always `None`
past_key_value = (key_states, value_states)
proj_shape = (bsz * self.num_heads, -1, self.head_dim)
query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
key_states = key_states.view(*proj_shape)
value_states = value_states.view(*proj_shape)
src_len = key_states.size(1)
attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
raise ValueError(
f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is"
f" {attn_weights.size()}"
)
if attention_mask is not None:
if attention_mask.size() != (bsz, 1, tgt_len, src_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
)
attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
attn_weights = nn.functional.softmax(attn_weights, dim=-1)
if layer_head_mask is not None:
if layer_head_mask.size() != (self.num_heads,):
raise ValueError(
f"Head mask for a single layer should be of size {(self.num_heads,)}, but is"
f" {layer_head_mask.size()}"
)
attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
if output_attentions:
# this operation is a bit awkward, but it's required to
# make sure that attn_weights keeps its gradient.
# In order to do so, attn_weights have to be reshaped
# twice and have to be reused in the following
attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len)
else:
attn_weights_reshaped = None
attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
attn_output = torch.bmm(attn_probs, value_states)
if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
raise ValueError(
f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is"
f" {attn_output.size()}"
)
attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
attn_output = attn_output.transpose(1, 2)
# Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be
# partitioned aross GPUs when using tensor-parallelism.
attn_output = attn_output.reshape(bsz, tgt_len, self.inner_dim)
attn_output = self.out_proj(attn_output)
return attn_output, attn_weights_reshaped, past_key_value
class LDMBertEncoderLayer(nn.Module):
def __init__(self, config: LDMBertConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = LDMBertAttention(
embed_dim=self.embed_dim,
num_heads=config.encoder_attention_heads,
head_dim=config.head_dim,
dropout=config.attention_dropout,
)
self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
self.dropout = config.dropout
self.activation_fn = ACT2FN[config.activation_function]
self.activation_dropout = config.activation_dropout
self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim)
self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim)
self.final_layer_norm = nn.LayerNorm(self.embed_dim)
def forward(
self,
hidden_states: torch.FloatTensor,
attention_mask: torch.FloatTensor,
layer_head_mask: torch.FloatTensor,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.FloatTensor, Optional[torch.FloatTensor]]:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(seq_len, batch, embed_dim)`
attention_mask (`torch.FloatTensor`): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
`(encoder_attention_heads,)`.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
"""
residual = hidden_states
hidden_states = self.self_attn_layer_norm(hidden_states)
hidden_states, attn_weights, _ = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
layer_head_mask=layer_head_mask,
output_attentions=output_attentions,
)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
residual = hidden_states
hidden_states = self.final_layer_norm(hidden_states)
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
hidden_states = self.fc2(hidden_states)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
if hidden_states.dtype == torch.float16 and (
torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any()
):
clamp_value = torch.finfo(hidden_states.dtype).max - 1000
hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
outputs = (hidden_states,)
if output_attentions:
outputs += (attn_weights,)
return outputs
# Copied from transformers.models.bart.modeling_bart.BartPretrainedModel with Bart->LDMBert
class LDMBertPreTrainedModel(PreTrainedModel):
config_class = LDMBertConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_keys_to_ignore_on_load_unexpected = [r"encoder\.version", r"decoder\.version"]
def _init_weights(self, module):
std = self.config.init_std
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=std)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
def _set_gradient_checkpointing(self, module, value=False):
if isinstance(module, (LDMBertEncoder,)):
module.gradient_checkpointing = value
@property
def dummy_inputs(self):
pad_token = self.config.pad_token_id
input_ids = torch.tensor([[0, 6, 10, 4, 2], [0, 8, 12, 2, pad_token]], device=self.device)
dummy_inputs = {
"attention_mask": input_ids.ne(pad_token),
"input_ids": input_ids,
}
return dummy_inputs
class LDMBertEncoder(LDMBertPreTrainedModel):
"""
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
[`LDMBertEncoderLayer`].
Args:
config: LDMBertConfig
embed_tokens (nn.Embedding): output embedding
"""
def __init__(self, config: LDMBertConfig):
super().__init__(config)
self.dropout = config.dropout
embed_dim = config.d_model
self.padding_idx = config.pad_token_id
self.max_source_positions = config.max_position_embeddings
self.embed_tokens = nn.Embedding(config.vocab_size, embed_dim)
self.embed_positions = nn.Embedding(config.max_position_embeddings, embed_dim)
self.layers = nn.ModuleList([LDMBertEncoderLayer(config) for _ in range(config.encoder_layers)])
self.layer_norm = nn.LayerNorm(embed_dim)
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.embed_tokens
def set_input_embeddings(self, value):
self.embed_tokens = value
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutput]:
r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [`BartTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
than the model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.BaseModelOutput`] instead of a plain tuple.
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# retrieve input_ids and inputs_embeds
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
input_shape = input_ids.size()
input_ids = input_ids.view(-1, input_shape[-1])
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids)
seq_len = input_shape[1]
if position_ids is None:
position_ids = torch.arange(seq_len, dtype=torch.long, device=inputs_embeds.device).expand((1, -1))
embed_pos = self.embed_positions(position_ids)
hidden_states = inputs_embeds + embed_pos
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
# expand attention_mask
if attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
attention_mask = _expand_mask(attention_mask, inputs_embeds.dtype)
encoder_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
# check if head_mask has a correct number of layers specified if desired
if head_mask is not None:
if head_mask.size()[0] != (len(self.layers)):
raise ValueError(
f"The head_mask should be specified for {len(self.layers)} layers, but it is for"
f" {head_mask.size()[0]}."
)
for idx, encoder_layer in enumerate(self.layers):
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if self.gradient_checkpointing and self.training:
def create_custom_forward(module):
def custom_forward(*inputs):
return module(*inputs, output_attentions)
return custom_forward
layer_outputs = torch.utils.checkpoint.checkpoint(
create_custom_forward(encoder_layer),
hidden_states,
attention_mask,
(head_mask[idx] if head_mask is not None else None),
)
else:
layer_outputs = encoder_layer(
hidden_states,
attention_mask,
layer_head_mask=(head_mask[idx] if head_mask is not None else None),
output_attentions=output_attentions,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
hidden_states = self.layer_norm(hidden_states)
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
return BaseModelOutput(
last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
)
class LDMBertModel(LDMBertPreTrainedModel):
def __init__(self, config: LDMBertConfig):
super().__init__(config)
self.model = LDMBertEncoder(config)
self.to_logits = nn.Linear(config.hidden_size, config.vocab_size)
def forward(
self,
input_ids=None,
attention_mask=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
outputs = self.model(
input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
return outputs
================================================
FILE: models/edict/my_diffusers/pipelines/latent_diffusion_uncond/__init__.py
================================================
# flake8: noqa
from .pipeline_latent_diffusion_uncond import LDMPipeline
================================================
FILE: models/edict/my_diffusers/pipelines/latent_diffusion_uncond/pipeline_latent_diffusion_uncond.py
================================================
import inspect
import warnings
from typing import Optional, Tuple, Union
import torch
from ...models import UNet2DModel, VQModel
from ...pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from ...schedulers import DDIMScheduler
class LDMPipeline(DiffusionPipeline):
r"""
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
Parameters:
vqvae ([`VQModel`]):
Vector-quantized (VQ) Model to encode and decode images to and from latent representations.
unet ([`UNet2DModel`]): U-Net architecture to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
[`DDIMScheduler`] is to be used in combination with `unet` to denoise the encoded image latens.
"""
def __init__(self, vqvae: VQModel, unet: UNet2DModel, scheduler: DDIMScheduler):
super().__init__()
scheduler = scheduler.set_format("pt")
self.register_modules(vqvae=vqvae, unet=unet, scheduler=scheduler)
@torch.no_grad()
def __call__(
self,
batch_size: int = 1,
generator: Optional[torch.Generator] = None,
eta: float = 0.0,
num_inference_steps: int = 50,
output_type: Optional[str] = "pil",
return_dict: bool = True,
**kwargs,
) -> Union[Tuple, ImagePipelineOutput]:
r"""
Args:
batch_size (`int`, *optional*, defaults to 1):
Number of images to generate.
generator (`torch.Generator`, *optional*):
A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
deterministic.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `nd.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipeline_utils.ImagePipelineOutput`] instead of a plain tuple.
Returns:
[`~pipeline_utils.ImagePipelineOutput`] or `tuple`: [`~pipelines.utils.ImagePipelineOutput`] if
`return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the
generated images.
"""
if "torch_device" in kwargs:
device = kwargs.pop("torch_device")
warnings.warn(
"`torch_device` is deprecated as an input argument to `__call__` and will be removed in v0.3.0."
" Consider using `pipe.to(torch_device)` instead."
)
# Set device as before (to be removed in 0.3.0)
if device is None:
device = "cuda" if torch.cuda.is_available() else "cpu"
self.to(device)
latents = torch.randn(
(batch_size, self.unet.in_channels, self.unet.sample_size, self.unet.sample_size),
generator=generator,
)
latents = latents.to(self.device)
self.scheduler.set_timesteps(num_inference_steps)
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_kwargs = {}
if accepts_eta:
extra_kwargs["eta"] = eta
for t in self.progress_bar(self.scheduler.timesteps):
# predict the noise residual
noise_prediction = self.unet(latents, t).sample
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_prediction, t, latents, **extra_kwargs).prev_sample
# decode the image latents with the VAE
image = self.vqvae.decode(latents).sample
image = (image / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).numpy()
if output_type == "pil":
image = self.numpy_to_pil(image)
if not return_dict:
return (image,)
return ImagePipelineOutput(images=image)
================================================
FILE: models/edict/my_diffusers/pipelines/pndm/__init__.py
================================================
# flake8: noqa
from .pipeline_pndm import PNDMPipeline
================================================
FILE: models/edict/my_diffusers/pipelines/pndm/pipeline_pndm.py
================================================
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import warnings
from typing import Optional, Tuple, Union
import torch
from ...models import UNet2DModel
from ...pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from ...schedulers import PNDMScheduler
class PNDMPipeline(DiffusionPipeline):
r"""
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
Parameters:
unet (`UNet2DModel`): U-Net architecture to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
The `PNDMScheduler` to be used in combination with `unet` to denoise the encoded image.
"""
unet: UNet2DModel
scheduler: PNDMScheduler
def __init__(self, unet: UNet2DModel, scheduler: PNDMScheduler):
super().__init__()
scheduler = scheduler.set_format("pt")
self.register_modules(unet=unet, scheduler=scheduler)
@torch.no_grad()
def __call__(
self,
batch_size: int = 1,
num_inference_steps: int = 50,
generator: Optional[torch.Generator] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
**kwargs,
) -> Union[ImagePipelineOutput, Tuple]:
r"""
Args:
batch_size (`int`, `optional`, defaults to 1): The number of images to generate.
num_inference_steps (`int`, `optional`, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
generator (`torch.Generator`, `optional`): A [torch
generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
deterministic.
output_type (`str`, `optional`, defaults to `"pil"`): The output format of the generate image. Choose
between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `nd.array`.
return_dict (`bool`, `optional`, defaults to `True`): Whether or not to return a
[`~pipeline_utils.ImagePipelineOutput`] instead of a plain tuple.
Returns:
[`~pipeline_utils.ImagePipelineOutput`] or `tuple`: [`~pipelines.utils.ImagePipelineOutput`] if
`return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the
generated images.
"""
# For more information on the sampling method you can take a look at Algorithm 2 of
# the official paper: https://arxiv.org/pdf/2202.09778.pdf
if "torch_device" in kwargs:
device = kwargs.pop("torch_device")
warnings.warn(
"`torch_device` is deprecated as an input argument to `__call__` and will be removed in v0.3.0."
" Consider using `pipe.to(torch_device)` instead."
)
# Set device as before (to be removed in 0.3.0)
if device is None:
device = "cuda" if torch.cuda.is_available() else "cpu"
self.to(device)
# Sample gaussian noise to begin loop
image = torch.randn(
(batch_size, self.unet.in_channels, self.unet.sample_size, self.unet.sample_size),
generator=generator,
)
image = image.to(self.device)
self.scheduler.set_timesteps(num_inference_steps)
for t in self.progress_bar(self.scheduler.timesteps):
model_output = self.unet(image, t).sample
image = self.scheduler.step(model_output, t, image).prev_sample
image = (image / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).numpy()
if output_type == "pil":
image = self.numpy_to_pil(image)
if not return_dict:
return (image,)
return ImagePipelineOutput(images=image)
================================================
FILE: models/edict/my_diffusers/pipelines/score_sde_ve/__init__.py
================================================
# flake8: noqa
from .pipeline_score_sde_ve import ScoreSdeVePipeline
================================================
FILE: models/edict/my_diffusers/pipelines/score_sde_ve/pipeline_score_sde_ve.py
================================================
#!/usr/bin/env python3
import warnings
from typing import Optional, Tuple, Union
import torch
from ...models import UNet2DModel
from ...pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from ...schedulers import ScoreSdeVeScheduler
class ScoreSdeVePipeline(DiffusionPipeline):
r"""
Parameters:
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
unet ([`UNet2DModel`]): U-Net architecture to denoise the encoded image. scheduler ([`SchedulerMixin`]):
The [`ScoreSdeVeScheduler`] scheduler to be used in combination with `unet` to denoise the encoded image.
"""
unet: UNet2DModel
scheduler: ScoreSdeVeScheduler
def __init__(self, unet: UNet2DModel, scheduler: DiffusionPipeline):
super().__init__()
self.register_modules(unet=unet, scheduler=scheduler)
@torch.no_grad()
def __call__(
self,
batch_size: int = 1,
num_inference_steps: int = 2000,
generator: Optional[torch.Generator] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
**kwargs,
) -> Union[ImagePipelineOutput, Tuple]:
r"""
Args:
batch_size (`int`, *optional*, defaults to 1):
The number of images to generate.
generator (`torch.Generator`, *optional*):
A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
deterministic.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `nd.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipeline_utils.ImagePipelineOutput`] instead of a plain tuple.
Returns:
[`~pipeline_utils.ImagePipelineOutput`] or `tuple`: [`~pipelines.utils.ImagePipelineOutput`] if
`return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the
generated images.
"""
if "torch_device" in kwargs:
device = kwargs.pop("torch_device")
warnings.warn(
"`torch_device` is deprecated as an input argument to `__call__` and will be removed in v0.3.0."
" Consider using `pipe.to(torch_device)` instead."
)
# Set device as before (to be removed in 0.3.0)
if device is None:
device = "cuda" if torch.cuda.is_available() else "cpu"
self.to(device)
img_size = self.unet.config.sample_size
shape = (batch_size, 3, img_size, img_size)
model = self.unet
sample = torch.randn(*shape, generator=generator) * self.scheduler.config.sigma_max
sample = sample.to(self.device)
self.scheduler.set_timesteps(num_inference_steps)
self.scheduler.set_sigmas(num_inference_steps)
for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
sigma_t = self.scheduler.sigmas[i] * torch.ones(shape[0], device=self.device)
# correction step
for _ in range(self.scheduler.correct_steps):
model_output = self.unet(sample, sigma_t).sample
sample = self.scheduler.step_correct(model_output, sample, generator=generator).prev_sample
# prediction step
model_output = model(sample, sigma_t).sample
output = self.scheduler.step_pred(model_output, t, sample, generator=generator)
sample, sample_mean = output.prev_sample, output.prev_sample_mean
sample = sample_mean.clamp(0, 1)
sample = sample.cpu().permute(0, 2, 3, 1).numpy()
if output_type == "pil":
sample = self.numpy_to_pil(sample)
if not return_dict:
return (sample,)
return ImagePipelineOutput(images=sample)
================================================
FILE: models/edict/my_diffusers/pipelines/stable_diffusion/__init__.py
================================================
from dataclasses import dataclass
from typing import List, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import BaseOutput, is_onnx_available, is_transformers_available
@dataclass
class StableDiffusionPipelineOutput(BaseOutput):
"""
Output class for Stable Diffusion pipelines.
Args:
images (`List[PIL.Image.Image]` or `np.ndarray`)
List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width,
num_channels)`. PIL images or numpy array present the denoised images of the diffusion pipeline.
nsfw_content_detected (`List[bool]`)
List of flags denoting whether the corresponding generated image likely represents "not-safe-for-work"
(nsfw) content.
"""
images: Union[List[PIL.Image.Image], np.ndarray]
nsfw_content_detected: List[bool]
if is_transformers_available():
from .pipeline_stable_diffusion import StableDiffusionPipeline
from .pipeline_stable_diffusion_img2img import StableDiffusionImg2ImgPipeline
from .pipeline_stable_diffusion_inpaint import StableDiffusionInpaintPipeline
from .safety_checker import StableDiffusionSafetyChecker
if is_transformers_available() and is_onnx_available():
from .pipeline_stable_diffusion_onnx import StableDiffusionOnnxPipeline
================================================
FILE: models/edict/my_diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py
================================================
import inspect
import warnings
from typing import List, Optional, Union
import torch
from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
from ...models import AutoencoderKL, UNet2DConditionModel
from ...pipeline_utils import DiffusionPipeline
from ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from . import StableDiffusionPipelineOutput
from .safety_checker import StableDiffusionSafetyChecker
class StableDiffusionPipeline(DiffusionPipeline):
r"""
Pipeline for text-to-image generation using Stable Diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
text_encoder ([`CLIPTextModel`]):
Frozen text-encoder. Stable Diffusion uses the text portion of
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
tokenizer (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latens. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offsensive or harmful.
Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details.
feature_extractor ([`CLIPFeatureExtractor`]):
Model that extracts features from generated images to be used as inputs for the `safety_checker`.
"""
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet2DConditionModel,
scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPFeatureExtractor,
):
super().__init__()
scheduler = scheduler.set_format("pt")
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
r"""
Enable sliced attention computation.
When this option is enabled, the attention module will split the input tensor in slices, to compute attention
in several steps. This is useful to save some memory in exchange for a small speed decrease.
Args:
slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case,
`attention_head_dim` must be a multiple of `slice_size`.
"""
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
slice_size = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(slice_size)
def disable_attention_slicing(self):
r"""
Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
back to computing attention in one step.
"""
# set slice_size = `None` to disable `attention slicing`
self.enable_attention_slicing(None)
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]],
height: Optional[int] = 512,
width: Optional[int] = 512,
num_inference_steps: Optional[int] = 50,
guidance_scale: Optional[float] = 7.5,
eta: Optional[float] = 0.0,
generator: Optional[torch.Generator] = None,
latents: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
**kwargs,
):
r"""
Function invoked when calling the pipeline for generation.
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
height (`int`, *optional*, defaults to 512):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to 512):
The width in pixels of the generated image.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
[`schedulers.DDIMScheduler`], will be ignored for others.
generator (`torch.Generator`, *optional*):
A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `nd.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
When returning a tuple, the first element is a list with the generated images, and the second element is a
list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
(nsfw) content, according to the `safety_checker`.
"""
if "torch_device" in kwargs:
device = kwargs.pop("torch_device")
warnings.warn(
"`torch_device` is deprecated as an input argument to `__call__` and will be removed in v0.3.0."
" Consider using `pipe.to(torch_device)` instead."
)
# Set device as before (to be removed in 0.3.0)
if device is None:
device = "cuda" if torch.cuda.is_available() else "cpu"
self.to(device)
if isinstance(prompt, str):
batch_size = 1
elif isinstance(prompt, list):
batch_size = len(prompt)
else:
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
# get prompt text embeddings
text_input = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_embeddings = self.text_encoder(text_input.input_ids.to(self.device))[0]
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
max_length = text_input.input_ids.shape[-1]
uncond_input = self.tokenizer(
[""] * batch_size, padding="max_length", max_length=max_length, return_tensors="pt"
)
uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
# get the initial random noise unless the user supplied it
# Unlike in other pipelines, latents need to be generated in the target device
# for 1-to-1 results reproducibility with the CompVis implementation.
# However this currently doesn't work in `mps`.
latents_device = "cpu" if self.device.type == "mps" else self.device
latents_shape = (batch_size, self.unet.in_channels, height // 8, width // 8)
if latents is None:
latents = torch.randn(
latents_shape,
generator=generator,
device=latents_device,
)
else:
if latents.shape != latents_shape:
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
latents = latents.to(self.device)
# set timesteps
accepts_offset = "offset" in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys())
extra_set_kwargs = {}
if accepts_offset:
extra_set_kwargs["offset"] = 1
self.scheduler.set_timesteps(num_inference_steps, **extra_set_kwargs)
# if we use LMSDiscreteScheduler, let's make sure latents are mulitplied by sigmas
if isinstance(self.scheduler, LMSDiscreteScheduler):
latents = latents * self.scheduler.sigmas[0]
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
if isinstance(self.scheduler, LMSDiscreteScheduler):
sigma = self.scheduler.sigmas[i]
# the model input needs to be scaled to match the continuous ODE formulation in K-LMS
latent_model_input = latent_model_input / ((sigma**2 + 1) ** 0.5)
# predict the noise residual
noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
if isinstance(self.scheduler, LMSDiscreteScheduler):
latents = self.scheduler.step(noise_pred, i, latents, **extra_step_kwargs).prev_sample
else:
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
# scale and decode the image latents with vae
latents = 1 / 0.18215 * latents
image = self.vae.decode(latents).sample
image = (image / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).numpy()
# run safety checker
safety_cheker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(self.device)
image, has_nsfw_concept = self.safety_checker(images=image, clip_input=safety_cheker_input.pixel_values)
if output_type == "pil":
image = self.numpy_to_pil(image)
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
================================================
FILE: models/edict/my_diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py
================================================
import inspect
from typing import List, Optional, Union
import numpy as np
import torch
import PIL
from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
from ...models import AutoencoderKL, UNet2DConditionModel
from ...pipeline_utils import DiffusionPipeline
from ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from . import StableDiffusionPipelineOutput
from .safety_checker import StableDiffusionSafetyChecker
def preprocess(image):
w, h = image.size
w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32
image = image.resize((w, h), resample=PIL.Image.LANCZOS)
image = np.array(image).astype(np.float32) / 255.0
image = image[None].transpose(0, 3, 1, 2)
image = torch.from_numpy(image)
return 2.0 * image - 1.0
class StableDiffusionImg2ImgPipeline(DiffusionPipeline):
r"""
Pipeline for text-guided image to image generation using Stable Diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
text_encoder ([`CLIPTextModel`]):
Frozen text-encoder. Stable Diffusion uses the text portion of
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
tokenizer (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latens. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offsensive or harmful.
Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details.
feature_extractor ([`CLIPFeatureExtractor`]):
Model that extracts features from generated images to be used as inputs for the `safety_checker`.
"""
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet2DConditionModel,
scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPFeatureExtractor,
):
super().__init__()
scheduler = scheduler.set_format("pt")
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
r"""
Enable sliced attention computation.
When this option is enabled, the attention module will split the input tensor in slices, to compute attention
in several steps. This is useful to save some memory in exchange for a small speed decrease.
Args:
slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case,
`attention_head_dim` must be a multiple of `slice_size`.
"""
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
slice_size = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(slice_size)
def disable_attention_slicing(self):
r"""
Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
back to computing attention in one step.
"""
# set slice_size = `None` to disable `set_attention_slice`
self.enable_attention_slice(None)
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]],
init_image: Union[torch.FloatTensor, PIL.Image.Image],
strength: float = 0.8,
num_inference_steps: Optional[int] = 50,
guidance_scale: Optional[float] = 7.5,
eta: Optional[float] = 0.0,
generator: Optional[torch.Generator] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
):
r"""
Function invoked when calling the pipeline for generation.
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
init_image (`torch.FloatTensor` or `PIL.Image.Image`):
`Image`, or tensor representing an image batch, that will be used as the starting point for the
process.
strength (`float`, *optional*, defaults to 0.8):
Conceptually, indicates how much to transform the reference `init_image`. Must be between 0 and 1.
`init_image` will be used as a starting point, adding more noise to it the larger the `strength`. The
number of denoising steps depends on the amount of noise initially added. When `strength` is 1, added
noise will be maximum and the denoising process will run for the full number of iterations specified in
`num_inference_steps`. A value of 1, therefore, essentially ignores `init_image`.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference. This parameter will be modulated by `strength`.
guidance_scale (`float`, *optional*, defaults to 7.5):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
[`schedulers.DDIMScheduler`], will be ignored for others.
generator (`torch.Generator`, *optional*):
A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
deterministic.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `nd.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
When returning a tuple, the first element is a list with the generated images, and the second element is a
list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
(nsfw) content, according to the `safety_checker`.
"""
if isinstance(prompt, str):
batch_size = 1
elif isinstance(prompt, list):
batch_size = len(prompt)
else:
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if strength < 0 or strength > 1:
raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
# set timesteps
accepts_offset = "offset" in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys())
extra_set_kwargs = {}
offset = 0
if accepts_offset:
offset = 1
extra_set_kwargs["offset"] = 1
self.scheduler.set_timesteps(num_inference_steps, **extra_set_kwargs)
if not isinstance(init_image, torch.FloatTensor):
init_image = preprocess(init_image)
# encode the init image into latents and scale the latents
init_latent_dist = self.vae.encode(init_image.to(self.device)).latent_dist
init_latents = init_latent_dist.sample(generator=generator)
init_latents = 0.18215 * init_latents
# expand init_latents for batch_size
init_latents = torch.cat([init_latents] * batch_size)
# get the original timestep using init_timestep
init_timestep = int(num_inference_steps * strength) + offset
init_timestep = min(init_timestep, num_inference_steps)
if isinstance(self.scheduler, LMSDiscreteScheduler):
timesteps = torch.tensor(
[num_inference_steps - init_timestep] * batch_size, dtype=torch.long, device=self.device
)
else:
timesteps = self.scheduler.timesteps[-init_timestep]
timesteps = torch.tensor([timesteps] * batch_size, dtype=torch.long, device=self.device)
# add noise to latents using the timesteps
noise = torch.randn(init_latents.shape, generator=generator, device=self.device)
init_latents = self.scheduler.add_noise(init_latents, noise, timesteps).to(self.device)
# get prompt text embeddings
text_input = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_embeddings = self.text_encoder(text_input.input_ids.to(self.device))[0]
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
max_length = text_input.input_ids.shape[-1]
uncond_input = self.tokenizer(
[""] * batch_size, padding="max_length", max_length=max_length, return_tensors="pt"
)
uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
latents = init_latents
t_start = max(num_inference_steps - init_timestep + offset, 0)
for i, t in enumerate(self.progress_bar(self.scheduler.timesteps[t_start:])):
t_index = t_start + i
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
# if we use LMSDiscreteScheduler, let's make sure latents are mulitplied by sigmas
if isinstance(self.scheduler, LMSDiscreteScheduler):
sigma = self.scheduler.sigmas[t_index]
# the model input needs to be scaled to match the continuous ODE formulation in K-LMS
latent_model_input = latent_model_input / ((sigma**2 + 1) ** 0.5)
latent_model_input = latent_model_input.to(self.unet.dtype)
t = t.to(self.unet.dtype)
# predict the noise residual
noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
if isinstance(self.scheduler, LMSDiscreteScheduler):
latents = self.scheduler.step(noise_pred, t_index, latents, **extra_step_kwargs).prev_sample
else:
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
# scale and decode the image latents with vae
latents = 1 / 0.18215 * latents
image = self.vae.decode(latents.to(self.vae.dtype)).sample
image = (image / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).numpy()
# run safety checker
safety_cheker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(self.device)
image, has_nsfw_concept = self.safety_checker(images=image, clip_input=safety_cheker_input.pixel_values)
if output_type == "pil":
image = self.numpy_to_pil(image)
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
================================================
FILE: models/edict/my_diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py
================================================
import inspect
from typing import List, Optional, Union
import numpy as np
import torch
import PIL
from tqdm.auto import tqdm
from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
from ...models import AutoencoderKL, UNet2DConditionModel
from ...pipeline_utils import DiffusionPipeline
from ...schedulers import DDIMScheduler, PNDMScheduler
from ...utils import logging
from . import StableDiffusionPipelineOutput
from .safety_checker import StableDiffusionSafetyChecker
logger = logging.get_logger(__name__)
def preprocess_image(image):
w, h = image.size
w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32
image = image.resize((w, h), resample=PIL.Image.LANCZOS)
image = np.array(image).astype(np.float32) / 255.0
image = image[None].transpose(0, 3, 1, 2)
image = torch.from_numpy(image)
return 2.0 * image - 1.0
def preprocess_mask(mask):
mask = mask.convert("L")
w, h = mask.size
w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32
mask = mask.resize((w // 8, h // 8), resample=PIL.Image.NEAREST)
mask = np.array(mask).astype(np.float32) / 255.0
mask = np.tile(mask, (4, 1, 1))
mask = mask[None].transpose(0, 1, 2, 3) # what does this step do?
mask = 1 - mask # repaint white, keep black
mask = torch.from_numpy(mask)
return mask
class StableDiffusionInpaintPipeline(DiffusionPipeline):
r"""
Pipeline for text-guided image inpainting using Stable Diffusion. *This is an experimental feature*.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
text_encoder ([`CLIPTextModel`]):
Frozen text-encoder. Stable Diffusion uses the text portion of
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
tokenizer (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latens. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offsensive or harmful.
Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details.
feature_extractor ([`CLIPFeatureExtractor`]):
Model that extracts features from generated images to be used as inputs for the `safety_checker`.
"""
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet2DConditionModel,
scheduler: Union[DDIMScheduler, PNDMScheduler],
safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPFeatureExtractor,
):
super().__init__()
scheduler = scheduler.set_format("pt")
logger.info("`StableDiffusionInpaintPipeline` is experimental and will very likely change in the future.")
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
r"""
Enable sliced attention computation.
When this option is enabled, the attention module will split the input tensor in slices, to compute attention
in several steps. This is useful to save some memory in exchange for a small speed decrease.
Args:
slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case,
`attention_head_dim` must be a multiple of `slice_size`.
"""
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
slice_size = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(slice_size)
def disable_attention_slicing(self):
r"""
Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
back to computing attention in one step.
"""
# set slice_size = `None` to disable `set_attention_slice`
self.enable_attention_slice(None)
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]],
init_image: Union[torch.FloatTensor, PIL.Image.Image],
mask_image: Union[torch.FloatTensor, PIL.Image.Image],
strength: float = 0.8,
num_inference_steps: Optional[int] = 50,
guidance_scale: Optional[float] = 7.5,
eta: Optional[float] = 0.0,
generator: Optional[torch.Generator] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
):
r"""
Function invoked when calling the pipeline for generation.
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
init_image (`torch.FloatTensor` or `PIL.Image.Image`):
`Image`, or tensor representing an image batch, that will be used as the starting point for the
process. This is the image whose masked region will be inpainted.
mask_image (`torch.FloatTensor` or `PIL.Image.Image`):
`Image`, or tensor representing an image batch, to mask `init_image`. White pixels in the mask will be
replaced by noise and therefore repainted, while black pixels will be preserved. The mask image will be
converted to a single channel (luminance) before use.
strength (`float`, *optional*, defaults to 0.8):
Conceptually, indicates how much to inpaint the masked area. Must be between 0 and 1. When `strength`
is 1, the denoising process will be run on the masked area for the full number of iterations specified
in `num_inference_steps`. `init_image` will be used as a reference for the masked area, adding more
noise to that region the larger the `strength`. If `strength` is 0, no inpainting will occur.
num_inference_steps (`int`, *optional*, defaults to 50):
The reference number of denoising steps. More denoising steps usually lead to a higher quality image at
the expense of slower inference. This parameter will be modulated by `strength`, as explained above.
guidance_scale (`float`, *optional*, defaults to 7.5):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
[`schedulers.DDIMScheduler`], will be ignored for others.
generator (`torch.Generator`, *optional*):
A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
deterministic.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `nd.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
When returning a tuple, the first element is a list with the generated images, and the second element is a
list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
(nsfw) content, according to the `safety_checker`.
"""
if isinstance(prompt, str):
batch_size = 1
elif isinstance(prompt, list):
batch_size = len(prompt)
else:
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if strength < 0 or strength > 1:
raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
# set timesteps
accepts_offset = "offset" in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys())
extra_set_kwargs = {}
offset = 0
if accepts_offset:
offset = 1
extra_set_kwargs["offset"] = 1
self.scheduler.set_timesteps(num_inference_steps, **extra_set_kwargs)
# preprocess image
init_image = preprocess_image(init_image).to(self.device)
# encode the init image into latents and scale the latents
init_latent_dist = self.vae.encode(init_image.to(self.device)).latent_dist
init_latents = init_latent_dist.sample(generator=generator)
init_latents = 0.18215 * init_latents
# Expand init_latents for batch_size
init_latents = torch.cat([init_latents] * batch_size)
init_latents_orig = init_latents
# preprocess mask
mask = preprocess_mask(mask_image).to(self.device)
mask = torch.cat([mask] * batch_size)
# check sizes
if not mask.shape == init_latents.shape:
raise ValueError("The mask and init_image should be the same size!")
# get the original timestep using init_timestep
init_timestep = int(num_inference_steps * strength) + offset
init_timestep = min(init_timestep, num_inference_steps)
timesteps = self.scheduler.timesteps[-init_timestep]
timesteps = torch.tensor([timesteps] * batch_size, dtype=torch.long, device=self.device)
# add noise to latents using the timesteps
noise = torch.randn(init_latents.shape, generator=generator, device=self.device)
init_latents = self.scheduler.add_noise(init_latents, noise, timesteps)
# get prompt text embeddings
text_input = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_embeddings = self.text_encoder(text_input.input_ids.to(self.device))[0]
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
max_length = text_input.input_ids.shape[-1]
uncond_input = self.tokenizer(
[""] * batch_size, padding="max_length", max_length=max_length, return_tensors="pt"
)
uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
latents = init_latents
t_start = max(num_inference_steps - init_timestep + offset, 0)
for i, t in tqdm(enumerate(self.scheduler.timesteps[t_start:])):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
# predict the noise residual
noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
# masking
init_latents_proper = self.scheduler.add_noise(init_latents_orig, noise, t)
latents = (init_latents_proper * mask) + (latents * (1 - mask))
# scale and decode the image latents with vae
latents = 1 / 0.18215 * latents
image = self.vae.decode(latents).sample
image = (image / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).numpy()
# run safety checker
safety_cheker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(self.device)
image, has_nsfw_concept = self.safety_checker(images=image, clip_input=safety_cheker_input.pixel_values)
if output_type == "pil":
image = self.numpy_to_pil(image)
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
================================================
FILE: models/edict/my_diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_onnx.py
================================================
import inspect
from typing import List, Optional, Union
import numpy as np
from transformers import CLIPFeatureExtractor, CLIPTokenizer
from ...onnx_utils import OnnxRuntimeModel
from ...pipeline_utils import DiffusionPipeline
from ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from . import StableDiffusionPipelineOutput
class StableDiffusionOnnxPipeline(DiffusionPipeline):
vae_decoder: OnnxRuntimeModel
text_encoder: OnnxRuntimeModel
tokenizer: CLIPTokenizer
unet: OnnxRuntimeModel
scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]
safety_checker: OnnxRuntimeModel
feature_extractor: CLIPFeatureExtractor
def __init__(
self,
vae_decoder: OnnxRuntimeModel,
text_encoder: OnnxRuntimeModel,
tokenizer: CLIPTokenizer,
unet: OnnxRuntimeModel,
scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
safety_checker: OnnxRuntimeModel,
feature_extractor: CLIPFeatureExtractor,
):
super().__init__()
scheduler = scheduler.set_format("np")
self.register_modules(
vae_decoder=vae_decoder,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
def __call__(
self,
prompt: Union[str, List[str]],
height: Optional[int] = 512,
width: Optional[int] = 512,
num_inference_steps: Optional[int] = 50,
guidance_scale: Optional[float] = 7.5,
eta: Optional[float] = 0.0,
latents: Optional[np.ndarray] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
**kwargs,
):
if isinstance(prompt, str):
batch_size = 1
elif isinstance(prompt, list):
batch_size = len(prompt)
else:
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
# get prompt text embeddings
text_input = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="np",
)
text_embeddings = self.text_encoder(input_ids=text_input.input_ids.astype(np.int32))[0]
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
max_length = text_input.input_ids.shape[-1]
uncond_input = self.tokenizer(
[""] * batch_size, padding="max_length", max_length=max_length, return_tensors="np"
)
uncond_embeddings = self.text_encoder(input_ids=uncond_input.input_ids.astype(np.int32))[0]
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
text_embeddings = np.concatenate([uncond_embeddings, text_embeddings])
# get the initial random noise unless the user supplied it
latents_shape = (batch_size, 4, height // 8, width // 8)
if latents is None:
latents = np.random.randn(*latents_shape).astype(np.float32)
elif latents.shape != latents_shape:
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
# set timesteps
accepts_offset = "offset" in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys())
extra_set_kwargs = {}
if accepts_offset:
extra_set_kwargs["offset"] = 1
self.scheduler.set_timesteps(num_inference_steps, **extra_set_kwargs)
# if we use LMSDiscreteScheduler, let's make sure latents are mulitplied by sigmas
if isinstance(self.scheduler, LMSDiscreteScheduler):
latents = latents * self.scheduler.sigmas[0]
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
# expand the latents if we are doing classifier free guidance
latent_model_input = np.concatenate([latents] * 2) if do_classifier_free_guidance else latents
if isinstance(self.scheduler, LMSDiscreteScheduler):
sigma = self.scheduler.sigmas[i]
# the model input needs to be scaled to match the continuous ODE formulation in K-LMS
latent_model_input = latent_model_input / ((sigma**2 + 1) ** 0.5)
# predict the noise residual
noise_pred = self.unet(
sample=latent_model_input, timestep=np.array([t]), encoder_hidden_states=text_embeddings
)
noise_pred = noise_pred[0]
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = np.split(noise_pred, 2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
if isinstance(self.scheduler, LMSDiscreteScheduler):
latents = self.scheduler.step(noise_pred, i, latents, **extra_step_kwargs).prev_sample
else:
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
# scale and decode the image latents with vae
latents = 1 / 0.18215 * latents
image = self.vae_decoder(latent_sample=latents)[0]
image = np.clip(image / 2 + 0.5, 0, 1)
image = image.transpose((0, 2, 3, 1))
# run safety checker
safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="np")
image, has_nsfw_concept = self.safety_checker(clip_input=safety_checker_input.pixel_values, images=image)
if output_type == "pil":
image = self.numpy_to_pil(image)
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
================================================
FILE: models/edict/my_diffusers/pipelines/stable_diffusion/safety_checker.py
================================================
import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
logger = logging.get_logger(__name__)
def cosine_distance(image_embeds, text_embeds):
normalized_image_embeds = nn.functional.normalize(image_embeds)
normalized_text_embeds = nn.functional.normalize(text_embeds)
return torch.mm(normalized_image_embeds, normalized_text_embeds.t())
class StableDiffusionSafetyChecker(PreTrainedModel):
config_class = CLIPConfig
def __init__(self, config: CLIPConfig):
super().__init__(config)
self.vision_model = CLIPVisionModel(config.vision_config)
self.visual_projection = nn.Linear(config.vision_config.hidden_size, config.projection_dim, bias=False)
self.concept_embeds = nn.Parameter(torch.ones(17, config.projection_dim), requires_grad=False)
self.special_care_embeds = nn.Parameter(torch.ones(3, config.projection_dim), requires_grad=False)
self.register_buffer("concept_embeds_weights", torch.ones(17))
self.register_buffer("special_care_embeds_weights", torch.ones(3))
@torch.no_grad()
def forward(self, clip_input, images):
pooled_output = self.vision_model(clip_input)[1] # pooled_output
image_embeds = self.visual_projection(pooled_output)
special_cos_dist = cosine_distance(image_embeds, self.special_care_embeds).cpu().numpy()
cos_dist = cosine_distance(image_embeds, self.concept_embeds).cpu().numpy()
result = []
batch_size = image_embeds.shape[0]
for i in range(batch_size):
result_img = {"special_scores": {}, "special_care": [], "concept_scores": {}, "bad_concepts": []}
# increase this value to create a stronger `nfsw` filter
# at the cost of increasing the possibility of filtering benign images
adjustment = 0.0
for concet_idx in range(len(special_cos_dist[0])):
concept_cos = special_cos_dist[i][concet_idx]
concept_threshold = self.special_care_embeds_weights[concet_idx].item()
result_img["special_scores"][concet_idx] = round(concept_cos - concept_threshold + adjustment, 3)
if result_img["special_scores"][concet_idx] > 0:
result_img["special_care"].append({concet_idx, result_img["special_scores"][concet_idx]})
adjustment = 0.01
for concet_idx in range(len(cos_dist[0])):
concept_cos = cos_dist[i][concet_idx]
concept_threshold = self.concept_embeds_weights[concet_idx].item()
result_img["concept_scores"][concet_idx] = round(concept_cos - concept_threshold + adjustment, 3)
if result_img["concept_scores"][concet_idx] > 0:
result_img["bad_concepts"].append(concet_idx)
result.append(result_img)
has_nsfw_concepts = [len(res["bad_concepts"]) > 0 for res in result]
for idx, has_nsfw_concept in enumerate(has_nsfw_concepts):
if has_nsfw_concept:
images[idx] = np.zeros(images[idx].shape) # black image
if any(has_nsfw_concepts):
logger.warning(
"Potential NSFW content was detected in one or more images. A black image will be returned instead."
" Try again with a different prompt and/or seed."
)
return images, has_nsfw_concepts
@torch.inference_mode()
def forward_onnx(self, clip_input: torch.FloatTensor, images: torch.FloatTensor):
pooled_output = self.vision_model(clip_input)[1] # pooled_output
image_embeds = self.visual_projection(pooled_output)
special_cos_dist = cosine_distance(image_embeds, self.special_care_embeds)
cos_dist = cosine_distance(image_embeds, self.concept_embeds)
# increase this value to create a stronger `nsfw` filter
# at the cost of increasing the possibility of filtering benign images
adjustment = 0.0
special_scores = special_cos_dist - self.special_care_embeds_weights + adjustment
# special_scores = special_scores.round(decimals=3)
special_care = torch.any(special_scores > 0, dim=1)
special_adjustment = special_care * 0.01
special_adjustment = special_adjustment.unsqueeze(1).expand(-1, cos_dist.shape[1])
concept_scores = (cos_dist - self.concept_embeds_weights) + special_adjustment
# concept_scores = concept_scores.round(decimals=3)
has_nsfw_concepts = torch.any(concept_scores > 0, dim=1)
images[has_nsfw_concepts] = 0.0 # black image
return images, has_nsfw_concepts
================================================
FILE: models/edict/my_diffusers/pipelines/stochastic_karras_ve/__init__.py
================================================
# flake8: noqa
from .pipeline_stochastic_karras_ve import KarrasVePipeline
================================================
FILE: models/edict/my_diffusers/pipelines/stochastic_karras_ve/pipeline_stochastic_karras_ve.py
================================================
#!/usr/bin/env python3
import warnings
from typing import Optional, Tuple, Union
import torch
from ...models import UNet2DModel
from ...pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from ...schedulers import KarrasVeScheduler
class KarrasVePipeline(DiffusionPipeline):
r"""
Stochastic sampling from Karras et al. [1] tailored to the Variance-Expanding (VE) models [2]. Use Algorithm 2 and
the VE column of Table 1 from [1] for reference.
[1] Karras, Tero, et al. "Elucidating the Design Space of Diffusion-Based Generative Models."
https://arxiv.org/abs/2206.00364 [2] Song, Yang, et al. "Score-based generative modeling through stochastic
differential equations." https://arxiv.org/abs/2011.13456
Parameters:
unet ([`UNet2DModel`]): U-Net architecture to denoise the encoded image.
scheduler ([`KarrasVeScheduler`]):
Scheduler for the diffusion process to be used in combination with `unet` to denoise the encoded image.
"""
# add type hints for linting
unet: UNet2DModel
scheduler: KarrasVeScheduler
def __init__(self, unet: UNet2DModel, scheduler: KarrasVeScheduler):
super().__init__()
scheduler = scheduler.set_format("pt")
self.register_modules(unet=unet, scheduler=scheduler)
@torch.no_grad()
def __call__(
self,
batch_size: int = 1,
num_inference_steps: int = 50,
generator: Optional[torch.Generator] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
**kwargs,
) -> Union[Tuple, ImagePipelineOutput]:
r"""
Args:
batch_size (`int`, *optional*, defaults to 1):
The number of images to generate.
generator (`torch.Generator`, *optional*):
A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
deterministic.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `nd.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipeline_utils.ImagePipelineOutput`] instead of a plain tuple.
Returns:
[`~pipeline_utils.ImagePipelineOutput`] or `tuple`: [`~pipelines.utils.ImagePipelineOutput`] if
`return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the
generated images.
"""
if "torch_device" in kwargs:
device = kwargs.pop("torch_device")
warnings.warn(
"`torch_device` is deprecated as an input argument to `__call__` and will be removed in v0.3.0."
" Consider using `pipe.to(torch_device)` instead."
)
# Set device as before (to be removed in 0.3.0)
if device is None:
device = "cuda" if torch.cuda.is_available() else "cpu"
self.to(device)
img_size = self.unet.config.sample_size
shape = (batch_size, 3, img_size, img_size)
model = self.unet
# sample x_0 ~ N(0, sigma_0^2 * I)
sample = torch.randn(*shape) * self.scheduler.config.sigma_max
sample = sample.to(self.device)
self.scheduler.set_timesteps(num_inference_steps)
for t in self.progress_bar(self.scheduler.timesteps):
# here sigma_t == t_i from the paper
sigma = self.scheduler.schedule[t]
sigma_prev = self.scheduler.schedule[t - 1] if t > 0 else 0
# 1. Select temporarily increased noise level sigma_hat
# 2. Add new noise to move from sample_i to sample_hat
sample_hat, sigma_hat = self.scheduler.add_noise_to_input(sample, sigma, generator=generator)
# 3. Predict the noise residual given the noise magnitude `sigma_hat`
# The model inputs and output are adjusted by following eq. (213) in [1].
model_output = (sigma_hat / 2) * model((sample_hat + 1) / 2, sigma_hat / 2).sample
# 4. Evaluate dx/dt at sigma_hat
# 5. Take Euler step from sigma to sigma_prev
step_output = self.scheduler.step(model_output, sigma_hat, sigma_prev, sample_hat)
if sigma_prev != 0:
# 6. Apply 2nd order correction
# The model inputs and output are adjusted by following eq. (213) in [1].
model_output = (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2, sigma_prev / 2).sample
step_output = self.scheduler.step_correct(
model_output,
sigma_hat,
sigma_prev,
sample_hat,
step_output.prev_sample,
step_output["derivative"],
)
sample = step_output.prev_sample
sample = (sample / 2 + 0.5).clamp(0, 1)
image = sample.cpu().permute(0, 2, 3, 1).numpy()
if output_type == "pil":
image = self.numpy_to_pil(sample)
if not return_dict:
return (image,)
return ImagePipelineOutput(images=image)
================================================
FILE: models/edict/my_diffusers/schedulers/__init__.py
================================================
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..utils import is_scipy_available
from .scheduling_ddim import DDIMScheduler
from .scheduling_ddpm import DDPMScheduler
from .scheduling_karras_ve import KarrasVeScheduler
from .scheduling_pndm import PNDMScheduler
from .scheduling_sde_ve import ScoreSdeVeScheduler
from .scheduling_sde_vp import ScoreSdeVpScheduler
from .scheduling_utils import SchedulerMixin
if is_scipy_available():
from .scheduling_lms_discrete import LMSDiscreteScheduler
else:
from ..utils.dummy_scipy_objects import * # noqa F403
================================================
FILE: models/edict/my_diffusers/schedulers/scheduling_ddim.py
================================================
# Copyright 2022 Stanford University Team and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
def betas_for_alpha_bar(num_diffusion_timesteps, max_beta=0.999):
"""
Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
(1-beta) over time from t = [0,1].
Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up
to that part of the diffusion process.
Args:
num_diffusion_timesteps (`int`): the number of betas to produce.
max_beta (`float`): the maximum beta to use; use values lower than 1 to
prevent singularities.
Returns:
betas (`np.ndarray`): the betas used by the scheduler to step the model outputs
"""
def alpha_bar(time_step):
return math.cos((time_step + 0.008) / 1.008 * math.pi / 2) ** 2
betas = []
for i in range(num_diffusion_timesteps):
t1 = i / num_diffusion_timesteps
t2 = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
return np.array(betas, dtype=np.float64)
class DDIMScheduler(SchedulerMixin, ConfigMixin):
"""
Denoising diffusion implicit models is a scheduler that extends the denoising procedure introduced in denoising
diffusion probabilistic models (DDPMs) with non-Markovian guidance.
[`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__`
function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`.
[`~ConfigMixin`] also provides general loading and saving functionality via the [`~ConfigMixin.save_config`] and
[`~ConfigMixin.from_config`] functios.
For more details, see the original paper: https://arxiv.org/abs/2010.02502
Args:
num_train_timesteps (`int`): number of diffusion steps used to train the model.
beta_start (`float`): the starting `beta` value of inference.
beta_end (`float`): the final `beta` value.
beta_schedule (`str`):
the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from
`linear`, `scaled_linear`, or `squaredcos_cap_v2`.
trained_betas (`np.ndarray`, optional): TODO
timestep_values (`np.ndarray`, optional): TODO
clip_sample (`bool`, default `True`):
option to clip predicted sample between -1 and 1 for numerical stability.
set_alpha_to_one (`bool`, default `True`):
if alpha for final step is 1 or the final alpha of the "non-previous" one.
tensor_format (`str`): whether the scheduler expects pytorch or numpy arrays.
"""
@register_to_config
def __init__(
self,
num_train_timesteps: int = 1000,
beta_start: float = 0.0001,
beta_end: float = 0.02,
beta_schedule: str = "linear",
trained_betas: Optional[np.ndarray] = None,
timestep_values: Optional[np.ndarray] = None,
clip_sample: bool = True,
set_alpha_to_one: bool = True,
tensor_format: str = "pt",
):
if trained_betas is not None:
self.betas = np.asarray(trained_betas)
if beta_schedule == "linear":
self.betas = np.linspace(beta_start, beta_end, num_train_timesteps, dtype=np.float64)
elif beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
self.betas = np.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=np.float64) ** 2
elif beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
self.betas = betas_for_alpha_bar(num_train_timesteps)
else:
raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}")
self.alphas = 1.0 - self.betas
self.alphas_cumprod = np.cumprod(self.alphas, axis=0)
# At every step in ddim, we are looking into the previous alphas_cumprod
# For the final step, there is no previous alphas_cumprod because we are already at 0
# `set_alpha_to_one` decides whether we set this paratemer simply to one or
# whether we use the final alpha of the "non-previous" one.
self.final_alpha_cumprod = np.array(1.0) if set_alpha_to_one else self.alphas_cumprod[0]
# setable values
self.num_inference_steps = None
self.timesteps = np.arange(0, num_train_timesteps)[::-1].copy()
self.tensor_format = tensor_format
self.set_format(tensor_format=tensor_format)
# print(self.alphas.shape)
def _get_variance(self, timestep, prev_timestep):
alpha_prod_t = self.alphas_cumprod[timestep]
alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
beta_prod_t = 1 - alpha_prod_t
beta_prod_t_prev = 1 - alpha_prod_t_prev
variance = (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)
return variance
def set_timesteps(self, num_inference_steps: int, offset: int = 0):
"""
Sets the discrete timesteps used for the diffusion chain. Supporting function to be run before inference.
Args:
num_inference_steps (`int`):
the number of diffusion steps used when generating samples with a pre-trained model.
offset (`int`): TODO
"""
self.num_inference_steps = num_inference_steps
if num_inference_steps <= 1000:
self.timesteps = np.arange(
0, self.config.num_train_timesteps, self.config.num_train_timesteps // self.num_inference_steps
)[::-1].copy()
else:
print("Hitting new logic, allowing fractional timesteps")
self.timesteps = np.linspace(
0, self.config.num_train_timesteps-1, self.num_inference_steps, endpoint=True
)[::-1].copy()
self.timesteps += offset
self.set_format(tensor_format=self.tensor_format)
def step(
self,
model_output: Union[torch.FloatTensor, np.ndarray],
timestep: int,
sample: Union[torch.FloatTensor, np.ndarray],
eta: float = 0.0,
use_clipped_model_output: bool = False,
generator=None,
return_dict: bool = True,
) -> Union[SchedulerOutput, Tuple]:
"""
Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion
process from the learned model outputs (most often the predicted noise).
Args:
model_output (`torch.FloatTensor` or `np.ndarray`): direct output from learned diffusion model.
timestep (`int`): current discrete timestep in the diffusion chain.
sample (`torch.FloatTensor` or `np.ndarray`):
current instance of sample being created by diffusion process.
eta (`float`): weight of noise for added noise in diffusion step.
use_clipped_model_output (`bool`): TODO
generator: random number generator.
return_dict (`bool`): option for returning tuple rather than SchedulerOutput class
Returns:
[`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`:
[`~schedulers.scheduling_utils.SchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When
returning a tuple, the first element is the sample tensor.
"""
if self.num_inference_steps is None:
raise ValueError(
"Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"
)
# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
# Ideally, read DDIM paper in-detail understanding
# Notation ( ->
# - pred_noise_t -> e_theta(x_t, t)
# - pred_original_sample -> f_theta(x_t, t) or x_0
# - std_dev_t -> sigma_t
# - eta -> η
# - pred_sample_direction -> "direction pointingc to x_t"
# - pred_prev_sample -> "x_t-1"
# 1. get previous step value (=t-1)
prev_timestep = timestep - self.config.num_train_timesteps // self.num_inference_steps
# 2. compute alphas, betas
alpha_prod_t = self.alphas_cumprod[timestep]
alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
beta_prod_t = 1 - alpha_prod_t
# 3. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)
# 4. Clip "predicted x_0"
if self.config.clip_sample:
pred_original_sample = self.clip(pred_original_sample, -1, 1)
# 5. compute variance: "sigma_t(η)" -> see formula (16)
# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
variance = self._get_variance(timestep, prev_timestep)
std_dev_t = eta * variance ** (0.5)
if use_clipped_model_output:
# the model_output is always re-derived from the clipped x_0 in Glide
model_output = (sample - alpha_prod_t ** (0.5) * pred_original_sample) / beta_prod_t ** (0.5)
# 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
pred_sample_direction = (1 - alpha_prod_t_prev - std_dev_t**2) ** (0.5) * model_output
# 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
prev_sample = alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction
if eta > 0:
device = model_output.device if torch.is_tensor(model_output) else "cpu"
noise = torch.randn(model_output.shape, generator=generator).to(device)
variance = self._get_variance(timestep, prev_timestep) ** (0.5) * eta * noise
if not torch.is_tensor(model_output):
variance = variance.numpy()
prev_sample = prev_sample + variance
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=prev_sample)
def add_noise(
self,
original_samples: Union[torch.FloatTensor, np.ndarray],
noise: Union[torch.FloatTensor, np.ndarray],
timesteps: Union[torch.IntTensor, np.ndarray],
) -> Union[torch.FloatTensor, np.ndarray]:
sqrt_alpha_prod = self.alphas_cumprod[timesteps] ** 0.5
sqrt_alpha_prod = self.match_shape(sqrt_alpha_prod, original_samples)
sqrt_one_minus_alpha_prod = (1 - self.alphas_cumprod[timesteps]) ** 0.5
sqrt_one_minus_alpha_prod = self.match_shape(sqrt_one_minus_alpha_prod, original_samples)
noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
return noisy_samples
def __len__(self):
return self.config.num_train_timesteps
================================================
FILE: models/edict/my_diffusers/schedulers/scheduling_ddpm.py
================================================
# Copyright 2022 UC Berkely Team and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
import math
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
def betas_for_alpha_bar(num_diffusion_timesteps, max_beta=0.999):
"""
Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
(1-beta) over time from t = [0,1].
Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up
to that part of the diffusion process.
Args:
num_diffusion_timesteps (`int`): the number of betas to produce.
max_beta (`float`): the maximum beta to use; use values lower than 1 to
prevent singularities.
Returns:
betas (`np.ndarray`): the betas used by the scheduler to step the model outputs
"""
def alpha_bar(time_step):
return math.cos((time_step + 0.008) / 1.008 * math.pi / 2) ** 2
betas = []
for i in range(num_diffusion_timesteps):
t1 = i / num_diffusion_timesteps
t2 = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
return np.array(betas, dtype=np.float32)
class DDPMScheduler(SchedulerMixin, ConfigMixin):
"""
Denoising diffusion probabilistic models (DDPMs) explores the connections between denoising score matching and
Langevin dynamics sampling.
[`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__`
function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`.
[`~ConfigMixin`] also provides general loading and saving functionality via the [`~ConfigMixin.save_config`] and
[`~ConfigMixin.from_config`] functios.
For more details, see the original paper: https://arxiv.org/abs/2006.11239
Args:
num_train_timesteps (`int`): number of diffusion steps used to train the model.
beta_start (`float`): the starting `beta` value of inference.
beta_end (`float`): the final `beta` value.
beta_schedule (`str`):
the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from
`linear`, `scaled_linear`, or `squaredcos_cap_v2`.
trained_betas (`np.ndarray`, optional): TODO
variance_type (`str`):
options to clip the variance used when adding noise to the denoised sample. Choose from `fixed_small`,
`fixed_small_log`, `fixed_large`, `fixed_large_log`, `learned` or `learned_range`.
clip_sample (`bool`, default `True`):
option to clip predicted sample between -1 and 1 for numerical stability.
tensor_format (`str`): whether the scheduler expects pytorch or numpy arrays.
"""
@register_to_config
def __init__(
self,
num_train_timesteps: int = 1000,
beta_start: float = 0.0001,
beta_end: float = 0.02,
beta_schedule: str = "linear",
trained_betas: Optional[np.ndarray] = None,
variance_type: str = "fixed_small",
clip_sample: bool = True,
tensor_format: str = "pt",
):
if trained_betas is not None:
self.betas = np.asarray(trained_betas)
elif beta_schedule == "linear":
self.betas = np.linspace(beta_start, beta_end, num_train_timesteps, dtype=np.float32)
elif beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
self.betas = np.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=np.float32) ** 2
elif beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
self.betas = betas_for_alpha_bar(num_train_timesteps)
else:
raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}")
self.alphas = 1.0 - self.betas
self.alphas_cumprod = np.cumprod(self.alphas, axis=0)
self.one = np.array(1.0)
# setable values
self.num_inference_steps = None
self.timesteps = np.arange(0, num_train_timesteps)[::-1].copy()
self.tensor_format = tensor_format
self.set_format(tensor_format=tensor_format)
self.variance_type = variance_type
def set_timesteps(self, num_inference_steps: int):
"""
Sets the discrete timesteps used for the diffusion chain. Supporting function to be run before inference.
Args:
num_inference_steps (`int`):
the number of diffusion steps used when generating samples with a pre-trained model.
"""
num_inference_steps = min(self.config.num_train_timesteps, num_inference_steps)
self.num_inference_steps = num_inference_steps
self.timesteps = np.arange(
0, self.config.num_train_timesteps, self.config.num_train_timesteps // self.num_inference_steps
)[::-1].copy()
self.set_format(tensor_format=self.tensor_format)
def _get_variance(self, t, predicted_variance=None, variance_type=None):
alpha_prod_t = self.alphas_cumprod[t]
alpha_prod_t_prev = self.alphas_cumprod[t - 1] if t > 0 else self.one
# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
# and sample from it to get previous sample
# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
if variance_type is None:
variance_type = self.config.variance_type
# hacks - were probs added for training stability
if variance_type == "fixed_small":
variance = self.clip(variance, min_value=1e-20)
# for rl-diffuser https://arxiv.org/abs/2205.09991
elif variance_type == "fixed_small_log":
variance = self.log(self.clip(variance, min_value=1e-20))
elif variance_type == "fixed_large":
variance = self.betas[t]
elif variance_type == "fixed_large_log":
# Glide max_log
variance = self.log(self.betas[t])
elif variance_type == "learned":
return predicted_variance
elif variance_type == "learned_range":
min_log = variance
max_log = self.betas[t]
frac = (predicted_variance + 1) / 2
variance = frac * max_log + (1 - frac) * min_log
return variance
def step(
self,
model_output: Union[torch.FloatTensor, np.ndarray],
timestep: int,
sample: Union[torch.FloatTensor, np.ndarray],
predict_epsilon=True,
generator=None,
return_dict: bool = True,
) -> Union[SchedulerOutput, Tuple]:
"""
Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion
process from the learned model outputs (most often the predicted noise).
Args:
model_output (`torch.FloatTensor` or `np.ndarray`): direct output from learned diffusion model.
timestep (`int`): current discrete timestep in the diffusion chain.
sample (`torch.FloatTensor` or `np.ndarray`):
current instance of sample being created by diffusion process.
eta (`float`): weight of noise for added noise in diffusion step.
predict_epsilon (`bool`):
optional flag to use when model predicts the samples directly instead of the noise, epsilon.
generator: random number generator.
return_dict (`bool`): option for returning tuple rather than SchedulerOutput class
Returns:
[`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`:
[`~schedulers.scheduling_utils.SchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When
returning a tuple, the first element is the sample tensor.
"""
t = timestep
if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
model_output, predicted_variance = torch.split(model_output, sample.shape[1], dim=1)
else:
predicted_variance = None
# 1. compute alphas, betas
alpha_prod_t = self.alphas_cumprod[t]
alpha_prod_t_prev = self.alphas_cumprod[t - 1] if t > 0 else self.one
beta_prod_t = 1 - alpha_prod_t
beta_prod_t_prev = 1 - alpha_prod_t_prev
# 2. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
if predict_epsilon:
pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)
else:
pred_original_sample = model_output
# 3. Clip "predicted x_0"
if self.config.clip_sample:
pred_original_sample = self.clip(pred_original_sample, -1, 1)
# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
pred_original_sample_coeff = (alpha_prod_t_prev ** (0.5) * self.betas[t]) / beta_prod_t
current_sample_coeff = self.alphas[t] ** (0.5) * beta_prod_t_prev / beta_prod_t
# 5. Compute predicted previous sample µ_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
pred_prev_sample = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
# 6. Add noise
variance = 0
if t > 0:
noise = self.randn_like(model_output, generator=generator)
variance = (self._get_variance(t, predicted_variance=predicted_variance) ** 0.5) * noise
pred_prev_sample = pred_prev_sample + variance
if not return_dict:
return (pred_prev_sample,)
return SchedulerOutput(prev_sample=pred_prev_sample)
def add_noise(
self,
original_samples: Union[torch.FloatTensor, np.ndarray],
noise: Union[torch.FloatTensor, np.ndarray],
timesteps: Union[torch.IntTensor, np.ndarray],
) -> Union[torch.FloatTensor, np.ndarray]:
sqrt_alpha_prod = self.alphas_cumprod[timesteps] ** 0.5
sqrt_alpha_prod = self.match_shape(sqrt_alpha_prod, original_samples)
sqrt_one_minus_alpha_prod = (1 - self.alphas_cumprod[timesteps]) ** 0.5
sqrt_one_minus_alpha_prod = self.match_shape(sqrt_one_minus_alpha_prod, original_samples)
noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
return noisy_samples
def __len__(self):
return self.config.num_train_timesteps
================================================
FILE: models/edict/my_diffusers/schedulers/scheduling_karras_ve.py
================================================
# Copyright 2022 NVIDIA and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils import SchedulerMixin
@dataclass
class KarrasVeOutput(BaseOutput):
"""
Output class for the scheduler's step function output.
Args:
prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the
denoising loop.
derivative (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
Derivate of predicted original image sample (x_0).
"""
prev_sample: torch.FloatTensor
derivative: torch.FloatTensor
class KarrasVeScheduler(SchedulerMixin, ConfigMixin):
"""
Stochastic sampling from Karras et al. [1] tailored to the Variance-Expanding (VE) models [2]. Use Algorithm 2 and
the VE column of Table 1 from [1] for reference.
[1] Karras, Tero, et al. "Elucidating the Design Space of Diffusion-Based Generative Models."
https://arxiv.org/abs/2206.00364 [2] Song, Yang, et al. "Score-based generative modeling through stochastic
differential equations." https://arxiv.org/abs/2011.13456
[`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__`
function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`.
[`~ConfigMixin`] also provides general loading and saving functionality via the [`~ConfigMixin.save_config`] and
[`~ConfigMixin.from_config`] functios.
For more details on the parameters, see the original paper's Appendix E.: "Elucidating the Design Space of
Diffusion-Based Generative Models." https://arxiv.org/abs/2206.00364. The grid search values used to find the
optimal {s_noise, s_churn, s_min, s_max} for a specific model are described in Table 5 of the paper.
Args:
sigma_min (`float`): minimum noise magnitude
sigma_max (`float`): maximum noise magnitude
s_noise (`float`): the amount of additional noise to counteract loss of detail during sampling.
A reasonable range is [1.000, 1.011].
s_churn (`float`): the parameter controlling the overall amount of stochasticity.
A reasonable range is [0, 100].
s_min (`float`): the start value of the sigma range where we add noise (enable stochasticity).
A reasonable range is [0, 10].
s_max (`float`): the end value of the sigma range where we add noise.
A reasonable range is [0.2, 80].
tensor_format (`str`): whether the scheduler expects pytorch or numpy arrays.
"""
@register_to_config
def __init__(
self,
sigma_min: float = 0.02,
sigma_max: float = 100,
s_noise: float = 1.007,
s_churn: float = 80,
s_min: float = 0.05,
s_max: float = 50,
tensor_format: str = "pt",
):
# setable values
self.num_inference_steps = None
self.timesteps = None
self.schedule = None # sigma(t_i)
self.tensor_format = tensor_format
self.set_format(tensor_format=tensor_format)
def set_timesteps(self, num_inference_steps: int):
"""
Sets the continuous timesteps used for the diffusion chain. Supporting function to be run before inference.
Args:
num_inference_steps (`int`):
the number of diffusion steps used when generating samples with a pre-trained model.
"""
self.num_inference_steps = num_inference_steps
self.timesteps = np.arange(0, self.num_inference_steps)[::-1].copy()
self.schedule = [
(self.sigma_max * (self.sigma_min**2 / self.sigma_max**2) ** (i / (num_inference_steps - 1)))
for i in self.timesteps
]
self.schedule = np.array(self.schedule, dtype=np.float32)
self.set_format(tensor_format=self.tensor_format)
def add_noise_to_input(
self, sample: Union[torch.FloatTensor, np.ndarray], sigma: float, generator: Optional[torch.Generator] = None
) -> Tuple[Union[torch.FloatTensor, np.ndarray], float]:
"""
Explicit Langevin-like "churn" step of adding noise to the sample according to a factor gamma_i ≥ 0 to reach a
higher noise level sigma_hat = sigma_i + gamma_i*sigma_i.
TODO Args:
"""
if self.s_min <= sigma <= self.s_max:
gamma = min(self.s_churn / self.num_inference_steps, 2**0.5 - 1)
else:
gamma = 0
# sample eps ~ N(0, S_noise^2 * I)
eps = self.s_noise * torch.randn(sample.shape, generator=generator).to(sample.device)
sigma_hat = sigma + gamma * sigma
sample_hat = sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)
return sample_hat, sigma_hat
def step(
self,
model_output: Union[torch.FloatTensor, np.ndarray],
sigma_hat: float,
sigma_prev: float,
sample_hat: Union[torch.FloatTensor, np.ndarray],
return_dict: bool = True,
) -> Union[KarrasVeOutput, Tuple]:
"""
Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion
process from the learned model outputs (most often the predicted noise).
Args:
model_output (`torch.FloatTensor` or `np.ndarray`): direct output from learned diffusion model.
sigma_hat (`float`): TODO
sigma_prev (`float`): TODO
sample_hat (`torch.FloatTensor` or `np.ndarray`): TODO
return_dict (`bool`): option for returning tuple rather than SchedulerOutput class
KarrasVeOutput: updated sample in the diffusion chain and derivative (TODO double check).
Returns:
[`~schedulers.scheduling_karras_ve.KarrasVeOutput`] or `tuple`:
[`~schedulers.scheduling_karras_ve.KarrasVeOutput`] if `return_dict` is True, otherwise a `tuple`. When
returning a tuple, the first element is the sample tensor.
"""
pred_original_sample = sample_hat + sigma_hat * model_output
derivative = (sample_hat - pred_original_sample) / sigma_hat
sample_prev = sample_hat + (sigma_prev - sigma_hat) * derivative
if not return_dict:
return (sample_prev, derivative)
return KarrasVeOutput(prev_sample=sample_prev, derivative=derivative)
def step_correct(
self,
model_output: Union[torch.FloatTensor, np.ndarray],
sigma_hat: float,
sigma_prev: float,
sample_hat: Union[torch.FloatTensor, np.ndarray],
sample_prev: Union[torch.FloatTensor, np.ndarray],
derivative: Union[torch.FloatTensor, np.ndarray],
return_dict: bool = True,
) -> Union[KarrasVeOutput, Tuple]:
"""
Correct the predicted sample based on the output model_output of the network. TODO complete description
Args:
model_output (`torch.FloatTensor` or `np.ndarray`): direct output from learned diffusion model.
sigma_hat (`float`): TODO
sigma_prev (`float`): TODO
sample_hat (`torch.FloatTensor` or `np.ndarray`): TODO
sample_prev (`torch.FloatTensor` or `np.ndarray`): TODO
derivative (`torch.FloatTensor` or `np.ndarray`): TODO
return_dict (`bool`): option for returning tuple rather than SchedulerOutput class
Returns:
prev_sample (TODO): updated sample in the diffusion chain. derivative (TODO): TODO
"""
pred_original_sample = sample_prev + sigma_prev * model_output
derivative_corr = (sample_prev - pred_original_sample) / sigma_prev
sample_prev = sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)
if not return_dict:
return (sample_prev, derivative)
return KarrasVeOutput(prev_sample=sample_prev, derivative=derivative)
def add_noise(self, original_samples, noise, timesteps):
raise NotImplementedError()
================================================
FILE: models/edict/my_diffusers/schedulers/scheduling_lms_discrete.py
================================================
# Copyright 2022 Katherine Crowson and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Optional, Tuple, Union
import numpy as np
import torch
from scipy import integrate
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class LMSDiscreteScheduler(SchedulerMixin, ConfigMixin):
"""
Linear Multistep Scheduler for discrete beta schedules. Based on the original k-diffusion implementation by
Katherine Crowson:
https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf5bd7a9cdee909e47/k_diffusion/sampling.py#L181
[`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__`
function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`.
[`~ConfigMixin`] also provides general loading and saving functionality via the [`~ConfigMixin.save_config`] and
[`~ConfigMixin.from_config`] functios.
Args:
num_train_timesteps (`int`): number of diffusion steps used to train the model.
beta_start (`float`): the starting `beta` value of inference.
beta_end (`float`): the final `beta` value.
beta_schedule (`str`):
the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from
`linear` or `scaled_linear`.
trained_betas (`np.ndarray`, optional): TODO
options to clip the variance used when adding noise to the denoised sample. Choose from `fixed_small`,
`fixed_small_log`, `fixed_large`, `fixed_large_log`, `learned` or `learned_range`.
timestep_values (`np.ndarry`, optional): TODO
tensor_format (`str`): whether the scheduler expects pytorch or numpy arrays.
"""
@register_to_config
def __init__(
self,
num_train_timesteps: int = 1000,
beta_start: float = 0.0001,
beta_end: float = 0.02,
beta_schedule: str = "linear",
trained_betas: Optional[np.ndarray] = None,
timestep_values: Optional[np.ndarray] = None,
tensor_format: str = "pt",
):
if trained_betas is not None:
self.betas = np.asarray(trained_betas)
if beta_schedule == "linear":
self.betas = np.linspace(beta_start, beta_end, num_train_timesteps, dtype=np.float32)
elif beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
self.betas = np.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=np.float32) ** 2
else:
raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}")
self.alphas = 1.0 - self.betas
self.alphas_cumprod = np.cumprod(self.alphas, axis=0)
self.sigmas = ((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5
# setable values
self.num_inference_steps = None
self.timesteps = np.arange(0, num_train_timesteps)[::-1].copy()
self.derivatives = []
self.tensor_format = tensor_format
self.set_format(tensor_format=tensor_format)
def get_lms_coefficient(self, order, t, current_order):
"""
Compute a linear multistep coefficient.
Args:
order (TODO):
t (TODO):
current_order (TODO):
"""
def lms_derivative(tau):
prod = 1.0
for k in range(order):
if current_order == k:
continue
prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
return prod
integrated_coeff = integrate.quad(lms_derivative, self.sigmas[t], self.sigmas[t + 1], epsrel=1e-4)[0]
return integrated_coeff
def set_timesteps(self, num_inference_steps: int):
"""
Sets the timesteps used for the diffusion chain. Supporting function to be run before inference.
Args:
num_inference_steps (`int`):
the number of diffusion steps used when generating samples with a pre-trained model.
"""
self.num_inference_steps = num_inference_steps
self.timesteps = np.linspace(self.num_train_timesteps - 1, 0, num_inference_steps, dtype=float)
low_idx = np.floor(self.timesteps).astype(int)
high_idx = np.ceil(self.timesteps).astype(int)
frac = np.mod(self.timesteps, 1.0)
sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
sigmas = (1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
self.sigmas = np.concatenate([sigmas, [0.0]])
self.derivatives = []
self.set_format(tensor_format=self.tensor_format)
def step(
self,
model_output: Union[torch.FloatTensor, np.ndarray],
timestep: int,
sample: Union[torch.FloatTensor, np.ndarray],
order: int = 4,
return_dict: bool = True,
) -> Union[SchedulerOutput, Tuple]:
"""
Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion
process from the learned model outputs (most often the predicted noise).
Args:
model_output (`torch.FloatTensor` or `np.ndarray`): direct output from learned diffusion model.
timestep (`int`): current discrete timestep in the diffusion chain.
sample (`torch.FloatTensor` or `np.ndarray`):
current instance of sample being created by diffusion process.
order: coefficient for multi-step inference.
return_dict (`bool`): option for returning tuple rather than SchedulerOutput class
Returns:
[`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`:
[`~schedulers.scheduling_utils.SchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When
returning a tuple, the first element is the sample tensor.
"""
sigma = self.sigmas[timestep]
# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
pred_original_sample = sample - sigma * model_output
# 2. Convert to an ODE derivative
derivative = (sample - pred_original_sample) / sigma
self.derivatives.append(derivative)
if len(self.derivatives) > order:
self.derivatives.pop(0)
# 3. Compute linear multistep coefficients
order = min(timestep + 1, order)
lms_coeffs = [self.get_lms_coefficient(order, timestep, curr_order) for curr_order in range(order)]
# 4. Compute previous sample based on the derivatives path
prev_sample = sample + sum(
coeff * derivative for coeff, derivative in zip(lms_coeffs, reversed(self.derivatives))
)
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=prev_sample)
def add_noise(
self,
original_samples: Union[torch.FloatTensor, np.ndarray],
noise: Union[torch.FloatTensor, np.ndarray],
timesteps: Union[torch.IntTensor, np.ndarray],
) -> Union[torch.FloatTensor, np.ndarray]:
sigmas = self.match_shape(self.sigmas[timesteps], noise)
noisy_samples = original_samples + noise * sigmas
return noisy_samples
def __len__(self):
return self.config.num_train_timesteps
================================================
FILE: models/edict/my_diffusers/schedulers/scheduling_pndm.py
================================================
# Copyright 2022 Zhejiang University Team and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
import math
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
def betas_for_alpha_bar(num_diffusion_timesteps, max_beta=0.999):
"""
Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
(1-beta) over time from t = [0,1].
Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up
to that part of the diffusion process.
Args:
num_diffusion_timesteps (`int`): the number of betas to produce.
max_beta (`float`): the maximum beta to use; use values lower than 1 to
prevent singularities.
Returns:
betas (`np.ndarray`): the betas used by the scheduler to step the model outputs
"""
def alpha_bar(time_step):
return math.cos((time_step + 0.008) / 1.008 * math.pi / 2) ** 2
betas = []
for i in range(num_diffusion_timesteps):
t1 = i / num_diffusion_timesteps
t2 = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
return np.array(betas, dtype=np.float32)
class PNDMScheduler(SchedulerMixin, ConfigMixin):
"""
Pseudo numerical methods for diffusion models (PNDM) proposes using more advanced ODE integration techniques,
namely Runge-Kutta method and a linear multi-step method.
[`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__`
function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`.
[`~ConfigMixin`] also provides general loading and saving functionality via the [`~ConfigMixin.save_config`] and
[`~ConfigMixin.from_config`] functios.
For more details, see the original paper: https://arxiv.org/abs/2202.09778
Args:
num_train_timesteps (`int`): number of diffusion steps used to train the model.
beta_start (`float`): the starting `beta` value of inference.
beta_end (`float`): the final `beta` value.
beta_schedule (`str`):
the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from
`linear`, `scaled_linear`, or `squaredcos_cap_v2`.
trained_betas (`np.ndarray`, optional): TODO
tensor_format (`str`): whether the scheduler expects pytorch or numpy arrays
skip_prk_steps (`bool`):
allows the scheduler to skip the Runge-Kutta steps that are defined in the original paper as being required
before plms steps; defaults to `False`.
"""
@register_to_config
def __init__(
self,
num_train_timesteps: int = 1000,
beta_start: float = 0.0001,
beta_end: float = 0.02,
beta_schedule: str = "linear",
trained_betas: Optional[np.ndarray] = None,
tensor_format: str = "pt",
skip_prk_steps: bool = False,
):
if trained_betas is not None:
self.betas = np.asarray(trained_betas)
if beta_schedule == "linear":
self.betas = np.linspace(beta_start, beta_end, num_train_timesteps, dtype=np.float32)
elif beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
self.betas = np.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=np.float32) ** 2
elif beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
self.betas = betas_for_alpha_bar(num_train_timesteps)
else:
raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}")
self.alphas = 1.0 - self.betas
self.alphas_cumprod = np.cumprod(self.alphas, axis=0)
self.one = np.array(1.0)
# For now we only support F-PNDM, i.e. the runge-kutta method
# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
# mainly at formula (9), (12), (13) and the Algorithm 2.
self.pndm_order = 4
# running values
self.cur_model_output = 0
self.counter = 0
self.cur_sample = None
self.ets = []
# setable values
self.num_inference_steps = None
self._timesteps = np.arange(0, num_train_timesteps)[::-1].copy()
self._offset = 0
self.prk_timesteps = None
self.plms_timesteps = None
self.timesteps = None
self.tensor_format = tensor_format
self.set_format(tensor_format=tensor_format)
def set_timesteps(self, num_inference_steps: int, offset: int = 0) -> torch.FloatTensor:
"""
Sets the discrete timesteps used for the diffusion chain. Supporting function to be run before inference.
Args:
num_inference_steps (`int`):
the number of diffusion steps used when generating samples with a pre-trained model.
offset (`int`): TODO
"""
self.num_inference_steps = num_inference_steps
self._timesteps = list(
range(0, self.config.num_train_timesteps, self.config.num_train_timesteps // num_inference_steps)
)
self._offset = offset
self._timesteps = np.array([t + self._offset for t in self._timesteps])
if self.config.skip_prk_steps:
# for some models like stable diffusion the prk steps can/should be skipped to
# produce better results. When using PNDM with `self.config.skip_prk_steps` the implementation
# is based on crowsonkb's PLMS sampler implementation: https://github.com/CompVis/latent-diffusion/pull/51
self.prk_timesteps = np.array([])
self.plms_timesteps = np.concatenate([self._timesteps[:-1], self._timesteps[-2:-1], self._timesteps[-1:]])[
::-1
].copy()
else:
prk_timesteps = np.array(self._timesteps[-self.pndm_order :]).repeat(2) + np.tile(
np.array([0, self.config.num_train_timesteps // num_inference_steps // 2]), self.pndm_order
)
self.prk_timesteps = (prk_timesteps[:-1].repeat(2)[1:-1])[::-1].copy()
self.plms_timesteps = self._timesteps[:-3][
::-1
].copy() # we copy to avoid having negative strides which are not supported by torch.from_numpy
self.timesteps = np.concatenate([self.prk_timesteps, self.plms_timesteps]).astype(np.int64)
self.ets = []
self.counter = 0
self.set_format(tensor_format=self.tensor_format)
def step(
self,
model_output: Union[torch.FloatTensor, np.ndarray],
timestep: int,
sample: Union[torch.FloatTensor, np.ndarray],
return_dict: bool = True,
) -> Union[SchedulerOutput, Tuple]:
"""
Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion
process from the learned model outputs (most often the predicted noise).
This function calls `step_prk()` or `step_plms()` depending on the internal variable `counter`.
Args:
model_output (`torch.FloatTensor` or `np.ndarray`): direct output from learned diffusion model.
timestep (`int`): current discrete timestep in the diffusion chain.
sample (`torch.FloatTensor` or `np.ndarray`):
current instance of sample being created by diffusion process.
return_dict (`bool`): option for returning tuple rather than SchedulerOutput class
Returns:
[`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`:
[`~schedulers.scheduling_utils.SchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When
returning a tuple, the first element is the sample tensor.
"""
if self.counter < len(self.prk_timesteps) and not self.config.skip_prk_steps:
return self.step_prk(model_output=model_output, timestep=timestep, sample=sample, return_dict=return_dict)
else:
return self.step_plms(model_output=model_output, timestep=timestep, sample=sample, return_dict=return_dict)
def step_prk(
self,
model_output: Union[torch.FloatTensor, np.ndarray],
timestep: int,
sample: Union[torch.FloatTensor, np.ndarray],
return_dict: bool = True,
) -> Union[SchedulerOutput, Tuple]:
"""
Step function propagating the sample with the Runge-Kutta method. RK takes 4 forward passes to approximate the
solution to the differential equation.
Args:
model_output (`torch.FloatTensor` or `np.ndarray`): direct output from learned diffusion model.
timestep (`int`): current discrete timestep in the diffusion chain.
sample (`torch.FloatTensor` or `np.ndarray`):
current instance of sample being created by diffusion process.
return_dict (`bool`): option for returning tuple rather than SchedulerOutput class
Returns:
[`~scheduling_utils.SchedulerOutput`] or `tuple`: [`~scheduling_utils.SchedulerOutput`] if `return_dict` is
True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor.
"""
if self.num_inference_steps is None:
raise ValueError(
"Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"
)
diff_to_prev = 0 if self.counter % 2 else self.config.num_train_timesteps // self.num_inference_steps // 2
prev_timestep = max(timestep - diff_to_prev, self.prk_timesteps[-1])
timestep = self.prk_timesteps[self.counter // 4 * 4]
if self.counter % 4 == 0:
self.cur_model_output += 1 / 6 * model_output
self.ets.append(model_output)
self.cur_sample = sample
elif (self.counter - 1) % 4 == 0:
self.cur_model_output += 1 / 3 * model_output
elif (self.counter - 2) % 4 == 0:
self.cur_model_output += 1 / 3 * model_output
elif (self.counter - 3) % 4 == 0:
model_output = self.cur_model_output + 1 / 6 * model_output
self.cur_model_output = 0
# cur_sample should not be `None`
cur_sample = self.cur_sample if self.cur_sample is not None else sample
prev_sample = self._get_prev_sample(cur_sample, timestep, prev_timestep, model_output)
self.counter += 1
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=prev_sample)
def step_plms(
self,
model_output: Union[torch.FloatTensor, np.ndarray],
timestep: int,
sample: Union[torch.FloatTensor, np.ndarray],
return_dict: bool = True,
) -> Union[SchedulerOutput, Tuple]:
"""
Step function propagating the sample with the linear multi-step method. This has one forward pass with multiple
times to approximate the solution.
Args:
model_output (`torch.FloatTensor` or `np.ndarray`): direct output from learned diffusion model.
timestep (`int`): current discrete timestep in the diffusion chain.
sample (`torch.FloatTensor` or `np.ndarray`):
current instance of sample being created by diffusion process.
return_dict (`bool`): option for returning tuple rather than SchedulerOutput class
Returns:
[`~scheduling_utils.SchedulerOutput`] or `tuple`: [`~scheduling_utils.SchedulerOutput`] if `return_dict` is
True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor.
"""
if self.num_inference_steps is None:
raise ValueError(
"Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"
)
if not self.config.skip_prk_steps and len(self.ets) < 3:
raise ValueError(
f"{self.__class__} can only be run AFTER scheduler has been run "
"in 'prk' mode for at least 12 iterations "
"See: https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/pipeline_pndm.py "
"for more information."
)
prev_timestep = max(timestep - self.config.num_train_timesteps // self.num_inference_steps, 0)
if self.counter != 1:
self.ets.append(model_output)
else:
prev_timestep = timestep
timestep = timestep + self.config.num_train_timesteps // self.num_inference_steps
if len(self.ets) == 1 and self.counter == 0:
model_output = model_output
self.cur_sample = sample
elif len(self.ets) == 1 and self.counter == 1:
model_output = (model_output + self.ets[-1]) / 2
sample = self.cur_sample
self.cur_sample = None
elif len(self.ets) == 2:
model_output = (3 * self.ets[-1] - self.ets[-2]) / 2
elif len(self.ets) == 3:
model_output = (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
else:
model_output = (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])
prev_sample = self._get_prev_sample(sample, timestep, prev_timestep, model_output)
self.counter += 1
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=prev_sample)
def _get_prev_sample(self, sample, timestep, timestep_prev, model_output):
# See formula (9) of PNDM paper https://arxiv.org/pdf/2202.09778.pdf
# this function computes x_(t−δ) using the formula of (9)
# Note that x_t needs to be added to both sides of the equation
# Notation ( ->
# alpha_prod_t -> α_t
# alpha_prod_t_prev -> α_(t−δ)
# beta_prod_t -> (1 - α_t)
# beta_prod_t_prev -> (1 - α_(t−δ))
# sample -> x_t
# model_output -> e_θ(x_t, t)
# prev_sample -> x_(t−δ)
alpha_prod_t = self.alphas_cumprod[timestep + 1 - self._offset]
alpha_prod_t_prev = self.alphas_cumprod[timestep_prev + 1 - self._offset]
beta_prod_t = 1 - alpha_prod_t
beta_prod_t_prev = 1 - alpha_prod_t_prev
# corresponds to (α_(t−δ) - α_t) divided by
# denominator of x_t in formula (9) and plus 1
# Note: (α_(t−δ) - α_t) / (sqrt(α_t) * (sqrt(α_(t−δ)) + sqr(α_t))) =
# sqrt(α_(t−δ)) / sqrt(α_t))
sample_coeff = (alpha_prod_t_prev / alpha_prod_t) ** (0.5)
# corresponds to denominator of e_θ(x_t, t) in formula (9)
model_output_denom_coeff = alpha_prod_t * beta_prod_t_prev ** (0.5) + (
alpha_prod_t * beta_prod_t * alpha_prod_t_prev
) ** (0.5)
# full formula (9)
prev_sample = (
sample_coeff * sample - (alpha_prod_t_prev - alpha_prod_t) * model_output / model_output_denom_coeff
)
return prev_sample
def add_noise(
self,
original_samples: Union[torch.FloatTensor, np.ndarray],
noise: Union[torch.FloatTensor, np.ndarray],
timesteps: Union[torch.IntTensor, np.ndarray],
) -> torch.Tensor:
# mps requires indices to be in the same device, so we use cpu as is the default with cuda
timesteps = timesteps.to(self.alphas_cumprod.device)
sqrt_alpha_prod = self.alphas_cumprod[timesteps] ** 0.5
sqrt_alpha_prod = self.match_shape(sqrt_alpha_prod, original_samples)
sqrt_one_minus_alpha_prod = (1 - self.alphas_cumprod[timesteps]) ** 0.5
sqrt_one_minus_alpha_prod = self.match_shape(sqrt_one_minus_alpha_prod, original_samples)
noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
return noisy_samples
def __len__(self):
return self.config.num_train_timesteps
================================================
FILE: models/edict/my_diffusers/schedulers/scheduling_sde_ve.py
================================================
# Copyright 2022 Google Brain and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import warnings
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils import SchedulerMixin, SchedulerOutput
@dataclass
class SdeVeOutput(BaseOutput):
"""
Output class for the ScoreSdeVeScheduler's step function output.
Args:
prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the
denoising loop.
prev_sample_mean (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
Mean averaged `prev_sample`. Same as `prev_sample`, only mean-averaged over previous timesteps.
"""
prev_sample: torch.FloatTensor
prev_sample_mean: torch.FloatTensor
class ScoreSdeVeScheduler(SchedulerMixin, ConfigMixin):
"""
The variance exploding stochastic differential equation (SDE) scheduler.
For more information, see the original paper: https://arxiv.org/abs/2011.13456
[`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__`
function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`.
[`~ConfigMixin`] also provides general loading and saving functionality via the [`~ConfigMixin.save_config`] and
[`~ConfigMixin.from_config`] functios.
Args:
snr (`float`):
coefficient weighting the step from the model_output sample (from the network) to the random noise.
sigma_min (`float`):
initial noise scale for sigma sequence in sampling procedure. The minimum sigma should mirror the
distribution of the data.
sigma_max (`float`): maximum value used for the range of continuous timesteps passed into the model.
sampling_eps (`float`): the end value of sampling, where timesteps decrease progessively from 1 to
epsilon.
correct_steps (`int`): number of correction steps performed on a produced sample.
tensor_format (`str`): "np" or "pt" for the expected format of samples passed to the Scheduler.
"""
@register_to_config
def __init__(
self,
num_train_timesteps: int = 2000,
snr: float = 0.15,
sigma_min: float = 0.01,
sigma_max: float = 1348.0,
sampling_eps: float = 1e-5,
correct_steps: int = 1,
tensor_format: str = "pt",
):
# setable values
self.timesteps = None
self.set_sigmas(num_train_timesteps, sigma_min, sigma_max, sampling_eps)
self.tensor_format = tensor_format
self.set_format(tensor_format=tensor_format)
def set_timesteps(self, num_inference_steps: int, sampling_eps: float = None):
"""
Sets the continuous timesteps used for the diffusion chain. Supporting function to be run before inference.
Args:
num_inference_steps (`int`):
the number of diffusion steps used when generating samples with a pre-trained model.
sampling_eps (`float`, optional): final timestep value (overrides value given at Scheduler instantiation).
"""
sampling_eps = sampling_eps if sampling_eps is not None else self.config.sampling_eps
tensor_format = getattr(self, "tensor_format", "pt")
if tensor_format == "np":
self.timesteps = np.linspace(1, sampling_eps, num_inference_steps)
elif tensor_format == "pt":
self.timesteps = torch.linspace(1, sampling_eps, num_inference_steps)
else:
raise ValueError(f"`self.tensor_format`: {self.tensor_format} is not valid.")
def set_sigmas(
self, num_inference_steps: int, sigma_min: float = None, sigma_max: float = None, sampling_eps: float = None
):
"""
Sets the noise scales used for the diffusion chain. Supporting function to be run before inference.
The sigmas control the weight of the `drift` and `diffusion` components of sample update.
Args:
num_inference_steps (`int`):
the number of diffusion steps used when generating samples with a pre-trained model.
sigma_min (`float`, optional):
initial noise scale value (overrides value given at Scheduler instantiation).
sigma_max (`float`, optional): final noise scale value (overrides value given at Scheduler instantiation).
sampling_eps (`float`, optional): final timestep value (overrides value given at Scheduler instantiation).
"""
sigma_min = sigma_min if sigma_min is not None else self.config.sigma_min
sigma_max = sigma_max if sigma_max is not None else self.config.sigma_max
sampling_eps = sampling_eps if sampling_eps is not None else self.config.sampling_eps
if self.timesteps is None:
self.set_timesteps(num_inference_steps, sampling_eps)
tensor_format = getattr(self, "tensor_format", "pt")
if tensor_format == "np":
self.discrete_sigmas = np.exp(np.linspace(np.log(sigma_min), np.log(sigma_max), num_inference_steps))
self.sigmas = np.array([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps])
elif tensor_format == "pt":
self.discrete_sigmas = torch.exp(torch.linspace(np.log(sigma_min), np.log(sigma_max), num_inference_steps))
self.sigmas = torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps])
else:
raise ValueError(f"`self.tensor_format`: {self.tensor_format} is not valid.")
def get_adjacent_sigma(self, timesteps, t):
tensor_format = getattr(self, "tensor_format", "pt")
if tensor_format == "np":
return np.where(timesteps == 0, np.zeros_like(t), self.discrete_sigmas[timesteps - 1])
elif tensor_format == "pt":
return torch.where(
timesteps == 0,
torch.zeros_like(t.to(timesteps.device)),
self.discrete_sigmas[timesteps - 1].to(timesteps.device),
)
raise ValueError(f"`self.tensor_format`: {self.tensor_format} is not valid.")
def set_seed(self, seed):
warnings.warn(
"The method `set_seed` is deprecated and will be removed in version `0.4.0`. Please consider passing a"
" generator instead.",
DeprecationWarning,
)
tensor_format = getattr(self, "tensor_format", "pt")
if tensor_format == "np":
np.random.seed(seed)
elif tensor_format == "pt":
torch.manual_seed(seed)
else:
raise ValueError(f"`self.tensor_format`: {self.tensor_format} is not valid.")
def step_pred(
self,
model_output: Union[torch.FloatTensor, np.ndarray],
timestep: int,
sample: Union[torch.FloatTensor, np.ndarray],
generator: Optional[torch.Generator] = None,
return_dict: bool = True,
**kwargs,
) -> Union[SdeVeOutput, Tuple]:
"""
Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion
process from the learned model outputs (most often the predicted noise).
Args:
model_output (`torch.FloatTensor` or `np.ndarray`): direct output from learned diffusion model.
timestep (`int`): current discrete timestep in the diffusion chain.
sample (`torch.FloatTensor` or `np.ndarray`):
current instance of sample being created by diffusion process.
generator: random number generator.
return_dict (`bool`): option for returning tuple rather than SchedulerOutput class
Returns:
[`~schedulers.scheduling_sde_ve.SdeVeOutput`] or `tuple`: [`~schedulers.scheduling_sde_ve.SdeVeOutput`] if
`return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor.
"""
if "seed" in kwargs and kwargs["seed"] is not None:
self.set_seed(kwargs["seed"])
if self.timesteps is None:
raise ValueError(
"`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"
)
timestep = timestep * torch.ones(
sample.shape[0], device=sample.device
) # torch.repeat_interleave(timestep, sample.shape[0])
timesteps = (timestep * (len(self.timesteps) - 1)).long()
# mps requires indices to be in the same device, so we use cpu as is the default with cuda
timesteps = timesteps.to(self.discrete_sigmas.device)
sigma = self.discrete_sigmas[timesteps].to(sample.device)
adjacent_sigma = self.get_adjacent_sigma(timesteps, timestep).to(sample.device)
drift = self.zeros_like(sample)
diffusion = (sigma**2 - adjacent_sigma**2) ** 0.5
# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
# also equation 47 shows the analog from SDE models to ancestral sampling methods
drift = drift - diffusion[:, None, None, None] ** 2 * model_output
# equation 6: sample noise for the diffusion term of
noise = self.randn_like(sample, generator=generator)
prev_sample_mean = sample - drift # subtract because `dt` is a small negative timestep
# TODO is the variable diffusion the correct scaling term for the noise?
prev_sample = prev_sample_mean + diffusion[:, None, None, None] * noise # add impact of diffusion field g
if not return_dict:
return (prev_sample, prev_sample_mean)
return SdeVeOutput(prev_sample=prev_sample, prev_sample_mean=prev_sample_mean)
def step_correct(
self,
model_output: Union[torch.FloatTensor, np.ndarray],
sample: Union[torch.FloatTensor, np.ndarray],
generator: Optional[torch.Generator] = None,
return_dict: bool = True,
**kwargs,
) -> Union[SchedulerOutput, Tuple]:
"""
Correct the predicted sample based on the output model_output of the network. This is often run repeatedly
after making the prediction for the previous timestep.
Args:
model_output (`torch.FloatTensor` or `np.ndarray`): direct output from learned diffusion model.
sample (`torch.FloatTensor` or `np.ndarray`):
current instance of sample being created by diffusion process.
generator: random number generator.
return_dict (`bool`): option for returning tuple rather than SchedulerOutput class
Returns:
[`~schedulers.scheduling_sde_ve.SdeVeOutput`] or `tuple`: [`~schedulers.scheduling_sde_ve.SdeVeOutput`] if
`return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor.
"""
if "seed" in kwargs and kwargs["seed"] is not None:
self.set_seed(kwargs["seed"])
if self.timesteps is None:
raise ValueError(
"`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"
)
# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
# sample noise for correction
noise = self.randn_like(sample, generator=generator)
# compute step size from the model_output, the noise, and the snr
grad_norm = self.norm(model_output)
noise_norm = self.norm(noise)
step_size = (self.config.snr * noise_norm / grad_norm) ** 2 * 2
step_size = step_size * torch.ones(sample.shape[0]).to(sample.device)
# self.repeat_scalar(step_size, sample.shape[0])
# compute corrected sample: model_output term and noise term
prev_sample_mean = sample + step_size[:, None, None, None] * model_output
prev_sample = prev_sample_mean + ((step_size * 2) ** 0.5)[:, None, None, None] * noise
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=prev_sample)
def __len__(self):
return self.config.num_train_timesteps
================================================
FILE: models/edict/my_diffusers/schedulers/scheduling_sde_vp.py
================================================
# Copyright 2022 Google Brain and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
# TODO(Patrick, Anton, Suraj) - make scheduler framework indepedent and clean-up a bit
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin
class ScoreSdeVpScheduler(SchedulerMixin, ConfigMixin):
"""
The variance preserving stochastic differential equation (SDE) scheduler.
[`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__`
function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`.
[`~ConfigMixin`] also provides general loading and saving functionality via the [`~ConfigMixin.save_config`] and
[`~ConfigMixin.from_config`] functios.
For more information, see the original paper: https://arxiv.org/abs/2011.13456
UNDER CONSTRUCTION
"""
@register_to_config
def __init__(self, num_train_timesteps=2000, beta_min=0.1, beta_max=20, sampling_eps=1e-3, tensor_format="np"):
self.sigmas = None
self.discrete_sigmas = None
self.timesteps = None
def set_timesteps(self, num_inference_steps):
self.timesteps = torch.linspace(1, self.config.sampling_eps, num_inference_steps)
def step_pred(self, score, x, t):
if self.timesteps is None:
raise ValueError(
"`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"
)
# TODO(Patrick) better comments + non-PyTorch
# postprocess model score
log_mean_coeff = (
-0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
)
std = torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff))
score = -score / std[:, None, None, None]
# compute
dt = -1.0 / len(self.timesteps)
beta_t = self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
drift = -0.5 * beta_t[:, None, None, None] * x
diffusion = torch.sqrt(beta_t)
drift = drift - diffusion[:, None, None, None] ** 2 * score
x_mean = x + drift * dt
# add noise
noise = torch.randn_like(x)
x = x_mean + diffusion[:, None, None, None] * np.sqrt(-dt) * noise
return x, x_mean
def __len__(self):
return self.config.num_train_timesteps
================================================
FILE: models/edict/my_diffusers/schedulers/scheduling_utils.py
================================================
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from dataclasses import dataclass
from typing import Union
import numpy as np
import torch
from ..utils import BaseOutput
SCHEDULER_CONFIG_NAME = "scheduler_config.json"
@dataclass
class SchedulerOutput(BaseOutput):
"""
Base class for the scheduler's step function output.
Args:
prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the
denoising loop.
"""
prev_sample: torch.FloatTensor
class SchedulerMixin:
"""
Mixin containing common functions for the schedulers.
"""
config_name = SCHEDULER_CONFIG_NAME
ignore_for_config = ["tensor_format"]
def set_format(self, tensor_format="pt"):
self.tensor_format = tensor_format
if tensor_format == "pt":
for key, value in vars(self).items():
if isinstance(value, np.ndarray):
setattr(self, key, torch.from_numpy(value))
return self
def clip(self, tensor, min_value=None, max_value=None):
tensor_format = getattr(self, "tensor_format", "pt")
if tensor_format == "np":
return np.clip(tensor, min_value, max_value)
elif tensor_format == "pt":
return torch.clamp(tensor, min_value, max_value)
raise ValueError(f"`self.tensor_format`: {self.tensor_format} is not valid.")
def log(self, tensor):
tensor_format = getattr(self, "tensor_format", "pt")
if tensor_format == "np":
return np.log(tensor)
elif tensor_format == "pt":
return torch.log(tensor)
raise ValueError(f"`self.tensor_format`: {self.tensor_format} is not valid.")
def match_shape(self, values: Union[np.ndarray, torch.Tensor], broadcast_array: Union[np.ndarray, torch.Tensor]):
"""
Turns a 1-D array into an array or tensor with len(broadcast_array.shape) dims.
Args:
values: an array or tensor of values to extract.
broadcast_array: an array with a larger shape of K dimensions with the batch
dimension equal to the length of timesteps.
Returns:
a tensor of shape [batch_size, 1, ...] where the shape has K dims.
"""
tensor_format = getattr(self, "tensor_format", "pt")
values = values.flatten()
while len(values.shape) < len(broadcast_array.shape):
values = values[..., None]
if tensor_format == "pt":
values = values.to(broadcast_array.device)
return values
def norm(self, tensor):
tensor_format = getattr(self, "tensor_format", "pt")
if tensor_format == "np":
return np.linalg.norm(tensor)
elif tensor_format == "pt":
return torch.norm(tensor.reshape(tensor.shape[0], -1), dim=-1).mean()
raise ValueError(f"`self.tensor_format`: {self.tensor_format} is not valid.")
def randn_like(self, tensor, generator=None):
tensor_format = getattr(self, "tensor_format", "pt")
if tensor_format == "np":
return np.random.randn(*np.shape(tensor))
elif tensor_format == "pt":
# return torch.randn_like(tensor)
return torch.randn(tensor.shape, layout=tensor.layout, generator=generator).to(tensor.device)
raise ValueError(f"`self.tensor_format`: {self.tensor_format} is not valid.")
def zeros_like(self, tensor):
tensor_format = getattr(self, "tensor_format", "pt")
if tensor_format == "np":
return np.zeros_like(tensor)
elif tensor_format == "pt":
return torch.zeros_like(tensor)
raise ValueError(f"`self.tensor_format`: {self.tensor_format} is not valid.")
================================================
FILE: models/edict/my_diffusers/testing_utils.py
================================================
import os
import random
import unittest
from distutils.util import strtobool
import torch
from packaging import version
global_rng = random.Random()
torch_device = "cuda" if torch.cuda.is_available() else "cpu"
is_torch_higher_equal_than_1_12 = version.parse(version.parse(torch.__version__).base_version) >= version.parse("1.12")
if is_torch_higher_equal_than_1_12:
torch_device = "mps" if torch.backends.mps.is_available() else torch_device
def parse_flag_from_env(key, default=False):
try:
value = os.environ[key]
except KeyError:
# KEY isn't set, default to `default`.
_value = default
else:
# KEY is set, convert it to True or False.
try:
_value = strtobool(value)
except ValueError:
# More values are supported, but let's keep the message simple.
raise ValueError(f"If set, {key} must be yes or no.")
return _value
_run_slow_tests = parse_flag_from_env("RUN_SLOW", default=False)
def floats_tensor(shape, scale=1.0, rng=None, name=None):
"""Creates a random float32 tensor"""
if rng is None:
rng = global_rng
total_dims = 1
for dim in shape:
total_dims *= dim
values = []
for _ in range(total_dims):
values.append(rng.random() * scale)
return torch.tensor(data=values, dtype=torch.float).view(shape).contiguous()
def slow(test_case):
"""
Decorator marking a test as slow.
Slow tests are skipped by default. Set the RUN_SLOW environment variable to a truthy value to run them.
"""
return unittest.skipUnless(_run_slow_tests, "test is slow")(test_case)
================================================
FILE: models/edict/my_diffusers/training_utils.py
================================================
import copy
import os
import random
import numpy as np
import torch
def enable_full_determinism(seed: int):
"""
Helper function for reproducible behavior during distributed training. See
- https://pytorch.org/docs/stable/notes/randomness.html for pytorch
"""
# set seed first
set_seed(seed)
# Enable PyTorch deterministic mode. This potentially requires either the environment
# variable 'CUDA_LAUNCH_BLOCKING' or 'CUBLAS_WORKSPACE_CONFIG' to be set,
# depending on the CUDA version, so we set them both here
os.environ["CUDA_LAUNCH_BLOCKING"] = "1"
os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":16:8"
torch.use_deterministic_algorithms(True)
# Enable CUDNN deterministic mode
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
def set_seed(seed: int):
"""
Args:
Helper function for reproducible behavior to set the seed in `random`, `numpy`, `torch`.
seed (`int`): The seed to set.
"""
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
# ^^ safe to call this function even if cuda is not available
class EMAModel:
"""
Exponential Moving Average of models weights
"""
def __init__(
self,
model,
update_after_step=0,
inv_gamma=1.0,
power=2 / 3,
min_value=0.0,
max_value=0.9999,
device=None,
):
"""
@crowsonkb's notes on EMA Warmup:
If gamma=1 and power=1, implements a simple average. gamma=1, power=2/3 are good values for models you plan
to train for a million or more steps (reaches decay factor 0.999 at 31.6K steps, 0.9999 at 1M steps),
gamma=1, power=3/4 for models you plan to train for less (reaches decay factor 0.999 at 10K steps, 0.9999
at 215.4k steps).
Args:
inv_gamma (float): Inverse multiplicative factor of EMA warmup. Default: 1.
power (float): Exponential factor of EMA warmup. Default: 2/3.
min_value (float): The minimum EMA decay rate. Default: 0.
"""
self.averaged_model = copy.deepcopy(model).eval()
self.averaged_model.requires_grad_(False)
self.update_after_step = update_after_step
self.inv_gamma = inv_gamma
self.power = power
self.min_value = min_value
self.max_value = max_value
if device is not None:
self.averaged_model = self.averaged_model.to(device=device)
self.decay = 0.0
self.optimization_step = 0
def get_decay(self, optimization_step):
"""
Compute the decay factor for the exponential moving average.
"""
step = max(0, optimization_step - self.update_after_step - 1)
value = 1 - (1 + step / self.inv_gamma) ** -self.power
if step <= 0:
return 0.0
return max(self.min_value, min(value, self.max_value))
@torch.no_grad()
def step(self, new_model):
ema_state_dict = {}
ema_params = self.averaged_model.state_dict()
self.decay = self.get_decay(self.optimization_step)
for key, param in new_model.named_parameters():
if isinstance(param, dict):
continue
try:
ema_param = ema_params[key]
except KeyError:
ema_param = param.float().clone() if param.ndim == 1 else copy.deepcopy(param)
ema_params[key] = ema_param
if not param.requires_grad:
ema_params[key].copy_(param.to(dtype=ema_param.dtype).data)
ema_param = ema_params[key]
else:
ema_param.mul_(self.decay)
ema_param.add_(param.data.to(dtype=ema_param.dtype), alpha=1 - self.decay)
ema_state_dict[key] = ema_param
for key, param in new_model.named_buffers():
ema_state_dict[key] = param
self.averaged_model.load_state_dict(ema_state_dict, strict=False)
self.optimization_step += 1
================================================
FILE: models/edict/my_diffusers/utils/__init__.py
================================================
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from .import_utils import (
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VARS_TRUE_VALUES,
USE_JAX,
USE_TF,
USE_TORCH,
DummyObject,
is_flax_available,
is_inflect_available,
is_modelcards_available,
is_onnx_available,
is_scipy_available,
is_tf_available,
is_torch_available,
is_transformers_available,
is_unidecode_available,
requires_backends,
)
from .logging import get_logger
from .outputs import BaseOutput
logger = get_logger(__name__)
hf_cache_home = os.path.expanduser(
os.getenv("HF_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "huggingface"))
)
default_cache_path = os.path.join(hf_cache_home, "diffusers")
CONFIG_NAME = "config.json"
HUGGINGFACE_CO_RESOLVE_ENDPOINT = "https://huggingface.co"
DIFFUSERS_CACHE = default_cache_path
DIFFUSERS_DYNAMIC_MODULE_NAME = "diffusers_modules"
HF_MODULES_CACHE = os.getenv("HF_MODULES_CACHE", os.path.join(hf_cache_home, "modules"))
================================================
FILE: models/edict/my_diffusers/utils/dummy_scipy_objects.py
================================================
# This file is autogenerated by the command `make fix-copies`, do not edit.
# flake8: noqa
from ..utils import DummyObject, requires_backends
class LMSDiscreteScheduler(metaclass=DummyObject):
_backends = ["scipy"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["scipy"])
================================================
FILE: models/edict/my_diffusers/utils/dummy_transformers_and_inflect_and_unidecode_objects.py
================================================
# This file is autogenerated by the command `make fix-copies`, do not edit.
# flake8: noqa
from ..utils import DummyObject, requires_backends
class GradTTSPipeline(metaclass=DummyObject):
_backends = ["transformers", "inflect", "unidecode"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["transformers", "inflect", "unidecode"])
================================================
FILE: models/edict/my_diffusers/utils/dummy_transformers_and_onnx_objects.py
================================================
# This file is autogenerated by the command `make fix-copies`, do not edit.
# flake8: noqa
from ..utils import DummyObject, requires_backends
class StableDiffusionOnnxPipeline(metaclass=DummyObject):
_backends = ["transformers", "onnx"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["transformers", "onnx"])
================================================
FILE: models/edict/my_diffusers/utils/dummy_transformers_objects.py
================================================
# This file is autogenerated by the command `make fix-copies`, do not edit.
# flake8: noqa
from ..utils import DummyObject, requires_backends
class LDMTextToImagePipeline(metaclass=DummyObject):
_backends = ["transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["transformers"])
class StableDiffusionImg2ImgPipeline(metaclass=DummyObject):
_backends = ["transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["transformers"])
class StableDiffusionInpaintPipeline(metaclass=DummyObject):
_backends = ["transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["transformers"])
class StableDiffusionPipeline(metaclass=DummyObject):
_backends = ["transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["transformers"])
================================================
FILE: models/edict/my_diffusers/utils/import_utils.py
================================================
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Import utilities: Utilities related to imports and our lazy inits.
"""
import importlib.util
import os
import sys
from collections import OrderedDict
from packaging import version
from . import logging
# The package importlib_metadata is in a different place, depending on the python version.
if sys.version_info < (3, 8):
import importlib_metadata
else:
import importlib.metadata as importlib_metadata
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
ENV_VARS_TRUE_VALUES = {"1", "ON", "YES", "TRUE"}
ENV_VARS_TRUE_AND_AUTO_VALUES = ENV_VARS_TRUE_VALUES.union({"AUTO"})
USE_TF = os.environ.get("USE_TF", "AUTO").upper()
USE_TORCH = os.environ.get("USE_TORCH", "AUTO").upper()
USE_JAX = os.environ.get("USE_FLAX", "AUTO").upper()
_torch_version = "N/A"
if USE_TORCH in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TF not in ENV_VARS_TRUE_VALUES:
_torch_available = importlib.util.find_spec("torch") is not None
if _torch_available:
try:
_torch_version = importlib_metadata.version("torch")
logger.info(f"PyTorch version {_torch_version} available.")
except importlib_metadata.PackageNotFoundError:
_torch_available = False
else:
logger.info("Disabling PyTorch because USE_TF is set")
_torch_available = False
_tf_version = "N/A"
if USE_TF in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TORCH not in ENV_VARS_TRUE_VALUES:
_tf_available = importlib.util.find_spec("tensorflow") is not None
if _tf_available:
candidates = (
"tensorflow",
"tensorflow-cpu",
"tensorflow-gpu",
"tf-nightly",
"tf-nightly-cpu",
"tf-nightly-gpu",
"intel-tensorflow",
"intel-tensorflow-avx512",
"tensorflow-rocm",
"tensorflow-macos",
"tensorflow-aarch64",
)
_tf_version = None
# For the metadata, we have to look for both tensorflow and tensorflow-cpu
for pkg in candidates:
try:
_tf_version = importlib_metadata.version(pkg)
break
except importlib_metadata.PackageNotFoundError:
pass
_tf_available = _tf_version is not None
if _tf_available:
if version.parse(_tf_version) < version.parse("2"):
logger.info(f"TensorFlow found but with version {_tf_version}. Diffusers requires version 2 minimum.")
_tf_available = False
else:
logger.info(f"TensorFlow version {_tf_version} available.")
else:
logger.info("Disabling Tensorflow because USE_TORCH is set")
_tf_available = False
if USE_JAX in ENV_VARS_TRUE_AND_AUTO_VALUES:
_flax_available = importlib.util.find_spec("jax") is not None and importlib.util.find_spec("flax") is not None
if _flax_available:
try:
_jax_version = importlib_metadata.version("jax")
_flax_version = importlib_metadata.version("flax")
logger.info(f"JAX version {_jax_version}, Flax version {_flax_version} available.")
except importlib_metadata.PackageNotFoundError:
_flax_available = False
else:
_flax_available = False
_transformers_available = importlib.util.find_spec("transformers") is not None
try:
_transformers_version = importlib_metadata.version("transformers")
logger.debug(f"Successfully imported transformers version {_transformers_version}")
except importlib_metadata.PackageNotFoundError:
_transformers_available = False
_inflect_available = importlib.util.find_spec("inflect") is not None
try:
_inflect_version = importlib_metadata.version("inflect")
logger.debug(f"Successfully imported inflect version {_inflect_version}")
except importlib_metadata.PackageNotFoundError:
_inflect_available = False
_unidecode_available = importlib.util.find_spec("unidecode") is not None
try:
_unidecode_version = importlib_metadata.version("unidecode")
logger.debug(f"Successfully imported unidecode version {_unidecode_version}")
except importlib_metadata.PackageNotFoundError:
_unidecode_available = False
_modelcards_available = importlib.util.find_spec("modelcards") is not None
try:
_modelcards_version = importlib_metadata.version("modelcards")
logger.debug(f"Successfully imported modelcards version {_modelcards_version}")
except importlib_metadata.PackageNotFoundError:
_modelcards_available = False
_onnx_available = importlib.util.find_spec("onnxruntime") is not None
try:
_onnxruntime_version = importlib_metadata.version("onnxruntime")
logger.debug(f"Successfully imported onnxruntime version {_onnxruntime_version}")
except importlib_metadata.PackageNotFoundError:
_onnx_available = False
_scipy_available = importlib.util.find_spec("scipy") is not None
try:
_scipy_version = importlib_metadata.version("scipy")
logger.debug(f"Successfully imported transformers version {_scipy_version}")
except importlib_metadata.PackageNotFoundError:
_scipy_available = False
def is_torch_available():
return _torch_available
def is_tf_available():
return _tf_available
def is_flax_available():
return _flax_available
def is_transformers_available():
return _transformers_available
def is_inflect_available():
return _inflect_available
def is_unidecode_available():
return _unidecode_available
def is_modelcards_available():
return _modelcards_available
def is_onnx_available():
return _onnx_available
def is_scipy_available():
return _scipy_available
# docstyle-ignore
FLAX_IMPORT_ERROR = """
{0} requires the FLAX library but it was not found in your environment. Checkout the instructions on the
installation page: https://github.com/google/flax and follow the ones that match your environment.
"""
# docstyle-ignore
INFLECT_IMPORT_ERROR = """
{0} requires the inflect library but it was not found in your environment. You can install it with pip: `pip install
inflect`
"""
# docstyle-ignore
PYTORCH_IMPORT_ERROR = """
{0} requires the PyTorch library but it was not found in your environment. Checkout the instructions on the
installation page: https://pytorch.org/get-started/locally/ and follow the ones that match your environment.
"""
# docstyle-ignore
ONNX_IMPORT_ERROR = """
{0} requires the onnxruntime library but it was not found in your environment. You can install it with pip: `pip
install onnxruntime`
"""
# docstyle-ignore
SCIPY_IMPORT_ERROR = """
{0} requires the scipy library but it was not found in your environment. You can install it with pip: `pip install
scipy`
"""
# docstyle-ignore
TENSORFLOW_IMPORT_ERROR = """
{0} requires the TensorFlow library but it was not found in your environment. Checkout the instructions on the
installation page: https://www.tensorflow.org/install and follow the ones that match your environment.
"""
# docstyle-ignore
TRANSFORMERS_IMPORT_ERROR = """
{0} requires the transformers library but it was not found in your environment. You can install it with pip: `pip
install transformers`
"""
# docstyle-ignore
UNIDECODE_IMPORT_ERROR = """
{0} requires the unidecode library but it was not found in your environment. You can install it with pip: `pip install
Unidecode`
"""
BACKENDS_MAPPING = OrderedDict(
[
("flax", (is_flax_available, FLAX_IMPORT_ERROR)),
("inflect", (is_inflect_available, INFLECT_IMPORT_ERROR)),
("onnx", (is_onnx_available, ONNX_IMPORT_ERROR)),
("scipy", (is_scipy_available, SCIPY_IMPORT_ERROR)),
("tf", (is_tf_available, TENSORFLOW_IMPORT_ERROR)),
("torch", (is_torch_available, PYTORCH_IMPORT_ERROR)),
("transformers", (is_transformers_available, TRANSFORMERS_IMPORT_ERROR)),
("unidecode", (is_unidecode_available, UNIDECODE_IMPORT_ERROR)),
]
)
def requires_backends(obj, backends):
if not isinstance(backends, (list, tuple)):
backends = [backends]
name = obj.__name__ if hasattr(obj, "__name__") else obj.__class__.__name__
checks = (BACKENDS_MAPPING[backend] for backend in backends)
failed = [msg.format(name) for available, msg in checks if not available()]
if failed:
raise ImportError("".join(failed))
class DummyObject(type):
"""
Metaclass for the dummy objects. Any class inheriting from it will return the ImportError generated by
`requires_backend` each time a user tries to access any method of that class.
"""
def __getattr__(cls, key):
if key.startswith("_"):
return super().__getattr__(cls, key)
requires_backends(cls, cls._backends)
================================================
FILE: models/edict/my_diffusers/utils/logging.py
================================================
# coding=utf-8
# Copyright 2020 Optuna, Hugging Face
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Logging utilities."""
import logging
import os
import sys
import threading
from logging import CRITICAL # NOQA
from logging import DEBUG # NOQA
from logging import ERROR # NOQA
from logging import FATAL # NOQA
from logging import INFO # NOQA
from logging import NOTSET # NOQA
from logging import WARN # NOQA
from logging import WARNING # NOQA
from typing import Optional
from tqdm import auto as tqdm_lib
_lock = threading.Lock()
_default_handler: Optional[logging.Handler] = None
log_levels = {
"debug": logging.DEBUG,
"info": logging.INFO,
"warning": logging.WARNING,
"error": logging.ERROR,
"critical": logging.CRITICAL,
}
_default_log_level = logging.WARNING
_tqdm_active = True
def _get_default_logging_level():
"""
If DIFFUSERS_VERBOSITY env var is set to one of the valid choices return that as the new default level. If it is
not - fall back to `_default_log_level`
"""
env_level_str = os.getenv("DIFFUSERS_VERBOSITY", None)
if env_level_str:
if env_level_str in log_levels:
return log_levels[env_level_str]
else:
logging.getLogger().warning(
f"Unknown option DIFFUSERS_VERBOSITY={env_level_str}, "
f"has to be one of: { ', '.join(log_levels.keys()) }"
)
return _default_log_level
def _get_library_name() -> str:
return __name__.split(".")[0]
def _get_library_root_logger() -> logging.Logger:
return logging.getLogger(_get_library_name())
def _configure_library_root_logger() -> None:
global _default_handler
with _lock:
if _default_handler:
# This library has already configured the library root logger.
return
_default_handler = logging.StreamHandler() # Set sys.stderr as stream.
_default_handler.flush = sys.stderr.flush
# Apply our default configuration to the library root logger.
library_root_logger = _get_library_root_logger()
library_root_logger.addHandler(_default_handler)
library_root_logger.setLevel(_get_default_logging_level())
library_root_logger.propagate = False
def _reset_library_root_logger() -> None:
global _default_handler
with _lock:
if not _default_handler:
return
library_root_logger = _get_library_root_logger()
library_root_logger.removeHandler(_default_handler)
library_root_logger.setLevel(logging.NOTSET)
_default_handler = None
def get_log_levels_dict():
return log_levels
def get_logger(name: Optional[str] = None) -> logging.Logger:
"""
Return a logger with the specified name.
This function is not supposed to be directly accessed unless you are writing a custom diffusers module.
"""
if name is None:
name = _get_library_name()
_configure_library_root_logger()
return logging.getLogger(name)
def get_verbosity() -> int:
"""
Return the current level for the 🤗 Diffusers' root logger as an int.
Returns:
`int`: The logging level.
🤗 Diffusers has following logging levels:
- 50: `diffusers.logging.CRITICAL` or `diffusers.logging.FATAL`
- 40: `diffusers.logging.ERROR`
- 30: `diffusers.logging.WARNING` or `diffusers.logging.WARN`
- 20: `diffusers.logging.INFO`
- 10: `diffusers.logging.DEBUG`
"""
_configure_library_root_logger()
return _get_library_root_logger().getEffectiveLevel()
def set_verbosity(verbosity: int) -> None:
"""
Set the verbosity level for the 🤗 Diffusers' root logger.
Args:
verbosity (`int`):
Logging level, e.g., one of:
- `diffusers.logging.CRITICAL` or `diffusers.logging.FATAL`
- `diffusers.logging.ERROR`
- `diffusers.logging.WARNING` or `diffusers.logging.WARN`
- `diffusers.logging.INFO`
- `diffusers.logging.DEBUG`
"""
_configure_library_root_logger()
_get_library_root_logger().setLevel(verbosity)
def set_verbosity_info():
"""Set the verbosity to the `INFO` level."""
return set_verbosity(INFO)
def set_verbosity_warning():
"""Set the verbosity to the `WARNING` level."""
return set_verbosity(WARNING)
def set_verbosity_debug():
"""Set the verbosity to the `DEBUG` level."""
return set_verbosity(DEBUG)
def set_verbosity_error():
"""Set the verbosity to the `ERROR` level."""
return set_verbosity(ERROR)
def disable_default_handler() -> None:
"""Disable the default handler of the HuggingFace Diffusers' root logger."""
_configure_library_root_logger()
assert _default_handler is not None
_get_library_root_logger().removeHandler(_default_handler)
def enable_default_handler() -> None:
"""Enable the default handler of the HuggingFace Diffusers' root logger."""
_configure_library_root_logger()
assert _default_handler is not None
_get_library_root_logger().addHandler(_default_handler)
def add_handler(handler: logging.Handler) -> None:
"""adds a handler to the HuggingFace Diffusers' root logger."""
_configure_library_root_logger()
assert handler is not None
_get_library_root_logger().addHandler(handler)
def remove_handler(handler: logging.Handler) -> None:
"""removes given handler from the HuggingFace Diffusers' root logger."""
_configure_library_root_logger()
assert handler is not None and handler not in _get_library_root_logger().handlers
_get_library_root_logger().removeHandler(handler)
def disable_propagation() -> None:
"""
Disable propagation of the library log outputs. Note that log propagation is disabled by default.
"""
_configure_library_root_logger()
_get_library_root_logger().propagate = False
def enable_propagation() -> None:
"""
Enable propagation of the library log outputs. Please disable the HuggingFace Diffusers' default handler to prevent
double logging if the root logger has been configured.
"""
_configure_library_root_logger()
_get_library_root_logger().propagate = True
def enable_explicit_format() -> None:
"""
Enable explicit formatting for every HuggingFace Diffusers' logger. The explicit formatter is as follows:
```
[LEVELNAME|FILENAME|LINE NUMBER] TIME >> MESSAGE
```
All handlers currently bound to the root logger are affected by this method.
"""
handlers = _get_library_root_logger().handlers
for handler in handlers:
formatter = logging.Formatter("[%(levelname)s|%(filename)s:%(lineno)s] %(asctime)s >> %(message)s")
handler.setFormatter(formatter)
def reset_format() -> None:
"""
Resets the formatting for HuggingFace Diffusers' loggers.
All handlers currently bound to the root logger are affected by this method.
"""
handlers = _get_library_root_logger().handlers
for handler in handlers:
handler.setFormatter(None)
def warning_advice(self, *args, **kwargs):
"""
This method is identical to `logger.warninging()`, but if env var DIFFUSERS_NO_ADVISORY_WARNINGS=1 is set, this
warning will not be printed
"""
no_advisory_warnings = os.getenv("DIFFUSERS_NO_ADVISORY_WARNINGS", False)
if no_advisory_warnings:
return
self.warning(*args, **kwargs)
logging.Logger.warning_advice = warning_advice
class EmptyTqdm:
"""Dummy tqdm which doesn't do anything."""
def __init__(self, *args, **kwargs): # pylint: disable=unused-argument
self._iterator = args[0] if args else None
def __iter__(self):
return iter(self._iterator)
def __getattr__(self, _):
"""Return empty function."""
def empty_fn(*args, **kwargs): # pylint: disable=unused-argument
return
return empty_fn
def __enter__(self):
return self
def __exit__(self, type_, value, traceback):
return
class _tqdm_cls:
def __call__(self, *args, **kwargs):
if _tqdm_active:
return tqdm_lib.tqdm(*args, **kwargs)
else:
return EmptyTqdm(*args, **kwargs)
def set_lock(self, *args, **kwargs):
self._lock = None
if _tqdm_active:
return tqdm_lib.tqdm.set_lock(*args, **kwargs)
def get_lock(self):
if _tqdm_active:
return tqdm_lib.tqdm.get_lock()
tqdm = _tqdm_cls()
def is_progress_bar_enabled() -> bool:
"""Return a boolean indicating whether tqdm progress bars are enabled."""
global _tqdm_active
return bool(_tqdm_active)
def enable_progress_bar():
"""Enable tqdm progress bar."""
global _tqdm_active
_tqdm_active = True
def disable_progress_bar():
"""Disable tqdm progress bar."""
global _tqdm_active
_tqdm_active = False
================================================
FILE: models/edict/my_diffusers/utils/model_card_template.md
================================================
---
{{ card_data }}
---
# {{ model_name | default("Diffusion Model") }}
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library
on the `{{ dataset_name }}` dataset.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: describe the data used to train the model]
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: {{ learning_rate }}
- train_batch_size: {{ train_batch_size }}
- eval_batch_size: {{ eval_batch_size }}
- gradient_accumulation_steps: {{ gradient_accumulation_steps }}
- optimizer: AdamW with betas=({{ adam_beta1 }}, {{ adam_beta2 }}), weight_decay={{ adam_weight_decay }} and epsilon={{ adam_epsilon }}
- lr_scheduler: {{ lr_scheduler }}
- lr_warmup_steps: {{ lr_warmup_steps }}
- ema_inv_gamma: {{ ema_inv_gamma }}
- ema_inv_gamma: {{ ema_power }}
- ema_inv_gamma: {{ ema_max_decay }}
- mixed_precision: {{ mixed_precision }}
### Training results
📈 [TensorBoard logs](https://huggingface.co/{{ repo_name }}/tensorboard?#scalars)
================================================
FILE: models/edict/my_diffusers/utils/outputs.py
================================================
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Generic utilities
"""
import warnings
from collections import OrderedDict
from dataclasses import fields
from typing import Any, Tuple
import numpy as np
from .import_utils import is_torch_available
def is_tensor(x):
"""
Tests if `x` is a `torch.Tensor` or `np.ndarray`.
"""
if is_torch_available():
import torch
if isinstance(x, torch.Tensor):
return True
return isinstance(x, np.ndarray)
class BaseOutput(OrderedDict):
"""
Base class for all model outputs as dataclass. Has a `__getitem__` that allows indexing by integer or slice (like a
tuple) or strings (like a dictionary) that will ignore the `None` attributes. Otherwise behaves like a regular
python dictionary.
You can't unpack a `BaseOutput` directly. Use the [`~utils.BaseOutput.to_tuple`] method to convert it to a tuple
before.
"""
def __post_init__(self):
class_fields = fields(self)
# Safety and consistency checks
if not len(class_fields):
raise ValueError(f"{self.__class__.__name__} has no fields.")
for field in class_fields:
v = getattr(self, field.name)
if v is not None:
self[field.name] = v
def __delitem__(self, *args, **kwargs):
raise Exception(f"You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.")
def setdefault(self, *args, **kwargs):
raise Exception(f"You cannot use ``setdefault`` on a {self.__class__.__name__} instance.")
def pop(self, *args, **kwargs):
raise Exception(f"You cannot use ``pop`` on a {self.__class__.__name__} instance.")
def update(self, *args, **kwargs):
raise Exception(f"You cannot use ``update`` on a {self.__class__.__name__} instance.")
def __getitem__(self, k):
if isinstance(k, str):
inner_dict = {k: v for (k, v) in self.items()}
if self.__class__.__name__ in ["StableDiffusionPipelineOutput", "ImagePipelineOutput"] and k == "sample":
warnings.warn(
"The keyword 'samples' is deprecated and will be removed in version 0.4.0. Please use `.images` or"
" `'images'` instead.",
DeprecationWarning,
)
return inner_dict["images"]
return inner_dict[k]
else:
return self.to_tuple()[k]
def __setattr__(self, name, value):
if name in self.keys() and value is not None:
# Don't call self.__setitem__ to avoid recursion errors
super().__setitem__(name, value)
super().__setattr__(name, value)
def __setitem__(self, key, value):
# Will raise a KeyException if needed
super().__setitem__(key, value)
# Don't call self.__setattr__ to avoid recursion errors
super().__setattr__(key, value)
def to_tuple(self) -> Tuple[Any]:
"""
Convert self to a tuple containing all the attributes/keys that are not `None`.
"""
return tuple(self[k] for k in self.keys())
================================================
FILE: models/edit_friendly_ddm/inversion_utils.py
================================================
import torch
import os
def load_real_image(folder = "data/", img_name = None, idx = 0, img_size=512, device='cuda'):
from ddm_inversion.utils import pil_to_tensor
from PIL import Image
from glob import glob
if img_name is not None:
path = os.path.join(folder, img_name)
else:
path = glob(folder + "*")[idx]
img = Image.open(path).resize((img_size,
img_size))
img = pil_to_tensor(img).to(device)
if img.shape[1]== 4:
img = img[:,:3,:,:]
return img
def mu_tilde(model, xt,x0, timestep):
"mu_tilde(x_t, x_0) DDPM paper eq. 7"
prev_timestep = timestep - model.scheduler.config.num_train_timesteps // model.scheduler.num_inference_steps
alpha_prod_t_prev = model.scheduler.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else model.scheduler.final_alpha_cumprod
alpha_t = model.scheduler.alphas[timestep]
beta_t = 1 - alpha_t
alpha_bar = model.scheduler.alphas_cumprod[timestep]
return ((alpha_prod_t_prev ** 0.5 * beta_t) / (1-alpha_bar)) * x0 + ((alpha_t**0.5 *(1-alpha_prod_t_prev)) / (1- alpha_bar))*xt
def sample_xts_from_x0(model, x0, num_inference_steps=50):
"""
Samples from P(x_1:T|x_0)
"""
# torch.manual_seed(43256465436)
alpha_bar = model.scheduler.alphas_cumprod
sqrt_one_minus_alpha_bar = (1-alpha_bar) ** 0.5
alphas = model.scheduler.alphas
betas = 1 - alphas
variance_noise_shape = (
num_inference_steps,
model.unet.in_channels,
model.unet.sample_size,
model.unet.sample_size)
timesteps = model.scheduler.timesteps.to(model.device)
t_to_idx = {int(v):k for k,v in enumerate(timesteps)}
xts = torch.zeros((num_inference_steps+1,model.unet.in_channels, model.unet.sample_size, model.unet.sample_size)).to(x0.device)
xts[0] = x0
for t in reversed(timesteps):
idx = num_inference_steps-t_to_idx[int(t)]
xts[idx] = x0 * (alpha_bar[t] ** 0.5) + torch.randn_like(x0) * sqrt_one_minus_alpha_bar[t]
return xts
def encode_text(model, prompts):
text_input = model.tokenizer(
prompts,
padding="max_length",
max_length=model.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
with torch.no_grad():
text_encoding = model.text_encoder(text_input.input_ids.to(model.device))[0]
return text_encoding
def forward_step(model, model_output, timestep, sample):
next_timestep = min(model.scheduler.config.num_train_timesteps - 2,
timestep + model.scheduler.config.num_train_timesteps // model.scheduler.num_inference_steps)
# 2. compute alphas, betas
alpha_prod_t = model.scheduler.alphas_cumprod[timestep]
# alpha_prod_t_next = self.scheduler.alphas_cumprod[next_timestep] if next_ltimestep >= 0 else self.scheduler.final_alpha_cumprod
beta_prod_t = 1 - alpha_prod_t
# 3. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)
# 5. TODO: simple noising implementatiom
next_sample = model.scheduler.add_noise(pred_original_sample,
model_output,
torch.LongTensor([next_timestep]))
return next_sample
def get_variance(model, timestep): #, prev_timestep):
prev_timestep = timestep - model.scheduler.config.num_train_timesteps // model.scheduler.num_inference_steps
alpha_prod_t = model.scheduler.alphas_cumprod[timestep]
alpha_prod_t_prev = model.scheduler.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else model.scheduler.final_alpha_cumprod
beta_prod_t = 1 - alpha_prod_t
beta_prod_t_prev = 1 - alpha_prod_t_prev
variance = (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)
return variance
def inversion_forward_process(model, x0,
etas = None,
prog_bar = False,
prompt = "",
cfg_scale = 3.5,
num_inference_steps=50, eps = None):
if not prompt=="":
text_embeddings = encode_text(model, prompt)
uncond_embedding = encode_text(model, "")
timesteps = model.scheduler.timesteps.to(model.device)
variance_noise_shape = (
num_inference_steps,
model.unet.in_channels,
model.unet.sample_size,
model.unet.sample_size)
if etas is None or (type(etas) in [int, float] and etas == 0):
eta_is_zero = True
zs = None
else:
eta_is_zero = False
if type(etas) in [int, float]: etas = [etas]*model.scheduler.num_inference_steps
xts = sample_xts_from_x0(model, x0, num_inference_steps=num_inference_steps)
alpha_bar = model.scheduler.alphas_cumprod
zs = torch.zeros(size=variance_noise_shape, device=model.device)
t_to_idx = {int(v):k for k,v in enumerate(timesteps)}
xt = x0
op = timesteps
for t in op:
# idx = t_to_idx[int(t)]
idx = num_inference_steps-t_to_idx[int(t)]-1
# 1. predict noise residual
if not eta_is_zero:
xt = xts[idx+1][None]
# xt = xts_cycle[idx+1][None]
with torch.no_grad():
out = model.unet.forward(xt, timestep = t, encoder_hidden_states = uncond_embedding)
if not prompt=="":
cond_out = model.unet.forward(xt, timestep=t, encoder_hidden_states = text_embeddings)
if not prompt=="":
## classifier free guidance
noise_pred = out.sample + cfg_scale * (cond_out.sample - out.sample)
else:
noise_pred = out.sample
if eta_is_zero:
# 2. compute more noisy image and set x_t -> x_t+1
xt = forward_step(model, noise_pred, t, xt)
else:
# xtm1 = xts[idx+1][None]
xtm1 = xts[idx][None]
# pred of x0
pred_original_sample = (xt - (1-alpha_bar[t]) ** 0.5 * noise_pred ) / alpha_bar[t] ** 0.5
# direction to xt
prev_timestep = t - model.scheduler.config.num_train_timesteps // model.scheduler.num_inference_steps
alpha_prod_t_prev = model.scheduler.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else model.scheduler.final_alpha_cumprod
variance = get_variance(model, t)
pred_sample_direction = (1 - alpha_prod_t_prev - etas[idx] * variance ) ** (0.5) * noise_pred
mu_xt = alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction
z = (xtm1 - mu_xt ) / ( etas[idx] * variance ** 0.5 )
zs[idx] = z
# correction to avoid error accumulation
xtm1 = mu_xt + ( etas[idx] * variance ** 0.5 )*z
xts[idx] = xtm1
if not zs is None:
zs[0] = torch.zeros_like(zs[0])
return xt, zs, xts
def reverse_step(model, model_output, timestep, sample, eta = 0, variance_noise=None):
# 1. get previous step value (=t-1)
prev_timestep = timestep - model.scheduler.config.num_train_timesteps // model.scheduler.num_inference_steps
# 2. compute alphas, betas
alpha_prod_t = model.scheduler.alphas_cumprod[timestep]
alpha_prod_t_prev = model.scheduler.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else model.scheduler.final_alpha_cumprod
beta_prod_t = 1 - alpha_prod_t
# 3. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)
# 5. compute variance: "sigma_t(η)" -> see formula (16)
# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
# variance = self.scheduler._get_variance(timestep, prev_timestep)
variance = get_variance(model, timestep) #, prev_timestep)
std_dev_t = eta * variance ** (0.5)
# Take care of asymetric reverse process (asyrp)
model_output_direction = model_output
# 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
# pred_sample_direction = (1 - alpha_prod_t_prev - std_dev_t**2) ** (0.5) * model_output_direction
pred_sample_direction = (1 - alpha_prod_t_prev - eta * variance) ** (0.5) * model_output_direction
# 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
prev_sample = alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction
# 8. Add noice if eta > 0
if eta > 0:
if variance_noise is None:
variance_noise = torch.randn(model_output.shape, device=model.device)
sigma_z = eta * variance ** (0.5) * variance_noise
prev_sample = prev_sample + sigma_z
return prev_sample
def inversion_reverse_process(model,
xT,
etas = 0,
prompts = "",
cfg_scales = None,
prog_bar = False,
zs = None,
controller=None,
asyrp = False):
batch_size = len(prompts)
cfg_scales_tensor = torch.Tensor(cfg_scales).view(-1,1,1,1).to(model.device)
text_embeddings = encode_text(model, prompts)
uncond_embedding = encode_text(model, [""] * batch_size)
if etas is None: etas = 0
if type(etas) in [int, float]: etas = [etas]*model.scheduler.num_inference_steps
assert len(etas) == model.scheduler.num_inference_steps
timesteps = model.scheduler.timesteps.to(model.device)
xt = xT.expand(batch_size, -1, -1, -1)
op = timesteps[-zs.shape[0]:]
t_to_idx = {int(v):k for k,v in enumerate(timesteps[-zs.shape[0]:])}
for t in op:
idx = model.scheduler.num_inference_steps-t_to_idx[int(t)]-(model.scheduler.num_inference_steps-zs.shape[0]+1)
## Unconditional embedding
with torch.no_grad():
uncond_out = model.unet.forward(xt, timestep = t,
encoder_hidden_states = uncond_embedding)
## Conditional embedding
if prompts:
with torch.no_grad():
cond_out = model.unet.forward(xt, timestep = t,
encoder_hidden_states = text_embeddings)
z = zs[idx] if not zs is None else None
z = z.expand(batch_size, -1, -1, -1)
if prompts:
## classifier free guidance
noise_pred = uncond_out.sample + cfg_scales_tensor * (cond_out.sample - uncond_out.sample)
else:
noise_pred = uncond_out.sample
# 2. compute less noisy image and set x_t -> x_t-1
xt = reverse_step(model, noise_pred, t, xt, eta = etas[idx], variance_noise = z)
if controller is not None:
xt = controller.step_callback(xt)
return xt, zs
================================================
FILE: models/edit_friendly_ddm/ptp_classes.py
================================================
"""
This code was originally taken from
https://github.com/google/prompt-to-prompt
"""
LOW_RESOURCE = True
MAX_NUM_WORDS = 77
from typing import Optional, Union, Tuple, List, Callable, Dict
import models.edit_friendly_ddm.ptp_utils as ptp_utils
import models.edit_friendly_ddm.seq_aligner as seq_aligner
import torch
import torch.nn.functional as nnf
import abc
import numpy as np
class LocalBlend:
def __call__(self, x_t, attention_store):
k = 1
maps = attention_store["down_cross"][2:4] + attention_store["up_cross"][:3]
maps = [item.reshape(self.alpha_layers.shape[0], -1, 1, 16, 16, MAX_NUM_WORDS) for item in maps]
maps = torch.cat(maps, dim=1)
maps = (maps * self.alpha_layers).sum(-1).mean(1)
mask = nnf.max_pool2d(maps, (k * 2 + 1, k * 2 +1), (1, 1), padding=(k, k))
mask = nnf.interpolate(mask, size=(x_t.shape[2:]))
mask = mask / mask.max(2, keepdims=True)[0].max(3, keepdims=True)[0]
mask = mask.gt(self.threshold)
mask = (mask[:1] + mask[1:]).float()
x_t = x_t[:1] + mask * (x_t - x_t[:1])
return x_t
def __init__(self, prompts: List[str], words: [List[List[str]]], threshold=.3, device=None, tokenizer=None):
alpha_layers = torch.zeros(len(prompts), 1, 1, 1, 1, MAX_NUM_WORDS)
for i, (prompt, words_) in enumerate(zip(prompts, words)):
if type(words_) is str:
words_ = [words_]
for word in words_:
ind = ptp_utils.get_word_inds(prompt, word, tokenizer)
alpha_layers[i, :, :, :, :, ind] = 1
self.alpha_layers = alpha_layers.to(device)
self.threshold = threshold
class AttentionControl(abc.ABC):
def step_callback(self, x_t):
return x_t
def between_steps(self):
return
@property
def num_uncond_att_layers(self):
return self.num_att_layers if LOW_RESOURCE else 0
@abc.abstractmethod
def forward (self, attn, is_cross: bool, place_in_unet: str):
raise NotImplementedError
def __call__(self, attn, is_cross: bool, place_in_unet: str):
if self.cur_att_layer >= self.num_uncond_att_layers:
if LOW_RESOURCE:
attn = self.forward(attn, is_cross, place_in_unet)
else:
h = attn.shape[0]
attn[h // 2:] = self.forward(attn[h // 2:], is_cross, place_in_unet)
self.cur_att_layer += 1
if self.cur_att_layer == self.num_att_layers + self.num_uncond_att_layers:
self.cur_att_layer = 0
self.cur_step += 1
self.between_steps()
return attn
def reset(self):
self.cur_step = 0
self.cur_att_layer = 0
def __init__(self):
self.cur_step = 0
self.num_att_layers = -1
self.cur_att_layer = 0
class EmptyControl(AttentionControl):
def forward (self, attn, is_cross: bool, place_in_unet: str):
return attn
class AttentionStore(AttentionControl):
@staticmethod
def get_empty_store():
return {"down_cross": [], "mid_cross": [], "up_cross": [],
"down_self": [], "mid_self": [], "up_self": []}
def forward(self, attn, is_cross: bool, place_in_unet: str):
key = f"{place_in_unet}_{'cross' if is_cross else 'self'}"
if attn.shape[1] <= 32 ** 2: # avoid memory overhead
self.step_store[key].append(attn)
return attn
def between_steps(self):
if len(self.attention_store) == 0:
self.attention_store = self.step_store
else:
for key in self.attention_store:
for i in range(len(self.attention_store[key])):
self.attention_store[key][i] += self.step_store[key][i]
self.step_store = self.get_empty_store()
def get_average_attention(self):
average_attention = {key: [item / self.cur_step for item in self.attention_store[key]] for key in self.attention_store}
return average_attention
def reset(self):
super(AttentionStore, self).reset()
self.step_store = self.get_empty_store()
self.attention_store = {}
def __init__(self):
super(AttentionStore, self).__init__()
self.step_store = self.get_empty_store()
self.attention_store = {}
class AttentionControlEdit(AttentionStore, abc.ABC):
def step_callback(self, x_t):
if self.local_blend is not None:
x_t = self.local_blend(x_t, self.attention_store)
return x_t
def replace_self_attention(self, attn_base, att_replace):
if att_replace.shape[2] <= 16 ** 2:
return attn_base.unsqueeze(0).expand(att_replace.shape[0], *attn_base.shape)
else:
return att_replace
@abc.abstractmethod
def replace_cross_attention(self, attn_base, att_replace):
raise NotImplementedError
def forward(self, attn, is_cross: bool, place_in_unet: str):
super(AttentionControlEdit, self).forward(attn, is_cross, place_in_unet)
if is_cross or (self.num_self_replace[0] <= self.cur_step < self.num_self_replace[1]):
h = attn.shape[0] // (self.batch_size)
attn = attn.reshape(self.batch_size, h, *attn.shape[1:])
attn_base, attn_repalce = attn[0], attn[1:]
if is_cross:
alpha_words = self.cross_replace_alpha[self.cur_step]
attn_repalce_new = self.replace_cross_attention(attn_base, attn_repalce) * alpha_words + (1 - alpha_words) * attn_repalce
attn[1:] = attn_repalce_new
else:
attn[1:] = self.replace_self_attention(attn_base, attn_repalce)
attn = attn.reshape(self.batch_size * h, *attn.shape[2:])
return attn
def __init__(self, prompts, num_steps: int,
cross_replace_steps: Union[float, Tuple[float, float], Dict[str, Tuple[float, float]]],
self_replace_steps: Union[float, Tuple[float, float]],
local_blend: Optional[LocalBlend],
device=None,
tokenizer=None):
super(AttentionControlEdit, self).__init__()
self.batch_size = len(prompts)
self.cross_replace_alpha = ptp_utils.get_time_words_attention_alpha(prompts, num_steps, cross_replace_steps, tokenizer).to(device)
if type(self_replace_steps) is float:
self_replace_steps = 0, self_replace_steps
self.num_self_replace = int(num_steps * self_replace_steps[0]), int(num_steps * self_replace_steps[1])
self.local_blend = local_blend
class AttentionReplace(AttentionControlEdit):
def replace_cross_attention(self, attn_base, att_replace):
return torch.einsum('hpw,bwn->bhpn', attn_base, self.mapper)
def __init__(self, prompts, num_steps: int, cross_replace_steps: float, self_replace_steps: float,
local_blend: Optional[LocalBlend] = None, model=None):
super(AttentionReplace, self).__init__(prompts, num_steps, cross_replace_steps, self_replace_steps, local_blend, device=model.device)
self.mapper = seq_aligner.get_replacement_mapper(prompts, model.tokenizer).to(model.device)
class AttentionRefine(AttentionControlEdit):
def replace_cross_attention(self, attn_base, att_replace):
attn_base_replace = attn_base[:, :, self.mapper].permute(2, 0, 1, 3)
attn_replace = attn_base_replace * self.alphas + att_replace * (1 - self.alphas)
return attn_replace
def __init__(self, prompts, num_steps: int, cross_replace_steps: float, self_replace_steps: float,
local_blend: Optional[LocalBlend] = None, model=None):
super(AttentionRefine, self).__init__(prompts, num_steps, cross_replace_steps, self_replace_steps, local_blend, device=model.device)
self.mapper, alphas = seq_aligner.get_refinement_mapper(prompts, model.tokenizer)
self.mapper, alphas = self.mapper.to(model.device), alphas.to(model.device)
self.alphas = alphas.reshape(alphas.shape[0], 1, 1, alphas.shape[1])
class AttentionReweight(AttentionControlEdit):
def replace_cross_attention(self, attn_base, att_replace):
if self.prev_controller is not None:
attn_base = self.prev_controller.replace_cross_attention(attn_base, att_replace)
attn_replace = attn_base[None, :, :, :] * self.equalizer[:, None, None, :]
return attn_replace
def __init__(self, prompts, num_steps: int, cross_replace_steps: float, self_replace_steps: float, equalizer,
local_blend: Optional[LocalBlend] = None, controller: Optional[AttentionControlEdit] = None, device=None, tokenizer=None):
super(AttentionReweight, self).__init__(prompts, num_steps, cross_replace_steps, self_replace_steps, local_blend)
self.equalizer = equalizer.to(device)
self.prev_controller = controller
def get_equalizer(text: str, word_select: Union[int, Tuple[int, ...]], values: Union[List[float],
Tuple[float, ...]], tokenizer=None):
if type(word_select) is int or type(word_select) is str:
word_select = (word_select,)
equalizer = torch.ones(len(values), 77)
values = torch.tensor(values, dtype=torch.float32)
for word in word_select:
inds = ptp_utils.get_word_inds(text, word, tokenizer)
equalizer[:, inds] = values
return equalizer
from PIL import Image
def aggregate_attention(attention_store: AttentionStore, res: int, from_where: List[str], is_cross: bool, select: int, prompts=None):
out = []
attention_maps = attention_store.get_average_attention()
num_pixels = res ** 2
for location in from_where:
for item in attention_maps[f"{location}_{'cross' if is_cross else 'self'}"]:
if item.shape[1] == num_pixels:
cross_maps = item.reshape(len(prompts), -1, res, res, item.shape[-1])[select]
out.append(cross_maps)
out = torch.cat(out, dim=0)
out = out.sum(0) / out.shape[0]
return out.cpu()
def show_cross_attention(attention_store: AttentionStore, res: int, from_where: List[str], select: int = 0, prompts=None, tokenizer=None):
tokens = tokenizer.encode(prompts[select])
decoder = tokenizer.decode
attention_maps = aggregate_attention(attention_store, res, from_where, True, select, prompts)
images = []
for i in range(len(tokens)):
image = attention_maps[:, :, i]
image = 255 * image / image.max()
image = image.unsqueeze(-1).expand(*image.shape, 3)
image = image.numpy().astype(np.uint8)
image = np.array(Image.fromarray(image).resize((256, 256)))
image = ptp_utils.text_under_image(image, decoder(int(tokens[i])))
images.append(image)
return(ptp_utils.view_images(np.stack(images, axis=0)))
def show_self_attention_comp(attention_store: AttentionStore, res: int, from_where: List[str],
max_com=10, select: int = 0):
attention_maps = aggregate_attention(attention_store, res, from_where, False, select).numpy().reshape((res ** 2, res ** 2))
u, s, vh = np.linalg.svd(attention_maps - np.mean(attention_maps, axis=1, keepdims=True))
images = []
for i in range(max_com):
image = vh[i].reshape(res, res)
image = image - image.min()
image = 255 * image / image.max()
image = np.repeat(np.expand_dims(image, axis=2), 3, axis=2).astype(np.uint8)
image = Image.fromarray(image).resize((256, 256))
image = np.array(image)
images.append(image)
ptp_utils.view_images(np.concatenate(images, axis=1))
def run_and_display(model, prompts, controller, latent=None, run_baseline=False, generator=None):
if run_baseline:
print("w.o. prompt-to-prompt")
images, latent = run_and_display(model, prompts, EmptyControl(), latent=latent, run_baseline=False, generator=generator)
print("with prompt-to-prompt")
images, x_t = ptp_utils.text2image_ld
def load_512(image_path, left=0, right=0, top=0, bottom=0, device=None):
if type(image_path) is str:
image = np.array(Image.open(image_path).convert('RGB'))[:, :, :3]
else:
image = image_path
h, w, c = image.shape
left = min(left, w-1)
right = min(right, w - left - 1)
top = min(top, h - left - 1)
bottom = min(bottom, h - top - 1)
image = image[top:h-bottom, left:w-right]
h, w, c = image.shape
if h < w:
offset = (w - h) // 2
image = image[:, offset:offset + h]
elif w < h:
offset = (h - w) // 2
image = image[offset:offset + w]
image = np.array(Image.fromarray(image).resize((512, 512)))
image = torch.from_numpy(image).float() / 127.5 - 1
image = image.permute(2, 0, 1).unsqueeze(0).to(device)
return image
================================================
FILE: models/edit_friendly_ddm/ptp_utils.py
================================================
"""
This code was originally taken from
https://github.com/google/prompt-to-prompt
"""
# Copyright 2022 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import torch
from PIL import Image, ImageDraw, ImageFont
import cv2
from typing import Optional, Union, Tuple, List, Callable, Dict
# from IPython.display import display
def text_under_image(image: np.ndarray, text: str, text_color: Tuple[int, int, int] = (0, 0, 0)):
h, w, c = image.shape
offset = int(h * .2)
img = np.ones((h + offset, w, c), dtype=np.uint8) * 255
font = cv2.FONT_HERSHEY_SIMPLEX
# font = ImageFont.truetype("/usr/share/fonts/truetype/noto/NotoMono-Regular.ttf", font_size)
img[:h] = image
textsize = cv2.getTextSize(text, font, 1, 2)[0]
text_x, text_y = (w - textsize[0]) // 2, h + offset - textsize[1] // 2
cv2.putText(img, text, (text_x, text_y ), font, 1, text_color, 2)
return img
def view_images(images, num_rows=1, offset_ratio=0.02):
if type(images) is list:
num_empty = len(images) % num_rows
elif images.ndim == 4:
num_empty = images.shape[0] % num_rows
else:
images = [images]
num_empty = 0
empty_images = np.ones(images[0].shape, dtype=np.uint8) * 255
images = [image.astype(np.uint8) for image in images] + [empty_images] * num_empty
num_items = len(images)
h, w, c = images[0].shape
offset = int(h * offset_ratio)
num_cols = num_items // num_rows
image_ = np.ones((h * num_rows + offset * (num_rows - 1),
w * num_cols + offset * (num_cols - 1), 3), dtype=np.uint8) * 255
for i in range(num_rows):
for j in range(num_cols):
image_[i * (h + offset): i * (h + offset) + h:, j * (w + offset): j * (w + offset) + w] = images[
i * num_cols + j]
pil_img = Image.fromarray(image_)
# display(pil_img)
return pil_img
def diffusion_step(model, controller, latents, context, t, guidance_scale, low_resource=False):
if low_resource:
noise_pred_uncond = model.unet(latents, t, encoder_hidden_states=context[0])["sample"]
noise_prediction_text = model.unet(latents, t, encoder_hidden_states=context[1])["sample"]
else:
latents_input = torch.cat([latents] * 2)
noise_pred = model.unet(latents_input, t, encoder_hidden_states=context)["sample"]
noise_pred_uncond, noise_prediction_text = noise_pred.chunk(2)
cfg_scales_tensor = torch.Tensor(guidance_scale).view(-1,1,1,1).to(model.device)
noise_pred = noise_pred_uncond + cfg_scales_tensor * (noise_prediction_text - noise_pred_uncond)
latents = model.scheduler.step(noise_pred, t, latents)["prev_sample"]
latents = controller.step_callback(latents)
return latents
def latent2image(vae, latents):
latents = 1 / 0.18215 * latents
image = vae.decode(latents)['sample']
image = (image / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).numpy()
image = (image * 255).astype(np.uint8)
return image
def init_latent(latent, model, height, width, generator, batch_size):
if latent is None:
latent = torch.randn(
(1, model.unet.in_channels, height // 8, width // 8),
generator=generator,
)
latents = latent.expand(batch_size, model.unet.in_channels, height // 8, width // 8).to(model.device)
return latent, latents
@torch.no_grad()
def text2image_ldm(
model,
prompt: List[str],
controller,
num_inference_steps: int = 50,
guidance_scale: Optional[float] = 7.,
generator: Optional[torch.Generator] = None,
latent: Optional[torch.FloatTensor] = None,
):
register_attention_control(model, controller)
height = width = 256
batch_size = len(prompt)
uncond_input = model.tokenizer([""] * batch_size, padding="max_length", max_length=77, return_tensors="pt")
uncond_embeddings = model.bert(uncond_input.input_ids.to(model.device))[0]
text_input = model.tokenizer(prompt, padding="max_length", max_length=77, return_tensors="pt")
text_embeddings = model.bert(text_input.input_ids.to(model.device))[0]
latent, latents = init_latent(latent, model, height, width, generator, batch_size)
context = torch.cat([uncond_embeddings, text_embeddings])
model.scheduler.set_timesteps(num_inference_steps)
for t in model.scheduler.timesteps:
latents = diffusion_step(model, controller, latents, context, t, guidance_scale)
image = latent2image(model.vqvae, latents)
return image, latent
@torch.no_grad()
def text2image_ldm_stable(
model,
prompt: List[str],
controller,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
generator: Optional[torch.Generator] = None,
latent: Optional[torch.FloatTensor] = None,
restored_wt = None,
restored_zs = None,
low_resource: bool = False,
):
register_attention_control(model, controller)
height = width = 512
batch_size = len(prompt)
text_input = model.tokenizer(
prompt,
padding="max_length",
max_length=model.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_embeddings = model.text_encoder(text_input.input_ids.to(model.device))[0]
max_length = text_input.input_ids.shape[-1]
uncond_input = model.tokenizer(
[""] * batch_size, padding="max_length", max_length=max_length, return_tensors="pt"
)
uncond_embeddings = model.text_encoder(uncond_input.input_ids.to(model.device))[0]
context = [uncond_embeddings, text_embeddings]
if not low_resource:
context = torch.cat(context)
latent, latents = init_latent(latent, model, height, width, generator, batch_size)
# set timesteps
# extra_set_kwargs = {"offset": 1}
model.scheduler.set_timesteps(num_inference_steps)#, **extra_set_kwargs)
for t in model.scheduler.timesteps:
latents = diffusion_step(model, controller, latents, context, t, guidance_scale, low_resource)
# image = latent2image(model.vae, latents)
return latents, latent
def register_attention_control(model, controller):
def ca_forward(self, place_in_unet):
to_out = self.to_out
if type(to_out) is torch.nn.modules.container.ModuleList:
to_out = self.to_out[0]
else:
to_out = self.to_out
def forward(x, context=None, mask=None):
batch_size, sequence_length, dim = x.shape
h = self.heads
q = self.to_q(x)
is_cross = context is not None
context = context if is_cross else x
k = self.to_k(context)
v = self.to_v(context)
q = self.reshape_heads_to_batch_dim(q)
k = self.reshape_heads_to_batch_dim(k)
v = self.reshape_heads_to_batch_dim(v)
sim = torch.einsum("b i d, b j d -> b i j", q, k) * self.scale
if mask is not None:
mask = mask.reshape(batch_size, -1)
max_neg_value = -torch.finfo(sim.dtype).max
mask = mask[:, None, :].repeat(h, 1, 1)
sim.masked_fill_(~mask, max_neg_value)
# attention, what we cannot get enough of
attn = sim.softmax(dim=-1)
attn = controller(attn, is_cross, place_in_unet)
out = torch.einsum("b i j, b j d -> b i d", attn, v)
out = self.reshape_batch_dim_to_heads(out)
return to_out(out)
return forward
class DummyController:
def __call__(self, *args):
return args[0]
def __init__(self):
self.num_att_layers = 0
if controller is None:
controller = DummyController()
def register_recr(net_, count, place_in_unet):
if net_.__class__.__name__ == 'CrossAttention':
net_.forward = ca_forward(net_, place_in_unet)
return count + 1
elif hasattr(net_, 'children'):
for net__ in net_.children():
count = register_recr(net__, count, place_in_unet)
return count
cross_att_count = 0
sub_nets = model.unet.named_children()
for net in sub_nets:
if "down" in net[0]:
cross_att_count += register_recr(net[1], 0, "down")
elif "up" in net[0]:
cross_att_count += register_recr(net[1], 0, "up")
elif "mid" in net[0]:
cross_att_count += register_recr(net[1], 0, "mid")
controller.num_att_layers = cross_att_count
def get_word_inds(text: str, word_place: int, tokenizer):
split_text = text.split(" ")
if type(word_place) is str:
word_place = [i for i, word in enumerate(split_text) if word_place == word]
elif type(word_place) is int:
word_place = [word_place]
out = []
if len(word_place) > 0:
words_encode = [tokenizer.decode([item]).strip("#") for item in tokenizer.encode(text)][1:-1]
cur_len, ptr = 0, 0
for i in range(len(words_encode)):
cur_len += len(words_encode[i])
if ptr in word_place:
out.append(i + 1)
if cur_len >= len(split_text[ptr]):
ptr += 1
cur_len = 0
return np.array(out)
def update_alpha_time_word(alpha, bounds: Union[float, Tuple[float, float]], prompt_ind: int,
word_inds: Optional[torch.Tensor]=None):
if type(bounds) is float:
bounds = 0, bounds
start, end = int(bounds[0] * alpha.shape[0]), int(bounds[1] * alpha.shape[0])
if word_inds is None:
word_inds = torch.arange(alpha.shape[2])
alpha[: start, prompt_ind, word_inds] = 0
alpha[start: end, prompt_ind, word_inds] = 1
alpha[end:, prompt_ind, word_inds] = 0
return alpha
def get_time_words_attention_alpha(prompts, num_steps,
cross_replace_steps: Union[float, Dict[str, Tuple[float, float]]],
tokenizer, max_num_words=77):
if type(cross_replace_steps) is not dict:
cross_replace_steps = {"default_": cross_replace_steps}
if "default_" not in cross_replace_steps:
cross_replace_steps["default_"] = (0., 1.)
alpha_time_words = torch.zeros(num_steps + 1, len(prompts) - 1, max_num_words)
for i in range(len(prompts) - 1):
alpha_time_words = update_alpha_time_word(alpha_time_words, cross_replace_steps["default_"],
i)
for key, item in cross_replace_steps.items():
if key != "default_":
inds = [get_word_inds(prompts[i], key, tokenizer) for i in range(1, len(prompts))]
for i, ind in enumerate(inds):
if len(ind) > 0:
alpha_time_words = update_alpha_time_word(alpha_time_words, item, i, ind)
alpha_time_words = alpha_time_words.reshape(num_steps + 1, len(prompts) - 1, 1, 1, max_num_words)
return alpha_time_words
================================================
FILE: models/edit_friendly_ddm/seq_aligner.py
================================================
"""
This code was originally taken from
https://github.com/google/prompt-to-prompt
"""
# Copyright 2022 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
import numpy as np
class ScoreParams:
def __init__(self, gap, match, mismatch):
self.gap = gap
self.match = match
self.mismatch = mismatch
def mis_match_char(self, x, y):
if x != y:
return self.mismatch
else:
return self.match
def get_matrix(size_x, size_y, gap):
matrix = []
for i in range(len(size_x) + 1):
sub_matrix = []
for j in range(len(size_y) + 1):
sub_matrix.append(0)
matrix.append(sub_matrix)
for j in range(1, len(size_y) + 1):
matrix[0][j] = j*gap
for i in range(1, len(size_x) + 1):
matrix[i][0] = i*gap
return matrix
def get_matrix(size_x, size_y, gap):
matrix = np.zeros((size_x + 1, size_y + 1), dtype=np.int32)
matrix[0, 1:] = (np.arange(size_y) + 1) * gap
matrix[1:, 0] = (np.arange(size_x) + 1) * gap
return matrix
def get_traceback_matrix(size_x, size_y):
matrix = np.zeros((size_x + 1, size_y +1), dtype=np.int32)
matrix[0, 1:] = 1
matrix[1:, 0] = 2
matrix[0, 0] = 4
return matrix
def global_align(x, y, score):
matrix = get_matrix(len(x), len(y), score.gap)
trace_back = get_traceback_matrix(len(x), len(y))
for i in range(1, len(x) + 1):
for j in range(1, len(y) + 1):
left = matrix[i, j - 1] + score.gap
up = matrix[i - 1, j] + score.gap
diag = matrix[i - 1, j - 1] + score.mis_match_char(x[i - 1], y[j - 1])
matrix[i, j] = max(left, up, diag)
if matrix[i, j] == left:
trace_back[i, j] = 1
elif matrix[i, j] == up:
trace_back[i, j] = 2
else:
trace_back[i, j] = 3
return matrix, trace_back
def get_aligned_sequences(x, y, trace_back):
x_seq = []
y_seq = []
i = len(x)
j = len(y)
mapper_y_to_x = []
while i > 0 or j > 0:
if trace_back[i, j] == 3:
x_seq.append(x[i-1])
y_seq.append(y[j-1])
i = i-1
j = j-1
mapper_y_to_x.append((j, i))
elif trace_back[i][j] == 1:
x_seq.append('-')
y_seq.append(y[j-1])
j = j-1
mapper_y_to_x.append((j, -1))
elif trace_back[i][j] == 2:
x_seq.append(x[i-1])
y_seq.append('-')
i = i-1
elif trace_back[i][j] == 4:
break
mapper_y_to_x.reverse()
return x_seq, y_seq, torch.tensor(mapper_y_to_x, dtype=torch.int64)
def get_mapper(x: str, y: str, tokenizer, max_len=77):
x_seq = tokenizer.encode(x)
y_seq = tokenizer.encode(y)
score = ScoreParams(0, 1, -1)
matrix, trace_back = global_align(x_seq, y_seq, score)
mapper_base = get_aligned_sequences(x_seq, y_seq, trace_back)[-1]
alphas = torch.ones(max_len)
alphas[: mapper_base.shape[0]] = mapper_base[:, 1].ne(-1).float()
mapper = torch.zeros(max_len, dtype=torch.int64)
mapper[:mapper_base.shape[0]] = mapper_base[:, 1]
mapper[mapper_base.shape[0]:] = len(y_seq) + torch.arange(max_len - len(y_seq))
return mapper, alphas
def get_refinement_mapper(prompts, tokenizer, max_len=77):
x_seq = prompts[0]
mappers, alphas = [], []
for i in range(1, len(prompts)):
mapper, alpha = get_mapper(x_seq, prompts[i], tokenizer, max_len)
mappers.append(mapper)
alphas.append(alpha)
return torch.stack(mappers), torch.stack(alphas)
def get_word_inds(text: str, word_place: int, tokenizer):
split_text = text.split(" ")
if type(word_place) is str:
word_place = [i for i, word in enumerate(split_text) if word_place == word]
elif type(word_place) is int:
word_place = [word_place]
out = []
if len(word_place) > 0:
words_encode = [tokenizer.decode([item]).strip("#") for item in tokenizer.encode(text)][1:-1]
cur_len, ptr = 0, 0
for i in range(len(words_encode)):
cur_len += len(words_encode[i])
if ptr in word_place:
out.append(i + 1)
if cur_len >= len(split_text[ptr]):
ptr += 1
cur_len = 0
return np.array(out)
def get_replacement_mapper_(x: str, y: str, tokenizer, max_len=77):
words_x = x.split(' ')
words_y = y.split(' ')
if len(words_x) != len(words_y):
raise ValueError(f"attention replacement edit can only be applied on prompts with the same length"
f" but prompt A has {len(words_x)} words and prompt B has {len(words_y)} words.")
inds_replace = [i for i in range(len(words_y)) if words_y[i] != words_x[i]]
inds_source = [get_word_inds(x, i, tokenizer) for i in inds_replace]
inds_target = [get_word_inds(y, i, tokenizer) for i in inds_replace]
mapper = np.zeros((max_len, max_len))
i = j = 0
cur_inds = 0
while i < max_len and j < max_len:
if cur_inds < len(inds_source) and inds_source[cur_inds][0] == i:
inds_source_, inds_target_ = inds_source[cur_inds], inds_target[cur_inds]
if len(inds_source_) == len(inds_target_):
mapper[inds_source_, inds_target_] = 1
else:
ratio = 1 / len(inds_target_)
for i_t in inds_target_:
mapper[inds_source_, i_t] = ratio
cur_inds += 1
i += len(inds_source_)
j += len(inds_target_)
elif cur_inds < len(inds_source):
mapper[i, j] = 1
i += 1
j += 1
else:
mapper[j, j] = 1
i += 1
j += 1
return torch.from_numpy(mapper).float()
def get_replacement_mapper(prompts, tokenizer, max_len=77):
x_seq = prompts[0]
mappers = []
for i in range(1, len(prompts)):
mapper = get_replacement_mapper_(x_seq, prompts[i], tokenizer, max_len)
mappers.append(mapper)
return torch.stack(mappers)
================================================
FILE: models/instructpix2pix/LICENSE
================================================
Copyright 2023 Timothy Brooks, Aleksander Holynski, Alexei A. Efros
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Portions of code and models (such as pretrained checkpoints, which are fine-tuned starting from released Stable Diffusion checkpoints) are derived from the Stable Diffusion codebase (https://github.com/CompVis/stable-diffusion). Further restrictions may apply. Please consult the Stable Diffusion license `stable_diffusion/LICENSE`. Modified code is denoted as such in comments at the start of each file.
================================================
FILE: models/instructpix2pix/README.md
================================================
# InstructPix2Pix: Learning to Follow Image Editing Instructions
### [Project Page](https://www.timothybrooks.com/instruct-pix2pix/) | [Paper](https://arxiv.org/abs/2211.09800) | [Data](http://instruct-pix2pix.eecs.berkeley.edu/)
PyTorch implementation of InstructPix2Pix, an instruction-based image editing model, based on the original [CompVis/stable_diffusion](https://github.com/CompVis/stable-diffusion) repo.
[InstructPix2Pix: Learning to Follow Image Editing Instructions](https://www.timothybrooks.com/instruct-pix2pix/)
[Tim Brooks](https://www.timothybrooks.com/)\*,
[Aleksander Holynski](https://holynski.org/)\*,
[Alexei A. Efros](https://people.eecs.berkeley.edu/~efros/)
UC Berkeley
\*denotes equal contribution
## TL;DR: quickstart
Follow the instructions below to download and run InstructPix2Pix on your own images. These instructions have been tested on a GPU with >18GB VRAM. If you don't have a GPU, you may need to change the default configuration, or check out [other ways of using the model](https://github.com/timothybrooks/instruct-pix2pix#other-ways-of-using-instructpix2pix).
### Set up a conda environment, and download a pretrained model:
```
conda env create -f environment.yaml
conda activate ip2p
bash scripts/download_checkpoints.sh
```
### Edit a single image:
```
python edit_cli.py --input imgs/example.jpg --output imgs/output.jpg --edit "turn him into a cyborg"
# Optionally, you can specify parameters to tune your result:
# python edit_cli.py --steps 100 --resolution 512 --seed 1371 --cfg-text 7.5 --cfg-image 1.2 --input imgs/example.jpg --output imgs/output.jpg --edit "turn him into a cyborg"
```
### Or launch your own interactive editing Gradio app:
```
python edit_app.py
```
_(For advice on how to get the best results by tuning parameters, see the [Tips](https://github.com/timothybrooks/instruct-pix2pix#tips) section)._
## Setup
Install all dependencies with:
```
conda env create -f environment.yaml
```
Download the pretrained models by running:
```
bash scripts/download_checkpoints.sh
```
## Generated Dataset
Our image editing model is trained on a generated dataset consisting of 454,445 examples. Each example contains (1) an input image, (2) an editing instruction, and (3) an output edited image. We provide two versions of the dataset, one in which each pair of edited images is generated 100 times, and the best examples are chosen based on CLIP metrics (Section 3.1.2 in the paper) (`clip-filtered-dataset`), and one in which examples are randomly chosen (`random-sample-dataset`).
For the released version of this dataset, we've additionally filtered prompts and images for NSFW content. After NSFW filtering, the GPT-3 generated dataset contains 451,990 examples. The final image-pair datasets contain:
| | # of image editing examples | Dataset size |
|--|-----------------------|----------------------- |
| `random-sample-dataset` |451990|727GB|
| `clip-filtered-dataset` |313010|436GB|
To download one of these datasets, along with the entire NSFW-filtered text data, run the following command with the appropriate dataset name:
```
bash scripts/download_data.sh clip-filtered-dataset
```
## Training InstructPix2Pix
InstructPix2Pix is trained by fine-tuning from an initial StableDiffusion checkpoint. The first step is to download a Stable Diffusion checkpoint. For our trained models, we used the v1.5 checkpoint as the starting point. To download the same ones we used, you can run the following script:
```
bash scripts/download_pretrained_sd.sh
```
If you'd like to use a different checkpoint, point to it in the config file `configs/train.yaml`, on line 8, after `ckpt_path:`.
Next, we need to change the config to point to our downloaded (or generated) dataset. If you're using the `clip-filtered-dataset` from above, you can skip this. Otherwise, you may need to edit lines 85 and 94 of the config (`data.params.train.params.path`, `data.params.validation.params.path`).
Finally, start a training job with the following command:
```
python main.py --name default --base configs/train.yaml --train --gpus 0,1,2,3,4,5,6,7
```
## Creating your own dataset
Our generated dataset of paired images and editing instructions is made in two phases: First, we use GPT-3 to generate text triplets: (a) a caption describing an image, (b) an edit instruction, (c) a caption describing the image after the edit. Then, we turn pairs of captions (before/after the edit) into pairs of images using Stable Diffusion and Prompt-to-Prompt.
### (1) Generate a dataset of captions and instructions
We provide our generated dataset of captions and edit instructions [here](https://instruct-pix2pix.eecs.berkeley.edu/gpt-generated-prompts.jsonl). If you plan to use our captions+instructions, skip to step (2). Otherwise, if you would like to create your own text dataset, please follow steps (1.1-1.3) below. Note that generating very large datasets using GPT-3 can be expensive.
#### (1.1) Manually write a dataset of instructions and captions
The first step of the process is fine-tuning GPT-3. To do this, we made a dataset of 700 examples broadly covering of edits that we might want our model to be able to perform. Our examples are available [here](https://instruct-pix2pix.eecs.berkeley.edu/human-written-prompts.jsonl). These should be diverse and cover a wide range of possible captions and types of edits. Ideally, they should avoid duplication or significant overlap of captions and instructions. It is also important to be mindful of limitations of Stable Diffusion and Prompt-to-Prompt in writing these examples, such as inability to perform large spatial transformations (e.g., moving the camera, zooming in, swapping object locations).
Input prompts should closely match the distribution of input prompts used to generate the larger dataset. We sampled the 700 input prompts from the _LAION Improved Aesthetics 6.5+_ dataset and also use this dataset for generating examples. We found this dataset is quite noisy (many of the captions are overly long and contain irrelevant text). For this reason, we also considered MSCOCO and LAION-COCO datasets, but ultimately chose _LAION Improved Aesthetics 6.5+_ due to its diversity of content, proper nouns, and artistic mediums. If you choose to use another dataset or combination of datasets as input to GPT-3 when generating examples, we recommend you sample the input prompts from the same distribution when manually writing training examples.
#### (1.2) Finetune GPT-3
The next step is to finetune a large language model on the manually written instructions/outputs to generate edit instructions and edited caption from a new input caption. For this, we finetune GPT-3's Davinci model via the OpenAI API, although other language models could be used.
To prepare training data for GPT-3, one must first create an OpenAI developer account to access the needed APIs, and [set up the API keys on your local device](https://beta.openai.com/docs/api-reference/introduction). Also, run the `prompts/prepare_for_gpt.py` script, which forms the prompts into the correct format by concatenating instructions and captions and adding delimiters and stop sequences.
```bash
python dataset_creation/prepare_for_gpt.py --input-path data/human-written-prompts.jsonl --output-path data/human-written-prompts-for-gpt.jsonl
```
Next, finetune GPT-3 via the OpenAI CLI. We provide an example below, although please refer to OpenAI's official documentation for this, as best practices may change. We trained the Davinci model for a single epoch. You can experiment with smaller less expensive GPT-3 variants or with open source language models, although this may negatively affect performance.
```bash
openai api fine_tunes.create -t data/human-written-prompts-for-gpt.jsonl -m davinci --n_epochs 1 --suffix "instruct-pix2pix"
```
You can test out the finetuned GPT-3 model by launching the provided Gradio app:
```bash
python prompt_app.py --openai-api-key OPENAI_KEY --openai-model OPENAI_MODEL_NAME
```
#### (1.3) Generate a large dataset of captions and instructions
We now use the finetuned GPT-3 model to generate a large dataset. Our dataset cost thousands of dollars to create. See `prompts/gen_instructions_and_captions.py` for the script which generates these examples. We recommend first generating a small number of examples (by setting a low value of `--num-samples`) and gradually increasing the scale to ensure the results are working as desired before increasing scale.
```bash
python dataset_creation/generate_txt_dataset.py --openai-api-key OPENAI_KEY --openai-model OPENAI_MODEL_NAME
```
If you are generating at a very large scale (e.g., 100K+), it will be noteably faster to generate the dataset with multiple processes running in parallel. This can be accomplished by setting `--partitions=N` to a higher number and running multiple processes, setting each `--partition` to the corresponding value.
```bash
python dataset_creation/generate_txt_dataset.py --openai-api-key OPENAI_KEY --openai-model OPENAI_MODEL_NAME --partitions=10 --partition=0
```
### (2) Turn paired captions into paired images
The next step is to turn pairs of text captions into pairs of images. For this, we need to copy some pre-trained Stable Diffusion checkpoints to `stable_diffusion/models/ldm/stable-diffusion-v1/`. You may have already done this if you followed the instructions above for training with our provided data, but if not, you can do this by running:
```bash
bash scripts/download_pretrained_sd.sh
```
For our model, we used [checkpoint v1.5](https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned.ckpt), and the [new autoencoder](https://huggingface.co/stabilityai/sd-vae-ft-mse-original/resolve/main/vae-ft-mse-840000-ema-pruned.ckpt), but other models may work as well. If you choose to use other models, make sure to change point to the corresponding checkpoints by passing in the `--ckpt` and `--vae-ckpt` arguments. Once all checkpoints have been downloaded, we can generate the dataset with the following command:
```
python dataset_creation/generate_img_dataset.py --out_dir data/instruct-pix2pix-dataset-000 --prompts_file path/to/generated_prompts.jsonl
```
This command operates on a single GPU (typically a V100 or A100). To parallelize over many GPUs/machines, set `--n-partitions` to the total number of parallel jobs and `--partition` to the index of each job.
```
python dataset_creation/generate_img_dataset.py --out_dir data/instruct-pix2pix-dataset-000 --prompts_file path/to/generated_prompts.jsonl --n-partitions 100 --partition 0
```
The default parameters match that of our dataset, although in practice you can use a smaller number of steps (e.g., `--steps=25`) to generate high quality data faster. By default, we generate 100 samples per prompt and use CLIP filtering to keep a max of 4 per prompt. You can experiment with fewer samples by setting `--n-samples`. The command below turns off CLIP filtering entirely and is therefore faster:
```
python dataset_creation/generate_img_dataset.py --out_dir data/instruct-pix2pix-dataset-000 --prompts_file path/to/generated_prompts.jsonl --n-samples 4 --clip-threshold 0 --clip-dir-threshold 0 --clip-img-threshold 0 --n-partitions 100 --partition 0
```
After generating all of the dataset examples, run the following command below to create a list of the examples. This is needed for the dataset onject to efficiently be able to sample examples without needing to iterate over the entire dataset directory at the start of each training run.
```
python dataset_creation/prepare_dataset.py data/instruct-pix2pix-dataset-000
```
## Evaluation
To generate plots like the ones in Figures 8 and 10 in the paper, run the following command:
```
python metrics/compute_metrics.py --ckpt /path/to/your/model.ckpt
```
## Tips
If you're not getting the quality result you want, there may be a few reasons:
1. **Is the image not changing enough?** Your Image CFG weight may be too high. This value dictates how similar the output should be to the input. It's possible your edit requires larger changes from the original image, and your Image CFG weight isn't allowing that. Alternatively, your Text CFG weight may be too low. This value dictates how much to listen to the text instruction. The default Image CFG of 1.5 and Text CFG of 7.5 are a good starting point, but aren't necessarily optimal for each edit. Try:
* Decreasing the Image CFG weight, or
* Increasing the Text CFG weight, or
2. Conversely, **is the image changing too much**, such that the details in the original image aren't preserved? Try:
* Increasing the Image CFG weight, or
* Decreasing the Text CFG weight
3. Try generating results with different random seeds by setting "Randomize Seed" and running generation multiple times. You can also try setting "Randomize CFG" to sample new Text CFG and Image CFG values each time.
4. Rephrasing the instruction sometimes improves results (e.g., "turn him into a dog" vs. "make him a dog" vs. "as a dog").
5. Increasing the number of steps sometimes improves results.
6. Do faces look weird? The Stable Diffusion autoencoder has a hard time with faces that are small in the image. Try cropping the image so the face takes up a larger portion of the frame.
## Comments
- Our codebase is based on the [Stable Diffusion codebase](https://github.com/CompVis/stable-diffusion).
## BibTeX
```
@article{brooks2022instructpix2pix,
title={InstructPix2Pix: Learning to Follow Image Editing Instructions},
author={Brooks, Tim and Holynski, Aleksander and Efros, Alexei A},
journal={arXiv preprint arXiv:2211.09800},
year={2022}
}
```
## Other ways of using InstructPix2Pix
### InstructPix2Pix on [HuggingFace](https://huggingface.co/spaces/timbrooks/instruct-pix2pix):
> A browser-based version of the demo is available as a [HuggingFace space](https://huggingface.co/spaces/timbrooks/instruct-pix2pix). For this version, you only need a browser, a picture you want to edit, and an instruction! Note that this is a shared online demo, and processing time may be slower during peak utilization.
### InstructPix2Pix on [Replicate](https://replicate.com/timothybrooks/instruct-pix2pix):
> Replicate provides a production-ready cloud API for running the InstructPix2Pix model. You can run the model from any environment using a simple API call with cURL, Python, JavaScript, or your language of choice. Replicate also provides a web interface for running the model and sharing predictions.
### InstructPix2Pix in [Imaginairy](https://github.com/brycedrennan/imaginAIry#-edit-images-with-instructions-alone-by-instructpix2pix):
> Imaginairy offers another way of easily installing InstructPix2Pix with a single command. It can run on devices without GPUs (like a Macbook!).
> ```bash
> pip install imaginairy --upgrade
> aimg edit any-image.jpg --gif "turn him into a cyborg"
> ```
> It also offers an easy way to perform a bunch of edits on an image, and can save edits out to an animated GIF:
> ```
> aimg edit --gif --surprise-me pearl-earring.jpg
> ```
> 
### InstructPix2Pix in [🧨 Diffusers](https://github.com/huggingface/diffusers):
> InstructPix2Pix in Diffusers is a bit more optimized, so it may be faster and more suitable for GPUs with less memory. Below are instructions for installing the library and editing an image:
> 1. Install diffusers and relevant dependencies:
>
> ```bash
> pip install transformers accelerate torch
>
> pip install git+https://github.com/huggingface/diffusers.git
> ```
>
> 2. Load the model and edit the image:
>
> ```python
>
> import torch
> from diffusers import StableDiffusionInstructPix2PixPipeline, EulerAncestralDiscreteScheduler
>
> model_id = "timbrooks/instruct-pix2pix"
> pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(model_id, torch_dtype=torch.float16, safety_checker=None)
> pipe.to("cuda")
> pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
> # `image` is an RGB PIL.Image
> images = pipe("turn him into cyborg", image=image).images
> images[0]
> ```
>
> For more information, check the docs [here](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/pix2pix).
================================================
FILE: models/instructpix2pix/configs/generate.yaml
================================================
# File modified by authors of InstructPix2Pix from original (https://github.com/CompVis/stable-diffusion).
# See more details in LICENSE.
model:
base_learning_rate: 1.0e-04
target: ldm.models.diffusion.ddpm_edit.LatentDiffusion
params:
linear_start: 0.00085
linear_end: 0.0120
num_timesteps_cond: 1
log_every_t: 200
timesteps: 1000
first_stage_key: edited
cond_stage_key: edit
# image_size: 64
# image_size: 32
image_size: 16
channels: 4
cond_stage_trainable: false # Note: different from the one we trained before
conditioning_key: hybrid
monitor: val/loss_simple_ema
scale_factor: 0.18215
use_ema: true
load_ema: true
scheduler_config: # 10000 warmup steps
target: ldm.lr_scheduler.LambdaLinearScheduler
params:
warm_up_steps: [ 0 ]
cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
f_start: [ 1.e-6 ]
f_max: [ 1. ]
f_min: [ 1. ]
unet_config:
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
params:
image_size: 32 # unused
in_channels: 8
out_channels: 4
model_channels: 320
attention_resolutions: [ 4, 2, 1 ]
num_res_blocks: 2
channel_mult: [ 1, 2, 4, 4 ]
num_heads: 8
use_spatial_transformer: True
transformer_depth: 1
context_dim: 768
use_checkpoint: True
legacy: False
first_stage_config:
target: ldm.models.autoencoder.AutoencoderKL
params:
embed_dim: 4
monitor: val/rec_loss
ddconfig:
double_z: true
z_channels: 4
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 4
- 4
num_res_blocks: 2
attn_resolutions: []
dropout: 0.0
lossconfig:
target: torch.nn.Identity
cond_stage_config:
target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
data:
target: main.DataModuleFromConfig
params:
batch_size: 128
num_workers: 1
wrap: false
validation:
target: edit_dataset.EditDataset
params:
path: data/clip-filtered-dataset
cache_dir: data/
cache_name: data_10k
split: val
min_text_sim: 0.2
min_image_sim: 0.75
min_direction_sim: 0.2
max_samples_per_prompt: 1
min_resize_res: 512
max_resize_res: 512
crop_res: 512
output_as_edit: False
real_input: True
================================================
FILE: models/instructpix2pix/configs/train.yaml
================================================
# File modified by authors of InstructPix2Pix from original (https://github.com/CompVis/stable-diffusion).
# See more details in LICENSE.
model:
base_learning_rate: 1.0e-04
target: ldm.models.diffusion.ddpm_edit.LatentDiffusion
params:
ckpt_path: stable_diffusion/models/ldm/stable-diffusion-v1/v1-5-pruned-emaonly.ckpt
linear_start: 0.00085
linear_end: 0.0120
num_timesteps_cond: 1
log_every_t: 200
timesteps: 1000
first_stage_key: edited
cond_stage_key: edit
image_size: 32
channels: 4
cond_stage_trainable: false # Note: different from the one we trained before
conditioning_key: hybrid
monitor: val/loss_simple_ema
scale_factor: 0.18215
use_ema: true
load_ema: false
scheduler_config: # 10000 warmup steps
target: ldm.lr_scheduler.LambdaLinearScheduler
params:
warm_up_steps: [ 0 ]
cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
f_start: [ 1.e-6 ]
f_max: [ 1. ]
f_min: [ 1. ]
unet_config:
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
params:
image_size: 32 # unused
in_channels: 8
out_channels: 4
model_channels: 320
attention_resolutions: [ 4, 2, 1 ]
num_res_blocks: 2
channel_mult: [ 1, 2, 4, 4 ]
num_heads: 8
use_spatial_transformer: True
transformer_depth: 1
context_dim: 768
use_checkpoint: True
legacy: False
first_stage_config:
target: ldm.models.autoencoder.AutoencoderKL
params:
embed_dim: 4
monitor: val/rec_loss
ddconfig:
double_z: true
z_channels: 4
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 4
- 4
num_res_blocks: 2
attn_resolutions: []
dropout: 0.0
lossconfig:
target: torch.nn.Identity
cond_stage_config:
target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
data:
target: main.DataModuleFromConfig
params:
batch_size: 32
num_workers: 2
train:
target: edit_dataset.EditDataset
params:
path: data/clip-filtered-dataset
split: train
min_resize_res: 256
max_resize_res: 256
crop_res: 256
flip_prob: 0.5
validation:
target: edit_dataset.EditDataset
params:
path: data/clip-filtered-dataset
split: val
min_resize_res: 256
max_resize_res: 256
crop_res: 256
lightning:
callbacks:
image_logger:
target: main.ImageLogger
params:
batch_frequency: 2000
max_images: 2
increase_log_steps: False
trainer:
max_epochs: 2000
benchmark: True
accumulate_grad_batches: 4
check_val_every_n_epoch: 4
================================================
FILE: models/instructpix2pix/dataset_creation/generate_img_dataset.py
================================================
import argparse
import json
import sys
from pathlib import Path
import k_diffusion
import numpy as np
import torch
import torch.nn as nn
from einops import rearrange, repeat
from omegaconf import OmegaConf
from PIL import Image
from pytorch_lightning import seed_everything
from tqdm import tqdm
sys.path.append("./")
sys.path.append("./stable_diffusion")
from ldm.modules.attention import CrossAttention
from ldm.util import instantiate_from_config
from metrics.clip_similarity import ClipSimilarity
################################################################################
# Modified K-diffusion Euler ancestral sampler with prompt-to-prompt.
# https://github.com/crowsonkb/k-diffusion/blob/master/k_diffusion/sampling.py
def append_dims(x, target_dims):
"""Appends dimensions to the end of a tensor until it has target_dims dimensions."""
dims_to_append = target_dims - x.ndim
if dims_to_append < 0:
raise ValueError(f"input has {x.ndim} dims but target_dims is {target_dims}, which is less")
return x[(...,) + (None,) * dims_to_append]
def to_d(x, sigma, denoised):
"""Converts a denoiser output to a Karras ODE derivative."""
return (x - denoised) / append_dims(sigma, x.ndim)
def get_ancestral_step(sigma_from, sigma_to):
"""Calculates the noise level (sigma_down) to step down to and the amount
of noise to add (sigma_up) when doing an ancestral sampling step."""
sigma_up = min(sigma_to, (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5)
sigma_down = (sigma_to**2 - sigma_up**2) ** 0.5
return sigma_down, sigma_up
def sample_euler_ancestral(model, x, sigmas, prompt2prompt_threshold=0.0, **extra_args):
"""Ancestral sampling with Euler method steps."""
s_in = x.new_ones([x.shape[0]])
for i in range(len(sigmas) - 1):
prompt_to_prompt = prompt2prompt_threshold > i / (len(sigmas) - 2)
for m in model.modules():
if isinstance(m, CrossAttention):
m.prompt_to_prompt = prompt_to_prompt
denoised = model(x, sigmas[i] * s_in, **extra_args)
sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1])
d = to_d(x, sigmas[i], denoised)
# Euler method
dt = sigma_down - sigmas[i]
x = x + d * dt
if sigmas[i + 1] > 0:
# Make noise the same across all samples in batch.
x = x + torch.randn_like(x[:1]) * sigma_up
return x
################################################################################
def load_model_from_config(config, ckpt, vae_ckpt=None, verbose=False):
print(f"Loading model from {ckpt}")
pl_sd = torch.load(ckpt, map_location="cpu")
if "global_step" in pl_sd:
print(f"Global Step: {pl_sd['global_step']}")
sd = pl_sd["state_dict"]
if vae_ckpt is not None:
print(f"Loading VAE from {vae_ckpt}")
vae_sd = torch.load(vae_ckpt, map_location="cpu")["state_dict"]
sd = {
k: vae_sd[k[len("first_stage_model.") :]] if k.startswith("first_stage_model.") else v
for k, v in sd.items()
}
model = instantiate_from_config(config.model)
m, u = model.load_state_dict(sd, strict=False)
if len(m) > 0 and verbose:
print("missing keys:")
print(m)
if len(u) > 0 and verbose:
print("unexpected keys:")
print(u)
return model
class CFGDenoiser(nn.Module):
def __init__(self, model):
super().__init__()
self.inner_model = model
def forward(self, x, sigma, uncond, cond, cfg_scale):
x_in = torch.cat([x] * 2)
sigma_in = torch.cat([sigma] * 2)
cond_in = torch.cat([uncond, cond])
uncond, cond = self.inner_model(x_in, sigma_in, cond=cond_in).chunk(2)
return uncond + (cond - uncond) * cfg_scale
def to_pil(image: torch.Tensor) -> Image.Image:
image = 255.0 * rearrange(image.cpu().numpy(), "c h w -> h w c")
image = Image.fromarray(image.astype(np.uint8))
return image
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"--out_dir",
type=str,
required=True,
help="Path to output dataset directory.",
)
parser.add_argument(
"--prompts_file",
type=str,
required=True,
help="Path to prompts .jsonl file.",
)
parser.add_argument(
"--ckpt",
type=str,
default="stable_diffusion/models/ldm/stable-diffusion-v1/v1-5-pruned-emaonly.ckpt",
help="Path to stable diffusion checkpoint.",
)
parser.add_argument(
"--vae-ckpt",
type=str,
default="stable_diffusion/models/ldm/stable-diffusion-v1/vae-ft-mse-840000-ema-pruned.ckpt",
help="Path to vae checkpoint.",
)
parser.add_argument(
"--steps",
type=int,
default=100,
help="Number of sampling steps.",
)
parser.add_argument(
"--n-samples",
type=int,
default=100,
help="Number of samples to generate per prompt (before CLIP filtering).",
)
parser.add_argument(
"--max-out-samples",
type=int,
default=4,
help="Max number of output samples to save per prompt (after CLIP filtering).",
)
parser.add_argument(
"--n-partitions",
type=int,
default=1,
help="Number of total partitions.",
)
parser.add_argument(
"--partition",
type=int,
default=0,
help="Partition index.",
)
parser.add_argument(
"--min-p2p",
type=float,
default=0.1,
help="Min prompt2prompt threshold (portion of denoising for which to fix self attention maps).",
)
parser.add_argument(
"--max-p2p",
type=float,
default=0.9,
help="Max prompt2prompt threshold (portion of denoising for which to fix self attention maps).",
)
parser.add_argument(
"--min-cfg",
type=float,
default=7.5,
help="Min classifier free guidance scale.",
)
parser.add_argument(
"--max-cfg",
type=float,
default=15,
help="Max classifier free guidance scale.",
)
parser.add_argument(
"--clip-threshold",
type=float,
default=0.2,
help="CLIP threshold for text-image similarity of each image.",
)
parser.add_argument(
"--clip-dir-threshold",
type=float,
default=0.2,
help="Directional CLIP threshold for similarity of change between pairs of text and pairs of images.",
)
parser.add_argument(
"--clip-img-threshold",
type=float,
default=0.7,
help="CLIP threshold for image-image similarity.",
)
opt = parser.parse_args()
global_seed = torch.randint(1 << 32, ()).item()
print(f"Global seed: {global_seed}")
seed_everything(global_seed)
model = load_model_from_config(
OmegaConf.load("stable_diffusion/configs/stable-diffusion/v1-inference.yaml"),
ckpt=opt.ckpt,
vae_ckpt=opt.vae_ckpt,
)
model.cuda().eval()
model_wrap = k_diffusion.external.CompVisDenoiser(model)
clip_similarity = ClipSimilarity().cuda()
out_dir = Path(opt.out_dir)
out_dir.mkdir(exist_ok=True, parents=True)
with open(opt.prompts_file) as fp:
prompts = [json.loads(line) for line in fp]
print(f"Partition index {opt.partition} ({opt.partition + 1} / {opt.n_partitions})")
prompts = np.array_split(list(enumerate(prompts)), opt.n_partitions)[opt.partition]
with torch.no_grad(), torch.autocast("cuda"), model.ema_scope():
uncond = model.get_learned_conditioning(2 * [""])
sigmas = model_wrap.get_sigmas(opt.steps)
for i, prompt in tqdm(prompts, desc="Prompts"):
prompt_dir = out_dir.joinpath(f"{i:07d}")
prompt_dir.mkdir(exist_ok=True)
with open(prompt_dir.joinpath("prompt.json"), "w") as fp:
json.dump(prompt, fp)
cond = model.get_learned_conditioning([prompt["caption"], prompt["output"]])
results = {}
with tqdm(total=opt.n_samples, desc="Samples") as progress_bar:
while len(results) < opt.n_samples:
seed = torch.randint(1 << 32, ()).item()
if seed in results:
continue
torch.manual_seed(seed)
x = torch.randn(1, 4, 512 // 8, 512 // 8, device="cuda") * sigmas[0]
x = repeat(x, "1 ... -> n ...", n=2)
model_wrap_cfg = CFGDenoiser(model_wrap)
p2p_threshold = opt.min_p2p + torch.rand(()).item() * (opt.max_p2p - opt.min_p2p)
cfg_scale = opt.min_cfg + torch.rand(()).item() * (opt.max_cfg - opt.min_cfg)
extra_args = {"cond": cond, "uncond": uncond, "cfg_scale": cfg_scale}
samples_ddim = sample_euler_ancestral(model_wrap_cfg, x, sigmas, p2p_threshold, **extra_args)
x_samples_ddim = model.decode_first_stage(samples_ddim)
x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
x0 = x_samples_ddim[0]
x1 = x_samples_ddim[1]
clip_sim_0, clip_sim_1, clip_sim_dir, clip_sim_image = clip_similarity(
x0[None], x1[None], [prompt["caption"]], [prompt["output"]]
)
results[seed] = dict(
image_0=to_pil(x0),
image_1=to_pil(x1),
p2p_threshold=p2p_threshold,
cfg_scale=cfg_scale,
clip_sim_0=clip_sim_0[0].item(),
clip_sim_1=clip_sim_1[0].item(),
clip_sim_dir=clip_sim_dir[0].item(),
clip_sim_image=clip_sim_image[0].item(),
)
progress_bar.update()
# CLIP filter to get best samples for each prompt.
metadata = [
(result["clip_sim_dir"], seed)
for seed, result in results.items()
if result["clip_sim_image"] >= opt.clip_img_threshold
and result["clip_sim_dir"] >= opt.clip_dir_threshold
and result["clip_sim_0"] >= opt.clip_threshold
and result["clip_sim_1"] >= opt.clip_threshold
]
metadata.sort(reverse=True)
for _, seed in metadata[: opt.max_out_samples]:
result = results[seed]
image_0 = result.pop("image_0")
image_1 = result.pop("image_1")
image_0.save(prompt_dir.joinpath(f"{seed}_0.jpg"), quality=100)
image_1.save(prompt_dir.joinpath(f"{seed}_1.jpg"), quality=100)
with open(prompt_dir.joinpath(f"metadata.jsonl"), "a") as fp:
fp.write(f"{json.dumps(dict(seed=seed, **result))}\n")
print("Done.")
if __name__ == "__main__":
main()
================================================
FILE: models/instructpix2pix/dataset_creation/generate_txt_dataset.py
================================================
from __future__ import annotations
import json
import time
from argparse import ArgumentParser
from pathlib import Path
from typing import Optional
import datasets
import numpy as np
import openai
from tqdm.auto import tqdm
DELIMITER_0 = "\n##\n"
DELIMITER_1 = "\n%%\n"
STOP = "\nEND"
def generate(
openai_model: str,
caption: str,
num_retries: int = 3,
max_tokens: int = 256,
temperature: float = 0.7,
top_p: float = 1.0,
frequency_penalty: float = 0.1,
presence_penalty: float = 0.0,
sleep_on_error: float = 1.0,
) -> Optional[tuple[str, str]]:
for _ in range(1 + num_retries):
try:
response = openai.Completion.create(
model=openai_model,
prompt=caption + DELIMITER_0,
temperature=temperature,
max_tokens=max_tokens,
top_p=top_p,
frequency_penalty=frequency_penalty,
presence_penalty=presence_penalty,
stop=[STOP],
)
except Exception as e:
print(e)
time.sleep(sleep_on_error)
continue
output = response["choices"][0]["text"].split(DELIMITER_1)
if len(output) == 2:
instruction, edited_caption = output
results = openai.Moderation.create([instruction, edited_caption])["results"]
if results[0]["flagged"] or results[1]["flagged"]:
continue
if caption.strip().strip(".!?").lower() != edited_caption.strip().strip(".!?").lower():
return instruction, edited_caption
def main(openai_model: str, num_samples: int, num_partitions: int, partition: int, seed: int):
dataset = datasets.load_dataset("ChristophSchuhmann/improved_aesthetics_6.5plus", split="train")
# Other datasets we considered that may be worth trying:
# dataset = datasets.load_dataset("ChristophSchuhmann/MS_COCO_2017_URL_TEXT", split="train")
# dataset = datasets.load_dataset("laion/laion-coco", split="train")
np.random.seed(seed)
permutation = np.array_split(np.random.permutation(len(dataset)), num_partitions)[partition]
dataset = dataset[permutation]
captions = dataset["TEXT"]
urls = dataset["URL"]
output_path = f"data/dataset=laion-aesthetics-6.5_model={openai_model}_samples={num_samples}_partition={partition}.jsonl" # fmt: skip
print(f"Prompt file path: {output_path}")
count = 0
caption_set = set()
url_set = set()
if Path(output_path).exists():
with open(output_path, "r") as f:
for line in tqdm(f, desc="Resuming from existing prompts"):
prompt = json.loads(line)
if prompt["caption"] not in caption_set and prompt["url"] not in url_set:
caption_set.add(prompt["caption"])
url_set.add(prompt["url"])
count += 1
with open(output_path, "a") as fp:
with tqdm(total=num_samples - count, desc="Generating instructions and edited captions") as progress_bar:
for caption, url in zip(captions, urls):
if caption in caption_set or url in url_set:
continue
if openai.Moderation.create(caption)["results"][0]["flagged"]:
continue
edit_output = generate(openai_model, caption)
if edit_output is not None:
edit, output = edit_output
fp.write(f"{json.dumps(dict(caption=caption, edit=edit, output=output, url=url))}\n")
count += 1
progress_bar.update()
caption_set.add(caption)
url_set.add(url)
if count == num_samples:
break
if __name__ == "__main__":
parser = ArgumentParser()
parser.add_argument("--openai-api-key", required=True, type=str)
parser.add_argument("--openai-model", required=True, type=str)
parser.add_argument("--num-samples", default=10000, type=int)
parser.add_argument("--num-partitions", default=1, type=int)
parser.add_argument("--partition", default=0, type=int)
parser.add_argument("--seed", default=0, type=int)
args = parser.parse_args()
openai.api_key = args.openai_api_key
main(args.openai_model, args.num_samples, args.num_partitions, args.partition, args.seed)
================================================
FILE: models/instructpix2pix/dataset_creation/prepare_dataset.py
================================================
import json
from argparse import ArgumentParser
from pathlib import Path
from tqdm.auto import tqdm
def main():
parser = ArgumentParser()
parser.add_argument("dataset_dir")
args = parser.parse_args()
dataset_dir = Path(args.dataset_dir)
seeds = []
with tqdm(desc="Listing dataset image seeds") as progress_bar:
for prompt_dir in dataset_dir.iterdir():
if prompt_dir.is_dir():
prompt_seeds = [image_path.name.split("_")[0] for image_path in sorted(prompt_dir.glob("*_0.jpg"))]
if len(prompt_seeds) > 0:
seeds.append((prompt_dir.name, prompt_seeds))
progress_bar.update()
seeds.sort()
with open(dataset_dir.joinpath("seeds.json"), "w") as f:
json.dump(seeds, f)
if __name__ == "__main__":
main()
================================================
FILE: models/instructpix2pix/dataset_creation/prepare_for_gpt.py
================================================
import json
from argparse import ArgumentParser
from generate_txt_dataset import DELIMITER_0, DELIMITER_1, STOP
def main(input_path: str, output_path: str):
with open(input_path) as f:
prompts = [json.loads(l) for l in f]
with open(output_path, "w") as f:
for prompt in prompts:
prompt_for_gpt = {
"prompt": f"{prompt['input']}{DELIMITER_0}",
"completion": f"{prompt['edit']}{DELIMITER_1}{prompt['output']}{STOP}",
}
f.write(f"{json.dumps(prompt_for_gpt)}\n")
if __name__ == "__main__":
parser = ArgumentParser()
parser.add_argument("--input-path", required=True, type=str)
parser.add_argument("--output-path", required=True, type=str)
args = parser.parse_args()
main(args.input_path, args.output_path)
================================================
FILE: models/instructpix2pix/edit_app.py
================================================
from __future__ import annotations
import math
import random
import sys
from argparse import ArgumentParser
import einops
import gradio as gr
import k_diffusion as K
import numpy as np
import torch
import torch.nn as nn
from einops import rearrange
from omegaconf import OmegaConf
from PIL import Image, ImageOps
from torch import autocast
sys.path.append("./stable_diffusion")
from stable_diffusion.ldm.util import instantiate_from_config
help_text = """
If you're not getting what you want, there may be a few reasons:
1. Is the image not changing enough? Your Image CFG weight may be too high. This value dictates how similar the output should be to the input. It's possible your edit requires larger changes from the original image, and your Image CFG weight isn't allowing that. Alternatively, your Text CFG weight may be too low. This value dictates how much to listen to the text instruction. The default Image CFG of 1.5 and Text CFG of 7.5 are a good starting point, but aren't necessarily optimal for each edit. Try:
* Decreasing the Image CFG weight, or
* Incerasing the Text CFG weight, or
2. Conversely, is the image changing too much, such that the details in the original image aren't preserved? Try:
* Increasing the Image CFG weight, or
* Decreasing the Text CFG weight
3. Try generating results with different random seeds by setting "Randomize Seed" and running generation multiple times. You can also try setting "Randomize CFG" to sample new Text CFG and Image CFG values each time.
4. Rephrasing the instruction sometimes improves results (e.g., "turn him into a dog" vs. "make him a dog" vs. "as a dog").
5. Increasing the number of steps sometimes improves results.
6. Do faces look weird? The Stable Diffusion autoencoder has a hard time with faces that are small in the image. Try:
* Cropping the image so the face takes up a larger portion of the frame.
"""
example_instructions = [
"Make it a picasso painting",
"as if it were by modigliani",
"convert to a bronze statue",
"Turn it into an anime.",
"have it look like a graphic novel",
"make him gain weight",
"what would he look like bald?",
"Have him smile",
"Put him in a cocktail party.",
"move him at the beach.",
"add dramatic lighting",
"Convert to black and white",
"What if it were snowing?",
"Give him a leather jacket",
"Turn him into a cyborg!",
"make him wear a beanie",
]
class CFGDenoiser(nn.Module):
def __init__(self, model):
super().__init__()
self.inner_model = model
def forward(self, z, sigma, cond, uncond, text_cfg_scale, image_cfg_scale):
cfg_z = einops.repeat(z, "1 ... -> n ...", n=3)
cfg_sigma = einops.repeat(sigma, "1 ... -> n ...", n=3)
cfg_cond = {
"c_crossattn": [torch.cat([cond["c_crossattn"][0], uncond["c_crossattn"][0], uncond["c_crossattn"][0]])],
"c_concat": [torch.cat([cond["c_concat"][0], cond["c_concat"][0], uncond["c_concat"][0]])],
}
out_cond, out_img_cond, out_uncond = self.inner_model(cfg_z, cfg_sigma, cond=cfg_cond).chunk(3)
return out_uncond + text_cfg_scale * (out_cond - out_img_cond) + image_cfg_scale * (out_img_cond - out_uncond)
def load_model_from_config(config, ckpt, vae_ckpt=None, verbose=False):
print(f"Loading model from {ckpt}")
pl_sd = torch.load(ckpt, map_location="cpu")
if "global_step" in pl_sd:
print(f"Global Step: {pl_sd['global_step']}")
sd = pl_sd["state_dict"]
if vae_ckpt is not None:
print(f"Loading VAE from {vae_ckpt}")
vae_sd = torch.load(vae_ckpt, map_location="cpu")["state_dict"]
sd = {
k: vae_sd[k[len("first_stage_model.") :]] if k.startswith("first_stage_model.") else v
for k, v in sd.items()
}
model = instantiate_from_config(config.model)
m, u = model.load_state_dict(sd, strict=False)
if len(m) > 0 and verbose:
print("missing keys:")
print(m)
if len(u) > 0 and verbose:
print("unexpected keys:")
print(u)
return model
def main():
parser = ArgumentParser()
parser.add_argument("--resolution", default=512, type=int)
parser.add_argument("--config", default="configs/generate.yaml", type=str)
parser.add_argument("--ckpt", default="checkpoints/instruct-pix2pix-00-22000.ckpt", type=str)
parser.add_argument("--vae-ckpt", default=None, type=str)
args = parser.parse_args()
config = OmegaConf.load(args.config)
model = load_model_from_config(config, args.ckpt, args.vae_ckpt)
model.eval().cuda()
model_wrap = K.external.CompVisDenoiser(model)
model_wrap_cfg = CFGDenoiser(model_wrap)
null_token = model.get_learned_conditioning([""])
example_image = Image.open("imgs/example.jpg").convert("RGB")
def load_example(
steps: int,
randomize_seed: bool,
seed: int,
randomize_cfg: bool,
text_cfg_scale: float,
image_cfg_scale: float,
):
example_instruction = random.choice(example_instructions)
return [example_image, example_instruction] + generate(
example_image,
example_instruction,
steps,
randomize_seed,
seed,
randomize_cfg,
text_cfg_scale,
image_cfg_scale,
)
def generate(
input_image: Image.Image,
instruction: str,
steps: int,
randomize_seed: bool,
seed: int,
randomize_cfg: bool,
text_cfg_scale: float,
image_cfg_scale: float,
):
seed = random.randint(0, 100000) if randomize_seed else seed
text_cfg_scale = round(random.uniform(6.0, 9.0), ndigits=2) if randomize_cfg else text_cfg_scale
image_cfg_scale = round(random.uniform(1.2, 1.8), ndigits=2) if randomize_cfg else image_cfg_scale
width, height = input_image.size
factor = args.resolution / max(width, height)
factor = math.ceil(min(width, height) * factor / 64) * 64 / min(width, height)
width = int((width * factor) // 64) * 64
height = int((height * factor) // 64) * 64
input_image = ImageOps.fit(input_image, (width, height), method=Image.Resampling.LANCZOS)
if instruction == "":
return [input_image, seed]
with torch.no_grad(), autocast("cuda"), model.ema_scope():
cond = {}
cond["c_crossattn"] = [model.get_learned_conditioning([instruction])]
input_image = 2 * torch.tensor(np.array(input_image)).float() / 255 - 1
input_image = rearrange(input_image, "h w c -> 1 c h w").to(model.device)
cond["c_concat"] = [model.encode_first_stage(input_image).mode()]
uncond = {}
uncond["c_crossattn"] = [null_token]
uncond["c_concat"] = [torch.zeros_like(cond["c_concat"][0])]
sigmas = model_wrap.get_sigmas(steps)
extra_args = {
"cond": cond,
"uncond": uncond,
"text_cfg_scale": text_cfg_scale,
"image_cfg_scale": image_cfg_scale,
}
torch.manual_seed(seed)
z = torch.randn_like(cond["c_concat"][0]) * sigmas[0]
z = K.sampling.sample_euler_ancestral(model_wrap_cfg, z, sigmas, extra_args=extra_args)
x = model.decode_first_stage(z)
x = torch.clamp((x + 1.0) / 2.0, min=0.0, max=1.0)
x = 255.0 * rearrange(x, "1 c h w -> h w c")
edited_image = Image.fromarray(x.type(torch.uint8).cpu().numpy())
return [seed, text_cfg_scale, image_cfg_scale, edited_image]
def reset():
return [0, "Randomize Seed", 1371, "Fix CFG", 7.5, 1.5, None]
with gr.Blocks(css="footer {visibility: hidden}") as demo:
with gr.Row():
with gr.Column(scale=1, min_width=100):
generate_button = gr.Button("Generate")
with gr.Column(scale=1, min_width=100):
load_button = gr.Button("Load Example")
with gr.Column(scale=1, min_width=100):
reset_button = gr.Button("Reset")
with gr.Column(scale=3):
instruction = gr.Textbox(lines=1, label="Edit Instruction", interactive=True)
with gr.Row():
input_image = gr.Image(label="Input Image", type="pil", interactive=True)
edited_image = gr.Image(label=f"Edited Image", type="pil", interactive=False)
input_image.style(height=512, width=512)
edited_image.style(height=512, width=512)
with gr.Row():
steps = gr.Number(value=100, precision=0, label="Steps", interactive=True)
randomize_seed = gr.Radio(
["Fix Seed", "Randomize Seed"],
value="Randomize Seed",
type="index",
show_label=False,
interactive=True,
)
seed = gr.Number(value=1371, precision=0, label="Seed", interactive=True)
randomize_cfg = gr.Radio(
["Fix CFG", "Randomize CFG"],
value="Fix CFG",
type="index",
show_label=False,
interactive=True,
)
text_cfg_scale = gr.Number(value=7.5, label=f"Text CFG", interactive=True)
image_cfg_scale = gr.Number(value=1.5, label=f"Image CFG", interactive=True)
gr.Markdown(help_text)
load_button.click(
fn=load_example,
inputs=[
steps,
randomize_seed,
seed,
randomize_cfg,
text_cfg_scale,
image_cfg_scale,
],
outputs=[input_image, instruction, seed, text_cfg_scale, image_cfg_scale, edited_image],
)
generate_button.click(
fn=generate,
inputs=[
input_image,
instruction,
steps,
randomize_seed,
seed,
randomize_cfg,
text_cfg_scale,
image_cfg_scale,
],
outputs=[seed, text_cfg_scale, image_cfg_scale, edited_image],
)
reset_button.click(
fn=reset,
inputs=[],
outputs=[steps, randomize_seed, seed, randomize_cfg, text_cfg_scale, image_cfg_scale, edited_image],
)
demo.queue(concurrency_count=1)
demo.launch(share=True)
if __name__ == "__main__":
main()
================================================
FILE: models/instructpix2pix/edit_cli.py
================================================
from __future__ import annotations
import math
import random
import sys
from argparse import ArgumentParser
import einops
import k_diffusion as K
import numpy as np
import torch
import torch.nn as nn
from einops import rearrange
from omegaconf import OmegaConf
from PIL import Image, ImageOps
from torch import autocast
sys.path.append("./stable_diffusion")
from stable_diffusion.ldm.util import instantiate_from_config
class CFGDenoiser(nn.Module):
def __init__(self, model):
super().__init__()
self.inner_model = model
def forward(self, z, sigma, cond, uncond, text_cfg_scale, image_cfg_scale):
cfg_z = einops.repeat(z, "1 ... -> n ...", n=3)
cfg_sigma = einops.repeat(sigma, "1 ... -> n ...", n=3)
cfg_cond = {
"c_crossattn": [torch.cat([cond["c_crossattn"][0], uncond["c_crossattn"][0], uncond["c_crossattn"][0]])],
"c_concat": [torch.cat([cond["c_concat"][0], cond["c_concat"][0], uncond["c_concat"][0]])],
}
out_cond, out_img_cond, out_uncond = self.inner_model(cfg_z, cfg_sigma, cond=cfg_cond).chunk(3)
return out_uncond + text_cfg_scale * (out_cond - out_img_cond) + image_cfg_scale * (out_img_cond - out_uncond)
def load_model_from_config(config, ckpt, vae_ckpt=None, verbose=False):
print(f"Loading model from {ckpt}")
pl_sd = torch.load(ckpt, map_location="cpu")
if "global_step" in pl_sd:
print(f"Global Step: {pl_sd['global_step']}")
sd = pl_sd["state_dict"]
if vae_ckpt is not None:
print(f"Loading VAE from {vae_ckpt}")
vae_sd = torch.load(vae_ckpt, map_location="cpu")["state_dict"]
sd = {
k: vae_sd[k[len("first_stage_model.") :]] if k.startswith("first_stage_model.") else v
for k, v in sd.items()
}
model = instantiate_from_config(config.model)
m, u = model.load_state_dict(sd, strict=False)
if len(m) > 0 and verbose:
print("missing keys:")
print(m)
if len(u) > 0 and verbose:
print("unexpected keys:")
print(u)
return model
def main():
parser = ArgumentParser()
parser.add_argument("--resolution", default=512, type=int)
parser.add_argument("--steps", default=100, type=int)
parser.add_argument("--config", default="configs/generate.yaml", type=str)
parser.add_argument("--ckpt", default="checkpoints/instruct-pix2pix-00-22000.ckpt", type=str)
parser.add_argument("--vae-ckpt", default=None, type=str)
parser.add_argument("--input", required=True, type=str)
parser.add_argument("--output", required=True, type=str)
parser.add_argument("--edit", required=True, type=str)
parser.add_argument("--cfg-text", default=7.5, type=float)
parser.add_argument("--cfg-image", default=1.5, type=float)
parser.add_argument("--seed", type=int)
args = parser.parse_args()
config = OmegaConf.load(args.config)
model = load_model_from_config(config, args.ckpt, args.vae_ckpt)
model.eval().cuda()
model_wrap = K.external.CompVisDenoiser(model)
model_wrap_cfg = CFGDenoiser(model_wrap)
null_token = model.get_learned_conditioning([""])
seed = random.randint(0, 100000) if args.seed is None else args.seed
input_image = Image.open(args.input).convert("RGB")
width, height = input_image.size
factor = args.resolution / max(width, height)
factor = math.ceil(min(width, height) * factor / 64) * 64 / min(width, height)
width = int((width * factor) // 64) * 64
height = int((height * factor) // 64) * 64
input_image = ImageOps.fit(input_image, (width, height), method=Image.Resampling.LANCZOS)
if args.edit == "":
input_image.save(args.output)
return
with torch.no_grad(), autocast("cuda"), model.ema_scope():
cond = {}
cond["c_crossattn"] = [model.get_learned_conditioning([args.edit])]
input_image = 2 * torch.tensor(np.array(input_image)).float() / 255 - 1
input_image = rearrange(input_image, "h w c -> 1 c h w").to(model.device)
cond["c_concat"] = [model.encode_first_stage(input_image).mode()]
uncond = {}
uncond["c_crossattn"] = [null_token]
uncond["c_concat"] = [torch.zeros_like(cond["c_concat"][0])]
sigmas = model_wrap.get_sigmas(args.steps)
extra_args = {
"cond": cond,
"uncond": uncond,
"text_cfg_scale": args.cfg_text,
"image_cfg_scale": args.cfg_image,
}
torch.manual_seed(seed)
z = torch.randn_like(cond["c_concat"][0]) * sigmas[0]
z = K.sampling.sample_euler_ancestral(model_wrap_cfg, z, sigmas, extra_args=extra_args)
x = model.decode_first_stage(z)
x = torch.clamp((x + 1.0) / 2.0, min=0.0, max=1.0)
x = 255.0 * rearrange(x, "1 c h w -> h w c")
edited_image = Image.fromarray(x.type(torch.uint8).cpu().numpy())
edited_image.save(args.output)
if __name__ == "__main__":
main()
================================================
FILE: models/instructpix2pix/edit_dataset.py
================================================
from __future__ import annotations
import json
import math
from pathlib import Path
from typing import Any
import numpy as np
import torch
import torchvision
from einops import rearrange
from PIL import Image
from torch.utils.data import Dataset
class EditDataset(Dataset):
def __init__(
self,
path: str,
split: str = "train",
splits: tuple[float, float, float] = (0.9, 0.05, 0.05),
min_resize_res: int = 256,
max_resize_res: int = 256,
crop_res: int = 256,
flip_prob: float = 0.0,
):
assert split in ("train", "val", "test")
assert sum(splits) == 1
self.path = path
self.min_resize_res = min_resize_res
self.max_resize_res = max_resize_res
self.crop_res = crop_res
self.flip_prob = flip_prob
with open(Path(self.path, "seeds.json")) as f:
self.seeds = json.load(f)
split_0, split_1 = {
"train": (0.0, splits[0]),
"val": (splits[0], splits[0] + splits[1]),
"test": (splits[0] + splits[1], 1.0),
}[split]
idx_0 = math.floor(split_0 * len(self.seeds))
idx_1 = math.floor(split_1 * len(self.seeds))
self.seeds = self.seeds[idx_0:idx_1]
def __len__(self) -> int:
return len(self.seeds)
def __getitem__(self, i: int) -> dict[str, Any]:
name, seeds = self.seeds[i]
propt_dir = Path(self.path, name)
seed = seeds[torch.randint(0, len(seeds), ()).item()]
with open(propt_dir.joinpath("prompt.json")) as fp:
prompt = json.load(fp)["edit"]
image_0 = Image.open(propt_dir.joinpath(f"{seed}_0.jpg"))
image_1 = Image.open(propt_dir.joinpath(f"{seed}_1.jpg"))
reize_res = torch.randint(self.min_resize_res, self.max_resize_res + 1, ()).item()
image_0 = image_0.resize((reize_res, reize_res), Image.Resampling.LANCZOS)
image_1 = image_1.resize((reize_res, reize_res), Image.Resampling.LANCZOS)
image_0 = rearrange(2 * torch.tensor(np.array(image_0)).float() / 255 - 1, "h w c -> c h w")
image_1 = rearrange(2 * torch.tensor(np.array(image_1)).float() / 255 - 1, "h w c -> c h w")
crop = torchvision.transforms.RandomCrop(self.crop_res)
flip = torchvision.transforms.RandomHorizontalFlip(float(self.flip_prob))
image_0, image_1 = flip(crop(torch.cat((image_0, image_1)))).chunk(2)
return dict(edited=image_1, edit=dict(c_concat=image_0, c_crossattn=prompt))
class EditDatasetEval(Dataset):
def __init__(
self,
path: str,
split: str = "train",
splits: tuple[float, float, float] = (0.9, 0.05, 0.05),
res: int = 256,
):
assert split in ("train", "val", "test")
assert sum(splits) == 1
self.path = path
self.res = res
with open(Path(self.path, "seeds.json")) as f:
self.seeds = json.load(f)
split_0, split_1 = {
"train": (0.0, splits[0]),
"val": (splits[0], splits[0] + splits[1]),
"test": (splits[0] + splits[1], 1.0),
}[split]
idx_0 = math.floor(split_0 * len(self.seeds))
idx_1 = math.floor(split_1 * len(self.seeds))
self.seeds = self.seeds[idx_0:idx_1]
def __len__(self) -> int:
return len(self.seeds)
def __getitem__(self, i: int) -> dict[str, Any]:
name, seeds = self.seeds[i]
propt_dir = Path(self.path, name)
seed = seeds[torch.randint(0, len(seeds), ()).item()]
with open(propt_dir.joinpath("prompt.json")) as fp:
prompt = json.load(fp)
edit = prompt["edit"]
input_prompt = prompt["input"]
output_prompt = prompt["output"]
image_0 = Image.open(propt_dir.joinpath(f"{seed}_0.jpg"))
reize_res = torch.randint(self.res, self.res + 1, ()).item()
image_0 = image_0.resize((reize_res, reize_res), Image.Resampling.LANCZOS)
image_0 = rearrange(2 * torch.tensor(np.array(image_0)).float() / 255 - 1, "h w c -> c h w")
return dict(image_0=image_0, input_prompt=input_prompt, edit=edit, output_prompt=output_prompt)
================================================
FILE: models/instructpix2pix/environment.yaml
================================================
# File modified by authors of InstructPix2Pix from original (https://github.com/CompVis/stable-diffusion).
# See more details in LICENSE.
name: ip2p
channels:
- pytorch
- defaults
dependencies:
- python=3.8.5
- pip=20.3
- cudatoolkit=11.3
- pytorch=1.11.0
- torchvision=0.12.0
- numpy=1.19.2
- pip:
- albumentations==0.4.3
- datasets==2.8.0
- diffusers
- opencv-python==4.1.2.30
- pudb==2019.2
- invisible-watermark
- imageio==2.9.0
- imageio-ffmpeg==0.4.2
- pytorch-lightning==1.4.2
- omegaconf==2.1.1
- test-tube>=0.7.5
- streamlit>=0.73.1
- einops==0.3.0
- torch-fidelity==0.3.0
- transformers==4.19.2
- torchmetrics==0.6.0
- kornia==0.6
- -e git+https://github.com/CompVis/taming-transformers.git@master#egg=taming-transformers
- -e git+https://github.com/openai/CLIP.git@main#egg=clip
- openai
- gradio
- seaborn
- git+https://github.com/crowsonkb/k-diffusion.git
================================================
FILE: models/instructpix2pix/main.py
================================================
import argparse, os, sys, datetime, glob
import numpy as np
import time
import torch
import torchvision
import pytorch_lightning as pl
import json
import pickle
from packaging import version
from omegaconf import OmegaConf
from torch.utils.data import DataLoader, Dataset
from functools import partial
from PIL import Image
import torch.distributed as dist
from pytorch_lightning import seed_everything
from pytorch_lightning.trainer import Trainer
from pytorch_lightning.callbacks import ModelCheckpoint, Callback, LearningRateMonitor
from pytorch_lightning.utilities.distributed import rank_zero_only
from pytorch_lightning.utilities import rank_zero_info
from pytorch_lightning.plugins import DDPPlugin
sys.path.append("./stable_diffusion")
from ldm.data.base import Txt2ImgIterableBaseDataset
from ldm.util import instantiate_from_config
def get_parser(**parser_kwargs):
def str2bool(v):
if isinstance(v, bool):
return v
if v.lower() in ("yes", "true", "t", "y", "1"):
return True
elif v.lower() in ("no", "false", "f", "n", "0"):
return False
else:
raise argparse.ArgumentTypeError("Boolean value expected.")
parser = argparse.ArgumentParser(**parser_kwargs)
parser.add_argument(
"-n",
"--name",
type=str,
const=True,
default="",
nargs="?",
help="postfix for logdir",
)
parser.add_argument(
"-r",
"--resume",
type=str,
const=True,
default="",
nargs="?",
help="resume from logdir or checkpoint in logdir",
)
parser.add_argument(
"-b",
"--base",
nargs="*",
metavar="base_config.yaml",
help="paths to base configs. Loaded from left-to-right. "
"Parameters can be overwritten or added with command-line options of the form `--key value`.",
default=list(),
)
parser.add_argument(
"-t",
"--train",
type=str2bool,
const=True,
default=False,
nargs="?",
help="train",
)
parser.add_argument(
"--no-test",
type=str2bool,
const=True,
default=False,
nargs="?",
help="disable test",
)
parser.add_argument(
"-p",
"--project",
help="name of new or path to existing project"
)
parser.add_argument(
"-d",
"--debug",
type=str2bool,
nargs="?",
const=True,
default=False,
help="enable post-mortem debugging",
)
parser.add_argument(
"-s",
"--seed",
type=int,
default=23,
help="seed for seed_everything",
)
parser.add_argument(
"-f",
"--postfix",
type=str,
default="",
help="post-postfix for default name",
)
parser.add_argument(
"-l",
"--logdir",
type=str,
default="logs",
help="directory for logging dat shit",
)
parser.add_argument(
"--scale_lr",
action="store_true",
default=False,
help="scale base-lr by ngpu * batch_size * n_accumulate",
)
return parser
def nondefault_trainer_args(opt):
parser = argparse.ArgumentParser()
parser = Trainer.add_argparse_args(parser)
args = parser.parse_args([])
return sorted(k for k in vars(args) if getattr(opt, k) != getattr(args, k))
class WrappedDataset(Dataset):
"""Wraps an arbitrary object with __len__ and __getitem__ into a pytorch dataset"""
def __init__(self, dataset):
self.data = dataset
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
return self.data[idx]
def worker_init_fn(_):
worker_info = torch.utils.data.get_worker_info()
dataset = worker_info.dataset
worker_id = worker_info.id
if isinstance(dataset, Txt2ImgIterableBaseDataset):
split_size = dataset.num_records // worker_info.num_workers
# reset num_records to the true number to retain reliable length information
dataset.sample_ids = dataset.valid_ids[worker_id * split_size:(worker_id + 1) * split_size]
current_id = np.random.choice(len(np.random.get_state()[1]), 1)
return np.random.seed(np.random.get_state()[1][current_id] + worker_id)
else:
return np.random.seed(np.random.get_state()[1][0] + worker_id)
class DataModuleFromConfig(pl.LightningDataModule):
def __init__(self, batch_size, train=None, validation=None, test=None, predict=None,
wrap=False, num_workers=None, shuffle_test_loader=False, use_worker_init_fn=False,
shuffle_val_dataloader=False):
super().__init__()
self.batch_size = batch_size
self.dataset_configs = dict()
self.num_workers = num_workers if num_workers is not None else batch_size * 2
self.use_worker_init_fn = use_worker_init_fn
if train is not None:
self.dataset_configs["train"] = train
self.train_dataloader = self._train_dataloader
if validation is not None:
self.dataset_configs["validation"] = validation
self.val_dataloader = partial(self._val_dataloader, shuffle=shuffle_val_dataloader)
if test is not None:
self.dataset_configs["test"] = test
self.test_dataloader = partial(self._test_dataloader, shuffle=shuffle_test_loader)
if predict is not None:
self.dataset_configs["predict"] = predict
self.predict_dataloader = self._predict_dataloader
self.wrap = wrap
def prepare_data(self):
for data_cfg in self.dataset_configs.values():
instantiate_from_config(data_cfg)
def setup(self, stage=None):
self.datasets = dict(
(k, instantiate_from_config(self.dataset_configs[k]))
for k in self.dataset_configs)
if self.wrap:
for k in self.datasets:
self.datasets[k] = WrappedDataset(self.datasets[k])
def _train_dataloader(self):
is_iterable_dataset = isinstance(self.datasets['train'], Txt2ImgIterableBaseDataset)
if is_iterable_dataset or self.use_worker_init_fn:
init_fn = worker_init_fn
else:
init_fn = None
return DataLoader(self.datasets["train"], batch_size=self.batch_size,
num_workers=self.num_workers, shuffle=False if is_iterable_dataset else True,
worker_init_fn=init_fn, persistent_workers=True)
def _val_dataloader(self, shuffle=False):
if isinstance(self.datasets['validation'], Txt2ImgIterableBaseDataset) or self.use_worker_init_fn:
init_fn = worker_init_fn
else:
init_fn = None
return DataLoader(self.datasets["validation"],
batch_size=self.batch_size,
num_workers=self.num_workers,
worker_init_fn=init_fn,
shuffle=shuffle, persistent_workers=True)
def _test_dataloader(self, shuffle=False):
is_iterable_dataset = isinstance(self.datasets['train'], Txt2ImgIterableBaseDataset)
if is_iterable_dataset or self.use_worker_init_fn:
init_fn = worker_init_fn
else:
init_fn = None
# do not shuffle dataloader for iterable dataset
shuffle = shuffle and (not is_iterable_dataset)
return DataLoader(self.datasets["test"], batch_size=self.batch_size,
num_workers=self.num_workers, worker_init_fn=init_fn, shuffle=shuffle, persistent_workers=True)
def _predict_dataloader(self, shuffle=False):
if isinstance(self.datasets['predict'], Txt2ImgIterableBaseDataset) or self.use_worker_init_fn:
init_fn = worker_init_fn
else:
init_fn = None
return DataLoader(self.datasets["predict"], batch_size=self.batch_size,
num_workers=self.num_workers, worker_init_fn=init_fn, persistent_workers=True)
class SetupCallback(Callback):
def __init__(self, resume, now, logdir, ckptdir, cfgdir, config, lightning_config):
super().__init__()
self.resume = resume
self.now = now
self.logdir = logdir
self.ckptdir = ckptdir
self.cfgdir = cfgdir
self.config = config
self.lightning_config = lightning_config
def on_keyboard_interrupt(self, trainer, pl_module):
if trainer.global_rank == 0:
print("Summoning checkpoint.")
ckpt_path = os.path.join(self.ckptdir, "last.ckpt")
trainer.save_checkpoint(ckpt_path)
def on_pretrain_routine_start(self, trainer, pl_module):
if trainer.global_rank == 0:
# Create logdirs and save configs
# os.makedirs(self.logdir, exist_ok=True)
# os.makedirs(self.ckptdir, exist_ok=True)
# os.makedirs(self.cfgdir, exist_ok=True)
if "callbacks" in self.lightning_config:
if 'metrics_over_trainsteps_checkpoint' in self.lightning_config['callbacks']:
os.makedirs(os.path.join(self.ckptdir, 'trainstep_checkpoints'), exist_ok=True)
print("Project config")
print(OmegaConf.to_yaml(self.config))
OmegaConf.save(self.config,
os.path.join(self.cfgdir, "{}-project.yaml".format(self.now)))
print("Lightning config")
print(OmegaConf.to_yaml(self.lightning_config))
OmegaConf.save(OmegaConf.create({"lightning": self.lightning_config}),
os.path.join(self.cfgdir, "{}-lightning.yaml".format(self.now)))
def get_world_size():
if not dist.is_available():
return 1
if not dist.is_initialized():
return 1
return dist.get_world_size()
def all_gather(data):
"""
Run all_gather on arbitrary picklable data (not necessarily tensors)
Args:
data: any picklable object
Returns:
list[data]: list of data gathered from each rank
"""
world_size = get_world_size()
if world_size == 1:
return [data]
# serialized to a Tensor
origin_size = None
if not isinstance(data, torch.Tensor):
buffer = pickle.dumps(data)
storage = torch.ByteStorage.from_buffer(buffer)
tensor = torch.ByteTensor(storage).to("cuda")
else:
origin_size = data.size()
tensor = data.reshape(-1)
tensor_type = tensor.dtype
# obtain Tensor size of each rank
local_size = torch.LongTensor([tensor.numel()]).to("cuda")
size_list = [torch.LongTensor([0]).to("cuda") for _ in range(world_size)]
dist.all_gather(size_list, local_size)
size_list = [int(size.item()) for size in size_list]
max_size = max(size_list)
# receiving Tensor from all ranks
# we pad the tensor because torch all_gather does not support
# gathering tensors of different shapes
tensor_list = []
for _ in size_list:
tensor_list.append(torch.FloatTensor(size=(max_size,)).cuda().to(tensor_type))
if local_size != max_size:
padding = torch.FloatTensor(size=(max_size - local_size,)).cuda().to(tensor_type)
tensor = torch.cat((tensor, padding), dim=0)
dist.all_gather(tensor_list, tensor)
data_list = []
for size, tensor in zip(size_list, tensor_list):
if origin_size is None:
buffer = tensor.cpu().numpy().tobytes()[:size]
data_list.append(pickle.loads(buffer))
else:
buffer = tensor[:size]
data_list.append(buffer)
if origin_size is not None:
new_shape = [-1] + list(origin_size[1:])
resized_list = []
for data in data_list:
# suppose the difference of tensor size exist in first dimension
data = data.reshape(new_shape)
resized_list.append(data)
return resized_list
else:
return data_list
class ImageLogger(Callback):
def __init__(self, batch_frequency, max_images, clamp=True, increase_log_steps=True,
rescale=True, disabled=False, log_on_batch_idx=False, log_first_step=False,
log_images_kwargs=None):
super().__init__()
self.rescale = rescale
self.batch_freq = batch_frequency
self.max_images = max_images
self.logger_log_images = {
pl.loggers.TestTubeLogger: self._testtube,
}
self.log_steps = [2 ** n for n in range(6, int(np.log2(self.batch_freq)) + 1)]
if not increase_log_steps:
self.log_steps = [self.batch_freq]
self.clamp = clamp
self.disabled = disabled
self.log_on_batch_idx = log_on_batch_idx
self.log_images_kwargs = log_images_kwargs if log_images_kwargs else {}
self.log_first_step = log_first_step
@rank_zero_only
def _testtube(self, pl_module, images, batch_idx, split):
for k in images:
grid = torchvision.utils.make_grid(images[k])
grid = (grid + 1.0) / 2.0 # -1,1 -> 0,1; c,h,w
tag = f"{split}/{k}"
pl_module.logger.experiment.add_image(
tag, grid,
global_step=pl_module.global_step)
@rank_zero_only
def log_local(self, save_dir, split, images, prompts,
global_step, current_epoch, batch_idx):
root = os.path.join(save_dir, "images", split)
names = {"reals": "before", "inputs": "after", "reconstruction": "before-vq", "samples": "after-gen"}
# print(root)
for k in images:
grid = torchvision.utils.make_grid(images[k], nrow=8)
if self.rescale:
grid = (grid + 1.0) / 2.0 # -1,1 -> 0,1; c,h,w
grid = grid.transpose(0, 1).transpose(1, 2).squeeze(-1)
grid = grid.numpy()
grid = (grid * 255).astype(np.uint8)
filename = "gs-{:06}_e-{:06}_b-{:06}_{}.png".format(
global_step,
current_epoch,
batch_idx,
names[k])
path = os.path.join(root, filename)
os.makedirs(os.path.split(path)[0], exist_ok=True)
# print(path)
Image.fromarray(grid).save(path)
filename = "gs-{:06}_e-{:06}_b-{:06}_prompt.json".format(
global_step,
current_epoch,
batch_idx)
path = os.path.join(root, filename)
with open(path, "w") as f:
for p in prompts:
f.write(f"{json.dumps(p)}\n")
def log_img(self, pl_module, batch, batch_idx, split="train"):
check_idx = batch_idx if self.log_on_batch_idx else pl_module.global_step
if (self.check_frequency(check_idx) and # batch_idx % self.batch_freq == 0
hasattr(pl_module, "log_images") and
callable(pl_module.log_images) and
self.max_images > 0) or (split == "val" and batch_idx == 0):
logger = type(pl_module.logger)
is_train = pl_module.training
if is_train:
pl_module.eval()
with torch.no_grad():
images = pl_module.log_images(batch, split=split, **self.log_images_kwargs)
prompts = batch["edit"]["c_crossattn"][:self.max_images]
prompts = [p for ps in all_gather(prompts) for p in ps]
for k in images:
N = min(images[k].shape[0], self.max_images)
images[k] = images[k][:N]
images[k] = torch.cat(all_gather(images[k][:N]))
if isinstance(images[k], torch.Tensor):
images[k] = images[k].detach().cpu()
if self.clamp:
images[k] = torch.clamp(images[k], -1., 1.)
self.log_local(pl_module.logger.save_dir, split, images, prompts,
pl_module.global_step, pl_module.current_epoch, batch_idx)
logger_log_images = self.logger_log_images.get(logger, lambda *args, **kwargs: None)
logger_log_images(pl_module, images, pl_module.global_step, split)
if is_train:
pl_module.train()
def check_frequency(self, check_idx):
if ((check_idx % self.batch_freq) == 0 or (check_idx in self.log_steps)) and (
check_idx > 0 or self.log_first_step):
if len(self.log_steps) > 0:
self.log_steps.pop(0)
return True
return False
def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
if not self.disabled and (pl_module.global_step > 0 or self.log_first_step):
self.log_img(pl_module, batch, batch_idx, split="train")
def on_validation_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
if not self.disabled and pl_module.global_step > 0:
self.log_img(pl_module, batch, batch_idx, split="val")
if hasattr(pl_module, 'calibrate_grad_norm'):
if (pl_module.calibrate_grad_norm and batch_idx % 25 == 0) and batch_idx > 0:
self.log_gradients(trainer, pl_module, batch_idx=batch_idx)
class CUDACallback(Callback):
# see https://github.com/SeanNaren/minGPT/blob/master/mingpt/callback.py
def on_train_epoch_start(self, trainer, pl_module):
# Reset the memory use counter
torch.cuda.reset_peak_memory_stats(trainer.root_gpu)
torch.cuda.synchronize(trainer.root_gpu)
self.start_time = time.time()
def on_train_epoch_end(self, trainer, pl_module, outputs):
torch.cuda.synchronize(trainer.root_gpu)
max_memory = torch.cuda.max_memory_allocated(trainer.root_gpu) / 2 ** 20
epoch_time = time.time() - self.start_time
try:
max_memory = trainer.training_type_plugin.reduce(max_memory)
epoch_time = trainer.training_type_plugin.reduce(epoch_time)
rank_zero_info(f"Average Epoch time: {epoch_time:.2f} seconds")
rank_zero_info(f"Average Peak memory {max_memory:.2f}MiB")
except AttributeError:
pass
if __name__ == "__main__":
# custom parser to specify config files, train, test and debug mode,
# postfix, resume.
# `--key value` arguments are interpreted as arguments to the trainer.
# `nested.key=value` arguments are interpreted as config parameters.
# configs are merged from left-to-right followed by command line parameters.
# model:
# base_learning_rate: float
# target: path to lightning module
# params:
# key: value
# data:
# target: main.DataModuleFromConfig
# params:
# batch_size: int
# wrap: bool
# train:
# target: path to train dataset
# params:
# key: value
# validation:
# target: path to validation dataset
# params:
# key: value
# test:
# target: path to test dataset
# params:
# key: value
# lightning: (optional, has sane defaults and can be specified on cmdline)
# trainer:
# additional arguments to trainer
# logger:
# logger to instantiate
# modelcheckpoint:
# modelcheckpoint to instantiate
# callbacks:
# callback1:
# target: importpath
# params:
# key: value
now = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S")
# add cwd for convenience and to make classes in this file available when
# running as `python main.py`
# (in particular `main.DataModuleFromConfig`)
sys.path.append(os.getcwd())
parser = get_parser()
parser = Trainer.add_argparse_args(parser)
opt, unknown = parser.parse_known_args()
assert opt.name
cfg_fname = os.path.split(opt.base[0])[-1]
cfg_name = os.path.splitext(cfg_fname)[0]
nowname = f"{cfg_name}_{opt.name}"
logdir = os.path.join(opt.logdir, nowname)
ckpt = os.path.join(logdir, "checkpoints", "last.ckpt")
resume = False
if os.path.isfile(ckpt):
opt.resume_from_checkpoint = ckpt
base_configs = sorted(glob.glob(os.path.join(logdir, "configs/*.yaml")))
opt.base = base_configs + opt.base
_tmp = logdir.split("/")
nowname = _tmp[-1]
resume = True
ckptdir = os.path.join(logdir, "checkpoints")
cfgdir = os.path.join(logdir, "configs")
os.makedirs(logdir, exist_ok=True)
os.makedirs(ckptdir, exist_ok=True)
os.makedirs(cfgdir, exist_ok=True)
try:
# init and save configs
configs = [OmegaConf.load(cfg) for cfg in opt.base]
cli = OmegaConf.from_dotlist(unknown)
config = OmegaConf.merge(*configs, cli)
if resume:
# By default, when finetuning from Stable Diffusion, we load the EMA-only checkpoint to initialize all weights.
# If resuming InstructPix2Pix from a finetuning checkpoint, instead load both EMA and non-EMA weights.
config.model.params.load_ema = True
lightning_config = config.pop("lightning", OmegaConf.create())
# merge trainer cli with config
trainer_config = lightning_config.get("trainer", OmegaConf.create())
# default to ddp
trainer_config["accelerator"] = "ddp"
for k in nondefault_trainer_args(opt):
trainer_config[k] = getattr(opt, k)
if not "gpus" in trainer_config:
del trainer_config["accelerator"]
cpu = True
else:
gpuinfo = trainer_config["gpus"]
print(f"Running on GPUs {gpuinfo}")
cpu = False
trainer_opt = argparse.Namespace(**trainer_config)
lightning_config.trainer = trainer_config
# model
model = instantiate_from_config(config.model)
# trainer and callbacks
trainer_kwargs = dict()
# default logger configs
default_logger_cfgs = {
"wandb": {
"target": "pytorch_lightning.loggers.WandbLogger",
"params": {
"name": nowname,
"save_dir": logdir,
"id": nowname,
}
},
"testtube": {
"target": "pytorch_lightning.loggers.TestTubeLogger",
"params": {
"name": "testtube",
"save_dir": logdir,
}
},
}
default_logger_cfg = default_logger_cfgs["wandb"]
if "logger" in lightning_config:
logger_cfg = lightning_config.logger
else:
logger_cfg = OmegaConf.create()
logger_cfg = OmegaConf.merge(default_logger_cfg, logger_cfg)
trainer_kwargs["logger"] = instantiate_from_config(logger_cfg)
# modelcheckpoint - use TrainResult/EvalResult(checkpoint_on=metric) to
# specify which metric is used to determine best models
default_modelckpt_cfg = {
"target": "pytorch_lightning.callbacks.ModelCheckpoint",
"params": {
"dirpath": ckptdir,
"filename": "{epoch:06}",
"verbose": True,
"save_last": True,
}
}
if "modelcheckpoint" in lightning_config:
modelckpt_cfg = lightning_config.modelcheckpoint
else:
modelckpt_cfg = OmegaConf.create()
modelckpt_cfg = OmegaConf.merge(default_modelckpt_cfg, modelckpt_cfg)
print(f"Merged modelckpt-cfg: \n{modelckpt_cfg}")
if version.parse(pl.__version__) < version.parse('1.4.0'):
trainer_kwargs["checkpoint_callback"] = instantiate_from_config(modelckpt_cfg)
# add callback which sets up log directory
default_callbacks_cfg = {
"setup_callback": {
"target": "main.SetupCallback",
"params": {
"resume": opt.resume,
"now": now,
"logdir": logdir,
"ckptdir": ckptdir,
"cfgdir": cfgdir,
"config": config,
"lightning_config": lightning_config,
}
},
"image_logger": {
"target": "main.ImageLogger",
"params": {
"batch_frequency": 750,
"max_images": 4,
"clamp": True
}
},
"learning_rate_logger": {
"target": "main.LearningRateMonitor",
"params": {
"logging_interval": "step",
# "log_momentum": True
}
},
"cuda_callback": {
"target": "main.CUDACallback"
},
}
if version.parse(pl.__version__) >= version.parse('1.4.0'):
default_callbacks_cfg.update({'checkpoint_callback': modelckpt_cfg})
if "callbacks" in lightning_config:
callbacks_cfg = lightning_config.callbacks
else:
callbacks_cfg = OmegaConf.create()
print(
'Caution: Saving checkpoints every n train steps without deleting. This might require some free space.')
default_metrics_over_trainsteps_ckpt_dict = {
'metrics_over_trainsteps_checkpoint': {
"target": 'pytorch_lightning.callbacks.ModelCheckpoint',
'params': {
"dirpath": os.path.join(ckptdir, 'trainstep_checkpoints'),
"filename": "{epoch:06}-{step:09}",
"verbose": True,
'save_top_k': -1,
'every_n_train_steps': 1000,
'save_weights_only': True
}
}
}
default_callbacks_cfg.update(default_metrics_over_trainsteps_ckpt_dict)
callbacks_cfg = OmegaConf.merge(default_callbacks_cfg, callbacks_cfg)
if 'ignore_keys_callback' in callbacks_cfg and hasattr(trainer_opt, 'resume_from_checkpoint'):
callbacks_cfg.ignore_keys_callback.params['ckpt_path'] = trainer_opt.resume_from_checkpoint
elif 'ignore_keys_callback' in callbacks_cfg:
del callbacks_cfg['ignore_keys_callback']
trainer_kwargs["callbacks"] = [instantiate_from_config(callbacks_cfg[k]) for k in callbacks_cfg]
trainer = Trainer.from_argparse_args(trainer_opt, plugins=DDPPlugin(find_unused_parameters=False), **trainer_kwargs)
trainer.logdir = logdir ###
# data
data = instantiate_from_config(config.data)
# NOTE according to https://pytorch-lightning.readthedocs.io/en/latest/datamodules.html
# calling these ourselves should not be necessary but it is.
# lightning still takes care of proper multiprocessing though
data.prepare_data()
data.setup()
print("#### Data #####")
for k in data.datasets:
print(f"{k}, {data.datasets[k].__class__.__name__}, {len(data.datasets[k])}")
# configure learning rate
bs, base_lr = config.data.params.batch_size, config.model.base_learning_rate
if not cpu:
ngpu = len(lightning_config.trainer.gpus.strip(",").split(','))
else:
ngpu = 1
if 'accumulate_grad_batches' in lightning_config.trainer:
accumulate_grad_batches = lightning_config.trainer.accumulate_grad_batches
else:
accumulate_grad_batches = 1
print(f"accumulate_grad_batches = {accumulate_grad_batches}")
lightning_config.trainer.accumulate_grad_batches = accumulate_grad_batches
if opt.scale_lr:
model.learning_rate = accumulate_grad_batches * ngpu * bs * base_lr
print(
"Setting learning rate to {:.2e} = {} (accumulate_grad_batches) * {} (num_gpus) * {} (batchsize) * {:.2e} (base_lr)".format(
model.learning_rate, accumulate_grad_batches, ngpu, bs, base_lr))
else:
model.learning_rate = base_lr
print("++++ NOT USING LR SCALING ++++")
print(f"Setting learning rate to {model.learning_rate:.2e}")
# allow checkpointing via USR1
def melk(*args, **kwargs):
# run all checkpoint hooks
if trainer.global_rank == 0:
print("Summoning checkpoint.")
ckpt_path = os.path.join(ckptdir, "last.ckpt")
trainer.save_checkpoint(ckpt_path)
def divein(*args, **kwargs):
if trainer.global_rank == 0:
import pudb;
pudb.set_trace()
import signal
signal.signal(signal.SIGUSR1, melk)
signal.signal(signal.SIGUSR2, divein)
# run
if opt.train:
try:
trainer.fit(model, data)
except Exception:
melk()
raise
if not opt.no_test and not trainer.interrupted:
trainer.test(model, data)
except Exception:
if opt.debug and trainer.global_rank == 0:
try:
import pudb as debugger
except ImportError:
import pdb as debugger
debugger.post_mortem()
raise
finally:
# move newly created debug project to debug_runs
if opt.debug and not opt.resume and trainer.global_rank == 0:
dst, name = os.path.split(logdir)
dst = os.path.join(dst, "debug_runs", name)
os.makedirs(os.path.split(dst)[0], exist_ok=True)
os.rename(logdir, dst)
if trainer.global_rank == 0:
print(trainer.profiler.summary())
================================================
FILE: models/instructpix2pix/metrics/clip_similarity.py
================================================
from __future__ import annotations
import clip
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
class ClipSimilarity(nn.Module):
def __init__(self, name: str = "ViT-L/14"):
super().__init__()
assert name in ("RN50", "RN101", "RN50x4", "RN50x16", "RN50x64", "ViT-B/32", "ViT-B/16", "ViT-L/14", "ViT-L/14@336px") # fmt: skip
self.size = {"RN50x4": 288, "RN50x16": 384, "RN50x64": 448, "ViT-L/14@336px": 336}.get(name, 224)
self.model, _ = clip.load(name, device="cpu", download_root="./")
self.model.eval().requires_grad_(False)
self.register_buffer("mean", torch.tensor((0.48145466, 0.4578275, 0.40821073)))
self.register_buffer("std", torch.tensor((0.26862954, 0.26130258, 0.27577711)))
def encode_text(self, text: list[str]) -> torch.Tensor:
text = clip.tokenize(text, truncate=True).to(next(self.parameters()).device)
text_features = self.model.encode_text(text)
text_features = text_features / text_features.norm(dim=1, keepdim=True)
return text_features
def encode_image(self, image: torch.Tensor) -> torch.Tensor: # Input images in range [0, 1].
image = F.interpolate(image.float(), size=self.size, mode="bicubic", align_corners=False)
image = image - rearrange(self.mean, "c -> 1 c 1 1")
image = image / rearrange(self.std, "c -> 1 c 1 1")
image_features = self.model.encode_image(image)
image_features = image_features / image_features.norm(dim=1, keepdim=True)
return image_features
def forward(
self, image_0: torch.Tensor, image_1: torch.Tensor, text_0: list[str], text_1: list[str]
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
image_features_0 = self.encode_image(image_0)
image_features_1 = self.encode_image(image_1)
text_features_0 = self.encode_text(text_0)
text_features_1 = self.encode_text(text_1)
sim_0 = F.cosine_similarity(image_features_0, text_features_0)
sim_1 = F.cosine_similarity(image_features_1, text_features_1)
sim_direction = F.cosine_similarity(image_features_1 - image_features_0, text_features_1 - text_features_0)
sim_image = F.cosine_similarity(image_features_0, image_features_1)
return sim_0, sim_1, sim_direction, sim_image
================================================
FILE: models/instructpix2pix/metrics/compute_metrics.py
================================================
from __future__ import annotations
import math
import random
import sys
from argparse import ArgumentParser
import einops
import k_diffusion as K
import numpy as np
import torch
import torch.nn as nn
from tqdm.auto import tqdm
from einops import rearrange
from omegaconf import OmegaConf
from PIL import Image, ImageOps
from torch import autocast
import json
import matplotlib.pyplot as plt
import seaborn
from pathlib import Path
sys.path.append("./")
from clip_similarity import ClipSimilarity
from edit_dataset import EditDatasetEval
sys.path.append("./stable_diffusion")
from ldm.util import instantiate_from_config
class CFGDenoiser(nn.Module):
def __init__(self, model):
super().__init__()
self.inner_model = model
def forward(self, z, sigma, cond, uncond, text_cfg_scale, image_cfg_scale):
cfg_z = einops.repeat(z, "1 ... -> n ...", n=3)
cfg_sigma = einops.repeat(sigma, "1 ... -> n ...", n=3)
cfg_cond = {
"c_crossattn": [torch.cat([cond["c_crossattn"][0], uncond["c_crossattn"][0], uncond["c_crossattn"][0]])],
"c_concat": [torch.cat([cond["c_concat"][0], cond["c_concat"][0], uncond["c_concat"][0]])],
}
out_cond, out_img_cond, out_uncond = self.inner_model(cfg_z, cfg_sigma, cond=cfg_cond).chunk(3)
return out_uncond + text_cfg_scale * (out_cond - out_img_cond) + image_cfg_scale * (out_img_cond - out_uncond)
def load_model_from_config(config, ckpt, vae_ckpt=None, verbose=False):
print(f"Loading model from {ckpt}")
pl_sd = torch.load(ckpt, map_location="cpu")
if "global_step" in pl_sd:
print(f"Global Step: {pl_sd['global_step']}")
sd = pl_sd["state_dict"]
if vae_ckpt is not None:
print(f"Loading VAE from {vae_ckpt}")
vae_sd = torch.load(vae_ckpt, map_location="cpu")["state_dict"]
sd = {
k: vae_sd[k[len("first_stage_model.") :]] if k.startswith("first_stage_model.") else v
for k, v in sd.items()
}
model = instantiate_from_config(config.model)
m, u = model.load_state_dict(sd, strict=False)
if len(m) > 0 and verbose:
print("missing keys:")
print(m)
if len(u) > 0 and verbose:
print("unexpected keys:")
print(u)
return model
class ImageEditor(nn.Module):
def __init__(self, config, ckpt, vae_ckpt=None):
super().__init__()
config = OmegaConf.load(config)
self.model = load_model_from_config(config, ckpt, vae_ckpt)
self.model.eval().cuda()
self.model_wrap = K.external.CompVisDenoiser(self.model)
self.model_wrap_cfg = CFGDenoiser(self.model_wrap)
self.null_token = self.model.get_learned_conditioning([""])
def forward(
self,
image: torch.Tensor,
edit: str,
scale_txt: float = 7.5,
scale_img: float = 1.0,
steps: int = 100,
) -> torch.Tensor:
assert image.dim() == 3
assert image.size(1) % 64 == 0
assert image.size(2) % 64 == 0
with torch.no_grad(), autocast("cuda"), self.model.ema_scope():
cond = {
"c_crossattn": [self.model.get_learned_conditioning([edit])],
"c_concat": [self.model.encode_first_stage(image[None]).mode()],
}
uncond = {
"c_crossattn": [self.model.get_learned_conditioning([""])],
"c_concat": [torch.zeros_like(cond["c_concat"][0])],
}
extra_args = {
"uncond": uncond,
"cond": cond,
"image_cfg_scale": scale_img,
"text_cfg_scale": scale_txt,
}
sigmas = self.model_wrap.get_sigmas(steps)
x = torch.randn_like(cond["c_concat"][0]) * sigmas[0]
x = K.sampling.sample_euler_ancestral(self.model_wrap_cfg, x, sigmas, extra_args=extra_args)
x = self.model.decode_first_stage(x)[0]
return x
def compute_metrics(config,
model_path,
vae_ckpt,
data_path,
output_path,
scales_img,
scales_txt,
num_samples = 5000,
split = "test",
steps = 50,
res = 512,
seed = 0):
editor = ImageEditor(config, model_path, vae_ckpt).cuda()
clip_similarity = ClipSimilarity().cuda()
outpath = Path(output_path, f"n={num_samples}_p={split}_s={steps}_r={res}_e={seed}.jsonl")
Path(output_path).mkdir(parents=True, exist_ok=True)
for scale_txt in scales_txt:
for scale_img in scales_img:
dataset = EditDatasetEval(
path=data_path,
split=split,
res=res
)
assert num_samples <= len(dataset)
print(f'Processing t={scale_txt}, i={scale_img}')
torch.manual_seed(seed)
perm = torch.randperm(len(dataset))
count = 0
i = 0
sim_0_avg = 0
sim_1_avg = 0
sim_direction_avg = 0
sim_image_avg = 0
count = 0
pbar = tqdm(total=num_samples)
while count < num_samples:
idx = perm[i].item()
sample = dataset[idx]
i += 1
gen = editor(sample["image_0"].cuda(), sample["edit"], scale_txt=scale_txt, scale_img=scale_img, steps=steps)
sim_0, sim_1, sim_direction, sim_image = clip_similarity(
sample["image_0"][None].cuda(), gen[None].cuda(), [sample["input_prompt"]], [sample["output_prompt"]]
)
sim_0_avg += sim_0.item()
sim_1_avg += sim_1.item()
sim_direction_avg += sim_direction.item()
sim_image_avg += sim_image.item()
count += 1
pbar.update(count)
pbar.close()
sim_0_avg /= count
sim_1_avg /= count
sim_direction_avg /= count
sim_image_avg /= count
with open(outpath, "a") as f:
f.write(f"{json.dumps(dict(sim_0=sim_0_avg, sim_1=sim_1_avg, sim_direction=sim_direction_avg, sim_image=sim_image_avg, num_samples=num_samples, split=split, scale_txt=scale_txt, scale_img=scale_img, steps=steps, res=res, seed=seed))}\n")
return outpath
def plot_metrics(metrics_file, output_path):
with open(metrics_file, 'r') as f:
data = [json.loads(line) for line in f]
plt.rcParams.update({'font.size': 11.5})
seaborn.set_style("darkgrid")
plt.figure(figsize=(20.5* 0.7, 10.8* 0.7), dpi=200)
x = [d["sim_direction"] for d in data]
y = [d["sim_image"] for d in data]
plt.plot(x, y, marker='o', linewidth=2, markersize=4)
plt.xlabel("CLIP Text-Image Direction Similarity", labelpad=10)
plt.ylabel("CLIP Image Similarity", labelpad=10)
plt.savefig(Path(output_path) / Path("plot.pdf"), bbox_inches="tight")
def main():
parser = ArgumentParser()
parser.add_argument("--resolution", default=512, type=int)
parser.add_argument("--steps", default=100, type=int)
parser.add_argument("--config", default="configs/generate.yaml", type=str)
parser.add_argument("--output_path", default="analysis/", type=str)
parser.add_argument("--ckpt", default="checkpoints/instruct-pix2pix-00-22000.ckpt", type=str)
parser.add_argument("--dataset", default="data/clip-filtered-dataset/", type=str)
parser.add_argument("--vae-ckpt", default=None, type=str)
args = parser.parse_args()
scales_img = [1.0, 1.2, 1.4, 1.6, 1.8, 2.0, 2.2]
scales_txt = [7.5]
metrics_file = compute_metrics(
args.config,
args.ckpt,
args.vae_ckpt,
args.dataset,
args.output_path,
scales_img,
scales_txt,
steps = args.steps,
)
plot_metrics(metrics_file, args.output_path)
if __name__ == "__main__":
main()
================================================
FILE: models/instructpix2pix/prompt_app.py
================================================
from __future__ import annotations
from argparse import ArgumentParser
import datasets
import gradio as gr
import numpy as np
import openai
from dataset_creation.generate_txt_dataset import generate
def main(openai_model: str):
dataset = datasets.load_dataset("ChristophSchuhmann/improved_aesthetics_6.5plus", split="train")
captions = dataset[np.random.permutation(len(dataset))]["TEXT"]
index = 0
def click_random():
nonlocal index
output = captions[index]
index = (index + 1) % len(captions)
return output
def click_generate(input: str):
if input == "":
raise gr.Error("Input caption is missing!")
edit_output = generate(openai_model, input)
if edit_output is None:
return "Failed :(", "Failed :("
return edit_output
with gr.Blocks(css="footer {visibility: hidden}") as demo:
txt_input = gr.Textbox(lines=3, label="Input Caption", interactive=True, placeholder="Type image caption here...") # fmt: skip
txt_edit = gr.Textbox(lines=1, label="GPT-3 Instruction", interactive=False)
txt_output = gr.Textbox(lines=3, label="GPT3 Edited Caption", interactive=False)
with gr.Row():
clear_btn = gr.Button("Clear")
random_btn = gr.Button("Random Input")
generate_btn = gr.Button("Generate Instruction + Edited Caption")
clear_btn.click(fn=lambda: ("", "", ""), inputs=[], outputs=[txt_input, txt_edit, txt_output])
random_btn.click(fn=click_random, inputs=[], outputs=[txt_input])
generate_btn.click(fn=click_generate, inputs=[txt_input], outputs=[txt_edit, txt_output])
demo.launch(share=True)
if __name__ == "__main__":
parser = ArgumentParser()
parser.add_argument("--openai-api-key", required=True, type=str)
parser.add_argument("--openai-model", required=True, type=str)
args = parser.parse_args()
openai.api_key = args.openai_api_key
main(args.openai_model)
================================================
FILE: models/instructpix2pix/scripts/download_checkpoints.sh
================================================
#!/bin/bash
SCRIPT_DIR=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )
mkdir -p $SCRIPT_DIR/../checkpoints
curl http://instruct-pix2pix.eecs.berkeley.edu/instruct-pix2pix-00-22000.ckpt -o $SCRIPT_DIR/../checkpoints/instruct-pix2pix-00-22000.ckpt
================================================
FILE: models/instructpix2pix/scripts/download_data.sh
================================================
#!/bin/bash
# Make data folder relative to script location
SCRIPT_DIR=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )
mkdir -p $SCRIPT_DIR/../data
# Copy text datasets
wget -q --show-progress http://instruct-pix2pix.eecs.berkeley.edu/gpt-generated-prompts.jsonl -O $SCRIPT_DIR/../data/gpt-generated-prompts.jsonl
wget -q --show-progress http://instruct-pix2pix.eecs.berkeley.edu/human-written-prompts.jsonl -O $SCRIPT_DIR/../data/human-written-prompts.jsonl
# If dataset name isn't provided, exit.
if [ -z $1 ]
then
exit 0
fi
# Copy dataset files
mkdir $SCRIPT_DIR/../data/$1
wget -A zip,json -R "index.html*" -q --show-progress -r --no-parent http://instruct-pix2pix.eecs.berkeley.edu/$1/ -nd -P $SCRIPT_DIR/../data/$1/
# Unzip to folders
unzip $SCRIPT_DIR/../data/$1/\*.zip -d $SCRIPT_DIR/../data/$1/
# Cleanup
rm -f $SCRIPT_DIR/../data/$1/*.zip
rm -f $SCRIPT_DIR/../data/$1/*.html
================================================
FILE: models/instructpix2pix/scripts/download_pretrained_sd.sh
================================================
#!/bin/bash
SCRIPT_DIR=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )
mkdir -p $SCRIPT_DIR/../stable_diffusion/models/ldm/stable-diffusion-v1
curl -L https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt -o $SCRIPT_DIR/../stable_diffusion/models/ldm/stable-diffusion-v1/v1-5-pruned-emaonly.ckpt
curl -L https://huggingface.co/stabilityai/sd-vae-ft-mse-original/resolve/main/vae-ft-mse-840000-ema-pruned.ckpt -o $SCRIPT_DIR/../stable_diffusion/models/ldm/stable-diffusion-v1/vae-ft-mse-840000-ema-pruned.ckpt
================================================
FILE: models/instructpix2pix/stable_diffusion/LICENSE
================================================
Copyright (c) 2022 Robin Rombach and Patrick Esser and contributors
CreativeML Open RAIL-M
dated August 22, 2022
Section I: PREAMBLE
Multimodal generative models are being widely adopted and used, and have the potential to transform the way artists, among other individuals, conceive and benefit from AI or ML technologies as a tool for content creation.
Notwithstanding the current and potential benefits that these artifacts can bring to society at large, there are also concerns about potential misuses of them, either due to their technical limitations or ethical considerations.
In short, this license strives for both the open and responsible downstream use of the accompanying model. When it comes to the open character, we took inspiration from open source permissive licenses regarding the grant of IP rights. Referring to the downstream responsible use, we added use-based restrictions not permitting the use of the Model in very specific scenarios, in order for the licensor to be able to enforce the license in case potential misuses of the Model may occur. At the same time, we strive to promote open and responsible research on generative models for art and content generation.
Even though downstream derivative versions of the model could be released under different licensing terms, the latter will always have to include - at minimum - the same use-based restrictions as the ones in the original license (this license). We believe in the intersection between open and responsible AI development; thus, this License aims to strike a balance between both in order to enable responsible open-science in the field of AI.
This License governs the use of the model (and its derivatives) and is informed by the model card associated with the model.
NOW THEREFORE, You and Licensor agree as follows:
1. Definitions
- "License" means the terms and conditions for use, reproduction, and Distribution as defined in this document.
- "Data" means a collection of information and/or content extracted from the dataset used with the Model, including to train, pretrain, or otherwise evaluate the Model. The Data is not licensed under this License.
- "Output" means the results of operating a Model as embodied in informational content resulting therefrom.
- "Model" means any accompanying machine-learning based assemblies (including checkpoints), consisting of learnt weights, parameters (including optimizer states), corresponding to the model architecture as embodied in the Complementary Material, that have been trained or tuned, in whole or in part on the Data, using the Complementary Material.
- "Derivatives of the Model" means all modifications to the Model, works based on the Model, or any other model which is created or initialized by transfer of patterns of the weights, parameters, activations or output of the Model, to the other model, in order to cause the other model to perform similarly to the Model, including - but not limited to - distillation methods entailing the use of intermediate data representations or methods based on the generation of synthetic data by the Model for training the other model.
- "Complementary Material" means the accompanying source code and scripts used to define, run, load, benchmark or evaluate the Model, and used to prepare data for training or evaluation, if any. This includes any accompanying documentation, tutorials, examples, etc, if any.
- "Distribution" means any transmission, reproduction, publication or other sharing of the Model or Derivatives of the Model to a third party, including providing the Model as a hosted service made available by electronic or other remote means - e.g. API-based or web access.
- "Licensor" means the copyright owner or entity authorized by the copyright owner that is granting the License, including the persons or entities that may have rights in the Model and/or distributing the Model.
- "You" (or "Your") means an individual or Legal Entity exercising permissions granted by this License and/or making use of the Model for whichever purpose and in any field of use, including usage of the Model in an end-use application - e.g. chatbot, translator, image generator.
- "Third Parties" means individuals or legal entities that are not under common control with Licensor or You.
- "Contribution" means any work of authorship, including the original version of the Model and any modifications or additions to that Model or Derivatives of the Model thereof, that is intentionally submitted to Licensor for inclusion in the Model by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, "submitted" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Model, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as "Not a Contribution."
- "Contributor" means Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Model.
Section II: INTELLECTUAL PROPERTY RIGHTS
Both copyright and patent grants apply to the Model, Derivatives of the Model and Complementary Material. The Model and Derivatives of the Model are subject to additional terms as described in Section III.
2. Grant of Copyright License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare, publicly display, publicly perform, sublicense, and distribute the Complementary Material, the Model, and Derivatives of the Model.
3. Grant of Patent License. Subject to the terms and conditions of this License and where and as applicable, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this paragraph) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Model and the Complementary Material, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Model to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Model and/or Complementary Material or a Contribution incorporated within the Model and/or Complementary Material constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for the Model and/or Work shall terminate as of the date such litigation is asserted or filed.
Section III: CONDITIONS OF USAGE, DISTRIBUTION AND REDISTRIBUTION
4. Distribution and Redistribution. You may host for Third Party remote access purposes (e.g. software-as-a-service), reproduce and distribute copies of the Model or Derivatives of the Model thereof in any medium, with or without modifications, provided that You meet the following conditions:
Use-based restrictions as referenced in paragraph 5 MUST be included as an enforceable provision by You in any type of legal agreement (e.g. a license) governing the use and/or distribution of the Model or Derivatives of the Model, and You shall give notice to subsequent users You Distribute to, that the Model or Derivatives of the Model are subject to paragraph 5. This provision does not apply to the use of Complementary Material.
You must give any Third Party recipients of the Model or Derivatives of the Model a copy of this License;
You must cause any modified files to carry prominent notices stating that You changed the files;
You must retain all copyright, patent, trademark, and attribution notices excluding those notices that do not pertain to any part of the Model, Derivatives of the Model.
You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions - respecting paragraph 4.a. - for use, reproduction, or Distribution of Your modifications, or for any such Derivatives of the Model as a whole, provided Your use, reproduction, and Distribution of the Model otherwise complies with the conditions stated in this License.
5. Use-based restrictions. The restrictions set forth in Attachment A are considered Use-based restrictions. Therefore You cannot use the Model and the Derivatives of the Model for the specified restricted uses. You may use the Model subject to this License, including only for lawful purposes and in accordance with the License. Use may include creating any content with, finetuning, updating, running, training, evaluating and/or reparametrizing the Model. You shall require all of Your users who use the Model or a Derivative of the Model to comply with the terms of this paragraph (paragraph 5).
6. The Output You Generate. Except as set forth herein, Licensor claims no rights in the Output You generate using the Model. You are accountable for the Output you generate and its subsequent uses. No use of the output can contravene any provision as stated in the License.
Section IV: OTHER PROVISIONS
7. Updates and Runtime Restrictions. To the maximum extent permitted by law, Licensor reserves the right to restrict (remotely or otherwise) usage of the Model in violation of this License, update the Model through electronic means, or modify the Output of the Model based on updates. You shall undertake reasonable efforts to use the latest version of the Model.
8. Trademarks and related. Nothing in this License permits You to make use of Licensors’ trademarks, trade names, logos or to otherwise suggest endorsement or misrepresent the relationship between the parties; and any rights not expressly granted herein are reserved by the Licensors.
9. Disclaimer of Warranty. Unless required by applicable law or agreed to in writing, Licensor provides the Model and the Complementary Material (and each Contributor provides its Contributions) on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using or redistributing the Model, Derivatives of the Model, and the Complementary Material and assume any risks associated with Your exercise of permissions under this License.
10. Limitation of Liability. In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall any Contributor be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Model and the Complementary Material (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if such Contributor has been advised of the possibility of such damages.
11. Accepting Warranty or Additional Liability. While redistributing the Model, Derivatives of the Model and the Complementary Material thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability.
12. If any provision of this License is held to be invalid, illegal or unenforceable, the remaining provisions shall be unaffected thereby and remain valid as if such provision had not been set forth herein.
END OF TERMS AND CONDITIONS
Attachment A
Use Restrictions
You agree not to use the Model or Derivatives of the Model:
- In any way that violates any applicable national, federal, state, local or international law or regulation;
- For the purpose of exploiting, harming or attempting to exploit or harm minors in any way;
- To generate or disseminate verifiably false information and/or content with the purpose of harming others;
- To generate or disseminate personal identifiable information that can be used to harm an individual;
- To defame, disparage or otherwise harass others;
- For fully automated decision making that adversely impacts an individual’s legal rights or otherwise creates or modifies a binding, enforceable obligation;
- For any use intended to or which has the effect of discriminating against or harming individuals or groups based on online or offline social behavior or known or predicted personal or personality characteristics;
- To exploit any of the vulnerabilities of a specific group of persons based on their age, social, physical or mental characteristics, in order to materially distort the behavior of a person pertaining to that group in a manner that causes or is likely to cause that person or another person physical or psychological harm;
- For any use intended to or which has the effect of discriminating against individuals or groups based on legally protected characteristics or categories;
- To provide medical advice and medical results interpretation;
- To generate or disseminate information for the purpose to be used for administration of justice, law enforcement, immigration or asylum processes, such as predicting an individual will commit fraud/crime commitment (e.g. by text profiling, drawing causal relationships between assertions made in documents, indiscriminate and arbitrarily-targeted use).
================================================
FILE: models/instructpix2pix/stable_diffusion/README.md
================================================
# Stable Diffusion
*Stable Diffusion was made possible thanks to a collaboration with [Stability AI](https://stability.ai/) and [Runway](https://runwayml.com/) and builds upon our previous work:*
[**High-Resolution Image Synthesis with Latent Diffusion Models**](https://ommer-lab.com/research/latent-diffusion-models/)
[Robin Rombach](https://github.com/rromb)\*,
[Andreas Blattmann](https://github.com/ablattmann)\*,
[Dominik Lorenz](https://github.com/qp-qp)\,
[Patrick Esser](https://github.com/pesser),
[Björn Ommer](https://hci.iwr.uni-heidelberg.de/Staff/bommer)
_[CVPR '22 Oral](https://openaccess.thecvf.com/content/CVPR2022/html/Rombach_High-Resolution_Image_Synthesis_With_Latent_Diffusion_Models_CVPR_2022_paper.html) |
[GitHub](https://github.com/CompVis/latent-diffusion) | [arXiv](https://arxiv.org/abs/2112.10752) | [Project page](https://ommer-lab.com/research/latent-diffusion-models/)_
[Stable Diffusion](#stable-diffusion-v1) is a latent text-to-image diffusion
model.
Thanks to a generous compute donation from [Stability AI](https://stability.ai/) and support from [LAION](https://laion.ai/), we were able to train a Latent Diffusion Model on 512x512 images from a subset of the [LAION-5B](https://laion.ai/blog/laion-5b/) database.
Similar to Google's [Imagen](https://arxiv.org/abs/2205.11487),
this model uses a frozen CLIP ViT-L/14 text encoder to condition the model on text prompts.
With its 860M UNet and 123M text encoder, the model is relatively lightweight and runs on a GPU with at least 10GB VRAM.
See [this section](#stable-diffusion-v1) below and the [model card](https://huggingface.co/CompVis/stable-diffusion).
## Requirements
A suitable [conda](https://conda.io/) environment named `ldm` can be created
and activated with:
```
conda env create -f environment.yaml
conda activate ldm
```
You can also update an existing [latent diffusion](https://github.com/CompVis/latent-diffusion) environment by running
```
conda install pytorch torchvision -c pytorch
pip install transformers==4.19.2 diffusers invisible-watermark
pip install -e .
```
## Stable Diffusion v1
Stable Diffusion v1 refers to a specific configuration of the model
architecture that uses a downsampling-factor 8 autoencoder with an 860M UNet
and CLIP ViT-L/14 text encoder for the diffusion model. The model was pretrained on 256x256 images and
then finetuned on 512x512 images.
*Note: Stable Diffusion v1 is a general text-to-image diffusion model and therefore mirrors biases and (mis-)conceptions that are present
in its training data.
Details on the training procedure and data, as well as the intended use of the model can be found in the corresponding [model card](Stable_Diffusion_v1_Model_Card.md).*
The weights are available via [the CompVis organization at Hugging Face](https://huggingface.co/CompVis) under [a license which contains specific use-based restrictions to prevent misuse and harm as informed by the model card, but otherwise remains permissive](LICENSE). While commercial use is permitted under the terms of the license, **we do not recommend using the provided weights for services or products without additional safety mechanisms and considerations**, since there are [known limitations and biases](Stable_Diffusion_v1_Model_Card.md#limitations-and-bias) of the weights, and research on safe and ethical deployment of general text-to-image models is an ongoing effort. **The weights are research artifacts and should be treated as such.**
[The CreativeML OpenRAIL M license](LICENSE) is an [Open RAIL M license](https://www.licenses.ai/blog/2022/8/18/naming-convention-of-responsible-ai-licenses), adapted from the work that [BigScience](https://bigscience.huggingface.co/) and [the RAIL Initiative](https://www.licenses.ai/) are jointly carrying in the area of responsible AI licensing. See also [the article about the BLOOM Open RAIL license](https://bigscience.huggingface.co/blog/the-bigscience-rail-license) on which our license is based.
### Weights
We currently provide the following checkpoints:
- `sd-v1-1.ckpt`: 237k steps at resolution `256x256` on [laion2B-en](https://huggingface.co/datasets/laion/laion2B-en).
194k steps at resolution `512x512` on [laion-high-resolution](https://huggingface.co/datasets/laion/laion-high-resolution) (170M examples from LAION-5B with resolution `>= 1024x1024`).
- `sd-v1-2.ckpt`: Resumed from `sd-v1-1.ckpt`.
515k steps at resolution `512x512` on [laion-aesthetics v2 5+](https://laion.ai/blog/laion-aesthetics/) (a subset of laion2B-en with estimated aesthetics score `> 5.0`, and additionally
filtered to images with an original size `>= 512x512`, and an estimated watermark probability `< 0.5`. The watermark estimate is from the [LAION-5B](https://laion.ai/blog/laion-5b/) metadata, the aesthetics score is estimated using the [LAION-Aesthetics Predictor V2](https://github.com/christophschuhmann/improved-aesthetic-predictor)).
- `sd-v1-3.ckpt`: Resumed from `sd-v1-2.ckpt`. 195k steps at resolution `512x512` on "laion-aesthetics v2 5+" and 10\% dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598).
- `sd-v1-4.ckpt`: Resumed from `sd-v1-2.ckpt`. 225k steps at resolution `512x512` on "laion-aesthetics v2 5+" and 10\% dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598).
Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0,
5.0, 6.0, 7.0, 8.0) and 50 PLMS sampling
steps show the relative improvements of the checkpoints:
### Text-to-Image with Stable Diffusion
Stable Diffusion is a latent diffusion model conditioned on the (non-pooled) text embeddings of a CLIP ViT-L/14 text encoder.
We provide a [reference script for sampling](#reference-sampling-script), but
there also exists a [diffusers integration](#diffusers-integration), which we
expect to see more active community development.
#### Reference Sampling Script
We provide a reference sampling script, which incorporates
- a [Safety Checker Module](https://github.com/CompVis/stable-diffusion/pull/36),
to reduce the probability of explicit outputs,
- an [invisible watermarking](https://github.com/ShieldMnt/invisible-watermark)
of the outputs, to help viewers [identify the images as machine-generated](scripts/tests/test_watermark.py).
After [obtaining the `stable-diffusion-v1-*-original` weights](#weights), link them
```
mkdir -p models/ldm/stable-diffusion-v1/
ln -s models/ldm/stable-diffusion-v1/model.ckpt
```
and sample with
```
python scripts/txt2img.py --prompt "a photograph of an astronaut riding a horse" --plms
```
By default, this uses a guidance scale of `--scale 7.5`, [Katherine Crowson's implementation](https://github.com/CompVis/latent-diffusion/pull/51) of the [PLMS](https://arxiv.org/abs/2202.09778) sampler,
and renders images of size 512x512 (which it was trained on) in 50 steps. All supported arguments are listed below (type `python scripts/txt2img.py --help`).
```commandline
usage: txt2img.py [-h] [--prompt [PROMPT]] [--outdir [OUTDIR]] [--skip_grid] [--skip_save] [--ddim_steps DDIM_STEPS] [--plms] [--laion400m] [--fixed_code] [--ddim_eta DDIM_ETA]
[--n_iter N_ITER] [--H H] [--W W] [--C C] [--f F] [--n_samples N_SAMPLES] [--n_rows N_ROWS] [--scale SCALE] [--from-file FROM_FILE] [--config CONFIG] [--ckpt CKPT]
[--seed SEED] [--precision {full,autocast}]
optional arguments:
-h, --help show this help message and exit
--prompt [PROMPT] the prompt to render
--outdir [OUTDIR] dir to write results to
--skip_grid do not save a grid, only individual samples. Helpful when evaluating lots of samples
--skip_save do not save individual samples. For speed measurements.
--ddim_steps DDIM_STEPS
number of ddim sampling steps
--plms use plms sampling
--laion400m uses the LAION400M model
--fixed_code if enabled, uses the same starting code across samples
--ddim_eta DDIM_ETA ddim eta (eta=0.0 corresponds to deterministic sampling
--n_iter N_ITER sample this often
--H H image height, in pixel space
--W W image width, in pixel space
--C C latent channels
--f F downsampling factor
--n_samples N_SAMPLES
how many samples to produce for each given prompt. A.k.a. batch size
--n_rows N_ROWS rows in the grid (default: n_samples)
--scale SCALE unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) - eps(x, empty))
--from-file FROM_FILE
if specified, load prompts from this file
--config CONFIG path to config which constructs model
--ckpt CKPT path to checkpoint of model
--seed SEED the seed (for reproducible sampling)
--precision {full,autocast}
evaluate at this precision
```
Note: The inference config for all v1 versions is designed to be used with EMA-only checkpoints.
For this reason `use_ema=False` is set in the configuration, otherwise the code will try to switch from
non-EMA to EMA weights. If you want to examine the effect of EMA vs no EMA, we provide "full" checkpoints
which contain both types of weights. For these, `use_ema=False` will load and use the non-EMA weights.
#### Diffusers Integration
A simple way to download and sample Stable Diffusion is by using the [diffusers library](https://github.com/huggingface/diffusers/tree/main#new--stable-diffusion-is-now-fully-compatible-with-diffusers):
```py
# make sure you're logged in with `huggingface-cli login`
from torch import autocast
from diffusers import StableDiffusionPipeline
pipe = StableDiffusionPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4",
use_auth_token=True
).to("cuda")
prompt = "a photo of an astronaut riding a horse on mars"
with autocast("cuda"):
image = pipe(prompt)["sample"][0]
image.save("astronaut_rides_horse.png")
```
### Image Modification with Stable Diffusion
By using a diffusion-denoising mechanism as first proposed by [SDEdit](https://arxiv.org/abs/2108.01073), the model can be used for different
tasks such as text-guided image-to-image translation and upscaling. Similar to the txt2img sampling script,
we provide a script to perform image modification with Stable Diffusion.
The following describes an example where a rough sketch made in [Pinta](https://www.pinta-project.com/) is converted into a detailed artwork.
```
python scripts/img2img.py --prompt "A fantasy landscape, trending on artstation" --init-img --strength 0.8
```
Here, strength is a value between 0.0 and 1.0, that controls the amount of noise that is added to the input image.
Values that approach 1.0 allow for lots of variations but will also produce images that are not semantically consistent with the input. See the following example.
**Input**
**Outputs**
This procedure can, for example, also be used to upscale samples from the base model.
## Comments
- Our codebase for the diffusion models builds heavily on [OpenAI's ADM codebase](https://github.com/openai/guided-diffusion)
and [https://github.com/lucidrains/denoising-diffusion-pytorch](https://github.com/lucidrains/denoising-diffusion-pytorch).
Thanks for open-sourcing!
- The implementation of the transformer encoder is from [x-transformers](https://github.com/lucidrains/x-transformers) by [lucidrains](https://github.com/lucidrains?tab=repositories).
## BibTeX
```
@misc{rombach2021highresolution,
title={High-Resolution Image Synthesis with Latent Diffusion Models},
author={Robin Rombach and Andreas Blattmann and Dominik Lorenz and Patrick Esser and Björn Ommer},
year={2021},
eprint={2112.10752},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
================================================
FILE: models/instructpix2pix/stable_diffusion/Stable_Diffusion_v1_Model_Card.md
================================================
# Stable Diffusion v1 Model Card
This model card focuses on the model associated with the Stable Diffusion model, available [here](https://github.com/CompVis/stable-diffusion).
## Model Details
- **Developed by:** Robin Rombach, Patrick Esser
- **Model type:** Diffusion-based text-to-image generation model
- **Language(s):** English
- **License:** [Proprietary](LICENSE)
- **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([CLIP ViT-L/14](https://arxiv.org/abs/2103.00020)) as suggested in the [Imagen paper](https://arxiv.org/abs/2205.11487).
- **Resources for more information:** [GitHub Repository](https://github.com/CompVis/stable-diffusion), [Paper](https://arxiv.org/abs/2112.10752).
- **Cite as:**
@InProceedings{Rombach_2022_CVPR,
author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},
title = {High-Resolution Image Synthesis With Latent Diffusion Models},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {10684-10695}
}
# Uses
## Direct Use
The model is intended for research purposes only. Possible research areas and
tasks include
- Safe deployment of models which have the potential to generate harmful content.
- Probing and understanding the limitations and biases of generative models.
- Generation of artworks and use in design and other artistic processes.
- Applications in educational or creative tools.
- Research on generative models.
Excluded uses are described below.
### Misuse, Malicious Use, and Out-of-Scope Use
_Note: This section is taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), but applies in the same way to Stable Diffusion v1_.
The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.
#### Out-of-Scope Use
The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.
#### Misuse and Malicious Use
Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to:
- Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc.
- Intentionally promoting or propagating discriminatory content or harmful stereotypes.
- Impersonating individuals without their consent.
- Sexual content without consent of the people who might see it.
- Mis- and disinformation
- Representations of egregious violence and gore
- Sharing of copyrighted or licensed material in violation of its terms of use.
- Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use.
## Limitations and Bias
### Limitations
- The model does not achieve perfect photorealism
- The model cannot render legible text
- The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere”
- Faces and people in general may not be generated properly.
- The model was trained mainly with English captions and will not work as well in other languages.
- The autoencoding part of the model is lossy
- The model was trained on a large-scale dataset
[LAION-5B](https://laion.ai/blog/laion-5b/) which contains adult material
and is not fit for product use without additional safety mechanisms and
considerations.
- No additional measures were used to deduplicate the dataset. As a result, we observe some degree of memorization for images that are duplicated in the training data.
The training data can be searched at [https://rom1504.github.io/clip-retrieval/](https://rom1504.github.io/clip-retrieval/) to possibly assist in the detection of memorized images.
### Bias
While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases.
Stable Diffusion v1 was primarily trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/),
which consists of images that are limited to English descriptions.
Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for.
This affects the overall output of the model, as white and western cultures are often set as the default. Further, the
ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts.
Stable Diffusion v1 mirrors and exacerbates biases to such a degree that viewer discretion must be advised irrespective of the input or its intent.
## Training
**Training Data**
The model developers used the following dataset for training the model:
- LAION-5B and subsets thereof (see next section)
**Training Procedure**
Stable Diffusion v1 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. During training,
- Images are encoded through an encoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4
- Text prompts are encoded through a ViT-L/14 text-encoder.
- The non-pooled output of the text encoder is fed into the UNet backbone of the latent diffusion model via cross-attention.
- The loss is a reconstruction objective between the noise that was added to the latent and the prediction made by the UNet.
We currently provide the following checkpoints:
- `sd-v1-1.ckpt`: 237k steps at resolution `256x256` on [laion2B-en](https://huggingface.co/datasets/laion/laion2B-en).
194k steps at resolution `512x512` on [laion-high-resolution](https://huggingface.co/datasets/laion/laion-high-resolution) (170M examples from LAION-5B with resolution `>= 1024x1024`).
- `sd-v1-2.ckpt`: Resumed from `sd-v1-1.ckpt`.
515k steps at resolution `512x512` on [laion-aesthetics v2 5+](https://laion.ai/blog/laion-aesthetics/) (a subset of laion2B-en with estimated aesthetics score `> 5.0`, and additionally
filtered to images with an original size `>= 512x512`, and an estimated watermark probability `< 0.5`. The watermark estimate is from the [LAION-5B](https://laion.ai/blog/laion-5b/) metadata, the aesthetics score is estimated using the [LAION-Aesthetics Predictor V2](https://github.com/christophschuhmann/improved-aesthetic-predictor)).
- `sd-v1-3.ckpt`: Resumed from `sd-v1-2.ckpt`. 195k steps at resolution `512x512` on "laion-aesthetics v2 5+" and 10\% dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598).
- `sd-v1-4.ckpt`: Resumed from `sd-v1-2.ckpt`. 225k steps at resolution `512x512` on "laion-aesthetics v2 5+" and 10\% dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598).
- **Hardware:** 32 x 8 x A100 GPUs
- **Optimizer:** AdamW
- **Gradient Accumulations**: 2
- **Batch:** 32 x 8 x 2 x 4 = 2048
- **Learning rate:** warmup to 0.0001 for 10,000 steps and then kept constant
## Evaluation Results
Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0,
5.0, 6.0, 7.0, 8.0) and 50 PLMS sampling
steps show the relative improvements of the checkpoints:
Evaluated using 50 PLMS steps and 10000 random prompts from the COCO2017 validation set, evaluated at 512x512 resolution. Not optimized for FID scores.
## Environmental Impact
**Stable Diffusion v1** **Estimated Emissions**
Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact.
- **Hardware Type:** A100 PCIe 40GB
- **Hours used:** 150000
- **Cloud Provider:** AWS
- **Compute Region:** US-east
- **Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid):** 11250 kg CO2 eq.
## Citation
@InProceedings{Rombach_2022_CVPR,
author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},
title = {High-Resolution Image Synthesis With Latent Diffusion Models},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {10684-10695}
}
*This model card was written by: Robin Rombach and Patrick Esser and is based on the [DALL-E Mini model card](https://huggingface.co/dalle-mini/dalle-mini).*
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FILE: models/instructpix2pix/stable_diffusion/configs/autoencoder/autoencoder_kl_16x16x16.yaml
================================================
model:
base_learning_rate: 4.5e-6
target: ldm.models.autoencoder.AutoencoderKL
params:
monitor: "val/rec_loss"
embed_dim: 16
lossconfig:
target: ldm.modules.losses.LPIPSWithDiscriminator
params:
disc_start: 50001
kl_weight: 0.000001
disc_weight: 0.5
ddconfig:
double_z: True
z_channels: 16
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult: [ 1,1,2,2,4] # num_down = len(ch_mult)-1
num_res_blocks: 2
attn_resolutions: [16]
dropout: 0.0
data:
target: main.DataModuleFromConfig
params:
batch_size: 12
wrap: True
train:
target: ldm.data.imagenet.ImageNetSRTrain
params:
size: 256
degradation: pil_nearest
validation:
target: ldm.data.imagenet.ImageNetSRValidation
params:
size: 256
degradation: pil_nearest
lightning:
callbacks:
image_logger:
target: main.ImageLogger
params:
batch_frequency: 1000
max_images: 8
increase_log_steps: True
trainer:
benchmark: True
accumulate_grad_batches: 2
================================================
FILE: models/instructpix2pix/stable_diffusion/configs/autoencoder/autoencoder_kl_32x32x4.yaml
================================================
model:
base_learning_rate: 4.5e-6
target: ldm.models.autoencoder.AutoencoderKL
params:
monitor: "val/rec_loss"
embed_dim: 4
lossconfig:
target: ldm.modules.losses.LPIPSWithDiscriminator
params:
disc_start: 50001
kl_weight: 0.000001
disc_weight: 0.5
ddconfig:
double_z: True
z_channels: 4
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult: [ 1,2,4,4 ] # num_down = len(ch_mult)-1
num_res_blocks: 2
attn_resolutions: [ ]
dropout: 0.0
data:
target: main.DataModuleFromConfig
params:
batch_size: 12
wrap: True
train:
target: ldm.data.imagenet.ImageNetSRTrain
params:
size: 256
degradation: pil_nearest
validation:
target: ldm.data.imagenet.ImageNetSRValidation
params:
size: 256
degradation: pil_nearest
lightning:
callbacks:
image_logger:
target: main.ImageLogger
params:
batch_frequency: 1000
max_images: 8
increase_log_steps: True
trainer:
benchmark: True
accumulate_grad_batches: 2
================================================
FILE: models/instructpix2pix/stable_diffusion/configs/autoencoder/autoencoder_kl_64x64x3.yaml
================================================
model:
base_learning_rate: 4.5e-6
target: ldm.models.autoencoder.AutoencoderKL
params:
monitor: "val/rec_loss"
embed_dim: 3
lossconfig:
target: ldm.modules.losses.LPIPSWithDiscriminator
params:
disc_start: 50001
kl_weight: 0.000001
disc_weight: 0.5
ddconfig:
double_z: True
z_channels: 3
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult: [ 1,2,4 ] # num_down = len(ch_mult)-1
num_res_blocks: 2
attn_resolutions: [ ]
dropout: 0.0
data:
target: main.DataModuleFromConfig
params:
batch_size: 12
wrap: True
train:
target: ldm.data.imagenet.ImageNetSRTrain
params:
size: 256
degradation: pil_nearest
validation:
target: ldm.data.imagenet.ImageNetSRValidation
params:
size: 256
degradation: pil_nearest
lightning:
callbacks:
image_logger:
target: main.ImageLogger
params:
batch_frequency: 1000
max_images: 8
increase_log_steps: True
trainer:
benchmark: True
accumulate_grad_batches: 2
================================================
FILE: models/instructpix2pix/stable_diffusion/configs/autoencoder/autoencoder_kl_8x8x64.yaml
================================================
model:
base_learning_rate: 4.5e-6
target: ldm.models.autoencoder.AutoencoderKL
params:
monitor: "val/rec_loss"
embed_dim: 64
lossconfig:
target: ldm.modules.losses.LPIPSWithDiscriminator
params:
disc_start: 50001
kl_weight: 0.000001
disc_weight: 0.5
ddconfig:
double_z: True
z_channels: 64
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult: [ 1,1,2,2,4,4] # num_down = len(ch_mult)-1
num_res_blocks: 2
attn_resolutions: [16,8]
dropout: 0.0
data:
target: main.DataModuleFromConfig
params:
batch_size: 12
wrap: True
train:
target: ldm.data.imagenet.ImageNetSRTrain
params:
size: 256
degradation: pil_nearest
validation:
target: ldm.data.imagenet.ImageNetSRValidation
params:
size: 256
degradation: pil_nearest
lightning:
callbacks:
image_logger:
target: main.ImageLogger
params:
batch_frequency: 1000
max_images: 8
increase_log_steps: True
trainer:
benchmark: True
accumulate_grad_batches: 2
================================================
FILE: models/instructpix2pix/stable_diffusion/configs/latent-diffusion/celebahq-ldm-vq-4.yaml
================================================
model:
base_learning_rate: 2.0e-06
target: ldm.models.diffusion.ddpm.LatentDiffusion
params:
linear_start: 0.0015
linear_end: 0.0195
num_timesteps_cond: 1
log_every_t: 200
timesteps: 1000
first_stage_key: image
image_size: 64
channels: 3
monitor: val/loss_simple_ema
unet_config:
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
params:
image_size: 64
in_channels: 3
out_channels: 3
model_channels: 224
attention_resolutions:
# note: this isn\t actually the resolution but
# the downsampling factor, i.e. this corresnponds to
# attention on spatial resolution 8,16,32, as the
# spatial reolution of the latents is 64 for f4
- 8
- 4
- 2
num_res_blocks: 2
channel_mult:
- 1
- 2
- 3
- 4
num_head_channels: 32
first_stage_config:
target: ldm.models.autoencoder.VQModelInterface
params:
embed_dim: 3
n_embed: 8192
ckpt_path: models/first_stage_models/vq-f4/model.ckpt
ddconfig:
double_z: false
z_channels: 3
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 4
num_res_blocks: 2
attn_resolutions: []
dropout: 0.0
lossconfig:
target: torch.nn.Identity
cond_stage_config: __is_unconditional__
data:
target: main.DataModuleFromConfig
params:
batch_size: 48
num_workers: 5
wrap: false
train:
target: taming.data.faceshq.CelebAHQTrain
params:
size: 256
validation:
target: taming.data.faceshq.CelebAHQValidation
params:
size: 256
lightning:
callbacks:
image_logger:
target: main.ImageLogger
params:
batch_frequency: 5000
max_images: 8
increase_log_steps: False
trainer:
benchmark: True
================================================
FILE: models/instructpix2pix/stable_diffusion/configs/latent-diffusion/cin-ldm-vq-f8.yaml
================================================
model:
base_learning_rate: 1.0e-06
target: ldm.models.diffusion.ddpm.LatentDiffusion
params:
linear_start: 0.0015
linear_end: 0.0195
num_timesteps_cond: 1
log_every_t: 200
timesteps: 1000
first_stage_key: image
cond_stage_key: class_label
image_size: 32
channels: 4
cond_stage_trainable: true
conditioning_key: crossattn
monitor: val/loss_simple_ema
unet_config:
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
params:
image_size: 32
in_channels: 4
out_channels: 4
model_channels: 256
attention_resolutions:
#note: this isn\t actually the resolution but
# the downsampling factor, i.e. this corresnponds to
# attention on spatial resolution 8,16,32, as the
# spatial reolution of the latents is 32 for f8
- 4
- 2
- 1
num_res_blocks: 2
channel_mult:
- 1
- 2
- 4
num_head_channels: 32
use_spatial_transformer: true
transformer_depth: 1
context_dim: 512
first_stage_config:
target: ldm.models.autoencoder.VQModelInterface
params:
embed_dim: 4
n_embed: 16384
ckpt_path: configs/first_stage_models/vq-f8/model.yaml
ddconfig:
double_z: false
z_channels: 4
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 2
- 4
num_res_blocks: 2
attn_resolutions:
- 32
dropout: 0.0
lossconfig:
target: torch.nn.Identity
cond_stage_config:
target: ldm.modules.encoders.modules.ClassEmbedder
params:
embed_dim: 512
key: class_label
data:
target: main.DataModuleFromConfig
params:
batch_size: 64
num_workers: 12
wrap: false
train:
target: ldm.data.imagenet.ImageNetTrain
params:
config:
size: 256
validation:
target: ldm.data.imagenet.ImageNetValidation
params:
config:
size: 256
lightning:
callbacks:
image_logger:
target: main.ImageLogger
params:
batch_frequency: 5000
max_images: 8
increase_log_steps: False
trainer:
benchmark: True
================================================
FILE: models/instructpix2pix/stable_diffusion/configs/latent-diffusion/cin256-v2.yaml
================================================
model:
base_learning_rate: 0.0001
target: ldm.models.diffusion.ddpm.LatentDiffusion
params:
linear_start: 0.0015
linear_end: 0.0195
num_timesteps_cond: 1
log_every_t: 200
timesteps: 1000
first_stage_key: image
cond_stage_key: class_label
image_size: 64
channels: 3
cond_stage_trainable: true
conditioning_key: crossattn
monitor: val/loss
use_ema: False
unet_config:
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
params:
image_size: 64
in_channels: 3
out_channels: 3
model_channels: 192
attention_resolutions:
- 8
- 4
- 2
num_res_blocks: 2
channel_mult:
- 1
- 2
- 3
- 5
num_heads: 1
use_spatial_transformer: true
transformer_depth: 1
context_dim: 512
first_stage_config:
target: ldm.models.autoencoder.VQModelInterface
params:
embed_dim: 3
n_embed: 8192
ddconfig:
double_z: false
z_channels: 3
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 4
num_res_blocks: 2
attn_resolutions: []
dropout: 0.0
lossconfig:
target: torch.nn.Identity
cond_stage_config:
target: ldm.modules.encoders.modules.ClassEmbedder
params:
n_classes: 1001
embed_dim: 512
key: class_label
================================================
FILE: models/instructpix2pix/stable_diffusion/configs/latent-diffusion/ffhq-ldm-vq-4.yaml
================================================
model:
base_learning_rate: 2.0e-06
target: ldm.models.diffusion.ddpm.LatentDiffusion
params:
linear_start: 0.0015
linear_end: 0.0195
num_timesteps_cond: 1
log_every_t: 200
timesteps: 1000
first_stage_key: image
image_size: 64
channels: 3
monitor: val/loss_simple_ema
unet_config:
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
params:
image_size: 64
in_channels: 3
out_channels: 3
model_channels: 224
attention_resolutions:
# note: this isn\t actually the resolution but
# the downsampling factor, i.e. this corresnponds to
# attention on spatial resolution 8,16,32, as the
# spatial reolution of the latents is 64 for f4
- 8
- 4
- 2
num_res_blocks: 2
channel_mult:
- 1
- 2
- 3
- 4
num_head_channels: 32
first_stage_config:
target: ldm.models.autoencoder.VQModelInterface
params:
embed_dim: 3
n_embed: 8192
ckpt_path: configs/first_stage_models/vq-f4/model.yaml
ddconfig:
double_z: false
z_channels: 3
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 4
num_res_blocks: 2
attn_resolutions: []
dropout: 0.0
lossconfig:
target: torch.nn.Identity
cond_stage_config: __is_unconditional__
data:
target: main.DataModuleFromConfig
params:
batch_size: 42
num_workers: 5
wrap: false
train:
target: taming.data.faceshq.FFHQTrain
params:
size: 256
validation:
target: taming.data.faceshq.FFHQValidation
params:
size: 256
lightning:
callbacks:
image_logger:
target: main.ImageLogger
params:
batch_frequency: 5000
max_images: 8
increase_log_steps: False
trainer:
benchmark: True
================================================
FILE: models/instructpix2pix/stable_diffusion/configs/latent-diffusion/lsun_bedrooms-ldm-vq-4.yaml
================================================
model:
base_learning_rate: 2.0e-06
target: ldm.models.diffusion.ddpm.LatentDiffusion
params:
linear_start: 0.0015
linear_end: 0.0195
num_timesteps_cond: 1
log_every_t: 200
timesteps: 1000
first_stage_key: image
image_size: 64
channels: 3
monitor: val/loss_simple_ema
unet_config:
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
params:
image_size: 64
in_channels: 3
out_channels: 3
model_channels: 224
attention_resolutions:
# note: this isn\t actually the resolution but
# the downsampling factor, i.e. this corresnponds to
# attention on spatial resolution 8,16,32, as the
# spatial reolution of the latents is 64 for f4
- 8
- 4
- 2
num_res_blocks: 2
channel_mult:
- 1
- 2
- 3
- 4
num_head_channels: 32
first_stage_config:
target: ldm.models.autoencoder.VQModelInterface
params:
ckpt_path: configs/first_stage_models/vq-f4/model.yaml
embed_dim: 3
n_embed: 8192
ddconfig:
double_z: false
z_channels: 3
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 4
num_res_blocks: 2
attn_resolutions: []
dropout: 0.0
lossconfig:
target: torch.nn.Identity
cond_stage_config: __is_unconditional__
data:
target: main.DataModuleFromConfig
params:
batch_size: 48
num_workers: 5
wrap: false
train:
target: ldm.data.lsun.LSUNBedroomsTrain
params:
size: 256
validation:
target: ldm.data.lsun.LSUNBedroomsValidation
params:
size: 256
lightning:
callbacks:
image_logger:
target: main.ImageLogger
params:
batch_frequency: 5000
max_images: 8
increase_log_steps: False
trainer:
benchmark: True
================================================
FILE: models/instructpix2pix/stable_diffusion/configs/latent-diffusion/lsun_churches-ldm-kl-8.yaml
================================================
model:
base_learning_rate: 5.0e-5 # set to target_lr by starting main.py with '--scale_lr False'
target: ldm.models.diffusion.ddpm.LatentDiffusion
params:
linear_start: 0.0015
linear_end: 0.0155
num_timesteps_cond: 1
log_every_t: 200
timesteps: 1000
loss_type: l1
first_stage_key: "image"
cond_stage_key: "image"
image_size: 32
channels: 4
cond_stage_trainable: False
concat_mode: False
scale_by_std: True
monitor: 'val/loss_simple_ema'
scheduler_config: # 10000 warmup steps
target: ldm.lr_scheduler.LambdaLinearScheduler
params:
warm_up_steps: [10000]
cycle_lengths: [10000000000000]
f_start: [1.e-6]
f_max: [1.]
f_min: [ 1.]
unet_config:
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
params:
image_size: 32
in_channels: 4
out_channels: 4
model_channels: 192
attention_resolutions: [ 1, 2, 4, 8 ] # 32, 16, 8, 4
num_res_blocks: 2
channel_mult: [ 1,2,2,4,4 ] # 32, 16, 8, 4, 2
num_heads: 8
use_scale_shift_norm: True
resblock_updown: True
first_stage_config:
target: ldm.models.autoencoder.AutoencoderKL
params:
embed_dim: 4
monitor: "val/rec_loss"
ckpt_path: "models/first_stage_models/kl-f8/model.ckpt"
ddconfig:
double_z: True
z_channels: 4
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult: [ 1,2,4,4 ] # num_down = len(ch_mult)-1
num_res_blocks: 2
attn_resolutions: [ ]
dropout: 0.0
lossconfig:
target: torch.nn.Identity
cond_stage_config: "__is_unconditional__"
data:
target: main.DataModuleFromConfig
params:
batch_size: 96
num_workers: 5
wrap: False
train:
target: ldm.data.lsun.LSUNChurchesTrain
params:
size: 256
validation:
target: ldm.data.lsun.LSUNChurchesValidation
params:
size: 256
lightning:
callbacks:
image_logger:
target: main.ImageLogger
params:
batch_frequency: 5000
max_images: 8
increase_log_steps: False
trainer:
benchmark: True
================================================
FILE: models/instructpix2pix/stable_diffusion/configs/latent-diffusion/txt2img-1p4B-eval.yaml
================================================
model:
base_learning_rate: 5.0e-05
target: ldm.models.diffusion.ddpm.LatentDiffusion
params:
linear_start: 0.00085
linear_end: 0.012
num_timesteps_cond: 1
log_every_t: 200
timesteps: 1000
first_stage_key: image
cond_stage_key: caption
image_size: 32
channels: 4
cond_stage_trainable: true
conditioning_key: crossattn
monitor: val/loss_simple_ema
scale_factor: 0.18215
use_ema: False
unet_config:
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
params:
image_size: 32
in_channels: 4
out_channels: 4
model_channels: 320
attention_resolutions:
- 4
- 2
- 1
num_res_blocks: 2
channel_mult:
- 1
- 2
- 4
- 4
num_heads: 8
use_spatial_transformer: true
transformer_depth: 1
context_dim: 1280
use_checkpoint: true
legacy: False
first_stage_config:
target: ldm.models.autoencoder.AutoencoderKL
params:
embed_dim: 4
monitor: val/rec_loss
ddconfig:
double_z: true
z_channels: 4
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 4
- 4
num_res_blocks: 2
attn_resolutions: []
dropout: 0.0
lossconfig:
target: torch.nn.Identity
cond_stage_config:
target: ldm.modules.encoders.modules.BERTEmbedder
params:
n_embed: 1280
n_layer: 32
================================================
FILE: models/instructpix2pix/stable_diffusion/configs/retrieval-augmented-diffusion/768x768.yaml
================================================
model:
base_learning_rate: 0.0001
target: ldm.models.diffusion.ddpm.LatentDiffusion
params:
linear_start: 0.0015
linear_end: 0.015
num_timesteps_cond: 1
log_every_t: 200
timesteps: 1000
first_stage_key: jpg
cond_stage_key: nix
image_size: 48
channels: 16
cond_stage_trainable: false
conditioning_key: crossattn
monitor: val/loss_simple_ema
scale_by_std: false
scale_factor: 0.22765929
unet_config:
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
params:
image_size: 48
in_channels: 16
out_channels: 16
model_channels: 448
attention_resolutions:
- 4
- 2
- 1
num_res_blocks: 2
channel_mult:
- 1
- 2
- 3
- 4
use_scale_shift_norm: false
resblock_updown: false
num_head_channels: 32
use_spatial_transformer: true
transformer_depth: 1
context_dim: 768
use_checkpoint: true
first_stage_config:
target: ldm.models.autoencoder.AutoencoderKL
params:
monitor: val/rec_loss
embed_dim: 16
ddconfig:
double_z: true
z_channels: 16
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 1
- 2
- 2
- 4
num_res_blocks: 2
attn_resolutions:
- 16
dropout: 0.0
lossconfig:
target: torch.nn.Identity
cond_stage_config:
target: torch.nn.Identity
================================================
FILE: models/instructpix2pix/stable_diffusion/configs/stable-diffusion/v1-inference.yaml
================================================
model:
base_learning_rate: 1.0e-04
target: ldm.models.diffusion.ddpm.LatentDiffusion
params:
linear_start: 0.00085
linear_end: 0.0120
num_timesteps_cond: 1
log_every_t: 200
timesteps: 1000
first_stage_key: "jpg"
cond_stage_key: "txt"
image_size: 64
channels: 4
cond_stage_trainable: false # Note: different from the one we trained before
conditioning_key: crossattn
monitor: val/loss_simple_ema
scale_factor: 0.18215
use_ema: False
scheduler_config: # 10000 warmup steps
target: ldm.lr_scheduler.LambdaLinearScheduler
params:
warm_up_steps: [ 10000 ]
cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
f_start: [ 1.e-6 ]
f_max: [ 1. ]
f_min: [ 1. ]
unet_config:
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
params:
image_size: 32 # unused
in_channels: 4
out_channels: 4
model_channels: 320
attention_resolutions: [ 4, 2, 1 ]
num_res_blocks: 2
channel_mult: [ 1, 2, 4, 4 ]
num_heads: 8
use_spatial_transformer: True
transformer_depth: 1
context_dim: 768
use_checkpoint: True
legacy: False
first_stage_config:
target: ldm.models.autoencoder.AutoencoderKL
params:
embed_dim: 4
monitor: val/rec_loss
ddconfig:
double_z: true
z_channels: 4
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 4
- 4
num_res_blocks: 2
attn_resolutions: []
dropout: 0.0
lossconfig:
target: torch.nn.Identity
cond_stage_config:
target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
================================================
FILE: models/instructpix2pix/stable_diffusion/environment.yaml
================================================
name: ldm
channels:
- pytorch
- defaults
dependencies:
- python=3.8.5
- pip=20.3
- cudatoolkit=11.3
- pytorch=1.11.0
- torchvision=0.12.0
- numpy=1.19.2
- pip:
- albumentations==0.4.3
- diffusers
- opencv-python==4.1.2.30
- pudb==2019.2
- invisible-watermark
- imageio==2.9.0
- imageio-ffmpeg==0.4.2
- pytorch-lightning==1.4.2
- omegaconf==2.1.1
- test-tube>=0.7.5
- streamlit>=0.73.1
- einops==0.3.0
- torch-fidelity==0.3.0
- transformers==4.19.2
- torchmetrics==0.6.0
- kornia==0.6
- -e git+https://github.com/CompVis/taming-transformers.git@master#egg=taming-transformers
- -e git+https://github.com/openai/CLIP.git@main#egg=clip
- -e .
================================================
FILE: models/instructpix2pix/stable_diffusion/ldm/lr_scheduler.py
================================================
import numpy as np
class LambdaWarmUpCosineScheduler:
"""
note: use with a base_lr of 1.0
"""
def __init__(self, warm_up_steps, lr_min, lr_max, lr_start, max_decay_steps, verbosity_interval=0):
self.lr_warm_up_steps = warm_up_steps
self.lr_start = lr_start
self.lr_min = lr_min
self.lr_max = lr_max
self.lr_max_decay_steps = max_decay_steps
self.last_lr = 0.
self.verbosity_interval = verbosity_interval
def schedule(self, n, **kwargs):
if self.verbosity_interval > 0:
if n % self.verbosity_interval == 0: print(f"current step: {n}, recent lr-multiplier: {self.last_lr}")
if n < self.lr_warm_up_steps:
lr = (self.lr_max - self.lr_start) / self.lr_warm_up_steps * n + self.lr_start
self.last_lr = lr
return lr
else:
t = (n - self.lr_warm_up_steps) / (self.lr_max_decay_steps - self.lr_warm_up_steps)
t = min(t, 1.0)
lr = self.lr_min + 0.5 * (self.lr_max - self.lr_min) * (
1 + np.cos(t * np.pi))
self.last_lr = lr
return lr
def __call__(self, n, **kwargs):
return self.schedule(n,**kwargs)
class LambdaWarmUpCosineScheduler2:
"""
supports repeated iterations, configurable via lists
note: use with a base_lr of 1.0.
"""
def __init__(self, warm_up_steps, f_min, f_max, f_start, cycle_lengths, verbosity_interval=0):
assert len(warm_up_steps) == len(f_min) == len(f_max) == len(f_start) == len(cycle_lengths)
self.lr_warm_up_steps = warm_up_steps
self.f_start = f_start
self.f_min = f_min
self.f_max = f_max
self.cycle_lengths = cycle_lengths
self.cum_cycles = np.cumsum([0] + list(self.cycle_lengths))
self.last_f = 0.
self.verbosity_interval = verbosity_interval
def find_in_interval(self, n):
interval = 0
for cl in self.cum_cycles[1:]:
if n <= cl:
return interval
interval += 1
def schedule(self, n, **kwargs):
cycle = self.find_in_interval(n)
n = n - self.cum_cycles[cycle]
if self.verbosity_interval > 0:
if n % self.verbosity_interval == 0: print(f"current step: {n}, recent lr-multiplier: {self.last_f}, "
f"current cycle {cycle}")
if n < self.lr_warm_up_steps[cycle]:
f = (self.f_max[cycle] - self.f_start[cycle]) / self.lr_warm_up_steps[cycle] * n + self.f_start[cycle]
self.last_f = f
return f
else:
t = (n - self.lr_warm_up_steps[cycle]) / (self.cycle_lengths[cycle] - self.lr_warm_up_steps[cycle])
t = min(t, 1.0)
f = self.f_min[cycle] + 0.5 * (self.f_max[cycle] - self.f_min[cycle]) * (
1 + np.cos(t * np.pi))
self.last_f = f
return f
def __call__(self, n, **kwargs):
return self.schedule(n, **kwargs)
class LambdaLinearScheduler(LambdaWarmUpCosineScheduler2):
def schedule(self, n, **kwargs):
cycle = self.find_in_interval(n)
n = n - self.cum_cycles[cycle]
if self.verbosity_interval > 0:
if n % self.verbosity_interval == 0: print(f"current step: {n}, recent lr-multiplier: {self.last_f}, "
f"current cycle {cycle}")
if n < self.lr_warm_up_steps[cycle]:
f = (self.f_max[cycle] - self.f_start[cycle]) / self.lr_warm_up_steps[cycle] * n + self.f_start[cycle]
self.last_f = f
return f
else:
f = self.f_min[cycle] + (self.f_max[cycle] - self.f_min[cycle]) * (self.cycle_lengths[cycle] - n) / (self.cycle_lengths[cycle])
self.last_f = f
return f
================================================
FILE: models/instructpix2pix/stable_diffusion/ldm/models/autoencoder.py
================================================
import torch
import pytorch_lightning as pl
import torch.nn.functional as F
from contextlib import contextmanager
from taming.modules.vqvae.quantize import VectorQuantizer2 as VectorQuantizer
from ldm.modules.diffusionmodules.model import Encoder, Decoder
from ldm.modules.distributions.distributions import DiagonalGaussianDistribution
from ldm.util import instantiate_from_config
class VQModel(pl.LightningModule):
def __init__(self,
ddconfig,
lossconfig,
n_embed,
embed_dim,
ckpt_path=None,
ignore_keys=[],
image_key="image",
colorize_nlabels=None,
monitor=None,
batch_resize_range=None,
scheduler_config=None,
lr_g_factor=1.0,
remap=None,
sane_index_shape=False, # tell vector quantizer to return indices as bhw
use_ema=False
):
super().__init__()
self.embed_dim = embed_dim
self.n_embed = n_embed
self.image_key = image_key
self.encoder = Encoder(**ddconfig)
self.decoder = Decoder(**ddconfig)
self.loss = instantiate_from_config(lossconfig)
self.quantize = VectorQuantizer(n_embed, embed_dim, beta=0.25,
remap=remap,
sane_index_shape=sane_index_shape)
self.quant_conv = torch.nn.Conv2d(ddconfig["z_channels"], embed_dim, 1)
self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
if colorize_nlabels is not None:
assert type(colorize_nlabels)==int
self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1))
if monitor is not None:
self.monitor = monitor
self.batch_resize_range = batch_resize_range
if self.batch_resize_range is not None:
print(f"{self.__class__.__name__}: Using per-batch resizing in range {batch_resize_range}.")
self.use_ema = use_ema
if self.use_ema:
self.model_ema = LitEma(self)
print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
if ckpt_path is not None:
self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
self.scheduler_config = scheduler_config
self.lr_g_factor = lr_g_factor
@contextmanager
def ema_scope(self, context=None):
if self.use_ema:
self.model_ema.store(self.parameters())
self.model_ema.copy_to(self)
if context is not None:
print(f"{context}: Switched to EMA weights")
try:
yield None
finally:
if self.use_ema:
self.model_ema.restore(self.parameters())
if context is not None:
print(f"{context}: Restored training weights")
def init_from_ckpt(self, path, ignore_keys=list()):
sd = torch.load(path, map_location="cpu")["state_dict"]
keys = list(sd.keys())
for k in keys:
for ik in ignore_keys:
if k.startswith(ik):
print("Deleting key {} from state_dict.".format(k))
del sd[k]
missing, unexpected = self.load_state_dict(sd, strict=False)
print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
if len(missing) > 0:
print(f"Missing Keys: {missing}")
print(f"Unexpected Keys: {unexpected}")
def on_train_batch_end(self, *args, **kwargs):
if self.use_ema:
self.model_ema(self)
def encode(self, x):
h = self.encoder(x)
h = self.quant_conv(h)
quant, emb_loss, info = self.quantize(h)
return quant, emb_loss, info
def encode_to_prequant(self, x):
h = self.encoder(x)
h = self.quant_conv(h)
return h
def decode(self, quant):
quant = self.post_quant_conv(quant)
dec = self.decoder(quant)
return dec
def decode_code(self, code_b):
quant_b = self.quantize.embed_code(code_b)
dec = self.decode(quant_b)
return dec
def forward(self, input, return_pred_indices=False):
quant, diff, (_,_,ind) = self.encode(input)
dec = self.decode(quant)
if return_pred_indices:
return dec, diff, ind
return dec, diff
def get_input(self, batch, k):
x = batch[k]
if len(x.shape) == 3:
x = x[..., None]
x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float()
if self.batch_resize_range is not None:
lower_size = self.batch_resize_range[0]
upper_size = self.batch_resize_range[1]
if self.global_step <= 4:
# do the first few batches with max size to avoid later oom
new_resize = upper_size
else:
new_resize = np.random.choice(np.arange(lower_size, upper_size+16, 16))
if new_resize != x.shape[2]:
x = F.interpolate(x, size=new_resize, mode="bicubic")
x = x.detach()
return x
def training_step(self, batch, batch_idx, optimizer_idx):
# https://github.com/pytorch/pytorch/issues/37142
# try not to fool the heuristics
x = self.get_input(batch, self.image_key)
xrec, qloss, ind = self(x, return_pred_indices=True)
if optimizer_idx == 0:
# autoencode
aeloss, log_dict_ae = self.loss(qloss, x, xrec, optimizer_idx, self.global_step,
last_layer=self.get_last_layer(), split="train",
predicted_indices=ind)
self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True)
return aeloss
if optimizer_idx == 1:
# discriminator
discloss, log_dict_disc = self.loss(qloss, x, xrec, optimizer_idx, self.global_step,
last_layer=self.get_last_layer(), split="train")
self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=True)
return discloss
def validation_step(self, batch, batch_idx):
log_dict = self._validation_step(batch, batch_idx)
with self.ema_scope():
log_dict_ema = self._validation_step(batch, batch_idx, suffix="_ema")
return log_dict
def _validation_step(self, batch, batch_idx, suffix=""):
x = self.get_input(batch, self.image_key)
xrec, qloss, ind = self(x, return_pred_indices=True)
aeloss, log_dict_ae = self.loss(qloss, x, xrec, 0,
self.global_step,
last_layer=self.get_last_layer(),
split="val"+suffix,
predicted_indices=ind
)
discloss, log_dict_disc = self.loss(qloss, x, xrec, 1,
self.global_step,
last_layer=self.get_last_layer(),
split="val"+suffix,
predicted_indices=ind
)
rec_loss = log_dict_ae[f"val{suffix}/rec_loss"]
self.log(f"val{suffix}/rec_loss", rec_loss,
prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True)
self.log(f"val{suffix}/aeloss", aeloss,
prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True)
if version.parse(pl.__version__) >= version.parse('1.4.0'):
del log_dict_ae[f"val{suffix}/rec_loss"]
self.log_dict(log_dict_ae)
self.log_dict(log_dict_disc)
return self.log_dict
def configure_optimizers(self):
lr_d = self.learning_rate
lr_g = self.lr_g_factor*self.learning_rate
print("lr_d", lr_d)
print("lr_g", lr_g)
opt_ae = torch.optim.Adam(list(self.encoder.parameters())+
list(self.decoder.parameters())+
list(self.quantize.parameters())+
list(self.quant_conv.parameters())+
list(self.post_quant_conv.parameters()),
lr=lr_g, betas=(0.5, 0.9))
opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(),
lr=lr_d, betas=(0.5, 0.9))
if self.scheduler_config is not None:
scheduler = instantiate_from_config(self.scheduler_config)
print("Setting up LambdaLR scheduler...")
scheduler = [
{
'scheduler': LambdaLR(opt_ae, lr_lambda=scheduler.schedule),
'interval': 'step',
'frequency': 1
},
{
'scheduler': LambdaLR(opt_disc, lr_lambda=scheduler.schedule),
'interval': 'step',
'frequency': 1
},
]
return [opt_ae, opt_disc], scheduler
return [opt_ae, opt_disc], []
def get_last_layer(self):
return self.decoder.conv_out.weight
def log_images(self, batch, only_inputs=False, plot_ema=False, **kwargs):
log = dict()
x = self.get_input(batch, self.image_key)
x = x.to(self.device)
if only_inputs:
log["inputs"] = x
return log
xrec, _ = self(x)
if x.shape[1] > 3:
# colorize with random projection
assert xrec.shape[1] > 3
x = self.to_rgb(x)
xrec = self.to_rgb(xrec)
log["inputs"] = x
log["reconstructions"] = xrec
if plot_ema:
with self.ema_scope():
xrec_ema, _ = self(x)
if x.shape[1] > 3: xrec_ema = self.to_rgb(xrec_ema)
log["reconstructions_ema"] = xrec_ema
return log
def to_rgb(self, x):
assert self.image_key == "segmentation"
if not hasattr(self, "colorize"):
self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x))
x = F.conv2d(x, weight=self.colorize)
x = 2.*(x-x.min())/(x.max()-x.min()) - 1.
return x
class VQModelInterface(VQModel):
def __init__(self, embed_dim, *args, **kwargs):
super().__init__(embed_dim=embed_dim, *args, **kwargs)
self.embed_dim = embed_dim
def encode(self, x):
h = self.encoder(x)
h = self.quant_conv(h)
return h
def decode(self, h, force_not_quantize=False):
# also go through quantization layer
if not force_not_quantize:
quant, emb_loss, info = self.quantize(h)
else:
quant = h
quant = self.post_quant_conv(quant)
dec = self.decoder(quant)
return dec
class AutoencoderKL(pl.LightningModule):
def __init__(self,
ddconfig,
lossconfig,
embed_dim,
ckpt_path=None,
ignore_keys=[],
image_key="image",
colorize_nlabels=None,
monitor=None,
):
super().__init__()
self.image_key = image_key
self.encoder = Encoder(**ddconfig)
self.decoder = Decoder(**ddconfig)
self.loss = instantiate_from_config(lossconfig)
assert ddconfig["double_z"]
self.quant_conv = torch.nn.Conv2d(2*ddconfig["z_channels"], 2*embed_dim, 1)
self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
self.embed_dim = embed_dim
if colorize_nlabels is not None:
assert type(colorize_nlabels)==int
self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1))
if monitor is not None:
self.monitor = monitor
if ckpt_path is not None:
self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
def init_from_ckpt(self, path, ignore_keys=list()):
sd = torch.load(path, map_location="cpu")["state_dict"]
keys = list(sd.keys())
for k in keys:
for ik in ignore_keys:
if k.startswith(ik):
print("Deleting key {} from state_dict.".format(k))
del sd[k]
self.load_state_dict(sd, strict=False)
print(f"Restored from {path}")
def encode(self, x):
h = self.encoder(x)
moments = self.quant_conv(h)
posterior = DiagonalGaussianDistribution(moments)
return posterior
def decode(self, z):
z = self.post_quant_conv(z)
dec = self.decoder(z)
return dec
def forward(self, input, sample_posterior=True):
posterior = self.encode(input)
if sample_posterior:
z = posterior.sample()
else:
z = posterior.mode()
dec = self.decode(z)
return dec, posterior
def get_input(self, batch, k):
x = batch[k]
if len(x.shape) == 3:
x = x[..., None]
x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float()
return x
def training_step(self, batch, batch_idx, optimizer_idx):
inputs = self.get_input(batch, self.image_key)
reconstructions, posterior = self(inputs)
if optimizer_idx == 0:
# train encoder+decoder+logvar
aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step,
last_layer=self.get_last_layer(), split="train")
self.log("aeloss", aeloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=False)
return aeloss
if optimizer_idx == 1:
# train the discriminator
discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step,
last_layer=self.get_last_layer(), split="train")
self.log("discloss", discloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=False)
return discloss
def validation_step(self, batch, batch_idx):
inputs = self.get_input(batch, self.image_key)
reconstructions, posterior = self(inputs)
aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, 0, self.global_step,
last_layer=self.get_last_layer(), split="val")
discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, 1, self.global_step,
last_layer=self.get_last_layer(), split="val")
self.log("val/rec_loss", log_dict_ae["val/rec_loss"])
self.log_dict(log_dict_ae)
self.log_dict(log_dict_disc)
return self.log_dict
def configure_optimizers(self):
lr = self.learning_rate
opt_ae = torch.optim.Adam(list(self.encoder.parameters())+
list(self.decoder.parameters())+
list(self.quant_conv.parameters())+
list(self.post_quant_conv.parameters()),
lr=lr, betas=(0.5, 0.9))
opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(),
lr=lr, betas=(0.5, 0.9))
return [opt_ae, opt_disc], []
def get_last_layer(self):
return self.decoder.conv_out.weight
@torch.no_grad()
def log_images(self, batch, only_inputs=False, **kwargs):
log = dict()
x = self.get_input(batch, self.image_key)
x = x.to(self.device)
if not only_inputs:
xrec, posterior = self(x)
if x.shape[1] > 3:
# colorize with random projection
assert xrec.shape[1] > 3
x = self.to_rgb(x)
xrec = self.to_rgb(xrec)
log["samples"] = self.decode(torch.randn_like(posterior.sample()))
log["reconstructions"] = xrec
log["inputs"] = x
return log
def to_rgb(self, x):
assert self.image_key == "segmentation"
if not hasattr(self, "colorize"):
self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x))
x = F.conv2d(x, weight=self.colorize)
x = 2.*(x-x.min())/(x.max()-x.min()) - 1.
return x
class IdentityFirstStage(torch.nn.Module):
def __init__(self, *args, vq_interface=False, **kwargs):
self.vq_interface = vq_interface # TODO: Should be true by default but check to not break older stuff
super().__init__()
def encode(self, x, *args, **kwargs):
return x
def decode(self, x, *args, **kwargs):
return x
def quantize(self, x, *args, **kwargs):
if self.vq_interface:
return x, None, [None, None, None]
return x
def forward(self, x, *args, **kwargs):
return x
================================================
FILE: models/instructpix2pix/stable_diffusion/ldm/models/diffusion/__init__.py
================================================
================================================
FILE: models/instructpix2pix/stable_diffusion/ldm/models/diffusion/classifier.py
================================================
import os
import torch
import pytorch_lightning as pl
from omegaconf import OmegaConf
from torch.nn import functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from copy import deepcopy
from einops import rearrange
from glob import glob
from natsort import natsorted
from ldm.modules.diffusionmodules.openaimodel import EncoderUNetModel, UNetModel
from ldm.util import log_txt_as_img, default, ismap, instantiate_from_config
__models__ = {
'class_label': EncoderUNetModel,
'segmentation': UNetModel
}
def disabled_train(self, mode=True):
"""Overwrite model.train with this function to make sure train/eval mode
does not change anymore."""
return self
class NoisyLatentImageClassifier(pl.LightningModule):
def __init__(self,
diffusion_path,
num_classes,
ckpt_path=None,
pool='attention',
label_key=None,
diffusion_ckpt_path=None,
scheduler_config=None,
weight_decay=1.e-2,
log_steps=10,
monitor='val/loss',
*args,
**kwargs):
super().__init__(*args, **kwargs)
self.num_classes = num_classes
# get latest config of diffusion model
diffusion_config = natsorted(glob(os.path.join(diffusion_path, 'configs', '*-project.yaml')))[-1]
self.diffusion_config = OmegaConf.load(diffusion_config).model
self.diffusion_config.params.ckpt_path = diffusion_ckpt_path
self.load_diffusion()
self.monitor = monitor
self.numd = self.diffusion_model.first_stage_model.encoder.num_resolutions - 1
self.log_time_interval = self.diffusion_model.num_timesteps // log_steps
self.log_steps = log_steps
self.label_key = label_key if not hasattr(self.diffusion_model, 'cond_stage_key') \
else self.diffusion_model.cond_stage_key
assert self.label_key is not None, 'label_key neither in diffusion model nor in model.params'
if self.label_key not in __models__:
raise NotImplementedError()
self.load_classifier(ckpt_path, pool)
self.scheduler_config = scheduler_config
self.use_scheduler = self.scheduler_config is not None
self.weight_decay = weight_decay
def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
sd = torch.load(path, map_location="cpu")
if "state_dict" in list(sd.keys()):
sd = sd["state_dict"]
keys = list(sd.keys())
for k in keys:
for ik in ignore_keys:
if k.startswith(ik):
print("Deleting key {} from state_dict.".format(k))
del sd[k]
missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
sd, strict=False)
print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
if len(missing) > 0:
print(f"Missing Keys: {missing}")
if len(unexpected) > 0:
print(f"Unexpected Keys: {unexpected}")
def load_diffusion(self):
model = instantiate_from_config(self.diffusion_config)
self.diffusion_model = model.eval()
self.diffusion_model.train = disabled_train
for param in self.diffusion_model.parameters():
param.requires_grad = False
def load_classifier(self, ckpt_path, pool):
model_config = deepcopy(self.diffusion_config.params.unet_config.params)
model_config.in_channels = self.diffusion_config.params.unet_config.params.out_channels
model_config.out_channels = self.num_classes
if self.label_key == 'class_label':
model_config.pool = pool
self.model = __models__[self.label_key](**model_config)
if ckpt_path is not None:
print('#####################################################################')
print(f'load from ckpt "{ckpt_path}"')
print('#####################################################################')
self.init_from_ckpt(ckpt_path)
@torch.no_grad()
def get_x_noisy(self, x, t, noise=None):
noise = default(noise, lambda: torch.randn_like(x))
continuous_sqrt_alpha_cumprod = None
if self.diffusion_model.use_continuous_noise:
continuous_sqrt_alpha_cumprod = self.diffusion_model.sample_continuous_noise_level(x.shape[0], t + 1)
# todo: make sure t+1 is correct here
return self.diffusion_model.q_sample(x_start=x, t=t, noise=noise,
continuous_sqrt_alpha_cumprod=continuous_sqrt_alpha_cumprod)
def forward(self, x_noisy, t, *args, **kwargs):
return self.model(x_noisy, t)
@torch.no_grad()
def get_input(self, batch, k):
x = batch[k]
if len(x.shape) == 3:
x = x[..., None]
x = rearrange(x, 'b h w c -> b c h w')
x = x.to(memory_format=torch.contiguous_format).float()
return x
@torch.no_grad()
def get_conditioning(self, batch, k=None):
if k is None:
k = self.label_key
assert k is not None, 'Needs to provide label key'
targets = batch[k].to(self.device)
if self.label_key == 'segmentation':
targets = rearrange(targets, 'b h w c -> b c h w')
for down in range(self.numd):
h, w = targets.shape[-2:]
targets = F.interpolate(targets, size=(h // 2, w // 2), mode='nearest')
# targets = rearrange(targets,'b c h w -> b h w c')
return targets
def compute_top_k(self, logits, labels, k, reduction="mean"):
_, top_ks = torch.topk(logits, k, dim=1)
if reduction == "mean":
return (top_ks == labels[:, None]).float().sum(dim=-1).mean().item()
elif reduction == "none":
return (top_ks == labels[:, None]).float().sum(dim=-1)
def on_train_epoch_start(self):
# save some memory
self.diffusion_model.model.to('cpu')
@torch.no_grad()
def write_logs(self, loss, logits, targets):
log_prefix = 'train' if self.training else 'val'
log = {}
log[f"{log_prefix}/loss"] = loss.mean()
log[f"{log_prefix}/acc@1"] = self.compute_top_k(
logits, targets, k=1, reduction="mean"
)
log[f"{log_prefix}/acc@5"] = self.compute_top_k(
logits, targets, k=5, reduction="mean"
)
self.log_dict(log, prog_bar=False, logger=True, on_step=self.training, on_epoch=True)
self.log('loss', log[f"{log_prefix}/loss"], prog_bar=True, logger=False)
self.log('global_step', self.global_step, logger=False, on_epoch=False, prog_bar=True)
lr = self.optimizers().param_groups[0]['lr']
self.log('lr_abs', lr, on_step=True, logger=True, on_epoch=False, prog_bar=True)
def shared_step(self, batch, t=None):
x, *_ = self.diffusion_model.get_input(batch, k=self.diffusion_model.first_stage_key)
targets = self.get_conditioning(batch)
if targets.dim() == 4:
targets = targets.argmax(dim=1)
if t is None:
t = torch.randint(0, self.diffusion_model.num_timesteps, (x.shape[0],), device=self.device).long()
else:
t = torch.full(size=(x.shape[0],), fill_value=t, device=self.device).long()
x_noisy = self.get_x_noisy(x, t)
logits = self(x_noisy, t)
loss = F.cross_entropy(logits, targets, reduction='none')
self.write_logs(loss.detach(), logits.detach(), targets.detach())
loss = loss.mean()
return loss, logits, x_noisy, targets
def training_step(self, batch, batch_idx):
loss, *_ = self.shared_step(batch)
return loss
def reset_noise_accs(self):
self.noisy_acc = {t: {'acc@1': [], 'acc@5': []} for t in
range(0, self.diffusion_model.num_timesteps, self.diffusion_model.log_every_t)}
def on_validation_start(self):
self.reset_noise_accs()
@torch.no_grad()
def validation_step(self, batch, batch_idx):
loss, *_ = self.shared_step(batch)
for t in self.noisy_acc:
_, logits, _, targets = self.shared_step(batch, t)
self.noisy_acc[t]['acc@1'].append(self.compute_top_k(logits, targets, k=1, reduction='mean'))
self.noisy_acc[t]['acc@5'].append(self.compute_top_k(logits, targets, k=5, reduction='mean'))
return loss
def configure_optimizers(self):
optimizer = AdamW(self.model.parameters(), lr=self.learning_rate, weight_decay=self.weight_decay)
if self.use_scheduler:
scheduler = instantiate_from_config(self.scheduler_config)
print("Setting up LambdaLR scheduler...")
scheduler = [
{
'scheduler': LambdaLR(optimizer, lr_lambda=scheduler.schedule),
'interval': 'step',
'frequency': 1
}]
return [optimizer], scheduler
return optimizer
@torch.no_grad()
def log_images(self, batch, N=8, *args, **kwargs):
log = dict()
x = self.get_input(batch, self.diffusion_model.first_stage_key)
log['inputs'] = x
y = self.get_conditioning(batch)
if self.label_key == 'class_label':
y = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"])
log['labels'] = y
if ismap(y):
log['labels'] = self.diffusion_model.to_rgb(y)
for step in range(self.log_steps):
current_time = step * self.log_time_interval
_, logits, x_noisy, _ = self.shared_step(batch, t=current_time)
log[f'inputs@t{current_time}'] = x_noisy
pred = F.one_hot(logits.argmax(dim=1), num_classes=self.num_classes)
pred = rearrange(pred, 'b h w c -> b c h w')
log[f'pred@t{current_time}'] = self.diffusion_model.to_rgb(pred)
for key in log:
log[key] = log[key][:N]
return log
================================================
FILE: models/instructpix2pix/stable_diffusion/ldm/models/diffusion/ddim.py
================================================
"""SAMPLING ONLY."""
import torch
import numpy as np
from tqdm import tqdm
from functools import partial
from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like, \
extract_into_tensor
class DDIMSampler(object):
def __init__(self, model, schedule="linear", **kwargs):
super().__init__()
self.model = model
self.ddpm_num_timesteps = model.num_timesteps
self.schedule = schedule
def register_buffer(self, name, attr):
if type(attr) == torch.Tensor:
if attr.device != torch.device("cuda"):
attr = attr.to(torch.device("cuda"))
setattr(self, name, attr)
def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True):
self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps,
num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose)
alphas_cumprod = self.model.alphas_cumprod
assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep'
to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device)
self.register_buffer('betas', to_torch(self.model.betas))
self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev))
# calculations for diffusion q(x_t | x_{t-1}) and others
self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu())))
self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu())))
self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu())))
self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu())))
self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1)))
# ddim sampling parameters
ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(),
ddim_timesteps=self.ddim_timesteps,
eta=ddim_eta,verbose=verbose)
self.register_buffer('ddim_sigmas', ddim_sigmas)
self.register_buffer('ddim_alphas', ddim_alphas)
self.register_buffer('ddim_alphas_prev', ddim_alphas_prev)
self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas))
sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(
(1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * (
1 - self.alphas_cumprod / self.alphas_cumprod_prev))
self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps)
@torch.no_grad()
def sample(self,
S,
batch_size,
shape,
conditioning=None,
callback=None,
normals_sequence=None,
img_callback=None,
quantize_x0=False,
eta=0.,
mask=None,
x0=None,
temperature=1.,
noise_dropout=0.,
score_corrector=None,
corrector_kwargs=None,
verbose=True,
x_T=None,
log_every_t=100,
unconditional_guidance_scale=1.,
unconditional_conditioning=None,
# this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
**kwargs
):
if conditioning is not None:
if isinstance(conditioning, dict):
cbs = conditioning[list(conditioning.keys())[0]].shape[0]
if cbs != batch_size:
print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
else:
if conditioning.shape[0] != batch_size:
print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
# sampling
C, H, W = shape
size = (batch_size, C, H, W)
print(f'Data shape for DDIM sampling is {size}, eta {eta}')
samples, intermediates = self.ddim_sampling(conditioning, size,
callback=callback,
img_callback=img_callback,
quantize_denoised=quantize_x0,
mask=mask, x0=x0,
ddim_use_original_steps=False,
noise_dropout=noise_dropout,
temperature=temperature,
score_corrector=score_corrector,
corrector_kwargs=corrector_kwargs,
x_T=x_T,
log_every_t=log_every_t,
unconditional_guidance_scale=unconditional_guidance_scale,
unconditional_conditioning=unconditional_conditioning,
)
return samples, intermediates
@torch.no_grad()
def ddim_sampling(self, cond, shape,
x_T=None, ddim_use_original_steps=False,
callback=None, timesteps=None, quantize_denoised=False,
mask=None, x0=None, img_callback=None, log_every_t=100,
temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
unconditional_guidance_scale=1., unconditional_conditioning=None,):
device = self.model.betas.device
b = shape[0]
if x_T is None:
img = torch.randn(shape, device=device)
else:
img = x_T
if timesteps is None:
timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps
elif timesteps is not None and not ddim_use_original_steps:
subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1
timesteps = self.ddim_timesteps[:subset_end]
intermediates = {'x_inter': [img], 'pred_x0': [img]}
time_range = reversed(range(0,timesteps)) if ddim_use_original_steps else np.flip(timesteps)
total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]
print(f"Running DDIM Sampling with {total_steps} timesteps")
iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps)
for i, step in enumerate(iterator):
index = total_steps - i - 1
ts = torch.full((b,), step, device=device, dtype=torch.long)
if mask is not None:
assert x0 is not None
img_orig = self.model.q_sample(x0, ts) # TODO: deterministic forward pass?
img = img_orig * mask + (1. - mask) * img
outs = self.p_sample_ddim(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps,
quantize_denoised=quantize_denoised, temperature=temperature,
noise_dropout=noise_dropout, score_corrector=score_corrector,
corrector_kwargs=corrector_kwargs,
unconditional_guidance_scale=unconditional_guidance_scale,
unconditional_conditioning=unconditional_conditioning)
img, pred_x0 = outs
if callback: callback(i)
if img_callback: img_callback(pred_x0, i)
if index % log_every_t == 0 or index == total_steps - 1:
intermediates['x_inter'].append(img)
intermediates['pred_x0'].append(pred_x0)
return img, intermediates
@torch.no_grad()
def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
unconditional_guidance_scale=1., unconditional_conditioning=None):
b, *_, device = *x.shape, x.device
if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
e_t = self.model.apply_model(x, t, c)
else:
x_in = torch.cat([x] * 2)
t_in = torch.cat([t] * 2)
c_in = torch.cat([unconditional_conditioning, c])
e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
if score_corrector is not None:
assert self.model.parameterization == "eps"
e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
# select parameters corresponding to the currently considered timestep
a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
# current prediction for x_0
pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
if quantize_denoised:
pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
# direction pointing to x_t
dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
if noise_dropout > 0.:
noise = torch.nn.functional.dropout(noise, p=noise_dropout)
x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
return x_prev, pred_x0
@torch.no_grad()
def stochastic_encode(self, x0, t, use_original_steps=False, noise=None):
# fast, but does not allow for exact reconstruction
# t serves as an index to gather the correct alphas
if use_original_steps:
sqrt_alphas_cumprod = self.sqrt_alphas_cumprod
sqrt_one_minus_alphas_cumprod = self.sqrt_one_minus_alphas_cumprod
else:
sqrt_alphas_cumprod = torch.sqrt(self.ddim_alphas)
sqrt_one_minus_alphas_cumprod = self.ddim_sqrt_one_minus_alphas
if noise is None:
noise = torch.randn_like(x0)
return (extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0 +
extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape) * noise)
@torch.no_grad()
def decode(self, x_latent, cond, t_start, unconditional_guidance_scale=1.0, unconditional_conditioning=None,
use_original_steps=False):
timesteps = np.arange(self.ddpm_num_timesteps) if use_original_steps else self.ddim_timesteps
timesteps = timesteps[:t_start]
time_range = np.flip(timesteps)
total_steps = timesteps.shape[0]
print(f"Running DDIM Sampling with {total_steps} timesteps")
iterator = tqdm(time_range, desc='Decoding image', total=total_steps)
x_dec = x_latent
for i, step in enumerate(iterator):
index = total_steps - i - 1
ts = torch.full((x_latent.shape[0],), step, device=x_latent.device, dtype=torch.long)
x_dec, _ = self.p_sample_ddim(x_dec, cond, ts, index=index, use_original_steps=use_original_steps,
unconditional_guidance_scale=unconditional_guidance_scale,
unconditional_conditioning=unconditional_conditioning)
return x_dec
================================================
FILE: models/instructpix2pix/stable_diffusion/ldm/models/diffusion/ddpm.py
================================================
"""
wild mixture of
https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py
https://github.com/CompVis/taming-transformers
-- merci
"""
import torch
import torch.nn as nn
import numpy as np
import pytorch_lightning as pl
from torch.optim.lr_scheduler import LambdaLR
from einops import rearrange, repeat
from contextlib import contextmanager
from functools import partial
from tqdm import tqdm
from torchvision.utils import make_grid
from pytorch_lightning.utilities.distributed import rank_zero_only
from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config
from ldm.modules.ema import LitEma
from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution
from ldm.models.autoencoder import VQModelInterface, IdentityFirstStage, AutoencoderKL
from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
from ldm.models.diffusion.ddim import DDIMSampler
__conditioning_keys__ = {'concat': 'c_concat',
'crossattn': 'c_crossattn',
'adm': 'y'}
def disabled_train(self, mode=True):
"""Overwrite model.train with this function to make sure train/eval mode
does not change anymore."""
return self
def uniform_on_device(r1, r2, shape, device):
return (r1 - r2) * torch.rand(*shape, device=device) + r2
class DDPM(pl.LightningModule):
# classic DDPM with Gaussian diffusion, in image space
def __init__(self,
unet_config,
timesteps=1000,
beta_schedule="linear",
loss_type="l2",
ckpt_path=None,
ignore_keys=[],
load_only_unet=False,
monitor="val/loss",
use_ema=True,
first_stage_key="image",
image_size=256,
channels=3,
log_every_t=100,
clip_denoised=True,
linear_start=1e-4,
linear_end=2e-2,
cosine_s=8e-3,
given_betas=None,
original_elbo_weight=0.,
v_posterior=0., # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta
l_simple_weight=1.,
conditioning_key=None,
parameterization="eps", # all assuming fixed variance schedules
scheduler_config=None,
use_positional_encodings=False,
learn_logvar=False,
logvar_init=0.,
):
super().__init__()
assert parameterization in ["eps", "x0"], 'currently only supporting "eps" and "x0"'
self.parameterization = parameterization
print(f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode")
self.cond_stage_model = None
self.clip_denoised = clip_denoised
self.log_every_t = log_every_t
self.first_stage_key = first_stage_key
self.image_size = image_size # try conv?
self.channels = channels
self.use_positional_encodings = use_positional_encodings
self.model = DiffusionWrapper(unet_config, conditioning_key)
count_params(self.model, verbose=True)
self.use_ema = use_ema
if self.use_ema:
self.model_ema = LitEma(self.model)
print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
self.use_scheduler = scheduler_config is not None
if self.use_scheduler:
self.scheduler_config = scheduler_config
self.v_posterior = v_posterior
self.original_elbo_weight = original_elbo_weight
self.l_simple_weight = l_simple_weight
if monitor is not None:
self.monitor = monitor
if ckpt_path is not None:
self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet)
self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps,
linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)
self.loss_type = loss_type
self.learn_logvar = learn_logvar
self.logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,))
if self.learn_logvar:
self.logvar = nn.Parameter(self.logvar, requires_grad=True)
def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000,
linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
if exists(given_betas):
betas = given_betas
else:
betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end,
cosine_s=cosine_s)
alphas = 1. - betas
alphas_cumprod = np.cumprod(alphas, axis=0)
alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
timesteps, = betas.shape
self.num_timesteps = int(timesteps)
self.linear_start = linear_start
self.linear_end = linear_end
assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep'
to_torch = partial(torch.tensor, dtype=torch.float32)
self.register_buffer('betas', to_torch(betas))
self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))
# calculations for diffusion q(x_t | x_{t-1}) and others
self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod)))
self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))
self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))
# calculations for posterior q(x_{t-1} | x_t, x_0)
posterior_variance = (1 - self.v_posterior) * betas * (1. - alphas_cumprod_prev) / (
1. - alphas_cumprod) + self.v_posterior * betas
# above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
self.register_buffer('posterior_variance', to_torch(posterior_variance))
# below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20))))
self.register_buffer('posterior_mean_coef1', to_torch(
betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)))
self.register_buffer('posterior_mean_coef2', to_torch(
(1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod)))
if self.parameterization == "eps":
lvlb_weights = self.betas ** 2 / (
2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod))
elif self.parameterization == "x0":
lvlb_weights = 0.5 * np.sqrt(torch.Tensor(alphas_cumprod)) / (2. * 1 - torch.Tensor(alphas_cumprod))
else:
raise NotImplementedError("mu not supported")
# TODO how to choose this term
lvlb_weights[0] = lvlb_weights[1]
self.register_buffer('lvlb_weights', lvlb_weights, persistent=False)
assert not torch.isnan(self.lvlb_weights).all()
@contextmanager
def ema_scope(self, context=None):
if self.use_ema:
self.model_ema.store(self.model.parameters())
self.model_ema.copy_to(self.model)
if context is not None:
print(f"{context}: Switched to EMA weights")
try:
yield None
finally:
if self.use_ema:
self.model_ema.restore(self.model.parameters())
if context is not None:
print(f"{context}: Restored training weights")
def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
sd = torch.load(path, map_location="cpu")
if "state_dict" in list(sd.keys()):
sd = sd["state_dict"]
keys = list(sd.keys())
for k in keys:
for ik in ignore_keys:
if k.startswith(ik):
print("Deleting key {} from state_dict.".format(k))
del sd[k]
missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
sd, strict=False)
print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
if len(missing) > 0:
print(f"Missing Keys: {missing}")
if len(unexpected) > 0:
print(f"Unexpected Keys: {unexpected}")
def q_mean_variance(self, x_start, t):
"""
Get the distribution q(x_t | x_0).
:param x_start: the [N x C x ...] tensor of noiseless inputs.
:param t: the number of diffusion steps (minus 1). Here, 0 means one step.
:return: A tuple (mean, variance, log_variance), all of x_start's shape.
"""
mean = (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start)
variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape)
log_variance = extract_into_tensor(self.log_one_minus_alphas_cumprod, t, x_start.shape)
return mean, variance, log_variance
def predict_start_from_noise(self, x_t, t, noise):
return (
extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t -
extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise
)
def q_posterior(self, x_start, x_t, t):
posterior_mean = (
extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start +
extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t
)
posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape)
posterior_log_variance_clipped = extract_into_tensor(self.posterior_log_variance_clipped, t, x_t.shape)
return posterior_mean, posterior_variance, posterior_log_variance_clipped
def p_mean_variance(self, x, t, clip_denoised: bool):
model_out = self.model(x, t)
if self.parameterization == "eps":
x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
elif self.parameterization == "x0":
x_recon = model_out
if clip_denoised:
x_recon.clamp_(-1., 1.)
model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
return model_mean, posterior_variance, posterior_log_variance
@torch.no_grad()
def p_sample(self, x, t, clip_denoised=True, repeat_noise=False):
b, *_, device = *x.shape, x.device
model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, clip_denoised=clip_denoised)
noise = noise_like(x.shape, device, repeat_noise)
# no noise when t == 0
nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
@torch.no_grad()
def p_sample_loop(self, shape, return_intermediates=False):
device = self.betas.device
b = shape[0]
img = torch.randn(shape, device=device)
intermediates = [img]
for i in tqdm(reversed(range(0, self.num_timesteps)), desc='Sampling t', total=self.num_timesteps):
img = self.p_sample(img, torch.full((b,), i, device=device, dtype=torch.long),
clip_denoised=self.clip_denoised)
if i % self.log_every_t == 0 or i == self.num_timesteps - 1:
intermediates.append(img)
if return_intermediates:
return img, intermediates
return img
@torch.no_grad()
def sample(self, batch_size=16, return_intermediates=False):
image_size = self.image_size
channels = self.channels
return self.p_sample_loop((batch_size, channels, image_size, image_size),
return_intermediates=return_intermediates)
def q_sample(self, x_start, t, noise=None):
noise = default(noise, lambda: torch.randn_like(x_start))
return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise)
def get_loss(self, pred, target, mean=True):
if self.loss_type == 'l1':
loss = (target - pred).abs()
if mean:
loss = loss.mean()
elif self.loss_type == 'l2':
if mean:
loss = torch.nn.functional.mse_loss(target, pred)
else:
loss = torch.nn.functional.mse_loss(target, pred, reduction='none')
else:
raise NotImplementedError("unknown loss type '{loss_type}'")
return loss
def p_losses(self, x_start, t, noise=None):
noise = default(noise, lambda: torch.randn_like(x_start))
x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
model_out = self.model(x_noisy, t)
loss_dict = {}
if self.parameterization == "eps":
target = noise
elif self.parameterization == "x0":
target = x_start
else:
raise NotImplementedError(f"Paramterization {self.parameterization} not yet supported")
loss = self.get_loss(model_out, target, mean=False).mean(dim=[1, 2, 3])
log_prefix = 'train' if self.training else 'val'
loss_dict.update({f'{log_prefix}/loss_simple': loss.mean()})
loss_simple = loss.mean() * self.l_simple_weight
loss_vlb = (self.lvlb_weights[t] * loss).mean()
loss_dict.update({f'{log_prefix}/loss_vlb': loss_vlb})
loss = loss_simple + self.original_elbo_weight * loss_vlb
loss_dict.update({f'{log_prefix}/loss': loss})
return loss, loss_dict
def forward(self, x, *args, **kwargs):
# b, c, h, w, device, img_size, = *x.shape, x.device, self.image_size
# assert h == img_size and w == img_size, f'height and width of image must be {img_size}'
t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
return self.p_losses(x, t, *args, **kwargs)
def get_input(self, batch, k):
x = batch[k]
if len(x.shape) == 3:
x = x[..., None]
x = rearrange(x, 'b h w c -> b c h w')
x = x.to(memory_format=torch.contiguous_format).float()
return x
def shared_step(self, batch):
x = self.get_input(batch, self.first_stage_key)
loss, loss_dict = self(x)
return loss, loss_dict
def training_step(self, batch, batch_idx):
loss, loss_dict = self.shared_step(batch)
self.log_dict(loss_dict, prog_bar=True,
logger=True, on_step=True, on_epoch=True)
self.log("global_step", self.global_step,
prog_bar=True, logger=True, on_step=True, on_epoch=False)
if self.use_scheduler:
lr = self.optimizers().param_groups[0]['lr']
self.log('lr_abs', lr, prog_bar=True, logger=True, on_step=True, on_epoch=False)
return loss
@torch.no_grad()
def validation_step(self, batch, batch_idx):
_, loss_dict_no_ema = self.shared_step(batch)
with self.ema_scope():
_, loss_dict_ema = self.shared_step(batch)
loss_dict_ema = {key + '_ema': loss_dict_ema[key] for key in loss_dict_ema}
self.log_dict(loss_dict_no_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
self.log_dict(loss_dict_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
def on_train_batch_end(self, *args, **kwargs):
if self.use_ema:
self.model_ema(self.model)
def _get_rows_from_list(self, samples):
n_imgs_per_row = len(samples)
denoise_grid = rearrange(samples, 'n b c h w -> b n c h w')
denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
return denoise_grid
@torch.no_grad()
def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs):
log = dict()
x = self.get_input(batch, self.first_stage_key)
N = min(x.shape[0], N)
n_row = min(x.shape[0], n_row)
x = x.to(self.device)[:N]
log["inputs"] = x
# get diffusion row
diffusion_row = list()
x_start = x[:n_row]
for t in range(self.num_timesteps):
if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
t = t.to(self.device).long()
noise = torch.randn_like(x_start)
x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
diffusion_row.append(x_noisy)
log["diffusion_row"] = self._get_rows_from_list(diffusion_row)
if sample:
# get denoise row
with self.ema_scope("Plotting"):
samples, denoise_row = self.sample(batch_size=N, return_intermediates=True)
log["samples"] = samples
log["denoise_row"] = self._get_rows_from_list(denoise_row)
if return_keys:
if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
return log
else:
return {key: log[key] for key in return_keys}
return log
def configure_optimizers(self):
lr = self.learning_rate
params = list(self.model.parameters())
if self.learn_logvar:
params = params + [self.logvar]
opt = torch.optim.AdamW(params, lr=lr)
return opt
class LatentDiffusion(DDPM):
"""main class"""
def __init__(self,
first_stage_config,
cond_stage_config,
num_timesteps_cond=None,
cond_stage_key="image",
cond_stage_trainable=False,
concat_mode=True,
cond_stage_forward=None,
conditioning_key=None,
scale_factor=1.0,
scale_by_std=False,
*args, **kwargs):
self.num_timesteps_cond = default(num_timesteps_cond, 1)
self.scale_by_std = scale_by_std
assert self.num_timesteps_cond <= kwargs['timesteps']
# for backwards compatibility after implementation of DiffusionWrapper
if conditioning_key is None:
conditioning_key = 'concat' if concat_mode else 'crossattn'
if cond_stage_config == '__is_unconditional__':
conditioning_key = None
ckpt_path = kwargs.pop("ckpt_path", None)
ignore_keys = kwargs.pop("ignore_keys", [])
super().__init__(conditioning_key=conditioning_key, *args, **kwargs)
self.concat_mode = concat_mode
self.cond_stage_trainable = cond_stage_trainable
self.cond_stage_key = cond_stage_key
try:
self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1
except:
self.num_downs = 0
if not scale_by_std:
self.scale_factor = scale_factor
else:
self.register_buffer('scale_factor', torch.tensor(scale_factor))
self.instantiate_first_stage(first_stage_config)
self.instantiate_cond_stage(cond_stage_config)
self.cond_stage_forward = cond_stage_forward
self.clip_denoised = False
self.bbox_tokenizer = None
self.restarted_from_ckpt = False
if ckpt_path is not None:
self.init_from_ckpt(ckpt_path, ignore_keys)
self.restarted_from_ckpt = True
def make_cond_schedule(self, ):
self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long)
ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long()
self.cond_ids[:self.num_timesteps_cond] = ids
@rank_zero_only
@torch.no_grad()
def on_train_batch_start(self, batch, batch_idx, dataloader_idx):
# only for very first batch
if self.scale_by_std and self.current_epoch == 0 and self.global_step == 0 and batch_idx == 0 and not self.restarted_from_ckpt:
assert self.scale_factor == 1., 'rather not use custom rescaling and std-rescaling simultaneously'
# set rescale weight to 1./std of encodings
print("### USING STD-RESCALING ###")
x = super().get_input(batch, self.first_stage_key)
x = x.to(self.device)
encoder_posterior = self.encode_first_stage(x)
z = self.get_first_stage_encoding(encoder_posterior).detach()
del self.scale_factor
self.register_buffer('scale_factor', 1. / z.flatten().std())
print(f"setting self.scale_factor to {self.scale_factor}")
print("### USING STD-RESCALING ###")
def register_schedule(self,
given_betas=None, beta_schedule="linear", timesteps=1000,
linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
super().register_schedule(given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s)
self.shorten_cond_schedule = self.num_timesteps_cond > 1
if self.shorten_cond_schedule:
self.make_cond_schedule()
def instantiate_first_stage(self, config):
model = instantiate_from_config(config)
self.first_stage_model = model.eval()
self.first_stage_model.train = disabled_train
for param in self.first_stage_model.parameters():
param.requires_grad = False
def instantiate_cond_stage(self, config):
if not self.cond_stage_trainable:
if config == "__is_first_stage__":
print("Using first stage also as cond stage.")
self.cond_stage_model = self.first_stage_model
elif config == "__is_unconditional__":
print(f"Training {self.__class__.__name__} as an unconditional model.")
self.cond_stage_model = None
# self.be_unconditional = True
else:
model = instantiate_from_config(config)
self.cond_stage_model = model.eval()
self.cond_stage_model.train = disabled_train
for param in self.cond_stage_model.parameters():
param.requires_grad = False
else:
assert config != '__is_first_stage__'
assert config != '__is_unconditional__'
model = instantiate_from_config(config)
self.cond_stage_model = model
def _get_denoise_row_from_list(self, samples, desc='', force_no_decoder_quantization=False):
denoise_row = []
for zd in tqdm(samples, desc=desc):
denoise_row.append(self.decode_first_stage(zd.to(self.device),
force_not_quantize=force_no_decoder_quantization))
n_imgs_per_row = len(denoise_row)
denoise_row = torch.stack(denoise_row) # n_log_step, n_row, C, H, W
denoise_grid = rearrange(denoise_row, 'n b c h w -> b n c h w')
denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
return denoise_grid
def get_first_stage_encoding(self, encoder_posterior):
if isinstance(encoder_posterior, DiagonalGaussianDistribution):
z = encoder_posterior.sample()
elif isinstance(encoder_posterior, torch.Tensor):
z = encoder_posterior
else:
raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented")
return self.scale_factor * z
def get_learned_conditioning(self, c):
if self.cond_stage_forward is None:
if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode):
c = self.cond_stage_model.encode(c)
if isinstance(c, DiagonalGaussianDistribution):
c = c.mode()
else:
c = self.cond_stage_model(c)
else:
assert hasattr(self.cond_stage_model, self.cond_stage_forward)
c = getattr(self.cond_stage_model, self.cond_stage_forward)(c)
return c
def meshgrid(self, h, w):
y = torch.arange(0, h).view(h, 1, 1).repeat(1, w, 1)
x = torch.arange(0, w).view(1, w, 1).repeat(h, 1, 1)
arr = torch.cat([y, x], dim=-1)
return arr
def delta_border(self, h, w):
"""
:param h: height
:param w: width
:return: normalized distance to image border,
wtith min distance = 0 at border and max dist = 0.5 at image center
"""
lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2)
arr = self.meshgrid(h, w) / lower_right_corner
dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0]
dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0]
edge_dist = torch.min(torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1)[0]
return edge_dist
def get_weighting(self, h, w, Ly, Lx, device):
weighting = self.delta_border(h, w)
weighting = torch.clip(weighting, self.split_input_params["clip_min_weight"],
self.split_input_params["clip_max_weight"], )
weighting = weighting.view(1, h * w, 1).repeat(1, 1, Ly * Lx).to(device)
if self.split_input_params["tie_braker"]:
L_weighting = self.delta_border(Ly, Lx)
L_weighting = torch.clip(L_weighting,
self.split_input_params["clip_min_tie_weight"],
self.split_input_params["clip_max_tie_weight"])
L_weighting = L_weighting.view(1, 1, Ly * Lx).to(device)
weighting = weighting * L_weighting
return weighting
def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1): # todo load once not every time, shorten code
"""
:param x: img of size (bs, c, h, w)
:return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1])
"""
bs, nc, h, w = x.shape
# number of crops in image
Ly = (h - kernel_size[0]) // stride[0] + 1
Lx = (w - kernel_size[1]) // stride[1] + 1
if uf == 1 and df == 1:
fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
unfold = torch.nn.Unfold(**fold_params)
fold = torch.nn.Fold(output_size=x.shape[2:], **fold_params)
weighting = self.get_weighting(kernel_size[0], kernel_size[1], Ly, Lx, x.device).to(x.dtype)
normalization = fold(weighting).view(1, 1, h, w) # normalizes the overlap
weighting = weighting.view((1, 1, kernel_size[0], kernel_size[1], Ly * Lx))
elif uf > 1 and df == 1:
fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
unfold = torch.nn.Unfold(**fold_params)
fold_params2 = dict(kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf),
dilation=1, padding=0,
stride=(stride[0] * uf, stride[1] * uf))
fold = torch.nn.Fold(output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2)
weighting = self.get_weighting(kernel_size[0] * uf, kernel_size[1] * uf, Ly, Lx, x.device).to(x.dtype)
normalization = fold(weighting).view(1, 1, h * uf, w * uf) # normalizes the overlap
weighting = weighting.view((1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx))
elif df > 1 and uf == 1:
fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
unfold = torch.nn.Unfold(**fold_params)
fold_params2 = dict(kernel_size=(kernel_size[0] // df, kernel_size[0] // df),
dilation=1, padding=0,
stride=(stride[0] // df, stride[1] // df))
fold = torch.nn.Fold(output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2)
weighting = self.get_weighting(kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device).to(x.dtype)
normalization = fold(weighting).view(1, 1, h // df, w // df) # normalizes the overlap
weighting = weighting.view((1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx))
else:
raise NotImplementedError
return fold, unfold, normalization, weighting
@torch.no_grad()
def get_input(self, batch, k, return_first_stage_outputs=False, force_c_encode=False,
cond_key=None, return_original_cond=False, bs=None):
x = super().get_input(batch, k)
if bs is not None:
x = x[:bs]
x = x.to(self.device)
encoder_posterior = self.encode_first_stage(x)
z = self.get_first_stage_encoding(encoder_posterior).detach()
if self.model.conditioning_key is not None:
if cond_key is None:
cond_key = self.cond_stage_key
if cond_key != self.first_stage_key:
if cond_key in ['caption', 'coordinates_bbox']:
xc = batch[cond_key]
elif cond_key == 'class_label':
xc = batch
else:
xc = super().get_input(batch, cond_key).to(self.device)
else:
xc = x
if not self.cond_stage_trainable or force_c_encode:
if isinstance(xc, dict) or isinstance(xc, list):
# import pudb; pudb.set_trace()
c = self.get_learned_conditioning(xc)
else:
c = self.get_learned_conditioning(xc.to(self.device))
else:
c = xc
if bs is not None:
c = c[:bs]
if self.use_positional_encodings:
pos_x, pos_y = self.compute_latent_shifts(batch)
ckey = __conditioning_keys__[self.model.conditioning_key]
c = {ckey: c, 'pos_x': pos_x, 'pos_y': pos_y}
else:
c = None
xc = None
if self.use_positional_encodings:
pos_x, pos_y = self.compute_latent_shifts(batch)
c = {'pos_x': pos_x, 'pos_y': pos_y}
out = [z, c]
if return_first_stage_outputs:
xrec = self.decode_first_stage(z)
out.extend([x, xrec])
if return_original_cond:
out.append(xc)
return out
@torch.no_grad()
def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
if predict_cids:
if z.dim() == 4:
z = torch.argmax(z.exp(), dim=1).long()
z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
z = rearrange(z, 'b h w c -> b c h w').contiguous()
z = 1. / self.scale_factor * z
if hasattr(self, "split_input_params"):
if self.split_input_params["patch_distributed_vq"]:
ks = self.split_input_params["ks"] # eg. (128, 128)
stride = self.split_input_params["stride"] # eg. (64, 64)
uf = self.split_input_params["vqf"]
bs, nc, h, w = z.shape
if ks[0] > h or ks[1] > w:
ks = (min(ks[0], h), min(ks[1], w))
print("reducing Kernel")
if stride[0] > h or stride[1] > w:
stride = (min(stride[0], h), min(stride[1], w))
print("reducing stride")
fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf)
z = unfold(z) # (bn, nc * prod(**ks), L)
# 1. Reshape to img shape
z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
# 2. apply model loop over last dim
if isinstance(self.first_stage_model, VQModelInterface):
output_list = [self.first_stage_model.decode(z[:, :, :, :, i],
force_not_quantize=predict_cids or force_not_quantize)
for i in range(z.shape[-1])]
else:
output_list = [self.first_stage_model.decode(z[:, :, :, :, i])
for i in range(z.shape[-1])]
o = torch.stack(output_list, axis=-1) # # (bn, nc, ks[0], ks[1], L)
o = o * weighting
# Reverse 1. reshape to img shape
o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
# stitch crops together
decoded = fold(o)
decoded = decoded / normalization # norm is shape (1, 1, h, w)
return decoded
else:
if isinstance(self.first_stage_model, VQModelInterface):
return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
else:
return self.first_stage_model.decode(z)
else:
if isinstance(self.first_stage_model, VQModelInterface):
return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
else:
return self.first_stage_model.decode(z)
# same as above but without decorator
def differentiable_decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
if predict_cids:
if z.dim() == 4:
z = torch.argmax(z.exp(), dim=1).long()
z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
z = rearrange(z, 'b h w c -> b c h w').contiguous()
z = 1. / self.scale_factor * z
if hasattr(self, "split_input_params"):
if self.split_input_params["patch_distributed_vq"]:
ks = self.split_input_params["ks"] # eg. (128, 128)
stride = self.split_input_params["stride"] # eg. (64, 64)
uf = self.split_input_params["vqf"]
bs, nc, h, w = z.shape
if ks[0] > h or ks[1] > w:
ks = (min(ks[0], h), min(ks[1], w))
print("reducing Kernel")
if stride[0] > h or stride[1] > w:
stride = (min(stride[0], h), min(stride[1], w))
print("reducing stride")
fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf)
z = unfold(z) # (bn, nc * prod(**ks), L)
# 1. Reshape to img shape
z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
# 2. apply model loop over last dim
if isinstance(self.first_stage_model, VQModelInterface):
output_list = [self.first_stage_model.decode(z[:, :, :, :, i],
force_not_quantize=predict_cids or force_not_quantize)
for i in range(z.shape[-1])]
else:
output_list = [self.first_stage_model.decode(z[:, :, :, :, i])
for i in range(z.shape[-1])]
o = torch.stack(output_list, axis=-1) # # (bn, nc, ks[0], ks[1], L)
o = o * weighting
# Reverse 1. reshape to img shape
o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
# stitch crops together
decoded = fold(o)
decoded = decoded / normalization # norm is shape (1, 1, h, w)
return decoded
else:
if isinstance(self.first_stage_model, VQModelInterface):
return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
else:
return self.first_stage_model.decode(z)
else:
if isinstance(self.first_stage_model, VQModelInterface):
return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
else:
return self.first_stage_model.decode(z)
@torch.no_grad()
def encode_first_stage(self, x):
if hasattr(self, "split_input_params"):
if self.split_input_params["patch_distributed_vq"]:
ks = self.split_input_params["ks"] # eg. (128, 128)
stride = self.split_input_params["stride"] # eg. (64, 64)
df = self.split_input_params["vqf"]
self.split_input_params['original_image_size'] = x.shape[-2:]
bs, nc, h, w = x.shape
if ks[0] > h or ks[1] > w:
ks = (min(ks[0], h), min(ks[1], w))
print("reducing Kernel")
if stride[0] > h or stride[1] > w:
stride = (min(stride[0], h), min(stride[1], w))
print("reducing stride")
fold, unfold, normalization, weighting = self.get_fold_unfold(x, ks, stride, df=df)
z = unfold(x) # (bn, nc * prod(**ks), L)
# Reshape to img shape
z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
output_list = [self.first_stage_model.encode(z[:, :, :, :, i])
for i in range(z.shape[-1])]
o = torch.stack(output_list, axis=-1)
o = o * weighting
# Reverse reshape to img shape
o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
# stitch crops together
decoded = fold(o)
decoded = decoded / normalization
return decoded
else:
return self.first_stage_model.encode(x)
else:
return self.first_stage_model.encode(x)
def shared_step(self, batch, **kwargs):
x, c = self.get_input(batch, self.first_stage_key)
loss = self(x, c)
return loss
def forward(self, x, c, *args, **kwargs):
t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
if self.model.conditioning_key is not None:
assert c is not None
if self.cond_stage_trainable:
c = self.get_learned_conditioning(c)
if self.shorten_cond_schedule: # TODO: drop this option
tc = self.cond_ids[t].to(self.device)
c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float()))
return self.p_losses(x, c, t, *args, **kwargs)
def _rescale_annotations(self, bboxes, crop_coordinates): # TODO: move to dataset
def rescale_bbox(bbox):
x0 = clamp((bbox[0] - crop_coordinates[0]) / crop_coordinates[2])
y0 = clamp((bbox[1] - crop_coordinates[1]) / crop_coordinates[3])
w = min(bbox[2] / crop_coordinates[2], 1 - x0)
h = min(bbox[3] / crop_coordinates[3], 1 - y0)
return x0, y0, w, h
return [rescale_bbox(b) for b in bboxes]
def apply_model(self, x_noisy, t, cond, return_ids=False):
if isinstance(cond, dict):
# hybrid case, cond is exptected to be a dict
pass
else:
if not isinstance(cond, list):
cond = [cond]
key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn'
cond = {key: cond}
if hasattr(self, "split_input_params"):
assert len(cond) == 1 # todo can only deal with one conditioning atm
assert not return_ids
ks = self.split_input_params["ks"] # eg. (128, 128)
stride = self.split_input_params["stride"] # eg. (64, 64)
h, w = x_noisy.shape[-2:]
fold, unfold, normalization, weighting = self.get_fold_unfold(x_noisy, ks, stride)
z = unfold(x_noisy) # (bn, nc * prod(**ks), L)
# Reshape to img shape
z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
z_list = [z[:, :, :, :, i] for i in range(z.shape[-1])]
if self.cond_stage_key in ["image", "LR_image", "segmentation",
'bbox_img'] and self.model.conditioning_key: # todo check for completeness
c_key = next(iter(cond.keys())) # get key
c = next(iter(cond.values())) # get value
assert (len(c) == 1) # todo extend to list with more than one elem
c = c[0] # get element
c = unfold(c)
c = c.view((c.shape[0], -1, ks[0], ks[1], c.shape[-1])) # (bn, nc, ks[0], ks[1], L )
cond_list = [{c_key: [c[:, :, :, :, i]]} for i in range(c.shape[-1])]
elif self.cond_stage_key == 'coordinates_bbox':
assert 'original_image_size' in self.split_input_params, 'BoudingBoxRescaling is missing original_image_size'
# assuming padding of unfold is always 0 and its dilation is always 1
n_patches_per_row = int((w - ks[0]) / stride[0] + 1)
full_img_h, full_img_w = self.split_input_params['original_image_size']
# as we are operating on latents, we need the factor from the original image size to the
# spatial latent size to properly rescale the crops for regenerating the bbox annotations
num_downs = self.first_stage_model.encoder.num_resolutions - 1
rescale_latent = 2 ** (num_downs)
# get top left postions of patches as conforming for the bbbox tokenizer, therefore we
# need to rescale the tl patch coordinates to be in between (0,1)
tl_patch_coordinates = [(rescale_latent * stride[0] * (patch_nr % n_patches_per_row) / full_img_w,
rescale_latent * stride[1] * (patch_nr // n_patches_per_row) / full_img_h)
for patch_nr in range(z.shape[-1])]
# patch_limits are tl_coord, width and height coordinates as (x_tl, y_tl, h, w)
patch_limits = [(x_tl, y_tl,
rescale_latent * ks[0] / full_img_w,
rescale_latent * ks[1] / full_img_h) for x_tl, y_tl in tl_patch_coordinates]
# patch_values = [(np.arange(x_tl,min(x_tl+ks, 1.)),np.arange(y_tl,min(y_tl+ks, 1.))) for x_tl, y_tl in tl_patch_coordinates]
# tokenize crop coordinates for the bounding boxes of the respective patches
patch_limits_tknzd = [torch.LongTensor(self.bbox_tokenizer._crop_encoder(bbox))[None].to(self.device)
for bbox in patch_limits] # list of length l with tensors of shape (1, 2)
print(patch_limits_tknzd[0].shape)
# cut tknzd crop position from conditioning
assert isinstance(cond, dict), 'cond must be dict to be fed into model'
cut_cond = cond['c_crossattn'][0][..., :-2].to(self.device)
print(cut_cond.shape)
adapted_cond = torch.stack([torch.cat([cut_cond, p], dim=1) for p in patch_limits_tknzd])
adapted_cond = rearrange(adapted_cond, 'l b n -> (l b) n')
print(adapted_cond.shape)
adapted_cond = self.get_learned_conditioning(adapted_cond)
print(adapted_cond.shape)
adapted_cond = rearrange(adapted_cond, '(l b) n d -> l b n d', l=z.shape[-1])
print(adapted_cond.shape)
cond_list = [{'c_crossattn': [e]} for e in adapted_cond]
else:
cond_list = [cond for i in range(z.shape[-1])] # Todo make this more efficient
# apply model by loop over crops
output_list = [self.model(z_list[i], t, **cond_list[i]) for i in range(z.shape[-1])]
assert not isinstance(output_list[0],
tuple) # todo cant deal with multiple model outputs check this never happens
o = torch.stack(output_list, axis=-1)
o = o * weighting
# Reverse reshape to img shape
o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
# stitch crops together
x_recon = fold(o) / normalization
else:
x_recon = self.model(x_noisy, t, **cond)
if isinstance(x_recon, tuple) and not return_ids:
return x_recon[0]
else:
return x_recon
def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
return (extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart) / \
extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
def _prior_bpd(self, x_start):
"""
Get the prior KL term for the variational lower-bound, measured in
bits-per-dim.
This term can't be optimized, as it only depends on the encoder.
:param x_start: the [N x C x ...] tensor of inputs.
:return: a batch of [N] KL values (in bits), one per batch element.
"""
batch_size = x_start.shape[0]
t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device)
qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t)
kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0)
return mean_flat(kl_prior) / np.log(2.0)
def p_losses(self, x_start, cond, t, noise=None):
noise = default(noise, lambda: torch.randn_like(x_start))
x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
model_output = self.apply_model(x_noisy, t, cond)
loss_dict = {}
prefix = 'train' if self.training else 'val'
if self.parameterization == "x0":
target = x_start
elif self.parameterization == "eps":
target = noise
else:
raise NotImplementedError()
loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3])
loss_dict.update({f'{prefix}/loss_simple': loss_simple.mean()})
logvar_t = self.logvar[t].to(self.device)
loss = loss_simple / torch.exp(logvar_t) + logvar_t
# loss = loss_simple / torch.exp(self.logvar) + self.logvar
if self.learn_logvar:
loss_dict.update({f'{prefix}/loss_gamma': loss.mean()})
loss_dict.update({'logvar': self.logvar.data.mean()})
loss = self.l_simple_weight * loss.mean()
loss_vlb = self.get_loss(model_output, target, mean=False).mean(dim=(1, 2, 3))
loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean()
loss_dict.update({f'{prefix}/loss_vlb': loss_vlb})
loss += (self.original_elbo_weight * loss_vlb)
loss_dict.update({f'{prefix}/loss': loss})
return loss, loss_dict
def p_mean_variance(self, x, c, t, clip_denoised: bool, return_codebook_ids=False, quantize_denoised=False,
return_x0=False, score_corrector=None, corrector_kwargs=None):
t_in = t
model_out = self.apply_model(x, t_in, c, return_ids=return_codebook_ids)
if score_corrector is not None:
assert self.parameterization == "eps"
model_out = score_corrector.modify_score(self, model_out, x, t, c, **corrector_kwargs)
if return_codebook_ids:
model_out, logits = model_out
if self.parameterization == "eps":
x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
elif self.parameterization == "x0":
x_recon = model_out
else:
raise NotImplementedError()
if clip_denoised:
x_recon.clamp_(-1., 1.)
if quantize_denoised:
x_recon, _, [_, _, indices] = self.first_stage_model.quantize(x_recon)
model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
if return_codebook_ids:
return model_mean, posterior_variance, posterior_log_variance, logits
elif return_x0:
return model_mean, posterior_variance, posterior_log_variance, x_recon
else:
return model_mean, posterior_variance, posterior_log_variance
@torch.no_grad()
def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False,
return_codebook_ids=False, quantize_denoised=False, return_x0=False,
temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None):
b, *_, device = *x.shape, x.device
outputs = self.p_mean_variance(x=x, c=c, t=t, clip_denoised=clip_denoised,
return_codebook_ids=return_codebook_ids,
quantize_denoised=quantize_denoised,
return_x0=return_x0,
score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
if return_codebook_ids:
raise DeprecationWarning("Support dropped.")
model_mean, _, model_log_variance, logits = outputs
elif return_x0:
model_mean, _, model_log_variance, x0 = outputs
else:
model_mean, _, model_log_variance = outputs
noise = noise_like(x.shape, device, repeat_noise) * temperature
if noise_dropout > 0.:
noise = torch.nn.functional.dropout(noise, p=noise_dropout)
# no noise when t == 0
nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
if return_codebook_ids:
return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, logits.argmax(dim=1)
if return_x0:
return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, x0
else:
return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
@torch.no_grad()
def progressive_denoising(self, cond, shape, verbose=True, callback=None, quantize_denoised=False,
img_callback=None, mask=None, x0=None, temperature=1., noise_dropout=0.,
score_corrector=None, corrector_kwargs=None, batch_size=None, x_T=None, start_T=None,
log_every_t=None):
if not log_every_t:
log_every_t = self.log_every_t
timesteps = self.num_timesteps
if batch_size is not None:
b = batch_size if batch_size is not None else shape[0]
shape = [batch_size] + list(shape)
else:
b = batch_size = shape[0]
if x_T is None:
img = torch.randn(shape, device=self.device)
else:
img = x_T
intermediates = []
if cond is not None:
if isinstance(cond, dict):
cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
else:
cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
if start_T is not None:
timesteps = min(timesteps, start_T)
iterator = tqdm(reversed(range(0, timesteps)), desc='Progressive Generation',
total=timesteps) if verbose else reversed(
range(0, timesteps))
if type(temperature) == float:
temperature = [temperature] * timesteps
for i in iterator:
ts = torch.full((b,), i, device=self.device, dtype=torch.long)
if self.shorten_cond_schedule:
assert self.model.conditioning_key != 'hybrid'
tc = self.cond_ids[ts].to(cond.device)
cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
img, x0_partial = self.p_sample(img, cond, ts,
clip_denoised=self.clip_denoised,
quantize_denoised=quantize_denoised, return_x0=True,
temperature=temperature[i], noise_dropout=noise_dropout,
score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
if mask is not None:
assert x0 is not None
img_orig = self.q_sample(x0, ts)
img = img_orig * mask + (1. - mask) * img
if i % log_every_t == 0 or i == timesteps - 1:
intermediates.append(x0_partial)
if callback: callback(i)
if img_callback: img_callback(img, i)
return img, intermediates
@torch.no_grad()
def p_sample_loop(self, cond, shape, return_intermediates=False,
x_T=None, verbose=True, callback=None, timesteps=None, quantize_denoised=False,
mask=None, x0=None, img_callback=None, start_T=None,
log_every_t=None):
if not log_every_t:
log_every_t = self.log_every_t
device = self.betas.device
b = shape[0]
if x_T is None:
img = torch.randn(shape, device=device)
else:
img = x_T
intermediates = [img]
if timesteps is None:
timesteps = self.num_timesteps
if start_T is not None:
timesteps = min(timesteps, start_T)
iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed(
range(0, timesteps))
if mask is not None:
assert x0 is not None
assert x0.shape[2:3] == mask.shape[2:3] # spatial size has to match
for i in iterator:
ts = torch.full((b,), i, device=device, dtype=torch.long)
if self.shorten_cond_schedule:
assert self.model.conditioning_key != 'hybrid'
tc = self.cond_ids[ts].to(cond.device)
cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
img = self.p_sample(img, cond, ts,
clip_denoised=self.clip_denoised,
quantize_denoised=quantize_denoised)
if mask is not None:
img_orig = self.q_sample(x0, ts)
img = img_orig * mask + (1. - mask) * img
if i % log_every_t == 0 or i == timesteps - 1:
intermediates.append(img)
if callback: callback(i)
if img_callback: img_callback(img, i)
if return_intermediates:
return img, intermediates
return img
@torch.no_grad()
def sample(self, cond, batch_size=16, return_intermediates=False, x_T=None,
verbose=True, timesteps=None, quantize_denoised=False,
mask=None, x0=None, shape=None,**kwargs):
if shape is None:
shape = (batch_size, self.channels, self.image_size, self.image_size)
if cond is not None:
if isinstance(cond, dict):
cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
else:
cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
return self.p_sample_loop(cond,
shape,
return_intermediates=return_intermediates, x_T=x_T,
verbose=verbose, timesteps=timesteps, quantize_denoised=quantize_denoised,
mask=mask, x0=x0)
@torch.no_grad()
def sample_log(self,cond,batch_size,ddim, ddim_steps,**kwargs):
if ddim:
ddim_sampler = DDIMSampler(self)
shape = (self.channels, self.image_size, self.image_size)
samples, intermediates =ddim_sampler.sample(ddim_steps,batch_size,
shape,cond,verbose=False,**kwargs)
else:
samples, intermediates = self.sample(cond=cond, batch_size=batch_size,
return_intermediates=True,**kwargs)
return samples, intermediates
@torch.no_grad()
def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None,
quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True,
plot_diffusion_rows=True, **kwargs):
use_ddim = ddim_steps is not None
log = dict()
z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key,
return_first_stage_outputs=True,
force_c_encode=True,
return_original_cond=True,
bs=N)
N = min(x.shape[0], N)
n_row = min(x.shape[0], n_row)
log["inputs"] = x
log["reconstruction"] = xrec
if self.model.conditioning_key is not None:
if hasattr(self.cond_stage_model, "decode"):
xc = self.cond_stage_model.decode(c)
log["conditioning"] = xc
elif self.cond_stage_key in ["caption"]:
xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["caption"])
log["conditioning"] = xc
elif self.cond_stage_key == 'class_label':
xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"])
log['conditioning'] = xc
elif isimage(xc):
log["conditioning"] = xc
if ismap(xc):
log["original_conditioning"] = self.to_rgb(xc)
if plot_diffusion_rows:
# get diffusion row
diffusion_row = list()
z_start = z[:n_row]
for t in range(self.num_timesteps):
if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
t = t.to(self.device).long()
noise = torch.randn_like(z_start)
z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)
diffusion_row.append(self.decode_first_stage(z_noisy))
diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W
diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w')
diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w')
diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0])
log["diffusion_row"] = diffusion_grid
if sample:
# get denoise row
with self.ema_scope("Plotting"):
samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim,
ddim_steps=ddim_steps,eta=ddim_eta)
# samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True)
x_samples = self.decode_first_stage(samples)
log["samples"] = x_samples
if plot_denoise_rows:
denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
log["denoise_row"] = denoise_grid
if quantize_denoised and not isinstance(self.first_stage_model, AutoencoderKL) and not isinstance(
self.first_stage_model, IdentityFirstStage):
# also display when quantizing x0 while sampling
with self.ema_scope("Plotting Quantized Denoised"):
samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim,
ddim_steps=ddim_steps,eta=ddim_eta,
quantize_denoised=True)
# samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True,
# quantize_denoised=True)
x_samples = self.decode_first_stage(samples.to(self.device))
log["samples_x0_quantized"] = x_samples
if inpaint:
# make a simple center square
b, h, w = z.shape[0], z.shape[2], z.shape[3]
mask = torch.ones(N, h, w).to(self.device)
# zeros will be filled in
mask[:, h // 4:3 * h // 4, w // 4:3 * w // 4] = 0.
mask = mask[:, None, ...]
with self.ema_scope("Plotting Inpaint"):
samples, _ = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, eta=ddim_eta,
ddim_steps=ddim_steps, x0=z[:N], mask=mask)
x_samples = self.decode_first_stage(samples.to(self.device))
log["samples_inpainting"] = x_samples
log["mask"] = mask
# outpaint
with self.ema_scope("Plotting Outpaint"):
samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,eta=ddim_eta,
ddim_steps=ddim_steps, x0=z[:N], mask=mask)
x_samples = self.decode_first_stage(samples.to(self.device))
log["samples_outpainting"] = x_samples
if plot_progressive_rows:
with self.ema_scope("Plotting Progressives"):
img, progressives = self.progressive_denoising(c,
shape=(self.channels, self.image_size, self.image_size),
batch_size=N)
prog_row = self._get_denoise_row_from_list(progressives, desc="Progressive Generation")
log["progressive_row"] = prog_row
if return_keys:
if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
return log
else:
return {key: log[key] for key in return_keys}
return log
def configure_optimizers(self):
lr = self.learning_rate
params = list(self.model.parameters())
if self.cond_stage_trainable:
print(f"{self.__class__.__name__}: Also optimizing conditioner params!")
params = params + list(self.cond_stage_model.parameters())
if self.learn_logvar:
print('Diffusion model optimizing logvar')
params.append(self.logvar)
opt = torch.optim.AdamW(params, lr=lr)
if self.use_scheduler:
assert 'target' in self.scheduler_config
scheduler = instantiate_from_config(self.scheduler_config)
print("Setting up LambdaLR scheduler...")
scheduler = [
{
'scheduler': LambdaLR(opt, lr_lambda=scheduler.schedule),
'interval': 'step',
'frequency': 1
}]
return [opt], scheduler
return opt
@torch.no_grad()
def to_rgb(self, x):
x = x.float()
if not hasattr(self, "colorize"):
self.colorize = torch.randn(3, x.shape[1], 1, 1).to(x)
x = nn.functional.conv2d(x, weight=self.colorize)
x = 2. * (x - x.min()) / (x.max() - x.min()) - 1.
return x
class DiffusionWrapper(pl.LightningModule):
def __init__(self, diff_model_config, conditioning_key):
super().__init__()
self.diffusion_model = instantiate_from_config(diff_model_config)
self.conditioning_key = conditioning_key
assert self.conditioning_key in [None, 'concat', 'crossattn', 'hybrid', 'adm']
def forward(self, x, t, c_concat: list = None, c_crossattn: list = None):
if self.conditioning_key is None:
out = self.diffusion_model(x, t)
elif self.conditioning_key == 'concat':
xc = torch.cat([x] + c_concat, dim=1)
out = self.diffusion_model(xc, t)
elif self.conditioning_key == 'crossattn':
cc = torch.cat(c_crossattn, 1)
out = self.diffusion_model(x, t, context=cc)
elif self.conditioning_key == 'hybrid':
xc = torch.cat([x] + c_concat, dim=1)
cc = torch.cat(c_crossattn, 1)
out = self.diffusion_model(xc, t, context=cc)
elif self.conditioning_key == 'adm':
cc = c_crossattn[0]
out = self.diffusion_model(x, t, y=cc)
else:
raise NotImplementedError()
return out
class Layout2ImgDiffusion(LatentDiffusion):
# TODO: move all layout-specific hacks to this class
def __init__(self, cond_stage_key, *args, **kwargs):
assert cond_stage_key == 'coordinates_bbox', 'Layout2ImgDiffusion only for cond_stage_key="coordinates_bbox"'
super().__init__(cond_stage_key=cond_stage_key, *args, **kwargs)
def log_images(self, batch, N=8, *args, **kwargs):
logs = super().log_images(batch=batch, N=N, *args, **kwargs)
key = 'train' if self.training else 'validation'
dset = self.trainer.datamodule.datasets[key]
mapper = dset.conditional_builders[self.cond_stage_key]
bbox_imgs = []
map_fn = lambda catno: dset.get_textual_label(dset.get_category_id(catno))
for tknzd_bbox in batch[self.cond_stage_key][:N]:
bboximg = mapper.plot(tknzd_bbox.detach().cpu(), map_fn, (256, 256))
bbox_imgs.append(bboximg)
cond_img = torch.stack(bbox_imgs, dim=0)
logs['bbox_image'] = cond_img
return logs
================================================
FILE: models/instructpix2pix/stable_diffusion/ldm/models/diffusion/ddpm_edit.py
================================================
"""
wild mixture of
https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py
https://github.com/CompVis/taming-transformers
-- merci
"""
# File modified by authors of InstructPix2Pix from original (https://github.com/CompVis/stable-diffusion).
# See more details in LICENSE.
import torch
import torch.nn as nn
import numpy as np
import pytorch_lightning as pl
from torch.optim.lr_scheduler import LambdaLR
from einops import rearrange, repeat
from contextlib import contextmanager
from functools import partial
from tqdm import tqdm
from torchvision.utils import make_grid
from pytorch_lightning.utilities.rank_zero import rank_zero_only
from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config
from ldm.modules.ema import LitEma
from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution
from ldm.models.autoencoder import VQModelInterface, IdentityFirstStage, AutoencoderKL
from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
from ldm.models.diffusion.ddim import DDIMSampler
__conditioning_keys__ = {'concat': 'c_concat',
'crossattn': 'c_crossattn',
'adm': 'y'}
def disabled_train(self, mode=True):
"""Overwrite model.train with this function to make sure train/eval mode
does not change anymore."""
return self
def uniform_on_device(r1, r2, shape, device):
return (r1 - r2) * torch.rand(*shape, device=device) + r2
class DDPM(pl.LightningModule):
# classic DDPM with Gaussian diffusion, in image space
def __init__(self,
unet_config,
timesteps=1000,
beta_schedule="linear",
loss_type="l2",
ckpt_path=None,
ignore_keys=[],
load_only_unet=False,
monitor="val/loss",
use_ema=True,
first_stage_key="image",
image_size=256,
channels=3,
log_every_t=100,
clip_denoised=True,
linear_start=1e-4,
linear_end=2e-2,
cosine_s=8e-3,
given_betas=None,
original_elbo_weight=0.,
v_posterior=0., # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta
l_simple_weight=1.,
conditioning_key=None,
parameterization="eps", # all assuming fixed variance schedules
scheduler_config=None,
use_positional_encodings=False,
learn_logvar=False,
logvar_init=0.,
load_ema=True,
):
super().__init__()
assert parameterization in ["eps", "x0"], 'currently only supporting "eps" and "x0"'
self.parameterization = parameterization
print(f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode")
self.cond_stage_model = None
self.clip_denoised = clip_denoised
self.log_every_t = log_every_t
self.first_stage_key = first_stage_key
self.image_size = image_size # try conv?
self.channels = channels
self.use_positional_encodings = use_positional_encodings
self.model = DiffusionWrapper(unet_config, conditioning_key)
count_params(self.model, verbose=True)
self.use_ema = use_ema
self.use_scheduler = scheduler_config is not None
if self.use_scheduler:
self.scheduler_config = scheduler_config
self.v_posterior = v_posterior
self.original_elbo_weight = original_elbo_weight
self.l_simple_weight = l_simple_weight
if monitor is not None:
self.monitor = monitor
if self.use_ema and load_ema:
self.model_ema = LitEma(self.model)
print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
if ckpt_path is not None:
self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet)
# If initialing from EMA-only checkpoint, create EMA model after loading.
if self.use_ema and not load_ema:
self.model_ema = LitEma(self.model)
print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps,
linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)
self.loss_type = loss_type
self.learn_logvar = learn_logvar
self.logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,))
if self.learn_logvar:
self.logvar = nn.Parameter(self.logvar, requires_grad=True)
def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000,
linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
if exists(given_betas):
betas = given_betas
else:
betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end,
cosine_s=cosine_s)
alphas = 1. - betas
alphas_cumprod = np.cumprod(alphas, axis=0)
alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
timesteps, = betas.shape
self.num_timesteps = int(timesteps)
self.linear_start = linear_start
self.linear_end = linear_end
assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep'
to_torch = partial(torch.tensor, dtype=torch.float32)
self.register_buffer('betas', to_torch(betas))
self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))
# calculations for diffusion q(x_t | x_{t-1}) and others
self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod)))
self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))
self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))
# calculations for posterior q(x_{t-1} | x_t, x_0)
posterior_variance = (1 - self.v_posterior) * betas * (1. - alphas_cumprod_prev) / (
1. - alphas_cumprod) + self.v_posterior * betas
# above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
self.register_buffer('posterior_variance', to_torch(posterior_variance))
# below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20))))
self.register_buffer('posterior_mean_coef1', to_torch(
betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)))
self.register_buffer('posterior_mean_coef2', to_torch(
(1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod)))
if self.parameterization == "eps":
lvlb_weights = self.betas ** 2 / (
2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod))
elif self.parameterization == "x0":
lvlb_weights = 0.5 * np.sqrt(torch.Tensor(alphas_cumprod)) / (2. * 1 - torch.Tensor(alphas_cumprod))
else:
raise NotImplementedError("mu not supported")
# TODO how to choose this term
lvlb_weights[0] = lvlb_weights[1]
self.register_buffer('lvlb_weights', lvlb_weights, persistent=False)
assert not torch.isnan(self.lvlb_weights).all()
@contextmanager
def ema_scope(self, context=None):
if self.use_ema:
self.model_ema.store(self.model.parameters())
self.model_ema.copy_to(self.model)
if context is not None:
print(f"{context}: Switched to EMA weights")
try:
yield None
finally:
if self.use_ema:
self.model_ema.restore(self.model.parameters())
if context is not None:
print(f"{context}: Restored training weights")
def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
sd = torch.load(path, map_location="cpu")
if "state_dict" in list(sd.keys()):
sd = sd["state_dict"]
keys = list(sd.keys())
# Our model adds additional channels to the first layer to condition on an input image.
# For the first layer, copy existing channel weights and initialize new channel weights to zero.
input_keys = [
"model.diffusion_model.input_blocks.0.0.weight",
"model_ema.diffusion_modelinput_blocks00weight",
]
self_sd = self.state_dict()
for input_key in input_keys:
if input_key not in sd or input_key not in self_sd:
continue
input_weight = self_sd[input_key]
if input_weight.size() != sd[input_key].size():
print(f"Manual init: {input_key}")
input_weight.zero_()
input_weight[:, :4, :, :].copy_(sd[input_key])
ignore_keys.append(input_key)
for k in keys:
for ik in ignore_keys:
if k.startswith(ik):
print("Deleting key {} from state_dict.".format(k))
del sd[k]
missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
sd, strict=False)
print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
if len(missing) > 0:
print(f"Missing Keys: {missing}")
if len(unexpected) > 0:
print(f"Unexpected Keys: {unexpected}")
def q_mean_variance(self, x_start, t):
"""
Get the distribution q(x_t | x_0).
:param x_start: the [N x C x ...] tensor of noiseless inputs.
:param t: the number of diffusion steps (minus 1). Here, 0 means one step.
:return: A tuple (mean, variance, log_variance), all of x_start's shape.
"""
mean = (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start)
variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape)
log_variance = extract_into_tensor(self.log_one_minus_alphas_cumprod, t, x_start.shape)
return mean, variance, log_variance
def predict_start_from_noise(self, x_t, t, noise):
return (
extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t -
extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise
)
def q_posterior(self, x_start, x_t, t):
posterior_mean = (
extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start +
extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t
)
posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape)
posterior_log_variance_clipped = extract_into_tensor(self.posterior_log_variance_clipped, t, x_t.shape)
return posterior_mean, posterior_variance, posterior_log_variance_clipped
def p_mean_variance(self, x, t, clip_denoised: bool):
model_out = self.model(x, t)
if self.parameterization == "eps":
x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
elif self.parameterization == "x0":
x_recon = model_out
if clip_denoised:
x_recon.clamp_(-1., 1.)
model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
return model_mean, posterior_variance, posterior_log_variance
@torch.no_grad()
def p_sample(self, x, t, clip_denoised=True, repeat_noise=False):
b, *_, device = *x.shape, x.device
model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, clip_denoised=clip_denoised)
noise = noise_like(x.shape, device, repeat_noise)
# no noise when t == 0
nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
@torch.no_grad()
def p_sample_loop(self, shape, return_intermediates=False):
device = self.betas.device
b = shape[0]
img = torch.randn(shape, device=device)
intermediates = [img]
for i in tqdm(reversed(range(0, self.num_timesteps)), desc='Sampling t', total=self.num_timesteps):
img = self.p_sample(img, torch.full((b,), i, device=device, dtype=torch.long),
clip_denoised=self.clip_denoised)
if i % self.log_every_t == 0 or i == self.num_timesteps - 1:
intermediates.append(img)
if return_intermediates:
return img, intermediates
return img
@torch.no_grad()
def sample(self, batch_size=16, return_intermediates=False):
image_size = self.image_size
channels = self.channels
return self.p_sample_loop((batch_size, channels, image_size, image_size),
return_intermediates=return_intermediates)
def q_sample(self, x_start, t, noise=None):
noise = default(noise, lambda: torch.randn_like(x_start))
return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise)
def get_loss(self, pred, target, mean=True):
if self.loss_type == 'l1':
loss = (target - pred).abs()
if mean:
loss = loss.mean()
elif self.loss_type == 'l2':
if mean:
loss = torch.nn.functional.mse_loss(target, pred)
else:
loss = torch.nn.functional.mse_loss(target, pred, reduction='none')
else:
raise NotImplementedError("unknown loss type '{loss_type}'")
return loss
def p_losses(self, x_start, t, noise=None):
noise = default(noise, lambda: torch.randn_like(x_start))
x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
model_out = self.model(x_noisy, t)
loss_dict = {}
if self.parameterization == "eps":
target = noise
elif self.parameterization == "x0":
target = x_start
else:
raise NotImplementedError(f"Paramterization {self.parameterization} not yet supported")
loss = self.get_loss(model_out, target, mean=False).mean(dim=[1, 2, 3])
log_prefix = 'train' if self.training else 'val'
loss_dict.update({f'{log_prefix}/loss_simple': loss.mean()})
loss_simple = loss.mean() * self.l_simple_weight
loss_vlb = (self.lvlb_weights[t] * loss).mean()
loss_dict.update({f'{log_prefix}/loss_vlb': loss_vlb})
loss = loss_simple + self.original_elbo_weight * loss_vlb
loss_dict.update({f'{log_prefix}/loss': loss})
return loss, loss_dict
def forward(self, x, *args, **kwargs):
# b, c, h, w, device, img_size, = *x.shape, x.device, self.image_size
# assert h == img_size and w == img_size, f'height and width of image must be {img_size}'
t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
return self.p_losses(x, t, *args, **kwargs)
def get_input(self, batch, k):
return batch[k]
def shared_step(self, batch):
x = self.get_input(batch, self.first_stage_key)
loss, loss_dict = self(x)
return loss, loss_dict
def training_step(self, batch, batch_idx):
loss, loss_dict = self.shared_step(batch)
self.log_dict(loss_dict, prog_bar=True,
logger=True, on_step=True, on_epoch=True)
self.log("global_step", self.global_step,
prog_bar=True, logger=True, on_step=True, on_epoch=False)
if self.use_scheduler:
lr = self.optimizers().param_groups[0]['lr']
self.log('lr_abs', lr, prog_bar=True, logger=True, on_step=True, on_epoch=False)
return loss
@torch.no_grad()
def validation_step(self, batch, batch_idx):
_, loss_dict_no_ema = self.shared_step(batch)
with self.ema_scope():
_, loss_dict_ema = self.shared_step(batch)
loss_dict_ema = {key + '_ema': loss_dict_ema[key] for key in loss_dict_ema}
self.log_dict(loss_dict_no_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
self.log_dict(loss_dict_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
def on_train_batch_end(self, *args, **kwargs):
if self.use_ema:
self.model_ema(self.model)
def _get_rows_from_list(self, samples):
n_imgs_per_row = len(samples)
denoise_grid = rearrange(samples, 'n b c h w -> b n c h w')
denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
return denoise_grid
@torch.no_grad()
def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs):
log = dict()
x = self.get_input(batch, self.first_stage_key)
N = min(x.shape[0], N)
n_row = min(x.shape[0], n_row)
x = x.to(self.device)[:N]
log["inputs"] = x
# get diffusion row
diffusion_row = list()
x_start = x[:n_row]
for t in range(self.num_timesteps):
if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
t = t.to(self.device).long()
noise = torch.randn_like(x_start)
x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
diffusion_row.append(x_noisy)
log["diffusion_row"] = self._get_rows_from_list(diffusion_row)
if sample:
# get denoise row
with self.ema_scope("Plotting"):
samples, denoise_row = self.sample(batch_size=N, return_intermediates=True)
log["samples"] = samples
log["denoise_row"] = self._get_rows_from_list(denoise_row)
if return_keys:
if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
return log
else:
return {key: log[key] for key in return_keys}
return log
def configure_optimizers(self):
lr = self.learning_rate
params = list(self.model.parameters())
if self.learn_logvar:
params = params + [self.logvar]
opt = torch.optim.AdamW(params, lr=lr)
return opt
class LatentDiffusion(DDPM):
"""main class"""
def __init__(self,
first_stage_config,
cond_stage_config,
num_timesteps_cond=None,
cond_stage_key="image",
cond_stage_trainable=False,
concat_mode=True,
cond_stage_forward=None,
conditioning_key=None,
scale_factor=1.0,
scale_by_std=False,
load_ema=True,
*args, **kwargs):
self.num_timesteps_cond = default(num_timesteps_cond, 1)
self.scale_by_std = scale_by_std
assert self.num_timesteps_cond <= kwargs['timesteps']
# for backwards compatibility after implementation of DiffusionWrapper
if conditioning_key is None:
conditioning_key = 'concat' if concat_mode else 'crossattn'
if cond_stage_config == '__is_unconditional__':
conditioning_key = None
ckpt_path = kwargs.pop("ckpt_path", None)
ignore_keys = kwargs.pop("ignore_keys", [])
super().__init__(conditioning_key=conditioning_key, *args, load_ema=load_ema, **kwargs)
self.concat_mode = concat_mode
self.cond_stage_trainable = cond_stage_trainable
self.cond_stage_key = cond_stage_key
try:
self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1
except:
self.num_downs = 0
if not scale_by_std:
self.scale_factor = scale_factor
else:
self.register_buffer('scale_factor', torch.tensor(scale_factor))
self.instantiate_first_stage(first_stage_config)
self.instantiate_cond_stage(cond_stage_config)
self.cond_stage_forward = cond_stage_forward
self.clip_denoised = False
self.bbox_tokenizer = None
self.restarted_from_ckpt = False
if ckpt_path is not None:
self.init_from_ckpt(ckpt_path, ignore_keys)
self.restarted_from_ckpt = True
if self.use_ema and not load_ema:
self.model_ema = LitEma(self.model)
print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
def make_cond_schedule(self, ):
self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long)
ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long()
self.cond_ids[:self.num_timesteps_cond] = ids
@rank_zero_only
@torch.no_grad()
def on_train_batch_start(self, batch, batch_idx, dataloader_idx):
# only for very first batch
if self.scale_by_std and self.current_epoch == 0 and self.global_step == 0 and batch_idx == 0 and not self.restarted_from_ckpt:
assert self.scale_factor == 1., 'rather not use custom rescaling and std-rescaling simultaneously'
# set rescale weight to 1./std of encodings
print("### USING STD-RESCALING ###")
x = super().get_input(batch, self.first_stage_key)
x = x.to(self.device)
encoder_posterior = self.encode_first_stage(x)
z = self.get_first_stage_encoding(encoder_posterior).detach()
del self.scale_factor
self.register_buffer('scale_factor', 1. / z.flatten().std())
print(f"setting self.scale_factor to {self.scale_factor}")
print("### USING STD-RESCALING ###")
def register_schedule(self,
given_betas=None, beta_schedule="linear", timesteps=1000,
linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
super().register_schedule(given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s)
self.shorten_cond_schedule = self.num_timesteps_cond > 1
if self.shorten_cond_schedule:
self.make_cond_schedule()
def instantiate_first_stage(self, config):
model = instantiate_from_config(config)
self.first_stage_model = model.eval()
self.first_stage_model.train = disabled_train
for param in self.first_stage_model.parameters():
param.requires_grad = False
def instantiate_cond_stage(self, config):
if not self.cond_stage_trainable:
if config == "__is_first_stage__":
print("Using first stage also as cond stage.")
self.cond_stage_model = self.first_stage_model
elif config == "__is_unconditional__":
print(f"Training {self.__class__.__name__} as an unconditional model.")
self.cond_stage_model = None
# self.be_unconditional = True
else:
model = instantiate_from_config(config)
self.cond_stage_model = model.eval()
self.cond_stage_model.train = disabled_train
for param in self.cond_stage_model.parameters():
param.requires_grad = False
else:
assert config != '__is_first_stage__'
assert config != '__is_unconditional__'
model = instantiate_from_config(config)
self.cond_stage_model = model
def _get_denoise_row_from_list(self, samples, desc='', force_no_decoder_quantization=False):
denoise_row = []
for zd in tqdm(samples, desc=desc):
denoise_row.append(self.decode_first_stage(zd.to(self.device),
force_not_quantize=force_no_decoder_quantization))
n_imgs_per_row = len(denoise_row)
denoise_row = torch.stack(denoise_row) # n_log_step, n_row, C, H, W
denoise_grid = rearrange(denoise_row, 'n b c h w -> b n c h w')
denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
return denoise_grid
def get_first_stage_encoding(self, encoder_posterior):
if isinstance(encoder_posterior, DiagonalGaussianDistribution):
z = encoder_posterior.sample()
elif isinstance(encoder_posterior, torch.Tensor):
z = encoder_posterior
else:
raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented")
return self.scale_factor * z
def get_learned_conditioning(self, c):
if self.cond_stage_forward is None:
if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode):
c = self.cond_stage_model.encode(c)
if isinstance(c, DiagonalGaussianDistribution):
c = c.mode()
else:
c = self.cond_stage_model(c)
else:
assert hasattr(self.cond_stage_model, self.cond_stage_forward)
c = getattr(self.cond_stage_model, self.cond_stage_forward)(c)
return c
def meshgrid(self, h, w):
y = torch.arange(0, h).view(h, 1, 1).repeat(1, w, 1)
x = torch.arange(0, w).view(1, w, 1).repeat(h, 1, 1)
arr = torch.cat([y, x], dim=-1)
return arr
def delta_border(self, h, w):
"""
:param h: height
:param w: width
:return: normalized distance to image border,
wtith min distance = 0 at border and max dist = 0.5 at image center
"""
lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2)
arr = self.meshgrid(h, w) / lower_right_corner
dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0]
dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0]
edge_dist = torch.min(torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1)[0]
return edge_dist
def get_weighting(self, h, w, Ly, Lx, device):
weighting = self.delta_border(h, w)
weighting = torch.clip(weighting, self.split_input_params["clip_min_weight"],
self.split_input_params["clip_max_weight"], )
weighting = weighting.view(1, h * w, 1).repeat(1, 1, Ly * Lx).to(device)
if self.split_input_params["tie_braker"]:
L_weighting = self.delta_border(Ly, Lx)
L_weighting = torch.clip(L_weighting,
self.split_input_params["clip_min_tie_weight"],
self.split_input_params["clip_max_tie_weight"])
L_weighting = L_weighting.view(1, 1, Ly * Lx).to(device)
weighting = weighting * L_weighting
return weighting
def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1): # todo load once not every time, shorten code
"""
:param x: img of size (bs, c, h, w)
:return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1])
"""
bs, nc, h, w = x.shape
# number of crops in image
Ly = (h - kernel_size[0]) // stride[0] + 1
Lx = (w - kernel_size[1]) // stride[1] + 1
if uf == 1 and df == 1:
fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
unfold = torch.nn.Unfold(**fold_params)
fold = torch.nn.Fold(output_size=x.shape[2:], **fold_params)
weighting = self.get_weighting(kernel_size[0], kernel_size[1], Ly, Lx, x.device).to(x.dtype)
normalization = fold(weighting).view(1, 1, h, w) # normalizes the overlap
weighting = weighting.view((1, 1, kernel_size[0], kernel_size[1], Ly * Lx))
elif uf > 1 and df == 1:
fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
unfold = torch.nn.Unfold(**fold_params)
fold_params2 = dict(kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf),
dilation=1, padding=0,
stride=(stride[0] * uf, stride[1] * uf))
fold = torch.nn.Fold(output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2)
weighting = self.get_weighting(kernel_size[0] * uf, kernel_size[1] * uf, Ly, Lx, x.device).to(x.dtype)
normalization = fold(weighting).view(1, 1, h * uf, w * uf) # normalizes the overlap
weighting = weighting.view((1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx))
elif df > 1 and uf == 1:
fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
unfold = torch.nn.Unfold(**fold_params)
fold_params2 = dict(kernel_size=(kernel_size[0] // df, kernel_size[0] // df),
dilation=1, padding=0,
stride=(stride[0] // df, stride[1] // df))
fold = torch.nn.Fold(output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2)
weighting = self.get_weighting(kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device).to(x.dtype)
normalization = fold(weighting).view(1, 1, h // df, w // df) # normalizes the overlap
weighting = weighting.view((1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx))
else:
raise NotImplementedError
return fold, unfold, normalization, weighting
@torch.no_grad()
def get_input(self, batch, k, return_first_stage_outputs=False, force_c_encode=False,
cond_key=None, return_original_cond=False, bs=None, uncond=0.05):
x = super().get_input(batch, k)
if bs is not None:
x = x[:bs]
x = x.to(self.device)
encoder_posterior = self.encode_first_stage(x)
z = self.get_first_stage_encoding(encoder_posterior).detach()
cond_key = cond_key or self.cond_stage_key
xc = super().get_input(batch, cond_key)
if bs is not None:
xc["c_crossattn"] = xc["c_crossattn"][:bs]
xc["c_concat"] = xc["c_concat"][:bs]
cond = {}
# To support classifier-free guidance, randomly drop out only text conditioning 5%, only image conditioning 5%, and both 5%.
random = torch.rand(x.size(0), device=x.device)
prompt_mask = rearrange(random < 2 * uncond, "n -> n 1 1")
input_mask = 1 - rearrange((random >= uncond).float() * (random < 3 * uncond).float(), "n -> n 1 1 1")
null_prompt = self.get_learned_conditioning([""])
cond["c_crossattn"] = [torch.where(prompt_mask, null_prompt, self.get_learned_conditioning(xc["c_crossattn"]).detach())]
cond["c_concat"] = [input_mask * self.encode_first_stage((xc["c_concat"].to(self.device))).mode().detach()]
out = [z, cond]
if return_first_stage_outputs:
xrec = self.decode_first_stage(z)
out.extend([x, xrec])
if return_original_cond:
out.append(xc)
return out
@torch.no_grad()
def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
if predict_cids:
if z.dim() == 4:
z = torch.argmax(z.exp(), dim=1).long()
z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
z = rearrange(z, 'b h w c -> b c h w').contiguous()
z = 1. / self.scale_factor * z
if hasattr(self, "split_input_params"):
if self.split_input_params["patch_distributed_vq"]:
ks = self.split_input_params["ks"] # eg. (128, 128)
stride = self.split_input_params["stride"] # eg. (64, 64)
uf = self.split_input_params["vqf"]
bs, nc, h, w = z.shape
if ks[0] > h or ks[1] > w:
ks = (min(ks[0], h), min(ks[1], w))
print("reducing Kernel")
if stride[0] > h or stride[1] > w:
stride = (min(stride[0], h), min(stride[1], w))
print("reducing stride")
fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf)
z = unfold(z) # (bn, nc * prod(**ks), L)
# 1. Reshape to img shape
z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
# 2. apply model loop over last dim
if isinstance(self.first_stage_model, VQModelInterface):
output_list = [self.first_stage_model.decode(z[:, :, :, :, i],
force_not_quantize=predict_cids or force_not_quantize)
for i in range(z.shape[-1])]
else:
output_list = [self.first_stage_model.decode(z[:, :, :, :, i])
for i in range(z.shape[-1])]
o = torch.stack(output_list, axis=-1) # # (bn, nc, ks[0], ks[1], L)
o = o * weighting
# Reverse 1. reshape to img shape
o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
# stitch crops together
decoded = fold(o)
decoded = decoded / normalization # norm is shape (1, 1, h, w)
return decoded
else:
if isinstance(self.first_stage_model, VQModelInterface):
return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
else:
return self.first_stage_model.decode(z)
else:
if isinstance(self.first_stage_model, VQModelInterface):
return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
else:
return self.first_stage_model.decode(z)
# same as above but without decorator
def differentiable_decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
if predict_cids:
if z.dim() == 4:
z = torch.argmax(z.exp(), dim=1).long()
z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
z = rearrange(z, 'b h w c -> b c h w').contiguous()
z = 1. / self.scale_factor * z
if hasattr(self, "split_input_params"):
if self.split_input_params["patch_distributed_vq"]:
ks = self.split_input_params["ks"] # eg. (128, 128)
stride = self.split_input_params["stride"] # eg. (64, 64)
uf = self.split_input_params["vqf"]
bs, nc, h, w = z.shape
if ks[0] > h or ks[1] > w:
ks = (min(ks[0], h), min(ks[1], w))
print("reducing Kernel")
if stride[0] > h or stride[1] > w:
stride = (min(stride[0], h), min(stride[1], w))
print("reducing stride")
fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf)
z = unfold(z) # (bn, nc * prod(**ks), L)
# 1. Reshape to img shape
z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
# 2. apply model loop over last dim
if isinstance(self.first_stage_model, VQModelInterface):
output_list = [self.first_stage_model.decode(z[:, :, :, :, i],
force_not_quantize=predict_cids or force_not_quantize)
for i in range(z.shape[-1])]
else:
output_list = [self.first_stage_model.decode(z[:, :, :, :, i])
for i in range(z.shape[-1])]
o = torch.stack(output_list, axis=-1) # # (bn, nc, ks[0], ks[1], L)
o = o * weighting
# Reverse 1. reshape to img shape
o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
# stitch crops together
decoded = fold(o)
decoded = decoded / normalization # norm is shape (1, 1, h, w)
return decoded
else:
if isinstance(self.first_stage_model, VQModelInterface):
return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
else:
return self.first_stage_model.decode(z)
else:
if isinstance(self.first_stage_model, VQModelInterface):
return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
else:
return self.first_stage_model.decode(z)
@torch.no_grad()
def encode_first_stage(self, x):
if hasattr(self, "split_input_params"):
if self.split_input_params["patch_distributed_vq"]:
ks = self.split_input_params["ks"] # eg. (128, 128)
stride = self.split_input_params["stride"] # eg. (64, 64)
df = self.split_input_params["vqf"]
self.split_input_params['original_image_size'] = x.shape[-2:]
bs, nc, h, w = x.shape
if ks[0] > h or ks[1] > w:
ks = (min(ks[0], h), min(ks[1], w))
print("reducing Kernel")
if stride[0] > h or stride[1] > w:
stride = (min(stride[0], h), min(stride[1], w))
print("reducing stride")
fold, unfold, normalization, weighting = self.get_fold_unfold(x, ks, stride, df=df)
z = unfold(x) # (bn, nc * prod(**ks), L)
# Reshape to img shape
z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
output_list = [self.first_stage_model.encode(z[:, :, :, :, i])
for i in range(z.shape[-1])]
o = torch.stack(output_list, axis=-1)
o = o * weighting
# Reverse reshape to img shape
o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
# stitch crops together
decoded = fold(o)
decoded = decoded / normalization
return decoded
else:
return self.first_stage_model.encode(x)
else:
return self.first_stage_model.encode(x)
def shared_step(self, batch, **kwargs):
x, c = self.get_input(batch, self.first_stage_key)
loss = self(x, c)
return loss
def forward(self, x, c, *args, **kwargs):
t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
if self.model.conditioning_key is not None:
assert c is not None
if self.cond_stage_trainable:
c = self.get_learned_conditioning(c)
if self.shorten_cond_schedule: # TODO: drop this option
tc = self.cond_ids[t].to(self.device)
c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float()))
return self.p_losses(x, c, t, *args, **kwargs)
def _rescale_annotations(self, bboxes, crop_coordinates): # TODO: move to dataset
def rescale_bbox(bbox):
x0 = clamp((bbox[0] - crop_coordinates[0]) / crop_coordinates[2])
y0 = clamp((bbox[1] - crop_coordinates[1]) / crop_coordinates[3])
w = min(bbox[2] / crop_coordinates[2], 1 - x0)
h = min(bbox[3] / crop_coordinates[3], 1 - y0)
return x0, y0, w, h
return [rescale_bbox(b) for b in bboxes]
def apply_model(self, x_noisy, t, cond, return_ids=False):
if isinstance(cond, dict):
# hybrid case, cond is exptected to be a dict
pass
else:
if not isinstance(cond, list):
cond = [cond]
key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn'
cond = {key: cond}
if hasattr(self, "split_input_params"):
assert len(cond) == 1 # todo can only deal with one conditioning atm
assert not return_ids
ks = self.split_input_params["ks"] # eg. (128, 128)
stride = self.split_input_params["stride"] # eg. (64, 64)
h, w = x_noisy.shape[-2:]
fold, unfold, normalization, weighting = self.get_fold_unfold(x_noisy, ks, stride)
z = unfold(x_noisy) # (bn, nc * prod(**ks), L)
# Reshape to img shape
z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
z_list = [z[:, :, :, :, i] for i in range(z.shape[-1])]
if self.cond_stage_key in ["image", "LR_image", "segmentation",
'bbox_img'] and self.model.conditioning_key: # todo check for completeness
c_key = next(iter(cond.keys())) # get key
c = next(iter(cond.values())) # get value
assert (len(c) == 1) # todo extend to list with more than one elem
c = c[0] # get element
c = unfold(c)
c = c.view((c.shape[0], -1, ks[0], ks[1], c.shape[-1])) # (bn, nc, ks[0], ks[1], L )
cond_list = [{c_key: [c[:, :, :, :, i]]} for i in range(c.shape[-1])]
elif self.cond_stage_key == 'coordinates_bbox':
assert 'original_image_size' in self.split_input_params, 'BoudingBoxRescaling is missing original_image_size'
# assuming padding of unfold is always 0 and its dilation is always 1
n_patches_per_row = int((w - ks[0]) / stride[0] + 1)
full_img_h, full_img_w = self.split_input_params['original_image_size']
# as we are operating on latents, we need the factor from the original image size to the
# spatial latent size to properly rescale the crops for regenerating the bbox annotations
num_downs = self.first_stage_model.encoder.num_resolutions - 1
rescale_latent = 2 ** (num_downs)
# get top left postions of patches as conforming for the bbbox tokenizer, therefore we
# need to rescale the tl patch coordinates to be in between (0,1)
tl_patch_coordinates = [(rescale_latent * stride[0] * (patch_nr % n_patches_per_row) / full_img_w,
rescale_latent * stride[1] * (patch_nr // n_patches_per_row) / full_img_h)
for patch_nr in range(z.shape[-1])]
# patch_limits are tl_coord, width and height coordinates as (x_tl, y_tl, h, w)
patch_limits = [(x_tl, y_tl,
rescale_latent * ks[0] / full_img_w,
rescale_latent * ks[1] / full_img_h) for x_tl, y_tl in tl_patch_coordinates]
# patch_values = [(np.arange(x_tl,min(x_tl+ks, 1.)),np.arange(y_tl,min(y_tl+ks, 1.))) for x_tl, y_tl in tl_patch_coordinates]
# tokenize crop coordinates for the bounding boxes of the respective patches
patch_limits_tknzd = [torch.LongTensor(self.bbox_tokenizer._crop_encoder(bbox))[None].to(self.device)
for bbox in patch_limits] # list of length l with tensors of shape (1, 2)
print(patch_limits_tknzd[0].shape)
# cut tknzd crop position from conditioning
assert isinstance(cond, dict), 'cond must be dict to be fed into model'
cut_cond = cond['c_crossattn'][0][..., :-2].to(self.device)
print(cut_cond.shape)
adapted_cond = torch.stack([torch.cat([cut_cond, p], dim=1) for p in patch_limits_tknzd])
adapted_cond = rearrange(adapted_cond, 'l b n -> (l b) n')
print(adapted_cond.shape)
adapted_cond = self.get_learned_conditioning(adapted_cond)
print(adapted_cond.shape)
adapted_cond = rearrange(adapted_cond, '(l b) n d -> l b n d', l=z.shape[-1])
print(adapted_cond.shape)
cond_list = [{'c_crossattn': [e]} for e in adapted_cond]
else:
cond_list = [cond for i in range(z.shape[-1])] # Todo make this more efficient
# apply model by loop over crops
output_list = [self.model(z_list[i], t, **cond_list[i]) for i in range(z.shape[-1])]
assert not isinstance(output_list[0],
tuple) # todo cant deal with multiple model outputs check this never happens
o = torch.stack(output_list, axis=-1)
o = o * weighting
# Reverse reshape to img shape
o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
# stitch crops together
x_recon = fold(o) / normalization
else:
x_recon = self.model(x_noisy, t, **cond)
if isinstance(x_recon, tuple) and not return_ids:
return x_recon[0]
else:
return x_recon
def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
return (extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart) / \
extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
def _prior_bpd(self, x_start):
"""
Get the prior KL term for the variational lower-bound, measured in
bits-per-dim.
This term can't be optimized, as it only depends on the encoder.
:param x_start: the [N x C x ...] tensor of inputs.
:return: a batch of [N] KL values (in bits), one per batch element.
"""
batch_size = x_start.shape[0]
t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device)
qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t)
kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0)
return mean_flat(kl_prior) / np.log(2.0)
def p_losses(self, x_start, cond, t, noise=None):
noise = default(noise, lambda: torch.randn_like(x_start))
x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
model_output = self.apply_model(x_noisy, t, cond)
loss_dict = {}
prefix = 'train' if self.training else 'val'
if self.parameterization == "x0":
target = x_start
elif self.parameterization == "eps":
target = noise
else:
raise NotImplementedError()
loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3])
loss_dict.update({f'{prefix}/loss_simple': loss_simple.mean()})
logvar_t = self.logvar[t].to(self.device)
loss = loss_simple / torch.exp(logvar_t) + logvar_t
# loss = loss_simple / torch.exp(self.logvar) + self.logvar
if self.learn_logvar:
loss_dict.update({f'{prefix}/loss_gamma': loss.mean()})
loss_dict.update({'logvar': self.logvar.data.mean()})
loss = self.l_simple_weight * loss.mean()
loss_vlb = self.get_loss(model_output, target, mean=False).mean(dim=(1, 2, 3))
loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean()
loss_dict.update({f'{prefix}/loss_vlb': loss_vlb})
loss += (self.original_elbo_weight * loss_vlb)
loss_dict.update({f'{prefix}/loss': loss})
return loss, loss_dict
def p_mean_variance(self, x, c, t, clip_denoised: bool, return_codebook_ids=False, quantize_denoised=False,
return_x0=False, score_corrector=None, corrector_kwargs=None):
t_in = t
model_out = self.apply_model(x, t_in, c, return_ids=return_codebook_ids)
if score_corrector is not None:
assert self.parameterization == "eps"
model_out = score_corrector.modify_score(self, model_out, x, t, c, **corrector_kwargs)
if return_codebook_ids:
model_out, logits = model_out
if self.parameterization == "eps":
x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
elif self.parameterization == "x0":
x_recon = model_out
else:
raise NotImplementedError()
if clip_denoised:
x_recon.clamp_(-1., 1.)
if quantize_denoised:
x_recon, _, [_, _, indices] = self.first_stage_model.quantize(x_recon)
model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
if return_codebook_ids:
return model_mean, posterior_variance, posterior_log_variance, logits
elif return_x0:
return model_mean, posterior_variance, posterior_log_variance, x_recon
else:
return model_mean, posterior_variance, posterior_log_variance
@torch.no_grad()
def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False,
return_codebook_ids=False, quantize_denoised=False, return_x0=False,
temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None):
b, *_, device = *x.shape, x.device
outputs = self.p_mean_variance(x=x, c=c, t=t, clip_denoised=clip_denoised,
return_codebook_ids=return_codebook_ids,
quantize_denoised=quantize_denoised,
return_x0=return_x0,
score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
if return_codebook_ids:
raise DeprecationWarning("Support dropped.")
model_mean, _, model_log_variance, logits = outputs
elif return_x0:
model_mean, _, model_log_variance, x0 = outputs
else:
model_mean, _, model_log_variance = outputs
noise = noise_like(x.shape, device, repeat_noise) * temperature
if noise_dropout > 0.:
noise = torch.nn.functional.dropout(noise, p=noise_dropout)
# no noise when t == 0
nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
if return_codebook_ids:
return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, logits.argmax(dim=1)
if return_x0:
return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, x0
else:
return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
@torch.no_grad()
def progressive_denoising(self, cond, shape, verbose=True, callback=None, quantize_denoised=False,
img_callback=None, mask=None, x0=None, temperature=1., noise_dropout=0.,
score_corrector=None, corrector_kwargs=None, batch_size=None, x_T=None, start_T=None,
log_every_t=None):
if not log_every_t:
log_every_t = self.log_every_t
timesteps = self.num_timesteps
if batch_size is not None:
b = batch_size if batch_size is not None else shape[0]
shape = [batch_size] + list(shape)
else:
b = batch_size = shape[0]
if x_T is None:
img = torch.randn(shape, device=self.device)
else:
img = x_T
intermediates = []
if cond is not None:
if isinstance(cond, dict):
cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
else:
cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
if start_T is not None:
timesteps = min(timesteps, start_T)
iterator = tqdm(reversed(range(0, timesteps)), desc='Progressive Generation',
total=timesteps) if verbose else reversed(
range(0, timesteps))
if type(temperature) == float:
temperature = [temperature] * timesteps
for i in iterator:
ts = torch.full((b,), i, device=self.device, dtype=torch.long)
if self.shorten_cond_schedule:
assert self.model.conditioning_key != 'hybrid'
tc = self.cond_ids[ts].to(cond.device)
cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
img, x0_partial = self.p_sample(img, cond, ts,
clip_denoised=self.clip_denoised,
quantize_denoised=quantize_denoised, return_x0=True,
temperature=temperature[i], noise_dropout=noise_dropout,
score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
if mask is not None:
assert x0 is not None
img_orig = self.q_sample(x0, ts)
img = img_orig * mask + (1. - mask) * img
if i % log_every_t == 0 or i == timesteps - 1:
intermediates.append(x0_partial)
if callback: callback(i)
if img_callback: img_callback(img, i)
return img, intermediates
@torch.no_grad()
def p_sample_loop(self, cond, shape, return_intermediates=False,
x_T=None, verbose=True, callback=None, timesteps=None, quantize_denoised=False,
mask=None, x0=None, img_callback=None, start_T=None,
log_every_t=None):
if not log_every_t:
log_every_t = self.log_every_t
device = self.betas.device
b = shape[0]
if x_T is None:
img = torch.randn(shape, device=device)
else:
img = x_T
intermediates = [img]
if timesteps is None:
timesteps = self.num_timesteps
if start_T is not None:
timesteps = min(timesteps, start_T)
iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed(
range(0, timesteps))
if mask is not None:
assert x0 is not None
assert x0.shape[2:3] == mask.shape[2:3] # spatial size has to match
for i in iterator:
ts = torch.full((b,), i, device=device, dtype=torch.long)
if self.shorten_cond_schedule:
assert self.model.conditioning_key != 'hybrid'
tc = self.cond_ids[ts].to(cond.device)
cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
img = self.p_sample(img, cond, ts,
clip_denoised=self.clip_denoised,
quantize_denoised=quantize_denoised)
if mask is not None:
img_orig = self.q_sample(x0, ts)
img = img_orig * mask + (1. - mask) * img
if i % log_every_t == 0 or i == timesteps - 1:
intermediates.append(img)
if callback: callback(i)
if img_callback: img_callback(img, i)
if return_intermediates:
return img, intermediates
return img
@torch.no_grad()
def sample(self, cond, batch_size=16, return_intermediates=False, x_T=None,
verbose=True, timesteps=None, quantize_denoised=False,
mask=None, x0=None, shape=None,**kwargs):
if shape is None:
shape = (batch_size, self.channels, self.image_size, self.image_size)
if cond is not None:
if isinstance(cond, dict):
cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
else:
cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
return self.p_sample_loop(cond,
shape,
return_intermediates=return_intermediates, x_T=x_T,
verbose=verbose, timesteps=timesteps, quantize_denoised=quantize_denoised,
mask=mask, x0=x0)
@torch.no_grad()
def sample_log(self,cond,batch_size,ddim, ddim_steps,**kwargs):
if ddim:
ddim_sampler = DDIMSampler(self)
shape = (self.channels, self.image_size, self.image_size)
samples, intermediates =ddim_sampler.sample(ddim_steps,batch_size,
shape,cond,verbose=False,**kwargs)
else:
samples, intermediates = self.sample(cond=cond, batch_size=batch_size,
return_intermediates=True,**kwargs)
return samples, intermediates
@torch.no_grad()
def log_images(self, batch, N=4, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None,
quantize_denoised=True, inpaint=False, plot_denoise_rows=False, plot_progressive_rows=False,
plot_diffusion_rows=False, **kwargs):
use_ddim = False
log = dict()
z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key,
return_first_stage_outputs=True,
force_c_encode=True,
return_original_cond=True,
bs=N, uncond=0)
N = min(x.shape[0], N)
n_row = min(x.shape[0], n_row)
log["inputs"] = x
log["reals"] = xc["c_concat"]
log["reconstruction"] = xrec
if self.model.conditioning_key is not None:
if hasattr(self.cond_stage_model, "decode"):
xc = self.cond_stage_model.decode(c)
log["conditioning"] = xc
elif self.cond_stage_key in ["caption"]:
xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["caption"])
log["conditioning"] = xc
elif self.cond_stage_key == 'class_label':
xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"])
log['conditioning'] = xc
elif isimage(xc):
log["conditioning"] = xc
if ismap(xc):
log["original_conditioning"] = self.to_rgb(xc)
if plot_diffusion_rows:
# get diffusion row
diffusion_row = list()
z_start = z[:n_row]
for t in range(self.num_timesteps):
if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
t = t.to(self.device).long()
noise = torch.randn_like(z_start)
z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)
diffusion_row.append(self.decode_first_stage(z_noisy))
diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W
diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w')
diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w')
diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0])
log["diffusion_row"] = diffusion_grid
if sample:
# get denoise row
with self.ema_scope("Plotting"):
samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim,
ddim_steps=ddim_steps,eta=ddim_eta)
# samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True)
x_samples = self.decode_first_stage(samples)
log["samples"] = x_samples
if plot_denoise_rows:
denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
log["denoise_row"] = denoise_grid
if quantize_denoised and not isinstance(self.first_stage_model, AutoencoderKL) and not isinstance(
self.first_stage_model, IdentityFirstStage):
# also display when quantizing x0 while sampling
with self.ema_scope("Plotting Quantized Denoised"):
samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim,
ddim_steps=ddim_steps,eta=ddim_eta,
quantize_denoised=True)
# samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True,
# quantize_denoised=True)
x_samples = self.decode_first_stage(samples.to(self.device))
log["samples_x0_quantized"] = x_samples
if inpaint:
# make a simple center square
b, h, w = z.shape[0], z.shape[2], z.shape[3]
mask = torch.ones(N, h, w).to(self.device)
# zeros will be filled in
mask[:, h // 4:3 * h // 4, w // 4:3 * w // 4] = 0.
mask = mask[:, None, ...]
with self.ema_scope("Plotting Inpaint"):
samples, _ = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, eta=ddim_eta,
ddim_steps=ddim_steps, x0=z[:N], mask=mask)
x_samples = self.decode_first_stage(samples.to(self.device))
log["samples_inpainting"] = x_samples
log["mask"] = mask
# outpaint
with self.ema_scope("Plotting Outpaint"):
samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,eta=ddim_eta,
ddim_steps=ddim_steps, x0=z[:N], mask=mask)
x_samples = self.decode_first_stage(samples.to(self.device))
log["samples_outpainting"] = x_samples
if plot_progressive_rows:
with self.ema_scope("Plotting Progressives"):
img, progressives = self.progressive_denoising(c,
shape=(self.channels, self.image_size, self.image_size),
batch_size=N)
prog_row = self._get_denoise_row_from_list(progressives, desc="Progressive Generation")
log["progressive_row"] = prog_row
if return_keys:
if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
return log
else:
return {key: log[key] for key in return_keys}
return log
def configure_optimizers(self):
lr = self.learning_rate
params = list(self.model.parameters())
if self.cond_stage_trainable:
print(f"{self.__class__.__name__}: Also optimizing conditioner params!")
params = params + list(self.cond_stage_model.parameters())
if self.learn_logvar:
print('Diffusion model optimizing logvar')
params.append(self.logvar)
opt = torch.optim.AdamW(params, lr=lr)
if self.use_scheduler:
assert 'target' in self.scheduler_config
scheduler = instantiate_from_config(self.scheduler_config)
print("Setting up LambdaLR scheduler...")
scheduler = [
{
'scheduler': LambdaLR(opt, lr_lambda=scheduler.schedule),
'interval': 'step',
'frequency': 1
}]
return [opt], scheduler
return opt
@torch.no_grad()
def to_rgb(self, x):
x = x.float()
if not hasattr(self, "colorize"):
self.colorize = torch.randn(3, x.shape[1], 1, 1).to(x)
x = nn.functional.conv2d(x, weight=self.colorize)
x = 2. * (x - x.min()) / (x.max() - x.min()) - 1.
return x
class DiffusionWrapper(pl.LightningModule):
def __init__(self, diff_model_config, conditioning_key):
super().__init__()
self.diffusion_model = instantiate_from_config(diff_model_config)
self.conditioning_key = conditioning_key
assert self.conditioning_key in [None, 'concat', 'crossattn', 'hybrid', 'adm']
def forward(self, x, t, c_concat: list = None, c_crossattn: list = None):
if self.conditioning_key is None:
out = self.diffusion_model(x, t)
elif self.conditioning_key == 'concat':
xc = torch.cat([x] + c_concat, dim=1)
out = self.diffusion_model(xc, t)
elif self.conditioning_key == 'crossattn':
cc = torch.cat(c_crossattn, 1)
out = self.diffusion_model(x, t, context=cc)
elif self.conditioning_key == 'hybrid':
xc = torch.cat([x] + c_concat, dim=1)
cc = torch.cat(c_crossattn, 1)
out = self.diffusion_model(xc, t, context=cc)
elif self.conditioning_key == 'adm':
cc = c_crossattn[0]
out = self.diffusion_model(x, t, y=cc)
else:
raise NotImplementedError()
return out
class Layout2ImgDiffusion(LatentDiffusion):
# TODO: move all layout-specific hacks to this class
def __init__(self, cond_stage_key, *args, **kwargs):
assert cond_stage_key == 'coordinates_bbox', 'Layout2ImgDiffusion only for cond_stage_key="coordinates_bbox"'
super().__init__(cond_stage_key=cond_stage_key, *args, **kwargs)
def log_images(self, batch, N=8, *args, **kwargs):
logs = super().log_images(batch=batch, N=N, *args, **kwargs)
key = 'train' if self.training else 'validation'
dset = self.trainer.datamodule.datasets[key]
mapper = dset.conditional_builders[self.cond_stage_key]
bbox_imgs = []
map_fn = lambda catno: dset.get_textual_label(dset.get_category_id(catno))
for tknzd_bbox in batch[self.cond_stage_key][:N]:
bboximg = mapper.plot(tknzd_bbox.detach().cpu(), map_fn, (256, 256))
bbox_imgs.append(bboximg)
cond_img = torch.stack(bbox_imgs, dim=0)
logs['bbox_image'] = cond_img
return logs
================================================
FILE: models/instructpix2pix/stable_diffusion/ldm/models/diffusion/dpm_solver/__init__.py
================================================
from .sampler import DPMSolverSampler
================================================
FILE: models/instructpix2pix/stable_diffusion/ldm/models/diffusion/dpm_solver/dpm_solver.py
================================================
import torch
import torch.nn.functional as F
import math
class NoiseScheduleVP:
def __init__(
self,
schedule='discrete',
betas=None,
alphas_cumprod=None,
continuous_beta_0=0.1,
continuous_beta_1=20.,
):
"""Create a wrapper class for the forward SDE (VP type).
***
Update: We support discrete-time diffusion models by implementing a picewise linear interpolation for log_alpha_t.
We recommend to use schedule='discrete' for the discrete-time diffusion models, especially for high-resolution images.
***
The forward SDE ensures that the condition distribution q_{t|0}(x_t | x_0) = N ( alpha_t * x_0, sigma_t^2 * I ).
We further define lambda_t = log(alpha_t) - log(sigma_t), which is the half-logSNR (described in the DPM-Solver paper).
Therefore, we implement the functions for computing alpha_t, sigma_t and lambda_t. For t in [0, T], we have:
log_alpha_t = self.marginal_log_mean_coeff(t)
sigma_t = self.marginal_std(t)
lambda_t = self.marginal_lambda(t)
Moreover, as lambda(t) is an invertible function, we also support its inverse function:
t = self.inverse_lambda(lambda_t)
===============================================================
We support both discrete-time DPMs (trained on n = 0, 1, ..., N-1) and continuous-time DPMs (trained on t in [t_0, T]).
1. For discrete-time DPMs:
For discrete-time DPMs trained on n = 0, 1, ..., N-1, we convert the discrete steps to continuous time steps by:
t_i = (i + 1) / N
e.g. for N = 1000, we have t_0 = 1e-3 and T = t_{N-1} = 1.
We solve the corresponding diffusion ODE from time T = 1 to time t_0 = 1e-3.
Args:
betas: A `torch.Tensor`. The beta array for the discrete-time DPM. (See the original DDPM paper for details)
alphas_cumprod: A `torch.Tensor`. The cumprod alphas for the discrete-time DPM. (See the original DDPM paper for details)
Note that we always have alphas_cumprod = cumprod(betas). Therefore, we only need to set one of `betas` and `alphas_cumprod`.
**Important**: Please pay special attention for the args for `alphas_cumprod`:
The `alphas_cumprod` is the \hat{alpha_n} arrays in the notations of DDPM. Specifically, DDPMs assume that
q_{t_n | 0}(x_{t_n} | x_0) = N ( \sqrt{\hat{alpha_n}} * x_0, (1 - \hat{alpha_n}) * I ).
Therefore, the notation \hat{alpha_n} is different from the notation alpha_t in DPM-Solver. In fact, we have
alpha_{t_n} = \sqrt{\hat{alpha_n}},
and
log(alpha_{t_n}) = 0.5 * log(\hat{alpha_n}).
2. For continuous-time DPMs:
We support two types of VPSDEs: linear (DDPM) and cosine (improved-DDPM). The hyperparameters for the noise
schedule are the default settings in DDPM and improved-DDPM:
Args:
beta_min: A `float` number. The smallest beta for the linear schedule.
beta_max: A `float` number. The largest beta for the linear schedule.
cosine_s: A `float` number. The hyperparameter in the cosine schedule.
cosine_beta_max: A `float` number. The hyperparameter in the cosine schedule.
T: A `float` number. The ending time of the forward process.
===============================================================
Args:
schedule: A `str`. The noise schedule of the forward SDE. 'discrete' for discrete-time DPMs,
'linear' or 'cosine' for continuous-time DPMs.
Returns:
A wrapper object of the forward SDE (VP type).
===============================================================
Example:
# For discrete-time DPMs, given betas (the beta array for n = 0, 1, ..., N - 1):
>>> ns = NoiseScheduleVP('discrete', betas=betas)
# For discrete-time DPMs, given alphas_cumprod (the \hat{alpha_n} array for n = 0, 1, ..., N - 1):
>>> ns = NoiseScheduleVP('discrete', alphas_cumprod=alphas_cumprod)
# For continuous-time DPMs (VPSDE), linear schedule:
>>> ns = NoiseScheduleVP('linear', continuous_beta_0=0.1, continuous_beta_1=20.)
"""
if schedule not in ['discrete', 'linear', 'cosine']:
raise ValueError("Unsupported noise schedule {}. The schedule needs to be 'discrete' or 'linear' or 'cosine'".format(schedule))
self.schedule = schedule
if schedule == 'discrete':
if betas is not None:
log_alphas = 0.5 * torch.log(1 - betas).cumsum(dim=0)
else:
assert alphas_cumprod is not None
log_alphas = 0.5 * torch.log(alphas_cumprod)
self.total_N = len(log_alphas)
self.T = 1.
self.t_array = torch.linspace(0., 1., self.total_N + 1)[1:].reshape((1, -1))
self.log_alpha_array = log_alphas.reshape((1, -1,))
else:
self.total_N = 1000
self.beta_0 = continuous_beta_0
self.beta_1 = continuous_beta_1
self.cosine_s = 0.008
self.cosine_beta_max = 999.
self.cosine_t_max = math.atan(self.cosine_beta_max * (1. + self.cosine_s) / math.pi) * 2. * (1. + self.cosine_s) / math.pi - self.cosine_s
self.cosine_log_alpha_0 = math.log(math.cos(self.cosine_s / (1. + self.cosine_s) * math.pi / 2.))
self.schedule = schedule
if schedule == 'cosine':
# For the cosine schedule, T = 1 will have numerical issues. So we manually set the ending time T.
# Note that T = 0.9946 may be not the optimal setting. However, we find it works well.
self.T = 0.9946
else:
self.T = 1.
def marginal_log_mean_coeff(self, t):
"""
Compute log(alpha_t) of a given continuous-time label t in [0, T].
"""
if self.schedule == 'discrete':
return interpolate_fn(t.reshape((-1, 1)), self.t_array.to(t.device), self.log_alpha_array.to(t.device)).reshape((-1))
elif self.schedule == 'linear':
return -0.25 * t ** 2 * (self.beta_1 - self.beta_0) - 0.5 * t * self.beta_0
elif self.schedule == 'cosine':
log_alpha_fn = lambda s: torch.log(torch.cos((s + self.cosine_s) / (1. + self.cosine_s) * math.pi / 2.))
log_alpha_t = log_alpha_fn(t) - self.cosine_log_alpha_0
return log_alpha_t
def marginal_alpha(self, t):
"""
Compute alpha_t of a given continuous-time label t in [0, T].
"""
return torch.exp(self.marginal_log_mean_coeff(t))
def marginal_std(self, t):
"""
Compute sigma_t of a given continuous-time label t in [0, T].
"""
return torch.sqrt(1. - torch.exp(2. * self.marginal_log_mean_coeff(t)))
def marginal_lambda(self, t):
"""
Compute lambda_t = log(alpha_t) - log(sigma_t) of a given continuous-time label t in [0, T].
"""
log_mean_coeff = self.marginal_log_mean_coeff(t)
log_std = 0.5 * torch.log(1. - torch.exp(2. * log_mean_coeff))
return log_mean_coeff - log_std
def inverse_lambda(self, lamb):
"""
Compute the continuous-time label t in [0, T] of a given half-logSNR lambda_t.
"""
if self.schedule == 'linear':
tmp = 2. * (self.beta_1 - self.beta_0) * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb))
Delta = self.beta_0**2 + tmp
return tmp / (torch.sqrt(Delta) + self.beta_0) / (self.beta_1 - self.beta_0)
elif self.schedule == 'discrete':
log_alpha = -0.5 * torch.logaddexp(torch.zeros((1,)).to(lamb.device), -2. * lamb)
t = interpolate_fn(log_alpha.reshape((-1, 1)), torch.flip(self.log_alpha_array.to(lamb.device), [1]), torch.flip(self.t_array.to(lamb.device), [1]))
return t.reshape((-1,))
else:
log_alpha = -0.5 * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb))
t_fn = lambda log_alpha_t: torch.arccos(torch.exp(log_alpha_t + self.cosine_log_alpha_0)) * 2. * (1. + self.cosine_s) / math.pi - self.cosine_s
t = t_fn(log_alpha)
return t
def model_wrapper(
model,
noise_schedule,
model_type="noise",
model_kwargs={},
guidance_type="uncond",
condition=None,
unconditional_condition=None,
guidance_scale=1.,
classifier_fn=None,
classifier_kwargs={},
):
"""Create a wrapper function for the noise prediction model.
DPM-Solver needs to solve the continuous-time diffusion ODEs. For DPMs trained on discrete-time labels, we need to
firstly wrap the model function to a noise prediction model that accepts the continuous time as the input.
We support four types of the diffusion model by setting `model_type`:
1. "noise": noise prediction model. (Trained by predicting noise).
2. "x_start": data prediction model. (Trained by predicting the data x_0 at time 0).
3. "v": velocity prediction model. (Trained by predicting the velocity).
The "v" prediction is derivation detailed in Appendix D of [1], and is used in Imagen-Video [2].
[1] Salimans, Tim, and Jonathan Ho. "Progressive distillation for fast sampling of diffusion models."
arXiv preprint arXiv:2202.00512 (2022).
[2] Ho, Jonathan, et al. "Imagen Video: High Definition Video Generation with Diffusion Models."
arXiv preprint arXiv:2210.02303 (2022).
4. "score": marginal score function. (Trained by denoising score matching).
Note that the score function and the noise prediction model follows a simple relationship:
```
noise(x_t, t) = -sigma_t * score(x_t, t)
```
We support three types of guided sampling by DPMs by setting `guidance_type`:
1. "uncond": unconditional sampling by DPMs.
The input `model` has the following format:
``
model(x, t_input, **model_kwargs) -> noise | x_start | v | score
``
2. "classifier": classifier guidance sampling [3] by DPMs and another classifier.
The input `model` has the following format:
``
model(x, t_input, **model_kwargs) -> noise | x_start | v | score
``
The input `classifier_fn` has the following format:
``
classifier_fn(x, t_input, cond, **classifier_kwargs) -> logits(x, t_input, cond)
``
[3] P. Dhariwal and A. Q. Nichol, "Diffusion models beat GANs on image synthesis,"
in Advances in Neural Information Processing Systems, vol. 34, 2021, pp. 8780-8794.
3. "classifier-free": classifier-free guidance sampling by conditional DPMs.
The input `model` has the following format:
``
model(x, t_input, cond, **model_kwargs) -> noise | x_start | v | score
``
And if cond == `unconditional_condition`, the model output is the unconditional DPM output.
[4] Ho, Jonathan, and Tim Salimans. "Classifier-free diffusion guidance."
arXiv preprint arXiv:2207.12598 (2022).
The `t_input` is the time label of the model, which may be discrete-time labels (i.e. 0 to 999)
or continuous-time labels (i.e. epsilon to T).
We wrap the model function to accept only `x` and `t_continuous` as inputs, and outputs the predicted noise:
``
def model_fn(x, t_continuous) -> noise:
t_input = get_model_input_time(t_continuous)
return noise_pred(model, x, t_input, **model_kwargs)
``
where `t_continuous` is the continuous time labels (i.e. epsilon to T). And we use `model_fn` for DPM-Solver.
===============================================================
Args:
model: A diffusion model with the corresponding format described above.
noise_schedule: A noise schedule object, such as NoiseScheduleVP.
model_type: A `str`. The parameterization type of the diffusion model.
"noise" or "x_start" or "v" or "score".
model_kwargs: A `dict`. A dict for the other inputs of the model function.
guidance_type: A `str`. The type of the guidance for sampling.
"uncond" or "classifier" or "classifier-free".
condition: A pytorch tensor. The condition for the guided sampling.
Only used for "classifier" or "classifier-free" guidance type.
unconditional_condition: A pytorch tensor. The condition for the unconditional sampling.
Only used for "classifier-free" guidance type.
guidance_scale: A `float`. The scale for the guided sampling.
classifier_fn: A classifier function. Only used for the classifier guidance.
classifier_kwargs: A `dict`. A dict for the other inputs of the classifier function.
Returns:
A noise prediction model that accepts the noised data and the continuous time as the inputs.
"""
def get_model_input_time(t_continuous):
"""
Convert the continuous-time `t_continuous` (in [epsilon, T]) to the model input time.
For discrete-time DPMs, we convert `t_continuous` in [1 / N, 1] to `t_input` in [0, 1000 * (N - 1) / N].
For continuous-time DPMs, we just use `t_continuous`.
"""
if noise_schedule.schedule == 'discrete':
return (t_continuous - 1. / noise_schedule.total_N) * 1000.
else:
return t_continuous
def noise_pred_fn(x, t_continuous, cond=None):
if t_continuous.reshape((-1,)).shape[0] == 1:
t_continuous = t_continuous.expand((x.shape[0]))
t_input = get_model_input_time(t_continuous)
if cond is None:
output = model(x, t_input, **model_kwargs)
else:
output = model(x, t_input, cond, **model_kwargs)
if model_type == "noise":
return output
elif model_type == "x_start":
alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous)
dims = x.dim()
return (x - expand_dims(alpha_t, dims) * output) / expand_dims(sigma_t, dims)
elif model_type == "v":
alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous)
dims = x.dim()
return expand_dims(alpha_t, dims) * output + expand_dims(sigma_t, dims) * x
elif model_type == "score":
sigma_t = noise_schedule.marginal_std(t_continuous)
dims = x.dim()
return -expand_dims(sigma_t, dims) * output
def cond_grad_fn(x, t_input):
"""
Compute the gradient of the classifier, i.e. nabla_{x} log p_t(cond | x_t).
"""
with torch.enable_grad():
x_in = x.detach().requires_grad_(True)
log_prob = classifier_fn(x_in, t_input, condition, **classifier_kwargs)
return torch.autograd.grad(log_prob.sum(), x_in)[0]
def model_fn(x, t_continuous):
"""
The noise predicition model function that is used for DPM-Solver.
"""
if t_continuous.reshape((-1,)).shape[0] == 1:
t_continuous = t_continuous.expand((x.shape[0]))
if guidance_type == "uncond":
return noise_pred_fn(x, t_continuous)
elif guidance_type == "classifier":
assert classifier_fn is not None
t_input = get_model_input_time(t_continuous)
cond_grad = cond_grad_fn(x, t_input)
sigma_t = noise_schedule.marginal_std(t_continuous)
noise = noise_pred_fn(x, t_continuous)
return noise - guidance_scale * expand_dims(sigma_t, dims=cond_grad.dim()) * cond_grad
elif guidance_type == "classifier-free":
if guidance_scale == 1. or unconditional_condition is None:
return noise_pred_fn(x, t_continuous, cond=condition)
else:
x_in = torch.cat([x] * 2)
t_in = torch.cat([t_continuous] * 2)
c_in = torch.cat([unconditional_condition, condition])
noise_uncond, noise = noise_pred_fn(x_in, t_in, cond=c_in).chunk(2)
return noise_uncond + guidance_scale * (noise - noise_uncond)
assert model_type in ["noise", "x_start", "v"]
assert guidance_type in ["uncond", "classifier", "classifier-free"]
return model_fn
class DPM_Solver:
def __init__(self, model_fn, noise_schedule, predict_x0=False, thresholding=False, max_val=1.):
"""Construct a DPM-Solver.
We support both the noise prediction model ("predicting epsilon") and the data prediction model ("predicting x0").
If `predict_x0` is False, we use the solver for the noise prediction model (DPM-Solver).
If `predict_x0` is True, we use the solver for the data prediction model (DPM-Solver++).
In such case, we further support the "dynamic thresholding" in [1] when `thresholding` is True.
The "dynamic thresholding" can greatly improve the sample quality for pixel-space DPMs with large guidance scales.
Args:
model_fn: A noise prediction model function which accepts the continuous-time input (t in [epsilon, T]):
``
def model_fn(x, t_continuous):
return noise
``
noise_schedule: A noise schedule object, such as NoiseScheduleVP.
predict_x0: A `bool`. If true, use the data prediction model; else, use the noise prediction model.
thresholding: A `bool`. Valid when `predict_x0` is True. Whether to use the "dynamic thresholding" in [1].
max_val: A `float`. Valid when both `predict_x0` and `thresholding` are True. The max value for thresholding.
[1] Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S Sara Mahdavi, Rapha Gontijo Lopes, et al. Photorealistic text-to-image diffusion models with deep language understanding. arXiv preprint arXiv:2205.11487, 2022b.
"""
self.model = model_fn
self.noise_schedule = noise_schedule
self.predict_x0 = predict_x0
self.thresholding = thresholding
self.max_val = max_val
def noise_prediction_fn(self, x, t):
"""
Return the noise prediction model.
"""
return self.model(x, t)
def data_prediction_fn(self, x, t):
"""
Return the data prediction model (with thresholding).
"""
noise = self.noise_prediction_fn(x, t)
dims = x.dim()
alpha_t, sigma_t = self.noise_schedule.marginal_alpha(t), self.noise_schedule.marginal_std(t)
x0 = (x - expand_dims(sigma_t, dims) * noise) / expand_dims(alpha_t, dims)
if self.thresholding:
p = 0.995 # A hyperparameter in the paper of "Imagen" [1].
s = torch.quantile(torch.abs(x0).reshape((x0.shape[0], -1)), p, dim=1)
s = expand_dims(torch.maximum(s, self.max_val * torch.ones_like(s).to(s.device)), dims)
x0 = torch.clamp(x0, -s, s) / s
return x0
def model_fn(self, x, t):
"""
Convert the model to the noise prediction model or the data prediction model.
"""
if self.predict_x0:
return self.data_prediction_fn(x, t)
else:
return self.noise_prediction_fn(x, t)
def get_time_steps(self, skip_type, t_T, t_0, N, device):
"""Compute the intermediate time steps for sampling.
Args:
skip_type: A `str`. The type for the spacing of the time steps. We support three types:
- 'logSNR': uniform logSNR for the time steps.
- 'time_uniform': uniform time for the time steps. (**Recommended for high-resolutional data**.)
- 'time_quadratic': quadratic time for the time steps. (Used in DDIM for low-resolutional data.)
t_T: A `float`. The starting time of the sampling (default is T).
t_0: A `float`. The ending time of the sampling (default is epsilon).
N: A `int`. The total number of the spacing of the time steps.
device: A torch device.
Returns:
A pytorch tensor of the time steps, with the shape (N + 1,).
"""
if skip_type == 'logSNR':
lambda_T = self.noise_schedule.marginal_lambda(torch.tensor(t_T).to(device))
lambda_0 = self.noise_schedule.marginal_lambda(torch.tensor(t_0).to(device))
logSNR_steps = torch.linspace(lambda_T.cpu().item(), lambda_0.cpu().item(), N + 1).to(device)
return self.noise_schedule.inverse_lambda(logSNR_steps)
elif skip_type == 'time_uniform':
return torch.linspace(t_T, t_0, N + 1).to(device)
elif skip_type == 'time_quadratic':
t_order = 2
t = torch.linspace(t_T**(1. / t_order), t_0**(1. / t_order), N + 1).pow(t_order).to(device)
return t
else:
raise ValueError("Unsupported skip_type {}, need to be 'logSNR' or 'time_uniform' or 'time_quadratic'".format(skip_type))
def get_orders_and_timesteps_for_singlestep_solver(self, steps, order, skip_type, t_T, t_0, device):
"""
Get the order of each step for sampling by the singlestep DPM-Solver.
We combine both DPM-Solver-1,2,3 to use all the function evaluations, which is named as "DPM-Solver-fast".
Given a fixed number of function evaluations by `steps`, the sampling procedure by DPM-Solver-fast is:
- If order == 1:
We take `steps` of DPM-Solver-1 (i.e. DDIM).
- If order == 2:
- Denote K = (steps // 2). We take K or (K + 1) intermediate time steps for sampling.
- If steps % 2 == 0, we use K steps of DPM-Solver-2.
- If steps % 2 == 1, we use K steps of DPM-Solver-2 and 1 step of DPM-Solver-1.
- If order == 3:
- Denote K = (steps // 3 + 1). We take K intermediate time steps for sampling.
- If steps % 3 == 0, we use (K - 2) steps of DPM-Solver-3, and 1 step of DPM-Solver-2 and 1 step of DPM-Solver-1.
- If steps % 3 == 1, we use (K - 1) steps of DPM-Solver-3 and 1 step of DPM-Solver-1.
- If steps % 3 == 2, we use (K - 1) steps of DPM-Solver-3 and 1 step of DPM-Solver-2.
============================================
Args:
order: A `int`. The max order for the solver (2 or 3).
steps: A `int`. The total number of function evaluations (NFE).
skip_type: A `str`. The type for the spacing of the time steps. We support three types:
- 'logSNR': uniform logSNR for the time steps.
- 'time_uniform': uniform time for the time steps. (**Recommended for high-resolutional data**.)
- 'time_quadratic': quadratic time for the time steps. (Used in DDIM for low-resolutional data.)
t_T: A `float`. The starting time of the sampling (default is T).
t_0: A `float`. The ending time of the sampling (default is epsilon).
device: A torch device.
Returns:
orders: A list of the solver order of each step.
"""
if order == 3:
K = steps // 3 + 1
if steps % 3 == 0:
orders = [3,] * (K - 2) + [2, 1]
elif steps % 3 == 1:
orders = [3,] * (K - 1) + [1]
else:
orders = [3,] * (K - 1) + [2]
elif order == 2:
if steps % 2 == 0:
K = steps // 2
orders = [2,] * K
else:
K = steps // 2 + 1
orders = [2,] * (K - 1) + [1]
elif order == 1:
K = 1
orders = [1,] * steps
else:
raise ValueError("'order' must be '1' or '2' or '3'.")
if skip_type == 'logSNR':
# To reproduce the results in DPM-Solver paper
timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, K, device)
else:
timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, steps, device)[torch.cumsum(torch.tensor([0,] + orders)).to(device)]
return timesteps_outer, orders
def denoise_to_zero_fn(self, x, s):
"""
Denoise at the final step, which is equivalent to solve the ODE from lambda_s to infty by first-order discretization.
"""
return self.data_prediction_fn(x, s)
def dpm_solver_first_update(self, x, s, t, model_s=None, return_intermediate=False):
"""
DPM-Solver-1 (equivalent to DDIM) from time `s` to time `t`.
Args:
x: A pytorch tensor. The initial value at time `s`.
s: A pytorch tensor. The starting time, with the shape (x.shape[0],).
t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
model_s: A pytorch tensor. The model function evaluated at time `s`.
If `model_s` is None, we evaluate the model by `x` and `s`; otherwise we directly use it.
return_intermediate: A `bool`. If true, also return the model value at time `s`.
Returns:
x_t: A pytorch tensor. The approximated solution at time `t`.
"""
ns = self.noise_schedule
dims = x.dim()
lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t)
h = lambda_t - lambda_s
log_alpha_s, log_alpha_t = ns.marginal_log_mean_coeff(s), ns.marginal_log_mean_coeff(t)
sigma_s, sigma_t = ns.marginal_std(s), ns.marginal_std(t)
alpha_t = torch.exp(log_alpha_t)
if self.predict_x0:
phi_1 = torch.expm1(-h)
if model_s is None:
model_s = self.model_fn(x, s)
x_t = (
expand_dims(sigma_t / sigma_s, dims) * x
- expand_dims(alpha_t * phi_1, dims) * model_s
)
if return_intermediate:
return x_t, {'model_s': model_s}
else:
return x_t
else:
phi_1 = torch.expm1(h)
if model_s is None:
model_s = self.model_fn(x, s)
x_t = (
expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
- expand_dims(sigma_t * phi_1, dims) * model_s
)
if return_intermediate:
return x_t, {'model_s': model_s}
else:
return x_t
def singlestep_dpm_solver_second_update(self, x, s, t, r1=0.5, model_s=None, return_intermediate=False, solver_type='dpm_solver'):
"""
Singlestep solver DPM-Solver-2 from time `s` to time `t`.
Args:
x: A pytorch tensor. The initial value at time `s`.
s: A pytorch tensor. The starting time, with the shape (x.shape[0],).
t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
r1: A `float`. The hyperparameter of the second-order solver.
model_s: A pytorch tensor. The model function evaluated at time `s`.
If `model_s` is None, we evaluate the model by `x` and `s`; otherwise we directly use it.
return_intermediate: A `bool`. If true, also return the model value at time `s` and `s1` (the intermediate time).
solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
Returns:
x_t: A pytorch tensor. The approximated solution at time `t`.
"""
if solver_type not in ['dpm_solver', 'taylor']:
raise ValueError("'solver_type' must be either 'dpm_solver' or 'taylor', got {}".format(solver_type))
if r1 is None:
r1 = 0.5
ns = self.noise_schedule
dims = x.dim()
lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t)
h = lambda_t - lambda_s
lambda_s1 = lambda_s + r1 * h
s1 = ns.inverse_lambda(lambda_s1)
log_alpha_s, log_alpha_s1, log_alpha_t = ns.marginal_log_mean_coeff(s), ns.marginal_log_mean_coeff(s1), ns.marginal_log_mean_coeff(t)
sigma_s, sigma_s1, sigma_t = ns.marginal_std(s), ns.marginal_std(s1), ns.marginal_std(t)
alpha_s1, alpha_t = torch.exp(log_alpha_s1), torch.exp(log_alpha_t)
if self.predict_x0:
phi_11 = torch.expm1(-r1 * h)
phi_1 = torch.expm1(-h)
if model_s is None:
model_s = self.model_fn(x, s)
x_s1 = (
expand_dims(sigma_s1 / sigma_s, dims) * x
- expand_dims(alpha_s1 * phi_11, dims) * model_s
)
model_s1 = self.model_fn(x_s1, s1)
if solver_type == 'dpm_solver':
x_t = (
expand_dims(sigma_t / sigma_s, dims) * x
- expand_dims(alpha_t * phi_1, dims) * model_s
- (0.5 / r1) * expand_dims(alpha_t * phi_1, dims) * (model_s1 - model_s)
)
elif solver_type == 'taylor':
x_t = (
expand_dims(sigma_t / sigma_s, dims) * x
- expand_dims(alpha_t * phi_1, dims) * model_s
+ (1. / r1) * expand_dims(alpha_t * ((torch.exp(-h) - 1.) / h + 1.), dims) * (model_s1 - model_s)
)
else:
phi_11 = torch.expm1(r1 * h)
phi_1 = torch.expm1(h)
if model_s is None:
model_s = self.model_fn(x, s)
x_s1 = (
expand_dims(torch.exp(log_alpha_s1 - log_alpha_s), dims) * x
- expand_dims(sigma_s1 * phi_11, dims) * model_s
)
model_s1 = self.model_fn(x_s1, s1)
if solver_type == 'dpm_solver':
x_t = (
expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
- expand_dims(sigma_t * phi_1, dims) * model_s
- (0.5 / r1) * expand_dims(sigma_t * phi_1, dims) * (model_s1 - model_s)
)
elif solver_type == 'taylor':
x_t = (
expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
- expand_dims(sigma_t * phi_1, dims) * model_s
- (1. / r1) * expand_dims(sigma_t * ((torch.exp(h) - 1.) / h - 1.), dims) * (model_s1 - model_s)
)
if return_intermediate:
return x_t, {'model_s': model_s, 'model_s1': model_s1}
else:
return x_t
def singlestep_dpm_solver_third_update(self, x, s, t, r1=1./3., r2=2./3., model_s=None, model_s1=None, return_intermediate=False, solver_type='dpm_solver'):
"""
Singlestep solver DPM-Solver-3 from time `s` to time `t`.
Args:
x: A pytorch tensor. The initial value at time `s`.
s: A pytorch tensor. The starting time, with the shape (x.shape[0],).
t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
r1: A `float`. The hyperparameter of the third-order solver.
r2: A `float`. The hyperparameter of the third-order solver.
model_s: A pytorch tensor. The model function evaluated at time `s`.
If `model_s` is None, we evaluate the model by `x` and `s`; otherwise we directly use it.
model_s1: A pytorch tensor. The model function evaluated at time `s1` (the intermediate time given by `r1`).
If `model_s1` is None, we evaluate the model at `s1`; otherwise we directly use it.
return_intermediate: A `bool`. If true, also return the model value at time `s`, `s1` and `s2` (the intermediate times).
solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
Returns:
x_t: A pytorch tensor. The approximated solution at time `t`.
"""
if solver_type not in ['dpm_solver', 'taylor']:
raise ValueError("'solver_type' must be either 'dpm_solver' or 'taylor', got {}".format(solver_type))
if r1 is None:
r1 = 1. / 3.
if r2 is None:
r2 = 2. / 3.
ns = self.noise_schedule
dims = x.dim()
lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t)
h = lambda_t - lambda_s
lambda_s1 = lambda_s + r1 * h
lambda_s2 = lambda_s + r2 * h
s1 = ns.inverse_lambda(lambda_s1)
s2 = ns.inverse_lambda(lambda_s2)
log_alpha_s, log_alpha_s1, log_alpha_s2, log_alpha_t = ns.marginal_log_mean_coeff(s), ns.marginal_log_mean_coeff(s1), ns.marginal_log_mean_coeff(s2), ns.marginal_log_mean_coeff(t)
sigma_s, sigma_s1, sigma_s2, sigma_t = ns.marginal_std(s), ns.marginal_std(s1), ns.marginal_std(s2), ns.marginal_std(t)
alpha_s1, alpha_s2, alpha_t = torch.exp(log_alpha_s1), torch.exp(log_alpha_s2), torch.exp(log_alpha_t)
if self.predict_x0:
phi_11 = torch.expm1(-r1 * h)
phi_12 = torch.expm1(-r2 * h)
phi_1 = torch.expm1(-h)
phi_22 = torch.expm1(-r2 * h) / (r2 * h) + 1.
phi_2 = phi_1 / h + 1.
phi_3 = phi_2 / h - 0.5
if model_s is None:
model_s = self.model_fn(x, s)
if model_s1 is None:
x_s1 = (
expand_dims(sigma_s1 / sigma_s, dims) * x
- expand_dims(alpha_s1 * phi_11, dims) * model_s
)
model_s1 = self.model_fn(x_s1, s1)
x_s2 = (
expand_dims(sigma_s2 / sigma_s, dims) * x
- expand_dims(alpha_s2 * phi_12, dims) * model_s
+ r2 / r1 * expand_dims(alpha_s2 * phi_22, dims) * (model_s1 - model_s)
)
model_s2 = self.model_fn(x_s2, s2)
if solver_type == 'dpm_solver':
x_t = (
expand_dims(sigma_t / sigma_s, dims) * x
- expand_dims(alpha_t * phi_1, dims) * model_s
+ (1. / r2) * expand_dims(alpha_t * phi_2, dims) * (model_s2 - model_s)
)
elif solver_type == 'taylor':
D1_0 = (1. / r1) * (model_s1 - model_s)
D1_1 = (1. / r2) * (model_s2 - model_s)
D1 = (r2 * D1_0 - r1 * D1_1) / (r2 - r1)
D2 = 2. * (D1_1 - D1_0) / (r2 - r1)
x_t = (
expand_dims(sigma_t / sigma_s, dims) * x
- expand_dims(alpha_t * phi_1, dims) * model_s
+ expand_dims(alpha_t * phi_2, dims) * D1
- expand_dims(alpha_t * phi_3, dims) * D2
)
else:
phi_11 = torch.expm1(r1 * h)
phi_12 = torch.expm1(r2 * h)
phi_1 = torch.expm1(h)
phi_22 = torch.expm1(r2 * h) / (r2 * h) - 1.
phi_2 = phi_1 / h - 1.
phi_3 = phi_2 / h - 0.5
if model_s is None:
model_s = self.model_fn(x, s)
if model_s1 is None:
x_s1 = (
expand_dims(torch.exp(log_alpha_s1 - log_alpha_s), dims) * x
- expand_dims(sigma_s1 * phi_11, dims) * model_s
)
model_s1 = self.model_fn(x_s1, s1)
x_s2 = (
expand_dims(torch.exp(log_alpha_s2 - log_alpha_s), dims) * x
- expand_dims(sigma_s2 * phi_12, dims) * model_s
- r2 / r1 * expand_dims(sigma_s2 * phi_22, dims) * (model_s1 - model_s)
)
model_s2 = self.model_fn(x_s2, s2)
if solver_type == 'dpm_solver':
x_t = (
expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
- expand_dims(sigma_t * phi_1, dims) * model_s
- (1. / r2) * expand_dims(sigma_t * phi_2, dims) * (model_s2 - model_s)
)
elif solver_type == 'taylor':
D1_0 = (1. / r1) * (model_s1 - model_s)
D1_1 = (1. / r2) * (model_s2 - model_s)
D1 = (r2 * D1_0 - r1 * D1_1) / (r2 - r1)
D2 = 2. * (D1_1 - D1_0) / (r2 - r1)
x_t = (
expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
- expand_dims(sigma_t * phi_1, dims) * model_s
- expand_dims(sigma_t * phi_2, dims) * D1
- expand_dims(sigma_t * phi_3, dims) * D2
)
if return_intermediate:
return x_t, {'model_s': model_s, 'model_s1': model_s1, 'model_s2': model_s2}
else:
return x_t
def multistep_dpm_solver_second_update(self, x, model_prev_list, t_prev_list, t, solver_type="dpm_solver"):
"""
Multistep solver DPM-Solver-2 from time `t_prev_list[-1]` to time `t`.
Args:
x: A pytorch tensor. The initial value at time `s`.
model_prev_list: A list of pytorch tensor. The previous computed model values.
t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (x.shape[0],)
t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
Returns:
x_t: A pytorch tensor. The approximated solution at time `t`.
"""
if solver_type not in ['dpm_solver', 'taylor']:
raise ValueError("'solver_type' must be either 'dpm_solver' or 'taylor', got {}".format(solver_type))
ns = self.noise_schedule
dims = x.dim()
model_prev_1, model_prev_0 = model_prev_list
t_prev_1, t_prev_0 = t_prev_list
lambda_prev_1, lambda_prev_0, lambda_t = ns.marginal_lambda(t_prev_1), ns.marginal_lambda(t_prev_0), ns.marginal_lambda(t)
log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t)
sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t)
alpha_t = torch.exp(log_alpha_t)
h_0 = lambda_prev_0 - lambda_prev_1
h = lambda_t - lambda_prev_0
r0 = h_0 / h
D1_0 = expand_dims(1. / r0, dims) * (model_prev_0 - model_prev_1)
if self.predict_x0:
if solver_type == 'dpm_solver':
x_t = (
expand_dims(sigma_t / sigma_prev_0, dims) * x
- expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * model_prev_0
- 0.5 * expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * D1_0
)
elif solver_type == 'taylor':
x_t = (
expand_dims(sigma_t / sigma_prev_0, dims) * x
- expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * model_prev_0
+ expand_dims(alpha_t * ((torch.exp(-h) - 1.) / h + 1.), dims) * D1_0
)
else:
if solver_type == 'dpm_solver':
x_t = (
expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x
- expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * model_prev_0
- 0.5 * expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * D1_0
)
elif solver_type == 'taylor':
x_t = (
expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x
- expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * model_prev_0
- expand_dims(sigma_t * ((torch.exp(h) - 1.) / h - 1.), dims) * D1_0
)
return x_t
def multistep_dpm_solver_third_update(self, x, model_prev_list, t_prev_list, t, solver_type='dpm_solver'):
"""
Multistep solver DPM-Solver-3 from time `t_prev_list[-1]` to time `t`.
Args:
x: A pytorch tensor. The initial value at time `s`.
model_prev_list: A list of pytorch tensor. The previous computed model values.
t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (x.shape[0],)
t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
Returns:
x_t: A pytorch tensor. The approximated solution at time `t`.
"""
ns = self.noise_schedule
dims = x.dim()
model_prev_2, model_prev_1, model_prev_0 = model_prev_list
t_prev_2, t_prev_1, t_prev_0 = t_prev_list
lambda_prev_2, lambda_prev_1, lambda_prev_0, lambda_t = ns.marginal_lambda(t_prev_2), ns.marginal_lambda(t_prev_1), ns.marginal_lambda(t_prev_0), ns.marginal_lambda(t)
log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t)
sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t)
alpha_t = torch.exp(log_alpha_t)
h_1 = lambda_prev_1 - lambda_prev_2
h_0 = lambda_prev_0 - lambda_prev_1
h = lambda_t - lambda_prev_0
r0, r1 = h_0 / h, h_1 / h
D1_0 = expand_dims(1. / r0, dims) * (model_prev_0 - model_prev_1)
D1_1 = expand_dims(1. / r1, dims) * (model_prev_1 - model_prev_2)
D1 = D1_0 + expand_dims(r0 / (r0 + r1), dims) * (D1_0 - D1_1)
D2 = expand_dims(1. / (r0 + r1), dims) * (D1_0 - D1_1)
if self.predict_x0:
x_t = (
expand_dims(sigma_t / sigma_prev_0, dims) * x
- expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * model_prev_0
+ expand_dims(alpha_t * ((torch.exp(-h) - 1.) / h + 1.), dims) * D1
- expand_dims(alpha_t * ((torch.exp(-h) - 1. + h) / h**2 - 0.5), dims) * D2
)
else:
x_t = (
expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x
- expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * model_prev_0
- expand_dims(sigma_t * ((torch.exp(h) - 1.) / h - 1.), dims) * D1
- expand_dims(sigma_t * ((torch.exp(h) - 1. - h) / h**2 - 0.5), dims) * D2
)
return x_t
def singlestep_dpm_solver_update(self, x, s, t, order, return_intermediate=False, solver_type='dpm_solver', r1=None, r2=None):
"""
Singlestep DPM-Solver with the order `order` from time `s` to time `t`.
Args:
x: A pytorch tensor. The initial value at time `s`.
s: A pytorch tensor. The starting time, with the shape (x.shape[0],).
t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
order: A `int`. The order of DPM-Solver. We only support order == 1 or 2 or 3.
return_intermediate: A `bool`. If true, also return the model value at time `s`, `s1` and `s2` (the intermediate times).
solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
r1: A `float`. The hyperparameter of the second-order or third-order solver.
r2: A `float`. The hyperparameter of the third-order solver.
Returns:
x_t: A pytorch tensor. The approximated solution at time `t`.
"""
if order == 1:
return self.dpm_solver_first_update(x, s, t, return_intermediate=return_intermediate)
elif order == 2:
return self.singlestep_dpm_solver_second_update(x, s, t, return_intermediate=return_intermediate, solver_type=solver_type, r1=r1)
elif order == 3:
return self.singlestep_dpm_solver_third_update(x, s, t, return_intermediate=return_intermediate, solver_type=solver_type, r1=r1, r2=r2)
else:
raise ValueError("Solver order must be 1 or 2 or 3, got {}".format(order))
def multistep_dpm_solver_update(self, x, model_prev_list, t_prev_list, t, order, solver_type='dpm_solver'):
"""
Multistep DPM-Solver with the order `order` from time `t_prev_list[-1]` to time `t`.
Args:
x: A pytorch tensor. The initial value at time `s`.
model_prev_list: A list of pytorch tensor. The previous computed model values.
t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (x.shape[0],)
t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
order: A `int`. The order of DPM-Solver. We only support order == 1 or 2 or 3.
solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
Returns:
x_t: A pytorch tensor. The approximated solution at time `t`.
"""
if order == 1:
return self.dpm_solver_first_update(x, t_prev_list[-1], t, model_s=model_prev_list[-1])
elif order == 2:
return self.multistep_dpm_solver_second_update(x, model_prev_list, t_prev_list, t, solver_type=solver_type)
elif order == 3:
return self.multistep_dpm_solver_third_update(x, model_prev_list, t_prev_list, t, solver_type=solver_type)
else:
raise ValueError("Solver order must be 1 or 2 or 3, got {}".format(order))
def dpm_solver_adaptive(self, x, order, t_T, t_0, h_init=0.05, atol=0.0078, rtol=0.05, theta=0.9, t_err=1e-5, solver_type='dpm_solver'):
"""
The adaptive step size solver based on singlestep DPM-Solver.
Args:
x: A pytorch tensor. The initial value at time `t_T`.
order: A `int`. The (higher) order of the solver. We only support order == 2 or 3.
t_T: A `float`. The starting time of the sampling (default is T).
t_0: A `float`. The ending time of the sampling (default is epsilon).
h_init: A `float`. The initial step size (for logSNR).
atol: A `float`. The absolute tolerance of the solver. For image data, the default setting is 0.0078, followed [1].
rtol: A `float`. The relative tolerance of the solver. The default setting is 0.05.
theta: A `float`. The safety hyperparameter for adapting the step size. The default setting is 0.9, followed [1].
t_err: A `float`. The tolerance for the time. We solve the diffusion ODE until the absolute error between the
current time and `t_0` is less than `t_err`. The default setting is 1e-5.
solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
Returns:
x_0: A pytorch tensor. The approximated solution at time `t_0`.
[1] A. Jolicoeur-Martineau, K. Li, R. Piché-Taillefer, T. Kachman, and I. Mitliagkas, "Gotta go fast when generating data with score-based models," arXiv preprint arXiv:2105.14080, 2021.
"""
ns = self.noise_schedule
s = t_T * torch.ones((x.shape[0],)).to(x)
lambda_s = ns.marginal_lambda(s)
lambda_0 = ns.marginal_lambda(t_0 * torch.ones_like(s).to(x))
h = h_init * torch.ones_like(s).to(x)
x_prev = x
nfe = 0
if order == 2:
r1 = 0.5
lower_update = lambda x, s, t: self.dpm_solver_first_update(x, s, t, return_intermediate=True)
higher_update = lambda x, s, t, **kwargs: self.singlestep_dpm_solver_second_update(x, s, t, r1=r1, solver_type=solver_type, **kwargs)
elif order == 3:
r1, r2 = 1. / 3., 2. / 3.
lower_update = lambda x, s, t: self.singlestep_dpm_solver_second_update(x, s, t, r1=r1, return_intermediate=True, solver_type=solver_type)
higher_update = lambda x, s, t, **kwargs: self.singlestep_dpm_solver_third_update(x, s, t, r1=r1, r2=r2, solver_type=solver_type, **kwargs)
else:
raise ValueError("For adaptive step size solver, order must be 2 or 3, got {}".format(order))
while torch.abs((s - t_0)).mean() > t_err:
t = ns.inverse_lambda(lambda_s + h)
x_lower, lower_noise_kwargs = lower_update(x, s, t)
x_higher = higher_update(x, s, t, **lower_noise_kwargs)
delta = torch.max(torch.ones_like(x).to(x) * atol, rtol * torch.max(torch.abs(x_lower), torch.abs(x_prev)))
norm_fn = lambda v: torch.sqrt(torch.square(v.reshape((v.shape[0], -1))).mean(dim=-1, keepdim=True))
E = norm_fn((x_higher - x_lower) / delta).max()
if torch.all(E <= 1.):
x = x_higher
s = t
x_prev = x_lower
lambda_s = ns.marginal_lambda(s)
h = torch.min(theta * h * torch.float_power(E, -1. / order).float(), lambda_0 - lambda_s)
nfe += order
print('adaptive solver nfe', nfe)
return x
def sample(self, x, steps=20, t_start=None, t_end=None, order=3, skip_type='time_uniform',
method='singlestep', lower_order_final=True, denoise_to_zero=False, solver_type='dpm_solver',
atol=0.0078, rtol=0.05,
):
"""
Compute the sample at time `t_end` by DPM-Solver, given the initial `x` at time `t_start`.
=====================================================
We support the following algorithms for both noise prediction model and data prediction model:
- 'singlestep':
Singlestep DPM-Solver (i.e. "DPM-Solver-fast" in the paper), which combines different orders of singlestep DPM-Solver.
We combine all the singlestep solvers with order <= `order` to use up all the function evaluations (steps).
The total number of function evaluations (NFE) == `steps`.
Given a fixed NFE == `steps`, the sampling procedure is:
- If `order` == 1:
- Denote K = steps. We use K steps of DPM-Solver-1 (i.e. DDIM).
- If `order` == 2:
- Denote K = (steps // 2) + (steps % 2). We take K intermediate time steps for sampling.
- If steps % 2 == 0, we use K steps of singlestep DPM-Solver-2.
- If steps % 2 == 1, we use (K - 1) steps of singlestep DPM-Solver-2 and 1 step of DPM-Solver-1.
- If `order` == 3:
- Denote K = (steps // 3 + 1). We take K intermediate time steps for sampling.
- If steps % 3 == 0, we use (K - 2) steps of singlestep DPM-Solver-3, and 1 step of singlestep DPM-Solver-2 and 1 step of DPM-Solver-1.
- If steps % 3 == 1, we use (K - 1) steps of singlestep DPM-Solver-3 and 1 step of DPM-Solver-1.
- If steps % 3 == 2, we use (K - 1) steps of singlestep DPM-Solver-3 and 1 step of singlestep DPM-Solver-2.
- 'multistep':
Multistep DPM-Solver with the order of `order`. The total number of function evaluations (NFE) == `steps`.
We initialize the first `order` values by lower order multistep solvers.
Given a fixed NFE == `steps`, the sampling procedure is:
Denote K = steps.
- If `order` == 1:
- We use K steps of DPM-Solver-1 (i.e. DDIM).
- If `order` == 2:
- We firstly use 1 step of DPM-Solver-1, then use (K - 1) step of multistep DPM-Solver-2.
- If `order` == 3:
- We firstly use 1 step of DPM-Solver-1, then 1 step of multistep DPM-Solver-2, then (K - 2) step of multistep DPM-Solver-3.
- 'singlestep_fixed':
Fixed order singlestep DPM-Solver (i.e. DPM-Solver-1 or singlestep DPM-Solver-2 or singlestep DPM-Solver-3).
We use singlestep DPM-Solver-`order` for `order`=1 or 2 or 3, with total [`steps` // `order`] * `order` NFE.
- 'adaptive':
Adaptive step size DPM-Solver (i.e. "DPM-Solver-12" and "DPM-Solver-23" in the paper).
We ignore `steps` and use adaptive step size DPM-Solver with a higher order of `order`.
You can adjust the absolute tolerance `atol` and the relative tolerance `rtol` to balance the computatation costs
(NFE) and the sample quality.
- If `order` == 2, we use DPM-Solver-12 which combines DPM-Solver-1 and singlestep DPM-Solver-2.
- If `order` == 3, we use DPM-Solver-23 which combines singlestep DPM-Solver-2 and singlestep DPM-Solver-3.
=====================================================
Some advices for choosing the algorithm:
- For **unconditional sampling** or **guided sampling with small guidance scale** by DPMs:
Use singlestep DPM-Solver ("DPM-Solver-fast" in the paper) with `order = 3`.
e.g.
>>> dpm_solver = DPM_Solver(model_fn, noise_schedule, predict_x0=False)
>>> x_sample = dpm_solver.sample(x, steps=steps, t_start=t_start, t_end=t_end, order=3,
skip_type='time_uniform', method='singlestep')
- For **guided sampling with large guidance scale** by DPMs:
Use multistep DPM-Solver with `predict_x0 = True` and `order = 2`.
e.g.
>>> dpm_solver = DPM_Solver(model_fn, noise_schedule, predict_x0=True)
>>> x_sample = dpm_solver.sample(x, steps=steps, t_start=t_start, t_end=t_end, order=2,
skip_type='time_uniform', method='multistep')
We support three types of `skip_type`:
- 'logSNR': uniform logSNR for the time steps. **Recommended for low-resolutional images**
- 'time_uniform': uniform time for the time steps. **Recommended for high-resolutional images**.
- 'time_quadratic': quadratic time for the time steps.
=====================================================
Args:
x: A pytorch tensor. The initial value at time `t_start`
e.g. if `t_start` == T, then `x` is a sample from the standard normal distribution.
steps: A `int`. The total number of function evaluations (NFE).
t_start: A `float`. The starting time of the sampling.
If `T` is None, we use self.noise_schedule.T (default is 1.0).
t_end: A `float`. The ending time of the sampling.
If `t_end` is None, we use 1. / self.noise_schedule.total_N.
e.g. if total_N == 1000, we have `t_end` == 1e-3.
For discrete-time DPMs:
- We recommend `t_end` == 1. / self.noise_schedule.total_N.
For continuous-time DPMs:
- We recommend `t_end` == 1e-3 when `steps` <= 15; and `t_end` == 1e-4 when `steps` > 15.
order: A `int`. The order of DPM-Solver.
skip_type: A `str`. The type for the spacing of the time steps. 'time_uniform' or 'logSNR' or 'time_quadratic'.
method: A `str`. The method for sampling. 'singlestep' or 'multistep' or 'singlestep_fixed' or 'adaptive'.
denoise_to_zero: A `bool`. Whether to denoise to time 0 at the final step.
Default is `False`. If `denoise_to_zero` is `True`, the total NFE is (`steps` + 1).
This trick is firstly proposed by DDPM (https://arxiv.org/abs/2006.11239) and
score_sde (https://arxiv.org/abs/2011.13456). Such trick can improve the FID
for diffusion models sampling by diffusion SDEs for low-resolutional images
(such as CIFAR-10). However, we observed that such trick does not matter for
high-resolutional images. As it needs an additional NFE, we do not recommend
it for high-resolutional images.
lower_order_final: A `bool`. Whether to use lower order solvers at the final steps.
Only valid for `method=multistep` and `steps < 15`. We empirically find that
this trick is a key to stabilizing the sampling by DPM-Solver with very few steps
(especially for steps <= 10). So we recommend to set it to be `True`.
solver_type: A `str`. The taylor expansion type for the solver. `dpm_solver` or `taylor`. We recommend `dpm_solver`.
atol: A `float`. The absolute tolerance of the adaptive step size solver. Valid when `method` == 'adaptive'.
rtol: A `float`. The relative tolerance of the adaptive step size solver. Valid when `method` == 'adaptive'.
Returns:
x_end: A pytorch tensor. The approximated solution at time `t_end`.
"""
t_0 = 1. / self.noise_schedule.total_N if t_end is None else t_end
t_T = self.noise_schedule.T if t_start is None else t_start
device = x.device
if method == 'adaptive':
with torch.no_grad():
x = self.dpm_solver_adaptive(x, order=order, t_T=t_T, t_0=t_0, atol=atol, rtol=rtol, solver_type=solver_type)
elif method == 'multistep':
assert steps >= order
timesteps = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=steps, device=device)
assert timesteps.shape[0] - 1 == steps
with torch.no_grad():
vec_t = timesteps[0].expand((x.shape[0]))
model_prev_list = [self.model_fn(x, vec_t)]
t_prev_list = [vec_t]
# Init the first `order` values by lower order multistep DPM-Solver.
for init_order in range(1, order):
vec_t = timesteps[init_order].expand(x.shape[0])
x = self.multistep_dpm_solver_update(x, model_prev_list, t_prev_list, vec_t, init_order, solver_type=solver_type)
model_prev_list.append(self.model_fn(x, vec_t))
t_prev_list.append(vec_t)
# Compute the remaining values by `order`-th order multistep DPM-Solver.
for step in range(order, steps + 1):
vec_t = timesteps[step].expand(x.shape[0])
if lower_order_final and steps < 15:
step_order = min(order, steps + 1 - step)
else:
step_order = order
x = self.multistep_dpm_solver_update(x, model_prev_list, t_prev_list, vec_t, step_order, solver_type=solver_type)
for i in range(order - 1):
t_prev_list[i] = t_prev_list[i + 1]
model_prev_list[i] = model_prev_list[i + 1]
t_prev_list[-1] = vec_t
# We do not need to evaluate the final model value.
if step < steps:
model_prev_list[-1] = self.model_fn(x, vec_t)
elif method in ['singlestep', 'singlestep_fixed']:
if method == 'singlestep':
timesteps_outer, orders = self.get_orders_and_timesteps_for_singlestep_solver(steps=steps, order=order, skip_type=skip_type, t_T=t_T, t_0=t_0, device=device)
elif method == 'singlestep_fixed':
K = steps // order
orders = [order,] * K
timesteps_outer = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=K, device=device)
for i, order in enumerate(orders):
t_T_inner, t_0_inner = timesteps_outer[i], timesteps_outer[i + 1]
timesteps_inner = self.get_time_steps(skip_type=skip_type, t_T=t_T_inner.item(), t_0=t_0_inner.item(), N=order, device=device)
lambda_inner = self.noise_schedule.marginal_lambda(timesteps_inner)
vec_s, vec_t = t_T_inner.tile(x.shape[0]), t_0_inner.tile(x.shape[0])
h = lambda_inner[-1] - lambda_inner[0]
r1 = None if order <= 1 else (lambda_inner[1] - lambda_inner[0]) / h
r2 = None if order <= 2 else (lambda_inner[2] - lambda_inner[0]) / h
x = self.singlestep_dpm_solver_update(x, vec_s, vec_t, order, solver_type=solver_type, r1=r1, r2=r2)
if denoise_to_zero:
x = self.denoise_to_zero_fn(x, torch.ones((x.shape[0],)).to(device) * t_0)
return x
#############################################################
# other utility functions
#############################################################
def interpolate_fn(x, xp, yp):
"""
A piecewise linear function y = f(x), using xp and yp as keypoints.
We implement f(x) in a differentiable way (i.e. applicable for autograd).
The function f(x) is well-defined for all x-axis. (For x beyond the bounds of xp, we use the outmost points of xp to define the linear function.)
Args:
x: PyTorch tensor with shape [N, C], where N is the batch size, C is the number of channels (we use C = 1 for DPM-Solver).
xp: PyTorch tensor with shape [C, K], where K is the number of keypoints.
yp: PyTorch tensor with shape [C, K].
Returns:
The function values f(x), with shape [N, C].
"""
N, K = x.shape[0], xp.shape[1]
all_x = torch.cat([x.unsqueeze(2), xp.unsqueeze(0).repeat((N, 1, 1))], dim=2)
sorted_all_x, x_indices = torch.sort(all_x, dim=2)
x_idx = torch.argmin(x_indices, dim=2)
cand_start_idx = x_idx - 1
start_idx = torch.where(
torch.eq(x_idx, 0),
torch.tensor(1, device=x.device),
torch.where(
torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx,
),
)
end_idx = torch.where(torch.eq(start_idx, cand_start_idx), start_idx + 2, start_idx + 1)
start_x = torch.gather(sorted_all_x, dim=2, index=start_idx.unsqueeze(2)).squeeze(2)
end_x = torch.gather(sorted_all_x, dim=2, index=end_idx.unsqueeze(2)).squeeze(2)
start_idx2 = torch.where(
torch.eq(x_idx, 0),
torch.tensor(0, device=x.device),
torch.where(
torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx,
),
)
y_positions_expanded = yp.unsqueeze(0).expand(N, -1, -1)
start_y = torch.gather(y_positions_expanded, dim=2, index=start_idx2.unsqueeze(2)).squeeze(2)
end_y = torch.gather(y_positions_expanded, dim=2, index=(start_idx2 + 1).unsqueeze(2)).squeeze(2)
cand = start_y + (x - start_x) * (end_y - start_y) / (end_x - start_x)
return cand
def expand_dims(v, dims):
"""
Expand the tensor `v` to the dim `dims`.
Args:
`v`: a PyTorch tensor with shape [N].
`dim`: a `int`.
Returns:
a PyTorch tensor with shape [N, 1, 1, ..., 1] and the total dimension is `dims`.
"""
return v[(...,) + (None,)*(dims - 1)]
================================================
FILE: models/instructpix2pix/stable_diffusion/ldm/models/diffusion/dpm_solver/sampler.py
================================================
"""SAMPLING ONLY."""
import torch
from .dpm_solver import NoiseScheduleVP, model_wrapper, DPM_Solver
class DPMSolverSampler(object):
def __init__(self, model, **kwargs):
super().__init__()
self.model = model
to_torch = lambda x: x.clone().detach().to(torch.float32).to(model.device)
self.register_buffer('alphas_cumprod', to_torch(model.alphas_cumprod))
def register_buffer(self, name, attr):
if type(attr) == torch.Tensor:
if attr.device != torch.device("cuda"):
attr = attr.to(torch.device("cuda"))
setattr(self, name, attr)
@torch.no_grad()
def sample(self,
S,
batch_size,
shape,
conditioning=None,
callback=None,
normals_sequence=None,
img_callback=None,
quantize_x0=False,
eta=0.,
mask=None,
x0=None,
temperature=1.,
noise_dropout=0.,
score_corrector=None,
corrector_kwargs=None,
verbose=True,
x_T=None,
log_every_t=100,
unconditional_guidance_scale=1.,
unconditional_conditioning=None,
# this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
**kwargs
):
if conditioning is not None:
if isinstance(conditioning, dict):
cbs = conditioning[list(conditioning.keys())[0]].shape[0]
if cbs != batch_size:
print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
else:
if conditioning.shape[0] != batch_size:
print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
# sampling
C, H, W = shape
size = (batch_size, C, H, W)
# print(f'Data shape for DPM-Solver sampling is {size}, sampling steps {S}')
device = self.model.betas.device
if x_T is None:
img = torch.randn(size, device=device)
else:
img = x_T
ns = NoiseScheduleVP('discrete', alphas_cumprod=self.alphas_cumprod)
model_fn = model_wrapper(
lambda x, t, c: self.model.apply_model(x, t, c),
ns,
model_type="noise",
guidance_type="classifier-free",
condition=conditioning,
unconditional_condition=unconditional_conditioning,
guidance_scale=unconditional_guidance_scale,
)
dpm_solver = DPM_Solver(model_fn, ns, predict_x0=True, thresholding=False)
x = dpm_solver.sample(img, steps=S, skip_type="time_uniform", method="multistep", order=2, lower_order_final=True)
return x.to(device), None
================================================
FILE: models/instructpix2pix/stable_diffusion/ldm/models/diffusion/plms.py
================================================
"""SAMPLING ONLY."""
import torch
import numpy as np
from tqdm import tqdm
from functools import partial
from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like
class PLMSSampler(object):
def __init__(self, model, schedule="linear", **kwargs):
super().__init__()
self.model = model
self.ddpm_num_timesteps = model.num_timesteps
self.schedule = schedule
def register_buffer(self, name, attr):
if type(attr) == torch.Tensor:
if attr.device != torch.device("cuda"):
attr = attr.to(torch.device("cuda"))
setattr(self, name, attr)
def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True):
if ddim_eta != 0:
raise ValueError('ddim_eta must be 0 for PLMS')
self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps,
num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose)
alphas_cumprod = self.model.alphas_cumprod
assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep'
to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device)
self.register_buffer('betas', to_torch(self.model.betas))
self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev))
# calculations for diffusion q(x_t | x_{t-1}) and others
self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu())))
self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu())))
self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu())))
self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu())))
self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1)))
# ddim sampling parameters
ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(),
ddim_timesteps=self.ddim_timesteps,
eta=ddim_eta,verbose=verbose)
self.register_buffer('ddim_sigmas', ddim_sigmas)
self.register_buffer('ddim_alphas', ddim_alphas)
self.register_buffer('ddim_alphas_prev', ddim_alphas_prev)
self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas))
sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(
(1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * (
1 - self.alphas_cumprod / self.alphas_cumprod_prev))
self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps)
@torch.no_grad()
def sample(self,
S,
batch_size,
shape,
conditioning=None,
callback=None,
normals_sequence=None,
img_callback=None,
quantize_x0=False,
eta=0.,
mask=None,
x0=None,
temperature=1.,
noise_dropout=0.,
score_corrector=None,
corrector_kwargs=None,
verbose=True,
x_T=None,
log_every_t=100,
unconditional_guidance_scale=1.,
unconditional_conditioning=None,
# this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
**kwargs
):
if conditioning is not None:
if isinstance(conditioning, dict):
cbs = conditioning[list(conditioning.keys())[0]].shape[0]
if cbs != batch_size:
print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
else:
if conditioning.shape[0] != batch_size:
print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
# sampling
C, H, W = shape
size = (batch_size, C, H, W)
print(f'Data shape for PLMS sampling is {size}')
samples, intermediates = self.plms_sampling(conditioning, size,
callback=callback,
img_callback=img_callback,
quantize_denoised=quantize_x0,
mask=mask, x0=x0,
ddim_use_original_steps=False,
noise_dropout=noise_dropout,
temperature=temperature,
score_corrector=score_corrector,
corrector_kwargs=corrector_kwargs,
x_T=x_T,
log_every_t=log_every_t,
unconditional_guidance_scale=unconditional_guidance_scale,
unconditional_conditioning=unconditional_conditioning,
)
return samples, intermediates
@torch.no_grad()
def plms_sampling(self, cond, shape,
x_T=None, ddim_use_original_steps=False,
callback=None, timesteps=None, quantize_denoised=False,
mask=None, x0=None, img_callback=None, log_every_t=100,
temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
unconditional_guidance_scale=1., unconditional_conditioning=None,):
device = self.model.betas.device
b = shape[0]
if x_T is None:
img = torch.randn(shape, device=device)
else:
img = x_T
if timesteps is None:
timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps
elif timesteps is not None and not ddim_use_original_steps:
subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1
timesteps = self.ddim_timesteps[:subset_end]
intermediates = {'x_inter': [img], 'pred_x0': [img]}
time_range = list(reversed(range(0,timesteps))) if ddim_use_original_steps else np.flip(timesteps)
total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]
print(f"Running PLMS Sampling with {total_steps} timesteps")
iterator = tqdm(time_range, desc='PLMS Sampler', total=total_steps)
old_eps = []
for i, step in enumerate(iterator):
index = total_steps - i - 1
ts = torch.full((b,), step, device=device, dtype=torch.long)
ts_next = torch.full((b,), time_range[min(i + 1, len(time_range) - 1)], device=device, dtype=torch.long)
if mask is not None:
assert x0 is not None
img_orig = self.model.q_sample(x0, ts) # TODO: deterministic forward pass?
img = img_orig * mask + (1. - mask) * img
outs = self.p_sample_plms(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps,
quantize_denoised=quantize_denoised, temperature=temperature,
noise_dropout=noise_dropout, score_corrector=score_corrector,
corrector_kwargs=corrector_kwargs,
unconditional_guidance_scale=unconditional_guidance_scale,
unconditional_conditioning=unconditional_conditioning,
old_eps=old_eps, t_next=ts_next)
img, pred_x0, e_t = outs
old_eps.append(e_t)
if len(old_eps) >= 4:
old_eps.pop(0)
if callback: callback(i)
if img_callback: img_callback(pred_x0, i)
if index % log_every_t == 0 or index == total_steps - 1:
intermediates['x_inter'].append(img)
intermediates['pred_x0'].append(pred_x0)
return img, intermediates
@torch.no_grad()
def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
unconditional_guidance_scale=1., unconditional_conditioning=None, old_eps=None, t_next=None):
b, *_, device = *x.shape, x.device
def get_model_output(x, t):
if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
e_t = self.model.apply_model(x, t, c)
else:
x_in = torch.cat([x] * 2)
t_in = torch.cat([t] * 2)
c_in = torch.cat([unconditional_conditioning, c])
e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
if score_corrector is not None:
assert self.model.parameterization == "eps"
e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
return e_t
alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
def get_x_prev_and_pred_x0(e_t, index):
# select parameters corresponding to the currently considered timestep
a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
# current prediction for x_0
pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
if quantize_denoised:
pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
# direction pointing to x_t
dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
if noise_dropout > 0.:
noise = torch.nn.functional.dropout(noise, p=noise_dropout)
x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
return x_prev, pred_x0
e_t = get_model_output(x, t)
if len(old_eps) == 0:
# Pseudo Improved Euler (2nd order)
x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t, index)
e_t_next = get_model_output(x_prev, t_next)
e_t_prime = (e_t + e_t_next) / 2
elif len(old_eps) == 1:
# 2nd order Pseudo Linear Multistep (Adams-Bashforth)
e_t_prime = (3 * e_t - old_eps[-1]) / 2
elif len(old_eps) == 2:
# 3nd order Pseudo Linear Multistep (Adams-Bashforth)
e_t_prime = (23 * e_t - 16 * old_eps[-1] + 5 * old_eps[-2]) / 12
elif len(old_eps) >= 3:
# 4nd order Pseudo Linear Multistep (Adams-Bashforth)
e_t_prime = (55 * e_t - 59 * old_eps[-1] + 37 * old_eps[-2] - 9 * old_eps[-3]) / 24
x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index)
return x_prev, pred_x0, e_t
================================================
FILE: models/instructpix2pix/stable_diffusion/ldm/modules/attention.py
================================================
# File modified by authors of InstructPix2Pix from original (https://github.com/CompVis/stable-diffusion).
# See more details in LICENSE.
from inspect import isfunction
import math
import torch
import torch.nn.functional as F
from torch import nn, einsum
from einops import rearrange, repeat
from ldm.modules.diffusionmodules.util import checkpoint
def exists(val):
return val is not None
def uniq(arr):
return{el: True for el in arr}.keys()
def default(val, d):
if exists(val):
return val
return d() if isfunction(d) else d
def max_neg_value(t):
return -torch.finfo(t.dtype).max
def init_(tensor):
dim = tensor.shape[-1]
std = 1 / math.sqrt(dim)
tensor.uniform_(-std, std)
return tensor
# feedforward
class GEGLU(nn.Module):
def __init__(self, dim_in, dim_out):
super().__init__()
self.proj = nn.Linear(dim_in, dim_out * 2)
def forward(self, x):
x, gate = self.proj(x).chunk(2, dim=-1)
return x * F.gelu(gate)
class FeedForward(nn.Module):
def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
super().__init__()
inner_dim = int(dim * mult)
dim_out = default(dim_out, dim)
project_in = nn.Sequential(
nn.Linear(dim, inner_dim),
nn.GELU()
) if not glu else GEGLU(dim, inner_dim)
self.net = nn.Sequential(
project_in,
nn.Dropout(dropout),
nn.Linear(inner_dim, dim_out)
)
def forward(self, x):
return self.net(x)
def zero_module(module):
"""
Zero out the parameters of a module and return it.
"""
for p in module.parameters():
p.detach().zero_()
return module
def Normalize(in_channels):
return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
class LinearAttention(nn.Module):
def __init__(self, dim, heads=4, dim_head=32):
super().__init__()
self.heads = heads
hidden_dim = dim_head * heads
self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias = False)
self.to_out = nn.Conv2d(hidden_dim, dim, 1)
def forward(self, x):
b, c, h, w = x.shape
qkv = self.to_qkv(x)
q, k, v = rearrange(qkv, 'b (qkv heads c) h w -> qkv b heads c (h w)', heads = self.heads, qkv=3)
k = k.softmax(dim=-1)
context = torch.einsum('bhdn,bhen->bhde', k, v)
out = torch.einsum('bhde,bhdn->bhen', context, q)
out = rearrange(out, 'b heads c (h w) -> b (heads c) h w', heads=self.heads, h=h, w=w)
return self.to_out(out)
class SpatialSelfAttention(nn.Module):
def __init__(self, in_channels):
super().__init__()
self.in_channels = in_channels
self.norm = Normalize(in_channels)
self.q = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.k = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.v = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.proj_out = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
def forward(self, x):
h_ = x
h_ = self.norm(h_)
q = self.q(h_)
k = self.k(h_)
v = self.v(h_)
# compute attention
b,c,h,w = q.shape
q = rearrange(q, 'b c h w -> b (h w) c')
k = rearrange(k, 'b c h w -> b c (h w)')
w_ = torch.einsum('bij,bjk->bik', q, k)
w_ = w_ * (int(c)**(-0.5))
w_ = torch.nn.functional.softmax(w_, dim=2)
# attend to values
v = rearrange(v, 'b c h w -> b c (h w)')
w_ = rearrange(w_, 'b i j -> b j i')
h_ = torch.einsum('bij,bjk->bik', v, w_)
h_ = rearrange(h_, 'b c (h w) -> b c h w', h=h)
h_ = self.proj_out(h_)
return x+h_
class CrossAttention(nn.Module):
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.):
super().__init__()
inner_dim = dim_head * heads
context_dim = default(context_dim, query_dim)
self.scale = dim_head ** -0.5
self.heads = heads
self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
self.to_out = nn.Sequential(
nn.Linear(inner_dim, query_dim),
nn.Dropout(dropout)
)
self.prompt_to_prompt = False
def forward(self, x, context=None, mask=None):
is_self_attn = context is None
h = self.heads
q = self.to_q(x)
context = default(context, x)
k = self.to_k(context)
v = self.to_v(context)
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
if self.prompt_to_prompt and is_self_attn:
# Unlike the original Prompt-to-Prompt which uses cross-attention layers, we copy attention maps for self-attention layers.
# There must be 4 elements in the batch: {conditional, unconditional} x {prompt 1, prompt 2}
assert x.size(0) == 4
sims = sim.chunk(4)
sim = torch.cat((sims[0], sims[0], sims[2], sims[2]))
if exists(mask):
mask = rearrange(mask, 'b ... -> b (...)')
max_neg_value = -torch.finfo(sim.dtype).max
mask = repeat(mask, 'b j -> (b h) () j', h=h)
sim.masked_fill_(~mask, max_neg_value)
# attention, what we cannot get enough of
attn = sim.softmax(dim=-1)
out = einsum('b i j, b j d -> b i d', attn, v)
out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
return self.to_out(out)
class BasicTransformerBlock(nn.Module):
def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True):
super().__init__()
self.attn1 = CrossAttention(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout) # is a self-attention
self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff)
self.attn2 = CrossAttention(query_dim=dim, context_dim=context_dim,
heads=n_heads, dim_head=d_head, dropout=dropout) # is self-attn if context is none
self.norm1 = nn.LayerNorm(dim)
self.norm2 = nn.LayerNorm(dim)
self.norm3 = nn.LayerNorm(dim)
self.checkpoint = checkpoint
def forward(self, x, context=None):
return checkpoint(self._forward, (x, context), self.parameters(), self.checkpoint)
def _forward(self, x, context=None):
x = self.attn1(self.norm1(x)) + x
x = self.attn2(self.norm2(x), context=context) + x
x = self.ff(self.norm3(x)) + x
return x
class SpatialTransformer(nn.Module):
"""
Transformer block for image-like data.
First, project the input (aka embedding)
and reshape to b, t, d.
Then apply standard transformer action.
Finally, reshape to image
"""
def __init__(self, in_channels, n_heads, d_head,
depth=1, dropout=0., context_dim=None):
super().__init__()
self.in_channels = in_channels
inner_dim = n_heads * d_head
self.norm = Normalize(in_channels)
self.proj_in = nn.Conv2d(in_channels,
inner_dim,
kernel_size=1,
stride=1,
padding=0)
self.transformer_blocks = nn.ModuleList(
[BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim)
for d in range(depth)]
)
self.proj_out = zero_module(nn.Conv2d(inner_dim,
in_channels,
kernel_size=1,
stride=1,
padding=0))
def forward(self, x, context=None):
# note: if no context is given, cross-attention defaults to self-attention
b, c, h, w = x.shape
x_in = x
x = self.norm(x)
x = self.proj_in(x)
x = rearrange(x, 'b c h w -> b (h w) c')
for block in self.transformer_blocks:
x = block(x, context=context)
x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w)
x = self.proj_out(x)
return x + x_in
================================================
FILE: models/instructpix2pix/stable_diffusion/ldm/modules/diffusionmodules/__init__.py
================================================
================================================
FILE: models/instructpix2pix/stable_diffusion/ldm/modules/diffusionmodules/model.py
================================================
# pytorch_diffusion + derived encoder decoder
import math
import torch
import torch.nn as nn
import numpy as np
from einops import rearrange
from ldm.util import instantiate_from_config
from ldm.modules.attention import LinearAttention
def get_timestep_embedding(timesteps, embedding_dim):
"""
This matches the implementation in Denoising Diffusion Probabilistic Models:
From Fairseq.
Build sinusoidal embeddings.
This matches the implementation in tensor2tensor, but differs slightly
from the description in Section 3.5 of "Attention Is All You Need".
"""
assert len(timesteps.shape) == 1
half_dim = embedding_dim // 2
emb = math.log(10000) / (half_dim - 1)
emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb)
emb = emb.to(device=timesteps.device)
emb = timesteps.float()[:, None] * emb[None, :]
emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
if embedding_dim % 2 == 1: # zero pad
emb = torch.nn.functional.pad(emb, (0,1,0,0))
return emb
def nonlinearity(x):
# swish
return x*torch.sigmoid(x)
def Normalize(in_channels, num_groups=32):
return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True)
class Upsample(nn.Module):
def __init__(self, in_channels, with_conv):
super().__init__()
self.with_conv = with_conv
if self.with_conv:
self.conv = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=3,
stride=1,
padding=1)
def forward(self, x):
x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
if self.with_conv:
x = self.conv(x)
return x
class Downsample(nn.Module):
def __init__(self, in_channels, with_conv):
super().__init__()
self.with_conv = with_conv
if self.with_conv:
# no asymmetric padding in torch conv, must do it ourselves
self.conv = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=3,
stride=2,
padding=0)
def forward(self, x):
if self.with_conv:
pad = (0,1,0,1)
x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
x = self.conv(x)
else:
x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2)
return x
class ResnetBlock(nn.Module):
def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False,
dropout, temb_channels=512):
super().__init__()
self.in_channels = in_channels
out_channels = in_channels if out_channels is None else out_channels
self.out_channels = out_channels
self.use_conv_shortcut = conv_shortcut
self.norm1 = Normalize(in_channels)
self.conv1 = torch.nn.Conv2d(in_channels,
out_channels,
kernel_size=3,
stride=1,
padding=1)
if temb_channels > 0:
self.temb_proj = torch.nn.Linear(temb_channels,
out_channels)
self.norm2 = Normalize(out_channels)
self.dropout = torch.nn.Dropout(dropout)
self.conv2 = torch.nn.Conv2d(out_channels,
out_channels,
kernel_size=3,
stride=1,
padding=1)
if self.in_channels != self.out_channels:
if self.use_conv_shortcut:
self.conv_shortcut = torch.nn.Conv2d(in_channels,
out_channels,
kernel_size=3,
stride=1,
padding=1)
else:
self.nin_shortcut = torch.nn.Conv2d(in_channels,
out_channels,
kernel_size=1,
stride=1,
padding=0)
def forward(self, x, temb):
h = x
h = self.norm1(h)
h = nonlinearity(h)
h = self.conv1(h)
if temb is not None:
h = h + self.temb_proj(nonlinearity(temb))[:,:,None,None]
h = self.norm2(h)
h = nonlinearity(h)
h = self.dropout(h)
h = self.conv2(h)
if self.in_channels != self.out_channels:
if self.use_conv_shortcut:
x = self.conv_shortcut(x)
else:
x = self.nin_shortcut(x)
return x+h
class LinAttnBlock(LinearAttention):
"""to match AttnBlock usage"""
def __init__(self, in_channels):
super().__init__(dim=in_channels, heads=1, dim_head=in_channels)
class AttnBlock(nn.Module):
def __init__(self, in_channels):
super().__init__()
self.in_channels = in_channels
self.norm = Normalize(in_channels)
self.q = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.k = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.v = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.proj_out = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
def forward(self, x):
h_ = x
h_ = self.norm(h_)
q = self.q(h_)
k = self.k(h_)
v = self.v(h_)
# compute attention
b,c,h,w = q.shape
q = q.reshape(b,c,h*w)
q = q.permute(0,2,1) # b,hw,c
k = k.reshape(b,c,h*w) # b,c,hw
w_ = torch.bmm(q,k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
w_ = w_ * (int(c)**(-0.5))
w_ = torch.nn.functional.softmax(w_, dim=2)
# attend to values
v = v.reshape(b,c,h*w)
w_ = w_.permute(0,2,1) # b,hw,hw (first hw of k, second of q)
h_ = torch.bmm(v,w_) # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
h_ = h_.reshape(b,c,h,w)
h_ = self.proj_out(h_)
return x+h_
def make_attn(in_channels, attn_type="vanilla"):
assert attn_type in ["vanilla", "linear", "none"], f'attn_type {attn_type} unknown'
print(f"making attention of type '{attn_type}' with {in_channels} in_channels")
if attn_type == "vanilla":
return AttnBlock(in_channels)
elif attn_type == "none":
return nn.Identity(in_channels)
else:
return LinAttnBlock(in_channels)
class Model(nn.Module):
def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
resolution, use_timestep=True, use_linear_attn=False, attn_type="vanilla"):
super().__init__()
if use_linear_attn: attn_type = "linear"
self.ch = ch
self.temb_ch = self.ch*4
self.num_resolutions = len(ch_mult)
self.num_res_blocks = num_res_blocks
self.resolution = resolution
self.in_channels = in_channels
self.use_timestep = use_timestep
if self.use_timestep:
# timestep embedding
self.temb = nn.Module()
self.temb.dense = nn.ModuleList([
torch.nn.Linear(self.ch,
self.temb_ch),
torch.nn.Linear(self.temb_ch,
self.temb_ch),
])
# downsampling
self.conv_in = torch.nn.Conv2d(in_channels,
self.ch,
kernel_size=3,
stride=1,
padding=1)
curr_res = resolution
in_ch_mult = (1,)+tuple(ch_mult)
self.down = nn.ModuleList()
for i_level in range(self.num_resolutions):
block = nn.ModuleList()
attn = nn.ModuleList()
block_in = ch*in_ch_mult[i_level]
block_out = ch*ch_mult[i_level]
for i_block in range(self.num_res_blocks):
block.append(ResnetBlock(in_channels=block_in,
out_channels=block_out,
temb_channels=self.temb_ch,
dropout=dropout))
block_in = block_out
if curr_res in attn_resolutions:
attn.append(make_attn(block_in, attn_type=attn_type))
down = nn.Module()
down.block = block
down.attn = attn
if i_level != self.num_resolutions-1:
down.downsample = Downsample(block_in, resamp_with_conv)
curr_res = curr_res // 2
self.down.append(down)
# middle
self.mid = nn.Module()
self.mid.block_1 = ResnetBlock(in_channels=block_in,
out_channels=block_in,
temb_channels=self.temb_ch,
dropout=dropout)
self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
self.mid.block_2 = ResnetBlock(in_channels=block_in,
out_channels=block_in,
temb_channels=self.temb_ch,
dropout=dropout)
# upsampling
self.up = nn.ModuleList()
for i_level in reversed(range(self.num_resolutions)):
block = nn.ModuleList()
attn = nn.ModuleList()
block_out = ch*ch_mult[i_level]
skip_in = ch*ch_mult[i_level]
for i_block in range(self.num_res_blocks+1):
if i_block == self.num_res_blocks:
skip_in = ch*in_ch_mult[i_level]
block.append(ResnetBlock(in_channels=block_in+skip_in,
out_channels=block_out,
temb_channels=self.temb_ch,
dropout=dropout))
block_in = block_out
if curr_res in attn_resolutions:
attn.append(make_attn(block_in, attn_type=attn_type))
up = nn.Module()
up.block = block
up.attn = attn
if i_level != 0:
up.upsample = Upsample(block_in, resamp_with_conv)
curr_res = curr_res * 2
self.up.insert(0, up) # prepend to get consistent order
# end
self.norm_out = Normalize(block_in)
self.conv_out = torch.nn.Conv2d(block_in,
out_ch,
kernel_size=3,
stride=1,
padding=1)
def forward(self, x, t=None, context=None):
#assert x.shape[2] == x.shape[3] == self.resolution
if context is not None:
# assume aligned context, cat along channel axis
x = torch.cat((x, context), dim=1)
if self.use_timestep:
# timestep embedding
assert t is not None
temb = get_timestep_embedding(t, self.ch)
temb = self.temb.dense[0](temb)
temb = nonlinearity(temb)
temb = self.temb.dense[1](temb)
else:
temb = None
# downsampling
hs = [self.conv_in(x)]
for i_level in range(self.num_resolutions):
for i_block in range(self.num_res_blocks):
h = self.down[i_level].block[i_block](hs[-1], temb)
if len(self.down[i_level].attn) > 0:
h = self.down[i_level].attn[i_block](h)
hs.append(h)
if i_level != self.num_resolutions-1:
hs.append(self.down[i_level].downsample(hs[-1]))
# middle
h = hs[-1]
h = self.mid.block_1(h, temb)
h = self.mid.attn_1(h)
h = self.mid.block_2(h, temb)
# upsampling
for i_level in reversed(range(self.num_resolutions)):
for i_block in range(self.num_res_blocks+1):
h = self.up[i_level].block[i_block](
torch.cat([h, hs.pop()], dim=1), temb)
if len(self.up[i_level].attn) > 0:
h = self.up[i_level].attn[i_block](h)
if i_level != 0:
h = self.up[i_level].upsample(h)
# end
h = self.norm_out(h)
h = nonlinearity(h)
h = self.conv_out(h)
return h
def get_last_layer(self):
return self.conv_out.weight
class Encoder(nn.Module):
def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
resolution, z_channels, double_z=True, use_linear_attn=False, attn_type="vanilla",
**ignore_kwargs):
super().__init__()
if use_linear_attn: attn_type = "linear"
self.ch = ch
self.temb_ch = 0
self.num_resolutions = len(ch_mult)
self.num_res_blocks = num_res_blocks
self.resolution = resolution
self.in_channels = in_channels
# downsampling
self.conv_in = torch.nn.Conv2d(in_channels,
self.ch,
kernel_size=3,
stride=1,
padding=1)
curr_res = resolution
in_ch_mult = (1,)+tuple(ch_mult)
self.in_ch_mult = in_ch_mult
self.down = nn.ModuleList()
for i_level in range(self.num_resolutions):
block = nn.ModuleList()
attn = nn.ModuleList()
block_in = ch*in_ch_mult[i_level]
block_out = ch*ch_mult[i_level]
for i_block in range(self.num_res_blocks):
block.append(ResnetBlock(in_channels=block_in,
out_channels=block_out,
temb_channels=self.temb_ch,
dropout=dropout))
block_in = block_out
if curr_res in attn_resolutions:
attn.append(make_attn(block_in, attn_type=attn_type))
down = nn.Module()
down.block = block
down.attn = attn
if i_level != self.num_resolutions-1:
down.downsample = Downsample(block_in, resamp_with_conv)
curr_res = curr_res // 2
self.down.append(down)
# middle
self.mid = nn.Module()
self.mid.block_1 = ResnetBlock(in_channels=block_in,
out_channels=block_in,
temb_channels=self.temb_ch,
dropout=dropout)
self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
self.mid.block_2 = ResnetBlock(in_channels=block_in,
out_channels=block_in,
temb_channels=self.temb_ch,
dropout=dropout)
# end
self.norm_out = Normalize(block_in)
self.conv_out = torch.nn.Conv2d(block_in,
2*z_channels if double_z else z_channels,
kernel_size=3,
stride=1,
padding=1)
def forward(self, x):
# timestep embedding
temb = None
# downsampling
hs = [self.conv_in(x)]
for i_level in range(self.num_resolutions):
for i_block in range(self.num_res_blocks):
h = self.down[i_level].block[i_block](hs[-1], temb)
if len(self.down[i_level].attn) > 0:
h = self.down[i_level].attn[i_block](h)
hs.append(h)
if i_level != self.num_resolutions-1:
hs.append(self.down[i_level].downsample(hs[-1]))
# middle
h = hs[-1]
h = self.mid.block_1(h, temb)
h = self.mid.attn_1(h)
h = self.mid.block_2(h, temb)
# end
h = self.norm_out(h)
h = nonlinearity(h)
h = self.conv_out(h)
return h
class Decoder(nn.Module):
def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
resolution, z_channels, give_pre_end=False, tanh_out=False, use_linear_attn=False,
attn_type="vanilla", **ignorekwargs):
super().__init__()
if use_linear_attn: attn_type = "linear"
self.ch = ch
self.temb_ch = 0
self.num_resolutions = len(ch_mult)
self.num_res_blocks = num_res_blocks
self.resolution = resolution
self.in_channels = in_channels
self.give_pre_end = give_pre_end
self.tanh_out = tanh_out
# compute in_ch_mult, block_in and curr_res at lowest res
in_ch_mult = (1,)+tuple(ch_mult)
block_in = ch*ch_mult[self.num_resolutions-1]
curr_res = resolution // 2**(self.num_resolutions-1)
self.z_shape = (1,z_channels,curr_res,curr_res)
print("Working with z of shape {} = {} dimensions.".format(
self.z_shape, np.prod(self.z_shape)))
# z to block_in
self.conv_in = torch.nn.Conv2d(z_channels,
block_in,
kernel_size=3,
stride=1,
padding=1)
# middle
self.mid = nn.Module()
self.mid.block_1 = ResnetBlock(in_channels=block_in,
out_channels=block_in,
temb_channels=self.temb_ch,
dropout=dropout)
self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
self.mid.block_2 = ResnetBlock(in_channels=block_in,
out_channels=block_in,
temb_channels=self.temb_ch,
dropout=dropout)
# upsampling
self.up = nn.ModuleList()
for i_level in reversed(range(self.num_resolutions)):
block = nn.ModuleList()
attn = nn.ModuleList()
block_out = ch*ch_mult[i_level]
for i_block in range(self.num_res_blocks+1):
block.append(ResnetBlock(in_channels=block_in,
out_channels=block_out,
temb_channels=self.temb_ch,
dropout=dropout))
block_in = block_out
if curr_res in attn_resolutions:
attn.append(make_attn(block_in, attn_type=attn_type))
up = nn.Module()
up.block = block
up.attn = attn
if i_level != 0:
up.upsample = Upsample(block_in, resamp_with_conv)
curr_res = curr_res * 2
self.up.insert(0, up) # prepend to get consistent order
# end
self.norm_out = Normalize(block_in)
self.conv_out = torch.nn.Conv2d(block_in,
out_ch,
kernel_size=3,
stride=1,
padding=1)
def forward(self, z):
#assert z.shape[1:] == self.z_shape[1:]
self.last_z_shape = z.shape
# timestep embedding
temb = None
# z to block_in
h = self.conv_in(z)
# middle
h = self.mid.block_1(h, temb)
h = self.mid.attn_1(h)
h = self.mid.block_2(h, temb)
# upsampling
for i_level in reversed(range(self.num_resolutions)):
for i_block in range(self.num_res_blocks+1):
h = self.up[i_level].block[i_block](h, temb)
if len(self.up[i_level].attn) > 0:
h = self.up[i_level].attn[i_block](h)
if i_level != 0:
h = self.up[i_level].upsample(h)
# end
if self.give_pre_end:
return h
h = self.norm_out(h)
h = nonlinearity(h)
h = self.conv_out(h)
if self.tanh_out:
h = torch.tanh(h)
return h
class SimpleDecoder(nn.Module):
def __init__(self, in_channels, out_channels, *args, **kwargs):
super().__init__()
self.model = nn.ModuleList([nn.Conv2d(in_channels, in_channels, 1),
ResnetBlock(in_channels=in_channels,
out_channels=2 * in_channels,
temb_channels=0, dropout=0.0),
ResnetBlock(in_channels=2 * in_channels,
out_channels=4 * in_channels,
temb_channels=0, dropout=0.0),
ResnetBlock(in_channels=4 * in_channels,
out_channels=2 * in_channels,
temb_channels=0, dropout=0.0),
nn.Conv2d(2*in_channels, in_channels, 1),
Upsample(in_channels, with_conv=True)])
# end
self.norm_out = Normalize(in_channels)
self.conv_out = torch.nn.Conv2d(in_channels,
out_channels,
kernel_size=3,
stride=1,
padding=1)
def forward(self, x):
for i, layer in enumerate(self.model):
if i in [1,2,3]:
x = layer(x, None)
else:
x = layer(x)
h = self.norm_out(x)
h = nonlinearity(h)
x = self.conv_out(h)
return x
class UpsampleDecoder(nn.Module):
def __init__(self, in_channels, out_channels, ch, num_res_blocks, resolution,
ch_mult=(2,2), dropout=0.0):
super().__init__()
# upsampling
self.temb_ch = 0
self.num_resolutions = len(ch_mult)
self.num_res_blocks = num_res_blocks
block_in = in_channels
curr_res = resolution // 2 ** (self.num_resolutions - 1)
self.res_blocks = nn.ModuleList()
self.upsample_blocks = nn.ModuleList()
for i_level in range(self.num_resolutions):
res_block = []
block_out = ch * ch_mult[i_level]
for i_block in range(self.num_res_blocks + 1):
res_block.append(ResnetBlock(in_channels=block_in,
out_channels=block_out,
temb_channels=self.temb_ch,
dropout=dropout))
block_in = block_out
self.res_blocks.append(nn.ModuleList(res_block))
if i_level != self.num_resolutions - 1:
self.upsample_blocks.append(Upsample(block_in, True))
curr_res = curr_res * 2
# end
self.norm_out = Normalize(block_in)
self.conv_out = torch.nn.Conv2d(block_in,
out_channels,
kernel_size=3,
stride=1,
padding=1)
def forward(self, x):
# upsampling
h = x
for k, i_level in enumerate(range(self.num_resolutions)):
for i_block in range(self.num_res_blocks + 1):
h = self.res_blocks[i_level][i_block](h, None)
if i_level != self.num_resolutions - 1:
h = self.upsample_blocks[k](h)
h = self.norm_out(h)
h = nonlinearity(h)
h = self.conv_out(h)
return h
class LatentRescaler(nn.Module):
def __init__(self, factor, in_channels, mid_channels, out_channels, depth=2):
super().__init__()
# residual block, interpolate, residual block
self.factor = factor
self.conv_in = nn.Conv2d(in_channels,
mid_channels,
kernel_size=3,
stride=1,
padding=1)
self.res_block1 = nn.ModuleList([ResnetBlock(in_channels=mid_channels,
out_channels=mid_channels,
temb_channels=0,
dropout=0.0) for _ in range(depth)])
self.attn = AttnBlock(mid_channels)
self.res_block2 = nn.ModuleList([ResnetBlock(in_channels=mid_channels,
out_channels=mid_channels,
temb_channels=0,
dropout=0.0) for _ in range(depth)])
self.conv_out = nn.Conv2d(mid_channels,
out_channels,
kernel_size=1,
)
def forward(self, x):
x = self.conv_in(x)
for block in self.res_block1:
x = block(x, None)
x = torch.nn.functional.interpolate(x, size=(int(round(x.shape[2]*self.factor)), int(round(x.shape[3]*self.factor))))
x = self.attn(x)
for block in self.res_block2:
x = block(x, None)
x = self.conv_out(x)
return x
class MergedRescaleEncoder(nn.Module):
def __init__(self, in_channels, ch, resolution, out_ch, num_res_blocks,
attn_resolutions, dropout=0.0, resamp_with_conv=True,
ch_mult=(1,2,4,8), rescale_factor=1.0, rescale_module_depth=1):
super().__init__()
intermediate_chn = ch * ch_mult[-1]
self.encoder = Encoder(in_channels=in_channels, num_res_blocks=num_res_blocks, ch=ch, ch_mult=ch_mult,
z_channels=intermediate_chn, double_z=False, resolution=resolution,
attn_resolutions=attn_resolutions, dropout=dropout, resamp_with_conv=resamp_with_conv,
out_ch=None)
self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=intermediate_chn,
mid_channels=intermediate_chn, out_channels=out_ch, depth=rescale_module_depth)
def forward(self, x):
x = self.encoder(x)
x = self.rescaler(x)
return x
class MergedRescaleDecoder(nn.Module):
def __init__(self, z_channels, out_ch, resolution, num_res_blocks, attn_resolutions, ch, ch_mult=(1,2,4,8),
dropout=0.0, resamp_with_conv=True, rescale_factor=1.0, rescale_module_depth=1):
super().__init__()
tmp_chn = z_channels*ch_mult[-1]
self.decoder = Decoder(out_ch=out_ch, z_channels=tmp_chn, attn_resolutions=attn_resolutions, dropout=dropout,
resamp_with_conv=resamp_with_conv, in_channels=None, num_res_blocks=num_res_blocks,
ch_mult=ch_mult, resolution=resolution, ch=ch)
self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=z_channels, mid_channels=tmp_chn,
out_channels=tmp_chn, depth=rescale_module_depth)
def forward(self, x):
x = self.rescaler(x)
x = self.decoder(x)
return x
class Upsampler(nn.Module):
def __init__(self, in_size, out_size, in_channels, out_channels, ch_mult=2):
super().__init__()
assert out_size >= in_size
num_blocks = int(np.log2(out_size//in_size))+1
factor_up = 1.+ (out_size % in_size)
print(f"Building {self.__class__.__name__} with in_size: {in_size} --> out_size {out_size} and factor {factor_up}")
self.rescaler = LatentRescaler(factor=factor_up, in_channels=in_channels, mid_channels=2*in_channels,
out_channels=in_channels)
self.decoder = Decoder(out_ch=out_channels, resolution=out_size, z_channels=in_channels, num_res_blocks=2,
attn_resolutions=[], in_channels=None, ch=in_channels,
ch_mult=[ch_mult for _ in range(num_blocks)])
def forward(self, x):
x = self.rescaler(x)
x = self.decoder(x)
return x
class Resize(nn.Module):
def __init__(self, in_channels=None, learned=False, mode="bilinear"):
super().__init__()
self.with_conv = learned
self.mode = mode
if self.with_conv:
print(f"Note: {self.__class__.__name} uses learned downsampling and will ignore the fixed {mode} mode")
raise NotImplementedError()
assert in_channels is not None
# no asymmetric padding in torch conv, must do it ourselves
self.conv = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=4,
stride=2,
padding=1)
def forward(self, x, scale_factor=1.0):
if scale_factor==1.0:
return x
else:
x = torch.nn.functional.interpolate(x, mode=self.mode, align_corners=False, scale_factor=scale_factor)
return x
class FirstStagePostProcessor(nn.Module):
def __init__(self, ch_mult:list, in_channels,
pretrained_model:nn.Module=None,
reshape=False,
n_channels=None,
dropout=0.,
pretrained_config=None):
super().__init__()
if pretrained_config is None:
assert pretrained_model is not None, 'Either "pretrained_model" or "pretrained_config" must not be None'
self.pretrained_model = pretrained_model
else:
assert pretrained_config is not None, 'Either "pretrained_model" or "pretrained_config" must not be None'
self.instantiate_pretrained(pretrained_config)
self.do_reshape = reshape
if n_channels is None:
n_channels = self.pretrained_model.encoder.ch
self.proj_norm = Normalize(in_channels,num_groups=in_channels//2)
self.proj = nn.Conv2d(in_channels,n_channels,kernel_size=3,
stride=1,padding=1)
blocks = []
downs = []
ch_in = n_channels
for m in ch_mult:
blocks.append(ResnetBlock(in_channels=ch_in,out_channels=m*n_channels,dropout=dropout))
ch_in = m * n_channels
downs.append(Downsample(ch_in, with_conv=False))
self.model = nn.ModuleList(blocks)
self.downsampler = nn.ModuleList(downs)
def instantiate_pretrained(self, config):
model = instantiate_from_config(config)
self.pretrained_model = model.eval()
# self.pretrained_model.train = False
for param in self.pretrained_model.parameters():
param.requires_grad = False
@torch.no_grad()
def encode_with_pretrained(self,x):
c = self.pretrained_model.encode(x)
if isinstance(c, DiagonalGaussianDistribution):
c = c.mode()
return c
def forward(self,x):
z_fs = self.encode_with_pretrained(x)
z = self.proj_norm(z_fs)
z = self.proj(z)
z = nonlinearity(z)
for submodel, downmodel in zip(self.model,self.downsampler):
z = submodel(z,temb=None)
z = downmodel(z)
if self.do_reshape:
z = rearrange(z,'b c h w -> b (h w) c')
return z
================================================
FILE: models/instructpix2pix/stable_diffusion/ldm/modules/diffusionmodules/openaimodel.py
================================================
from abc import abstractmethod
from functools import partial
import math
from typing import Iterable
import numpy as np
import torch as th
import torch.nn as nn
import torch.nn.functional as F
from ldm.modules.diffusionmodules.util import (
checkpoint,
conv_nd,
linear,
avg_pool_nd,
zero_module,
normalization,
timestep_embedding,
)
from ldm.modules.attention import SpatialTransformer
# dummy replace
def convert_module_to_f16(x):
pass
def convert_module_to_f32(x):
pass
## go
class AttentionPool2d(nn.Module):
"""
Adapted from CLIP: https://github.com/openai/CLIP/blob/main/clip/model.py
"""
def __init__(
self,
spacial_dim: int,
embed_dim: int,
num_heads_channels: int,
output_dim: int = None,
):
super().__init__()
self.positional_embedding = nn.Parameter(th.randn(embed_dim, spacial_dim ** 2 + 1) / embed_dim ** 0.5)
self.qkv_proj = conv_nd(1, embed_dim, 3 * embed_dim, 1)
self.c_proj = conv_nd(1, embed_dim, output_dim or embed_dim, 1)
self.num_heads = embed_dim // num_heads_channels
self.attention = QKVAttention(self.num_heads)
def forward(self, x):
b, c, *_spatial = x.shape
x = x.reshape(b, c, -1) # NC(HW)
x = th.cat([x.mean(dim=-1, keepdim=True), x], dim=-1) # NC(HW+1)
x = x + self.positional_embedding[None, :, :].to(x.dtype) # NC(HW+1)
x = self.qkv_proj(x)
x = self.attention(x)
x = self.c_proj(x)
return x[:, :, 0]
class TimestepBlock(nn.Module):
"""
Any module where forward() takes timestep embeddings as a second argument.
"""
@abstractmethod
def forward(self, x, emb):
"""
Apply the module to `x` given `emb` timestep embeddings.
"""
class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
"""
A sequential module that passes timestep embeddings to the children that
support it as an extra input.
"""
def forward(self, x, emb, context=None):
for layer in self:
if isinstance(layer, TimestepBlock):
x = layer(x, emb)
elif isinstance(layer, SpatialTransformer):
x = layer(x, context)
else:
x = layer(x)
return x
class Upsample(nn.Module):
"""
An upsampling layer with an optional convolution.
:param channels: channels in the inputs and outputs.
:param use_conv: a bool determining if a convolution is applied.
:param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
upsampling occurs in the inner-two dimensions.
"""
def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1):
super().__init__()
self.channels = channels
self.out_channels = out_channels or channels
self.use_conv = use_conv
self.dims = dims
if use_conv:
self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=padding)
def forward(self, x):
assert x.shape[1] == self.channels
if self.dims == 3:
x = F.interpolate(
x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest"
)
else:
x = F.interpolate(x, scale_factor=2, mode="nearest")
if self.use_conv:
x = self.conv(x)
return x
class TransposedUpsample(nn.Module):
'Learned 2x upsampling without padding'
def __init__(self, channels, out_channels=None, ks=5):
super().__init__()
self.channels = channels
self.out_channels = out_channels or channels
self.up = nn.ConvTranspose2d(self.channels,self.out_channels,kernel_size=ks,stride=2)
def forward(self,x):
return self.up(x)
class Downsample(nn.Module):
"""
A downsampling layer with an optional convolution.
:param channels: channels in the inputs and outputs.
:param use_conv: a bool determining if a convolution is applied.
:param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
downsampling occurs in the inner-two dimensions.
"""
def __init__(self, channels, use_conv, dims=2, out_channels=None,padding=1):
super().__init__()
self.channels = channels
self.out_channels = out_channels or channels
self.use_conv = use_conv
self.dims = dims
stride = 2 if dims != 3 else (1, 2, 2)
if use_conv:
self.op = conv_nd(
dims, self.channels, self.out_channels, 3, stride=stride, padding=padding
)
else:
assert self.channels == self.out_channels
self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride)
def forward(self, x):
assert x.shape[1] == self.channels
return self.op(x)
class ResBlock(TimestepBlock):
"""
A residual block that can optionally change the number of channels.
:param channels: the number of input channels.
:param emb_channels: the number of timestep embedding channels.
:param dropout: the rate of dropout.
:param out_channels: if specified, the number of out channels.
:param use_conv: if True and out_channels is specified, use a spatial
convolution instead of a smaller 1x1 convolution to change the
channels in the skip connection.
:param dims: determines if the signal is 1D, 2D, or 3D.
:param use_checkpoint: if True, use gradient checkpointing on this module.
:param up: if True, use this block for upsampling.
:param down: if True, use this block for downsampling.
"""
def __init__(
self,
channels,
emb_channels,
dropout,
out_channels=None,
use_conv=False,
use_scale_shift_norm=False,
dims=2,
use_checkpoint=False,
up=False,
down=False,
):
super().__init__()
self.channels = channels
self.emb_channels = emb_channels
self.dropout = dropout
self.out_channels = out_channels or channels
self.use_conv = use_conv
self.use_checkpoint = use_checkpoint
self.use_scale_shift_norm = use_scale_shift_norm
self.in_layers = nn.Sequential(
normalization(channels),
nn.SiLU(),
conv_nd(dims, channels, self.out_channels, 3, padding=1),
)
self.updown = up or down
if up:
self.h_upd = Upsample(channels, False, dims)
self.x_upd = Upsample(channels, False, dims)
elif down:
self.h_upd = Downsample(channels, False, dims)
self.x_upd = Downsample(channels, False, dims)
else:
self.h_upd = self.x_upd = nn.Identity()
self.emb_layers = nn.Sequential(
nn.SiLU(),
linear(
emb_channels,
2 * self.out_channels if use_scale_shift_norm else self.out_channels,
),
)
self.out_layers = nn.Sequential(
normalization(self.out_channels),
nn.SiLU(),
nn.Dropout(p=dropout),
zero_module(
conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1)
),
)
if self.out_channels == channels:
self.skip_connection = nn.Identity()
elif use_conv:
self.skip_connection = conv_nd(
dims, channels, self.out_channels, 3, padding=1
)
else:
self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)
def forward(self, x, emb):
"""
Apply the block to a Tensor, conditioned on a timestep embedding.
:param x: an [N x C x ...] Tensor of features.
:param emb: an [N x emb_channels] Tensor of timestep embeddings.
:return: an [N x C x ...] Tensor of outputs.
"""
return checkpoint(
self._forward, (x, emb), self.parameters(), self.use_checkpoint
)
def _forward(self, x, emb):
if self.updown:
in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
h = in_rest(x)
h = self.h_upd(h)
x = self.x_upd(x)
h = in_conv(h)
else:
h = self.in_layers(x)
emb_out = self.emb_layers(emb).type(h.dtype)
while len(emb_out.shape) < len(h.shape):
emb_out = emb_out[..., None]
if self.use_scale_shift_norm:
out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
scale, shift = th.chunk(emb_out, 2, dim=1)
h = out_norm(h) * (1 + scale) + shift
h = out_rest(h)
else:
h = h + emb_out
h = self.out_layers(h)
return self.skip_connection(x) + h
class AttentionBlock(nn.Module):
"""
An attention block that allows spatial positions to attend to each other.
Originally ported from here, but adapted to the N-d case.
https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66.
"""
def __init__(
self,
channels,
num_heads=1,
num_head_channels=-1,
use_checkpoint=False,
use_new_attention_order=False,
):
super().__init__()
self.channels = channels
if num_head_channels == -1:
self.num_heads = num_heads
else:
assert (
channels % num_head_channels == 0
), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
self.num_heads = channels // num_head_channels
self.use_checkpoint = use_checkpoint
self.norm = normalization(channels)
self.qkv = conv_nd(1, channels, channels * 3, 1)
if use_new_attention_order:
# split qkv before split heads
self.attention = QKVAttention(self.num_heads)
else:
# split heads before split qkv
self.attention = QKVAttentionLegacy(self.num_heads)
self.proj_out = zero_module(conv_nd(1, channels, channels, 1))
def forward(self, x):
return checkpoint(self._forward, (x,), self.parameters(), True) # TODO: check checkpoint usage, is True # TODO: fix the .half call!!!
#return pt_checkpoint(self._forward, x) # pytorch
def _forward(self, x):
b, c, *spatial = x.shape
x = x.reshape(b, c, -1)
qkv = self.qkv(self.norm(x))
h = self.attention(qkv)
h = self.proj_out(h)
return (x + h).reshape(b, c, *spatial)
def count_flops_attn(model, _x, y):
"""
A counter for the `thop` package to count the operations in an
attention operation.
Meant to be used like:
macs, params = thop.profile(
model,
inputs=(inputs, timestamps),
custom_ops={QKVAttention: QKVAttention.count_flops},
)
"""
b, c, *spatial = y[0].shape
num_spatial = int(np.prod(spatial))
# We perform two matmuls with the same number of ops.
# The first computes the weight matrix, the second computes
# the combination of the value vectors.
matmul_ops = 2 * b * (num_spatial ** 2) * c
model.total_ops += th.DoubleTensor([matmul_ops])
class QKVAttentionLegacy(nn.Module):
"""
A module which performs QKV attention. Matches legacy QKVAttention + input/ouput heads shaping
"""
def __init__(self, n_heads):
super().__init__()
self.n_heads = n_heads
def forward(self, qkv):
"""
Apply QKV attention.
:param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs.
:return: an [N x (H * C) x T] tensor after attention.
"""
bs, width, length = qkv.shape
assert width % (3 * self.n_heads) == 0
ch = width // (3 * self.n_heads)
q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1)
scale = 1 / math.sqrt(math.sqrt(ch))
weight = th.einsum(
"bct,bcs->bts", q * scale, k * scale
) # More stable with f16 than dividing afterwards
weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
a = th.einsum("bts,bcs->bct", weight, v)
return a.reshape(bs, -1, length)
@staticmethod
def count_flops(model, _x, y):
return count_flops_attn(model, _x, y)
class QKVAttention(nn.Module):
"""
A module which performs QKV attention and splits in a different order.
"""
def __init__(self, n_heads):
super().__init__()
self.n_heads = n_heads
def forward(self, qkv):
"""
Apply QKV attention.
:param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs.
:return: an [N x (H * C) x T] tensor after attention.
"""
bs, width, length = qkv.shape
assert width % (3 * self.n_heads) == 0
ch = width // (3 * self.n_heads)
q, k, v = qkv.chunk(3, dim=1)
scale = 1 / math.sqrt(math.sqrt(ch))
weight = th.einsum(
"bct,bcs->bts",
(q * scale).view(bs * self.n_heads, ch, length),
(k * scale).view(bs * self.n_heads, ch, length),
) # More stable with f16 than dividing afterwards
weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
a = th.einsum("bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length))
return a.reshape(bs, -1, length)
@staticmethod
def count_flops(model, _x, y):
return count_flops_attn(model, _x, y)
class UNetModel(nn.Module):
"""
The full UNet model with attention and timestep embedding.
:param in_channels: channels in the input Tensor.
:param model_channels: base channel count for the model.
:param out_channels: channels in the output Tensor.
:param num_res_blocks: number of residual blocks per downsample.
:param attention_resolutions: a collection of downsample rates at which
attention will take place. May be a set, list, or tuple.
For example, if this contains 4, then at 4x downsampling, attention
will be used.
:param dropout: the dropout probability.
:param channel_mult: channel multiplier for each level of the UNet.
:param conv_resample: if True, use learned convolutions for upsampling and
downsampling.
:param dims: determines if the signal is 1D, 2D, or 3D.
:param num_classes: if specified (as an int), then this model will be
class-conditional with `num_classes` classes.
:param use_checkpoint: use gradient checkpointing to reduce memory usage.
:param num_heads: the number of attention heads in each attention layer.
:param num_heads_channels: if specified, ignore num_heads and instead use
a fixed channel width per attention head.
:param num_heads_upsample: works with num_heads to set a different number
of heads for upsampling. Deprecated.
:param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
:param resblock_updown: use residual blocks for up/downsampling.
:param use_new_attention_order: use a different attention pattern for potentially
increased efficiency.
"""
def __init__(
self,
image_size,
in_channels,
model_channels,
out_channels,
num_res_blocks,
attention_resolutions,
dropout=0,
channel_mult=(1, 2, 4, 8),
conv_resample=True,
dims=2,
num_classes=None,
use_checkpoint=False,
use_fp16=False,
num_heads=-1,
num_head_channels=-1,
num_heads_upsample=-1,
use_scale_shift_norm=False,
resblock_updown=False,
use_new_attention_order=False,
use_spatial_transformer=False, # custom transformer support
transformer_depth=1, # custom transformer support
context_dim=None, # custom transformer support
n_embed=None, # custom support for prediction of discrete ids into codebook of first stage vq model
legacy=True,
):
super().__init__()
if use_spatial_transformer:
assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...'
if context_dim is not None:
assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...'
from omegaconf.listconfig import ListConfig
if type(context_dim) == ListConfig:
context_dim = list(context_dim)
if num_heads_upsample == -1:
num_heads_upsample = num_heads
if num_heads == -1:
assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set'
if num_head_channels == -1:
assert num_heads != -1, 'Either num_heads or num_head_channels has to be set'
self.image_size = image_size
self.in_channels = in_channels
self.model_channels = model_channels
self.out_channels = out_channels
self.num_res_blocks = num_res_blocks
self.attention_resolutions = attention_resolutions
self.dropout = dropout
self.channel_mult = channel_mult
self.conv_resample = conv_resample
self.num_classes = num_classes
self.use_checkpoint = use_checkpoint
self.dtype = th.float16 if use_fp16 else th.float32
self.num_heads = num_heads
self.num_head_channels = num_head_channels
self.num_heads_upsample = num_heads_upsample
self.predict_codebook_ids = n_embed is not None
time_embed_dim = model_channels * 4
self.time_embed = nn.Sequential(
linear(model_channels, time_embed_dim),
nn.SiLU(),
linear(time_embed_dim, time_embed_dim),
)
if self.num_classes is not None:
self.label_emb = nn.Embedding(num_classes, time_embed_dim)
self.input_blocks = nn.ModuleList(
[
TimestepEmbedSequential(
conv_nd(dims, in_channels, model_channels, 3, padding=1)
)
]
)
self._feature_size = model_channels
input_block_chans = [model_channels]
ch = model_channels
ds = 1
for level, mult in enumerate(channel_mult):
for _ in range(num_res_blocks):
layers = [
ResBlock(
ch,
time_embed_dim,
dropout,
out_channels=mult * model_channels,
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
)
]
ch = mult * model_channels
if ds in attention_resolutions:
if num_head_channels == -1:
dim_head = ch // num_heads
else:
num_heads = ch // num_head_channels
dim_head = num_head_channels
if legacy:
#num_heads = 1
dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
layers.append(
AttentionBlock(
ch,
use_checkpoint=use_checkpoint,
num_heads=num_heads,
num_head_channels=dim_head,
use_new_attention_order=use_new_attention_order,
) if not use_spatial_transformer else SpatialTransformer(
ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim
)
)
self.input_blocks.append(TimestepEmbedSequential(*layers))
self._feature_size += ch
input_block_chans.append(ch)
if level != len(channel_mult) - 1:
out_ch = ch
self.input_blocks.append(
TimestepEmbedSequential(
ResBlock(
ch,
time_embed_dim,
dropout,
out_channels=out_ch,
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
down=True,
)
if resblock_updown
else Downsample(
ch, conv_resample, dims=dims, out_channels=out_ch
)
)
)
ch = out_ch
input_block_chans.append(ch)
ds *= 2
self._feature_size += ch
if num_head_channels == -1:
dim_head = ch // num_heads
else:
num_heads = ch // num_head_channels
dim_head = num_head_channels
if legacy:
#num_heads = 1
dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
self.middle_block = TimestepEmbedSequential(
ResBlock(
ch,
time_embed_dim,
dropout,
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
),
AttentionBlock(
ch,
use_checkpoint=use_checkpoint,
num_heads=num_heads,
num_head_channels=dim_head,
use_new_attention_order=use_new_attention_order,
) if not use_spatial_transformer else SpatialTransformer(
ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim
),
ResBlock(
ch,
time_embed_dim,
dropout,
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
),
)
self._feature_size += ch
self.output_blocks = nn.ModuleList([])
for level, mult in list(enumerate(channel_mult))[::-1]:
for i in range(num_res_blocks + 1):
ich = input_block_chans.pop()
layers = [
ResBlock(
ch + ich,
time_embed_dim,
dropout,
out_channels=model_channels * mult,
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
)
]
ch = model_channels * mult
if ds in attention_resolutions:
if num_head_channels == -1:
dim_head = ch // num_heads
else:
num_heads = ch // num_head_channels
dim_head = num_head_channels
if legacy:
#num_heads = 1
dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
layers.append(
AttentionBlock(
ch,
use_checkpoint=use_checkpoint,
num_heads=num_heads_upsample,
num_head_channels=dim_head,
use_new_attention_order=use_new_attention_order,
) if not use_spatial_transformer else SpatialTransformer(
ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim
)
)
if level and i == num_res_blocks:
out_ch = ch
layers.append(
ResBlock(
ch,
time_embed_dim,
dropout,
out_channels=out_ch,
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
up=True,
)
if resblock_updown
else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch)
)
ds //= 2
self.output_blocks.append(TimestepEmbedSequential(*layers))
self._feature_size += ch
self.out = nn.Sequential(
normalization(ch),
nn.SiLU(),
zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)),
)
if self.predict_codebook_ids:
self.id_predictor = nn.Sequential(
normalization(ch),
conv_nd(dims, model_channels, n_embed, 1),
#nn.LogSoftmax(dim=1) # change to cross_entropy and produce non-normalized logits
)
def convert_to_fp16(self):
"""
Convert the torso of the model to float16.
"""
self.input_blocks.apply(convert_module_to_f16)
self.middle_block.apply(convert_module_to_f16)
self.output_blocks.apply(convert_module_to_f16)
def convert_to_fp32(self):
"""
Convert the torso of the model to float32.
"""
self.input_blocks.apply(convert_module_to_f32)
self.middle_block.apply(convert_module_to_f32)
self.output_blocks.apply(convert_module_to_f32)
def forward(self, x, timesteps=None, context=None, y=None,**kwargs):
"""
Apply the model to an input batch.
:param x: an [N x C x ...] Tensor of inputs.
:param timesteps: a 1-D batch of timesteps.
:param context: conditioning plugged in via crossattn
:param y: an [N] Tensor of labels, if class-conditional.
:return: an [N x C x ...] Tensor of outputs.
"""
assert (y is not None) == (
self.num_classes is not None
), "must specify y if and only if the model is class-conditional"
hs = []
t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
emb = self.time_embed(t_emb)
if self.num_classes is not None:
assert y.shape == (x.shape[0],)
emb = emb + self.label_emb(y)
h = x.type(self.dtype)
for module in self.input_blocks:
h = module(h, emb, context)
hs.append(h)
h = self.middle_block(h, emb, context)
for module in self.output_blocks:
h = th.cat([h, hs.pop()], dim=1)
h = module(h, emb, context)
h = h.type(x.dtype)
if self.predict_codebook_ids:
return self.id_predictor(h)
else:
return self.out(h)
class EncoderUNetModel(nn.Module):
"""
The half UNet model with attention and timestep embedding.
For usage, see UNet.
"""
def __init__(
self,
image_size,
in_channels,
model_channels,
out_channels,
num_res_blocks,
attention_resolutions,
dropout=0,
channel_mult=(1, 2, 4, 8),
conv_resample=True,
dims=2,
use_checkpoint=False,
use_fp16=False,
num_heads=1,
num_head_channels=-1,
num_heads_upsample=-1,
use_scale_shift_norm=False,
resblock_updown=False,
use_new_attention_order=False,
pool="adaptive",
*args,
**kwargs
):
super().__init__()
if num_heads_upsample == -1:
num_heads_upsample = num_heads
self.in_channels = in_channels
self.model_channels = model_channels
self.out_channels = out_channels
self.num_res_blocks = num_res_blocks
self.attention_resolutions = attention_resolutions
self.dropout = dropout
self.channel_mult = channel_mult
self.conv_resample = conv_resample
self.use_checkpoint = use_checkpoint
self.dtype = th.float16 if use_fp16 else th.float32
self.num_heads = num_heads
self.num_head_channels = num_head_channels
self.num_heads_upsample = num_heads_upsample
time_embed_dim = model_channels * 4
self.time_embed = nn.Sequential(
linear(model_channels, time_embed_dim),
nn.SiLU(),
linear(time_embed_dim, time_embed_dim),
)
self.input_blocks = nn.ModuleList(
[
TimestepEmbedSequential(
conv_nd(dims, in_channels, model_channels, 3, padding=1)
)
]
)
self._feature_size = model_channels
input_block_chans = [model_channels]
ch = model_channels
ds = 1
for level, mult in enumerate(channel_mult):
for _ in range(num_res_blocks):
layers = [
ResBlock(
ch,
time_embed_dim,
dropout,
out_channels=mult * model_channels,
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
)
]
ch = mult * model_channels
if ds in attention_resolutions:
layers.append(
AttentionBlock(
ch,
use_checkpoint=use_checkpoint,
num_heads=num_heads,
num_head_channels=num_head_channels,
use_new_attention_order=use_new_attention_order,
)
)
self.input_blocks.append(TimestepEmbedSequential(*layers))
self._feature_size += ch
input_block_chans.append(ch)
if level != len(channel_mult) - 1:
out_ch = ch
self.input_blocks.append(
TimestepEmbedSequential(
ResBlock(
ch,
time_embed_dim,
dropout,
out_channels=out_ch,
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
down=True,
)
if resblock_updown
else Downsample(
ch, conv_resample, dims=dims, out_channels=out_ch
)
)
)
ch = out_ch
input_block_chans.append(ch)
ds *= 2
self._feature_size += ch
self.middle_block = TimestepEmbedSequential(
ResBlock(
ch,
time_embed_dim,
dropout,
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
),
AttentionBlock(
ch,
use_checkpoint=use_checkpoint,
num_heads=num_heads,
num_head_channels=num_head_channels,
use_new_attention_order=use_new_attention_order,
),
ResBlock(
ch,
time_embed_dim,
dropout,
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
),
)
self._feature_size += ch
self.pool = pool
if pool == "adaptive":
self.out = nn.Sequential(
normalization(ch),
nn.SiLU(),
nn.AdaptiveAvgPool2d((1, 1)),
zero_module(conv_nd(dims, ch, out_channels, 1)),
nn.Flatten(),
)
elif pool == "attention":
assert num_head_channels != -1
self.out = nn.Sequential(
normalization(ch),
nn.SiLU(),
AttentionPool2d(
(image_size // ds), ch, num_head_channels, out_channels
),
)
elif pool == "spatial":
self.out = nn.Sequential(
nn.Linear(self._feature_size, 2048),
nn.ReLU(),
nn.Linear(2048, self.out_channels),
)
elif pool == "spatial_v2":
self.out = nn.Sequential(
nn.Linear(self._feature_size, 2048),
normalization(2048),
nn.SiLU(),
nn.Linear(2048, self.out_channels),
)
else:
raise NotImplementedError(f"Unexpected {pool} pooling")
def convert_to_fp16(self):
"""
Convert the torso of the model to float16.
"""
self.input_blocks.apply(convert_module_to_f16)
self.middle_block.apply(convert_module_to_f16)
def convert_to_fp32(self):
"""
Convert the torso of the model to float32.
"""
self.input_blocks.apply(convert_module_to_f32)
self.middle_block.apply(convert_module_to_f32)
def forward(self, x, timesteps):
"""
Apply the model to an input batch.
:param x: an [N x C x ...] Tensor of inputs.
:param timesteps: a 1-D batch of timesteps.
:return: an [N x K] Tensor of outputs.
"""
emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
results = []
h = x.type(self.dtype)
for module in self.input_blocks:
h = module(h, emb)
if self.pool.startswith("spatial"):
results.append(h.type(x.dtype).mean(dim=(2, 3)))
h = self.middle_block(h, emb)
if self.pool.startswith("spatial"):
results.append(h.type(x.dtype).mean(dim=(2, 3)))
h = th.cat(results, axis=-1)
return self.out(h)
else:
h = h.type(x.dtype)
return self.out(h)
================================================
FILE: models/instructpix2pix/stable_diffusion/ldm/modules/diffusionmodules/util.py
================================================
# adopted from
# https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py
# and
# https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
# and
# https://github.com/openai/guided-diffusion/blob/0ba878e517b276c45d1195eb29f6f5f72659a05b/guided_diffusion/nn.py
#
# thanks!
import os
import math
import torch
import torch.nn as nn
import numpy as np
from einops import repeat
from ldm.util import instantiate_from_config
def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
if schedule == "linear":
betas = (
torch.linspace(linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64) ** 2
)
elif schedule == "cosine":
timesteps = (
torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s
)
alphas = timesteps / (1 + cosine_s) * np.pi / 2
alphas = torch.cos(alphas).pow(2)
alphas = alphas / alphas[0]
betas = 1 - alphas[1:] / alphas[:-1]
betas = np.clip(betas, a_min=0, a_max=0.999)
elif schedule == "sqrt_linear":
betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64)
elif schedule == "sqrt":
betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) ** 0.5
else:
raise ValueError(f"schedule '{schedule}' unknown.")
return betas.numpy()
def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True):
if ddim_discr_method == 'uniform':
c = num_ddpm_timesteps // num_ddim_timesteps
ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c)))
elif ddim_discr_method == 'quad':
ddim_timesteps = ((np.linspace(0, np.sqrt(num_ddpm_timesteps * .8), num_ddim_timesteps)) ** 2).astype(int)
else:
raise NotImplementedError(f'There is no ddim discretization method called "{ddim_discr_method}"')
# assert ddim_timesteps.shape[0] == num_ddim_timesteps
# add one to get the final alpha values right (the ones from first scale to data during sampling)
steps_out = ddim_timesteps + 1
if verbose:
print(f'Selected timesteps for ddim sampler: {steps_out}')
return steps_out
def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True):
# select alphas for computing the variance schedule
alphas = alphacums[ddim_timesteps]
alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist())
# according the the formula provided in https://arxiv.org/abs/2010.02502
sigmas = eta * np.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev))
if verbose:
print(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}')
print(f'For the chosen value of eta, which is {eta}, '
f'this results in the following sigma_t schedule for ddim sampler {sigmas}')
return sigmas, alphas, alphas_prev
def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999):
"""
Create a beta schedule that discretizes the given alpha_t_bar function,
which defines the cumulative product of (1-beta) over time from t = [0,1].
:param num_diffusion_timesteps: the number of betas to produce.
:param alpha_bar: a lambda that takes an argument t from 0 to 1 and
produces the cumulative product of (1-beta) up to that
part of the diffusion process.
:param max_beta: the maximum beta to use; use values lower than 1 to
prevent singularities.
"""
betas = []
for i in range(num_diffusion_timesteps):
t1 = i / num_diffusion_timesteps
t2 = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
return np.array(betas)
def extract_into_tensor(a, t, x_shape):
b, *_ = t.shape
out = a.gather(-1, t)
return out.reshape(b, *((1,) * (len(x_shape) - 1)))
def checkpoint(func, inputs, params, flag):
"""
Evaluate a function without caching intermediate activations, allowing for
reduced memory at the expense of extra compute in the backward pass.
:param func: the function to evaluate.
:param inputs: the argument sequence to pass to `func`.
:param params: a sequence of parameters `func` depends on but does not
explicitly take as arguments.
:param flag: if False, disable gradient checkpointing.
"""
if flag:
args = tuple(inputs) + tuple(params)
return CheckpointFunction.apply(func, len(inputs), *args)
else:
return func(*inputs)
class CheckpointFunction(torch.autograd.Function):
@staticmethod
def forward(ctx, run_function, length, *args):
ctx.run_function = run_function
ctx.input_tensors = list(args[:length])
ctx.input_params = list(args[length:])
with torch.no_grad():
output_tensors = ctx.run_function(*ctx.input_tensors)
return output_tensors
@staticmethod
def backward(ctx, *output_grads):
ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors]
with torch.enable_grad():
# Fixes a bug where the first op in run_function modifies the
# Tensor storage in place, which is not allowed for detach()'d
# Tensors.
shallow_copies = [x.view_as(x) for x in ctx.input_tensors]
output_tensors = ctx.run_function(*shallow_copies)
input_grads = torch.autograd.grad(
output_tensors,
ctx.input_tensors + ctx.input_params,
output_grads,
allow_unused=True,
)
del ctx.input_tensors
del ctx.input_params
del output_tensors
return (None, None) + input_grads
def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False):
"""
Create sinusoidal timestep embeddings.
:param timesteps: a 1-D Tensor of N indices, one per batch element.
These may be fractional.
:param dim: the dimension of the output.
:param max_period: controls the minimum frequency of the embeddings.
:return: an [N x dim] Tensor of positional embeddings.
"""
if not repeat_only:
half = dim // 2
freqs = torch.exp(
-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
).to(device=timesteps.device)
args = timesteps[:, None].float() * freqs[None]
embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
if dim % 2:
embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
else:
embedding = repeat(timesteps, 'b -> b d', d=dim)
return embedding
def zero_module(module):
"""
Zero out the parameters of a module and return it.
"""
for p in module.parameters():
p.detach().zero_()
return module
def scale_module(module, scale):
"""
Scale the parameters of a module and return it.
"""
for p in module.parameters():
p.detach().mul_(scale)
return module
def mean_flat(tensor):
"""
Take the mean over all non-batch dimensions.
"""
return tensor.mean(dim=list(range(1, len(tensor.shape))))
def normalization(channels):
"""
Make a standard normalization layer.
:param channels: number of input channels.
:return: an nn.Module for normalization.
"""
return GroupNorm32(32, channels)
# PyTorch 1.7 has SiLU, but we support PyTorch 1.5.
class SiLU(nn.Module):
def forward(self, x):
return x * torch.sigmoid(x)
class GroupNorm32(nn.GroupNorm):
def forward(self, x):
return super().forward(x.float()).type(x.dtype)
def conv_nd(dims, *args, **kwargs):
"""
Create a 1D, 2D, or 3D convolution module.
"""
if dims == 1:
return nn.Conv1d(*args, **kwargs)
elif dims == 2:
return nn.Conv2d(*args, **kwargs)
elif dims == 3:
return nn.Conv3d(*args, **kwargs)
raise ValueError(f"unsupported dimensions: {dims}")
def linear(*args, **kwargs):
"""
Create a linear module.
"""
return nn.Linear(*args, **kwargs)
def avg_pool_nd(dims, *args, **kwargs):
"""
Create a 1D, 2D, or 3D average pooling module.
"""
if dims == 1:
return nn.AvgPool1d(*args, **kwargs)
elif dims == 2:
return nn.AvgPool2d(*args, **kwargs)
elif dims == 3:
return nn.AvgPool3d(*args, **kwargs)
raise ValueError(f"unsupported dimensions: {dims}")
class HybridConditioner(nn.Module):
def __init__(self, c_concat_config, c_crossattn_config):
super().__init__()
self.concat_conditioner = instantiate_from_config(c_concat_config)
self.crossattn_conditioner = instantiate_from_config(c_crossattn_config)
def forward(self, c_concat, c_crossattn):
c_concat = self.concat_conditioner(c_concat)
c_crossattn = self.crossattn_conditioner(c_crossattn)
return {'c_concat': [c_concat], 'c_crossattn': [c_crossattn]}
def noise_like(shape, device, repeat=False):
repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1)))
noise = lambda: torch.randn(shape, device=device)
return repeat_noise() if repeat else noise()
================================================
FILE: models/instructpix2pix/stable_diffusion/ldm/modules/distributions/__init__.py
================================================
================================================
FILE: models/instructpix2pix/stable_diffusion/ldm/modules/distributions/distributions.py
================================================
import torch
import numpy as np
class AbstractDistribution:
def sample(self):
raise NotImplementedError()
def mode(self):
raise NotImplementedError()
class DiracDistribution(AbstractDistribution):
def __init__(self, value):
self.value = value
def sample(self):
return self.value
def mode(self):
return self.value
class DiagonalGaussianDistribution(object):
def __init__(self, parameters, deterministic=False):
self.parameters = parameters
self.mean, self.logvar = torch.chunk(parameters, 2, dim=1)
self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
self.deterministic = deterministic
self.std = torch.exp(0.5 * self.logvar)
self.var = torch.exp(self.logvar)
if self.deterministic:
self.var = self.std = torch.zeros_like(self.mean).to(device=self.parameters.device)
def sample(self):
x = self.mean + self.std * torch.randn(self.mean.shape).to(device=self.parameters.device)
return x
def kl(self, other=None):
if self.deterministic:
return torch.Tensor([0.])
else:
if other is None:
return 0.5 * torch.sum(torch.pow(self.mean, 2)
+ self.var - 1.0 - self.logvar,
dim=[1, 2, 3])
else:
return 0.5 * torch.sum(
torch.pow(self.mean - other.mean, 2) / other.var
+ self.var / other.var - 1.0 - self.logvar + other.logvar,
dim=[1, 2, 3])
def nll(self, sample, dims=[1,2,3]):
if self.deterministic:
return torch.Tensor([0.])
logtwopi = np.log(2.0 * np.pi)
return 0.5 * torch.sum(
logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var,
dim=dims)
def mode(self):
return self.mean
def normal_kl(mean1, logvar1, mean2, logvar2):
"""
source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12
Compute the KL divergence between two gaussians.
Shapes are automatically broadcasted, so batches can be compared to
scalars, among other use cases.
"""
tensor = None
for obj in (mean1, logvar1, mean2, logvar2):
if isinstance(obj, torch.Tensor):
tensor = obj
break
assert tensor is not None, "at least one argument must be a Tensor"
# Force variances to be Tensors. Broadcasting helps convert scalars to
# Tensors, but it does not work for torch.exp().
logvar1, logvar2 = [
x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor)
for x in (logvar1, logvar2)
]
return 0.5 * (
-1.0
+ logvar2
- logvar1
+ torch.exp(logvar1 - logvar2)
+ ((mean1 - mean2) ** 2) * torch.exp(-logvar2)
)
================================================
FILE: models/instructpix2pix/stable_diffusion/ldm/modules/ema.py
================================================
import torch
from torch import nn
class LitEma(nn.Module):
def __init__(self, model, decay=0.9999, use_num_upates=True):
super().__init__()
if decay < 0.0 or decay > 1.0:
raise ValueError('Decay must be between 0 and 1')
self.m_name2s_name = {}
self.register_buffer('decay', torch.tensor(decay, dtype=torch.float32))
self.register_buffer('num_updates', torch.tensor(0,dtype=torch.int) if use_num_upates
else torch.tensor(-1,dtype=torch.int))
for name, p in model.named_parameters():
if p.requires_grad:
#remove as '.'-character is not allowed in buffers
s_name = name.replace('.','')
self.m_name2s_name.update({name:s_name})
self.register_buffer(s_name,p.clone().detach().data)
self.collected_params = []
def forward(self,model):
decay = self.decay
if self.num_updates >= 0:
self.num_updates += 1
decay = min(self.decay,(1 + self.num_updates) / (10 + self.num_updates))
one_minus_decay = 1.0 - decay
with torch.no_grad():
m_param = dict(model.named_parameters())
shadow_params = dict(self.named_buffers())
for key in m_param:
if m_param[key].requires_grad:
sname = self.m_name2s_name[key]
shadow_params[sname] = shadow_params[sname].type_as(m_param[key])
shadow_params[sname].sub_(one_minus_decay * (shadow_params[sname] - m_param[key]))
else:
assert not key in self.m_name2s_name
def copy_to(self, model):
m_param = dict(model.named_parameters())
shadow_params = dict(self.named_buffers())
for key in m_param:
if m_param[key].requires_grad:
m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data)
else:
assert not key in self.m_name2s_name
def store(self, parameters):
"""
Save the current parameters for restoring later.
Args:
parameters: Iterable of `torch.nn.Parameter`; the parameters to be
temporarily stored.
"""
self.collected_params = [param.clone() for param in parameters]
def restore(self, parameters):
"""
Restore the parameters stored with the `store` method.
Useful to validate the model with EMA parameters without affecting the
original optimization process. Store the parameters before the
`copy_to` method. After validation (or model saving), use this to
restore the former parameters.
Args:
parameters: Iterable of `torch.nn.Parameter`; the parameters to be
updated with the stored parameters.
"""
for c_param, param in zip(self.collected_params, parameters):
param.data.copy_(c_param.data)
================================================
FILE: models/instructpix2pix/stable_diffusion/ldm/modules/encoders/__init__.py
================================================
================================================
FILE: models/instructpix2pix/stable_diffusion/ldm/modules/encoders/modules.py
================================================
import torch
import torch.nn as nn
from functools import partial
import clip
from einops import rearrange, repeat
from transformers import CLIPTokenizer, CLIPTextModel
import kornia
from ldm.modules.x_transformer import Encoder, TransformerWrapper # TODO: can we directly rely on lucidrains code and simply add this as a reuirement? --> test
class AbstractEncoder(nn.Module):
def __init__(self):
super().__init__()
def encode(self, *args, **kwargs):
raise NotImplementedError
class ClassEmbedder(nn.Module):
def __init__(self, embed_dim, n_classes=1000, key='class'):
super().__init__()
self.key = key
self.embedding = nn.Embedding(n_classes, embed_dim)
def forward(self, batch, key=None):
if key is None:
key = self.key
# this is for use in crossattn
c = batch[key][:, None]
c = self.embedding(c)
return c
class TransformerEmbedder(AbstractEncoder):
"""Some transformer encoder layers"""
def __init__(self, n_embed, n_layer, vocab_size, max_seq_len=77, device="cuda"):
super().__init__()
self.device = device
self.transformer = TransformerWrapper(num_tokens=vocab_size, max_seq_len=max_seq_len,
attn_layers=Encoder(dim=n_embed, depth=n_layer))
def forward(self, tokens):
tokens = tokens.to(self.device) # meh
z = self.transformer(tokens, return_embeddings=True)
return z
def encode(self, x):
return self(x)
class BERTTokenizer(AbstractEncoder):
""" Uses a pretrained BERT tokenizer by huggingface. Vocab size: 30522 (?)"""
def __init__(self, device="cuda", vq_interface=True, max_length=77):
super().__init__()
from transformers import BertTokenizerFast # TODO: add to reuquirements
self.tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased")
self.device = device
self.vq_interface = vq_interface
self.max_length = max_length
def forward(self, text):
batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
tokens = batch_encoding["input_ids"].to(self.device)
return tokens
@torch.no_grad()
def encode(self, text):
tokens = self(text)
if not self.vq_interface:
return tokens
return None, None, [None, None, tokens]
def decode(self, text):
return text
class BERTEmbedder(AbstractEncoder):
"""Uses the BERT tokenizr model and add some transformer encoder layers"""
def __init__(self, n_embed, n_layer, vocab_size=30522, max_seq_len=77,
device="cuda",use_tokenizer=True, embedding_dropout=0.0):
super().__init__()
self.use_tknz_fn = use_tokenizer
if self.use_tknz_fn:
self.tknz_fn = BERTTokenizer(vq_interface=False, max_length=max_seq_len)
self.device = device
self.transformer = TransformerWrapper(num_tokens=vocab_size, max_seq_len=max_seq_len,
attn_layers=Encoder(dim=n_embed, depth=n_layer),
emb_dropout=embedding_dropout)
def forward(self, text):
if self.use_tknz_fn:
tokens = self.tknz_fn(text)#.to(self.device)
else:
tokens = text
z = self.transformer(tokens, return_embeddings=True)
return z
def encode(self, text):
# output of length 77
return self(text)
class SpatialRescaler(nn.Module):
def __init__(self,
n_stages=1,
method='bilinear',
multiplier=0.5,
in_channels=3,
out_channels=None,
bias=False):
super().__init__()
self.n_stages = n_stages
assert self.n_stages >= 0
assert method in ['nearest','linear','bilinear','trilinear','bicubic','area']
self.multiplier = multiplier
self.interpolator = partial(torch.nn.functional.interpolate, mode=method)
self.remap_output = out_channels is not None
if self.remap_output:
print(f'Spatial Rescaler mapping from {in_channels} to {out_channels} channels after resizing.')
self.channel_mapper = nn.Conv2d(in_channels,out_channels,1,bias=bias)
def forward(self,x):
for stage in range(self.n_stages):
x = self.interpolator(x, scale_factor=self.multiplier)
if self.remap_output:
x = self.channel_mapper(x)
return x
def encode(self, x):
return self(x)
class FrozenCLIPEmbedder(AbstractEncoder):
"""Uses the CLIP transformer encoder for text (from Hugging Face)"""
def __init__(self, version="openai/clip-vit-large-patch14", device="cuda", max_length=77):
super().__init__()
self.tokenizer = CLIPTokenizer.from_pretrained(version)
self.transformer = CLIPTextModel.from_pretrained(version)
self.device = device
self.max_length = max_length
self.freeze()
def freeze(self):
self.transformer = self.transformer.eval()
for param in self.parameters():
param.requires_grad = False
def forward(self, text):
batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
tokens = batch_encoding["input_ids"].to(self.device)
outputs = self.transformer(input_ids=tokens)
z = outputs.last_hidden_state
return z
def encode(self, text):
return self(text)
class FrozenCLIPTextEmbedder(nn.Module):
"""
Uses the CLIP transformer encoder for text.
"""
def __init__(self, version='ViT-L/14', device="cuda", max_length=77, n_repeat=1, normalize=True):
super().__init__()
self.model, _ = clip.load(version, jit=False, device="cpu")
self.device = device
self.max_length = max_length
self.n_repeat = n_repeat
self.normalize = normalize
def freeze(self):
self.model = self.model.eval()
for param in self.parameters():
param.requires_grad = False
def forward(self, text):
tokens = clip.tokenize(text).to(self.device)
z = self.model.encode_text(tokens)
if self.normalize:
z = z / torch.linalg.norm(z, dim=1, keepdim=True)
return z
def encode(self, text):
z = self(text)
if z.ndim==2:
z = z[:, None, :]
z = repeat(z, 'b 1 d -> b k d', k=self.n_repeat)
return z
class FrozenClipImageEmbedder(nn.Module):
"""
Uses the CLIP image encoder.
"""
def __init__(
self,
model,
jit=False,
device='cuda' if torch.cuda.is_available() else 'cpu',
antialias=False,
):
super().__init__()
self.model, _ = clip.load(name=model, device=device, jit=jit)
self.antialias = antialias
self.register_buffer('mean', torch.Tensor([0.48145466, 0.4578275, 0.40821073]), persistent=False)
self.register_buffer('std', torch.Tensor([0.26862954, 0.26130258, 0.27577711]), persistent=False)
def preprocess(self, x):
# normalize to [0,1]
x = kornia.geometry.resize(x, (224, 224),
interpolation='bicubic',align_corners=True,
antialias=self.antialias)
x = (x + 1.) / 2.
# renormalize according to clip
x = kornia.enhance.normalize(x, self.mean, self.std)
return x
def forward(self, x):
# x is assumed to be in range [-1,1]
return self.model.encode_image(self.preprocess(x))
if __name__ == "__main__":
from ldm.util import count_params
model = FrozenCLIPEmbedder()
count_params(model, verbose=True)
================================================
FILE: models/instructpix2pix/stable_diffusion/ldm/modules/image_degradation/__init__.py
================================================
from ldm.modules.image_degradation.bsrgan import degradation_bsrgan_variant as degradation_fn_bsr
from ldm.modules.image_degradation.bsrgan_light import degradation_bsrgan_variant as degradation_fn_bsr_light
================================================
FILE: models/instructpix2pix/stable_diffusion/ldm/modules/image_degradation/bsrgan.py
================================================
# -*- coding: utf-8 -*-
"""
# --------------------------------------------
# Super-Resolution
# --------------------------------------------
#
# Kai Zhang (cskaizhang@gmail.com)
# https://github.com/cszn
# From 2019/03--2021/08
# --------------------------------------------
"""
import numpy as np
import cv2
import torch
from functools import partial
import random
from scipy import ndimage
import scipy
import scipy.stats as ss
from scipy.interpolate import interp2d
from scipy.linalg import orth
import albumentations
import ldm.modules.image_degradation.utils_image as util
def modcrop_np(img, sf):
'''
Args:
img: numpy image, WxH or WxHxC
sf: scale factor
Return:
cropped image
'''
w, h = img.shape[:2]
im = np.copy(img)
return im[:w - w % sf, :h - h % sf, ...]
"""
# --------------------------------------------
# anisotropic Gaussian kernels
# --------------------------------------------
"""
def analytic_kernel(k):
"""Calculate the X4 kernel from the X2 kernel (for proof see appendix in paper)"""
k_size = k.shape[0]
# Calculate the big kernels size
big_k = np.zeros((3 * k_size - 2, 3 * k_size - 2))
# Loop over the small kernel to fill the big one
for r in range(k_size):
for c in range(k_size):
big_k[2 * r:2 * r + k_size, 2 * c:2 * c + k_size] += k[r, c] * k
# Crop the edges of the big kernel to ignore very small values and increase run time of SR
crop = k_size // 2
cropped_big_k = big_k[crop:-crop, crop:-crop]
# Normalize to 1
return cropped_big_k / cropped_big_k.sum()
def anisotropic_Gaussian(ksize=15, theta=np.pi, l1=6, l2=6):
""" generate an anisotropic Gaussian kernel
Args:
ksize : e.g., 15, kernel size
theta : [0, pi], rotation angle range
l1 : [0.1,50], scaling of eigenvalues
l2 : [0.1,l1], scaling of eigenvalues
If l1 = l2, will get an isotropic Gaussian kernel.
Returns:
k : kernel
"""
v = np.dot(np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]), np.array([1., 0.]))
V = np.array([[v[0], v[1]], [v[1], -v[0]]])
D = np.array([[l1, 0], [0, l2]])
Sigma = np.dot(np.dot(V, D), np.linalg.inv(V))
k = gm_blur_kernel(mean=[0, 0], cov=Sigma, size=ksize)
return k
def gm_blur_kernel(mean, cov, size=15):
center = size / 2.0 + 0.5
k = np.zeros([size, size])
for y in range(size):
for x in range(size):
cy = y - center + 1
cx = x - center + 1
k[y, x] = ss.multivariate_normal.pdf([cx, cy], mean=mean, cov=cov)
k = k / np.sum(k)
return k
def shift_pixel(x, sf, upper_left=True):
"""shift pixel for super-resolution with different scale factors
Args:
x: WxHxC or WxH
sf: scale factor
upper_left: shift direction
"""
h, w = x.shape[:2]
shift = (sf - 1) * 0.5
xv, yv = np.arange(0, w, 1.0), np.arange(0, h, 1.0)
if upper_left:
x1 = xv + shift
y1 = yv + shift
else:
x1 = xv - shift
y1 = yv - shift
x1 = np.clip(x1, 0, w - 1)
y1 = np.clip(y1, 0, h - 1)
if x.ndim == 2:
x = interp2d(xv, yv, x)(x1, y1)
if x.ndim == 3:
for i in range(x.shape[-1]):
x[:, :, i] = interp2d(xv, yv, x[:, :, i])(x1, y1)
return x
def blur(x, k):
'''
x: image, NxcxHxW
k: kernel, Nx1xhxw
'''
n, c = x.shape[:2]
p1, p2 = (k.shape[-2] - 1) // 2, (k.shape[-1] - 1) // 2
x = torch.nn.functional.pad(x, pad=(p1, p2, p1, p2), mode='replicate')
k = k.repeat(1, c, 1, 1)
k = k.view(-1, 1, k.shape[2], k.shape[3])
x = x.view(1, -1, x.shape[2], x.shape[3])
x = torch.nn.functional.conv2d(x, k, bias=None, stride=1, padding=0, groups=n * c)
x = x.view(n, c, x.shape[2], x.shape[3])
return x
def gen_kernel(k_size=np.array([15, 15]), scale_factor=np.array([4, 4]), min_var=0.6, max_var=10., noise_level=0):
""""
# modified version of https://github.com/assafshocher/BlindSR_dataset_generator
# Kai Zhang
# min_var = 0.175 * sf # variance of the gaussian kernel will be sampled between min_var and max_var
# max_var = 2.5 * sf
"""
# Set random eigen-vals (lambdas) and angle (theta) for COV matrix
lambda_1 = min_var + np.random.rand() * (max_var - min_var)
lambda_2 = min_var + np.random.rand() * (max_var - min_var)
theta = np.random.rand() * np.pi # random theta
noise = -noise_level + np.random.rand(*k_size) * noise_level * 2
# Set COV matrix using Lambdas and Theta
LAMBDA = np.diag([lambda_1, lambda_2])
Q = np.array([[np.cos(theta), -np.sin(theta)],
[np.sin(theta), np.cos(theta)]])
SIGMA = Q @ LAMBDA @ Q.T
INV_SIGMA = np.linalg.inv(SIGMA)[None, None, :, :]
# Set expectation position (shifting kernel for aligned image)
MU = k_size // 2 - 0.5 * (scale_factor - 1) # - 0.5 * (scale_factor - k_size % 2)
MU = MU[None, None, :, None]
# Create meshgrid for Gaussian
[X, Y] = np.meshgrid(range(k_size[0]), range(k_size[1]))
Z = np.stack([X, Y], 2)[:, :, :, None]
# Calcualte Gaussian for every pixel of the kernel
ZZ = Z - MU
ZZ_t = ZZ.transpose(0, 1, 3, 2)
raw_kernel = np.exp(-0.5 * np.squeeze(ZZ_t @ INV_SIGMA @ ZZ)) * (1 + noise)
# shift the kernel so it will be centered
# raw_kernel_centered = kernel_shift(raw_kernel, scale_factor)
# Normalize the kernel and return
# kernel = raw_kernel_centered / np.sum(raw_kernel_centered)
kernel = raw_kernel / np.sum(raw_kernel)
return kernel
def fspecial_gaussian(hsize, sigma):
hsize = [hsize, hsize]
siz = [(hsize[0] - 1.0) / 2.0, (hsize[1] - 1.0) / 2.0]
std = sigma
[x, y] = np.meshgrid(np.arange(-siz[1], siz[1] + 1), np.arange(-siz[0], siz[0] + 1))
arg = -(x * x + y * y) / (2 * std * std)
h = np.exp(arg)
h[h < scipy.finfo(float).eps * h.max()] = 0
sumh = h.sum()
if sumh != 0:
h = h / sumh
return h
def fspecial_laplacian(alpha):
alpha = max([0, min([alpha, 1])])
h1 = alpha / (alpha + 1)
h2 = (1 - alpha) / (alpha + 1)
h = [[h1, h2, h1], [h2, -4 / (alpha + 1), h2], [h1, h2, h1]]
h = np.array(h)
return h
def fspecial(filter_type, *args, **kwargs):
'''
python code from:
https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py
'''
if filter_type == 'gaussian':
return fspecial_gaussian(*args, **kwargs)
if filter_type == 'laplacian':
return fspecial_laplacian(*args, **kwargs)
"""
# --------------------------------------------
# degradation models
# --------------------------------------------
"""
def bicubic_degradation(x, sf=3):
'''
Args:
x: HxWxC image, [0, 1]
sf: down-scale factor
Return:
bicubicly downsampled LR image
'''
x = util.imresize_np(x, scale=1 / sf)
return x
def srmd_degradation(x, k, sf=3):
''' blur + bicubic downsampling
Args:
x: HxWxC image, [0, 1]
k: hxw, double
sf: down-scale factor
Return:
downsampled LR image
Reference:
@inproceedings{zhang2018learning,
title={Learning a single convolutional super-resolution network for multiple degradations},
author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
pages={3262--3271},
year={2018}
}
'''
x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap') # 'nearest' | 'mirror'
x = bicubic_degradation(x, sf=sf)
return x
def dpsr_degradation(x, k, sf=3):
''' bicubic downsampling + blur
Args:
x: HxWxC image, [0, 1]
k: hxw, double
sf: down-scale factor
Return:
downsampled LR image
Reference:
@inproceedings{zhang2019deep,
title={Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels},
author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
pages={1671--1681},
year={2019}
}
'''
x = bicubic_degradation(x, sf=sf)
x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
return x
def classical_degradation(x, k, sf=3):
''' blur + downsampling
Args:
x: HxWxC image, [0, 1]/[0, 255]
k: hxw, double
sf: down-scale factor
Return:
downsampled LR image
'''
x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
# x = filters.correlate(x, np.expand_dims(np.flip(k), axis=2))
st = 0
return x[st::sf, st::sf, ...]
def add_sharpening(img, weight=0.5, radius=50, threshold=10):
"""USM sharpening. borrowed from real-ESRGAN
Input image: I; Blurry image: B.
1. K = I + weight * (I - B)
2. Mask = 1 if abs(I - B) > threshold, else: 0
3. Blur mask:
4. Out = Mask * K + (1 - Mask) * I
Args:
img (Numpy array): Input image, HWC, BGR; float32, [0, 1].
weight (float): Sharp weight. Default: 1.
radius (float): Kernel size of Gaussian blur. Default: 50.
threshold (int):
"""
if radius % 2 == 0:
radius += 1
blur = cv2.GaussianBlur(img, (radius, radius), 0)
residual = img - blur
mask = np.abs(residual) * 255 > threshold
mask = mask.astype('float32')
soft_mask = cv2.GaussianBlur(mask, (radius, radius), 0)
K = img + weight * residual
K = np.clip(K, 0, 1)
return soft_mask * K + (1 - soft_mask) * img
def add_blur(img, sf=4):
wd2 = 4.0 + sf
wd = 2.0 + 0.2 * sf
if random.random() < 0.5:
l1 = wd2 * random.random()
l2 = wd2 * random.random()
k = anisotropic_Gaussian(ksize=2 * random.randint(2, 11) + 3, theta=random.random() * np.pi, l1=l1, l2=l2)
else:
k = fspecial('gaussian', 2 * random.randint(2, 11) + 3, wd * random.random())
img = ndimage.filters.convolve(img, np.expand_dims(k, axis=2), mode='mirror')
return img
def add_resize(img, sf=4):
rnum = np.random.rand()
if rnum > 0.8: # up
sf1 = random.uniform(1, 2)
elif rnum < 0.7: # down
sf1 = random.uniform(0.5 / sf, 1)
else:
sf1 = 1.0
img = cv2.resize(img, (int(sf1 * img.shape[1]), int(sf1 * img.shape[0])), interpolation=random.choice([1, 2, 3]))
img = np.clip(img, 0.0, 1.0)
return img
# def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
# noise_level = random.randint(noise_level1, noise_level2)
# rnum = np.random.rand()
# if rnum > 0.6: # add color Gaussian noise
# img += np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
# elif rnum < 0.4: # add grayscale Gaussian noise
# img += np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
# else: # add noise
# L = noise_level2 / 255.
# D = np.diag(np.random.rand(3))
# U = orth(np.random.rand(3, 3))
# conv = np.dot(np.dot(np.transpose(U), D), U)
# img += np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
# img = np.clip(img, 0.0, 1.0)
# return img
def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
noise_level = random.randint(noise_level1, noise_level2)
rnum = np.random.rand()
if rnum > 0.6: # add color Gaussian noise
img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
elif rnum < 0.4: # add grayscale Gaussian noise
img = img + np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
else: # add noise
L = noise_level2 / 255.
D = np.diag(np.random.rand(3))
U = orth(np.random.rand(3, 3))
conv = np.dot(np.dot(np.transpose(U), D), U)
img = img + np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
img = np.clip(img, 0.0, 1.0)
return img
def add_speckle_noise(img, noise_level1=2, noise_level2=25):
noise_level = random.randint(noise_level1, noise_level2)
img = np.clip(img, 0.0, 1.0)
rnum = random.random()
if rnum > 0.6:
img += img * np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
elif rnum < 0.4:
img += img * np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
else:
L = noise_level2 / 255.
D = np.diag(np.random.rand(3))
U = orth(np.random.rand(3, 3))
conv = np.dot(np.dot(np.transpose(U), D), U)
img += img * np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
img = np.clip(img, 0.0, 1.0)
return img
def add_Poisson_noise(img):
img = np.clip((img * 255.0).round(), 0, 255) / 255.
vals = 10 ** (2 * random.random() + 2.0) # [2, 4]
if random.random() < 0.5:
img = np.random.poisson(img * vals).astype(np.float32) / vals
else:
img_gray = np.dot(img[..., :3], [0.299, 0.587, 0.114])
img_gray = np.clip((img_gray * 255.0).round(), 0, 255) / 255.
noise_gray = np.random.poisson(img_gray * vals).astype(np.float32) / vals - img_gray
img += noise_gray[:, :, np.newaxis]
img = np.clip(img, 0.0, 1.0)
return img
def add_JPEG_noise(img):
quality_factor = random.randint(30, 95)
img = cv2.cvtColor(util.single2uint(img), cv2.COLOR_RGB2BGR)
result, encimg = cv2.imencode('.jpg', img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor])
img = cv2.imdecode(encimg, 1)
img = cv2.cvtColor(util.uint2single(img), cv2.COLOR_BGR2RGB)
return img
def random_crop(lq, hq, sf=4, lq_patchsize=64):
h, w = lq.shape[:2]
rnd_h = random.randint(0, h - lq_patchsize)
rnd_w = random.randint(0, w - lq_patchsize)
lq = lq[rnd_h:rnd_h + lq_patchsize, rnd_w:rnd_w + lq_patchsize, :]
rnd_h_H, rnd_w_H = int(rnd_h * sf), int(rnd_w * sf)
hq = hq[rnd_h_H:rnd_h_H + lq_patchsize * sf, rnd_w_H:rnd_w_H + lq_patchsize * sf, :]
return lq, hq
def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None):
"""
This is the degradation model of BSRGAN from the paper
"Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
----------
img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf)
sf: scale factor
isp_model: camera ISP model
Returns
-------
img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
"""
isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
sf_ori = sf
h1, w1 = img.shape[:2]
img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop
h, w = img.shape[:2]
if h < lq_patchsize * sf or w < lq_patchsize * sf:
raise ValueError(f'img size ({h1}X{w1}) is too small!')
hq = img.copy()
if sf == 4 and random.random() < scale2_prob: # downsample1
if np.random.rand() < 0.5:
img = cv2.resize(img, (int(1 / 2 * img.shape[1]), int(1 / 2 * img.shape[0])),
interpolation=random.choice([1, 2, 3]))
else:
img = util.imresize_np(img, 1 / 2, True)
img = np.clip(img, 0.0, 1.0)
sf = 2
shuffle_order = random.sample(range(7), 7)
idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
if idx1 > idx2: # keep downsample3 last
shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]
for i in shuffle_order:
if i == 0:
img = add_blur(img, sf=sf)
elif i == 1:
img = add_blur(img, sf=sf)
elif i == 2:
a, b = img.shape[1], img.shape[0]
# downsample2
if random.random() < 0.75:
sf1 = random.uniform(1, 2 * sf)
img = cv2.resize(img, (int(1 / sf1 * img.shape[1]), int(1 / sf1 * img.shape[0])),
interpolation=random.choice([1, 2, 3]))
else:
k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
k_shifted = shift_pixel(k, sf)
k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel
img = ndimage.filters.convolve(img, np.expand_dims(k_shifted, axis=2), mode='mirror')
img = img[0::sf, 0::sf, ...] # nearest downsampling
img = np.clip(img, 0.0, 1.0)
elif i == 3:
# downsample3
img = cv2.resize(img, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
img = np.clip(img, 0.0, 1.0)
elif i == 4:
# add Gaussian noise
img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25)
elif i == 5:
# add JPEG noise
if random.random() < jpeg_prob:
img = add_JPEG_noise(img)
elif i == 6:
# add processed camera sensor noise
if random.random() < isp_prob and isp_model is not None:
with torch.no_grad():
img, hq = isp_model.forward(img.copy(), hq)
# add final JPEG compression noise
img = add_JPEG_noise(img)
# random crop
img, hq = random_crop(img, hq, sf_ori, lq_patchsize)
return img, hq
# todo no isp_model?
def degradation_bsrgan_variant(image, sf=4, isp_model=None):
"""
This is the degradation model of BSRGAN from the paper
"Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
----------
sf: scale factor
isp_model: camera ISP model
Returns
-------
img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
"""
image = util.uint2single(image)
isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
sf_ori = sf
h1, w1 = image.shape[:2]
image = image.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop
h, w = image.shape[:2]
hq = image.copy()
if sf == 4 and random.random() < scale2_prob: # downsample1
if np.random.rand() < 0.5:
image = cv2.resize(image, (int(1 / 2 * image.shape[1]), int(1 / 2 * image.shape[0])),
interpolation=random.choice([1, 2, 3]))
else:
image = util.imresize_np(image, 1 / 2, True)
image = np.clip(image, 0.0, 1.0)
sf = 2
shuffle_order = random.sample(range(7), 7)
idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
if idx1 > idx2: # keep downsample3 last
shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]
for i in shuffle_order:
if i == 0:
image = add_blur(image, sf=sf)
elif i == 1:
image = add_blur(image, sf=sf)
elif i == 2:
a, b = image.shape[1], image.shape[0]
# downsample2
if random.random() < 0.75:
sf1 = random.uniform(1, 2 * sf)
image = cv2.resize(image, (int(1 / sf1 * image.shape[1]), int(1 / sf1 * image.shape[0])),
interpolation=random.choice([1, 2, 3]))
else:
k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
k_shifted = shift_pixel(k, sf)
k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel
image = ndimage.filters.convolve(image, np.expand_dims(k_shifted, axis=2), mode='mirror')
image = image[0::sf, 0::sf, ...] # nearest downsampling
image = np.clip(image, 0.0, 1.0)
elif i == 3:
# downsample3
image = cv2.resize(image, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
image = np.clip(image, 0.0, 1.0)
elif i == 4:
# add Gaussian noise
image = add_Gaussian_noise(image, noise_level1=2, noise_level2=25)
elif i == 5:
# add JPEG noise
if random.random() < jpeg_prob:
image = add_JPEG_noise(image)
# elif i == 6:
# # add processed camera sensor noise
# if random.random() < isp_prob and isp_model is not None:
# with torch.no_grad():
# img, hq = isp_model.forward(img.copy(), hq)
# add final JPEG compression noise
image = add_JPEG_noise(image)
image = util.single2uint(image)
example = {"image":image}
return example
# TODO incase there is a pickle error one needs to replace a += x with a = a + x in add_speckle_noise etc...
def degradation_bsrgan_plus(img, sf=4, shuffle_prob=0.5, use_sharp=True, lq_patchsize=64, isp_model=None):
"""
This is an extended degradation model by combining
the degradation models of BSRGAN and Real-ESRGAN
----------
img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf)
sf: scale factor
use_shuffle: the degradation shuffle
use_sharp: sharpening the img
Returns
-------
img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
"""
h1, w1 = img.shape[:2]
img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop
h, w = img.shape[:2]
if h < lq_patchsize * sf or w < lq_patchsize * sf:
raise ValueError(f'img size ({h1}X{w1}) is too small!')
if use_sharp:
img = add_sharpening(img)
hq = img.copy()
if random.random() < shuffle_prob:
shuffle_order = random.sample(range(13), 13)
else:
shuffle_order = list(range(13))
# local shuffle for noise, JPEG is always the last one
shuffle_order[2:6] = random.sample(shuffle_order[2:6], len(range(2, 6)))
shuffle_order[9:13] = random.sample(shuffle_order[9:13], len(range(9, 13)))
poisson_prob, speckle_prob, isp_prob = 0.1, 0.1, 0.1
for i in shuffle_order:
if i == 0:
img = add_blur(img, sf=sf)
elif i == 1:
img = add_resize(img, sf=sf)
elif i == 2:
img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25)
elif i == 3:
if random.random() < poisson_prob:
img = add_Poisson_noise(img)
elif i == 4:
if random.random() < speckle_prob:
img = add_speckle_noise(img)
elif i == 5:
if random.random() < isp_prob and isp_model is not None:
with torch.no_grad():
img, hq = isp_model.forward(img.copy(), hq)
elif i == 6:
img = add_JPEG_noise(img)
elif i == 7:
img = add_blur(img, sf=sf)
elif i == 8:
img = add_resize(img, sf=sf)
elif i == 9:
img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25)
elif i == 10:
if random.random() < poisson_prob:
img = add_Poisson_noise(img)
elif i == 11:
if random.random() < speckle_prob:
img = add_speckle_noise(img)
elif i == 12:
if random.random() < isp_prob and isp_model is not None:
with torch.no_grad():
img, hq = isp_model.forward(img.copy(), hq)
else:
print('check the shuffle!')
# resize to desired size
img = cv2.resize(img, (int(1 / sf * hq.shape[1]), int(1 / sf * hq.shape[0])),
interpolation=random.choice([1, 2, 3]))
# add final JPEG compression noise
img = add_JPEG_noise(img)
# random crop
img, hq = random_crop(img, hq, sf, lq_patchsize)
return img, hq
if __name__ == '__main__':
print("hey")
img = util.imread_uint('utils/test.png', 3)
print(img)
img = util.uint2single(img)
print(img)
img = img[:448, :448]
h = img.shape[0] // 4
print("resizing to", h)
sf = 4
deg_fn = partial(degradation_bsrgan_variant, sf=sf)
for i in range(20):
print(i)
img_lq = deg_fn(img)
print(img_lq)
img_lq_bicubic = albumentations.SmallestMaxSize(max_size=h, interpolation=cv2.INTER_CUBIC)(image=img)["image"]
print(img_lq.shape)
print("bicubic", img_lq_bicubic.shape)
print(img_hq.shape)
lq_nearest = cv2.resize(util.single2uint(img_lq), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
interpolation=0)
lq_bicubic_nearest = cv2.resize(util.single2uint(img_lq_bicubic), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
interpolation=0)
img_concat = np.concatenate([lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1)
util.imsave(img_concat, str(i) + '.png')
================================================
FILE: models/instructpix2pix/stable_diffusion/ldm/modules/image_degradation/bsrgan_light.py
================================================
# -*- coding: utf-8 -*-
import numpy as np
import cv2
import torch
from functools import partial
import random
from scipy import ndimage
import scipy
import scipy.stats as ss
from scipy.interpolate import interp2d
from scipy.linalg import orth
import albumentations
import ldm.modules.image_degradation.utils_image as util
"""
# --------------------------------------------
# Super-Resolution
# --------------------------------------------
#
# Kai Zhang (cskaizhang@gmail.com)
# https://github.com/cszn
# From 2019/03--2021/08
# --------------------------------------------
"""
def modcrop_np(img, sf):
'''
Args:
img: numpy image, WxH or WxHxC
sf: scale factor
Return:
cropped image
'''
w, h = img.shape[:2]
im = np.copy(img)
return im[:w - w % sf, :h - h % sf, ...]
"""
# --------------------------------------------
# anisotropic Gaussian kernels
# --------------------------------------------
"""
def analytic_kernel(k):
"""Calculate the X4 kernel from the X2 kernel (for proof see appendix in paper)"""
k_size = k.shape[0]
# Calculate the big kernels size
big_k = np.zeros((3 * k_size - 2, 3 * k_size - 2))
# Loop over the small kernel to fill the big one
for r in range(k_size):
for c in range(k_size):
big_k[2 * r:2 * r + k_size, 2 * c:2 * c + k_size] += k[r, c] * k
# Crop the edges of the big kernel to ignore very small values and increase run time of SR
crop = k_size // 2
cropped_big_k = big_k[crop:-crop, crop:-crop]
# Normalize to 1
return cropped_big_k / cropped_big_k.sum()
def anisotropic_Gaussian(ksize=15, theta=np.pi, l1=6, l2=6):
""" generate an anisotropic Gaussian kernel
Args:
ksize : e.g., 15, kernel size
theta : [0, pi], rotation angle range
l1 : [0.1,50], scaling of eigenvalues
l2 : [0.1,l1], scaling of eigenvalues
If l1 = l2, will get an isotropic Gaussian kernel.
Returns:
k : kernel
"""
v = np.dot(np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]), np.array([1., 0.]))
V = np.array([[v[0], v[1]], [v[1], -v[0]]])
D = np.array([[l1, 0], [0, l2]])
Sigma = np.dot(np.dot(V, D), np.linalg.inv(V))
k = gm_blur_kernel(mean=[0, 0], cov=Sigma, size=ksize)
return k
def gm_blur_kernel(mean, cov, size=15):
center = size / 2.0 + 0.5
k = np.zeros([size, size])
for y in range(size):
for x in range(size):
cy = y - center + 1
cx = x - center + 1
k[y, x] = ss.multivariate_normal.pdf([cx, cy], mean=mean, cov=cov)
k = k / np.sum(k)
return k
def shift_pixel(x, sf, upper_left=True):
"""shift pixel for super-resolution with different scale factors
Args:
x: WxHxC or WxH
sf: scale factor
upper_left: shift direction
"""
h, w = x.shape[:2]
shift = (sf - 1) * 0.5
xv, yv = np.arange(0, w, 1.0), np.arange(0, h, 1.0)
if upper_left:
x1 = xv + shift
y1 = yv + shift
else:
x1 = xv - shift
y1 = yv - shift
x1 = np.clip(x1, 0, w - 1)
y1 = np.clip(y1, 0, h - 1)
if x.ndim == 2:
x = interp2d(xv, yv, x)(x1, y1)
if x.ndim == 3:
for i in range(x.shape[-1]):
x[:, :, i] = interp2d(xv, yv, x[:, :, i])(x1, y1)
return x
def blur(x, k):
'''
x: image, NxcxHxW
k: kernel, Nx1xhxw
'''
n, c = x.shape[:2]
p1, p2 = (k.shape[-2] - 1) // 2, (k.shape[-1] - 1) // 2
x = torch.nn.functional.pad(x, pad=(p1, p2, p1, p2), mode='replicate')
k = k.repeat(1, c, 1, 1)
k = k.view(-1, 1, k.shape[2], k.shape[3])
x = x.view(1, -1, x.shape[2], x.shape[3])
x = torch.nn.functional.conv2d(x, k, bias=None, stride=1, padding=0, groups=n * c)
x = x.view(n, c, x.shape[2], x.shape[3])
return x
def gen_kernel(k_size=np.array([15, 15]), scale_factor=np.array([4, 4]), min_var=0.6, max_var=10., noise_level=0):
""""
# modified version of https://github.com/assafshocher/BlindSR_dataset_generator
# Kai Zhang
# min_var = 0.175 * sf # variance of the gaussian kernel will be sampled between min_var and max_var
# max_var = 2.5 * sf
"""
# Set random eigen-vals (lambdas) and angle (theta) for COV matrix
lambda_1 = min_var + np.random.rand() * (max_var - min_var)
lambda_2 = min_var + np.random.rand() * (max_var - min_var)
theta = np.random.rand() * np.pi # random theta
noise = -noise_level + np.random.rand(*k_size) * noise_level * 2
# Set COV matrix using Lambdas and Theta
LAMBDA = np.diag([lambda_1, lambda_2])
Q = np.array([[np.cos(theta), -np.sin(theta)],
[np.sin(theta), np.cos(theta)]])
SIGMA = Q @ LAMBDA @ Q.T
INV_SIGMA = np.linalg.inv(SIGMA)[None, None, :, :]
# Set expectation position (shifting kernel for aligned image)
MU = k_size // 2 - 0.5 * (scale_factor - 1) # - 0.5 * (scale_factor - k_size % 2)
MU = MU[None, None, :, None]
# Create meshgrid for Gaussian
[X, Y] = np.meshgrid(range(k_size[0]), range(k_size[1]))
Z = np.stack([X, Y], 2)[:, :, :, None]
# Calcualte Gaussian for every pixel of the kernel
ZZ = Z - MU
ZZ_t = ZZ.transpose(0, 1, 3, 2)
raw_kernel = np.exp(-0.5 * np.squeeze(ZZ_t @ INV_SIGMA @ ZZ)) * (1 + noise)
# shift the kernel so it will be centered
# raw_kernel_centered = kernel_shift(raw_kernel, scale_factor)
# Normalize the kernel and return
# kernel = raw_kernel_centered / np.sum(raw_kernel_centered)
kernel = raw_kernel / np.sum(raw_kernel)
return kernel
def fspecial_gaussian(hsize, sigma):
hsize = [hsize, hsize]
siz = [(hsize[0] - 1.0) / 2.0, (hsize[1] - 1.0) / 2.0]
std = sigma
[x, y] = np.meshgrid(np.arange(-siz[1], siz[1] + 1), np.arange(-siz[0], siz[0] + 1))
arg = -(x * x + y * y) / (2 * std * std)
h = np.exp(arg)
h[h < scipy.finfo(float).eps * h.max()] = 0
sumh = h.sum()
if sumh != 0:
h = h / sumh
return h
def fspecial_laplacian(alpha):
alpha = max([0, min([alpha, 1])])
h1 = alpha / (alpha + 1)
h2 = (1 - alpha) / (alpha + 1)
h = [[h1, h2, h1], [h2, -4 / (alpha + 1), h2], [h1, h2, h1]]
h = np.array(h)
return h
def fspecial(filter_type, *args, **kwargs):
'''
python code from:
https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py
'''
if filter_type == 'gaussian':
return fspecial_gaussian(*args, **kwargs)
if filter_type == 'laplacian':
return fspecial_laplacian(*args, **kwargs)
"""
# --------------------------------------------
# degradation models
# --------------------------------------------
"""
def bicubic_degradation(x, sf=3):
'''
Args:
x: HxWxC image, [0, 1]
sf: down-scale factor
Return:
bicubicly downsampled LR image
'''
x = util.imresize_np(x, scale=1 / sf)
return x
def srmd_degradation(x, k, sf=3):
''' blur + bicubic downsampling
Args:
x: HxWxC image, [0, 1]
k: hxw, double
sf: down-scale factor
Return:
downsampled LR image
Reference:
@inproceedings{zhang2018learning,
title={Learning a single convolutional super-resolution network for multiple degradations},
author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
pages={3262--3271},
year={2018}
}
'''
x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap') # 'nearest' | 'mirror'
x = bicubic_degradation(x, sf=sf)
return x
def dpsr_degradation(x, k, sf=3):
''' bicubic downsampling + blur
Args:
x: HxWxC image, [0, 1]
k: hxw, double
sf: down-scale factor
Return:
downsampled LR image
Reference:
@inproceedings{zhang2019deep,
title={Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels},
author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
pages={1671--1681},
year={2019}
}
'''
x = bicubic_degradation(x, sf=sf)
x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
return x
def classical_degradation(x, k, sf=3):
''' blur + downsampling
Args:
x: HxWxC image, [0, 1]/[0, 255]
k: hxw, double
sf: down-scale factor
Return:
downsampled LR image
'''
x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
# x = filters.correlate(x, np.expand_dims(np.flip(k), axis=2))
st = 0
return x[st::sf, st::sf, ...]
def add_sharpening(img, weight=0.5, radius=50, threshold=10):
"""USM sharpening. borrowed from real-ESRGAN
Input image: I; Blurry image: B.
1. K = I + weight * (I - B)
2. Mask = 1 if abs(I - B) > threshold, else: 0
3. Blur mask:
4. Out = Mask * K + (1 - Mask) * I
Args:
img (Numpy array): Input image, HWC, BGR; float32, [0, 1].
weight (float): Sharp weight. Default: 1.
radius (float): Kernel size of Gaussian blur. Default: 50.
threshold (int):
"""
if radius % 2 == 0:
radius += 1
blur = cv2.GaussianBlur(img, (radius, radius), 0)
residual = img - blur
mask = np.abs(residual) * 255 > threshold
mask = mask.astype('float32')
soft_mask = cv2.GaussianBlur(mask, (radius, radius), 0)
K = img + weight * residual
K = np.clip(K, 0, 1)
return soft_mask * K + (1 - soft_mask) * img
def add_blur(img, sf=4):
wd2 = 4.0 + sf
wd = 2.0 + 0.2 * sf
wd2 = wd2/4
wd = wd/4
if random.random() < 0.5:
l1 = wd2 * random.random()
l2 = wd2 * random.random()
k = anisotropic_Gaussian(ksize=random.randint(2, 11) + 3, theta=random.random() * np.pi, l1=l1, l2=l2)
else:
k = fspecial('gaussian', random.randint(2, 4) + 3, wd * random.random())
img = ndimage.filters.convolve(img, np.expand_dims(k, axis=2), mode='mirror')
return img
def add_resize(img, sf=4):
rnum = np.random.rand()
if rnum > 0.8: # up
sf1 = random.uniform(1, 2)
elif rnum < 0.7: # down
sf1 = random.uniform(0.5 / sf, 1)
else:
sf1 = 1.0
img = cv2.resize(img, (int(sf1 * img.shape[1]), int(sf1 * img.shape[0])), interpolation=random.choice([1, 2, 3]))
img = np.clip(img, 0.0, 1.0)
return img
# def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
# noise_level = random.randint(noise_level1, noise_level2)
# rnum = np.random.rand()
# if rnum > 0.6: # add color Gaussian noise
# img += np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
# elif rnum < 0.4: # add grayscale Gaussian noise
# img += np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
# else: # add noise
# L = noise_level2 / 255.
# D = np.diag(np.random.rand(3))
# U = orth(np.random.rand(3, 3))
# conv = np.dot(np.dot(np.transpose(U), D), U)
# img += np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
# img = np.clip(img, 0.0, 1.0)
# return img
def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
noise_level = random.randint(noise_level1, noise_level2)
rnum = np.random.rand()
if rnum > 0.6: # add color Gaussian noise
img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
elif rnum < 0.4: # add grayscale Gaussian noise
img = img + np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
else: # add noise
L = noise_level2 / 255.
D = np.diag(np.random.rand(3))
U = orth(np.random.rand(3, 3))
conv = np.dot(np.dot(np.transpose(U), D), U)
img = img + np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
img = np.clip(img, 0.0, 1.0)
return img
def add_speckle_noise(img, noise_level1=2, noise_level2=25):
noise_level = random.randint(noise_level1, noise_level2)
img = np.clip(img, 0.0, 1.0)
rnum = random.random()
if rnum > 0.6:
img += img * np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
elif rnum < 0.4:
img += img * np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
else:
L = noise_level2 / 255.
D = np.diag(np.random.rand(3))
U = orth(np.random.rand(3, 3))
conv = np.dot(np.dot(np.transpose(U), D), U)
img += img * np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
img = np.clip(img, 0.0, 1.0)
return img
def add_Poisson_noise(img):
img = np.clip((img * 255.0).round(), 0, 255) / 255.
vals = 10 ** (2 * random.random() + 2.0) # [2, 4]
if random.random() < 0.5:
img = np.random.poisson(img * vals).astype(np.float32) / vals
else:
img_gray = np.dot(img[..., :3], [0.299, 0.587, 0.114])
img_gray = np.clip((img_gray * 255.0).round(), 0, 255) / 255.
noise_gray = np.random.poisson(img_gray * vals).astype(np.float32) / vals - img_gray
img += noise_gray[:, :, np.newaxis]
img = np.clip(img, 0.0, 1.0)
return img
def add_JPEG_noise(img):
quality_factor = random.randint(80, 95)
img = cv2.cvtColor(util.single2uint(img), cv2.COLOR_RGB2BGR)
result, encimg = cv2.imencode('.jpg', img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor])
img = cv2.imdecode(encimg, 1)
img = cv2.cvtColor(util.uint2single(img), cv2.COLOR_BGR2RGB)
return img
def random_crop(lq, hq, sf=4, lq_patchsize=64):
h, w = lq.shape[:2]
rnd_h = random.randint(0, h - lq_patchsize)
rnd_w = random.randint(0, w - lq_patchsize)
lq = lq[rnd_h:rnd_h + lq_patchsize, rnd_w:rnd_w + lq_patchsize, :]
rnd_h_H, rnd_w_H = int(rnd_h * sf), int(rnd_w * sf)
hq = hq[rnd_h_H:rnd_h_H + lq_patchsize * sf, rnd_w_H:rnd_w_H + lq_patchsize * sf, :]
return lq, hq
def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None):
"""
This is the degradation model of BSRGAN from the paper
"Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
----------
img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf)
sf: scale factor
isp_model: camera ISP model
Returns
-------
img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
"""
isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
sf_ori = sf
h1, w1 = img.shape[:2]
img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop
h, w = img.shape[:2]
if h < lq_patchsize * sf or w < lq_patchsize * sf:
raise ValueError(f'img size ({h1}X{w1}) is too small!')
hq = img.copy()
if sf == 4 and random.random() < scale2_prob: # downsample1
if np.random.rand() < 0.5:
img = cv2.resize(img, (int(1 / 2 * img.shape[1]), int(1 / 2 * img.shape[0])),
interpolation=random.choice([1, 2, 3]))
else:
img = util.imresize_np(img, 1 / 2, True)
img = np.clip(img, 0.0, 1.0)
sf = 2
shuffle_order = random.sample(range(7), 7)
idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
if idx1 > idx2: # keep downsample3 last
shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]
for i in shuffle_order:
if i == 0:
img = add_blur(img, sf=sf)
elif i == 1:
img = add_blur(img, sf=sf)
elif i == 2:
a, b = img.shape[1], img.shape[0]
# downsample2
if random.random() < 0.75:
sf1 = random.uniform(1, 2 * sf)
img = cv2.resize(img, (int(1 / sf1 * img.shape[1]), int(1 / sf1 * img.shape[0])),
interpolation=random.choice([1, 2, 3]))
else:
k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
k_shifted = shift_pixel(k, sf)
k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel
img = ndimage.filters.convolve(img, np.expand_dims(k_shifted, axis=2), mode='mirror')
img = img[0::sf, 0::sf, ...] # nearest downsampling
img = np.clip(img, 0.0, 1.0)
elif i == 3:
# downsample3
img = cv2.resize(img, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
img = np.clip(img, 0.0, 1.0)
elif i == 4:
# add Gaussian noise
img = add_Gaussian_noise(img, noise_level1=2, noise_level2=8)
elif i == 5:
# add JPEG noise
if random.random() < jpeg_prob:
img = add_JPEG_noise(img)
elif i == 6:
# add processed camera sensor noise
if random.random() < isp_prob and isp_model is not None:
with torch.no_grad():
img, hq = isp_model.forward(img.copy(), hq)
# add final JPEG compression noise
img = add_JPEG_noise(img)
# random crop
img, hq = random_crop(img, hq, sf_ori, lq_patchsize)
return img, hq
# todo no isp_model?
def degradation_bsrgan_variant(image, sf=4, isp_model=None):
"""
This is the degradation model of BSRGAN from the paper
"Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
----------
sf: scale factor
isp_model: camera ISP model
Returns
-------
img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
"""
image = util.uint2single(image)
isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
sf_ori = sf
h1, w1 = image.shape[:2]
image = image.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop
h, w = image.shape[:2]
hq = image.copy()
if sf == 4 and random.random() < scale2_prob: # downsample1
if np.random.rand() < 0.5:
image = cv2.resize(image, (int(1 / 2 * image.shape[1]), int(1 / 2 * image.shape[0])),
interpolation=random.choice([1, 2, 3]))
else:
image = util.imresize_np(image, 1 / 2, True)
image = np.clip(image, 0.0, 1.0)
sf = 2
shuffle_order = random.sample(range(7), 7)
idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
if idx1 > idx2: # keep downsample3 last
shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]
for i in shuffle_order:
if i == 0:
image = add_blur(image, sf=sf)
# elif i == 1:
# image = add_blur(image, sf=sf)
if i == 0:
pass
elif i == 2:
a, b = image.shape[1], image.shape[0]
# downsample2
if random.random() < 0.8:
sf1 = random.uniform(1, 2 * sf)
image = cv2.resize(image, (int(1 / sf1 * image.shape[1]), int(1 / sf1 * image.shape[0])),
interpolation=random.choice([1, 2, 3]))
else:
k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
k_shifted = shift_pixel(k, sf)
k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel
image = ndimage.filters.convolve(image, np.expand_dims(k_shifted, axis=2), mode='mirror')
image = image[0::sf, 0::sf, ...] # nearest downsampling
image = np.clip(image, 0.0, 1.0)
elif i == 3:
# downsample3
image = cv2.resize(image, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
image = np.clip(image, 0.0, 1.0)
elif i == 4:
# add Gaussian noise
image = add_Gaussian_noise(image, noise_level1=1, noise_level2=2)
elif i == 5:
# add JPEG noise
if random.random() < jpeg_prob:
image = add_JPEG_noise(image)
#
# elif i == 6:
# # add processed camera sensor noise
# if random.random() < isp_prob and isp_model is not None:
# with torch.no_grad():
# img, hq = isp_model.forward(img.copy(), hq)
# add final JPEG compression noise
image = add_JPEG_noise(image)
image = util.single2uint(image)
example = {"image": image}
return example
if __name__ == '__main__':
print("hey")
img = util.imread_uint('utils/test.png', 3)
img = img[:448, :448]
h = img.shape[0] // 4
print("resizing to", h)
sf = 4
deg_fn = partial(degradation_bsrgan_variant, sf=sf)
for i in range(20):
print(i)
img_hq = img
img_lq = deg_fn(img)["image"]
img_hq, img_lq = util.uint2single(img_hq), util.uint2single(img_lq)
print(img_lq)
img_lq_bicubic = albumentations.SmallestMaxSize(max_size=h, interpolation=cv2.INTER_CUBIC)(image=img_hq)["image"]
print(img_lq.shape)
print("bicubic", img_lq_bicubic.shape)
print(img_hq.shape)
lq_nearest = cv2.resize(util.single2uint(img_lq), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
interpolation=0)
lq_bicubic_nearest = cv2.resize(util.single2uint(img_lq_bicubic),
(int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
interpolation=0)
img_concat = np.concatenate([lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1)
util.imsave(img_concat, str(i) + '.png')
================================================
FILE: models/instructpix2pix/stable_diffusion/ldm/modules/image_degradation/utils_image.py
================================================
import os
import math
import random
import numpy as np
import torch
import cv2
from torchvision.utils import make_grid
from datetime import datetime
#import matplotlib.pyplot as plt # TODO: check with Dominik, also bsrgan.py vs bsrgan_light.py
os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
'''
# --------------------------------------------
# Kai Zhang (github: https://github.com/cszn)
# 03/Mar/2019
# --------------------------------------------
# https://github.com/twhui/SRGAN-pyTorch
# https://github.com/xinntao/BasicSR
# --------------------------------------------
'''
IMG_EXTENSIONS = ['.jpg', '.JPG', '.jpeg', '.JPEG', '.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP', '.tif']
def is_image_file(filename):
return any(filename.endswith(extension) for extension in IMG_EXTENSIONS)
def get_timestamp():
return datetime.now().strftime('%y%m%d-%H%M%S')
def imshow(x, title=None, cbar=False, figsize=None):
plt.figure(figsize=figsize)
plt.imshow(np.squeeze(x), interpolation='nearest', cmap='gray')
if title:
plt.title(title)
if cbar:
plt.colorbar()
plt.show()
def surf(Z, cmap='rainbow', figsize=None):
plt.figure(figsize=figsize)
ax3 = plt.axes(projection='3d')
w, h = Z.shape[:2]
xx = np.arange(0,w,1)
yy = np.arange(0,h,1)
X, Y = np.meshgrid(xx, yy)
ax3.plot_surface(X,Y,Z,cmap=cmap)
#ax3.contour(X,Y,Z, zdim='z',offset=-2，cmap=cmap)
plt.show()
'''
# --------------------------------------------
# get image pathes
# --------------------------------------------
'''
def get_image_paths(dataroot):
paths = None # return None if dataroot is None
if dataroot is not None:
paths = sorted(_get_paths_from_images(dataroot))
return paths
def _get_paths_from_images(path):
assert os.path.isdir(path), '{:s} is not a valid directory'.format(path)
images = []
for dirpath, _, fnames in sorted(os.walk(path)):
for fname in sorted(fnames):
if is_image_file(fname):
img_path = os.path.join(dirpath, fname)
images.append(img_path)
assert images, '{:s} has no valid image file'.format(path)
return images
'''
# --------------------------------------------
# split large images into small images
# --------------------------------------------
'''
def patches_from_image(img, p_size=512, p_overlap=64, p_max=800):
w, h = img.shape[:2]
patches = []
if w > p_max and h > p_max:
w1 = list(np.arange(0, w-p_size, p_size-p_overlap, dtype=np.int))
h1 = list(np.arange(0, h-p_size, p_size-p_overlap, dtype=np.int))
w1.append(w-p_size)
h1.append(h-p_size)
# print(w1)
# print(h1)
for i in w1:
for j in h1:
patches.append(img[i:i+p_size, j:j+p_size,:])
else:
patches.append(img)
return patches
def imssave(imgs, img_path):
"""
imgs: list, N images of size WxHxC
"""
img_name, ext = os.path.splitext(os.path.basename(img_path))
for i, img in enumerate(imgs):
if img.ndim == 3:
img = img[:, :, [2, 1, 0]]
new_path = os.path.join(os.path.dirname(img_path), img_name+str('_s{:04d}'.format(i))+'.png')
cv2.imwrite(new_path, img)
def split_imageset(original_dataroot, taget_dataroot, n_channels=3, p_size=800, p_overlap=96, p_max=1000):
"""
split the large images from original_dataroot into small overlapped images with size (p_size)x(p_size),
and save them into taget_dataroot; only the images with larger size than (p_max)x(p_max)
will be splitted.
Args:
original_dataroot:
taget_dataroot:
p_size: size of small images
p_overlap: patch size in training is a good choice
p_max: images with smaller size than (p_max)x(p_max) keep unchanged.
"""
paths = get_image_paths(original_dataroot)
for img_path in paths:
# img_name, ext = os.path.splitext(os.path.basename(img_path))
img = imread_uint(img_path, n_channels=n_channels)
patches = patches_from_image(img, p_size, p_overlap, p_max)
imssave(patches, os.path.join(taget_dataroot,os.path.basename(img_path)))
#if original_dataroot == taget_dataroot:
#del img_path
'''
# --------------------------------------------
# makedir
# --------------------------------------------
'''
def mkdir(path):
if not os.path.exists(path):
os.makedirs(path)
def mkdirs(paths):
if isinstance(paths, str):
mkdir(paths)
else:
for path in paths:
mkdir(path)
def mkdir_and_rename(path):
if os.path.exists(path):
new_name = path + '_archived_' + get_timestamp()
print('Path already exists. Rename it to [{:s}]'.format(new_name))
os.rename(path, new_name)
os.makedirs(path)
'''
# --------------------------------------------
# read image from path
# opencv is fast, but read BGR numpy image
# --------------------------------------------
'''
# --------------------------------------------
# get uint8 image of size HxWxn_channles (RGB)
# --------------------------------------------
def imread_uint(path, n_channels=3):
# input: path
# output: HxWx3(RGB or GGG), or HxWx1 (G)
if n_channels == 1:
img = cv2.imread(path, 0) # cv2.IMREAD_GRAYSCALE
img = np.expand_dims(img, axis=2) # HxWx1
elif n_channels == 3:
img = cv2.imread(path, cv2.IMREAD_UNCHANGED) # BGR or G
if img.ndim == 2:
img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB) # GGG
else:
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # RGB
return img
# --------------------------------------------
# matlab's imwrite
# --------------------------------------------
def imsave(img, img_path):
img = np.squeeze(img)
if img.ndim == 3:
img = img[:, :, [2, 1, 0]]
cv2.imwrite(img_path, img)
def imwrite(img, img_path):
img = np.squeeze(img)
if img.ndim == 3:
img = img[:, :, [2, 1, 0]]
cv2.imwrite(img_path, img)
# --------------------------------------------
# get single image of size HxWxn_channles (BGR)
# --------------------------------------------
def read_img(path):
# read image by cv2
# return: Numpy float32, HWC, BGR, [0,1]
img = cv2.imread(path, cv2.IMREAD_UNCHANGED) # cv2.IMREAD_GRAYSCALE
img = img.astype(np.float32) / 255.
if img.ndim == 2:
img = np.expand_dims(img, axis=2)
# some images have 4 channels
if img.shape[2] > 3:
img = img[:, :, :3]
return img
'''
# --------------------------------------------
# image format conversion
# --------------------------------------------
# numpy(single) <---> numpy(unit)
# numpy(single) <---> tensor
# numpy(unit) <---> tensor
# --------------------------------------------
'''
# --------------------------------------------
# numpy(single) [0, 1] <---> numpy(unit)
# --------------------------------------------
def uint2single(img):
return np.float32(img/255.)
def single2uint(img):
return np.uint8((img.clip(0, 1)*255.).round())
def uint162single(img):
return np.float32(img/65535.)
def single2uint16(img):
return np.uint16((img.clip(0, 1)*65535.).round())
# --------------------------------------------
# numpy(unit) (HxWxC or HxW) <---> tensor
# --------------------------------------------
# convert uint to 4-dimensional torch tensor
def uint2tensor4(img):
if img.ndim == 2:
img = np.expand_dims(img, axis=2)
return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.).unsqueeze(0)
# convert uint to 3-dimensional torch tensor
def uint2tensor3(img):
if img.ndim == 2:
img = np.expand_dims(img, axis=2)
return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.)
# convert 2/3/4-dimensional torch tensor to uint
def tensor2uint(img):
img = img.data.squeeze().float().clamp_(0, 1).cpu().numpy()
if img.ndim == 3:
img = np.transpose(img, (1, 2, 0))
return np.uint8((img*255.0).round())
# --------------------------------------------
# numpy(single) (HxWxC) <---> tensor
# --------------------------------------------
# convert single (HxWxC) to 3-dimensional torch tensor
def single2tensor3(img):
return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float()
# convert single (HxWxC) to 4-dimensional torch tensor
def single2tensor4(img):
return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().unsqueeze(0)
# convert torch tensor to single
def tensor2single(img):
img = img.data.squeeze().float().cpu().numpy()
if img.ndim == 3:
img = np.transpose(img, (1, 2, 0))
return img
# convert torch tensor to single
def tensor2single3(img):
img = img.data.squeeze().float().cpu().numpy()
if img.ndim == 3:
img = np.transpose(img, (1, 2, 0))
elif img.ndim == 2:
img = np.expand_dims(img, axis=2)
return img
def single2tensor5(img):
return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1, 3).float().unsqueeze(0)
def single32tensor5(img):
return torch.from_numpy(np.ascontiguousarray(img)).float().unsqueeze(0).unsqueeze(0)
def single42tensor4(img):
return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1, 3).float()
# from skimage.io import imread, imsave
def tensor2img(tensor, out_type=np.uint8, min_max=(0, 1)):
'''
Converts a torch Tensor into an image Numpy array of BGR channel order
Input: 4D(B,(3/1),H,W), 3D(C,H,W), or 2D(H,W), any range, RGB channel order
Output: 3D(H,W,C) or 2D(H,W), [0,255], np.uint8 (default)
'''
tensor = tensor.squeeze().float().cpu().clamp_(*min_max) # squeeze first, then clamp
tensor = (tensor - min_max[0]) / (min_max[1] - min_max[0]) # to range [0,1]
n_dim = tensor.dim()
if n_dim == 4:
n_img = len(tensor)
img_np = make_grid(tensor, nrow=int(math.sqrt(n_img)), normalize=False).numpy()
img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0)) # HWC, BGR
elif n_dim == 3:
img_np = tensor.numpy()
img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0)) # HWC, BGR
elif n_dim == 2:
img_np = tensor.numpy()
else:
raise TypeError(
'Only support 4D, 3D and 2D tensor. But received with dimension: {:d}'.format(n_dim))
if out_type == np.uint8:
img_np = (img_np * 255.0).round()
# Important. Unlike matlab, numpy.unit8() WILL NOT round by default.
return img_np.astype(out_type)
'''
# --------------------------------------------
# Augmentation, flipe and/or rotate
# --------------------------------------------
# The following two are enough.
# (1) augmet_img: numpy image of WxHxC or WxH
# (2) augment_img_tensor4: tensor image 1xCxWxH
# --------------------------------------------
'''
def augment_img(img, mode=0):
'''Kai Zhang (github: https://github.com/cszn)
'''
if mode == 0:
return img
elif mode == 1:
return np.flipud(np.rot90(img))
elif mode == 2:
return np.flipud(img)
elif mode == 3:
return np.rot90(img, k=3)
elif mode == 4:
return np.flipud(np.rot90(img, k=2))
elif mode == 5:
return np.rot90(img)
elif mode == 6:
return np.rot90(img, k=2)
elif mode == 7:
return np.flipud(np.rot90(img, k=3))
def augment_img_tensor4(img, mode=0):
'''Kai Zhang (github: https://github.com/cszn)
'''
if mode == 0:
return img
elif mode == 1:
return img.rot90(1, [2, 3]).flip([2])
elif mode == 2:
return img.flip([2])
elif mode == 3:
return img.rot90(3, [2, 3])
elif mode == 4:
return img.rot90(2, [2, 3]).flip([2])
elif mode == 5:
return img.rot90(1, [2, 3])
elif mode == 6:
return img.rot90(2, [2, 3])
elif mode == 7:
return img.rot90(3, [2, 3]).flip([2])
def augment_img_tensor(img, mode=0):
'''Kai Zhang (github: https://github.com/cszn)
'''
img_size = img.size()
img_np = img.data.cpu().numpy()
if len(img_size) == 3:
img_np = np.transpose(img_np, (1, 2, 0))
elif len(img_size) == 4:
img_np = np.transpose(img_np, (2, 3, 1, 0))
img_np = augment_img(img_np, mode=mode)
img_tensor = torch.from_numpy(np.ascontiguousarray(img_np))
if len(img_size) == 3:
img_tensor = img_tensor.permute(2, 0, 1)
elif len(img_size) == 4:
img_tensor = img_tensor.permute(3, 2, 0, 1)
return img_tensor.type_as(img)
def augment_img_np3(img, mode=0):
if mode == 0:
return img
elif mode == 1:
return img.transpose(1, 0, 2)
elif mode == 2:
return img[::-1, :, :]
elif mode == 3:
img = img[::-1, :, :]
img = img.transpose(1, 0, 2)
return img
elif mode == 4:
return img[:, ::-1, :]
elif mode == 5:
img = img[:, ::-1, :]
img = img.transpose(1, 0, 2)
return img
elif mode == 6:
img = img[:, ::-1, :]
img = img[::-1, :, :]
return img
elif mode == 7:
img = img[:, ::-1, :]
img = img[::-1, :, :]
img = img.transpose(1, 0, 2)
return img
def augment_imgs(img_list, hflip=True, rot=True):
# horizontal flip OR rotate
hflip = hflip and random.random() < 0.5
vflip = rot and random.random() < 0.5
rot90 = rot and random.random() < 0.5
def _augment(img):
if hflip:
img = img[:, ::-1, :]
if vflip:
img = img[::-1, :, :]
if rot90:
img = img.transpose(1, 0, 2)
return img
return [_augment(img) for img in img_list]
'''
# --------------------------------------------
# modcrop and shave
# --------------------------------------------
'''
def modcrop(img_in, scale):
# img_in: Numpy, HWC or HW
img = np.copy(img_in)
if img.ndim == 2:
H, W = img.shape
H_r, W_r = H % scale, W % scale
img = img[:H - H_r, :W - W_r]
elif img.ndim == 3:
H, W, C = img.shape
H_r, W_r = H % scale, W % scale
img = img[:H - H_r, :W - W_r, :]
else:
raise ValueError('Wrong img ndim: [{:d}].'.format(img.ndim))
return img
def shave(img_in, border=0):
# img_in: Numpy, HWC or HW
img = np.copy(img_in)
h, w = img.shape[:2]
img = img[border:h-border, border:w-border]
return img
'''
# --------------------------------------------
# image processing process on numpy image
# channel_convert(in_c, tar_type, img_list):
# rgb2ycbcr(img, only_y=True):
# bgr2ycbcr(img, only_y=True):
# ycbcr2rgb(img):
# --------------------------------------------
'''
def rgb2ycbcr(img, only_y=True):
'''same as matlab rgb2ycbcr
only_y: only return Y channel
Input:
uint8, [0, 255]
float, [0, 1]
'''
in_img_type = img.dtype
img.astype(np.float32)
if in_img_type != np.uint8:
img *= 255.
# convert
if only_y:
rlt = np.dot(img, [65.481, 128.553, 24.966]) / 255.0 + 16.0
else:
rlt = np.matmul(img, [[65.481, -37.797, 112.0], [128.553, -74.203, -93.786],
[24.966, 112.0, -18.214]]) / 255.0 + [16, 128, 128]
if in_img_type == np.uint8:
rlt = rlt.round()
else:
rlt /= 255.
return rlt.astype(in_img_type)
def ycbcr2rgb(img):
'''same as matlab ycbcr2rgb
Input:
uint8, [0, 255]
float, [0, 1]
'''
in_img_type = img.dtype
img.astype(np.float32)
if in_img_type != np.uint8:
img *= 255.
# convert
rlt = np.matmul(img, [[0.00456621, 0.00456621, 0.00456621], [0, -0.00153632, 0.00791071],
[0.00625893, -0.00318811, 0]]) * 255.0 + [-222.921, 135.576, -276.836]
if in_img_type == np.uint8:
rlt = rlt.round()
else:
rlt /= 255.
return rlt.astype(in_img_type)
def bgr2ycbcr(img, only_y=True):
'''bgr version of rgb2ycbcr
only_y: only return Y channel
Input:
uint8, [0, 255]
float, [0, 1]
'''
in_img_type = img.dtype
img.astype(np.float32)
if in_img_type != np.uint8:
img *= 255.
# convert
if only_y:
rlt = np.dot(img, [24.966, 128.553, 65.481]) / 255.0 + 16.0
else:
rlt = np.matmul(img, [[24.966, 112.0, -18.214], [128.553, -74.203, -93.786],
[65.481, -37.797, 112.0]]) / 255.0 + [16, 128, 128]
if in_img_type == np.uint8:
rlt = rlt.round()
else:
rlt /= 255.
return rlt.astype(in_img_type)
def channel_convert(in_c, tar_type, img_list):
# conversion among BGR, gray and y
if in_c == 3 and tar_type == 'gray': # BGR to gray
gray_list = [cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) for img in img_list]
return [np.expand_dims(img, axis=2) for img in gray_list]
elif in_c == 3 and tar_type == 'y': # BGR to y
y_list = [bgr2ycbcr(img, only_y=True) for img in img_list]
return [np.expand_dims(img, axis=2) for img in y_list]
elif in_c == 1 and tar_type == 'RGB': # gray/y to BGR
return [cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) for img in img_list]
else:
return img_list
'''
# --------------------------------------------
# metric, PSNR and SSIM
# --------------------------------------------
'''
# --------------------------------------------
# PSNR
# --------------------------------------------
def calculate_psnr(img1, img2, border=0):
# img1 and img2 have range [0, 255]
#img1 = img1.squeeze()
#img2 = img2.squeeze()
if not img1.shape == img2.shape:
raise ValueError('Input images must have the same dimensions.')
h, w = img1.shape[:2]
img1 = img1[border:h-border, border:w-border]
img2 = img2[border:h-border, border:w-border]
img1 = img1.astype(np.float64)
img2 = img2.astype(np.float64)
mse = np.mean((img1 - img2)**2)
if mse == 0:
return float('inf')
return 20 * math.log10(255.0 / math.sqrt(mse))
# --------------------------------------------
# SSIM
# --------------------------------------------
def calculate_ssim(img1, img2, border=0):
'''calculate SSIM
the same outputs as MATLAB's
img1, img2: [0, 255]
'''
#img1 = img1.squeeze()
#img2 = img2.squeeze()
if not img1.shape == img2.shape:
raise ValueError('Input images must have the same dimensions.')
h, w = img1.shape[:2]
img1 = img1[border:h-border, border:w-border]
img2 = img2[border:h-border, border:w-border]
if img1.ndim == 2:
return ssim(img1, img2)
elif img1.ndim == 3:
if img1.shape[2] == 3:
ssims = []
for i in range(3):
ssims.append(ssim(img1[:,:,i], img2[:,:,i]))
return np.array(ssims).mean()
elif img1.shape[2] == 1:
return ssim(np.squeeze(img1), np.squeeze(img2))
else:
raise ValueError('Wrong input image dimensions.')
def ssim(img1, img2):
C1 = (0.01 * 255)**2
C2 = (0.03 * 255)**2
img1 = img1.astype(np.float64)
img2 = img2.astype(np.float64)
kernel = cv2.getGaussianKernel(11, 1.5)
window = np.outer(kernel, kernel.transpose())
mu1 = cv2.filter2D(img1, -1, window)[5:-5, 5:-5] # valid
mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5]
mu1_sq = mu1**2
mu2_sq = mu2**2
mu1_mu2 = mu1 * mu2
sigma1_sq = cv2.filter2D(img1**2, -1, window)[5:-5, 5:-5] - mu1_sq
sigma2_sq = cv2.filter2D(img2**2, -1, window)[5:-5, 5:-5] - mu2_sq
sigma12 = cv2.filter2D(img1 * img2, -1, window)[5:-5, 5:-5] - mu1_mu2
ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) *
(sigma1_sq + sigma2_sq + C2))
return ssim_map.mean()
'''
# --------------------------------------------
# matlab's bicubic imresize (numpy and torch) [0, 1]
# --------------------------------------------
'''
# matlab 'imresize' function, now only support 'bicubic'
def cubic(x):
absx = torch.abs(x)
absx2 = absx**2
absx3 = absx**3
return (1.5*absx3 - 2.5*absx2 + 1) * ((absx <= 1).type_as(absx)) + \
(-0.5*absx3 + 2.5*absx2 - 4*absx + 2) * (((absx > 1)*(absx <= 2)).type_as(absx))
def calculate_weights_indices(in_length, out_length, scale, kernel, kernel_width, antialiasing):
if (scale < 1) and (antialiasing):
# Use a modified kernel to simultaneously interpolate and antialias- larger kernel width
kernel_width = kernel_width / scale
# Output-space coordinates
x = torch.linspace(1, out_length, out_length)
# Input-space coordinates. Calculate the inverse mapping such that 0.5
# in output space maps to 0.5 in input space, and 0.5+scale in output
# space maps to 1.5 in input space.
u = x / scale + 0.5 * (1 - 1 / scale)
# What is the left-most pixel that can be involved in the computation?
left = torch.floor(u - kernel_width / 2)
# What is the maximum number of pixels that can be involved in the
# computation? Note: it's OK to use an extra pixel here; if the
# corresponding weights are all zero, it will be eliminated at the end
# of this function.
P = math.ceil(kernel_width) + 2
# The indices of the input pixels involved in computing the k-th output
# pixel are in row k of the indices matrix.
indices = left.view(out_length, 1).expand(out_length, P) + torch.linspace(0, P - 1, P).view(
1, P).expand(out_length, P)
# The weights used to compute the k-th output pixel are in row k of the
# weights matrix.
distance_to_center = u.view(out_length, 1).expand(out_length, P) - indices
# apply cubic kernel
if (scale < 1) and (antialiasing):
weights = scale * cubic(distance_to_center * scale)
else:
weights = cubic(distance_to_center)
# Normalize the weights matrix so that each row sums to 1.
weights_sum = torch.sum(weights, 1).view(out_length, 1)
weights = weights / weights_sum.expand(out_length, P)
# If a column in weights is all zero, get rid of it. only consider the first and last column.
weights_zero_tmp = torch.sum((weights == 0), 0)
if not math.isclose(weights_zero_tmp[0], 0, rel_tol=1e-6):
indices = indices.narrow(1, 1, P - 2)
weights = weights.narrow(1, 1, P - 2)
if not math.isclose(weights_zero_tmp[-1], 0, rel_tol=1e-6):
indices = indices.narrow(1, 0, P - 2)
weights = weights.narrow(1, 0, P - 2)
weights = weights.contiguous()
indices = indices.contiguous()
sym_len_s = -indices.min() + 1
sym_len_e = indices.max() - in_length
indices = indices + sym_len_s - 1
return weights, indices, int(sym_len_s), int(sym_len_e)
# --------------------------------------------
# imresize for tensor image [0, 1]
# --------------------------------------------
def imresize(img, scale, antialiasing=True):
# Now the scale should be the same for H and W
# input: img: pytorch tensor, CHW or HW [0,1]
# output: CHW or HW [0,1] w/o round
need_squeeze = True if img.dim() == 2 else False
if need_squeeze:
img.unsqueeze_(0)
in_C, in_H, in_W = img.size()
out_C, out_H, out_W = in_C, math.ceil(in_H * scale), math.ceil(in_W * scale)
kernel_width = 4
kernel = 'cubic'
# Return the desired dimension order for performing the resize. The
# strategy is to perform the resize first along the dimension with the
# smallest scale factor.
# Now we do not support this.
# get weights and indices
weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices(
in_H, out_H, scale, kernel, kernel_width, antialiasing)
weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices(
in_W, out_W, scale, kernel, kernel_width, antialiasing)
# process H dimension
# symmetric copying
img_aug = torch.FloatTensor(in_C, in_H + sym_len_Hs + sym_len_He, in_W)
img_aug.narrow(1, sym_len_Hs, in_H).copy_(img)
sym_patch = img[:, :sym_len_Hs, :]
inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
sym_patch_inv = sym_patch.index_select(1, inv_idx)
img_aug.narrow(1, 0, sym_len_Hs).copy_(sym_patch_inv)
sym_patch = img[:, -sym_len_He:, :]
inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
sym_patch_inv = sym_patch.index_select(1, inv_idx)
img_aug.narrow(1, sym_len_Hs + in_H, sym_len_He).copy_(sym_patch_inv)
out_1 = torch.FloatTensor(in_C, out_H, in_W)
kernel_width = weights_H.size(1)
for i in range(out_H):
idx = int(indices_H[i][0])
for j in range(out_C):
out_1[j, i, :] = img_aug[j, idx:idx + kernel_width, :].transpose(0, 1).mv(weights_H[i])
# process W dimension
# symmetric copying
out_1_aug = torch.FloatTensor(in_C, out_H, in_W + sym_len_Ws + sym_len_We)
out_1_aug.narrow(2, sym_len_Ws, in_W).copy_(out_1)
sym_patch = out_1[:, :, :sym_len_Ws]
inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long()
sym_patch_inv = sym_patch.index_select(2, inv_idx)
out_1_aug.narrow(2, 0, sym_len_Ws).copy_(sym_patch_inv)
sym_patch = out_1[:, :, -sym_len_We:]
inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long()
sym_patch_inv = sym_patch.index_select(2, inv_idx)
out_1_aug.narrow(2, sym_len_Ws + in_W, sym_len_We).copy_(sym_patch_inv)
out_2 = torch.FloatTensor(in_C, out_H, out_W)
kernel_width = weights_W.size(1)
for i in range(out_W):
idx = int(indices_W[i][0])
for j in range(out_C):
out_2[j, :, i] = out_1_aug[j, :, idx:idx + kernel_width].mv(weights_W[i])
if need_squeeze:
out_2.squeeze_()
return out_2
# --------------------------------------------
# imresize for numpy image [0, 1]
# --------------------------------------------
def imresize_np(img, scale, antialiasing=True):
# Now the scale should be the same for H and W
# input: img: Numpy, HWC or HW [0,1]
# output: HWC or HW [0,1] w/o round
img = torch.from_numpy(img)
need_squeeze = True if img.dim() == 2 else False
if need_squeeze:
img.unsqueeze_(2)
in_H, in_W, in_C = img.size()
out_C, out_H, out_W = in_C, math.ceil(in_H * scale), math.ceil(in_W * scale)
kernel_width = 4
kernel = 'cubic'
# Return the desired dimension order for performing the resize. The
# strategy is to perform the resize first along the dimension with the
# smallest scale factor.
# Now we do not support this.
# get weights and indices
weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices(
in_H, out_H, scale, kernel, kernel_width, antialiasing)
weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices(
in_W, out_W, scale, kernel, kernel_width, antialiasing)
# process H dimension
# symmetric copying
img_aug = torch.FloatTensor(in_H + sym_len_Hs + sym_len_He, in_W, in_C)
img_aug.narrow(0, sym_len_Hs, in_H).copy_(img)
sym_patch = img[:sym_len_Hs, :, :]
inv_idx = torch.arange(sym_patch.size(0) - 1, -1, -1).long()
sym_patch_inv = sym_patch.index_select(0, inv_idx)
img_aug.narrow(0, 0, sym_len_Hs).copy_(sym_patch_inv)
sym_patch = img[-sym_len_He:, :, :]
inv_idx = torch.arange(sym_patch.size(0) - 1, -1, -1).long()
sym_patch_inv = sym_patch.index_select(0, inv_idx)
img_aug.narrow(0, sym_len_Hs + in_H, sym_len_He).copy_(sym_patch_inv)
out_1 = torch.FloatTensor(out_H, in_W, in_C)
kernel_width = weights_H.size(1)
for i in range(out_H):
idx = int(indices_H[i][0])
for j in range(out_C):
out_1[i, :, j] = img_aug[idx:idx + kernel_width, :, j].transpose(0, 1).mv(weights_H[i])
# process W dimension
# symmetric copying
out_1_aug = torch.FloatTensor(out_H, in_W + sym_len_Ws + sym_len_We, in_C)
out_1_aug.narrow(1, sym_len_Ws, in_W).copy_(out_1)
sym_patch = out_1[:, :sym_len_Ws, :]
inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
sym_patch_inv = sym_patch.index_select(1, inv_idx)
out_1_aug.narrow(1, 0, sym_len_Ws).copy_(sym_patch_inv)
sym_patch = out_1[:, -sym_len_We:, :]
inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
sym_patch_inv = sym_patch.index_select(1, inv_idx)
out_1_aug.narrow(1, sym_len_Ws + in_W, sym_len_We).copy_(sym_patch_inv)
out_2 = torch.FloatTensor(out_H, out_W, in_C)
kernel_width = weights_W.size(1)
for i in range(out_W):
idx = int(indices_W[i][0])
for j in range(out_C):
out_2[:, i, j] = out_1_aug[:, idx:idx + kernel_width, j].mv(weights_W[i])
if need_squeeze:
out_2.squeeze_()
return out_2.numpy()
if __name__ == '__main__':
print('---')
# img = imread_uint('test.bmp', 3)
# img = uint2single(img)
# img_bicubic = imresize_np(img, 1/4)
================================================
FILE: models/instructpix2pix/stable_diffusion/ldm/modules/losses/__init__.py
================================================
from ldm.modules.losses.contperceptual import LPIPSWithDiscriminator
================================================
FILE: models/instructpix2pix/stable_diffusion/ldm/modules/losses/contperceptual.py
================================================
import torch
import torch.nn as nn
from taming.modules.losses.vqperceptual import * # TODO: taming dependency yes/no?
class LPIPSWithDiscriminator(nn.Module):
def __init__(self, disc_start, logvar_init=0.0, kl_weight=1.0, pixelloss_weight=1.0,
disc_num_layers=3, disc_in_channels=3, disc_factor=1.0, disc_weight=1.0,
perceptual_weight=1.0, use_actnorm=False, disc_conditional=False,
disc_loss="hinge"):
super().__init__()
assert disc_loss in ["hinge", "vanilla"]
self.kl_weight = kl_weight
self.pixel_weight = pixelloss_weight
self.perceptual_loss = LPIPS().eval()
self.perceptual_weight = perceptual_weight
# output log variance
self.logvar = nn.Parameter(torch.ones(size=()) * logvar_init)
self.discriminator = NLayerDiscriminator(input_nc=disc_in_channels,
n_layers=disc_num_layers,
use_actnorm=use_actnorm
).apply(weights_init)
self.discriminator_iter_start = disc_start
self.disc_loss = hinge_d_loss if disc_loss == "hinge" else vanilla_d_loss
self.disc_factor = disc_factor
self.discriminator_weight = disc_weight
self.disc_conditional = disc_conditional
def calculate_adaptive_weight(self, nll_loss, g_loss, last_layer=None):
if last_layer is not None:
nll_grads = torch.autograd.grad(nll_loss, last_layer, retain_graph=True)[0]
g_grads = torch.autograd.grad(g_loss, last_layer, retain_graph=True)[0]
else:
nll_grads = torch.autograd.grad(nll_loss, self.last_layer[0], retain_graph=True)[0]
g_grads = torch.autograd.grad(g_loss, self.last_layer[0], retain_graph=True)[0]
d_weight = torch.norm(nll_grads) / (torch.norm(g_grads) + 1e-4)
d_weight = torch.clamp(d_weight, 0.0, 1e4).detach()
d_weight = d_weight * self.discriminator_weight
return d_weight
def forward(self, inputs, reconstructions, posteriors, optimizer_idx,
global_step, last_layer=None, cond=None, split="train",
weights=None):
rec_loss = torch.abs(inputs.contiguous() - reconstructions.contiguous())
if self.perceptual_weight > 0:
p_loss = self.perceptual_loss(inputs.contiguous(), reconstructions.contiguous())
rec_loss = rec_loss + self.perceptual_weight * p_loss
nll_loss = rec_loss / torch.exp(self.logvar) + self.logvar
weighted_nll_loss = nll_loss
if weights is not None:
weighted_nll_loss = weights*nll_loss
weighted_nll_loss = torch.sum(weighted_nll_loss) / weighted_nll_loss.shape[0]
nll_loss = torch.sum(nll_loss) / nll_loss.shape[0]
kl_loss = posteriors.kl()
kl_loss = torch.sum(kl_loss) / kl_loss.shape[0]
# now the GAN part
if optimizer_idx == 0:
# generator update
if cond is None:
assert not self.disc_conditional
logits_fake = self.discriminator(reconstructions.contiguous())
else:
assert self.disc_conditional
logits_fake = self.discriminator(torch.cat((reconstructions.contiguous(), cond), dim=1))
g_loss = -torch.mean(logits_fake)
if self.disc_factor > 0.0:
try:
d_weight = self.calculate_adaptive_weight(nll_loss, g_loss, last_layer=last_layer)
except RuntimeError:
assert not self.training
d_weight = torch.tensor(0.0)
else:
d_weight = torch.tensor(0.0)
disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start)
loss = weighted_nll_loss + self.kl_weight * kl_loss + d_weight * disc_factor * g_loss
log = {"{}/total_loss".format(split): loss.clone().detach().mean(), "{}/logvar".format(split): self.logvar.detach(),
"{}/kl_loss".format(split): kl_loss.detach().mean(), "{}/nll_loss".format(split): nll_loss.detach().mean(),
"{}/rec_loss".format(split): rec_loss.detach().mean(),
"{}/d_weight".format(split): d_weight.detach(),
"{}/disc_factor".format(split): torch.tensor(disc_factor),
"{}/g_loss".format(split): g_loss.detach().mean(),
}
return loss, log
if optimizer_idx == 1:
# second pass for discriminator update
if cond is None:
logits_real = self.discriminator(inputs.contiguous().detach())
logits_fake = self.discriminator(reconstructions.contiguous().detach())
else:
logits_real = self.discriminator(torch.cat((inputs.contiguous().detach(), cond), dim=1))
logits_fake = self.discriminator(torch.cat((reconstructions.contiguous().detach(), cond), dim=1))
disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start)
d_loss = disc_factor * self.disc_loss(logits_real, logits_fake)
log = {"{}/disc_loss".format(split): d_loss.clone().detach().mean(),
"{}/logits_real".format(split): logits_real.detach().mean(),
"{}/logits_fake".format(split): logits_fake.detach().mean()
}
return d_loss, log
================================================
FILE: models/instructpix2pix/stable_diffusion/ldm/modules/losses/vqperceptual.py
================================================
import torch
from torch import nn
import torch.nn.functional as F
from einops import repeat
from taming.modules.discriminator.model import NLayerDiscriminator, weights_init
from taming.modules.losses.lpips import LPIPS
from taming.modules.losses.vqperceptual import hinge_d_loss, vanilla_d_loss
def hinge_d_loss_with_exemplar_weights(logits_real, logits_fake, weights):
assert weights.shape[0] == logits_real.shape[0] == logits_fake.shape[0]
loss_real = torch.mean(F.relu(1. - logits_real), dim=[1,2,3])
loss_fake = torch.mean(F.relu(1. + logits_fake), dim=[1,2,3])
loss_real = (weights * loss_real).sum() / weights.sum()
loss_fake = (weights * loss_fake).sum() / weights.sum()
d_loss = 0.5 * (loss_real + loss_fake)
return d_loss
def adopt_weight(weight, global_step, threshold=0, value=0.):
if global_step < threshold:
weight = value
return weight
def measure_perplexity(predicted_indices, n_embed):
# src: https://github.com/karpathy/deep-vector-quantization/blob/main/model.py
# eval cluster perplexity. when perplexity == num_embeddings then all clusters are used exactly equally
encodings = F.one_hot(predicted_indices, n_embed).float().reshape(-1, n_embed)
avg_probs = encodings.mean(0)
perplexity = (-(avg_probs * torch.log(avg_probs + 1e-10)).sum()).exp()
cluster_use = torch.sum(avg_probs > 0)
return perplexity, cluster_use
def l1(x, y):
return torch.abs(x-y)
def l2(x, y):
return torch.pow((x-y), 2)
class VQLPIPSWithDiscriminator(nn.Module):
def __init__(self, disc_start, codebook_weight=1.0, pixelloss_weight=1.0,
disc_num_layers=3, disc_in_channels=3, disc_factor=1.0, disc_weight=1.0,
perceptual_weight=1.0, use_actnorm=False, disc_conditional=False,
disc_ndf=64, disc_loss="hinge", n_classes=None, perceptual_loss="lpips",
pixel_loss="l1"):
super().__init__()
assert disc_loss in ["hinge", "vanilla"]
assert perceptual_loss in ["lpips", "clips", "dists"]
assert pixel_loss in ["l1", "l2"]
self.codebook_weight = codebook_weight
self.pixel_weight = pixelloss_weight
if perceptual_loss == "lpips":
print(f"{self.__class__.__name__}: Running with LPIPS.")
self.perceptual_loss = LPIPS().eval()
else:
raise ValueError(f"Unknown perceptual loss: >> {perceptual_loss} <<")
self.perceptual_weight = perceptual_weight
if pixel_loss == "l1":
self.pixel_loss = l1
else:
self.pixel_loss = l2
self.discriminator = NLayerDiscriminator(input_nc=disc_in_channels,
n_layers=disc_num_layers,
use_actnorm=use_actnorm,
ndf=disc_ndf
).apply(weights_init)
self.discriminator_iter_start = disc_start
if disc_loss == "hinge":
self.disc_loss = hinge_d_loss
elif disc_loss == "vanilla":
self.disc_loss = vanilla_d_loss
else:
raise ValueError(f"Unknown GAN loss '{disc_loss}'.")
print(f"VQLPIPSWithDiscriminator running with {disc_loss} loss.")
self.disc_factor = disc_factor
self.discriminator_weight = disc_weight
self.disc_conditional = disc_conditional
self.n_classes = n_classes
def calculate_adaptive_weight(self, nll_loss, g_loss, last_layer=None):
if last_layer is not None:
nll_grads = torch.autograd.grad(nll_loss, last_layer, retain_graph=True)[0]
g_grads = torch.autograd.grad(g_loss, last_layer, retain_graph=True)[0]
else:
nll_grads = torch.autograd.grad(nll_loss, self.last_layer[0], retain_graph=True)[0]
g_grads = torch.autograd.grad(g_loss, self.last_layer[0], retain_graph=True)[0]
d_weight = torch.norm(nll_grads) / (torch.norm(g_grads) + 1e-4)
d_weight = torch.clamp(d_weight, 0.0, 1e4).detach()
d_weight = d_weight * self.discriminator_weight
return d_weight
def forward(self, codebook_loss, inputs, reconstructions, optimizer_idx,
global_step, last_layer=None, cond=None, split="train", predicted_indices=None):
if not exists(codebook_loss):
codebook_loss = torch.tensor([0.]).to(inputs.device)
#rec_loss = torch.abs(inputs.contiguous() - reconstructions.contiguous())
rec_loss = self.pixel_loss(inputs.contiguous(), reconstructions.contiguous())
if self.perceptual_weight > 0:
p_loss = self.perceptual_loss(inputs.contiguous(), reconstructions.contiguous())
rec_loss = rec_loss + self.perceptual_weight * p_loss
else:
p_loss = torch.tensor([0.0])
nll_loss = rec_loss
#nll_loss = torch.sum(nll_loss) / nll_loss.shape[0]
nll_loss = torch.mean(nll_loss)
# now the GAN part
if optimizer_idx == 0:
# generator update
if cond is None:
assert not self.disc_conditional
logits_fake = self.discriminator(reconstructions.contiguous())
else:
assert self.disc_conditional
logits_fake = self.discriminator(torch.cat((reconstructions.contiguous(), cond), dim=1))
g_loss = -torch.mean(logits_fake)
try:
d_weight = self.calculate_adaptive_weight(nll_loss, g_loss, last_layer=last_layer)
except RuntimeError:
assert not self.training
d_weight = torch.tensor(0.0)
disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start)
loss = nll_loss + d_weight * disc_factor * g_loss + self.codebook_weight * codebook_loss.mean()
log = {"{}/total_loss".format(split): loss.clone().detach().mean(),
"{}/quant_loss".format(split): codebook_loss.detach().mean(),
"{}/nll_loss".format(split): nll_loss.detach().mean(),
"{}/rec_loss".format(split): rec_loss.detach().mean(),
"{}/p_loss".format(split): p_loss.detach().mean(),
"{}/d_weight".format(split): d_weight.detach(),
"{}/disc_factor".format(split): torch.tensor(disc_factor),
"{}/g_loss".format(split): g_loss.detach().mean(),
}
if predicted_indices is not None:
assert self.n_classes is not None
with torch.no_grad():
perplexity, cluster_usage = measure_perplexity(predicted_indices, self.n_classes)
log[f"{split}/perplexity"] = perplexity
log[f"{split}/cluster_usage"] = cluster_usage
return loss, log
if optimizer_idx == 1:
# second pass for discriminator update
if cond is None:
logits_real = self.discriminator(inputs.contiguous().detach())
logits_fake = self.discriminator(reconstructions.contiguous().detach())
else:
logits_real = self.discriminator(torch.cat((inputs.contiguous().detach(), cond), dim=1))
logits_fake = self.discriminator(torch.cat((reconstructions.contiguous().detach(), cond), dim=1))
disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start)
d_loss = disc_factor * self.disc_loss(logits_real, logits_fake)
log = {"{}/disc_loss".format(split): d_loss.clone().detach().mean(),
"{}/logits_real".format(split): logits_real.detach().mean(),
"{}/logits_fake".format(split): logits_fake.detach().mean()
}
return d_loss, log
================================================
FILE: models/instructpix2pix/stable_diffusion/ldm/modules/x_transformer.py
================================================
"""shout-out to https://github.com/lucidrains/x-transformers/tree/main/x_transformers"""
import torch
from torch import nn, einsum
import torch.nn.functional as F
from functools import partial
from inspect import isfunction
from collections import namedtuple
from einops import rearrange, repeat, reduce
# constants
DEFAULT_DIM_HEAD = 64
Intermediates = namedtuple('Intermediates', [
'pre_softmax_attn',
'post_softmax_attn'
])
LayerIntermediates = namedtuple('Intermediates', [
'hiddens',
'attn_intermediates'
])
class AbsolutePositionalEmbedding(nn.Module):
def __init__(self, dim, max_seq_len):
super().__init__()
self.emb = nn.Embedding(max_seq_len, dim)
self.init_()
def init_(self):
nn.init.normal_(self.emb.weight, std=0.02)
def forward(self, x):
n = torch.arange(x.shape[1], device=x.device)
return self.emb(n)[None, :, :]
class FixedPositionalEmbedding(nn.Module):
def __init__(self, dim):
super().__init__()
inv_freq = 1. / (10000 ** (torch.arange(0, dim, 2).float() / dim))
self.register_buffer('inv_freq', inv_freq)
def forward(self, x, seq_dim=1, offset=0):
t = torch.arange(x.shape[seq_dim], device=x.device).type_as(self.inv_freq) + offset
sinusoid_inp = torch.einsum('i , j -> i j', t, self.inv_freq)
emb = torch.cat((sinusoid_inp.sin(), sinusoid_inp.cos()), dim=-1)
return emb[None, :, :]
# helpers
def exists(val):
return val is not None
def default(val, d):
if exists(val):
return val
return d() if isfunction(d) else d
def always(val):
def inner(*args, **kwargs):
return val
return inner
def not_equals(val):
def inner(x):
return x != val
return inner
def equals(val):
def inner(x):
return x == val
return inner
def max_neg_value(tensor):
return -torch.finfo(tensor.dtype).max
# keyword argument helpers
def pick_and_pop(keys, d):
values = list(map(lambda key: d.pop(key), keys))
return dict(zip(keys, values))
def group_dict_by_key(cond, d):
return_val = [dict(), dict()]
for key in d.keys():
match = bool(cond(key))
ind = int(not match)
return_val[ind][key] = d[key]
return (*return_val,)
def string_begins_with(prefix, str):
return str.startswith(prefix)
def group_by_key_prefix(prefix, d):
return group_dict_by_key(partial(string_begins_with, prefix), d)
def groupby_prefix_and_trim(prefix, d):
kwargs_with_prefix, kwargs = group_dict_by_key(partial(string_begins_with, prefix), d)
kwargs_without_prefix = dict(map(lambda x: (x[0][len(prefix):], x[1]), tuple(kwargs_with_prefix.items())))
return kwargs_without_prefix, kwargs
# classes
class Scale(nn.Module):
def __init__(self, value, fn):
super().__init__()
self.value = value
self.fn = fn
def forward(self, x, **kwargs):
x, *rest = self.fn(x, **kwargs)
return (x * self.value, *rest)
class Rezero(nn.Module):
def __init__(self, fn):
super().__init__()
self.fn = fn
self.g = nn.Parameter(torch.zeros(1))
def forward(self, x, **kwargs):
x, *rest = self.fn(x, **kwargs)
return (x * self.g, *rest)
class ScaleNorm(nn.Module):
def __init__(self, dim, eps=1e-5):
super().__init__()
self.scale = dim ** -0.5
self.eps = eps
self.g = nn.Parameter(torch.ones(1))
def forward(self, x):
norm = torch.norm(x, dim=-1, keepdim=True) * self.scale
return x / norm.clamp(min=self.eps) * self.g
class RMSNorm(nn.Module):
def __init__(self, dim, eps=1e-8):
super().__init__()
self.scale = dim ** -0.5
self.eps = eps
self.g = nn.Parameter(torch.ones(dim))
def forward(self, x):
norm = torch.norm(x, dim=-1, keepdim=True) * self.scale
return x / norm.clamp(min=self.eps) * self.g
class Residual(nn.Module):
def forward(self, x, residual):
return x + residual
class GRUGating(nn.Module):
def __init__(self, dim):
super().__init__()
self.gru = nn.GRUCell(dim, dim)
def forward(self, x, residual):
gated_output = self.gru(
rearrange(x, 'b n d -> (b n) d'),
rearrange(residual, 'b n d -> (b n) d')
)
return gated_output.reshape_as(x)
# feedforward
class GEGLU(nn.Module):
def __init__(self, dim_in, dim_out):
super().__init__()
self.proj = nn.Linear(dim_in, dim_out * 2)
def forward(self, x):
x, gate = self.proj(x).chunk(2, dim=-1)
return x * F.gelu(gate)
class FeedForward(nn.Module):
def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
super().__init__()
inner_dim = int(dim * mult)
dim_out = default(dim_out, dim)
project_in = nn.Sequential(
nn.Linear(dim, inner_dim),
nn.GELU()
) if not glu else GEGLU(dim, inner_dim)
self.net = nn.Sequential(
project_in,
nn.Dropout(dropout),
nn.Linear(inner_dim, dim_out)
)
def forward(self, x):
return self.net(x)
# attention.
class Attention(nn.Module):
def __init__(
self,
dim,
dim_head=DEFAULT_DIM_HEAD,
heads=8,
causal=False,
mask=None,
talking_heads=False,
sparse_topk=None,
use_entmax15=False,
num_mem_kv=0,
dropout=0.,
on_attn=False
):
super().__init__()
if use_entmax15:
raise NotImplementedError("Check out entmax activation instead of softmax activation!")
self.scale = dim_head ** -0.5
self.heads = heads
self.causal = causal
self.mask = mask
inner_dim = dim_head * heads
self.to_q = nn.Linear(dim, inner_dim, bias=False)
self.to_k = nn.Linear(dim, inner_dim, bias=False)
self.to_v = nn.Linear(dim, inner_dim, bias=False)
self.dropout = nn.Dropout(dropout)
# talking heads
self.talking_heads = talking_heads
if talking_heads:
self.pre_softmax_proj = nn.Parameter(torch.randn(heads, heads))
self.post_softmax_proj = nn.Parameter(torch.randn(heads, heads))
# explicit topk sparse attention
self.sparse_topk = sparse_topk
# entmax
#self.attn_fn = entmax15 if use_entmax15 else F.softmax
self.attn_fn = F.softmax
# add memory key / values
self.num_mem_kv = num_mem_kv
if num_mem_kv > 0:
self.mem_k = nn.Parameter(torch.randn(heads, num_mem_kv, dim_head))
self.mem_v = nn.Parameter(torch.randn(heads, num_mem_kv, dim_head))
# attention on attention
self.attn_on_attn = on_attn
self.to_out = nn.Sequential(nn.Linear(inner_dim, dim * 2), nn.GLU()) if on_attn else nn.Linear(inner_dim, dim)
def forward(
self,
x,
context=None,
mask=None,
context_mask=None,
rel_pos=None,
sinusoidal_emb=None,
prev_attn=None,
mem=None
):
b, n, _, h, talking_heads, device = *x.shape, self.heads, self.talking_heads, x.device
kv_input = default(context, x)
q_input = x
k_input = kv_input
v_input = kv_input
if exists(mem):
k_input = torch.cat((mem, k_input), dim=-2)
v_input = torch.cat((mem, v_input), dim=-2)
if exists(sinusoidal_emb):
# in shortformer, the query would start at a position offset depending on the past cached memory
offset = k_input.shape[-2] - q_input.shape[-2]
q_input = q_input + sinusoidal_emb(q_input, offset=offset)
k_input = k_input + sinusoidal_emb(k_input)
q = self.to_q(q_input)
k = self.to_k(k_input)
v = self.to_v(v_input)
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h=h), (q, k, v))
input_mask = None
if any(map(exists, (mask, context_mask))):
q_mask = default(mask, lambda: torch.ones((b, n), device=device).bool())
k_mask = q_mask if not exists(context) else context_mask
k_mask = default(k_mask, lambda: torch.ones((b, k.shape[-2]), device=device).bool())
q_mask = rearrange(q_mask, 'b i -> b () i ()')
k_mask = rearrange(k_mask, 'b j -> b () () j')
input_mask = q_mask * k_mask
if self.num_mem_kv > 0:
mem_k, mem_v = map(lambda t: repeat(t, 'h n d -> b h n d', b=b), (self.mem_k, self.mem_v))
k = torch.cat((mem_k, k), dim=-2)
v = torch.cat((mem_v, v), dim=-2)
if exists(input_mask):
input_mask = F.pad(input_mask, (self.num_mem_kv, 0), value=True)
dots = einsum('b h i d, b h j d -> b h i j', q, k) * self.scale
mask_value = max_neg_value(dots)
if exists(prev_attn):
dots = dots + prev_attn
pre_softmax_attn = dots
if talking_heads:
dots = einsum('b h i j, h k -> b k i j', dots, self.pre_softmax_proj).contiguous()
if exists(rel_pos):
dots = rel_pos(dots)
if exists(input_mask):
dots.masked_fill_(~input_mask, mask_value)
del input_mask
if self.causal:
i, j = dots.shape[-2:]
r = torch.arange(i, device=device)
mask = rearrange(r, 'i -> () () i ()') < rearrange(r, 'j -> () () () j')
mask = F.pad(mask, (j - i, 0), value=False)
dots.masked_fill_(mask, mask_value)
del mask
if exists(self.sparse_topk) and self.sparse_topk < dots.shape[-1]:
top, _ = dots.topk(self.sparse_topk, dim=-1)
vk = top[..., -1].unsqueeze(-1).expand_as(dots)
mask = dots < vk
dots.masked_fill_(mask, mask_value)
del mask
attn = self.attn_fn(dots, dim=-1)
post_softmax_attn = attn
attn = self.dropout(attn)
if talking_heads:
attn = einsum('b h i j, h k -> b k i j', attn, self.post_softmax_proj).contiguous()
out = einsum('b h i j, b h j d -> b h i d', attn, v)
out = rearrange(out, 'b h n d -> b n (h d)')
intermediates = Intermediates(
pre_softmax_attn=pre_softmax_attn,
post_softmax_attn=post_softmax_attn
)
return self.to_out(out), intermediates
class AttentionLayers(nn.Module):
def __init__(
self,
dim,
depth,
heads=8,
causal=False,
cross_attend=False,
only_cross=False,
use_scalenorm=False,
use_rmsnorm=False,
use_rezero=False,
rel_pos_num_buckets=32,
rel_pos_max_distance=128,
position_infused_attn=False,
custom_layers=None,
sandwich_coef=None,
par_ratio=None,
residual_attn=False,
cross_residual_attn=False,
macaron=False,
pre_norm=True,
gate_residual=False,
**kwargs
):
super().__init__()
ff_kwargs, kwargs = groupby_prefix_and_trim('ff_', kwargs)
attn_kwargs, _ = groupby_prefix_and_trim('attn_', kwargs)
dim_head = attn_kwargs.get('dim_head', DEFAULT_DIM_HEAD)
self.dim = dim
self.depth = depth
self.layers = nn.ModuleList([])
self.has_pos_emb = position_infused_attn
self.pia_pos_emb = FixedPositionalEmbedding(dim) if position_infused_attn else None
self.rotary_pos_emb = always(None)
assert rel_pos_num_buckets <= rel_pos_max_distance, 'number of relative position buckets must be less than the relative position max distance'
self.rel_pos = None
self.pre_norm = pre_norm
self.residual_attn = residual_attn
self.cross_residual_attn = cross_residual_attn
norm_class = ScaleNorm if use_scalenorm else nn.LayerNorm
norm_class = RMSNorm if use_rmsnorm else norm_class
norm_fn = partial(norm_class, dim)
norm_fn = nn.Identity if use_rezero else norm_fn
branch_fn = Rezero if use_rezero else None
if cross_attend and not only_cross:
default_block = ('a', 'c', 'f')
elif cross_attend and only_cross:
default_block = ('c', 'f')
else:
default_block = ('a', 'f')
if macaron:
default_block = ('f',) + default_block
if exists(custom_layers):
layer_types = custom_layers
elif exists(par_ratio):
par_depth = depth * len(default_block)
assert 1 < par_ratio <= par_depth, 'par ratio out of range'
default_block = tuple(filter(not_equals('f'), default_block))
par_attn = par_depth // par_ratio
depth_cut = par_depth * 2 // 3 # 2 / 3 attention layer cutoff suggested by PAR paper
par_width = (depth_cut + depth_cut // par_attn) // par_attn
assert len(default_block) <= par_width, 'default block is too large for par_ratio'
par_block = default_block + ('f',) * (par_width - len(default_block))
par_head = par_block * par_attn
layer_types = par_head + ('f',) * (par_depth - len(par_head))
elif exists(sandwich_coef):
assert sandwich_coef > 0 and sandwich_coef <= depth, 'sandwich coefficient should be less than the depth'
layer_types = ('a',) * sandwich_coef + default_block * (depth - sandwich_coef) + ('f',) * sandwich_coef
else:
layer_types = default_block * depth
self.layer_types = layer_types
self.num_attn_layers = len(list(filter(equals('a'), layer_types)))
for layer_type in self.layer_types:
if layer_type == 'a':
layer = Attention(dim, heads=heads, causal=causal, **attn_kwargs)
elif layer_type == 'c':
layer = Attention(dim, heads=heads, **attn_kwargs)
elif layer_type == 'f':
layer = FeedForward(dim, **ff_kwargs)
layer = layer if not macaron else Scale(0.5, layer)
else:
raise Exception(f'invalid layer type {layer_type}')
if isinstance(layer, Attention) and exists(branch_fn):
layer = branch_fn(layer)
if gate_residual:
residual_fn = GRUGating(dim)
else:
residual_fn = Residual()
self.layers.append(nn.ModuleList([
norm_fn(),
layer,
residual_fn
]))
def forward(
self,
x,
context=None,
mask=None,
context_mask=None,
mems=None,
return_hiddens=False
):
hiddens = []
intermediates = []
prev_attn = None
prev_cross_attn = None
mems = mems.copy() if exists(mems) else [None] * self.num_attn_layers
for ind, (layer_type, (norm, block, residual_fn)) in enumerate(zip(self.layer_types, self.layers)):
is_last = ind == (len(self.layers) - 1)
if layer_type == 'a':
hiddens.append(x)
layer_mem = mems.pop(0)
residual = x
if self.pre_norm:
x = norm(x)
if layer_type == 'a':
out, inter = block(x, mask=mask, sinusoidal_emb=self.pia_pos_emb, rel_pos=self.rel_pos,
prev_attn=prev_attn, mem=layer_mem)
elif layer_type == 'c':
out, inter = block(x, context=context, mask=mask, context_mask=context_mask, prev_attn=prev_cross_attn)
elif layer_type == 'f':
out = block(x)
x = residual_fn(out, residual)
if layer_type in ('a', 'c'):
intermediates.append(inter)
if layer_type == 'a' and self.residual_attn:
prev_attn = inter.pre_softmax_attn
elif layer_type == 'c' and self.cross_residual_attn:
prev_cross_attn = inter.pre_softmax_attn
if not self.pre_norm and not is_last:
x = norm(x)
if return_hiddens:
intermediates = LayerIntermediates(
hiddens=hiddens,
attn_intermediates=intermediates
)
return x, intermediates
return x
class Encoder(AttentionLayers):
def __init__(self, **kwargs):
assert 'causal' not in kwargs, 'cannot set causality on encoder'
super().__init__(causal=False, **kwargs)
class TransformerWrapper(nn.Module):
def __init__(
self,
*,
num_tokens,
max_seq_len,
attn_layers,
emb_dim=None,
max_mem_len=0.,
emb_dropout=0.,
num_memory_tokens=None,
tie_embedding=False,
use_pos_emb=True
):
super().__init__()
assert isinstance(attn_layers, AttentionLayers), 'attention layers must be one of Encoder or Decoder'
dim = attn_layers.dim
emb_dim = default(emb_dim, dim)
self.max_seq_len = max_seq_len
self.max_mem_len = max_mem_len
self.num_tokens = num_tokens
self.token_emb = nn.Embedding(num_tokens, emb_dim)
self.pos_emb = AbsolutePositionalEmbedding(emb_dim, max_seq_len) if (
use_pos_emb and not attn_layers.has_pos_emb) else always(0)
self.emb_dropout = nn.Dropout(emb_dropout)
self.project_emb = nn.Linear(emb_dim, dim) if emb_dim != dim else nn.Identity()
self.attn_layers = attn_layers
self.norm = nn.LayerNorm(dim)
self.init_()
self.to_logits = nn.Linear(dim, num_tokens) if not tie_embedding else lambda t: t @ self.token_emb.weight.t()
# memory tokens (like [cls]) from Memory Transformers paper
num_memory_tokens = default(num_memory_tokens, 0)
self.num_memory_tokens = num_memory_tokens
if num_memory_tokens > 0:
self.memory_tokens = nn.Parameter(torch.randn(num_memory_tokens, dim))
# let funnel encoder know number of memory tokens, if specified
if hasattr(attn_layers, 'num_memory_tokens'):
attn_layers.num_memory_tokens = num_memory_tokens
def init_(self):
nn.init.normal_(self.token_emb.weight, std=0.02)
def forward(
self,
x,
return_embeddings=False,
mask=None,
return_mems=False,
return_attn=False,
mems=None,
**kwargs
):
b, n, device, num_mem = *x.shape, x.device, self.num_memory_tokens
x = self.token_emb(x)
x += self.pos_emb(x)
x = self.emb_dropout(x)
x = self.project_emb(x)
if num_mem > 0:
mem = repeat(self.memory_tokens, 'n d -> b n d', b=b)
x = torch.cat((mem, x), dim=1)
# auto-handle masking after appending memory tokens
if exists(mask):
mask = F.pad(mask, (num_mem, 0), value=True)
x, intermediates = self.attn_layers(x, mask=mask, mems=mems, return_hiddens=True, **kwargs)
x = self.norm(x)
mem, x = x[:, :num_mem], x[:, num_mem:]
out = self.to_logits(x) if not return_embeddings else x
if return_mems:
hiddens = intermediates.hiddens
new_mems = list(map(lambda pair: torch.cat(pair, dim=-2), zip(mems, hiddens))) if exists(mems) else hiddens
new_mems = list(map(lambda t: t[..., -self.max_mem_len:, :].detach(), new_mems))
return out, new_mems
if return_attn:
attn_maps = list(map(lambda t: t.post_softmax_attn, intermediates.attn_intermediates))
return out, attn_maps
return out
================================================
FILE: models/instructpix2pix/stable_diffusion/ldm/util.py
================================================
import importlib
import torch
import numpy as np
from collections import abc
from einops import rearrange
from functools import partial
import multiprocessing as mp
from threading import Thread
from queue import Queue
from inspect import isfunction
from PIL import Image, ImageDraw, ImageFont
def log_txt_as_img(wh, xc, size=10):
# wh a tuple of (width, height)
# xc a list of captions to plot
b = len(xc)
txts = list()
for bi in range(b):
txt = Image.new("RGB", wh, color="white")
draw = ImageDraw.Draw(txt)
font = ImageFont.truetype('data/DejaVuSans.ttf', size=size)
nc = int(40 * (wh[0] / 256))
lines = "\n".join(xc[bi][start:start + nc] for start in range(0, len(xc[bi]), nc))
try:
draw.text((0, 0), lines, fill="black", font=font)
except UnicodeEncodeError:
print("Cant encode string for logging. Skipping.")
txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0
txts.append(txt)
txts = np.stack(txts)
txts = torch.tensor(txts)
return txts
def ismap(x):
if not isinstance(x, torch.Tensor):
return False
return (len(x.shape) == 4) and (x.shape[1] > 3)
def isimage(x):
if not isinstance(x, torch.Tensor):
return False
return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1)
def exists(x):
return x is not None
def default(val, d):
if exists(val):
return val
return d() if isfunction(d) else d
def mean_flat(tensor):
"""
https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/nn.py#L86
Take the mean over all non-batch dimensions.
"""
return tensor.mean(dim=list(range(1, len(tensor.shape))))
def count_params(model, verbose=False):
total_params = sum(p.numel() for p in model.parameters())
if verbose:
print(f"{model.__class__.__name__} has {total_params * 1.e-6:.2f} M params.")
return total_params
def instantiate_from_config(config):
if not "target" in config:
if config == '__is_first_stage__':
return None
elif config == "__is_unconditional__":
return None
raise KeyError("Expected key `target` to instantiate.")
return get_obj_from_str(config["target"])(**config.get("params", dict()))
def get_obj_from_str(string, reload=False):
module, cls = string.rsplit(".", 1)
if reload:
module_imp = importlib.import_module(module)
importlib.reload(module_imp)
return getattr(importlib.import_module(module, package=None), cls)
def _do_parallel_data_prefetch(func, Q, data, idx, idx_to_fn=False):
# create dummy dataset instance
# run prefetching
if idx_to_fn:
res = func(data, worker_id=idx)
else:
res = func(data)
Q.put([idx, res])
Q.put("Done")
def parallel_data_prefetch(
func: callable, data, n_proc, target_data_type="ndarray", cpu_intensive=True, use_worker_id=False
):
# if target_data_type not in ["ndarray", "list"]:
# raise ValueError(
# "Data, which is passed to parallel_data_prefetch has to be either of type list or ndarray."
# )
if isinstance(data, np.ndarray) and target_data_type == "list":
raise ValueError("list expected but function got ndarray.")
elif isinstance(data, abc.Iterable):
if isinstance(data, dict):
print(
f'WARNING:"data" argument passed to parallel_data_prefetch is a dict: Using only its values and disregarding keys.'
)
data = list(data.values())
if target_data_type == "ndarray":
data = np.asarray(data)
else:
data = list(data)
else:
raise TypeError(
f"The data, that shall be processed parallel has to be either an np.ndarray or an Iterable, but is actually {type(data)}."
)
if cpu_intensive:
Q = mp.Queue(1000)
proc = mp.Process
else:
Q = Queue(1000)
proc = Thread
# spawn processes
if target_data_type == "ndarray":
arguments = [
[func, Q, part, i, use_worker_id]
for i, part in enumerate(np.array_split(data, n_proc))
]
else:
step = (
int(len(data) / n_proc + 1)
if len(data) % n_proc != 0
else int(len(data) / n_proc)
)
arguments = [
[func, Q, part, i, use_worker_id]
for i, part in enumerate(
[data[i: i + step] for i in range(0, len(data), step)]
)
]
processes = []
for i in range(n_proc):
p = proc(target=_do_parallel_data_prefetch, args=arguments[i])
processes += [p]
# start processes
print(f"Start prefetching...")
import time
start = time.time()
gather_res = [[] for _ in range(n_proc)]
try:
for p in processes:
p.start()
k = 0
while k < n_proc:
# get result
res = Q.get()
if res == "Done":
k += 1
else:
gather_res[res[0]] = res[1]
except Exception as e:
print("Exception: ", e)
for p in processes:
p.terminate()
raise e
finally:
for p in processes:
p.join()
print(f"Prefetching complete. [{time.time() - start} sec.]")
if target_data_type == 'ndarray':
if not isinstance(gather_res[0], np.ndarray):
return np.concatenate([np.asarray(r) for r in gather_res], axis=0)
# order outputs
return np.concatenate(gather_res, axis=0)
elif target_data_type == 'list':
out = []
for r in gather_res:
out.extend(r)
return out
else:
return gather_res
================================================
FILE: models/instructpix2pix/stable_diffusion/main.py
================================================
import argparse, os, sys, datetime, glob, importlib, csv
import numpy as np
import time
import torch
import torchvision
import pytorch_lightning as pl
from packaging import version
from omegaconf import OmegaConf
from torch.utils.data import random_split, DataLoader, Dataset, Subset
from functools import partial
from PIL import Image
from pytorch_lightning import seed_everything
from pytorch_lightning.trainer import Trainer
from pytorch_lightning.callbacks import ModelCheckpoint, Callback, LearningRateMonitor
from pytorch_lightning.utilities.distributed import rank_zero_only
from pytorch_lightning.utilities import rank_zero_info
from ldm.data.base import Txt2ImgIterableBaseDataset
from ldm.util import instantiate_from_config
def get_parser(**parser_kwargs):
def str2bool(v):
if isinstance(v, bool):
return v
if v.lower() in ("yes", "true", "t", "y", "1"):
return True
elif v.lower() in ("no", "false", "f", "n", "0"):
return False
else:
raise argparse.ArgumentTypeError("Boolean value expected.")
parser = argparse.ArgumentParser(**parser_kwargs)
parser.add_argument(
"-n",
"--name",
type=str,
const=True,
default="",
nargs="?",
help="postfix for logdir",
)
parser.add_argument(
"-r",
"--resume",
type=str,
const=True,
default="",
nargs="?",
help="resume from logdir or checkpoint in logdir",
)
parser.add_argument(
"-b",
"--base",
nargs="*",
metavar="base_config.yaml",
help="paths to base configs. Loaded from left-to-right. "
"Parameters can be overwritten or added with command-line options of the form `--key value`.",
default=list(),
)
parser.add_argument(
"-t",
"--train",
type=str2bool,
const=True,
default=False,
nargs="?",
help="train",
)
parser.add_argument(
"--no-test",
type=str2bool,
const=True,
default=False,
nargs="?",
help="disable test",
)
parser.add_argument(
"-p",
"--project",
help="name of new or path to existing project"
)
parser.add_argument(
"-d",
"--debug",
type=str2bool,
nargs="?",
const=True,
default=False,
help="enable post-mortem debugging",
)
parser.add_argument(
"-s",
"--seed",
type=int,
default=23,
help="seed for seed_everything",
)
parser.add_argument(
"-f",
"--postfix",
type=str,
default="",
help="post-postfix for default name",
)
parser.add_argument(
"-l",
"--logdir",
type=str,
default="logs",
help="directory for logging dat shit",
)
parser.add_argument(
"--scale_lr",
type=str2bool,
nargs="?",
const=True,
default=True,
help="scale base-lr by ngpu * batch_size * n_accumulate",
)
return parser
def nondefault_trainer_args(opt):
parser = argparse.ArgumentParser()
parser = Trainer.add_argparse_args(parser)
args = parser.parse_args([])
return sorted(k for k in vars(args) if getattr(opt, k) != getattr(args, k))
class WrappedDataset(Dataset):
"""Wraps an arbitrary object with __len__ and __getitem__ into a pytorch dataset"""
def __init__(self, dataset):
self.data = dataset
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
return self.data[idx]
def worker_init_fn(_):
worker_info = torch.utils.data.get_worker_info()
dataset = worker_info.dataset
worker_id = worker_info.id
if isinstance(dataset, Txt2ImgIterableBaseDataset):
split_size = dataset.num_records // worker_info.num_workers
# reset num_records to the true number to retain reliable length information
dataset.sample_ids = dataset.valid_ids[worker_id * split_size:(worker_id + 1) * split_size]
current_id = np.random.choice(len(np.random.get_state()[1]), 1)
return np.random.seed(np.random.get_state()[1][current_id] + worker_id)
else:
return np.random.seed(np.random.get_state()[1][0] + worker_id)
class DataModuleFromConfig(pl.LightningDataModule):
def __init__(self, batch_size, train=None, validation=None, test=None, predict=None,
wrap=False, num_workers=None, shuffle_test_loader=False, use_worker_init_fn=False,
shuffle_val_dataloader=False):
super().__init__()
self.batch_size = batch_size
self.dataset_configs = dict()
self.num_workers = num_workers if num_workers is not None else batch_size * 2
self.use_worker_init_fn = use_worker_init_fn
if train is not None:
self.dataset_configs["train"] = train
self.train_dataloader = self._train_dataloader
if validation is not None:
self.dataset_configs["validation"] = validation
self.val_dataloader = partial(self._val_dataloader, shuffle=shuffle_val_dataloader)
if test is not None:
self.dataset_configs["test"] = test
self.test_dataloader = partial(self._test_dataloader, shuffle=shuffle_test_loader)
if predict is not None:
self.dataset_configs["predict"] = predict
self.predict_dataloader = self._predict_dataloader
self.wrap = wrap
def prepare_data(self):
for data_cfg in self.dataset_configs.values():
instantiate_from_config(data_cfg)
def setup(self, stage=None):
self.datasets = dict(
(k, instantiate_from_config(self.dataset_configs[k]))
for k in self.dataset_configs)
if self.wrap:
for k in self.datasets:
self.datasets[k] = WrappedDataset(self.datasets[k])
def _train_dataloader(self):
is_iterable_dataset = isinstance(self.datasets['train'], Txt2ImgIterableBaseDataset)
if is_iterable_dataset or self.use_worker_init_fn:
init_fn = worker_init_fn
else:
init_fn = None
return DataLoader(self.datasets["train"], batch_size=self.batch_size,
num_workers=self.num_workers, shuffle=False if is_iterable_dataset else True,
worker_init_fn=init_fn)
def _val_dataloader(self, shuffle=False):
if isinstance(self.datasets['validation'], Txt2ImgIterableBaseDataset) or self.use_worker_init_fn:
init_fn = worker_init_fn
else:
init_fn = None
return DataLoader(self.datasets["validation"],
batch_size=self.batch_size,
num_workers=self.num_workers,
worker_init_fn=init_fn,
shuffle=shuffle)
def _test_dataloader(self, shuffle=False):
is_iterable_dataset = isinstance(self.datasets['train'], Txt2ImgIterableBaseDataset)
if is_iterable_dataset or self.use_worker_init_fn:
init_fn = worker_init_fn
else:
init_fn = None
# do not shuffle dataloader for iterable dataset
shuffle = shuffle and (not is_iterable_dataset)
return DataLoader(self.datasets["test"], batch_size=self.batch_size,
num_workers=self.num_workers, worker_init_fn=init_fn, shuffle=shuffle)
def _predict_dataloader(self, shuffle=False):
if isinstance(self.datasets['predict'], Txt2ImgIterableBaseDataset) or self.use_worker_init_fn:
init_fn = worker_init_fn
else:
init_fn = None
return DataLoader(self.datasets["predict"], batch_size=self.batch_size,
num_workers=self.num_workers, worker_init_fn=init_fn)
class SetupCallback(Callback):
def __init__(self, resume, now, logdir, ckptdir, cfgdir, config, lightning_config):
super().__init__()
self.resume = resume
self.now = now
self.logdir = logdir
self.ckptdir = ckptdir
self.cfgdir = cfgdir
self.config = config
self.lightning_config = lightning_config
def on_keyboard_interrupt(self, trainer, pl_module):
if trainer.global_rank == 0:
print("Summoning checkpoint.")
ckpt_path = os.path.join(self.ckptdir, "last.ckpt")
trainer.save_checkpoint(ckpt_path)
def on_pretrain_routine_start(self, trainer, pl_module):
if trainer.global_rank == 0:
# Create logdirs and save configs
os.makedirs(self.logdir, exist_ok=True)
os.makedirs(self.ckptdir, exist_ok=True)
os.makedirs(self.cfgdir, exist_ok=True)
if "callbacks" in self.lightning_config:
if 'metrics_over_trainsteps_checkpoint' in self.lightning_config['callbacks']:
os.makedirs(os.path.join(self.ckptdir, 'trainstep_checkpoints'), exist_ok=True)
print("Project config")
print(OmegaConf.to_yaml(self.config))
OmegaConf.save(self.config,
os.path.join(self.cfgdir, "{}-project.yaml".format(self.now)))
print("Lightning config")
print(OmegaConf.to_yaml(self.lightning_config))
OmegaConf.save(OmegaConf.create({"lightning": self.lightning_config}),
os.path.join(self.cfgdir, "{}-lightning.yaml".format(self.now)))
else:
# ModelCheckpoint callback created log directory --- remove it
if not self.resume and os.path.exists(self.logdir):
dst, name = os.path.split(self.logdir)
dst = os.path.join(dst, "child_runs", name)
os.makedirs(os.path.split(dst)[0], exist_ok=True)
try:
os.rename(self.logdir, dst)
except FileNotFoundError:
pass
class ImageLogger(Callback):
def __init__(self, batch_frequency, max_images, clamp=True, increase_log_steps=True,
rescale=True, disabled=False, log_on_batch_idx=False, log_first_step=False,
log_images_kwargs=None):
super().__init__()
self.rescale = rescale
self.batch_freq = batch_frequency
self.max_images = max_images
self.logger_log_images = {
pl.loggers.TestTubeLogger: self._testtube,
}
self.log_steps = [2 ** n for n in range(int(np.log2(self.batch_freq)) + 1)]
if not increase_log_steps:
self.log_steps = [self.batch_freq]
self.clamp = clamp
self.disabled = disabled
self.log_on_batch_idx = log_on_batch_idx
self.log_images_kwargs = log_images_kwargs if log_images_kwargs else {}
self.log_first_step = log_first_step
@rank_zero_only
def _testtube(self, pl_module, images, batch_idx, split):
for k in images:
grid = torchvision.utils.make_grid(images[k])
grid = (grid + 1.0) / 2.0 # -1,1 -> 0,1; c,h,w
tag = f"{split}/{k}"
pl_module.logger.experiment.add_image(
tag, grid,
global_step=pl_module.global_step)
@rank_zero_only
def log_local(self, save_dir, split, images,
global_step, current_epoch, batch_idx):
root = os.path.join(save_dir, "images", split)
for k in images:
grid = torchvision.utils.make_grid(images[k], nrow=4)
if self.rescale:
grid = (grid + 1.0) / 2.0 # -1,1 -> 0,1; c,h,w
grid = grid.transpose(0, 1).transpose(1, 2).squeeze(-1)
grid = grid.numpy()
grid = (grid * 255).astype(np.uint8)
filename = "{}_gs-{:06}_e-{:06}_b-{:06}.png".format(
k,
global_step,
current_epoch,
batch_idx)
path = os.path.join(root, filename)
os.makedirs(os.path.split(path)[0], exist_ok=True)
Image.fromarray(grid).save(path)
def log_img(self, pl_module, batch, batch_idx, split="train"):
check_idx = batch_idx if self.log_on_batch_idx else pl_module.global_step
if (self.check_frequency(check_idx) and # batch_idx % self.batch_freq == 0
hasattr(pl_module, "log_images") and
callable(pl_module.log_images) and
self.max_images > 0):
logger = type(pl_module.logger)
is_train = pl_module.training
if is_train:
pl_module.eval()
with torch.no_grad():
images = pl_module.log_images(batch, split=split, **self.log_images_kwargs)
for k in images:
N = min(images[k].shape[0], self.max_images)
images[k] = images[k][:N]
if isinstance(images[k], torch.Tensor):
images[k] = images[k].detach().cpu()
if self.clamp:
images[k] = torch.clamp(images[k], -1., 1.)
self.log_local(pl_module.logger.save_dir, split, images,
pl_module.global_step, pl_module.current_epoch, batch_idx)
logger_log_images = self.logger_log_images.get(logger, lambda *args, **kwargs: None)
logger_log_images(pl_module, images, pl_module.global_step, split)
if is_train:
pl_module.train()
def check_frequency(self, check_idx):
if ((check_idx % self.batch_freq) == 0 or (check_idx in self.log_steps)) and (
check_idx > 0 or self.log_first_step):
try:
self.log_steps.pop(0)
except IndexError as e:
print(e)
pass
return True
return False
def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
if not self.disabled and (pl_module.global_step > 0 or self.log_first_step):
self.log_img(pl_module, batch, batch_idx, split="train")
def on_validation_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
if not self.disabled and pl_module.global_step > 0:
self.log_img(pl_module, batch, batch_idx, split="val")
if hasattr(pl_module, 'calibrate_grad_norm'):
if (pl_module.calibrate_grad_norm and batch_idx % 25 == 0) and batch_idx > 0:
self.log_gradients(trainer, pl_module, batch_idx=batch_idx)
class CUDACallback(Callback):
# see https://github.com/SeanNaren/minGPT/blob/master/mingpt/callback.py
def on_train_epoch_start(self, trainer, pl_module):
# Reset the memory use counter
torch.cuda.reset_peak_memory_stats(trainer.root_gpu)
torch.cuda.synchronize(trainer.root_gpu)
self.start_time = time.time()
def on_train_epoch_end(self, trainer, pl_module, outputs):
torch.cuda.synchronize(trainer.root_gpu)
max_memory = torch.cuda.max_memory_allocated(trainer.root_gpu) / 2 ** 20
epoch_time = time.time() - self.start_time
try:
max_memory = trainer.training_type_plugin.reduce(max_memory)
epoch_time = trainer.training_type_plugin.reduce(epoch_time)
rank_zero_info(f"Average Epoch time: {epoch_time:.2f} seconds")
rank_zero_info(f"Average Peak memory {max_memory:.2f}MiB")
except AttributeError:
pass
if __name__ == "__main__":
# custom parser to specify config files, train, test and debug mode,
# postfix, resume.
# `--key value` arguments are interpreted as arguments to the trainer.
# `nested.key=value` arguments are interpreted as config parameters.
# configs are merged from left-to-right followed by command line parameters.
# model:
# base_learning_rate: float
# target: path to lightning module
# params:
# key: value
# data:
# target: main.DataModuleFromConfig
# params:
# batch_size: int
# wrap: bool
# train:
# target: path to train dataset
# params:
# key: value
# validation:
# target: path to validation dataset
# params:
# key: value
# test:
# target: path to test dataset
# params:
# key: value
# lightning: (optional, has sane defaults and can be specified on cmdline)
# trainer:
# additional arguments to trainer
# logger:
# logger to instantiate
# modelcheckpoint:
# modelcheckpoint to instantiate
# callbacks:
# callback1:
# target: importpath
# params:
# key: value
now = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S")
# add cwd for convenience and to make classes in this file available when
# running as `python main.py`
# (in particular `main.DataModuleFromConfig`)
sys.path.append(os.getcwd())
parser = get_parser()
parser = Trainer.add_argparse_args(parser)
opt, unknown = parser.parse_known_args()
if opt.name and opt.resume:
raise ValueError(
"-n/--name and -r/--resume cannot be specified both."
"If you want to resume training in a new log folder, "
"use -n/--name in combination with --resume_from_checkpoint"
)
if opt.resume:
if not os.path.exists(opt.resume):
raise ValueError("Cannot find {}".format(opt.resume))
if os.path.isfile(opt.resume):
paths = opt.resume.split("/")
# idx = len(paths)-paths[::-1].index("logs")+1
# logdir = "/".join(paths[:idx])
logdir = "/".join(paths[:-2])
ckpt = opt.resume
else:
assert os.path.isdir(opt.resume), opt.resume
logdir = opt.resume.rstrip("/")
ckpt = os.path.join(logdir, "checkpoints", "last.ckpt")
opt.resume_from_checkpoint = ckpt
base_configs = sorted(glob.glob(os.path.join(logdir, "configs/*.yaml")))
opt.base = base_configs + opt.base
_tmp = logdir.split("/")
nowname = _tmp[-1]
else:
if opt.name:
name = "_" + opt.name
elif opt.base:
cfg_fname = os.path.split(opt.base[0])[-1]
cfg_name = os.path.splitext(cfg_fname)[0]
name = "_" + cfg_name
else:
name = ""
nowname = now + name + opt.postfix
logdir = os.path.join(opt.logdir, nowname)
ckptdir = os.path.join(logdir, "checkpoints")
cfgdir = os.path.join(logdir, "configs")
seed_everything(opt.seed)
try:
# init and save configs
configs = [OmegaConf.load(cfg) for cfg in opt.base]
cli = OmegaConf.from_dotlist(unknown)
config = OmegaConf.merge(*configs, cli)
lightning_config = config.pop("lightning", OmegaConf.create())
# merge trainer cli with config
trainer_config = lightning_config.get("trainer", OmegaConf.create())
# default to ddp
trainer_config["accelerator"] = "ddp"
for k in nondefault_trainer_args(opt):
trainer_config[k] = getattr(opt, k)
if not "gpus" in trainer_config:
del trainer_config["accelerator"]
cpu = True
else:
gpuinfo = trainer_config["gpus"]
print(f"Running on GPUs {gpuinfo}")
cpu = False
trainer_opt = argparse.Namespace(**trainer_config)
lightning_config.trainer = trainer_config
# model
model = instantiate_from_config(config.model)
# trainer and callbacks
trainer_kwargs = dict()
# default logger configs
default_logger_cfgs = {
"wandb": {
"target": "pytorch_lightning.loggers.WandbLogger",
"params": {
"name": nowname,
"save_dir": logdir,
"offline": opt.debug,
"id": nowname,
}
},
"testtube": {
"target": "pytorch_lightning.loggers.TestTubeLogger",
"params": {
"name": "testtube",
"save_dir": logdir,
}
},
}
default_logger_cfg = default_logger_cfgs["testtube"]
if "logger" in lightning_config:
logger_cfg = lightning_config.logger
else:
logger_cfg = OmegaConf.create()
logger_cfg = OmegaConf.merge(default_logger_cfg, logger_cfg)
trainer_kwargs["logger"] = instantiate_from_config(logger_cfg)
# modelcheckpoint - use TrainResult/EvalResult(checkpoint_on=metric) to
# specify which metric is used to determine best models
default_modelckpt_cfg = {
"target": "pytorch_lightning.callbacks.ModelCheckpoint",
"params": {
"dirpath": ckptdir,
"filename": "{epoch:06}",
"verbose": True,
"save_last": True,
}
}
if hasattr(model, "monitor"):
print(f"Monitoring {model.monitor} as checkpoint metric.")
default_modelckpt_cfg["params"]["monitor"] = model.monitor
default_modelckpt_cfg["params"]["save_top_k"] = 3
if "modelcheckpoint" in lightning_config:
modelckpt_cfg = lightning_config.modelcheckpoint
else:
modelckpt_cfg = OmegaConf.create()
modelckpt_cfg = OmegaConf.merge(default_modelckpt_cfg, modelckpt_cfg)
print(f"Merged modelckpt-cfg: \n{modelckpt_cfg}")
if version.parse(pl.__version__) < version.parse('1.4.0'):
trainer_kwargs["checkpoint_callback"] = instantiate_from_config(modelckpt_cfg)
# add callback which sets up log directory
default_callbacks_cfg = {
"setup_callback": {
"target": "main.SetupCallback",
"params": {
"resume": opt.resume,
"now": now,
"logdir": logdir,
"ckptdir": ckptdir,
"cfgdir": cfgdir,
"config": config,
"lightning_config": lightning_config,
}
},
"image_logger": {
"target": "main.ImageLogger",
"params": {
"batch_frequency": 750,
"max_images": 4,
"clamp": True
}
},
"learning_rate_logger": {
"target": "main.LearningRateMonitor",
"params": {
"logging_interval": "step",
# "log_momentum": True
}
},
"cuda_callback": {
"target": "main.CUDACallback"
},
}
if version.parse(pl.__version__) >= version.parse('1.4.0'):
default_callbacks_cfg.update({'checkpoint_callback': modelckpt_cfg})
if "callbacks" in lightning_config:
callbacks_cfg = lightning_config.callbacks
else:
callbacks_cfg = OmegaConf.create()
if 'metrics_over_trainsteps_checkpoint' in callbacks_cfg:
print(
'Caution: Saving checkpoints every n train steps without deleting. This might require some free space.')
default_metrics_over_trainsteps_ckpt_dict = {
'metrics_over_trainsteps_checkpoint':
{"target": 'pytorch_lightning.callbacks.ModelCheckpoint',
'params': {
"dirpath": os.path.join(ckptdir, 'trainstep_checkpoints'),
"filename": "{epoch:06}-{step:09}",
"verbose": True,
'save_top_k': -1,
'every_n_train_steps': 10000,
'save_weights_only': True
}
}
}
default_callbacks_cfg.update(default_metrics_over_trainsteps_ckpt_dict)
callbacks_cfg = OmegaConf.merge(default_callbacks_cfg, callbacks_cfg)
if 'ignore_keys_callback' in callbacks_cfg and hasattr(trainer_opt, 'resume_from_checkpoint'):
callbacks_cfg.ignore_keys_callback.params['ckpt_path'] = trainer_opt.resume_from_checkpoint
elif 'ignore_keys_callback' in callbacks_cfg:
del callbacks_cfg['ignore_keys_callback']
trainer_kwargs["callbacks"] = [instantiate_from_config(callbacks_cfg[k]) for k in callbacks_cfg]
trainer = Trainer.from_argparse_args(trainer_opt, **trainer_kwargs)
trainer.logdir = logdir ###
# data
data = instantiate_from_config(config.data)
# NOTE according to https://pytorch-lightning.readthedocs.io/en/latest/datamodules.html
# calling these ourselves should not be necessary but it is.
# lightning still takes care of proper multiprocessing though
data.prepare_data()
data.setup()
print("#### Data #####")
for k in data.datasets:
print(f"{k}, {data.datasets[k].__class__.__name__}, {len(data.datasets[k])}")
# configure learning rate
bs, base_lr = config.data.params.batch_size, config.model.base_learning_rate
if not cpu:
ngpu = len(lightning_config.trainer.gpus.strip(",").split(','))
else:
ngpu = 1
if 'accumulate_grad_batches' in lightning_config.trainer:
accumulate_grad_batches = lightning_config.trainer.accumulate_grad_batches
else:
accumulate_grad_batches = 1
print(f"accumulate_grad_batches = {accumulate_grad_batches}")
lightning_config.trainer.accumulate_grad_batches = accumulate_grad_batches
if opt.scale_lr:
model.learning_rate = accumulate_grad_batches * ngpu * bs * base_lr
print(
"Setting learning rate to {:.2e} = {} (accumulate_grad_batches) * {} (num_gpus) * {} (batchsize) * {:.2e} (base_lr)".format(
model.learning_rate, accumulate_grad_batches, ngpu, bs, base_lr))
else:
model.learning_rate = base_lr
print("++++ NOT USING LR SCALING ++++")
print(f"Setting learning rate to {model.learning_rate:.2e}")
# allow checkpointing via USR1
def melk(*args, **kwargs):
# run all checkpoint hooks
if trainer.global_rank == 0:
print("Summoning checkpoint.")
ckpt_path = os.path.join(ckptdir, "last.ckpt")
trainer.save_checkpoint(ckpt_path)
def divein(*args, **kwargs):
if trainer.global_rank == 0:
import pudb;
pudb.set_trace()
import signal
signal.signal(signal.SIGUSR1, melk)
signal.signal(signal.SIGUSR2, divein)
# run
if opt.train:
try:
trainer.fit(model, data)
except Exception:
melk()
raise
if not opt.no_test and not trainer.interrupted:
trainer.test(model, data)
except Exception:
if opt.debug and trainer.global_rank == 0:
try:
import pudb as debugger
except ImportError:
import pdb as debugger
debugger.post_mortem()
raise
finally:
# move newly created debug project to debug_runs
if opt.debug and not opt.resume and trainer.global_rank == 0:
dst, name = os.path.split(logdir)
dst = os.path.join(dst, "debug_runs", name)
os.makedirs(os.path.split(dst)[0], exist_ok=True)
os.rename(logdir, dst)
try:
if trainer.global_rank == 0:
print(trainer.profiler.summary())
except:
pass
================================================
FILE: models/instructpix2pix/stable_diffusion/models/first_stage_models/kl-f16/config.yaml
================================================
model:
base_learning_rate: 4.5e-06
target: ldm.models.autoencoder.AutoencoderKL
params:
monitor: val/rec_loss
embed_dim: 16
lossconfig:
target: ldm.modules.losses.LPIPSWithDiscriminator
params:
disc_start: 50001
kl_weight: 1.0e-06
disc_weight: 0.5
ddconfig:
double_z: true
z_channels: 16
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 1
- 2
- 2
- 4
num_res_blocks: 2
attn_resolutions:
- 16
dropout: 0.0
data:
target: main.DataModuleFromConfig
params:
batch_size: 6
wrap: true
train:
target: ldm.data.openimages.FullOpenImagesTrain
params:
size: 384
crop_size: 256
validation:
target: ldm.data.openimages.FullOpenImagesValidation
params:
size: 384
crop_size: 256
================================================
FILE: models/instructpix2pix/stable_diffusion/models/first_stage_models/kl-f32/config.yaml
================================================
model:
base_learning_rate: 4.5e-06
target: ldm.models.autoencoder.AutoencoderKL
params:
monitor: val/rec_loss
embed_dim: 64
lossconfig:
target: ldm.modules.losses.LPIPSWithDiscriminator
params:
disc_start: 50001
kl_weight: 1.0e-06
disc_weight: 0.5
ddconfig:
double_z: true
z_channels: 64
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 1
- 2
- 2
- 4
- 4
num_res_blocks: 2
attn_resolutions:
- 16
- 8
dropout: 0.0
data:
target: main.DataModuleFromConfig
params:
batch_size: 6
wrap: true
train:
target: ldm.data.openimages.FullOpenImagesTrain
params:
size: 384
crop_size: 256
validation:
target: ldm.data.openimages.FullOpenImagesValidation
params:
size: 384
crop_size: 256
================================================
FILE: models/instructpix2pix/stable_diffusion/models/first_stage_models/kl-f4/config.yaml
================================================
model:
base_learning_rate: 4.5e-06
target: ldm.models.autoencoder.AutoencoderKL
params:
monitor: val/rec_loss
embed_dim: 3
lossconfig:
target: ldm.modules.losses.LPIPSWithDiscriminator
params:
disc_start: 50001
kl_weight: 1.0e-06
disc_weight: 0.5
ddconfig:
double_z: true
z_channels: 3
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 4
num_res_blocks: 2
attn_resolutions: []
dropout: 0.0
data:
target: main.DataModuleFromConfig
params:
batch_size: 10
wrap: true
train:
target: ldm.data.openimages.FullOpenImagesTrain
params:
size: 384
crop_size: 256
validation:
target: ldm.data.openimages.FullOpenImagesValidation
params:
size: 384
crop_size: 256
================================================
FILE: models/instructpix2pix/stable_diffusion/models/first_stage_models/kl-f8/config.yaml
================================================
model:
base_learning_rate: 4.5e-06
target: ldm.models.autoencoder.AutoencoderKL
params:
monitor: val/rec_loss
embed_dim: 4
lossconfig:
target: ldm.modules.losses.LPIPSWithDiscriminator
params:
disc_start: 50001
kl_weight: 1.0e-06
disc_weight: 0.5
ddconfig:
double_z: true
z_channels: 4
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 4
- 4
num_res_blocks: 2
attn_resolutions: []
dropout: 0.0
data:
target: main.DataModuleFromConfig
params:
batch_size: 4
wrap: true
train:
target: ldm.data.openimages.FullOpenImagesTrain
params:
size: 384
crop_size: 256
validation:
target: ldm.data.openimages.FullOpenImagesValidation
params:
size: 384
crop_size: 256
================================================
FILE: models/instructpix2pix/stable_diffusion/models/first_stage_models/vq-f16/config.yaml
================================================
model:
base_learning_rate: 4.5e-06
target: ldm.models.autoencoder.VQModel
params:
embed_dim: 8
n_embed: 16384
ddconfig:
double_z: false
z_channels: 8
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 1
- 2
- 2
- 4
num_res_blocks: 2
attn_resolutions:
- 16
dropout: 0.0
lossconfig:
target: taming.modules.losses.vqperceptual.VQLPIPSWithDiscriminator
params:
disc_conditional: false
disc_in_channels: 3
disc_start: 250001
disc_weight: 0.75
disc_num_layers: 2
codebook_weight: 1.0
data:
target: main.DataModuleFromConfig
params:
batch_size: 14
num_workers: 20
wrap: true
train:
target: ldm.data.openimages.FullOpenImagesTrain
params:
size: 384
crop_size: 256
validation:
target: ldm.data.openimages.FullOpenImagesValidation
params:
size: 384
crop_size: 256
================================================
FILE: models/instructpix2pix/stable_diffusion/models/first_stage_models/vq-f4/config.yaml
================================================
model:
base_learning_rate: 4.5e-06
target: ldm.models.autoencoder.VQModel
params:
embed_dim: 3
n_embed: 8192
monitor: val/rec_loss
ddconfig:
double_z: false
z_channels: 3
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 4
num_res_blocks: 2
attn_resolutions: []
dropout: 0.0
lossconfig:
target: taming.modules.losses.vqperceptual.VQLPIPSWithDiscriminator
params:
disc_conditional: false
disc_in_channels: 3
disc_start: 0
disc_weight: 0.75
codebook_weight: 1.0
data:
target: main.DataModuleFromConfig
params:
batch_size: 8
num_workers: 16
wrap: true
train:
target: ldm.data.openimages.FullOpenImagesTrain
params:
crop_size: 256
validation:
target: ldm.data.openimages.FullOpenImagesValidation
params:
crop_size: 256
================================================
FILE: models/instructpix2pix/stable_diffusion/models/first_stage_models/vq-f4-noattn/config.yaml
================================================
model:
base_learning_rate: 4.5e-06
target: ldm.models.autoencoder.VQModel
params:
embed_dim: 3
n_embed: 8192
monitor: val/rec_loss
ddconfig:
attn_type: none
double_z: false
z_channels: 3
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 4
num_res_blocks: 2
attn_resolutions: []
dropout: 0.0
lossconfig:
target: taming.modules.losses.vqperceptual.VQLPIPSWithDiscriminator
params:
disc_conditional: false
disc_in_channels: 3
disc_start: 11
disc_weight: 0.75
codebook_weight: 1.0
data:
target: main.DataModuleFromConfig
params:
batch_size: 8
num_workers: 12
wrap: true
train:
target: ldm.data.openimages.FullOpenImagesTrain
params:
crop_size: 256
validation:
target: ldm.data.openimages.FullOpenImagesValidation
params:
crop_size: 256
================================================
FILE: models/instructpix2pix/stable_diffusion/models/first_stage_models/vq-f8/config.yaml
================================================
model:
base_learning_rate: 4.5e-06
target: ldm.models.autoencoder.VQModel
params:
embed_dim: 4
n_embed: 16384
monitor: val/rec_loss
ddconfig:
double_z: false
z_channels: 4
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 2
- 4
num_res_blocks: 2
attn_resolutions:
- 32
dropout: 0.0
lossconfig:
target: taming.modules.losses.vqperceptual.VQLPIPSWithDiscriminator
params:
disc_conditional: false
disc_in_channels: 3
disc_num_layers: 2
disc_start: 1
disc_weight: 0.6
codebook_weight: 1.0
data:
target: main.DataModuleFromConfig
params:
batch_size: 10
num_workers: 20
wrap: true
train:
target: ldm.data.openimages.FullOpenImagesTrain
params:
size: 384
crop_size: 256
validation:
target: ldm.data.openimages.FullOpenImagesValidation
params:
size: 384
crop_size: 256
================================================
FILE: models/instructpix2pix/stable_diffusion/models/first_stage_models/vq-f8-n256/config.yaml
================================================
model:
base_learning_rate: 4.5e-06
target: ldm.models.autoencoder.VQModel
params:
embed_dim: 4
n_embed: 256
monitor: val/rec_loss
ddconfig:
double_z: false
z_channels: 4
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 2
- 4
num_res_blocks: 2
attn_resolutions:
- 32
dropout: 0.0
lossconfig:
target: taming.modules.losses.vqperceptual.VQLPIPSWithDiscriminator
params:
disc_conditional: false
disc_in_channels: 3
disc_start: 250001
disc_weight: 0.75
codebook_weight: 1.0
data:
target: main.DataModuleFromConfig
params:
batch_size: 10
num_workers: 20
wrap: true
train:
target: ldm.data.openimages.FullOpenImagesTrain
params:
size: 384
crop_size: 256
validation:
target: ldm.data.openimages.FullOpenImagesValidation
params:
size: 384
crop_size: 256
================================================
FILE: models/instructpix2pix/stable_diffusion/models/ldm/bsr_sr/config.yaml
================================================
model:
base_learning_rate: 1.0e-06
target: ldm.models.diffusion.ddpm.LatentDiffusion
params:
linear_start: 0.0015
linear_end: 0.0155
log_every_t: 100
timesteps: 1000
loss_type: l2
first_stage_key: image
cond_stage_key: LR_image
image_size: 64
channels: 3
concat_mode: true
cond_stage_trainable: false
unet_config:
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
params:
image_size: 64
in_channels: 6
out_channels: 3
model_channels: 160
attention_resolutions:
- 16
- 8
num_res_blocks: 2
channel_mult:
- 1
- 2
- 2
- 4
num_head_channels: 32
first_stage_config:
target: ldm.models.autoencoder.VQModelInterface
params:
embed_dim: 3
n_embed: 8192
monitor: val/rec_loss
ddconfig:
double_z: false
z_channels: 3
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 4
num_res_blocks: 2
attn_resolutions: []
dropout: 0.0
lossconfig:
target: torch.nn.Identity
cond_stage_config:
target: torch.nn.Identity
data:
target: main.DataModuleFromConfig
params:
batch_size: 64
wrap: false
num_workers: 12
train:
target: ldm.data.openimages.SuperresOpenImagesAdvancedTrain
params:
size: 256
degradation: bsrgan_light
downscale_f: 4
min_crop_f: 0.5
max_crop_f: 1.0
random_crop: true
validation:
target: ldm.data.openimages.SuperresOpenImagesAdvancedValidation
params:
size: 256
degradation: bsrgan_light
downscale_f: 4
min_crop_f: 0.5
max_crop_f: 1.0
random_crop: true
================================================
FILE: models/instructpix2pix/stable_diffusion/models/ldm/celeba256/config.yaml
================================================
model:
base_learning_rate: 2.0e-06
target: ldm.models.diffusion.ddpm.LatentDiffusion
params:
linear_start: 0.0015
linear_end: 0.0195
num_timesteps_cond: 1
log_every_t: 200
timesteps: 1000
first_stage_key: image
cond_stage_key: class_label
image_size: 64
channels: 3
cond_stage_trainable: false
concat_mode: false
monitor: val/loss
unet_config:
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
params:
image_size: 64
in_channels: 3
out_channels: 3
model_channels: 224
attention_resolutions:
- 8
- 4
- 2
num_res_blocks: 2
channel_mult:
- 1
- 2
- 3
- 4
num_head_channels: 32
first_stage_config:
target: ldm.models.autoencoder.VQModelInterface
params:
embed_dim: 3
n_embed: 8192
ddconfig:
double_z: false
z_channels: 3
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 4
num_res_blocks: 2
attn_resolutions: []
dropout: 0.0
lossconfig:
target: torch.nn.Identity
cond_stage_config: __is_unconditional__
data:
target: main.DataModuleFromConfig
params:
batch_size: 48
num_workers: 5
wrap: false
train:
target: ldm.data.faceshq.CelebAHQTrain
params:
size: 256
validation:
target: ldm.data.faceshq.CelebAHQValidation
params:
size: 256
================================================
FILE: models/instructpix2pix/stable_diffusion/models/ldm/cin256/config.yaml
================================================
model:
base_learning_rate: 1.0e-06
target: ldm.models.diffusion.ddpm.LatentDiffusion
params:
linear_start: 0.0015
linear_end: 0.0195
num_timesteps_cond: 1
log_every_t: 200
timesteps: 1000
first_stage_key: image
cond_stage_key: class_label
image_size: 32
channels: 4
cond_stage_trainable: true
conditioning_key: crossattn
monitor: val/loss_simple_ema
unet_config:
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
params:
image_size: 32
in_channels: 4
out_channels: 4
model_channels: 256
attention_resolutions:
- 4
- 2
- 1
num_res_blocks: 2
channel_mult:
- 1
- 2
- 4
num_head_channels: 32
use_spatial_transformer: true
transformer_depth: 1
context_dim: 512
first_stage_config:
target: ldm.models.autoencoder.VQModelInterface
params:
embed_dim: 4
n_embed: 16384
ddconfig:
double_z: false
z_channels: 4
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 2
- 4
num_res_blocks: 2
attn_resolutions:
- 32
dropout: 0.0
lossconfig:
target: torch.nn.Identity
cond_stage_config:
target: ldm.modules.encoders.modules.ClassEmbedder
params:
embed_dim: 512
key: class_label
data:
target: main.DataModuleFromConfig
params:
batch_size: 64
num_workers: 12
wrap: false
train:
target: ldm.data.imagenet.ImageNetTrain
params:
config:
size: 256
validation:
target: ldm.data.imagenet.ImageNetValidation
params:
config:
size: 256
================================================
FILE: models/instructpix2pix/stable_diffusion/models/ldm/ffhq256/config.yaml
================================================
model:
base_learning_rate: 2.0e-06
target: ldm.models.diffusion.ddpm.LatentDiffusion
params:
linear_start: 0.0015
linear_end: 0.0195
num_timesteps_cond: 1
log_every_t: 200
timesteps: 1000
first_stage_key: image
cond_stage_key: class_label
image_size: 64
channels: 3
cond_stage_trainable: false
concat_mode: false
monitor: val/loss
unet_config:
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
params:
image_size: 64
in_channels: 3
out_channels: 3
model_channels: 224
attention_resolutions:
- 8
- 4
- 2
num_res_blocks: 2
channel_mult:
- 1
- 2
- 3
- 4
num_head_channels: 32
first_stage_config:
target: ldm.models.autoencoder.VQModelInterface
params:
embed_dim: 3
n_embed: 8192
ddconfig:
double_z: false
z_channels: 3
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 4
num_res_blocks: 2
attn_resolutions: []
dropout: 0.0
lossconfig:
target: torch.nn.Identity
cond_stage_config: __is_unconditional__
data:
target: main.DataModuleFromConfig
params:
batch_size: 42
num_workers: 5
wrap: false
train:
target: ldm.data.faceshq.FFHQTrain
params:
size: 256
validation:
target: ldm.data.faceshq.FFHQValidation
params:
size: 256
================================================
FILE: models/instructpix2pix/stable_diffusion/models/ldm/inpainting_big/config.yaml
================================================
model:
base_learning_rate: 1.0e-06
target: ldm.models.diffusion.ddpm.LatentDiffusion
params:
linear_start: 0.0015
linear_end: 0.0205
log_every_t: 100
timesteps: 1000
loss_type: l1
first_stage_key: image
cond_stage_key: masked_image
image_size: 64
channels: 3
concat_mode: true
monitor: val/loss
scheduler_config:
target: ldm.lr_scheduler.LambdaWarmUpCosineScheduler
params:
verbosity_interval: 0
warm_up_steps: 1000
max_decay_steps: 50000
lr_start: 0.001
lr_max: 0.1
lr_min: 0.0001
unet_config:
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
params:
image_size: 64
in_channels: 7
out_channels: 3
model_channels: 256
attention_resolutions:
- 8
- 4
- 2
num_res_blocks: 2
channel_mult:
- 1
- 2
- 3
- 4
num_heads: 8
resblock_updown: true
first_stage_config:
target: ldm.models.autoencoder.VQModelInterface
params:
embed_dim: 3
n_embed: 8192
monitor: val/rec_loss
ddconfig:
attn_type: none
double_z: false
z_channels: 3
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 4
num_res_blocks: 2
attn_resolutions: []
dropout: 0.0
lossconfig:
target: ldm.modules.losses.contperceptual.DummyLoss
cond_stage_config: __is_first_stage__
================================================
FILE: models/instructpix2pix/stable_diffusion/models/ldm/layout2img-openimages256/config.yaml
================================================
model:
base_learning_rate: 2.0e-06
target: ldm.models.diffusion.ddpm.LatentDiffusion
params:
linear_start: 0.0015
linear_end: 0.0205
log_every_t: 100
timesteps: 1000
loss_type: l1
first_stage_key: image
cond_stage_key: coordinates_bbox
image_size: 64
channels: 3
conditioning_key: crossattn
cond_stage_trainable: true
unet_config:
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
params:
image_size: 64
in_channels: 3
out_channels: 3
model_channels: 128
attention_resolutions:
- 8
- 4
- 2
num_res_blocks: 2
channel_mult:
- 1
- 2
- 3
- 4
num_head_channels: 32
use_spatial_transformer: true
transformer_depth: 3
context_dim: 512
first_stage_config:
target: ldm.models.autoencoder.VQModelInterface
params:
embed_dim: 3
n_embed: 8192
monitor: val/rec_loss
ddconfig:
double_z: false
z_channels: 3
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 4
num_res_blocks: 2
attn_resolutions: []
dropout: 0.0
lossconfig:
target: torch.nn.Identity
cond_stage_config:
target: ldm.modules.encoders.modules.BERTEmbedder
params:
n_embed: 512
n_layer: 16
vocab_size: 8192
max_seq_len: 92
use_tokenizer: false
monitor: val/loss_simple_ema
data:
target: main.DataModuleFromConfig
params:
batch_size: 24
wrap: false
num_workers: 10
train:
target: ldm.data.openimages.OpenImagesBBoxTrain
params:
size: 256
validation:
target: ldm.data.openimages.OpenImagesBBoxValidation
params:
size: 256
================================================
FILE: models/instructpix2pix/stable_diffusion/models/ldm/lsun_beds256/config.yaml
================================================
model:
base_learning_rate: 2.0e-06
target: ldm.models.diffusion.ddpm.LatentDiffusion
params:
linear_start: 0.0015
linear_end: 0.0195
num_timesteps_cond: 1
log_every_t: 200
timesteps: 1000
first_stage_key: image
cond_stage_key: class_label
image_size: 64
channels: 3
cond_stage_trainable: false
concat_mode: false
monitor: val/loss
unet_config:
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
params:
image_size: 64
in_channels: 3
out_channels: 3
model_channels: 224
attention_resolutions:
- 8
- 4
- 2
num_res_blocks: 2
channel_mult:
- 1
- 2
- 3
- 4
num_head_channels: 32
first_stage_config:
target: ldm.models.autoencoder.VQModelInterface
params:
embed_dim: 3
n_embed: 8192
ddconfig:
double_z: false
z_channels: 3
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 4
num_res_blocks: 2
attn_resolutions: []
dropout: 0.0
lossconfig:
target: torch.nn.Identity
cond_stage_config: __is_unconditional__
data:
target: main.DataModuleFromConfig
params:
batch_size: 48
num_workers: 5
wrap: false
train:
target: ldm.data.lsun.LSUNBedroomsTrain
params:
size: 256
validation:
target: ldm.data.lsun.LSUNBedroomsValidation
params:
size: 256
================================================
FILE: models/instructpix2pix/stable_diffusion/models/ldm/lsun_churches256/config.yaml
================================================
model:
base_learning_rate: 5.0e-05
target: ldm.models.diffusion.ddpm.LatentDiffusion
params:
linear_start: 0.0015
linear_end: 0.0155
num_timesteps_cond: 1
log_every_t: 200
timesteps: 1000
loss_type: l1
first_stage_key: image
cond_stage_key: image
image_size: 32
channels: 4
cond_stage_trainable: false
concat_mode: false
scale_by_std: true
monitor: val/loss_simple_ema
scheduler_config:
target: ldm.lr_scheduler.LambdaLinearScheduler
params:
warm_up_steps:
- 10000
cycle_lengths:
- 10000000000000
f_start:
- 1.0e-06
f_max:
- 1.0
f_min:
- 1.0
unet_config:
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
params:
image_size: 32
in_channels: 4
out_channels: 4
model_channels: 192
attention_resolutions:
- 1
- 2
- 4
- 8
num_res_blocks: 2
channel_mult:
- 1
- 2
- 2
- 4
- 4
num_heads: 8
use_scale_shift_norm: true
resblock_updown: true
first_stage_config:
target: ldm.models.autoencoder.AutoencoderKL
params:
embed_dim: 4
monitor: val/rec_loss
ddconfig:
double_z: true
z_channels: 4
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 4
- 4
num_res_blocks: 2
attn_resolutions: []
dropout: 0.0
lossconfig:
target: torch.nn.Identity
cond_stage_config: '__is_unconditional__'
data:
target: main.DataModuleFromConfig
params:
batch_size: 96
num_workers: 5
wrap: false
train:
target: ldm.data.lsun.LSUNChurchesTrain
params:
size: 256
validation:
target: ldm.data.lsun.LSUNChurchesValidation
params:
size: 256
================================================
FILE: models/instructpix2pix/stable_diffusion/models/ldm/semantic_synthesis256/config.yaml
================================================
model:
base_learning_rate: 1.0e-06
target: ldm.models.diffusion.ddpm.LatentDiffusion
params:
linear_start: 0.0015
linear_end: 0.0205
log_every_t: 100
timesteps: 1000
loss_type: l1
first_stage_key: image
cond_stage_key: segmentation
image_size: 64
channels: 3
concat_mode: true
cond_stage_trainable: true
unet_config:
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
params:
image_size: 64
in_channels: 6
out_channels: 3
model_channels: 128
attention_resolutions:
- 32
- 16
- 8
num_res_blocks: 2
channel_mult:
- 1
- 4
- 8
num_heads: 8
first_stage_config:
target: ldm.models.autoencoder.VQModelInterface
params:
embed_dim: 3
n_embed: 8192
ddconfig:
double_z: false
z_channels: 3
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 4
num_res_blocks: 2
attn_resolutions: []
dropout: 0.0
lossconfig:
target: torch.nn.Identity
cond_stage_config:
target: ldm.modules.encoders.modules.SpatialRescaler
params:
n_stages: 2
in_channels: 182
out_channels: 3
================================================
FILE: models/instructpix2pix/stable_diffusion/models/ldm/semantic_synthesis512/config.yaml
================================================
model:
base_learning_rate: 1.0e-06
target: ldm.models.diffusion.ddpm.LatentDiffusion
params:
linear_start: 0.0015
linear_end: 0.0205
log_every_t: 100
timesteps: 1000
loss_type: l1
first_stage_key: image
cond_stage_key: segmentation
image_size: 128
channels: 3
concat_mode: true
cond_stage_trainable: true
unet_config:
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
params:
image_size: 128
in_channels: 6
out_channels: 3
model_channels: 128
attention_resolutions:
- 32
- 16
- 8
num_res_blocks: 2
channel_mult:
- 1
- 4
- 8
num_heads: 8
first_stage_config:
target: ldm.models.autoencoder.VQModelInterface
params:
embed_dim: 3
n_embed: 8192
monitor: val/rec_loss
ddconfig:
double_z: false
z_channels: 3
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 4
num_res_blocks: 2
attn_resolutions: []
dropout: 0.0
lossconfig:
target: torch.nn.Identity
cond_stage_config:
target: ldm.modules.encoders.modules.SpatialRescaler
params:
n_stages: 2
in_channels: 182
out_channels: 3
data:
target: main.DataModuleFromConfig
params:
batch_size: 8
wrap: false
num_workers: 10
train:
target: ldm.data.landscapes.RFWTrain
params:
size: 768
crop_size: 512
segmentation_to_float32: true
validation:
target: ldm.data.landscapes.RFWValidation
params:
size: 768
crop_size: 512
segmentation_to_float32: true
================================================
FILE: models/instructpix2pix/stable_diffusion/models/ldm/text2img256/config.yaml
================================================
model:
base_learning_rate: 2.0e-06
target: ldm.models.diffusion.ddpm.LatentDiffusion
params:
linear_start: 0.0015
linear_end: 0.0195
num_timesteps_cond: 1
log_every_t: 200
timesteps: 1000
first_stage_key: image
cond_stage_key: caption
image_size: 64
channels: 3
cond_stage_trainable: true
conditioning_key: crossattn
monitor: val/loss_simple_ema
unet_config:
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
params:
image_size: 64
in_channels: 3
out_channels: 3
model_channels: 192
attention_resolutions:
- 8
- 4
- 2
num_res_blocks: 2
channel_mult:
- 1
- 2
- 3
- 5
num_head_channels: 32
use_spatial_transformer: true
transformer_depth: 1
context_dim: 640
first_stage_config:
target: ldm.models.autoencoder.VQModelInterface
params:
embed_dim: 3
n_embed: 8192
ddconfig:
double_z: false
z_channels: 3
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 4
num_res_blocks: 2
attn_resolutions: []
dropout: 0.0
lossconfig:
target: torch.nn.Identity
cond_stage_config:
target: ldm.modules.encoders.modules.BERTEmbedder
params:
n_embed: 640
n_layer: 32
data:
target: main.DataModuleFromConfig
params:
batch_size: 28
num_workers: 10
wrap: false
train:
target: ldm.data.previews.pytorch_dataset.PreviewsTrain
params:
size: 256
validation:
target: ldm.data.previews.pytorch_dataset.PreviewsValidation
params:
size: 256
================================================
FILE: models/instructpix2pix/stable_diffusion/notebook_helpers.py
================================================
from torchvision.datasets.utils import download_url
from ldm.util import instantiate_from_config
import torch
import os
# todo ?
from google.colab import files
from IPython.display import Image as ipyimg
import ipywidgets as widgets
from PIL import Image
from numpy import asarray
from einops import rearrange, repeat
import torch, torchvision
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.util import ismap
import time
from omegaconf import OmegaConf
def download_models(mode):
if mode == "superresolution":
# this is the small bsr light model
url_conf = 'https://heibox.uni-heidelberg.de/f/31a76b13ea27482981b4/?dl=1'
url_ckpt = 'https://heibox.uni-heidelberg.de/f/578df07c8fc04ffbadf3/?dl=1'
path_conf = 'logs/diffusion/superresolution_bsr/configs/project.yaml'
path_ckpt = 'logs/diffusion/superresolution_bsr/checkpoints/last.ckpt'
download_url(url_conf, path_conf)
download_url(url_ckpt, path_ckpt)
path_conf = path_conf + '/?dl=1' # fix it
path_ckpt = path_ckpt + '/?dl=1' # fix it
return path_conf, path_ckpt
else:
raise NotImplementedError
def load_model_from_config(config, ckpt):
print(f"Loading model from {ckpt}")
pl_sd = torch.load(ckpt, map_location="cpu")
global_step = pl_sd["global_step"]
sd = pl_sd["state_dict"]
model = instantiate_from_config(config.model)
m, u = model.load_state_dict(sd, strict=False)
model.cuda()
model.eval()
return {"model": model}, global_step
def get_model(mode):
path_conf, path_ckpt = download_models(mode)
config = OmegaConf.load(path_conf)
model, step = load_model_from_config(config, path_ckpt)
return model
def get_custom_cond(mode):
dest = "data/example_conditioning"
if mode == "superresolution":
uploaded_img = files.upload()
filename = next(iter(uploaded_img))
name, filetype = filename.split(".") # todo assumes just one dot in name !
os.rename(f"{filename}", f"{dest}/{mode}/custom_{name}.{filetype}")
elif mode == "text_conditional":
w = widgets.Text(value='A cake with cream!', disabled=True)
display(w)
with open(f"{dest}/{mode}/custom_{w.value[:20]}.txt", 'w') as f:
f.write(w.value)
elif mode == "class_conditional":
w = widgets.IntSlider(min=0, max=1000)
display(w)
with open(f"{dest}/{mode}/custom.txt", 'w') as f:
f.write(w.value)
else:
raise NotImplementedError(f"cond not implemented for mode{mode}")
def get_cond_options(mode):
path = "data/example_conditioning"
path = os.path.join(path, mode)
onlyfiles = [f for f in sorted(os.listdir(path))]
return path, onlyfiles
def select_cond_path(mode):
path = "data/example_conditioning" # todo
path = os.path.join(path, mode)
onlyfiles = [f for f in sorted(os.listdir(path))]
selected = widgets.RadioButtons(
options=onlyfiles,
description='Select conditioning:',
disabled=False
)
display(selected)
selected_path = os.path.join(path, selected.value)
return selected_path
def get_cond(mode, selected_path):
example = dict()
if mode == "superresolution":
up_f = 4
visualize_cond_img(selected_path)
c = Image.open(selected_path)
c = torch.unsqueeze(torchvision.transforms.ToTensor()(c), 0)
c_up = torchvision.transforms.functional.resize(c, size=[up_f * c.shape[2], up_f * c.shape[3]], antialias=True)
c_up = rearrange(c_up, '1 c h w -> 1 h w c')
c = rearrange(c, '1 c h w -> 1 h w c')
c = 2. * c - 1.
c = c.to(torch.device("cuda"))
example["LR_image"] = c
example["image"] = c_up
return example
def visualize_cond_img(path):
display(ipyimg(filename=path))
def run(model, selected_path, task, custom_steps, resize_enabled=False, classifier_ckpt=None, global_step=None):
example = get_cond(task, selected_path)
save_intermediate_vid = False
n_runs = 1
masked = False
guider = None
ckwargs = None
mode = 'ddim'
ddim_use_x0_pred = False
temperature = 1.
eta = 1.
make_progrow = True
custom_shape = None
height, width = example["image"].shape[1:3]
split_input = height >= 128 and width >= 128
if split_input:
ks = 128
stride = 64
vqf = 4 #
model.split_input_params = {"ks": (ks, ks), "stride": (stride, stride),
"vqf": vqf,
"patch_distributed_vq": True,
"tie_braker": False,
"clip_max_weight": 0.5,
"clip_min_weight": 0.01,
"clip_max_tie_weight": 0.5,
"clip_min_tie_weight": 0.01}
else:
if hasattr(model, "split_input_params"):
delattr(model, "split_input_params")
invert_mask = False
x_T = None
for n in range(n_runs):
if custom_shape is not None:
x_T = torch.randn(1, custom_shape[1], custom_shape[2], custom_shape[3]).to(model.device)
x_T = repeat(x_T, '1 c h w -> b c h w', b=custom_shape[0])
logs = make_convolutional_sample(example, model,
mode=mode, custom_steps=custom_steps,
eta=eta, swap_mode=False , masked=masked,
invert_mask=invert_mask, quantize_x0=False,
custom_schedule=None, decode_interval=10,
resize_enabled=resize_enabled, custom_shape=custom_shape,
temperature=temperature, noise_dropout=0.,
corrector=guider, corrector_kwargs=ckwargs, x_T=x_T, save_intermediate_vid=save_intermediate_vid,
make_progrow=make_progrow,ddim_use_x0_pred=ddim_use_x0_pred
)
return logs
@torch.no_grad()
def convsample_ddim(model, cond, steps, shape, eta=1.0, callback=None, normals_sequence=None,
mask=None, x0=None, quantize_x0=False, img_callback=None,
temperature=1., noise_dropout=0., score_corrector=None,
corrector_kwargs=None, x_T=None, log_every_t=None
):
ddim = DDIMSampler(model)
bs = shape[0] # dont know where this comes from but wayne
shape = shape[1:] # cut batch dim
print(f"Sampling with eta = {eta}; steps: {steps}")
samples, intermediates = ddim.sample(steps, batch_size=bs, shape=shape, conditioning=cond, callback=callback,
normals_sequence=normals_sequence, quantize_x0=quantize_x0, eta=eta,
mask=mask, x0=x0, temperature=temperature, verbose=False,
score_corrector=score_corrector,
corrector_kwargs=corrector_kwargs, x_T=x_T)
return samples, intermediates
@torch.no_grad()
def make_convolutional_sample(batch, model, mode="vanilla", custom_steps=None, eta=1.0, swap_mode=False, masked=False,
invert_mask=True, quantize_x0=False, custom_schedule=None, decode_interval=1000,
resize_enabled=False, custom_shape=None, temperature=1., noise_dropout=0., corrector=None,
corrector_kwargs=None, x_T=None, save_intermediate_vid=False, make_progrow=True,ddim_use_x0_pred=False):
log = dict()
z, c, x, xrec, xc = model.get_input(batch, model.first_stage_key,
return_first_stage_outputs=True,
force_c_encode=not (hasattr(model, 'split_input_params')
and model.cond_stage_key == 'coordinates_bbox'),
return_original_cond=True)
log_every_t = 1 if save_intermediate_vid else None
if custom_shape is not None:
z = torch.randn(custom_shape)
print(f"Generating {custom_shape[0]} samples of shape {custom_shape[1:]}")
z0 = None
log["input"] = x
log["reconstruction"] = xrec
if ismap(xc):
log["original_conditioning"] = model.to_rgb(xc)
if hasattr(model, 'cond_stage_key'):
log[model.cond_stage_key] = model.to_rgb(xc)
else:
log["original_conditioning"] = xc if xc is not None else torch.zeros_like(x)
if model.cond_stage_model:
log[model.cond_stage_key] = xc if xc is not None else torch.zeros_like(x)
if model.cond_stage_key =='class_label':
log[model.cond_stage_key] = xc[model.cond_stage_key]
with model.ema_scope("Plotting"):
t0 = time.time()
img_cb = None
sample, intermediates = convsample_ddim(model, c, steps=custom_steps, shape=z.shape,
eta=eta,
quantize_x0=quantize_x0, img_callback=img_cb, mask=None, x0=z0,
temperature=temperature, noise_dropout=noise_dropout,
score_corrector=corrector, corrector_kwargs=corrector_kwargs,
x_T=x_T, log_every_t=log_every_t)
t1 = time.time()
if ddim_use_x0_pred:
sample = intermediates['pred_x0'][-1]
x_sample = model.decode_first_stage(sample)
try:
x_sample_noquant = model.decode_first_stage(sample, force_not_quantize=True)
log["sample_noquant"] = x_sample_noquant
log["sample_diff"] = torch.abs(x_sample_noquant - x_sample)
except:
pass
log["sample"] = x_sample
log["time"] = t1 - t0
return log
================================================
FILE: models/instructpix2pix/stable_diffusion/scripts/download_first_stages.sh
================================================
#!/bin/bash
wget -O models/first_stage_models/kl-f4/model.zip https://ommer-lab.com/files/latent-diffusion/kl-f4.zip
wget -O models/first_stage_models/kl-f8/model.zip https://ommer-lab.com/files/latent-diffusion/kl-f8.zip
wget -O models/first_stage_models/kl-f16/model.zip https://ommer-lab.com/files/latent-diffusion/kl-f16.zip
wget -O models/first_stage_models/kl-f32/model.zip https://ommer-lab.com/files/latent-diffusion/kl-f32.zip
wget -O models/first_stage_models/vq-f4/model.zip https://ommer-lab.com/files/latent-diffusion/vq-f4.zip
wget -O models/first_stage_models/vq-f4-noattn/model.zip https://ommer-lab.com/files/latent-diffusion/vq-f4-noattn.zip
wget -O models/first_stage_models/vq-f8/model.zip https://ommer-lab.com/files/latent-diffusion/vq-f8.zip
wget -O models/first_stage_models/vq-f8-n256/model.zip https://ommer-lab.com/files/latent-diffusion/vq-f8-n256.zip
wget -O models/first_stage_models/vq-f16/model.zip https://ommer-lab.com/files/latent-diffusion/vq-f16.zip
cd models/first_stage_models/kl-f4
unzip -o model.zip
cd ../kl-f8
unzip -o model.zip
cd ../kl-f16
unzip -o model.zip
cd ../kl-f32
unzip -o model.zip
cd ../vq-f4
unzip -o model.zip
cd ../vq-f4-noattn
unzip -o model.zip
cd ../vq-f8
unzip -o model.zip
cd ../vq-f8-n256
unzip -o model.zip
cd ../vq-f16
unzip -o model.zip
cd ../..
================================================
FILE: models/instructpix2pix/stable_diffusion/scripts/download_models.sh
================================================
#!/bin/bash
wget -O models/ldm/celeba256/celeba-256.zip https://ommer-lab.com/files/latent-diffusion/celeba.zip
wget -O models/ldm/ffhq256/ffhq-256.zip https://ommer-lab.com/files/latent-diffusion/ffhq.zip
wget -O models/ldm/lsun_churches256/lsun_churches-256.zip https://ommer-lab.com/files/latent-diffusion/lsun_churches.zip
wget -O models/ldm/lsun_beds256/lsun_beds-256.zip https://ommer-lab.com/files/latent-diffusion/lsun_bedrooms.zip
wget -O models/ldm/text2img256/model.zip https://ommer-lab.com/files/latent-diffusion/text2img.zip
wget -O models/ldm/cin256/model.zip https://ommer-lab.com/files/latent-diffusion/cin.zip
wget -O models/ldm/semantic_synthesis512/model.zip https://ommer-lab.com/files/latent-diffusion/semantic_synthesis.zip
wget -O models/ldm/semantic_synthesis256/model.zip https://ommer-lab.com/files/latent-diffusion/semantic_synthesis256.zip
wget -O models/ldm/bsr_sr/model.zip https://ommer-lab.com/files/latent-diffusion/sr_bsr.zip
wget -O models/ldm/layout2img-openimages256/model.zip https://ommer-lab.com/files/latent-diffusion/layout2img_model.zip
wget -O models/ldm/inpainting_big/model.zip https://ommer-lab.com/files/latent-diffusion/inpainting_big.zip
cd models/ldm/celeba256
unzip -o celeba-256.zip
cd ../ffhq256
unzip -o ffhq-256.zip
cd ../lsun_churches256
unzip -o lsun_churches-256.zip
cd ../lsun_beds256
unzip -o lsun_beds-256.zip
cd ../text2img256
unzip -o model.zip
cd ../cin256
unzip -o model.zip
cd ../semantic_synthesis512
unzip -o model.zip
cd ../semantic_synthesis256
unzip -o model.zip
cd ../bsr_sr
unzip -o model.zip
cd ../layout2img-openimages256
unzip -o model.zip
cd ../inpainting_big
unzip -o model.zip
cd ../..
================================================
FILE: models/instructpix2pix/stable_diffusion/scripts/img2img.py
================================================
"""make variations of input image"""
import argparse, os, sys, glob
import PIL
import torch
import numpy as np
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from itertools import islice
from einops import rearrange, repeat
from torchvision.utils import make_grid
from torch import autocast
from contextlib import nullcontext
import time
from pytorch_lightning import seed_everything
from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.plms import PLMSSampler
def chunk(it, size):
it = iter(it)
return iter(lambda: tuple(islice(it, size)), ())
def load_model_from_config(config, ckpt, verbose=False):
print(f"Loading model from {ckpt}")
pl_sd = torch.load(ckpt, map_location="cpu")
if "global_step" in pl_sd:
print(f"Global Step: {pl_sd['global_step']}")
sd = pl_sd["state_dict"]
model = instantiate_from_config(config.model)
m, u = model.load_state_dict(sd, strict=False)
if len(m) > 0 and verbose:
print("missing keys:")
print(m)
if len(u) > 0 and verbose:
print("unexpected keys:")
print(u)
model.cuda()
model.eval()
return model
def load_img(path):
image = Image.open(path).convert("RGB")
w, h = image.size
print(f"loaded input image of size ({w}, {h}) from {path}")
w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32
image = image.resize((w, h), resample=PIL.Image.LANCZOS)
image = np.array(image).astype(np.float32) / 255.0
image = image[None].transpose(0, 3, 1, 2)
image = torch.from_numpy(image)
return 2.*image - 1.
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"--prompt",
type=str,
nargs="?",
default="a painting of a virus monster playing guitar",
help="the prompt to render"
)
parser.add_argument(
"--init-img",
type=str,
nargs="?",
help="path to the input image"
)
parser.add_argument(
"--outdir",
type=str,
nargs="?",
help="dir to write results to",
default="outputs/img2img-samples"
)
parser.add_argument(
"--skip_grid",
action='store_true',
help="do not save a grid, only individual samples. Helpful when evaluating lots of samples",
)
parser.add_argument(
"--skip_save",
action='store_true',
help="do not save indiviual samples. For speed measurements.",
)
parser.add_argument(
"--ddim_steps",
type=int,
default=50,
help="number of ddim sampling steps",
)
parser.add_argument(
"--plms",
action='store_true',
help="use plms sampling",
)
parser.add_argument(
"--fixed_code",
action='store_true',
help="if enabled, uses the same starting code across all samples ",
)
parser.add_argument(
"--ddim_eta",
type=float,
default=0.0,
help="ddim eta (eta=0.0 corresponds to deterministic sampling",
)
parser.add_argument(
"--n_iter",
type=int,
default=1,
help="sample this often",
)
parser.add_argument(
"--C",
type=int,
default=4,
help="latent channels",
)
parser.add_argument(
"--f",
type=int,
default=8,
help="downsampling factor, most often 8 or 16",
)
parser.add_argument(
"--n_samples",
type=int,
default=2,
help="how many samples to produce for each given prompt. A.k.a batch size",
)
parser.add_argument(
"--n_rows",
type=int,
default=0,
help="rows in the grid (default: n_samples)",
)
parser.add_argument(
"--scale",
type=float,
default=5.0,
help="unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) - eps(x, empty))",
)
parser.add_argument(
"--strength",
type=float,
default=0.75,
help="strength for noising/unnoising. 1.0 corresponds to full destruction of information in init image",
)
parser.add_argument(
"--from-file",
type=str,
help="if specified, load prompts from this file",
)
parser.add_argument(
"--config",
type=str,
default="configs/stable-diffusion/v1-inference.yaml",
help="path to config which constructs model",
)
parser.add_argument(
"--ckpt",
type=str,
default="models/ldm/stable-diffusion-v1/model.ckpt",
help="path to checkpoint of model",
)
parser.add_argument(
"--seed",
type=int,
default=42,
help="the seed (for reproducible sampling)",
)
parser.add_argument(
"--precision",
type=str,
help="evaluate at this precision",
choices=["full", "autocast"],
default="autocast"
)
opt = parser.parse_args()
seed_everything(opt.seed)
config = OmegaConf.load(f"{opt.config}")
model = load_model_from_config(config, f"{opt.ckpt}")
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
model = model.to(device)
if opt.plms:
raise NotImplementedError("PLMS sampler not (yet) supported")
sampler = PLMSSampler(model)
else:
sampler = DDIMSampler(model)
os.makedirs(opt.outdir, exist_ok=True)
outpath = opt.outdir
batch_size = opt.n_samples
n_rows = opt.n_rows if opt.n_rows > 0 else batch_size
if not opt.from_file:
prompt = opt.prompt
assert prompt is not None
data = [batch_size * [prompt]]
else:
print(f"reading prompts from {opt.from_file}")
with open(opt.from_file, "r") as f:
data = f.read().splitlines()
data = list(chunk(data, batch_size))
sample_path = os.path.join(outpath, "samples")
os.makedirs(sample_path, exist_ok=True)
base_count = len(os.listdir(sample_path))
grid_count = len(os.listdir(outpath)) - 1
assert os.path.isfile(opt.init_img)
init_image = load_img(opt.init_img).to(device)
init_image = repeat(init_image, '1 ... -> b ...', b=batch_size)
init_latent = model.get_first_stage_encoding(model.encode_first_stage(init_image)) # move to latent space
sampler.make_schedule(ddim_num_steps=opt.ddim_steps, ddim_eta=opt.ddim_eta, verbose=False)
assert 0. <= opt.strength <= 1., 'can only work with strength in [0.0, 1.0]'
t_enc = int(opt.strength * opt.ddim_steps)
print(f"target t_enc is {t_enc} steps")
precision_scope = autocast if opt.precision == "autocast" else nullcontext
with torch.no_grad():
with precision_scope("cuda"):
with model.ema_scope():
tic = time.time()
all_samples = list()
for n in trange(opt.n_iter, desc="Sampling"):
for prompts in tqdm(data, desc="data"):
uc = None
if opt.scale != 1.0:
uc = model.get_learned_conditioning(batch_size * [""])
if isinstance(prompts, tuple):
prompts = list(prompts)
c = model.get_learned_conditioning(prompts)
# encode (scaled latent)
z_enc = sampler.stochastic_encode(init_latent, torch.tensor([t_enc]*batch_size).to(device))
# decode it
samples = sampler.decode(z_enc, c, t_enc, unconditional_guidance_scale=opt.scale,
unconditional_conditioning=uc,)
x_samples = model.decode_first_stage(samples)
x_samples = torch.clamp((x_samples + 1.0) / 2.0, min=0.0, max=1.0)
if not opt.skip_save:
for x_sample in x_samples:
x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
Image.fromarray(x_sample.astype(np.uint8)).save(
os.path.join(sample_path, f"{base_count:05}.png"))
base_count += 1
all_samples.append(x_samples)
if not opt.skip_grid:
# additionally, save as grid
grid = torch.stack(all_samples, 0)
grid = rearrange(grid, 'n b c h w -> (n b) c h w')
grid = make_grid(grid, nrow=n_rows)
# to image
grid = 255. * rearrange(grid, 'c h w -> h w c').cpu().numpy()
Image.fromarray(grid.astype(np.uint8)).save(os.path.join(outpath, f'grid-{grid_count:04}.png'))
grid_count += 1
toc = time.time()
print(f"Your samples are ready and waiting for you here: \n{outpath} \n"
f" \nEnjoy.")
if __name__ == "__main__":
main()
================================================
FILE: models/instructpix2pix/stable_diffusion/scripts/inpaint.py
================================================
import argparse, os, sys, glob
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm
import numpy as np
import torch
from main import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
def make_batch(image, mask, device):
image = np.array(Image.open(image).convert("RGB"))
image = image.astype(np.float32)/255.0
image = image[None].transpose(0,3,1,2)
image = torch.from_numpy(image)
mask = np.array(Image.open(mask).convert("L"))
mask = mask.astype(np.float32)/255.0
mask = mask[None,None]
mask[mask < 0.5] = 0
mask[mask >= 0.5] = 1
mask = torch.from_numpy(mask)
masked_image = (1-mask)*image
batch = {"image": image, "mask": mask, "masked_image": masked_image}
for k in batch:
batch[k] = batch[k].to(device=device)
batch[k] = batch[k]*2.0-1.0
return batch
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--indir",
type=str,
nargs="?",
help="dir containing image-mask pairs (`example.png` and `example_mask.png`)",
)
parser.add_argument(
"--outdir",
type=str,
nargs="?",
help="dir to write results to",
)
parser.add_argument(
"--steps",
type=int,
default=50,
help="number of ddim sampling steps",
)
opt = parser.parse_args()
masks = sorted(glob.glob(os.path.join(opt.indir, "*_mask.png")))
images = [x.replace("_mask.png", ".png") for x in masks]
print(f"Found {len(masks)} inputs.")
config = OmegaConf.load("models/ldm/inpainting_big/config.yaml")
model = instantiate_from_config(config.model)
model.load_state_dict(torch.load("models/ldm/inpainting_big/last.ckpt")["state_dict"],
strict=False)
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
model = model.to(device)
sampler = DDIMSampler(model)
os.makedirs(opt.outdir, exist_ok=True)
with torch.no_grad():
with model.ema_scope():
for image, mask in tqdm(zip(images, masks)):
outpath = os.path.join(opt.outdir, os.path.split(image)[1])
batch = make_batch(image, mask, device=device)
# encode masked image and concat downsampled mask
c = model.cond_stage_model.encode(batch["masked_image"])
cc = torch.nn.functional.interpolate(batch["mask"],
size=c.shape[-2:])
c = torch.cat((c, cc), dim=1)
shape = (c.shape[1]-1,)+c.shape[2:]
samples_ddim, _ = sampler.sample(S=opt.steps,
conditioning=c,
batch_size=c.shape[0],
shape=shape,
verbose=False)
x_samples_ddim = model.decode_first_stage(samples_ddim)
image = torch.clamp((batch["image"]+1.0)/2.0,
min=0.0, max=1.0)
mask = torch.clamp((batch["mask"]+1.0)/2.0,
min=0.0, max=1.0)
predicted_image = torch.clamp((x_samples_ddim+1.0)/2.0,
min=0.0, max=1.0)
inpainted = (1-mask)*image+mask*predicted_image
inpainted = inpainted.cpu().numpy().transpose(0,2,3,1)[0]*255
Image.fromarray(inpainted.astype(np.uint8)).save(outpath)
================================================
FILE: models/instructpix2pix/stable_diffusion/scripts/knn2img.py
================================================
import argparse, os, sys, glob
import clip
import torch
import torch.nn as nn
import numpy as np
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from itertools import islice
from einops import rearrange, repeat
from torchvision.utils import make_grid
import scann
import time
from multiprocessing import cpu_count
from ldm.util import instantiate_from_config, parallel_data_prefetch
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.plms import PLMSSampler
from ldm.modules.encoders.modules import FrozenClipImageEmbedder, FrozenCLIPTextEmbedder
DATABASES = [
"openimages",
"artbench-art_nouveau",
"artbench-baroque",
"artbench-expressionism",
"artbench-impressionism",
"artbench-post_impressionism",
"artbench-realism",
"artbench-romanticism",
"artbench-renaissance",
"artbench-surrealism",
"artbench-ukiyo_e",
]
def chunk(it, size):
it = iter(it)
return iter(lambda: tuple(islice(it, size)), ())
def load_model_from_config(config, ckpt, verbose=False):
print(f"Loading model from {ckpt}")
pl_sd = torch.load(ckpt, map_location="cpu")
if "global_step" in pl_sd:
print(f"Global Step: {pl_sd['global_step']}")
sd = pl_sd["state_dict"]
model = instantiate_from_config(config.model)
m, u = model.load_state_dict(sd, strict=False)
if len(m) > 0 and verbose:
print("missing keys:")
print(m)
if len(u) > 0 and verbose:
print("unexpected keys:")
print(u)
model.cuda()
model.eval()
return model
class Searcher(object):
def __init__(self, database, retriever_version='ViT-L/14'):
assert database in DATABASES
# self.database = self.load_database(database)
self.database_name = database
self.searcher_savedir = f'data/rdm/searchers/{self.database_name}'
self.database_path = f'data/rdm/retrieval_databases/{self.database_name}'
self.retriever = self.load_retriever(version=retriever_version)
self.database = {'embedding': [],
'img_id': [],
'patch_coords': []}
self.load_database()
self.load_searcher()
def train_searcher(self, k,
metric='dot_product',
searcher_savedir=None):
print('Start training searcher')
searcher = scann.scann_ops_pybind.builder(self.database['embedding'] /
np.linalg.norm(self.database['embedding'], axis=1)[:, np.newaxis],
k, metric)
self.searcher = searcher.score_brute_force().build()
print('Finish training searcher')
if searcher_savedir is not None:
print(f'Save trained searcher under "{searcher_savedir}"')
os.makedirs(searcher_savedir, exist_ok=True)
self.searcher.serialize(searcher_savedir)
def load_single_file(self, saved_embeddings):
compressed = np.load(saved_embeddings)
self.database = {key: compressed[key] for key in compressed.files}
print('Finished loading of clip embeddings.')
def load_multi_files(self, data_archive):
out_data = {key: [] for key in self.database}
for d in tqdm(data_archive, desc=f'Loading datapool from {len(data_archive)} individual files.'):
for key in d.files:
out_data[key].append(d[key])
return out_data
def load_database(self):
print(f'Load saved patch embedding from "{self.database_path}"')
file_content = glob.glob(os.path.join(self.database_path, '*.npz'))
if len(file_content) == 1:
self.load_single_file(file_content[0])
elif len(file_content) > 1:
data = [np.load(f) for f in file_content]
prefetched_data = parallel_data_prefetch(self.load_multi_files, data,
n_proc=min(len(data), cpu_count()), target_data_type='dict')
self.database = {key: np.concatenate([od[key] for od in prefetched_data], axis=1)[0] for key in
self.database}
else:
raise ValueError(f'No npz-files in specified path "{self.database_path}" is this directory existing?')
print(f'Finished loading of retrieval database of length {self.database["embedding"].shape[0]}.')
def load_retriever(self, version='ViT-L/14', ):
model = FrozenClipImageEmbedder(model=version)
if torch.cuda.is_available():
model.cuda()
model.eval()
return model
def load_searcher(self):
print(f'load searcher for database {self.database_name} from {self.searcher_savedir}')
self.searcher = scann.scann_ops_pybind.load_searcher(self.searcher_savedir)
print('Finished loading searcher.')
def search(self, x, k):
if self.searcher is None and self.database['embedding'].shape[0] < 2e4:
self.train_searcher(k) # quickly fit searcher on the fly for small databases
assert self.searcher is not None, 'Cannot search with uninitialized searcher'
if isinstance(x, torch.Tensor):
x = x.detach().cpu().numpy()
if len(x.shape) == 3:
x = x[:, 0]
query_embeddings = x / np.linalg.norm(x, axis=1)[:, np.newaxis]
start = time.time()
nns, distances = self.searcher.search_batched(query_embeddings, final_num_neighbors=k)
end = time.time()
out_embeddings = self.database['embedding'][nns]
out_img_ids = self.database['img_id'][nns]
out_pc = self.database['patch_coords'][nns]
out = {'nn_embeddings': out_embeddings / np.linalg.norm(out_embeddings, axis=-1)[..., np.newaxis],
'img_ids': out_img_ids,
'patch_coords': out_pc,
'queries': x,
'exec_time': end - start,
'nns': nns,
'q_embeddings': query_embeddings}
return out
def __call__(self, x, n):
return self.search(x, n)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# TODO: add n_neighbors and modes (text-only, text-image-retrieval, image-image retrieval etc)
# TODO: add 'image variation' mode when knn=0 but a single image is given instead of a text prompt?
parser.add_argument(
"--prompt",
type=str,
nargs="?",
default="a painting of a virus monster playing guitar",
help="the prompt to render"
)
parser.add_argument(
"--outdir",
type=str,
nargs="?",
help="dir to write results to",
default="outputs/txt2img-samples"
)
parser.add_argument(
"--skip_grid",
action='store_true',
help="do not save a grid, only individual samples. Helpful when evaluating lots of samples",
)
parser.add_argument(
"--ddim_steps",
type=int,
default=50,
help="number of ddim sampling steps",
)
parser.add_argument(
"--n_repeat",
type=int,
default=1,
help="number of repeats in CLIP latent space",
)
parser.add_argument(
"--plms",
action='store_true',
help="use plms sampling",
)
parser.add_argument(
"--ddim_eta",
type=float,
default=0.0,
help="ddim eta (eta=0.0 corresponds to deterministic sampling",
)
parser.add_argument(
"--n_iter",
type=int,
default=1,
help="sample this often",
)
parser.add_argument(
"--H",
type=int,
default=768,
help="image height, in pixel space",
)
parser.add_argument(
"--W",
type=int,
default=768,
help="image width, in pixel space",
)
parser.add_argument(
"--n_samples",
type=int,
default=3,
help="how many samples to produce for each given prompt. A.k.a batch size",
)
parser.add_argument(
"--n_rows",
type=int,
default=0,
help="rows in the grid (default: n_samples)",
)
parser.add_argument(
"--scale",
type=float,
default=5.0,
help="unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) - eps(x, empty))",
)
parser.add_argument(
"--from-file",
type=str,
help="if specified, load prompts from this file",
)
parser.add_argument(
"--config",
type=str,
default="configs/retrieval-augmented-diffusion/768x768.yaml",
help="path to config which constructs model",
)
parser.add_argument(
"--ckpt",
type=str,
default="models/rdm/rdm768x768/model.ckpt",
help="path to checkpoint of model",
)
parser.add_argument(
"--clip_type",
type=str,
default="ViT-L/14",
help="which CLIP model to use for retrieval and NN encoding",
)
parser.add_argument(
"--database",
type=str,
default='artbench-surrealism',
choices=DATABASES,
help="The database used for the search, only applied when --use_neighbors=True",
)
parser.add_argument(
"--use_neighbors",
default=False,
action='store_true',
help="Include neighbors in addition to text prompt for conditioning",
)
parser.add_argument(
"--knn",
default=10,
type=int,
help="The number of included neighbors, only applied when --use_neighbors=True",
)
opt = parser.parse_args()
config = OmegaConf.load(f"{opt.config}")
model = load_model_from_config(config, f"{opt.ckpt}")
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
model = model.to(device)
clip_text_encoder = FrozenCLIPTextEmbedder(opt.clip_type).to(device)
if opt.plms:
sampler = PLMSSampler(model)
else:
sampler = DDIMSampler(model)
os.makedirs(opt.outdir, exist_ok=True)
outpath = opt.outdir
batch_size = opt.n_samples
n_rows = opt.n_rows if opt.n_rows > 0 else batch_size
if not opt.from_file:
prompt = opt.prompt
assert prompt is not None
data = [batch_size * [prompt]]
else:
print(f"reading prompts from {opt.from_file}")
with open(opt.from_file, "r") as f:
data = f.read().splitlines()
data = list(chunk(data, batch_size))
sample_path = os.path.join(outpath, "samples")
os.makedirs(sample_path, exist_ok=True)
base_count = len(os.listdir(sample_path))
grid_count = len(os.listdir(outpath)) - 1
print(f"sampling scale for cfg is {opt.scale:.2f}")
searcher = None
if opt.use_neighbors:
searcher = Searcher(opt.database)
with torch.no_grad():
with model.ema_scope():
for n in trange(opt.n_iter, desc="Sampling"):
all_samples = list()
for prompts in tqdm(data, desc="data"):
print("sampling prompts:", prompts)
if isinstance(prompts, tuple):
prompts = list(prompts)
c = clip_text_encoder.encode(prompts)
uc = None
if searcher is not None:
nn_dict = searcher(c, opt.knn)
c = torch.cat([c, torch.from_numpy(nn_dict['nn_embeddings']).cuda()], dim=1)
if opt.scale != 1.0:
uc = torch.zeros_like(c)
if isinstance(prompts, tuple):
prompts = list(prompts)
shape = [16, opt.H // 16, opt.W // 16] # note: currently hardcoded for f16 model
samples_ddim, _ = sampler.sample(S=opt.ddim_steps,
conditioning=c,
batch_size=c.shape[0],
shape=shape,
verbose=False,
unconditional_guidance_scale=opt.scale,
unconditional_conditioning=uc,
eta=opt.ddim_eta,
)
x_samples_ddim = model.decode_first_stage(samples_ddim)
x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
for x_sample in x_samples_ddim:
x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
Image.fromarray(x_sample.astype(np.uint8)).save(
os.path.join(sample_path, f"{base_count:05}.png"))
base_count += 1
all_samples.append(x_samples_ddim)
if not opt.skip_grid:
# additionally, save as grid
grid = torch.stack(all_samples, 0)
grid = rearrange(grid, 'n b c h w -> (n b) c h w')
grid = make_grid(grid, nrow=n_rows)
# to image
grid = 255. * rearrange(grid, 'c h w -> h w c').cpu().numpy()
Image.fromarray(grid.astype(np.uint8)).save(os.path.join(outpath, f'grid-{grid_count:04}.png'))
grid_count += 1
print(f"Your samples are ready and waiting for you here: \n{outpath} \nEnjoy.")
================================================
FILE: models/instructpix2pix/stable_diffusion/scripts/latent_imagenet_diffusion.ipynb.REMOVED.git-id
================================================
607f94fc7d3ef6d8d1627017215476d9dfc7ddc4
================================================
FILE: models/instructpix2pix/stable_diffusion/scripts/sample_diffusion.py
================================================
import argparse, os, sys, glob, datetime, yaml
import torch
import time
import numpy as np
from tqdm import trange
from omegaconf import OmegaConf
from PIL import Image
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.util import instantiate_from_config
rescale = lambda x: (x + 1.) / 2.
def custom_to_pil(x):
x = x.detach().cpu()
x = torch.clamp(x, -1., 1.)
x = (x + 1.) / 2.
x = x.permute(1, 2, 0).numpy()
x = (255 * x).astype(np.uint8)
x = Image.fromarray(x)
if not x.mode == "RGB":
x = x.convert("RGB")
return x
def custom_to_np(x):
# saves the batch in adm style as in https://github.com/openai/guided-diffusion/blob/main/scripts/image_sample.py
sample = x.detach().cpu()
sample = ((sample + 1) * 127.5).clamp(0, 255).to(torch.uint8)
sample = sample.permute(0, 2, 3, 1)
sample = sample.contiguous()
return sample
def logs2pil(logs, keys=["sample"]):
imgs = dict()
for k in logs:
try:
if len(logs[k].shape) == 4:
img = custom_to_pil(logs[k][0, ...])
elif len(logs[k].shape) == 3:
img = custom_to_pil(logs[k])
else:
print(f"Unknown format for key {k}. ")
img = None
except:
img = None
imgs[k] = img
return imgs
@torch.no_grad()
def convsample(model, shape, return_intermediates=True,
verbose=True,
make_prog_row=False):
if not make_prog_row:
return model.p_sample_loop(None, shape,
return_intermediates=return_intermediates, verbose=verbose)
else:
return model.progressive_denoising(
None, shape, verbose=True
)
@torch.no_grad()
def convsample_ddim(model, steps, shape, eta=1.0
):
ddim = DDIMSampler(model)
bs = shape[0]
shape = shape[1:]
samples, intermediates = ddim.sample(steps, batch_size=bs, shape=shape, eta=eta, verbose=False,)
return samples, intermediates
@torch.no_grad()
def make_convolutional_sample(model, batch_size, vanilla=False, custom_steps=None, eta=1.0,):
log = dict()
shape = [batch_size,
model.model.diffusion_model.in_channels,
model.model.diffusion_model.image_size,
model.model.diffusion_model.image_size]
with model.ema_scope("Plotting"):
t0 = time.time()
if vanilla:
sample, progrow = convsample(model, shape,
make_prog_row=True)
else:
sample, intermediates = convsample_ddim(model, steps=custom_steps, shape=shape,
eta=eta)
t1 = time.time()
x_sample = model.decode_first_stage(sample)
log["sample"] = x_sample
log["time"] = t1 - t0
log['throughput'] = sample.shape[0] / (t1 - t0)
print(f'Throughput for this batch: {log["throughput"]}')
return log
def run(model, logdir, batch_size=50, vanilla=False, custom_steps=None, eta=None, n_samples=50000, nplog=None):
if vanilla:
print(f'Using Vanilla DDPM sampling with {model.num_timesteps} sampling steps.')
else:
print(f'Using DDIM sampling with {custom_steps} sampling steps and eta={eta}')
tstart = time.time()
n_saved = len(glob.glob(os.path.join(logdir,'*.png')))-1
# path = logdir
if model.cond_stage_model is None:
all_images = []
print(f"Running unconditional sampling for {n_samples} samples")
for _ in trange(n_samples // batch_size, desc="Sampling Batches (unconditional)"):
logs = make_convolutional_sample(model, batch_size=batch_size,
vanilla=vanilla, custom_steps=custom_steps,
eta=eta)
n_saved = save_logs(logs, logdir, n_saved=n_saved, key="sample")
all_images.extend([custom_to_np(logs["sample"])])
if n_saved >= n_samples:
print(f'Finish after generating {n_saved} samples')
break
all_img = np.concatenate(all_images, axis=0)
all_img = all_img[:n_samples]
shape_str = "x".join([str(x) for x in all_img.shape])
nppath = os.path.join(nplog, f"{shape_str}-samples.npz")
np.savez(nppath, all_img)
else:
raise NotImplementedError('Currently only sampling for unconditional models supported.')
print(f"sampling of {n_saved} images finished in {(time.time() - tstart) / 60.:.2f} minutes.")
def save_logs(logs, path, n_saved=0, key="sample", np_path=None):
for k in logs:
if k == key:
batch = logs[key]
if np_path is None:
for x in batch:
img = custom_to_pil(x)
imgpath = os.path.join(path, f"{key}_{n_saved:06}.png")
img.save(imgpath)
n_saved += 1
else:
npbatch = custom_to_np(batch)
shape_str = "x".join([str(x) for x in npbatch.shape])
nppath = os.path.join(np_path, f"{n_saved}-{shape_str}-samples.npz")
np.savez(nppath, npbatch)
n_saved += npbatch.shape[0]
return n_saved
def get_parser():
parser = argparse.ArgumentParser()
parser.add_argument(
"-r",
"--resume",
type=str,
nargs="?",
help="load from logdir or checkpoint in logdir",
)
parser.add_argument(
"-n",
"--n_samples",
type=int,
nargs="?",
help="number of samples to draw",
default=50000
)
parser.add_argument(
"-e",
"--eta",
type=float,
nargs="?",
help="eta for ddim sampling (0.0 yields deterministic sampling)",
default=1.0
)
parser.add_argument(
"-v",
"--vanilla_sample",
default=False,
action='store_true',
help="vanilla sampling (default option is DDIM sampling)?",
)
parser.add_argument(
"-l",
"--logdir",
type=str,
nargs="?",
help="extra logdir",
default="none"
)
parser.add_argument(
"-c",
"--custom_steps",
type=int,
nargs="?",
help="number of steps for ddim and fastdpm sampling",
default=50
)
parser.add_argument(
"--batch_size",
type=int,
nargs="?",
help="the bs",
default=10
)
return parser
def load_model_from_config(config, sd):
model = instantiate_from_config(config)
model.load_state_dict(sd,strict=False)
model.cuda()
model.eval()
return model
def load_model(config, ckpt, gpu, eval_mode):
if ckpt:
print(f"Loading model from {ckpt}")
pl_sd = torch.load(ckpt, map_location="cpu")
global_step = pl_sd["global_step"]
else:
pl_sd = {"state_dict": None}
global_step = None
model = load_model_from_config(config.model,
pl_sd["state_dict"])
return model, global_step
if __name__ == "__main__":
now = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
sys.path.append(os.getcwd())
command = " ".join(sys.argv)
parser = get_parser()
opt, unknown = parser.parse_known_args()
ckpt = None
if not os.path.exists(opt.resume):
raise ValueError("Cannot find {}".format(opt.resume))
if os.path.isfile(opt.resume):
# paths = opt.resume.split("/")
try:
logdir = '/'.join(opt.resume.split('/')[:-1])
# idx = len(paths)-paths[::-1].index("logs")+1
print(f'Logdir is {logdir}')
except ValueError:
paths = opt.resume.split("/")
idx = -2 # take a guess: path/to/logdir/checkpoints/model.ckpt
logdir = "/".join(paths[:idx])
ckpt = opt.resume
else:
assert os.path.isdir(opt.resume), f"{opt.resume} is not a directory"
logdir = opt.resume.rstrip("/")
ckpt = os.path.join(logdir, "model.ckpt")
base_configs = sorted(glob.glob(os.path.join(logdir, "config.yaml")))
opt.base = base_configs
configs = [OmegaConf.load(cfg) for cfg in opt.base]
cli = OmegaConf.from_dotlist(unknown)
config = OmegaConf.merge(*configs, cli)
gpu = True
eval_mode = True
if opt.logdir != "none":
locallog = logdir.split(os.sep)[-1]
if locallog == "": locallog = logdir.split(os.sep)[-2]
print(f"Switching logdir from '{logdir}' to '{os.path.join(opt.logdir, locallog)}'")
logdir = os.path.join(opt.logdir, locallog)
print(config)
model, global_step = load_model(config, ckpt, gpu, eval_mode)
print(f"global step: {global_step}")
print(75 * "=")
print("logging to:")
logdir = os.path.join(logdir, "samples", f"{global_step:08}", now)
imglogdir = os.path.join(logdir, "img")
numpylogdir = os.path.join(logdir, "numpy")
os.makedirs(imglogdir)
os.makedirs(numpylogdir)
print(logdir)
print(75 * "=")
# write config out
sampling_file = os.path.join(logdir, "sampling_config.yaml")
sampling_conf = vars(opt)
with open(sampling_file, 'w') as f:
yaml.dump(sampling_conf, f, default_flow_style=False)
print(sampling_conf)
run(model, imglogdir, eta=opt.eta,
vanilla=opt.vanilla_sample, n_samples=opt.n_samples, custom_steps=opt.custom_steps,
batch_size=opt.batch_size, nplog=numpylogdir)
print("done.")
================================================
FILE: models/instructpix2pix/stable_diffusion/scripts/tests/test_watermark.py
================================================
import cv2
import fire
from imwatermark import WatermarkDecoder
def testit(img_path):
bgr = cv2.imread(img_path)
decoder = WatermarkDecoder('bytes', 136)
watermark = decoder.decode(bgr, 'dwtDct')
try:
dec = watermark.decode('utf-8')
except:
dec = "null"
print(dec)
if __name__ == "__main__":
fire.Fire(testit)
================================================
FILE: models/instructpix2pix/stable_diffusion/scripts/train_searcher.py
================================================
import os, sys
import numpy as np
import scann
import argparse
import glob
from multiprocessing import cpu_count
from tqdm import tqdm
from ldm.util import parallel_data_prefetch
def search_bruteforce(searcher):
return searcher.score_brute_force().build()
def search_partioned_ah(searcher, dims_per_block, aiq_threshold, reorder_k,
partioning_trainsize, num_leaves, num_leaves_to_search):
return searcher.tree(num_leaves=num_leaves,
num_leaves_to_search=num_leaves_to_search,
training_sample_size=partioning_trainsize). \
score_ah(dims_per_block, anisotropic_quantization_threshold=aiq_threshold).reorder(reorder_k).build()
def search_ah(searcher, dims_per_block, aiq_threshold, reorder_k):
return searcher.score_ah(dims_per_block, anisotropic_quantization_threshold=aiq_threshold).reorder(
reorder_k).build()
def load_datapool(dpath):
def load_single_file(saved_embeddings):
compressed = np.load(saved_embeddings)
database = {key: compressed[key] for key in compressed.files}
return database
def load_multi_files(data_archive):
database = {key: [] for key in data_archive[0].files}
for d in tqdm(data_archive, desc=f'Loading datapool from {len(data_archive)} individual files.'):
for key in d.files:
database[key].append(d[key])
return database
print(f'Load saved patch embedding from "{dpath}"')
file_content = glob.glob(os.path.join(dpath, '*.npz'))
if len(file_content) == 1:
data_pool = load_single_file(file_content[0])
elif len(file_content) > 1:
data = [np.load(f) for f in file_content]
prefetched_data = parallel_data_prefetch(load_multi_files, data,
n_proc=min(len(data), cpu_count()), target_data_type='dict')
data_pool = {key: np.concatenate([od[key] for od in prefetched_data], axis=1)[0] for key in prefetched_data[0].keys()}
else:
raise ValueError(f'No npz-files in specified path "{dpath}" is this directory existing?')
print(f'Finished loading of retrieval database of length {data_pool["embedding"].shape[0]}.')
return data_pool
def train_searcher(opt,
metric='dot_product',
partioning_trainsize=None,
reorder_k=None,
# todo tune
aiq_thld=0.2,
dims_per_block=2,
num_leaves=None,
num_leaves_to_search=None,):
data_pool = load_datapool(opt.database)
k = opt.knn
if not reorder_k:
reorder_k = 2 * k
# normalize
# embeddings =
searcher = scann.scann_ops_pybind.builder(data_pool['embedding'] / np.linalg.norm(data_pool['embedding'], axis=1)[:, np.newaxis], k, metric)
pool_size = data_pool['embedding'].shape[0]
print(*(['#'] * 100))
print('Initializing scaNN searcher with the following values:')
print(f'k: {k}')
print(f'metric: {metric}')
print(f'reorder_k: {reorder_k}')
print(f'anisotropic_quantization_threshold: {aiq_thld}')
print(f'dims_per_block: {dims_per_block}')
print(*(['#'] * 100))
print('Start training searcher....')
print(f'N samples in pool is {pool_size}')
# this reflects the recommended design choices proposed at
# https://github.com/google-research/google-research/blob/aca5f2e44e301af172590bb8e65711f0c9ee0cfd/scann/docs/algorithms.md
if pool_size < 2e4:
print('Using brute force search.')
searcher = search_bruteforce(searcher)
elif 2e4 <= pool_size and pool_size < 1e5:
print('Using asymmetric hashing search and reordering.')
searcher = search_ah(searcher, dims_per_block, aiq_thld, reorder_k)
else:
print('Using using partioning, asymmetric hashing search and reordering.')
if not partioning_trainsize:
partioning_trainsize = data_pool['embedding'].shape[0] // 10
if not num_leaves:
num_leaves = int(np.sqrt(pool_size))
if not num_leaves_to_search:
num_leaves_to_search = max(num_leaves // 20, 1)
print('Partitioning params:')
print(f'num_leaves: {num_leaves}')
print(f'num_leaves_to_search: {num_leaves_to_search}')
# self.searcher = self.search_ah(searcher, dims_per_block, aiq_thld, reorder_k)
searcher = search_partioned_ah(searcher, dims_per_block, aiq_thld, reorder_k,
partioning_trainsize, num_leaves, num_leaves_to_search)
print('Finish training searcher')
searcher_savedir = opt.target_path
os.makedirs(searcher_savedir, exist_ok=True)
searcher.serialize(searcher_savedir)
print(f'Saved trained searcher under "{searcher_savedir}"')
if __name__ == '__main__':
sys.path.append(os.getcwd())
parser = argparse.ArgumentParser()
parser.add_argument('--database',
'-d',
default='data/rdm/retrieval_databases/openimages',
type=str,
help='path to folder containing the clip feature of the database')
parser.add_argument('--target_path',
'-t',
default='data/rdm/searchers/openimages',
type=str,
help='path to the target folder where the searcher shall be stored.')
parser.add_argument('--knn',
'-k',
default=20,
type=int,
help='number of nearest neighbors, for which the searcher shall be optimized')
opt, _ = parser.parse_known_args()
train_searcher(opt,)
================================================
FILE: models/instructpix2pix/stable_diffusion/scripts/txt2img.py
================================================
import argparse, os, sys, glob
import cv2
import torch
import numpy as np
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from imwatermark import WatermarkEncoder
from itertools import islice
from einops import rearrange
from torchvision.utils import make_grid
import time
from pytorch_lightning import seed_everything
from torch import autocast
from contextlib import contextmanager, nullcontext
from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.plms import PLMSSampler
from ldm.models.diffusion.dpm_solver import DPMSolverSampler
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from transformers import AutoFeatureExtractor
# load safety model
safety_model_id = "CompVis/stable-diffusion-safety-checker"
safety_feature_extractor = AutoFeatureExtractor.from_pretrained(safety_model_id)
safety_checker = StableDiffusionSafetyChecker.from_pretrained(safety_model_id)
def chunk(it, size):
it = iter(it)
return iter(lambda: tuple(islice(it, size)), ())
def numpy_to_pil(images):
"""
Convert a numpy image or a batch of images to a PIL image.
"""
if images.ndim == 3:
images = images[None, ...]
images = (images * 255).round().astype("uint8")
pil_images = [Image.fromarray(image) for image in images]
return pil_images
def load_model_from_config(config, ckpt, verbose=False):
print(f"Loading model from {ckpt}")
pl_sd = torch.load(ckpt, map_location="cpu")
if "global_step" in pl_sd:
print(f"Global Step: {pl_sd['global_step']}")
sd = pl_sd["state_dict"]
model = instantiate_from_config(config.model)
m, u = model.load_state_dict(sd, strict=False)
if len(m) > 0 and verbose:
print("missing keys:")
print(m)
if len(u) > 0 and verbose:
print("unexpected keys:")
print(u)
model.cuda()
model.eval()
return model
def put_watermark(img, wm_encoder=None):
if wm_encoder is not None:
img = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
img = wm_encoder.encode(img, 'dwtDct')
img = Image.fromarray(img[:, :, ::-1])
return img
def load_replacement(x):
try:
hwc = x.shape
y = Image.open("assets/rick.jpeg").convert("RGB").resize((hwc[1], hwc[0]))
y = (np.array(y)/255.0).astype(x.dtype)
assert y.shape == x.shape
return y
except Exception:
return x
def check_safety(x_image):
safety_checker_input = safety_feature_extractor(numpy_to_pil(x_image), return_tensors="pt")
x_checked_image, has_nsfw_concept = safety_checker(images=x_image, clip_input=safety_checker_input.pixel_values)
assert x_checked_image.shape[0] == len(has_nsfw_concept)
for i in range(len(has_nsfw_concept)):
if has_nsfw_concept[i]:
x_checked_image[i] = load_replacement(x_checked_image[i])
return x_checked_image, has_nsfw_concept
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"--prompt",
type=str,
nargs="?",
default="a painting of a virus monster playing guitar",
help="the prompt to render"
)
parser.add_argument(
"--outdir",
type=str,
nargs="?",
help="dir to write results to",
default="outputs/txt2img-samples"
)
parser.add_argument(
"--skip_grid",
action='store_true',
help="do not save a grid, only individual samples. Helpful when evaluating lots of samples",
)
parser.add_argument(
"--skip_save",
action='store_true',
help="do not save individual samples. For speed measurements.",
)
parser.add_argument(
"--ddim_steps",
type=int,
default=50,
help="number of ddim sampling steps",
)
parser.add_argument(
"--plms",
action='store_true',
help="use plms sampling",
)
parser.add_argument(
"--dpm_solver",
action='store_true',
help="use dpm_solver sampling",
)
parser.add_argument(
"--laion400m",
action='store_true',
help="uses the LAION400M model",
)
parser.add_argument(
"--fixed_code",
action='store_true',
help="if enabled, uses the same starting code across samples ",
)
parser.add_argument(
"--ddim_eta",
type=float,
default=0.0,
help="ddim eta (eta=0.0 corresponds to deterministic sampling",
)
parser.add_argument(
"--n_iter",
type=int,
default=2,
help="sample this often",
)
parser.add_argument(
"--H",
type=int,
default=512,
help="image height, in pixel space",
)
parser.add_argument(
"--W",
type=int,
default=512,
help="image width, in pixel space",
)
parser.add_argument(
"--C",
type=int,
default=4,
help="latent channels",
)
parser.add_argument(
"--f",
type=int,
default=8,
help="downsampling factor",
)
parser.add_argument(
"--n_samples",
type=int,
default=3,
help="how many samples to produce for each given prompt. A.k.a. batch size",
)
parser.add_argument(
"--n_rows",
type=int,
default=0,
help="rows in the grid (default: n_samples)",
)
parser.add_argument(
"--scale",
type=float,
default=7.5,
help="unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) - eps(x, empty))",
)
parser.add_argument(
"--from-file",
type=str,
help="if specified, load prompts from this file",
)
parser.add_argument(
"--config",
type=str,
default="configs/stable-diffusion/v1-inference.yaml",
help="path to config which constructs model",
)
parser.add_argument(
"--ckpt",
type=str,
default="models/ldm/stable-diffusion-v1/model.ckpt",
help="path to checkpoint of model",
)
parser.add_argument(
"--seed",
type=int,
default=42,
help="the seed (for reproducible sampling)",
)
parser.add_argument(
"--precision",
type=str,
help="evaluate at this precision",
choices=["full", "autocast"],
default="autocast"
)
opt = parser.parse_args()
if opt.laion400m:
print("Falling back to LAION 400M model...")
opt.config = "configs/latent-diffusion/txt2img-1p4B-eval.yaml"
opt.ckpt = "models/ldm/text2img-large/model.ckpt"
opt.outdir = "outputs/txt2img-samples-laion400m"
seed_everything(opt.seed)
config = OmegaConf.load(f"{opt.config}")
model = load_model_from_config(config, f"{opt.ckpt}")
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
model = model.to(device)
if opt.dpm_solver:
sampler = DPMSolverSampler(model)
elif opt.plms:
sampler = PLMSSampler(model)
else:
sampler = DDIMSampler(model)
os.makedirs(opt.outdir, exist_ok=True)
outpath = opt.outdir
print("Creating invisible watermark encoder (see https://github.com/ShieldMnt/invisible-watermark)...")
wm = "StableDiffusionV1"
wm_encoder = WatermarkEncoder()
wm_encoder.set_watermark('bytes', wm.encode('utf-8'))
batch_size = opt.n_samples
n_rows = opt.n_rows if opt.n_rows > 0 else batch_size
if not opt.from_file:
prompt = opt.prompt
assert prompt is not None
data = [batch_size * [prompt]]
else:
print(f"reading prompts from {opt.from_file}")
with open(opt.from_file, "r") as f:
data = f.read().splitlines()
data = list(chunk(data, batch_size))
sample_path = os.path.join(outpath, "samples")
os.makedirs(sample_path, exist_ok=True)
base_count = len(os.listdir(sample_path))
grid_count = len(os.listdir(outpath)) - 1
start_code = None
if opt.fixed_code:
start_code = torch.randn([opt.n_samples, opt.C, opt.H // opt.f, opt.W // opt.f], device=device)
precision_scope = autocast if opt.precision=="autocast" else nullcontext
with torch.no_grad():
with precision_scope("cuda"):
with model.ema_scope():
tic = time.time()
all_samples = list()
for n in trange(opt.n_iter, desc="Sampling"):
for prompts in tqdm(data, desc="data"):
uc = None
if opt.scale != 1.0:
uc = model.get_learned_conditioning(batch_size * [""])
if isinstance(prompts, tuple):
prompts = list(prompts)
c = model.get_learned_conditioning(prompts)
shape = [opt.C, opt.H // opt.f, opt.W // opt.f]
samples_ddim, _ = sampler.sample(S=opt.ddim_steps,
conditioning=c,
batch_size=opt.n_samples,
shape=shape,
verbose=False,
unconditional_guidance_scale=opt.scale,
unconditional_conditioning=uc,
eta=opt.ddim_eta,
x_T=start_code)
x_samples_ddim = model.decode_first_stage(samples_ddim)
x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
x_samples_ddim = x_samples_ddim.cpu().permute(0, 2, 3, 1).numpy()
x_checked_image, has_nsfw_concept = check_safety(x_samples_ddim)
x_checked_image_torch = torch.from_numpy(x_checked_image).permute(0, 3, 1, 2)
if not opt.skip_save:
for x_sample in x_checked_image_torch:
x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
img = Image.fromarray(x_sample.astype(np.uint8))
img = put_watermark(img, wm_encoder)
img.save(os.path.join(sample_path, f"{base_count:05}.png"))
base_count += 1
if not opt.skip_grid:
all_samples.append(x_checked_image_torch)
if not opt.skip_grid:
# additionally, save as grid
grid = torch.stack(all_samples, 0)
grid = rearrange(grid, 'n b c h w -> (n b) c h w')
grid = make_grid(grid, nrow=n_rows)
# to image
grid = 255. * rearrange(grid, 'c h w -> h w c').cpu().numpy()
img = Image.fromarray(grid.astype(np.uint8))
img = put_watermark(img, wm_encoder)
img.save(os.path.join(outpath, f'grid-{grid_count:04}.png'))
grid_count += 1
toc = time.time()
print(f"Your samples are ready and waiting for you here: \n{outpath} \n"
f" \nEnjoy.")
if __name__ == "__main__":
main()
================================================
FILE: models/instructpix2pix/stable_diffusion/setup.py
================================================
from setuptools import setup, find_packages
setup(
name='latent-diffusion',
version='0.0.1',
description='',
packages=find_packages(),
install_requires=[
'torch',
'numpy',
'tqdm',
],
)
================================================
FILE: models/masactrl/diffuser_utils.py
================================================
"""
Util functions based on Diffuser framework.
"""
import torch
import numpy as np
from tqdm import tqdm
from PIL import Image
from diffusers import StableDiffusionPipeline
class MasaCtrlPipeline(StableDiffusionPipeline):
def next_step(
self,
model_output: torch.FloatTensor,
timestep: int,
x: torch.FloatTensor,
eta=0.,
verbose=False
):
"""
Inverse sampling for DDIM Inversion
"""
if verbose:
print("timestep: ", timestep)
next_step = timestep
timestep = min(timestep - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps, 999)
alpha_prod_t = self.scheduler.alphas_cumprod[timestep] if timestep >= 0 else self.scheduler.final_alpha_cumprod
alpha_prod_t_next = self.scheduler.alphas_cumprod[next_step]
beta_prod_t = 1 - alpha_prod_t
pred_x0 = (x - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5
pred_dir = (1 - alpha_prod_t_next)**0.5 * model_output
x_next = alpha_prod_t_next**0.5 * pred_x0 + pred_dir
return x_next, pred_x0
def step(
self,
model_output: torch.FloatTensor,
timestep: int,
x: torch.FloatTensor,
eta: float=0.0,
verbose=False,
):
"""
predict the sampe the next step in the denoise process.
"""
prev_timestep = timestep - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
alpha_prod_t = self.scheduler.alphas_cumprod[timestep]
alpha_prod_t_prev = self.scheduler.alphas_cumprod[prev_timestep] if prev_timestep > 0 else self.scheduler.final_alpha_cumprod
beta_prod_t = 1 - alpha_prod_t
pred_x0 = (x - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5
pred_dir = (1 - alpha_prod_t_prev)**0.5 * model_output
x_prev = alpha_prod_t_prev**0.5 * pred_x0 + pred_dir
return x_prev, pred_x0
@torch.no_grad()
def image2latent(self, image):
DEVICE = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
if type(image) is Image:
image = np.array(image)
image = torch.from_numpy(image).float() / 127.5 - 1
image = image.permute(2, 0, 1).unsqueeze(0).to(DEVICE)
# input image density range [-1, 1]
latents = self.vae.encode(image)['latent_dist'].mean
latents = latents * 0.18215
return latents
@torch.no_grad()
def latent2image(self, latents, return_type='np'):
latents = 1 / 0.18215 * latents.detach()
image = self.vae.decode(latents)['sample']
if return_type == 'np':
image = (image / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).numpy()[0]
image = (image * 255).astype(np.uint8)
elif return_type == "pt":
image = (image / 2 + 0.5).clamp(0, 1)
return image
def latent2image_grad(self, latents):
latents = 1 / 0.18215 * latents
image = self.vae.decode(latents)['sample']
return image # range [-1, 1]
@torch.no_grad()
def __call__(
self,
prompt,
batch_size=1,
height=512,
width=512,
num_inference_steps=50,
guidance_scale=7.5,
eta=0.0,
latents=None,
unconditioning=None,
neg_prompt=None,
ref_intermediate_latents=None,
return_intermediates=False,
noise_loss_list=None,
**kwds):
DEVICE = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
if isinstance(prompt, list):
batch_size = len(prompt)
elif isinstance(prompt, str):
if batch_size > 1:
prompt = [prompt] * batch_size
# text embeddings
text_input = self.tokenizer(
prompt,
padding="max_length",
max_length=77,
return_tensors="pt"
)
text_embeddings = self.text_encoder(text_input.input_ids.to(DEVICE))[0]
print("input text embeddings :", text_embeddings.shape)
if kwds.get("dir"):
dir = text_embeddings[-2] - text_embeddings[-1]
u, s, v = torch.pca_lowrank(dir.transpose(-1, -2), q=1, center=True)
text_embeddings[-1] = text_embeddings[-1] + kwds.get("dir") * v
print(u.shape)
print(v.shape)
# define initial latents
latents_shape = (batch_size, self.unet.in_channels, height//8, width//8)
if latents is None:
latents = torch.randn(latents_shape, device=DEVICE)
else:
assert latents.shape == latents_shape, f"The shape of input latent tensor {latents.shape} should equal to predefined one."
# unconditional embedding for classifier free guidance
if guidance_scale > 1.:
max_length = text_input.input_ids.shape[-1]
if neg_prompt:
uc_text = neg_prompt
else:
uc_text = ""
# uc_text = "ugly, tiling, poorly drawn hands, poorly drawn feet, body out of frame, cut off, low contrast, underexposed, distorted face"
unconditional_input = self.tokenizer(
[uc_text] * batch_size,
padding="max_length",
max_length=77,
return_tensors="pt"
)
# unconditional_input.input_ids = unconditional_input.input_ids[:, 1:]
unconditional_embeddings = self.text_encoder(unconditional_input.input_ids.to(DEVICE))[0]
text_embeddings = torch.cat([unconditional_embeddings, text_embeddings], dim=0)
print("latents shape: ", latents.shape)
# iterative sampling
self.scheduler.set_timesteps(num_inference_steps)
# print("Valid timesteps: ", reversed(self.scheduler.timesteps))
latents_list = [latents]
pred_x0_list = [latents]
for i, t in enumerate(tqdm(self.scheduler.timesteps, desc="DDIM Sampler")):
if ref_intermediate_latents is not None:
# note that the batch_size >= 2
latents_ref = ref_intermediate_latents[-1 - i]
_, latents_cur = latents.chunk(2)
latents = torch.cat([latents_ref, latents_cur])
if guidance_scale > 1.:
model_inputs = torch.cat([latents] * 2)
else:
model_inputs = latents
if unconditioning is not None and isinstance(unconditioning, list):
_, text_embeddings = text_embeddings.chunk(2)
text_embeddings = torch.cat([unconditioning[i].expand(*text_embeddings.shape), text_embeddings])
# predict the noise
noise_pred = self.unet(model_inputs, t, encoder_hidden_states=text_embeddings).sample
if guidance_scale > 1.:
noise_pred_uncon, noise_pred_con = noise_pred.chunk(2, dim=0)
noise_pred = noise_pred_uncon + guidance_scale * (noise_pred_con - noise_pred_uncon)
# compute the previous noise sample x_t -> x_t-1
latents, pred_x0 = self.step(noise_pred, t, latents)
if noise_loss_list is not None:
latents = torch.concat((latents[:1]+noise_loss_list[i][:1],latents[1:]))
latents_list.append(latents)
pred_x0_list.append(pred_x0)
image = self.latent2image(latents, return_type="pt")
if return_intermediates:
pred_x0_list = [self.latent2image(img, return_type="pt") for img in pred_x0_list]
latents_list = [self.latent2image(img, return_type="pt") for img in latents_list]
return image, pred_x0_list, latents_list
return image
@torch.no_grad()
def invert(
self,
image: torch.Tensor,
prompt,
num_inference_steps=50,
guidance_scale=7.5,
eta=0.0,
return_intermediates=False,
**kwds):
"""
invert a real image into noise map with determinisc DDIM inversion
"""
DEVICE = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
batch_size = image.shape[0]
if isinstance(prompt, list):
if batch_size == 1:
image = image.expand(len(prompt), -1, -1, -1)
elif isinstance(prompt, str):
if batch_size > 1:
prompt = [prompt] * batch_size
# text embeddings
text_input = self.tokenizer(
prompt,
padding="max_length",
max_length=77,
return_tensors="pt"
)
text_embeddings = self.text_encoder(text_input.input_ids.to(DEVICE))[0]
print("input text embeddings :", text_embeddings.shape)
# define initial latents
latents = self.image2latent(image)
start_latents = latents
# print(latents)
# exit()
# unconditional embedding for classifier free guidance
if guidance_scale > 1.:
max_length = text_input.input_ids.shape[-1]
unconditional_input = self.tokenizer(
[""] * batch_size,
padding="max_length",
max_length=77,
return_tensors="pt"
)
unconditional_embeddings = self.text_encoder(unconditional_input.input_ids.to(DEVICE))[0]
text_embeddings = torch.cat([unconditional_embeddings, text_embeddings], dim=0)
print("latents shape: ", latents.shape)
# interative sampling
self.scheduler.set_timesteps(num_inference_steps)
print("Valid timesteps: ", reversed(self.scheduler.timesteps))
# print("attributes: ", self.scheduler.__dict__)
latents_list = [latents]
pred_x0_list = [latents]
for i, t in enumerate(tqdm(reversed(self.scheduler.timesteps), desc="DDIM Inversion")):
if guidance_scale > 1.:
model_inputs = torch.cat([latents] * 2)
else:
model_inputs = latents
# predict the noise
noise_pred = self.unet(model_inputs, t, encoder_hidden_states=text_embeddings).sample
if guidance_scale > 1.:
noise_pred_uncon, noise_pred_con = noise_pred.chunk(2, dim=0)
noise_pred = noise_pred_uncon + guidance_scale * (noise_pred_con - noise_pred_uncon)
# compute the previous noise sample x_t-1 -> x_t
latents, pred_x0 = self.next_step(noise_pred, t, latents)
latents_list.append(latents)
pred_x0_list.append(pred_x0)
if return_intermediates:
# return the intermediate laters during inversion
# pred_x0_list = [self.latent2image(img, return_type="pt") for img in pred_x0_list]
return latents, latents_list
return latents, start_latents
================================================
FILE: models/masactrl/masactrl.py
================================================
import os
import torch
import torch.nn.functional as F
import numpy as np
from einops import rearrange
from .masactrl_utils import AttentionBase
from torchvision.utils import save_image
class MutualSelfAttentionControl(AttentionBase):
MODEL_TYPE = {
"SD": 16,
"SDXL": 70
}
def __init__(self, start_step=4, start_layer=10, layer_idx=None, step_idx=None, total_steps=50, model_type="SD"):
"""
Mutual self-attention control for Stable-Diffusion model
Args:
start_step: the step to start mutual self-attention control
start_layer: the layer to start mutual self-attention control
layer_idx: list of the layers to apply mutual self-attention control
step_idx: list the steps to apply mutual self-attention control
total_steps: the total number of steps
model_type: the model type, SD or SDXL
"""
super().__init__()
self.total_steps = total_steps
self.total_layers = self.MODEL_TYPE.get(model_type, 16)
self.start_step = start_step
self.start_layer = start_layer
self.layer_idx = layer_idx if layer_idx is not None else list(range(start_layer, self.total_layers))
self.step_idx = step_idx if step_idx is not None else list(range(start_step, total_steps))
print("MasaCtrl at denoising steps: ", self.step_idx)
print("MasaCtrl at U-Net layers: ", self.layer_idx)
def attn_batch(self, q, k, v, sim, attn, is_cross, place_in_unet, num_heads, **kwargs):
"""
Performing attention for a batch of queries, keys, and values
"""
b = q.shape[0] // num_heads
q = rearrange(q, "(b h) n d -> h (b n) d", h=num_heads)
k = rearrange(k, "(b h) n d -> h (b n) d", h=num_heads)
v = rearrange(v, "(b h) n d -> h (b n) d", h=num_heads)
sim = torch.einsum("h i d, h j d -> h i j", q, k) * kwargs.get("scale")
attn = sim.softmax(-1)
out = torch.einsum("h i j, h j d -> h i d", attn, v)
out = rearrange(out, "h (b n) d -> b n (h d)", b=b)
return out
def forward(self, q, k, v, sim, attn, is_cross, place_in_unet, num_heads, **kwargs):
"""
Attention forward function
"""
if is_cross or self.cur_step not in self.step_idx or self.cur_att_layer // 2 not in self.layer_idx:
return super().forward(q, k, v, sim, attn, is_cross, place_in_unet, num_heads, **kwargs)
qu, qc = q.chunk(2)
ku, kc = k.chunk(2)
vu, vc = v.chunk(2)
attnu, attnc = attn.chunk(2)
out_u = self.attn_batch(qu, ku[:num_heads], vu[:num_heads], sim[:num_heads], attnu, is_cross, place_in_unet, num_heads, **kwargs)
out_c = self.attn_batch(qc, kc[:num_heads], vc[:num_heads], sim[:num_heads], attnc, is_cross, place_in_unet, num_heads, **kwargs)
out = torch.cat([out_u, out_c], dim=0)
return out
class MutualSelfAttentionControlUnion(MutualSelfAttentionControl):
def __init__(self, start_step=4, start_layer=10, layer_idx=None, step_idx=None, total_steps=50, model_type="SD"):
"""
Mutual self-attention control for Stable-Diffusion model with unition source and target [K, V]
Args:
start_step: the step to start mutual self-attention control
start_layer: the layer to start mutual self-attention control
layer_idx: list of the layers to apply mutual self-attention control
step_idx: list the steps to apply mutual self-attention control
total_steps: the total number of steps
model_type: the model type, SD or SDXL
"""
super().__init__(start_step, start_layer, layer_idx, step_idx, total_steps, model_type)
def forward(self, q, k, v, sim, attn, is_cross, place_in_unet, num_heads, **kwargs):
"""
Attention forward function
"""
if is_cross or self.cur_step not in self.step_idx or self.cur_att_layer // 2 not in self.layer_idx:
return super().forward(q, k, v, sim, attn, is_cross, place_in_unet, num_heads, **kwargs)
qu_s, qu_t, qc_s, qc_t = q.chunk(4)
ku_s, ku_t, kc_s, kc_t = k.chunk(4)
vu_s, vu_t, vc_s, vc_t = v.chunk(4)
attnu_s, attnu_t, attnc_s, attnc_t = attn.chunk(4)
# source image branch
out_u_s = super().forward(qu_s, ku_s, vu_s, sim, attnu_s, is_cross, place_in_unet, num_heads, **kwargs)
out_c_s = super().forward(qc_s, kc_s, vc_s, sim, attnc_s, is_cross, place_in_unet, num_heads, **kwargs)
# target image branch, concatenating source and target [K, V]
out_u_t = self.attn_batch(qu_t, torch.cat([ku_s, ku_t]), torch.cat([vu_s, vu_t]), sim[:num_heads], attnu_t, is_cross, place_in_unet, num_heads, **kwargs)
out_c_t = self.attn_batch(qc_t, torch.cat([kc_s, kc_t]), torch.cat([vc_s, vc_t]), sim[:num_heads], attnc_t, is_cross, place_in_unet, num_heads, **kwargs)
out = torch.cat([out_u_s, out_u_t, out_c_s, out_c_t], dim=0)
return out
class MutualSelfAttentionControlMask(MutualSelfAttentionControl):
def __init__(self, start_step=4, start_layer=10, layer_idx=None, step_idx=None, total_steps=50, mask_s=None, mask_t=None, mask_save_dir=None, model_type="SD"):
"""
Maske-guided MasaCtrl to alleviate the problem of fore- and background confusion
Args:
start_step: the step to start mutual self-attention control
start_layer: the layer to start mutual self-attention control
layer_idx: list of the layers to apply mutual self-attention control
step_idx: list the steps to apply mutual self-attention control
total_steps: the total number of steps
mask_s: source mask with shape (h, w)
mask_t: target mask with same shape as source mask
mask_save_dir: the path to save the mask image
model_type: the model type, SD or SDXL
"""
super().__init__(start_step, start_layer, layer_idx, step_idx, total_steps, model_type)
self.mask_s = mask_s # source mask with shape (h, w)
self.mask_t = mask_t # target mask with same shape as source mask
print("Using mask-guided MasaCtrl")
if mask_save_dir is not None:
os.makedirs(mask_save_dir, exist_ok=True)
save_image(self.mask_s.unsqueeze(0).unsqueeze(0), os.path.join(mask_save_dir, "mask_s.png"))
save_image(self.mask_t.unsqueeze(0).unsqueeze(0), os.path.join(mask_save_dir, "mask_t.png"))
def attn_batch(self, q, k, v, sim, attn, is_cross, place_in_unet, num_heads, **kwargs):
B = q.shape[0] // num_heads
H = W = int(np.sqrt(q.shape[1]))
q = rearrange(q, "(b h) n d -> h (b n) d", h=num_heads)
k = rearrange(k, "(b h) n d -> h (b n) d", h=num_heads)
v = rearrange(v, "(b h) n d -> h (b n) d", h=num_heads)
sim = torch.einsum("h i d, h j d -> h i j", q, k) * kwargs.get("scale")
if kwargs.get("is_mask_attn") and self.mask_s is not None:
print("masked attention")
mask = self.mask_s.unsqueeze(0).unsqueeze(0)
mask = F.interpolate(mask, (H, W)).flatten(0).unsqueeze(0)
mask = mask.flatten()
# background
sim_bg = sim + mask.masked_fill(mask == 1, torch.finfo(sim.dtype).min)
# object
sim_fg = sim + mask.masked_fill(mask == 0, torch.finfo(sim.dtype).min)
sim = torch.cat([sim_fg, sim_bg], dim=0)
attn = sim.softmax(-1)
if len(attn) == 2 * len(v):
v = torch.cat([v] * 2)
out = torch.einsum("h i j, h j d -> h i d", attn, v)
out = rearrange(out, "(h1 h) (b n) d -> (h1 b) n (h d)", b=B, h=num_heads)
return out
def forward(self, q, k, v, sim, attn, is_cross, place_in_unet, num_heads, **kwargs):
"""
Attention forward function
"""
if is_cross or self.cur_step not in self.step_idx or self.cur_att_layer // 2 not in self.layer_idx:
return super().forward(q, k, v, sim, attn, is_cross, place_in_unet, num_heads, **kwargs)
B = q.shape[0] // num_heads // 2
H = W = int(np.sqrt(q.shape[1]))
qu, qc = q.chunk(2)
ku, kc = k.chunk(2)
vu, vc = v.chunk(2)
attnu, attnc = attn.chunk(2)
out_u_source = self.attn_batch(qu[:num_heads], ku[:num_heads], vu[:num_heads], sim[:num_heads], attnu, is_cross, place_in_unet, num_heads, **kwargs)
out_c_source = self.attn_batch(qc[:num_heads], kc[:num_heads], vc[:num_heads], sim[:num_heads], attnc, is_cross, place_in_unet, num_heads, **kwargs)
out_u_target = self.attn_batch(qu[-num_heads:], ku[:num_heads], vu[:num_heads], sim[:num_heads], attnu, is_cross, place_in_unet, num_heads, is_mask_attn=True, **kwargs)
out_c_target = self.attn_batch(qc[-num_heads:], kc[:num_heads], vc[:num_heads], sim[:num_heads], attnc, is_cross, place_in_unet, num_heads, is_mask_attn=True, **kwargs)
if self.mask_s is not None and self.mask_t is not None:
out_u_target_fg, out_u_target_bg = out_u_target.chunk(2, 0)
out_c_target_fg, out_c_target_bg = out_c_target.chunk(2, 0)
mask = F.interpolate(self.mask_t.unsqueeze(0).unsqueeze(0), (H, W))
mask = mask.reshape(-1, 1) # (hw, 1)
out_u_target = out_u_target_fg * mask + out_u_target_bg * (1 - mask)
out_c_target = out_c_target_fg * mask + out_c_target_bg * (1 - mask)
out = torch.cat([out_u_source, out_u_target, out_c_source, out_c_target], dim=0)
return out
class MutualSelfAttentionControlMaskAuto(MutualSelfAttentionControl):
def __init__(self, start_step=4, start_layer=10, layer_idx=None, step_idx=None, total_steps=50, thres=0.1, ref_token_idx=[1], cur_token_idx=[1], mask_save_dir=None, model_type="SD"):
"""
MasaCtrl with mask auto generation from cross-attention map
Args:
start_step: the step to start mutual self-attention control
start_layer: the layer to start mutual self-attention control
layer_idx: list of the layers to apply mutual self-attention control
step_idx: list the steps to apply mutual self-attention control
total_steps: the total number of steps
thres: the thereshold for mask thresholding
ref_token_idx: the token index list for cross-attention map aggregation
cur_token_idx: the token index list for cross-attention map aggregation
mask_save_dir: the path to save the mask image
"""
super().__init__(start_step, start_layer, layer_idx, step_idx, total_steps, model_type)
print("Using MutualSelfAttentionControlMaskAuto")
self.thres = thres
self.ref_token_idx = ref_token_idx
self.cur_token_idx = cur_token_idx
self.self_attns = []
self.cross_attns = []
self.cross_attns_mask = None
self.self_attns_mask = None
self.mask_save_dir = mask_save_dir
if self.mask_save_dir is not None:
os.makedirs(self.mask_save_dir, exist_ok=True)
def after_step(self):
self.self_attns = []
self.cross_attns = []
def attn_batch(self, q, k, v, sim, attn, is_cross, place_in_unet, num_heads, **kwargs):
"""
Performing attention for a batch of queries, keys, and values
"""
B = q.shape[0] // num_heads
H = W = int(np.sqrt(q.shape[1]))
q = rearrange(q, "(b h) n d -> h (b n) d", h=num_heads)
k = rearrange(k, "(b h) n d -> h (b n) d", h=num_heads)
v = rearrange(v, "(b h) n d -> h (b n) d", h=num_heads)
sim = torch.einsum("h i d, h j d -> h i j", q, k) * kwargs.get("scale")
if self.self_attns_mask is not None:
# binarize the mask
mask = self.self_attns_mask
thres = self.thres
mask[mask >= thres] = 1
mask[mask < thres] = 0
sim_fg = sim + mask.masked_fill(mask == 0, torch.finfo(sim.dtype).min)
sim_bg = sim + mask.masked_fill(mask == 1, torch.finfo(sim.dtype).min)
sim = torch.cat([sim_fg, sim_bg])
attn = sim.softmax(-1)
if len(attn) == 2 * len(v):
v = torch.cat([v] * 2)
out = torch.einsum("h i j, h j d -> h i d", attn, v)
out = rearrange(out, "(h1 h) (b n) d -> (h1 b) n (h d)", b=B, h=num_heads)
return out
def aggregate_cross_attn_map(self, idx):
attn_map = torch.stack(self.cross_attns, dim=1).mean(1) # (B, N, dim)
B = attn_map.shape[0]
res = int(np.sqrt(attn_map.shape[-2]))
attn_map = attn_map.reshape(-1, res, res, attn_map.shape[-1])
image = attn_map[..., idx]
if isinstance(idx, list):
image = image.sum(-1)
image_min = image.min(dim=1, keepdim=True)[0].min(dim=2, keepdim=True)[0]
image_max = image.max(dim=1, keepdim=True)[0].max(dim=2, keepdim=True)[0]
image = (image - image_min) / (image_max - image_min)
return image
def forward(self, q, k, v, sim, attn, is_cross, place_in_unet, num_heads, **kwargs):
"""
Attention forward function
"""
if is_cross:
# save cross attention map with res 16 * 16
if attn.shape[1] == 16 * 16:
self.cross_attns.append(attn.reshape(-1, num_heads, *attn.shape[-2:]).mean(1))
if is_cross or self.cur_step not in self.step_idx or self.cur_att_layer // 2 not in self.layer_idx:
return super().forward(q, k, v, sim, attn, is_cross, place_in_unet, num_heads, **kwargs)
B = q.shape[0] // num_heads // 2
H = W = int(np.sqrt(q.shape[1]))
qu, qc = q.chunk(2)
ku, kc = k.chunk(2)
vu, vc = v.chunk(2)
attnu, attnc = attn.chunk(2)
out_u_source = self.attn_batch(qu[:num_heads], ku[:num_heads], vu[:num_heads], sim[:num_heads], attnu, is_cross, place_in_unet, num_heads, **kwargs)
out_c_source = self.attn_batch(qc[:num_heads], kc[:num_heads], vc[:num_heads], sim[:num_heads], attnc, is_cross, place_in_unet, num_heads, **kwargs)
if len(self.cross_attns) == 0:
self.self_attns_mask = None
out_u_target = self.attn_batch(qu[-num_heads:], ku[:num_heads], vu[:num_heads], sim[:num_heads], attnu, is_cross, place_in_unet, num_heads, **kwargs)
out_c_target = self.attn_batch(qc[-num_heads:], kc[:num_heads], vc[:num_heads], sim[:num_heads], attnc, is_cross, place_in_unet, num_heads, **kwargs)
else:
mask = self.aggregate_cross_attn_map(idx=self.ref_token_idx) # (2, H, W)
mask_source = mask[-2] # (H, W)
res = int(np.sqrt(q.shape[1]))
self.self_attns_mask = F.interpolate(mask_source.unsqueeze(0).unsqueeze(0), (res, res)).flatten()
if self.mask_save_dir is not None:
H = W = int(np.sqrt(self.self_attns_mask.shape[0]))
mask_image = self.self_attns_mask.reshape(H, W).unsqueeze(0)
save_image(mask_image, os.path.join(self.mask_save_dir, f"mask_s_{self.cur_step}_{self.cur_att_layer}.png"))
out_u_target = self.attn_batch(qu[-num_heads:], ku[:num_heads], vu[:num_heads], sim[:num_heads], attnu, is_cross, place_in_unet, num_heads, **kwargs)
out_c_target = self.attn_batch(qc[-num_heads:], kc[:num_heads], vc[:num_heads], sim[:num_heads], attnc, is_cross, place_in_unet, num_heads, **kwargs)
if self.self_attns_mask is not None:
mask = self.aggregate_cross_attn_map(idx=self.cur_token_idx) # (2, H, W)
mask_target = mask[-1] # (H, W)
res = int(np.sqrt(q.shape[1]))
spatial_mask = F.interpolate(mask_target.unsqueeze(0).unsqueeze(0), (res, res)).reshape(-1, 1)
if self.mask_save_dir is not None:
H = W = int(np.sqrt(spatial_mask.shape[0]))
mask_image = spatial_mask.reshape(H, W).unsqueeze(0)
save_image(mask_image, os.path.join(self.mask_save_dir, f"mask_t_{self.cur_step}_{self.cur_att_layer}.png"))
# binarize the mask
thres = self.thres
spatial_mask[spatial_mask >= thres] = 1
spatial_mask[spatial_mask < thres] = 0
out_u_target_fg, out_u_target_bg = out_u_target.chunk(2)
out_c_target_fg, out_c_target_bg = out_c_target.chunk(2)
out_u_target = out_u_target_fg * spatial_mask + out_u_target_bg * (1 - spatial_mask)
out_c_target = out_c_target_fg * spatial_mask + out_c_target_bg * (1 - spatial_mask)
# set self self-attention mask to None
self.self_attns_mask = None
out = torch.cat([out_u_source, out_u_target, out_c_source, out_c_target], dim=0)
return out
================================================
FILE: models/masactrl/masactrl_utils.py
================================================
import os
import cv2
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional, Union, Tuple, List, Callable, Dict
from torchvision.utils import save_image
from einops import rearrange, repeat
class AttentionBase:
def __init__(self):
self.cur_step = 0
self.num_att_layers = -1
self.cur_att_layer = 0
def after_step(self):
pass
def __call__(self, q, k, v, sim, attn, is_cross, place_in_unet, num_heads, **kwargs):
out = self.forward(q, k, v, sim, attn, is_cross, place_in_unet, num_heads, **kwargs)
self.cur_att_layer += 1
if self.cur_att_layer == self.num_att_layers:
self.cur_att_layer = 0
self.cur_step += 1
# after step
self.after_step()
return out
def forward(self, q, k, v, sim, attn, is_cross, place_in_unet, num_heads, **kwargs):
out = torch.einsum('b i j, b j d -> b i d', attn, v)
out = rearrange(out, '(b h) n d -> b n (h d)', h=num_heads)
return out
def reset(self):
self.cur_step = 0
self.cur_att_layer = 0
class AttentionStore(AttentionBase):
def __init__(self, res=[32], min_step=0, max_step=1000):
super().__init__()
self.res = res
self.min_step = min_step
self.max_step = max_step
self.valid_steps = 0
self.self_attns = [] # store the all attns
self.cross_attns = []
self.self_attns_step = [] # store the attns in each step
self.cross_attns_step = []
def after_step(self):
if self.cur_step > self.min_step and self.cur_step < self.max_step:
self.valid_steps += 1
if len(self.self_attns) == 0:
self.self_attns = self.self_attns_step
self.cross_attns = self.cross_attns_step
else:
for i in range(len(self.self_attns)):
self.self_attns[i] += self.self_attns_step[i]
self.cross_attns[i] += self.cross_attns_step[i]
self.self_attns_step.clear()
self.cross_attns_step.clear()
def forward(self, q, k, v, sim, attn, is_cross, place_in_unet, num_heads, **kwargs):
if attn.shape[1] <= 64 ** 2: # avoid OOM
if is_cross:
self.cross_attns_step.append(attn)
else:
self.self_attns_step.append(attn)
return super().forward(q, k, v, sim, attn, is_cross, place_in_unet, num_heads, **kwargs)
def regiter_attention_editor_diffusers(model, editor: AttentionBase):
"""
Register a attention editor to Diffuser Pipeline, refer from [Prompt-to-Prompt]
"""
def ca_forward(self, place_in_unet):
def forward(x, encoder_hidden_states=None, attention_mask=None, context=None, mask=None):
"""
The attention is similar to the original implementation of LDM CrossAttention class
except adding some modifications on the attention
"""
if encoder_hidden_states is not None:
context = encoder_hidden_states
if attention_mask is not None:
mask = attention_mask
to_out = self.to_out
if isinstance(to_out, nn.modules.container.ModuleList):
to_out = self.to_out[0]
else:
to_out = self.to_out
h = self.heads
q = self.to_q(x)
is_cross = context is not None
context = context if is_cross else x
k = self.to_k(context)
v = self.to_v(context)
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
sim = torch.einsum('b i d, b j d -> b i j', q, k) * self.scale
if mask is not None:
mask = rearrange(mask, 'b ... -> b (...)')
max_neg_value = -torch.finfo(sim.dtype).max
mask = repeat(mask, 'b j -> (b h) () j', h=h)
mask = mask[:, None, :].repeat(h, 1, 1)
sim.masked_fill_(~mask, max_neg_value)
attn = sim.softmax(dim=-1)
# the only difference
out = editor(
q, k, v, sim, attn, is_cross, place_in_unet,
self.heads, scale=self.scale)
return to_out(out)
return forward
def register_editor(net, count, place_in_unet):
for name, subnet in net.named_children():
if net.__class__.__name__ == 'Attention': # spatial Transformer layer
net.forward = ca_forward(net, place_in_unet)
return count + 1
elif hasattr(net, 'children'):
count = register_editor(subnet, count, place_in_unet)
return count
cross_att_count = 0
for net_name, net in model.unet.named_children():
if "down" in net_name:
cross_att_count += register_editor(net, 0, "down")
elif "mid" in net_name:
cross_att_count += register_editor(net, 0, "mid")
elif "up" in net_name:
cross_att_count += register_editor(net, 0, "up")
editor.num_att_layers = cross_att_count
def regiter_attention_editor_ldm(model, editor: AttentionBase):
"""
Register a attention editor to Stable Diffusion model, refer from [Prompt-to-Prompt]
"""
def ca_forward(self, place_in_unet):
def forward(x, encoder_hidden_states=None, attention_mask=None, context=None, mask=None):
"""
The attention is similar to the original implementation of LDM CrossAttention class
except adding some modifications on the attention
"""
if encoder_hidden_states is not None:
context = encoder_hidden_states
if attention_mask is not None:
mask = attention_mask
to_out = self.to_out
if isinstance(to_out, nn.modules.container.ModuleList):
to_out = self.to_out[0]
else:
to_out = self.to_out
h = self.heads
q = self.to_q(x)
is_cross = context is not None
context = context if is_cross else x
k = self.to_k(context)
v = self.to_v(context)
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
sim = torch.einsum('b i d, b j d -> b i j', q, k) * self.scale
if mask is not None:
mask = rearrange(mask, 'b ... -> b (...)')
max_neg_value = -torch.finfo(sim.dtype).max
mask = repeat(mask, 'b j -> (b h) () j', h=h)
mask = mask[:, None, :].repeat(h, 1, 1)
sim.masked_fill_(~mask, max_neg_value)
attn = sim.softmax(dim=-1)
# the only difference
out = editor(
q, k, v, sim, attn, is_cross, place_in_unet,
self.heads, scale=self.scale)
return to_out(out)
return forward
def register_editor(net, count, place_in_unet):
for name, subnet in net.named_children():
if net.__class__.__name__ == 'CrossAttention': # spatial Transformer layer
net.forward = ca_forward(net, place_in_unet)
return count + 1
elif hasattr(net, 'children'):
count = register_editor(subnet, count, place_in_unet)
return count
cross_att_count = 0
for net_name, net in model.model.diffusion_model.named_children():
if "input" in net_name:
cross_att_count += register_editor(net, 0, "input")
elif "middle" in net_name:
cross_att_count += register_editor(net, 0, "middle")
elif "output" in net_name:
cross_att_count += register_editor(net, 0, "output")
editor.num_att_layers = cross_att_count
================================================
FILE: models/p2p/attention_control.py
================================================
import torch
import torch.nn.functional as nnf
import abc
from utils.utils import get_word_inds, get_time_words_attention_alpha
from models.p2p import seq_aligner
MAX_NUM_WORDS = 77
LATENT_SIZE = (64, 64)
LOW_RESOURCE = False
def register_attention_control(model, controller):
def ca_forward(self, place_in_unet):
to_out = self.to_out
if type(to_out) is torch.nn.modules.container.ModuleList:
to_out = self.to_out[0]
else:
to_out = self.to_out
def forward(x, context=None, mask=None, **kwargs):
if isinstance(context, dict): # NOTE: compatible with ELITE (0.11.1)
context = context['CONTEXT_TENSOR']
batch_size, sequence_length, dim = x.shape
h = self.heads
q = self.to_q(x)
is_cross = context is not None
context = context if is_cross else x
k = self.to_k(context)
v = self.to_v(context)
q = self.reshape_heads_to_batch_dim(q)
k = self.reshape_heads_to_batch_dim(k)
v = self.reshape_heads_to_batch_dim(v)
sim = torch.einsum("b i d, b j d -> b i j", q, k) * self.scale
if mask is not None:
mask = mask.reshape(batch_size, -1)
max_neg_value = -torch.finfo(sim.dtype).max
mask = mask[:, None, :].repeat(h, 1, 1)
sim.masked_fill_(~mask, max_neg_value)
# attention, what we cannot get enough of
attn = sim.softmax(dim=-1)
attn = controller(attn, is_cross, place_in_unet)
out = torch.einsum("b i j, b j d -> b i d", attn, v)
out = self.reshape_batch_dim_to_heads(out)
return to_out(out)
return forward
class DummyController:
def __call__(self, *args):
return args[0]
def __init__(self):
self.num_att_layers = 0
if controller is None:
controller = DummyController()
def register_recr(net_, count, place_in_unet):
if net_.__class__.__name__ == 'CrossAttention':
net_.forward = ca_forward(net_, place_in_unet)
return count + 1
elif hasattr(net_, 'children'):
for net__ in net_.children():
count = register_recr(net__, count, place_in_unet)
return count
cross_att_count = 0
sub_nets = model.unet.named_children()
for net in sub_nets:
if "down" in net[0]:
cross_att_count += register_recr(net[1], 0, "down")
elif "up" in net[0]:
cross_att_count += register_recr(net[1], 0, "up")
elif "mid" in net[0]:
cross_att_count += register_recr(net[1], 0, "mid")
controller.num_att_layers = cross_att_count
def get_equalizer(text, word_select, values, tokenizer=None):
if type(word_select) is int or type(word_select) is str:
word_select = (word_select,)
equalizer = torch.ones(1, 77)
for word, val in zip(word_select, values):
inds = get_word_inds(text, word, tokenizer)
equalizer[:, inds] = val
return equalizer
class LocalBlend:
def get_mask(self, maps, alpha, use_pool):
k = 1
maps = (maps * alpha).sum(-1).mean(1)
if use_pool:
maps = nnf.max_pool2d(maps, (k * 2 + 1, k * 2 +1), (1, 1), padding=(k, k))
mask = nnf.interpolate(maps, size=LATENT_SIZE)
mask = mask / mask.max(2, keepdims=True)[0].max(3, keepdims=True)[0]
mask = mask.gt(self.th[1-int(use_pool)])
mask = mask[:1] + mask
return mask
def __call__(self, x_t, attention_store):
self.counter += 1
if self.counter > self.start_blend:
maps = attention_store["down_cross"][2:4] + attention_store["up_cross"][:3]
maps = [item.reshape(self.alpha_layers.shape[0], -1, 1, 16, 16, MAX_NUM_WORDS) for item in maps]
maps = torch.cat(maps, dim=1)
mask = self.get_mask(maps, self.alpha_layers, True)
if self.substruct_layers is not None:
maps_sub = ~self.get_mask(maps, self.substruct_layers, False)
mask = mask * maps_sub
mask = mask.float()
x_t = x_t[:1] + mask * (x_t - x_t[:1])
return x_t
def __init__(self, prompts, words, substruct_words=None, start_blend=0.2, th=(.3, .3),
tokenizer=None, device="cuda",num_ddim_steps=50):
alpha_layers = torch.zeros(len(prompts), 1, 1, 1, 1, MAX_NUM_WORDS)
for i, (prompt, words_) in enumerate(zip(prompts, words)):
if type(words_) is str:
words_ = [words_]
for word in words_:
ind = get_word_inds(prompt, word, tokenizer)
alpha_layers[i, :, :, :, :, ind] = 1
if substruct_words is not None:
substruct_layers = torch.zeros(len(prompts), 1, 1, 1, 1, MAX_NUM_WORDS)
for i, (prompt, words_) in enumerate(zip(prompts, substruct_words)):
if type(words_) is str:
words_ = [words_]
for word in words_:
ind = get_word_inds(prompt, word, tokenizer)
substruct_layers[i, :, :, :, :, ind] = 1
self.substruct_layers = substruct_layers.to(device)
else:
self.substruct_layers = None
self.alpha_layers = alpha_layers.to(device)
self.start_blend = int(start_blend * num_ddim_steps)
self.counter = 0
self.th=th
class EmptyControl:
def step_callback(self, x_t):
return x_t
def between_steps(self):
return
def __call__(self, attn, is_cross, place_in_unet):
return attn
class AttentionControl(abc.ABC):
def step_callback(self, x_t):
return x_t
def between_steps(self):
return
@property
def num_uncond_att_layers(self):
return self.num_att_layers if LOW_RESOURCE else 0
@abc.abstractmethod
def forward (self, attn, is_cross, place_in_unet):
raise NotImplementedError
def __call__(self, attn, is_cross, place_in_unet):
if self.cur_att_layer >= self.num_uncond_att_layers:
if LOW_RESOURCE:
attn = self.forward(attn, is_cross, place_in_unet)
else:
h = attn.shape[0]
attn[h // 2:] = self.forward(attn[h // 2:], is_cross, place_in_unet)
self.cur_att_layer += 1
if self.cur_att_layer == self.num_att_layers + self.num_uncond_att_layers:
self.cur_att_layer = 0
self.cur_step += 1
self.between_steps()
return attn
def reset(self):
self.cur_step = 0
self.cur_att_layer = 0
def __init__(self):
self.cur_step = 0
self.num_att_layers = -1
self.cur_att_layer = 0
class SpatialReplace(EmptyControl):
def step_callback(self, x_t):
if self.cur_step < self.stop_inject:
b = x_t.shape[0]
x_t = x_t[:1].expand(b, *x_t.shape[1:])
return x_t
def __init__(self, stop_inject,num_ddim_steps=50):
super(SpatialReplace, self).__init__()
self.stop_inject = int((1 - stop_inject) * num_ddim_steps)
class AttentionStore(AttentionControl):
@staticmethod
def get_empty_store():
return {"down_cross": [], "mid_cross": [], "up_cross": [],
"down_self": [], "mid_self": [], "up_self": []}
def forward(self, attn, is_cross, place_in_unet):
key = f"{place_in_unet}_{'cross' if is_cross else 'self'}"
if attn.shape[1] <= 32 ** 2: # avoid memory overhead
self.step_store[key].append(attn)
return attn
def between_steps(self):
if len(self.attention_store) == 0:
self.attention_store = self.step_store
else:
for key in self.attention_store:
for i in range(len(self.attention_store[key])):
self.attention_store[key][i] += self.step_store[key][i]
self.step_store = self.get_empty_store()
def get_average_attention(self):
average_attention = {key: [item / self.cur_step for item in self.attention_store[key]] for key in self.attention_store}
return average_attention
def reset(self):
super(AttentionStore, self).reset()
self.step_store = self.get_empty_store()
self.attention_store = {}
def __init__(self):
super(AttentionStore, self).__init__()
self.step_store = self.get_empty_store()
self.attention_store = {}
class AttentionControlEdit(AttentionStore, abc.ABC):
def step_callback(self, x_t):
if self.local_blend is not None:
x_t = self.local_blend(x_t, self.attention_store)
return x_t
def replace_self_attention(self, attn_base, att_replace, place_in_unet):
if att_replace.shape[2] <= 32 ** 2:
attn_base = attn_base.unsqueeze(0).expand(att_replace.shape[0], *attn_base.shape)
return attn_base
else:
return att_replace
@abc.abstractmethod
def replace_cross_attention(self, attn_base, att_replace):
raise NotImplementedError
def forward(self, attn, is_cross, place_in_unet):
super(AttentionControlEdit, self).forward(attn, is_cross, place_in_unet)
if is_cross or (self.num_self_replace[0] <= self.cur_step < self.num_self_replace[1]):
h = attn.shape[0] // (self.batch_size)
attn = attn.reshape(self.batch_size, h, *attn.shape[1:])
attn_base, attn_repalce = attn[0], attn[1:]
if is_cross:
alpha_words = self.cross_replace_alpha[self.cur_step]
attn_repalce_new = self.replace_cross_attention(attn_base, attn_repalce) * alpha_words + (1 - alpha_words) * attn_repalce
attn[1:] = attn_repalce_new
else:
attn[1:] = self.replace_self_attention(attn_base, attn_repalce, place_in_unet)
attn = attn.reshape(self.batch_size * h, *attn.shape[2:])
return attn
def __init__(self,
prompts,
num_steps,
cross_replace_steps,
self_replace_steps,
local_blend,
tokenizer=None,
device="cuda"):
super(AttentionControlEdit, self).__init__()
self.batch_size = len(prompts)
self.cross_replace_alpha = get_time_words_attention_alpha(prompts, num_steps, cross_replace_steps, tokenizer).to(device)
if type(self_replace_steps) is float:
self_replace_steps = 0, self_replace_steps
self.num_self_replace = int(num_steps * self_replace_steps[0]), int(num_steps * self_replace_steps[1])
self.local_blend = local_blend
class AttentionReplace(AttentionControlEdit):
def replace_cross_attention(self, attn_base, att_replace):
return torch.einsum('hpw,bwn->bhpn', attn_base, self.mapper)
def __init__(self, prompts, num_steps, cross_replace_steps, self_replace_steps,
local_blend = None, tokenizer=None,device="cuda"):
super(AttentionReplace, self).__init__(prompts=prompts,
num_steps=num_steps,
cross_replace_steps=cross_replace_steps,
self_replace_steps=self_replace_steps,
local_blend=local_blend,
device=device)
self.mapper = seq_aligner.get_replacement_mapper(prompts, tokenizer).to(device)
class AttentionRefine(AttentionControlEdit):
def replace_cross_attention(self, attn_base, att_replace):
attn_base_replace = attn_base[:, :, self.mapper].permute(2, 0, 1, 3)
attn_replace = attn_base_replace * self.alphas + att_replace * (1 - self.alphas)
# attn_replace = attn_replace / attn_replace.sum(-1, keepdims=True)
return attn_replace
def __init__(self, prompts, num_steps, cross_replace_steps, self_replace_steps,
local_blend = None, tokenizer=None,device="cuda"):
super(AttentionRefine, self).__init__(prompts=prompts,
num_steps=num_steps,
cross_replace_steps=cross_replace_steps,
self_replace_steps=self_replace_steps,
local_blend=local_blend,
device=device)
self.mapper, alphas = seq_aligner.get_refinement_mapper(prompts, tokenizer)
self.mapper, alphas = self.mapper.to(device), alphas.to(device)
self.alphas = alphas.reshape(alphas.shape[0], 1, 1, alphas.shape[1])
class AttentionReweight(AttentionControlEdit):
def replace_cross_attention(self, attn_base, att_replace):
if self.prev_controller is not None:
attn_base = self.prev_controller.replace_cross_attention(attn_base, att_replace)
attn_replace = attn_base[None, :, :, :] * self.equalizer[:, None, None, :]
# attn_replace = attn_replace / attn_replace.sum(-1, keepdims=True)
return attn_replace
def __init__(self,
prompts,
num_steps,
cross_replace_steps,
self_replace_steps,
equalizer,
local_blend = None,
controller = None,
device="cuda"):
super(AttentionReweight, self).__init__(prompts=prompts,
num_steps=num_steps,
cross_replace_steps=cross_replace_steps,
self_replace_steps=self_replace_steps,
local_blend=local_blend,
device=device)
self.equalizer = equalizer.to(device)
self.prev_controller = controller
def make_controller(pipeline,
prompts,
is_replace_controller,
cross_replace_steps,
self_replace_steps,
blend_words=None,
equilizer_params=None,
num_ddim_steps=50,
device="cuda") -> AttentionControlEdit:
if blend_words is None:
lb = None
else:
lb = LocalBlend(prompts, blend_words, tokenizer=pipeline.tokenizer, device=device,num_ddim_steps=num_ddim_steps)
if is_replace_controller:
controller = AttentionReplace(prompts,
num_ddim_steps,
cross_replace_steps=cross_replace_steps,
self_replace_steps=self_replace_steps,
local_blend=lb,
tokenizer=pipeline.tokenizer)
else:
controller = AttentionRefine(prompts,
num_ddim_steps,
cross_replace_steps=cross_replace_steps,
self_replace_steps=self_replace_steps,
local_blend=lb,
tokenizer=pipeline.tokenizer)
if equilizer_params is not None:
eq = get_equalizer(prompts[1],
equilizer_params["words"],
equilizer_params["values"],
tokenizer=pipeline.tokenizer)
controller = AttentionReweight(prompts,
num_ddim_steps,
cross_replace_steps=cross_replace_steps,
self_replace_steps=self_replace_steps,
equalizer=eq,
local_blend=lb,
controller=controller)
return controller
================================================
FILE: models/p2p/inversion.py
================================================
import torch
import numpy as np
from PIL import Image
import torch.nn.functional as nnf
from torch.optim.adam import Adam
from models.p2p.attention_control import register_attention_control
from utils.utils import slerp_tensor, image2latent, latent2image
class NegativePromptInversion:
def prev_step(self, model_output, timestep, sample):
prev_timestep = timestep - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
alpha_prod_t = self.scheduler.alphas_cumprod[timestep]
alpha_prod_t_prev = self.scheduler.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.scheduler.final_alpha_cumprod
beta_prod_t = 1 - alpha_prod_t
pred_original_sample = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
pred_sample_direction = (1 - alpha_prod_t_prev) ** 0.5 * model_output
prev_sample = alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction
return prev_sample
def next_step(self, model_output, timestep, sample):
timestep, next_timestep = min(timestep - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps, 999), timestep
alpha_prod_t = self.scheduler.alphas_cumprod[timestep] if timestep >= 0 else self.scheduler.final_alpha_cumprod
alpha_prod_t_next = self.scheduler.alphas_cumprod[next_timestep]
beta_prod_t = 1 - alpha_prod_t
next_original_sample = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
next_sample_direction = (1 - alpha_prod_t_next) ** 0.5 * model_output
next_sample = alpha_prod_t_next ** 0.5 * next_original_sample + next_sample_direction
return next_sample
def get_noise_pred_single(self, latents, t, context):
noise_pred = self.model.unet(latents, t, encoder_hidden_states=context)["sample"]
return noise_pred
@torch.no_grad()
def init_prompt(self, prompt):
uncond_input = self.model.tokenizer(
[""], padding="max_length", max_length=self.model.tokenizer.model_max_length,
return_tensors="pt"
)
uncond_embeddings = self.model.text_encoder(uncond_input.input_ids.to(self.model.device))[0]
text_input = self.model.tokenizer(
[prompt],
padding="max_length",
max_length=self.model.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_embeddings = self.model.text_encoder(text_input.input_ids.to(self.model.device))[0]
self.context = torch.cat([uncond_embeddings, text_embeddings])
self.prompt = prompt
@torch.no_grad()
def ddim_loop(self, latent):
uncond_embeddings, cond_embeddings = self.context.chunk(2)
all_latent = [latent]
latent = latent.clone().detach()
print("DDIM Inversion ...")
for i in range(self.num_ddim_steps):
t = self.model.scheduler.timesteps[len(self.model.scheduler.timesteps) - i - 1]
noise_pred = self.get_noise_pred_single(latent, t, cond_embeddings)
latent = self.next_step(noise_pred, t, latent)
all_latent.append(latent)
return all_latent
@property
def scheduler(self):
return self.model.scheduler
@torch.no_grad()
def ddim_inversion(self, image):
latent = image2latent(self.model.vae, image)
image_rec = latent2image(self.model.vae, latent)[0]
ddim_latents = self.ddim_loop(latent)
return image_rec, ddim_latents, latent
def invert(self, image_gt, prompt, npi_interp=0.0):
"""
Get DDIM Inversion of the image
Parameters:
image_gt - the gt image with a size of [512,512,3], the channel follows the rgb of PIL.Image. i.e. RGB.
prompt - this is the prompt used for DDIM Inversion
npi_interp - the interpolation ratio among conditional embedding and unconditional embedding
num_ddim_steps - the number of ddim steps
Returns:
image_rec - the image reconstructed by VAE decoder with a size of [512,512,3], the channel follows the rgb of PIL.Image. i.e. RGB.
image_rec_latent - the image latent with a size of [64,64,4]
ddim_latents - the ddim inversion latents 50*[64,4,4], the first latent is the image_rec_latent, the last latent is noise (but in fact not pure noise)
uncond_embeddings - the fake uncond_embeddings, in fact is cond_embedding or a interpolation among cond_embedding and uncond_embedding
"""
self.init_prompt(prompt)
register_attention_control(self.model, None)
image_rec, ddim_latents, image_rec_latent = self.ddim_inversion(image_gt)
uncond_embeddings, cond_embeddings = self.context.chunk(2)
if npi_interp > 0.0: # do vector interpolation among cond_embedding and uncond_embedding
cond_embeddings = slerp_tensor(npi_interp, cond_embeddings, uncond_embeddings)
uncond_embeddings = [cond_embeddings] * self.num_ddim_steps
return image_rec, image_rec_latent, ddim_latents, uncond_embeddings
def __init__(self, model,num_ddim_steps):
self.model = model
self.tokenizer = self.model.tokenizer
self.prompt = None
self.context = None
self.num_ddim_steps=num_ddim_steps
class NullInversion:
def prev_step(self, model_output, timestep: int, sample):
prev_timestep = timestep - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
alpha_prod_t = self.scheduler.alphas_cumprod[timestep]
alpha_prod_t_prev = self.scheduler.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.scheduler.final_alpha_cumprod
beta_prod_t = 1 - alpha_prod_t
pred_original_sample = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
pred_sample_direction = (1 - alpha_prod_t_prev) ** 0.5 * model_output
prev_sample = alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction
return prev_sample
def next_step(self, model_output, timestep: int, sample):
timestep, next_timestep = min(timestep - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps, 999), timestep
alpha_prod_t = self.scheduler.alphas_cumprod[timestep] if timestep >= 0 else self.scheduler.final_alpha_cumprod
alpha_prod_t_next = self.scheduler.alphas_cumprod[next_timestep]
beta_prod_t = 1 - alpha_prod_t
next_original_sample = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
next_sample_direction = (1 - alpha_prod_t_next) ** 0.5 * model_output
next_sample = alpha_prod_t_next ** 0.5 * next_original_sample + next_sample_direction
return next_sample
def get_noise_pred_single(self, latents, t, context):
noise_pred = self.model.unet(latents, t, encoder_hidden_states=context)["sample"]
return noise_pred
def get_noise_pred(self, latents, t, guidance_scale, is_forward=True, context=None):
latents_input = torch.cat([latents] * 2)
if context is None:
context = self.context
guidance_scale = 1 if is_forward else guidance_scale
noise_pred = self.model.unet(latents_input, t, encoder_hidden_states=context)["sample"]
noise_pred_uncond, noise_prediction_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_prediction_text - noise_pred_uncond)
if is_forward:
latents = self.next_step(noise_pred, t, latents)
else:
latents = self.prev_step(noise_pred, t, latents)
return latents
@torch.no_grad()
def init_prompt(self, prompt: str):
uncond_input = self.model.tokenizer(
[""],
padding="max_length",
max_length=self.model.tokenizer.model_max_length,
return_tensors="pt"
)
uncond_embeddings = self.model.text_encoder(uncond_input.input_ids.to(self.model.device))[0]
text_input = self.model.tokenizer(
[prompt],
padding="max_length",
max_length=self.model.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_embeddings = self.model.text_encoder(text_input.input_ids.to(self.model.device))[0]
self.context = torch.cat([uncond_embeddings, text_embeddings])
self.prompt = prompt
@torch.no_grad()
def ddim_loop(self, latent):
uncond_embeddings, cond_embeddings = self.context.chunk(2)
all_latent = [latent]
latent = latent.clone().detach()
for i in range(self.num_ddim_steps):
t = self.model.scheduler.timesteps[len(self.model.scheduler.timesteps) - i - 1]
noise_pred = self.get_noise_pred_single(latent, t, cond_embeddings)
latent = self.next_step(noise_pred, t, latent)
all_latent.append(latent)
return all_latent
@property
def scheduler(self):
return self.model.scheduler
@torch.no_grad()
def ddim_inversion(self, image):
latent = image2latent(self.model.vae, image)
image_rec = latent2image(self.model.vae, latent)[0]
ddim_latents = self.ddim_loop(latent)
return image_rec, ddim_latents
def null_optimization(self, latents, num_inner_steps, epsilon, guidance_scale):
uncond_embeddings, cond_embeddings = self.context.chunk(2)
uncond_embeddings_list = []
latent_cur = latents[-1]
for i in range(self.num_ddim_steps):
uncond_embeddings = uncond_embeddings.clone().detach()
t = self.model.scheduler.timesteps[i]
if num_inner_steps!=0:
uncond_embeddings.requires_grad = True
optimizer = Adam([uncond_embeddings], lr=1e-2 * (1. - i / 100.))
latent_prev = latents[len(latents) - i - 2]
with torch.no_grad():
noise_pred_cond = self.get_noise_pred_single(latent_cur, t, cond_embeddings)
for j in range(num_inner_steps):
noise_pred_uncond = self.get_noise_pred_single(latent_cur, t, uncond_embeddings)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_cond - noise_pred_uncond)
latents_prev_rec = self.prev_step(noise_pred, t, latent_cur)
loss = nnf.mse_loss(latents_prev_rec, latent_prev)
optimizer.zero_grad()
loss.backward()
optimizer.step()
loss_item = loss.item()
if loss_item < epsilon + i * 2e-5:
break
uncond_embeddings_list.append(uncond_embeddings[:1].detach())
with torch.no_grad():
context = torch.cat([uncond_embeddings, cond_embeddings])
latent_cur = self.get_noise_pred(latent_cur, t, guidance_scale, False, context)
return uncond_embeddings_list
def invert(self, image_gt, prompt, guidance_scale, num_inner_steps=10, early_stop_epsilon=1e-5):
self.init_prompt(prompt)
register_attention_control(self.model, None)
image_rec, ddim_latents = self.ddim_inversion(image_gt)
uncond_embeddings = self.null_optimization(ddim_latents, num_inner_steps, early_stop_epsilon,guidance_scale)
return image_gt, image_rec, ddim_latents, uncond_embeddings
def __init__(self, model,num_ddim_steps):
self.model = model
self.tokenizer = self.model.tokenizer
self.prompt = None
self.context = None
self.num_ddim_steps=num_ddim_steps
class DirectInversion:
def prev_step(self, model_output, timestep: int, sample):
prev_timestep = timestep - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
alpha_prod_t = self.scheduler.alphas_cumprod[timestep]
alpha_prod_t_prev = self.scheduler.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.scheduler.final_alpha_cumprod
beta_prod_t = 1 - alpha_prod_t
pred_original_sample = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
pred_sample_direction = (1 - alpha_prod_t_prev) ** 0.5 * model_output
prev_sample = alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction
difference_scale_pred_original_sample= - beta_prod_t ** 0.5 / alpha_prod_t ** 0.5
difference_scale_pred_sample_direction = (1 - alpha_prod_t_prev) ** 0.5
difference_scale = alpha_prod_t_prev ** 0.5 * difference_scale_pred_original_sample + difference_scale_pred_sample_direction
return prev_sample,difference_scale
def next_step(self, model_output, timestep: int, sample):
timestep, next_timestep = min(timestep - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps, 999), timestep
alpha_prod_t = self.scheduler.alphas_cumprod[timestep] if timestep >= 0 else self.scheduler.final_alpha_cumprod
alpha_prod_t_next = self.scheduler.alphas_cumprod[next_timestep]
beta_prod_t = 1 - alpha_prod_t
next_original_sample = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
next_sample_direction = (1 - alpha_prod_t_next) ** 0.5 * model_output
next_sample = alpha_prod_t_next ** 0.5 * next_original_sample + next_sample_direction
return next_sample
def get_noise_pred_single(self, latents, t, context):
noise_pred = self.model.unet(latents, t, encoder_hidden_states=context)["sample"]
return noise_pred
def get_noise_pred(self, latents, t, guidance_scale, is_forward=True, context=None):
latents_input = torch.cat([latents] * 2)
if context is None:
context = self.context
guidance_scale = 1 if is_forward else guidance_scale
noise_pred = self.model.unet(latents_input, t, encoder_hidden_states=context)["sample"]
noise_pred_uncond, noise_prediction_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_prediction_text - noise_pred_uncond)
if is_forward:
latents = self.next_step(noise_pred, t, latents)
else:
latents = self.prev_step(noise_pred, t, latents)
return latents
@torch.no_grad()
def init_prompt(self, prompt: str):
uncond_input = self.model.tokenizer(
[""]*len(prompt), padding="max_length", max_length=self.model.tokenizer.model_max_length,
return_tensors="pt"
)
uncond_embeddings = self.model.text_encoder(uncond_input.input_ids.to(self.model.device))[0]
text_input = self.model.tokenizer(
prompt,
padding="max_length",
max_length=self.model.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_embeddings = self.model.text_encoder(text_input.input_ids.to(self.model.device))[0]
self.context = torch.cat([uncond_embeddings, text_embeddings])
self.prompt = prompt
@torch.no_grad()
def ddim_loop(self, latent):
uncond_embeddings, cond_embeddings = self.context.chunk(2)
cond_embeddings=cond_embeddings[[0]]
all_latent = [latent]
latent = latent.clone().detach()
for i in range(self.num_ddim_steps):
t = self.model.scheduler.timesteps[len(self.model.scheduler.timesteps) - i - 1]
noise_pred = self.get_noise_pred_single(latent, t, cond_embeddings)
latent = self.next_step(noise_pred, t, latent)
all_latent.append(latent)
return all_latent
@torch.no_grad()
def ddim_null_loop(self, latent):
uncond_embeddings, cond_embeddings = self.context.chunk(2)
uncond_embeddings=uncond_embeddings[[0]]
all_latent = [latent]
latent = latent.clone().detach()
for i in range(self.num_ddim_steps):
t = self.model.scheduler.timesteps[len(self.model.scheduler.timesteps) - i - 1]
noise_pred = self.get_noise_pred_single(latent, t, uncond_embeddings)
latent = self.next_step(noise_pred, t, latent)
all_latent.append(latent)
return all_latent
@torch.no_grad()
def ddim_with_guidance_scale_loop(self, latent,guidance_scale):
uncond_embeddings, cond_embeddings = self.context.chunk(2)
uncond_embeddings=uncond_embeddings[[0]]
cond_embeddings=cond_embeddings[[0]]
all_latent = [latent]
latent = latent.clone().detach()
for i in range(self.num_ddim_steps):
t = self.model.scheduler.timesteps[len(self.model.scheduler.timesteps) - i - 1]
uncond_noise_pred = self.get_noise_pred_single(latent, t, uncond_embeddings)
cond_noise_pred = self.get_noise_pred_single(latent, t, cond_embeddings)
noise_pred = uncond_noise_pred + guidance_scale * (cond_noise_pred - uncond_noise_pred)
latent = self.next_step(noise_pred, t, latent)
all_latent.append(latent)
return all_latent
@property
def scheduler(self):
return self.model.scheduler
@torch.no_grad()
def ddim_inversion(self, image):
latent = image2latent(self.model.vae, image)
image_rec = latent2image(self.model.vae, latent)[0]
ddim_latents = self.ddim_loop(latent)
return image_rec, ddim_latents
@torch.no_grad()
def ddim_null_inversion(self, image):
latent = image2latent(self.model.vae, image)
image_rec = latent2image(self.model.vae, latent)[0]
ddim_latents = self.ddim_null_loop(latent)
return image_rec, ddim_latents
@torch.no_grad()
def ddim_with_guidance_scale_inversion(self, image,guidance_scale):
latent = image2latent(self.model.vae, image)
image_rec = latent2image(self.model.vae, latent)[0]
ddim_latents = self.ddim_with_guidance_scale_loop(latent,guidance_scale)
return image_rec, ddim_latents
def offset_calculate(self, latents, num_inner_steps, epsilon, guidance_scale):
noise_loss_list = []
latent_cur = torch.concat([latents[-1]]*(self.context.shape[0]//2))
for i in range(self.num_ddim_steps):
latent_prev = torch.concat([latents[len(latents) - i - 2]]*latent_cur.shape[0])
t = self.model.scheduler.timesteps[i]
with torch.no_grad():
noise_pred = self.get_noise_pred_single(torch.concat([latent_cur]*2), t, self.context)
noise_pred_uncond, noise_pred_cond = noise_pred.chunk(2)
noise_pred_w_guidance = noise_pred_uncond + guidance_scale * (noise_pred_cond - noise_pred_uncond)
latents_prev_rec, _ = self.prev_step(noise_pred_w_guidance, t, latent_cur)
loss = latent_prev - latents_prev_rec
noise_loss_list.append(loss.detach())
latent_cur = latents_prev_rec + loss
return noise_loss_list
def invert(self, image_gt, prompt, guidance_scale, num_inner_steps=10, early_stop_epsilon=1e-5):
self.init_prompt(prompt)
register_attention_control(self.model, None)
image_rec, ddim_latents = self.ddim_inversion(image_gt)
noise_loss_list = self.offset_calculate(ddim_latents, num_inner_steps, early_stop_epsilon,guidance_scale)
return image_gt, image_rec, ddim_latents, noise_loss_list
def invert_without_attn_controller(self, image_gt, prompt, guidance_scale, num_inner_steps=10, early_stop_epsilon=1e-5):
self.init_prompt(prompt)
image_rec, ddim_latents = self.ddim_inversion(image_gt)
noise_loss_list = self.offset_calculate(ddim_latents, num_inner_steps, early_stop_epsilon,guidance_scale)
return image_gt, image_rec, ddim_latents, noise_loss_list
def invert_with_guidance_scale_vary_guidance(self, image_gt, prompt, inverse_guidance_scale, forward_guidance_scale, num_inner_steps=10, early_stop_epsilon=1e-5):
self.init_prompt(prompt)
register_attention_control(self.model, None)
image_rec, ddim_latents = self.ddim_with_guidance_scale_inversion(image_gt,inverse_guidance_scale)
noise_loss_list = self.offset_calculate(ddim_latents, num_inner_steps, early_stop_epsilon,forward_guidance_scale)
return image_gt, image_rec, ddim_latents, noise_loss_list
def null_latent_calculate(self, latents, num_inner_steps, epsilon, guidance_scale):
noise_loss_list = []
latent_cur = torch.concat([latents[-1]]*(self.context.shape[0]//2))
uncond_embeddings, cond_embeddings = self.context.chunk(2)
for i in range(self.num_ddim_steps):
latent_prev = torch.concat([latents[len(latents) - i - 2]]*latent_cur.shape[0])
t = self.model.scheduler.timesteps[i]
if num_inner_steps!=0:
uncond_embeddings = uncond_embeddings.clone().detach()
uncond_embeddings.requires_grad = True
optimizer = Adam([uncond_embeddings], lr=1e-2 * (1. - i / 100.))
for j in range(num_inner_steps):
latents_input = torch.cat([latent_cur] * 2)
noise_pred = self.model.unet(latents_input, t, encoder_hidden_states=torch.cat([uncond_embeddings, cond_embeddings]))["sample"]
noise_pred_uncond, noise_prediction_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_prediction_text - noise_pred_uncond)
latents_prev_rec = self.prev_step(noise_pred, t, latent_cur)[0]
loss = nnf.mse_loss(latents_prev_rec[[0]], latent_prev[[0]])
optimizer.zero_grad()
loss.backward()
optimizer.step()
loss_item = loss.item()
if loss_item < epsilon + i * 2e-5:
break
with torch.no_grad():
noise_pred = self.get_noise_pred_single(torch.concat([latent_cur]*2), t, self.context)
noise_pred_uncond, noise_pred_cond = noise_pred.chunk(2)
noise_pred_w_guidance = noise_pred_uncond + guidance_scale * (noise_pred_cond - noise_pred_uncond)
latents_prev_rec, _ = self.prev_step(noise_pred_w_guidance, t, latent_cur)
latent_cur = self.get_noise_pred(latent_cur, t,guidance_scale, False, torch.cat([uncond_embeddings, cond_embeddings]))[0]
loss = latent_cur - latents_prev_rec
noise_loss_list.append(loss.detach())
latent_cur = latents_prev_rec + loss
return noise_loss_list
def invert_null_latent(self, image_gt, prompt, guidance_scale, num_inner_steps=10, early_stop_epsilon=1e-5):
self.init_prompt(prompt)
register_attention_control(self.model, None)
image_rec, ddim_latents = self.ddim_inversion(image_gt)
latent_list = self.null_latent_calculate(ddim_latents, num_inner_steps, early_stop_epsilon,guidance_scale)
return image_gt, image_rec, ddim_latents, latent_list
def offset_calculate_not_full(self, latents, num_inner_steps, epsilon, guidance_scale,scale):
noise_loss_list = []
latent_cur = torch.concat([latents[-1]]*(self.context.shape[0]//2))
for i in range(self.num_ddim_steps):
latent_prev = torch.concat([latents[len(latents) - i - 2]]*latent_cur.shape[0])
t = self.model.scheduler.timesteps[i]
with torch.no_grad():
noise_pred = self.get_noise_pred_single(torch.concat([latent_cur]*2), t, self.context)
noise_pred_uncond, noise_pred_cond = noise_pred.chunk(2)
noise_pred_w_guidance = noise_pred_uncond + guidance_scale * (noise_pred_cond - noise_pred_uncond)
latents_prev_rec, _ = self.prev_step(noise_pred_w_guidance, t, latent_cur)
loss = latent_prev - latents_prev_rec
loss=loss*scale
noise_loss_list.append(loss.detach())
latent_cur = latents_prev_rec + loss
return noise_loss_list
def invert_not_full(self, image_gt, prompt, guidance_scale, num_inner_steps=10, early_stop_epsilon=1e-5,scale=1.):
self.init_prompt(prompt)
register_attention_control(self.model, None)
image_rec, ddim_latents = self.ddim_inversion(image_gt)
noise_loss_list = self.offset_calculate_not_full(ddim_latents, num_inner_steps, early_stop_epsilon,guidance_scale,scale)
return image_gt, image_rec, ddim_latents, noise_loss_list
def offset_calculate_skip_step(self, latents, num_inner_steps, epsilon, guidance_scale,skip_step):
noise_loss_list = []
latent_cur = torch.concat([latents[-1]]*(self.context.shape[0]//2))
for i in range(self.num_ddim_steps):
latent_prev = torch.concat([latents[len(latents) - i - 2]]*latent_cur.shape[0])
t = self.model.scheduler.timesteps[i]
with torch.no_grad():
noise_pred = self.get_noise_pred_single(torch.concat([latent_cur]*2), t, self.context)
noise_pred_uncond, noise_pred_cond = noise_pred.chunk(2)
noise_pred_w_guidance = noise_pred_uncond + guidance_scale * (noise_pred_cond - noise_pred_uncond)
latents_prev_rec, _ = self.prev_step(noise_pred_w_guidance, t, latent_cur)
if (i%skip_step)==0:
loss = latent_prev - latents_prev_rec
else:
loss=torch.zeros_like(latent_prev)
noise_loss_list.append(loss.detach())
latent_cur = latents_prev_rec + loss
return noise_loss_list
def invert_skip_step(self, image_gt, prompt, guidance_scale, skip_step,num_inner_steps=10, early_stop_epsilon=1e-5,scale=1.):
self.init_prompt(prompt)
register_attention_control(self.model, None)
image_rec, ddim_latents = self.ddim_inversion(image_gt)
noise_loss_list = self.offset_calculate_skip_step(ddim_latents, num_inner_steps, early_stop_epsilon,guidance_scale,skip_step)
return image_gt, image_rec, ddim_latents, noise_loss_list
def __init__(self, model,num_ddim_steps):
self.model = model
self.tokenizer = self.model.tokenizer
self.prompt = None
self.context = None
self.num_ddim_steps=num_ddim_steps
================================================
FILE: models/p2p/p2p_guidance_forward.py
================================================
import torch
from models.p2p.attention_control import register_attention_control
from utils.utils import init_latent
def p2p_guidance_diffusion_step(model, controller, latents, context, t, guidance_scale, low_resource=False):
if low_resource:
noise_pred_uncond = model.unet(latents, t, encoder_hidden_states=context[0])["sample"]
noise_prediction_text = model.unet(latents, t, encoder_hidden_states=context[1])["sample"]
else:
latents_input = torch.cat([latents] * 2)
noise_pred = model.unet(latents_input, t, encoder_hidden_states=context)["sample"]
noise_pred_uncond, noise_prediction_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_prediction_text - noise_pred_uncond)
latents = model.scheduler.step(noise_pred, t, latents)["prev_sample"]
latents = controller.step_callback(latents)
return latents
@torch.no_grad()
def p2p_guidance_forward(
model,
prompt,
controller,
num_inference_steps: int = 50,
guidance_scale = 7.5,
generator = None,
latent = None,
uncond_embeddings=None
):
batch_size = len(prompt)
register_attention_control(model, controller)
height = width = 512
text_input = model.tokenizer(
prompt,
padding="max_length",
max_length=model.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_embeddings = model.text_encoder(text_input.input_ids.to(model.device))[0]
max_length = text_input.input_ids.shape[-1]
if uncond_embeddings is None:
uncond_input = model.tokenizer(
[""] * batch_size, padding="max_length", max_length=max_length, return_tensors="pt"
)
uncond_embeddings_ = model.text_encoder(uncond_input.input_ids.to(model.device))[0]
else:
uncond_embeddings_ = None
latent, latents = init_latent(latent, model, height, width, generator, batch_size)
model.scheduler.set_timesteps(num_inference_steps)
for i, t in enumerate(model.scheduler.timesteps):
if uncond_embeddings_ is None:
context = torch.cat([uncond_embeddings[i].expand(*text_embeddings.shape), text_embeddings])
else:
context = torch.cat([uncond_embeddings_, text_embeddings])
latents = p2p_guidance_diffusion_step(model, controller, latents, context, t, guidance_scale, low_resource=False)
return latents, latent
@torch.no_grad()
def p2p_guidance_forward_single_branch(
model,
prompt,
controller,
num_inference_steps: int = 50,
guidance_scale = 7.5,
generator = None,
latent = None,
uncond_embeddings=None
):
batch_size = len(prompt)
register_attention_control(model, controller)
height = width = 512
text_input = model.tokenizer(
prompt,
padding="max_length",
max_length=model.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_embeddings = model.text_encoder(text_input.input_ids.to(model.device))[0]
max_length = text_input.input_ids.shape[-1]
uncond_input = model.tokenizer(
[""] * batch_size, padding="max_length", max_length=max_length, return_tensors="pt"
)
uncond_embeddings_ = model.text_encoder(uncond_input.input_ids.to(model.device))[0]
latent, latents = init_latent(latent, model, height, width, generator, batch_size)
model.scheduler.set_timesteps(num_inference_steps)
for i, t in enumerate(model.scheduler.timesteps):
context = torch.cat([torch.cat([uncond_embeddings[i],uncond_embeddings_[1:]]), text_embeddings])
latents = p2p_guidance_diffusion_step(model, controller, latents, context, t, guidance_scale, low_resource=False)
return latents, latent
def direct_inversion_p2p_guidance_diffusion_step(model, controller, latents, context, t, guidance_scale, noise_loss, low_resource=False,add_offset=True):
if low_resource:
noise_pred_uncond = model.unet(latents, t, encoder_hidden_states=context[0])["sample"]
noise_prediction_text = model.unet(latents, t, encoder_hidden_states=context[1])["sample"]
else:
latents_input = torch.cat([latents] * 2)
noise_pred = model.unet(latents_input, t, encoder_hidden_states=context)["sample"]
noise_pred_uncond, noise_prediction_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_prediction_text - noise_pred_uncond)
latents = model.scheduler.step(noise_pred, t, latents)["prev_sample"]
if add_offset:
latents = torch.concat((latents[:1]+noise_loss[:1],latents[1:]))
latents = controller.step_callback(latents)
return latents
def direct_inversion_p2p_guidance_diffusion_step_add_target(model, controller, latents, context, t, guidance_scale, noise_loss, low_resource=False,add_offset=True):
if low_resource:
noise_pred_uncond = model.unet(latents, t, encoder_hidden_states=context[0])["sample"]
noise_prediction_text = model.unet(latents, t, encoder_hidden_states=context[1])["sample"]
else:
latents_input = torch.cat([latents] * 2)
noise_pred = model.unet(latents_input, t, encoder_hidden_states=context)["sample"]
noise_pred_uncond, noise_prediction_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_prediction_text - noise_pred_uncond)
latents = model.scheduler.step(noise_pred, t, latents)["prev_sample"]
if add_offset:
latents = torch.concat((latents[:1]+noise_loss[:1],latents[1:]+noise_loss[1:]))
latents = controller.step_callback(latents)
return latents
@torch.no_grad()
def direct_inversion_p2p_guidance_forward(
model,
prompt,
controller,
latent=None,
num_inference_steps: int = 50,
guidance_scale = 7.5,
generator = None,
noise_loss_list = None,
add_offset=True
):
batch_size = len(prompt)
register_attention_control(model, controller)
height = width = 512
text_input = model.tokenizer(
prompt,
padding="max_length",
max_length=model.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_embeddings = model.text_encoder(text_input.input_ids.to(model.device))[0]
max_length = text_input.input_ids.shape[-1]
uncond_input = model.tokenizer(
[""] * batch_size, padding="max_length", max_length=max_length, return_tensors="pt"
)
uncond_embeddings = model.text_encoder(uncond_input.input_ids.to(model.device))[0]
latent, latents = init_latent(latent, model, height, width, generator, batch_size)
model.scheduler.set_timesteps(num_inference_steps)
for i, t in enumerate(model.scheduler.timesteps):
context = torch.cat([uncond_embeddings, text_embeddings])
latents = direct_inversion_p2p_guidance_diffusion_step(model, controller, latents, context, t, guidance_scale, noise_loss_list[i],low_resource=False,add_offset=add_offset)
return latents, latent
@torch.no_grad()
def direct_inversion_p2p_guidance_forward_add_target(
model,
prompt,
controller,
latent=None,
num_inference_steps: int = 50,
guidance_scale = 7.5,
generator = None,
noise_loss_list = None,
add_offset=True
):
batch_size = len(prompt)
register_attention_control(model, controller)
height = width = 512
text_input = model.tokenizer(
prompt,
padding="max_length",
max_length=model.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_embeddings = model.text_encoder(text_input.input_ids.to(model.device))[0]
max_length = text_input.input_ids.shape[-1]
uncond_input = model.tokenizer(
[""] * batch_size, padding="max_length", max_length=max_length, return_tensors="pt"
)
uncond_embeddings = model.text_encoder(uncond_input.input_ids.to(model.device))[0]
latent, latents = init_latent(latent, model, height, width, generator, batch_size)
model.scheduler.set_timesteps(num_inference_steps)
for i, t in enumerate(model.scheduler.timesteps):
context = torch.cat([uncond_embeddings, text_embeddings])
latents = direct_inversion_p2p_guidance_diffusion_step_add_target(model, controller, latents, context, t, guidance_scale, noise_loss_list[i],low_resource=False,add_offset=add_offset)
return latents, latent
================================================
FILE: models/p2p/proximal_guidance_forward.py
================================================
import torch
import torch.nn.functional as F
from models.p2p.attention_control import register_attention_control
from utils.utils import init_latent
def dilate(image, kernel_size, stride=1, padding=0):
"""
Perform dilation on a binary image using a square kernel.
"""
# Ensure the image is binary
assert image.max() <= 1 and image.min() >= 0
# Get the maximum value in each neighborhood
dilated_image = F.max_pool2d(image, kernel_size, stride, padding)
return dilated_image
def proximal_guidance_diffusion_step(model, controller, latents, context, t, guidance_scale, low_resource=False,
edit_stage=True, prox=None, quantile=0.7,
image_enc=None, recon_lr=0.1, recon_t=400,
inversion_guidance=False, x_stars=None, i=0,
dilate_mask=0,):
if low_resource:
noise_pred_uncond = model.unet(latents, t, encoder_hidden_states=context[0])["sample"]
noise_prediction_text = model.unet(latents, t, encoder_hidden_states=context[1])["sample"]
else:
latents_input = torch.cat([latents] * 2)
noise_pred = model.unet(latents_input, t, encoder_hidden_states=context)["sample"]
noise_pred_uncond, noise_prediction_text = noise_pred.chunk(2)
step_kwargs = {
'ref_image': None,
'recon_lr': 0,
'recon_mask': None,
}
mask_edit = None
if edit_stage and prox is not None:
if prox == 'l1':
score_delta = noise_prediction_text - noise_pred_uncond
if quantile > 0:
threshold = score_delta.abs().quantile(quantile)
else:
threshold = -quantile # if quantile is negative, use it as a fixed threshold
score_delta -= score_delta.clamp(-threshold, threshold)
score_delta = torch.where(score_delta > 0, score_delta-threshold, score_delta)
score_delta = torch.where(score_delta < 0, score_delta+threshold, score_delta)
if (recon_t > 0 and t < recon_t) or (recon_t < 0 and t > -recon_t):
step_kwargs['ref_image'] = image_enc
step_kwargs['recon_lr'] = recon_lr
mask_edit = (score_delta.abs() > threshold).float()
if dilate_mask > 0:
radius = int(dilate_mask)
mask_edit = dilate(mask_edit.float(), kernel_size=2*radius+1, padding=radius)
step_kwargs['recon_mask'] = 1 - mask_edit
elif prox == 'l0':
score_delta = noise_prediction_text - noise_pred_uncond
if quantile > 0:
threshold = score_delta.abs().quantile(quantile)
else:
threshold = -quantile # if quantile is negative, use it as a fixed threshold
score_delta -= score_delta.clamp(-threshold, threshold)
if (recon_t > 0 and t < recon_t) or (recon_t < 0 and t > -recon_t):
step_kwargs['ref_image'] = image_enc
step_kwargs['recon_lr'] = recon_lr
mask_edit = (score_delta.abs() > threshold).float()
if dilate_mask > 0:
radius = int(dilate_mask)
mask_edit = dilate(mask_edit.float(), kernel_size=2*radius+1, padding=radius)
step_kwargs['recon_mask'] = 1 - mask_edit
else:
raise NotImplementedError
noise_pred = noise_pred_uncond + guidance_scale * score_delta
else:
noise_pred = noise_pred_uncond + guidance_scale * (noise_prediction_text - noise_pred_uncond)
latents = model.scheduler.step(noise_pred, t, latents, **step_kwargs)["prev_sample"]
if mask_edit is not None and inversion_guidance and (recon_t > 0 and t < recon_t) or (recon_t < 0 and t > -recon_t):
recon_mask = 1 - mask_edit
latents = latents - recon_lr * (latents - x_stars[len(x_stars)-i-2].expand_as(latents)) * recon_mask
latents = controller.step_callback(latents)
return latents
@torch.no_grad()
def proximal_guidance_forward(
model,
prompt,
controller,
guidance_scale = 7.5,
generator = None,
latent = None,
uncond_embeddings=None,
edit_stage=True,
prox=None,
quantile=0.7,
image_enc=None,
recon_lr=0.1,
recon_t=400,
inversion_guidance=False,
x_stars=None,
dilate_mask=None
):
"""
Get DDIM Forward result
Parameters:
model - the diffusion model
prompt - forward prompt
controller - prompt2prompt attention controller
guidance_scale - generation guidance scale
generator - the generator of random noisy latent (only needed if latent=None)
latent - the x_T step latent
uncond_embeddings - the generated unconditional embeddings
edit_stage - doing inference
prox -
quantile - quantile of the proximal guidance's threshold
image_enc -
recon_lr -
recon_t -
inversion_guidance -
x_stars -
Returns:
image_rec - the image reconstructed by VAE decoder with a size of [512,512,3], the channel follows the rgb of PIL.Image. i.e. RGB.
image_rec_latent - the image latent with a size of [64,64,4]
ddim_latents - the ddim inversion latents 50*[64,4,4], the first latent is the image_rec_latent, the last latent is noise (but in fact not pure noise)
uncond_embeddings - the fake uncond_embeddings, in fact is cond_embedding or a interpolation among cond_embedding and uncond_embedding
"""
batch_size = len(prompt)
register_attention_control(model, controller)
height = width = 512
text_input = model.tokenizer(
prompt,
padding="max_length",
max_length=model.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_embeddings = model.text_encoder(text_input.input_ids.to(model.device))[0]
if uncond_embeddings is None:
uncond_input = model.tokenizer(
[""] * batch_size,
padding="max_length",
max_length=model.tokenizer.model_max_length,
truncation=True,
return_tensors="pt"
)
uncond_embeddings_ = model.text_encoder(uncond_input.input_ids.to(model.device))[0]
else:
uncond_embeddings_ = None
latent, latents = init_latent(latent, model, height, width, generator, batch_size)
for i, t in enumerate(model.scheduler.timesteps):
if uncond_embeddings_ is None:
context = torch.cat([uncond_embeddings[i].expand(*text_embeddings.shape), text_embeddings])
else:
context = torch.cat([uncond_embeddings_, text_embeddings])
latents = proximal_guidance_diffusion_step(model, controller, latents, context, t, guidance_scale, low_resource=False,
edit_stage=edit_stage, prox=prox, quantile=quantile,
image_enc=image_enc, recon_lr=recon_lr, recon_t=recon_t,
inversion_guidance=inversion_guidance, x_stars=x_stars, i=i,dilate_mask=dilate_mask)
return latents, latent
================================================
FILE: models/p2p/scheduler_dev.py
================================================
from typing import List, Optional, Tuple, Union
import torch
from diffusers import DDIMScheduler
from diffusers.schedulers.scheduling_ddim import DDIMScheduler, DDIMSchedulerOutput
import torch.nn.functional as F
class DDIMSchedulerDev(DDIMScheduler):
def step(
self,
model_output,
timestep,
sample,
eta = 0.0,
use_clipped_model_output = False,
generator=None,
variance_noise = None,
return_dict = True,
**kwargs,
):
if self.num_inference_steps is None:
raise ValueError(
"Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"
)
# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
# Ideally, read DDIM paper in-detail understanding
# Notation ( ->
# - pred_noise_t -> e_theta(x_t, t)
# - pred_original_sample -> f_theta(x_t, t) or x_0
# - std_dev_t -> sigma_t
# - eta -> η
# - pred_sample_direction -> "direction pointing to x_t"
# - pred_prev_sample -> "x_t-1"
# 1. get previous step value (=t-1)
# prev_timestep = timestep_next
prev_timestep = timestep - self.config.num_train_timesteps // self.num_inference_steps
# 2. compute alphas, betas
alpha_prod_t = self.alphas_cumprod[timestep]
alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
beta_prod_t = 1 - alpha_prod_t
# 3. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
if self.config.prediction_type == "epsilon":
pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)
elif self.config.prediction_type == "sample":
pred_original_sample = model_output
elif self.config.prediction_type == "v_prediction":
pred_original_sample = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
# predict V
model_output = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
else:
raise ValueError(
f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"
" `v_prediction`"
)
# 4. Clip "predicted x_0"
if kwargs.get("clip_sample", False):
pred_original_sample = torch.clamp(pred_original_sample, -1, 1)
if kwargs.get("ref_image", None) is not None and kwargs.get("recon_lr", 0.0) > 0.0:
ref_image = kwargs.get("ref_image").expand_as(pred_original_sample)
recon_lr = kwargs.get("recon_lr", 0.0)
recon_mask = kwargs.get("recon_mask", None)
if recon_mask is not None:
recon_mask = recon_mask.expand_as(pred_original_sample).float()
pred_original_sample = pred_original_sample - recon_lr * (pred_original_sample - ref_image) * recon_mask
else:
pred_original_sample = pred_original_sample - recon_lr * (pred_original_sample - ref_image)
# 5. compute variance: "sigma_t(η)" -> see formula (16)
# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
if eta > 0:
variance = self._get_variance(timestep, prev_timestep)
std_dev_t = eta * variance ** (0.5)
else:
std_dev_t = 0.
if use_clipped_model_output:
# the model_output is always re-derived from the clipped x_0 in Glide
model_output = (sample - alpha_prod_t ** (0.5) * pred_original_sample) / beta_prod_t ** (0.5)
# 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
pred_sample_direction = (1 - alpha_prod_t_prev - std_dev_t**2) ** (0.5) * model_output
# 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
prev_sample = alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction
if eta > 0:
# randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
device = model_output.device
if variance_noise is not None and generator is not None:
raise ValueError(
"Cannot pass both generator and variance_noise. Please make sure that either `generator` or"
" `variance_noise` stays `None`."
)
if variance_noise is None:
if device.type == "mps":
# randn does not work reproducibly on mps
variance_noise = torch.randn(model_output.shape, dtype=model_output.dtype, generator=generator)
variance_noise = variance_noise.to(device)
else:
variance_noise = torch.randn(
model_output.shape, generator=generator, device=device, dtype=model_output.dtype
)
variance = self._get_variance(timestep, prev_timestep) ** (0.5) * eta * variance_noise
prev_sample = prev_sample + variance
if not return_dict:
return (prev_sample,)
return DDIMSchedulerOutput(prev_sample=prev_sample, pred_original_sample=pred_original_sample)
================================================
FILE: models/p2p/seq_aligner.py
================================================
# Copyright 2022 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
import numpy as np
class ScoreParams:
def __init__(self, gap, match, mismatch):
self.gap = gap
self.match = match
self.mismatch = mismatch
def mis_match_char(self, x, y):
if x != y:
return self.mismatch
else:
return self.match
def get_matrix(size_x, size_y, gap):
matrix = []
for i in range(len(size_x) + 1):
sub_matrix = []
for j in range(len(size_y) + 1):
sub_matrix.append(0)
matrix.append(sub_matrix)
for j in range(1, len(size_y) + 1):
matrix[0][j] = j*gap
for i in range(1, len(size_x) + 1):
matrix[i][0] = i*gap
return matrix
def get_matrix(size_x, size_y, gap):
matrix = np.zeros((size_x + 1, size_y + 1), dtype=np.int32)
matrix[0, 1:] = (np.arange(size_y) + 1) * gap
matrix[1:, 0] = (np.arange(size_x) + 1) * gap
return matrix
def get_traceback_matrix(size_x, size_y):
matrix = np.zeros((size_x + 1, size_y +1), dtype=np.int32)
matrix[0, 1:] = 1
matrix[1:, 0] = 2
matrix[0, 0] = 4
return matrix
def global_align(x, y, score):
matrix = get_matrix(len(x), len(y), score.gap)
trace_back = get_traceback_matrix(len(x), len(y))
for i in range(1, len(x) + 1):
for j in range(1, len(y) + 1):
left = matrix[i, j - 1] + score.gap
up = matrix[i - 1, j] + score.gap
diag = matrix[i - 1, j - 1] + score.mis_match_char(x[i - 1], y[j - 1])
matrix[i, j] = max(left, up, diag)
if matrix[i, j] == left:
trace_back[i, j] = 1
elif matrix[i, j] == up:
trace_back[i, j] = 2
else:
trace_back[i, j] = 3
return matrix, trace_back
def get_aligned_sequences(x, y, trace_back):
x_seq = []
y_seq = []
i = len(x)
j = len(y)
mapper_y_to_x = []
while i > 0 or j > 0:
if trace_back[i, j] == 3:
x_seq.append(x[i-1])
y_seq.append(y[j-1])
i = i-1
j = j-1
mapper_y_to_x.append((j, i))
elif trace_back[i][j] == 1:
x_seq.append('-')
y_seq.append(y[j-1])
j = j-1
mapper_y_to_x.append((j, -1))
elif trace_back[i][j] == 2:
x_seq.append(x[i-1])
y_seq.append('-')
i = i-1
elif trace_back[i][j] == 4:
break
mapper_y_to_x.reverse()
return x_seq, y_seq, torch.tensor(mapper_y_to_x, dtype=torch.int64)
def get_mapper(x, y, tokenizer, max_len=77):
x_seq = tokenizer.encode(x)
y_seq = tokenizer.encode(y)
score = ScoreParams(0, 1, -1)
matrix, trace_back = global_align(x_seq, y_seq, score)
mapper_base = get_aligned_sequences(x_seq, y_seq, trace_back)[-1]
alphas = torch.ones(max_len)
alphas[: mapper_base.shape[0]] = mapper_base[:, 1].ne(-1).float()
mapper = torch.zeros(max_len, dtype=torch.int64)
mapper[:mapper_base.shape[0]] = mapper_base[:, 1]
mapper[mapper_base.shape[0]:] = len(y_seq) + torch.arange(max_len - len(y_seq))
return mapper, alphas
def get_refinement_mapper(prompts, tokenizer, max_len=77):
x_seq = prompts[0]
mappers, alphas = [], []
for i in range(1, len(prompts)):
mapper, alpha = get_mapper(x_seq, prompts[i], tokenizer, max_len)
mappers.append(mapper)
alphas.append(alpha)
return torch.stack(mappers), torch.stack(alphas)
def get_word_inds(text, word_place, tokenizer):
split_text = text.split(" ")
if type(word_place) is str:
word_place = [i for i, word in enumerate(split_text) if word_place == word]
elif type(word_place) is int:
word_place = [word_place]
out = []
if len(word_place) > 0:
words_encode = [tokenizer.decode([item]).strip("#") for item in tokenizer.encode(text)][1:-1]
cur_len, ptr = 0, 0
for i in range(len(words_encode)):
cur_len += len(words_encode[i])
if ptr in word_place:
out.append(i + 1)
if cur_len >= len(split_text[ptr]):
ptr += 1
cur_len = 0
return np.array(out)
def get_replacement_mapper_(x, y, tokenizer, max_len=77):
words_x = x.split(' ')
words_y = y.split(' ')
if len(words_x) != len(words_y):
raise ValueError(f"attention replacement edit can only be applied on prompts with the same length"
f" but prompt A has {len(words_x)} words and prompt B has {len(words_y)} words.")
inds_replace = [i for i in range(len(words_y)) if words_y[i] != words_x[i]]
inds_source = [get_word_inds(x, i, tokenizer) for i in inds_replace]
inds_target = [get_word_inds(y, i, tokenizer) for i in inds_replace]
mapper = np.zeros((max_len, max_len))
i = j = 0
cur_inds = 0
while i < max_len and j < max_len:
if cur_inds < len(inds_source) and inds_source[cur_inds][0] == i:
inds_source_, inds_target_ = inds_source[cur_inds], inds_target[cur_inds]
if len(inds_source_) == len(inds_target_):
mapper[inds_source_, inds_target_] = 1
else:
ratio = 1 / len(inds_target_)
for i_t in inds_target_:
mapper[inds_source_, i_t] = ratio
cur_inds += 1
i += len(inds_source_)
j += len(inds_target_)
elif cur_inds < len(inds_source):
mapper[i, j] = 1
i += 1
j += 1
else:
mapper[j, j] = 1
i += 1
j += 1
return torch.from_numpy(mapper).float()
def get_replacement_mapper(prompts, tokenizer, max_len=77):
x_seq = prompts[0]
mappers = []
for i in range(1, len(prompts)):
mapper = get_replacement_mapper_(x_seq, prompts[i], tokenizer, max_len)
mappers.append(mapper)
return torch.stack(mappers)
================================================
FILE: models/p2p_editor.py
================================================
from models.p2p.scheduler_dev import DDIMSchedulerDev
from models.p2p.inversion import NegativePromptInversion, NullInversion, DirectInversion
from models.p2p.attention_control import EmptyControl, AttentionStore, make_controller
from models.p2p.p2p_guidance_forward import p2p_guidance_forward, direct_inversion_p2p_guidance_forward, direct_inversion_p2p_guidance_forward_add_target,p2p_guidance_forward_single_branch
from models.p2p.proximal_guidance_forward import proximal_guidance_forward
from diffusers import StableDiffusionPipeline
from utils.utils import load_512, latent2image, txt_draw
from PIL import Image
import numpy as np
class P2PEditor:
def __init__(self, method_list, device, num_ddim_steps=50) -> None:
self.device=device
self.method_list=method_list
self.num_ddim_steps=num_ddim_steps
# init model
self.scheduler = DDIMSchedulerDev(beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
clip_sample=False,
set_alpha_to_one=False)
self.ldm_stable = StableDiffusionPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4", scheduler=self.scheduler).to(device)
self.ldm_stable.scheduler.set_timesteps(self.num_ddim_steps)
def __call__(self,
edit_method,
image_path,
prompt_src,
prompt_tar,
guidance_scale=7.5,
proximal=None,
quantile=0.7,
use_reconstruction_guidance=False,
recon_t=400,
recon_lr=0.1,
cross_replace_steps=0.4,
self_replace_steps=0.6,
blend_word=None,
eq_params=None,
is_replace_controller=False,
use_inversion_guidance=False,
dilate_mask=1,):
if edit_method=="ddim+p2p":
return self.edit_image_ddim(image_path, prompt_src, prompt_tar, guidance_scale=guidance_scale,
cross_replace_steps=cross_replace_steps, self_replace_steps=self_replace_steps,
blend_word=blend_word, eq_params=eq_params, is_replace_controller=is_replace_controller)
elif edit_method in ["null-text-inversion+p2p", "null-text-inversion+p2p_a800", "null-text-inversion+p2p_3090"]:
return self.edit_image_null_text_inversion(image_path, prompt_src, prompt_tar, guidance_scale=guidance_scale,
cross_replace_steps=cross_replace_steps, self_replace_steps=self_replace_steps,
blend_word=blend_word, eq_params=eq_params, is_replace_controller=is_replace_controller)
elif edit_method == "ablation_null-text-inversion_single_branch+p2p":
return self.edit_image_null_text_inversion_single_branch(image_path, prompt_src, prompt_tar, guidance_scale=guidance_scale,
cross_replace_steps=cross_replace_steps, self_replace_steps=self_replace_steps,
blend_word=blend_word, eq_params=eq_params, is_replace_controller=is_replace_controller)
elif edit_method=="negative-prompt-inversion+p2p":
return self.edit_image_negative_prompt_inversion(image_path=image_path, prompt_src=prompt_src, prompt_tar=prompt_tar,
guidance_scale=guidance_scale, proximal=None, quantile=quantile, use_reconstruction_guidance=use_reconstruction_guidance,
recon_t=recon_t, recon_lr=recon_lr, cross_replace_steps=cross_replace_steps,
self_replace_steps=self_replace_steps, blend_word=blend_word, eq_params=eq_params,
is_replace_controller=is_replace_controller, use_inversion_guidance=use_inversion_guidance,
dilate_mask=dilate_mask)
elif edit_method=="directinversion+p2p":
return self.edit_image_directinversion(image_path=image_path, prompt_src=prompt_src, prompt_tar=prompt_tar, guidance_scale=guidance_scale,
cross_replace_steps=cross_replace_steps, self_replace_steps=self_replace_steps,
blend_word=blend_word, eq_params=eq_params, is_replace_controller=is_replace_controller)
elif edit_method in ["directinversion+p2p_guidance_0_1", "directinversion+p2p_guidance_0_5","directinversion+p2p_guidance_0_25", \
"directinversion+p2p_guidance_0_75", "directinversion+p2p_guidance_1_1", "directinversion+p2p_guidance_1_5", "directinversion+p2p_guidance_1_25", \
"directinversion+p2p_guidance_1_75", "directinversion+p2p_guidance_25_1", "directinversion+p2p_guidance_25_5", "directinversion+p2p_guidance_25_25", \
"directinversion+p2p_guidance_25_75", "directinversion+p2p_guidance_5_1", "directinversion+p2p_guidance_5_5", \
"directinversion+p2p_guidance_5_25", "directinversion+p2p_guidance_5_75", "directinversion+p2p_guidance_75_1", \
"directinversion+p2p_guidance_75_5", "directinversion+p2p_guidance_75_25", "directinversion+p2p_guidance_75_75"]:
guidance_scale={
"0":0,
"1":1,
"25":2.5,
"5":5,
"75":7.5}
inverse_guidance_scale=guidance_scale[edit_method.split("_")[-2]]
forward_guidance_scale=guidance_scale[edit_method.split("_")[-1]]
return self.edit_image_directinversion_vary_guidance_scale(image_path=image_path, prompt_src=prompt_src, prompt_tar=prompt_tar,
inverse_guidance_scale=inverse_guidance_scale,
forward_guidance_scale=forward_guidance_scale,
cross_replace_steps=cross_replace_steps, self_replace_steps=self_replace_steps,
blend_word=blend_word, eq_params=eq_params, is_replace_controller=is_replace_controller)
elif edit_method=="null-text-inversion+proximal-guidance":
return self.edit_image_null_text_inversion_proximal_guidanca(image_path=image_path, prompt_src=prompt_src, prompt_tar=prompt_tar,
guidance_scale=guidance_scale, proximal=proximal, quantile=quantile, use_reconstruction_guidance=use_reconstruction_guidance,
recon_t=recon_t, recon_lr=recon_lr, cross_replace_steps=cross_replace_steps,
self_replace_steps=self_replace_steps, blend_word=blend_word, eq_params=eq_params,
is_replace_controller=is_replace_controller, use_inversion_guidance=use_inversion_guidance,
dilate_mask=dilate_mask)
elif edit_method=="negative-prompt-inversion+proximal-guidance":
return self.edit_image_negative_prompt_inversion(image_path=image_path, prompt_src=prompt_src, prompt_tar=prompt_tar,
guidance_scale=guidance_scale, proximal=proximal, quantile=quantile, use_reconstruction_guidance=use_reconstruction_guidance,
recon_t=recon_t, recon_lr=recon_lr, cross_replace_steps=cross_replace_steps,
self_replace_steps=self_replace_steps, blend_word=blend_word, eq_params=eq_params,
is_replace_controller=is_replace_controller, use_inversion_guidance=use_inversion_guidance,
dilate_mask=dilate_mask)
elif edit_method=="ablation_null-latent-inversion+p2p":
return self.edit_image_null_latent_inversion(image_path, prompt_src, prompt_tar, guidance_scale=guidance_scale,
cross_replace_steps=cross_replace_steps, self_replace_steps=self_replace_steps,
blend_word=blend_word, eq_params=eq_params, is_replace_controller=is_replace_controller)
elif edit_method in [
"ablation_directinversion_08+p2p",
"ablation_directinversion_04+p2p",
]:
scale=float(edit_method.split("+")[0].split("_")[-1])/10
return self.edit_image_directinversion_not_full(image_path=image_path, prompt_src=prompt_src, prompt_tar=prompt_tar, guidance_scale=guidance_scale,
cross_replace_steps=cross_replace_steps, self_replace_steps=self_replace_steps,
blend_word=blend_word, eq_params=eq_params, is_replace_controller=is_replace_controller,scale=scale)
elif edit_method in [
"ablation_directinversion_interval_2+p2p",
"ablation_directinversion_interval_5+p2p",
"ablation_directinversion_interval_10+p2p",
"ablation_directinversion_interval_24+p2p",
"ablation_directinversion_interval_49+p2p",
]:
skip_step=int(edit_method.split("+")[0].split("_")[-1])
return self.edit_image_directinversion_skip_step(image_path=image_path, prompt_src=prompt_src, prompt_tar=prompt_tar, guidance_scale=guidance_scale,
cross_replace_steps=cross_replace_steps, self_replace_steps=self_replace_steps,
blend_word=blend_word, eq_params=eq_params, is_replace_controller=is_replace_controller,skip_step=skip_step)
elif edit_method=="ablation_directinversion_add-target+p2p":
return self.edit_image_directinversion_add_target(image_path=image_path, prompt_src=prompt_src, prompt_tar=prompt_tar, guidance_scale=guidance_scale,
cross_replace_steps=cross_replace_steps, self_replace_steps=self_replace_steps,
blend_word=blend_word, eq_params=eq_params, is_replace_controller=is_replace_controller)
elif edit_method=="ablation_directinversion_add-source+p2p":
return self.edit_image_directinversion_add_source(image_path=image_path, prompt_src=prompt_src, prompt_tar=prompt_tar, guidance_scale=guidance_scale,
cross_replace_steps=cross_replace_steps, self_replace_steps=self_replace_steps,
blend_word=blend_word, eq_params=eq_params, is_replace_controller=is_replace_controller)
else:
raise NotImplementedError(f"No edit method named {edit_method}")
def edit_image_ddim(
self,
image_path,
prompt_src,
prompt_tar,
guidance_scale=7.5,
cross_replace_steps=0.4,
self_replace_steps=0.6,
blend_word=None,
eq_params=None,
is_replace_controller=False,
):
image_gt = load_512(image_path)
prompts = [prompt_src, prompt_tar]
null_inversion = NullInversion(model=self.ldm_stable,
num_ddim_steps=self.num_ddim_steps)
_, _, x_stars, uncond_embeddings = null_inversion.invert(
image_gt=image_gt, prompt=prompt_src,guidance_scale=guidance_scale,num_inner_steps=0)
x_t = x_stars[-1]
controller = AttentionStore()
reconstruct_latent, x_t = p2p_guidance_forward(model=self.ldm_stable,
prompt=[prompt_src],
controller=controller,
latent=x_t,
num_inference_steps=self.num_ddim_steps,
guidance_scale=guidance_scale,
generator=None,
uncond_embeddings=uncond_embeddings)
reconstruct_image = latent2image(model=self.ldm_stable.vae, latents=reconstruct_latent)[0]
image_instruct = txt_draw(f"source prompt: {prompt_src}\ntarget prompt: {prompt_tar}")
########## edit ##########
cross_replace_steps = {
'default_': cross_replace_steps,
}
controller = make_controller(pipeline=self.ldm_stable,
prompts=prompts,
is_replace_controller=is_replace_controller,
cross_replace_steps=cross_replace_steps,
self_replace_steps=self_replace_steps,
blend_words=blend_word,
equilizer_params=eq_params,
num_ddim_steps=self.num_ddim_steps,
device=self.device)
latents, _ = p2p_guidance_forward(model=self.ldm_stable,
prompt=prompts,
controller=controller,
latent=x_t,
num_inference_steps=self.num_ddim_steps,
guidance_scale=guidance_scale,
generator=None,
uncond_embeddings=uncond_embeddings)
images = latent2image(model=self.ldm_stable.vae, latents=latents)
return Image.fromarray(np.concatenate((image_instruct, image_gt, reconstruct_image,images[-1]),axis=1))
def edit_image_null_text_inversion(
self,
image_path,
prompt_src,
prompt_tar,
guidance_scale=7.5,
cross_replace_steps=0.4,
self_replace_steps=0.6,
blend_word=None,
eq_params=None,
is_replace_controller=False,
):
image_gt = load_512(image_path)
prompts = [prompt_src, prompt_tar]
null_inversion = NullInversion(model=self.ldm_stable,
num_ddim_steps=self.num_ddim_steps)
_, _, x_stars, uncond_embeddings = null_inversion.invert(
image_gt=image_gt, prompt=prompt_src,guidance_scale=guidance_scale)
x_t = x_stars[-1]
controller = AttentionStore()
reconstruct_latent, x_t = p2p_guidance_forward(model=self.ldm_stable,
prompt=[prompt_src],
controller=controller,
latent=x_t,
num_inference_steps=self.num_ddim_steps,
guidance_scale=guidance_scale,
generator=None,
uncond_embeddings=uncond_embeddings)
reconstruct_image = latent2image(model=self.ldm_stable.vae, latents=reconstruct_latent)[0]
image_instruct = txt_draw(f"source prompt: {prompt_src}\ntarget prompt: {prompt_tar}")
########## edit ##########
cross_replace_steps = {
'default_': cross_replace_steps,
}
controller = make_controller(pipeline=self.ldm_stable,
prompts=prompts,
is_replace_controller=is_replace_controller,
cross_replace_steps=cross_replace_steps,
self_replace_steps=self_replace_steps,
blend_words=blend_word,
equilizer_params=eq_params,
num_ddim_steps=self.num_ddim_steps,
device=self.device)
latents, _ = p2p_guidance_forward(model=self.ldm_stable,
prompt=prompts,
controller=controller,
latent=x_t,
num_inference_steps=self.num_ddim_steps,
guidance_scale=guidance_scale,
generator=None,
uncond_embeddings=uncond_embeddings)
images = latent2image(model=self.ldm_stable.vae, latents=latents)
return Image.fromarray(np.concatenate((image_instruct, image_gt, reconstruct_image,images[-1]),axis=1))
def edit_image_null_text_inversion_single_branch(
self,
image_path,
prompt_src,
prompt_tar,
guidance_scale=7.5,
cross_replace_steps=0.4,
self_replace_steps=0.6,
blend_word=None,
eq_params=None,
is_replace_controller=False,
):
image_gt = load_512(image_path)
prompts = [prompt_src, prompt_tar]
null_inversion = NullInversion(model=self.ldm_stable,
num_ddim_steps=self.num_ddim_steps)
_, _, x_stars, uncond_embeddings = null_inversion.invert(
image_gt=image_gt, prompt=prompt_src,guidance_scale=guidance_scale)
x_t = x_stars[-1]
controller = AttentionStore()
reconstruct_latent, x_t = p2p_guidance_forward_single_branch(model=self.ldm_stable,
prompt=[prompt_src],
controller=controller,
latent=x_t,
num_inference_steps=self.num_ddim_steps,
guidance_scale=guidance_scale,
generator=None,
uncond_embeddings=uncond_embeddings)
reconstruct_image = latent2image(model=self.ldm_stable.vae, latents=reconstruct_latent)[0]
image_instruct = txt_draw(f"source prompt: {prompt_src}\ntarget prompt: {prompt_tar}")
########## edit ##########
cross_replace_steps = {
'default_': cross_replace_steps,
}
controller = make_controller(pipeline=self.ldm_stable,
prompts=prompts,
is_replace_controller=is_replace_controller,
cross_replace_steps=cross_replace_steps,
self_replace_steps=self_replace_steps,
blend_words=blend_word,
equilizer_params=eq_params,
num_ddim_steps=self.num_ddim_steps,
device=self.device)
latents, _ = p2p_guidance_forward_single_branch(model=self.ldm_stable,
prompt=prompts,
controller=controller,
latent=x_t,
num_inference_steps=self.num_ddim_steps,
guidance_scale=guidance_scale,
generator=None,
uncond_embeddings=uncond_embeddings)
images = latent2image(model=self.ldm_stable.vae, latents=latents)
return Image.fromarray(np.concatenate((image_instruct, image_gt, reconstruct_image,images[-1]),axis=1))
def edit_image_negative_prompt_inversion(
self,
image_path,
prompt_src,
prompt_tar,
guidance_scale=7.5,
proximal=None,
quantile=0.7,
use_reconstruction_guidance=False,
recon_t=400,
recon_lr=0.1,
npi_interp=0,
cross_replace_steps=0.4,
self_replace_steps=0.6,
blend_word=None,
eq_params=None,
is_replace_controller=False,
use_inversion_guidance=False,
dilate_mask=1,
):
image_gt = load_512(image_path)
prompts = [prompt_src, prompt_tar]
null_inversion = NegativePromptInversion(model=self.ldm_stable,
num_ddim_steps=self.num_ddim_steps)
_, image_enc_latent, x_stars, uncond_embeddings = null_inversion.invert(
image_gt=image_gt, prompt=prompt_src, npi_interp=npi_interp)
x_t = x_stars[-1]
controller = AttentionStore()
reconstruct_latent, x_t = proximal_guidance_forward(
model=self.ldm_stable,
prompt=[prompt_src],
controller=controller,
latent=x_t,
guidance_scale=guidance_scale,
generator=None,
uncond_embeddings=uncond_embeddings,
edit_stage=False,
prox=None,
quantile=quantile,
image_enc=None,
recon_lr=recon_lr,
recon_t=recon_t,
inversion_guidance=False,
x_stars=None,
dilate_mask=dilate_mask)
reconstruct_image = latent2image(model=self.ldm_stable.vae, latents=reconstruct_latent)[0]
image_instruct = txt_draw(f"source prompt: {prompt_src}\ntarget prompt: {prompt_tar}")
########## edit ##########
cross_replace_steps = {
'default_': cross_replace_steps,
}
controller = make_controller(pipeline=self.ldm_stable,
prompts=prompts,
is_replace_controller=is_replace_controller,
cross_replace_steps=cross_replace_steps,
self_replace_steps=self_replace_steps,
blend_words=blend_word,
equilizer_params=eq_params,
num_ddim_steps=self.num_ddim_steps,
device=self.device)
latents, _ = proximal_guidance_forward(
model=self.ldm_stable,
prompt=prompts,
controller=controller,
latent=x_t,
guidance_scale=guidance_scale,
generator=None,
uncond_embeddings=uncond_embeddings,
edit_stage=True,
prox=proximal,
quantile=quantile,
image_enc=image_enc_latent if use_reconstruction_guidance else None,
recon_lr=recon_lr
if use_reconstruction_guidance or use_inversion_guidance else 0,
recon_t=recon_t
if use_reconstruction_guidance or use_inversion_guidance else 1000,
x_stars=x_stars,
dilate_mask=dilate_mask)
images = latent2image(model=self.ldm_stable.vae, latents=latents)
return Image.fromarray(np.concatenate((image_instruct, image_gt, reconstruct_image,images[-1]),axis=1))
def edit_image_directinversion(
self,
image_path,
prompt_src,
prompt_tar,
guidance_scale=7.5,
cross_replace_steps=0.4,
self_replace_steps=0.6,
blend_word=None,
eq_params=None,
is_replace_controller=False,
):
image_gt = load_512(image_path)
prompts = [prompt_src, prompt_tar]
null_inversion = DirectInversion(model=self.ldm_stable,
num_ddim_steps=self.num_ddim_steps)
_, _, x_stars, noise_loss_list = null_inversion.invert(
image_gt=image_gt, prompt=prompts,guidance_scale=guidance_scale)
x_t = x_stars[-1]
controller = AttentionStore()
reconstruct_latent, x_t = direct_inversion_p2p_guidance_forward(model=self.ldm_stable,
prompt=prompts,
controller=controller,
noise_loss_list=noise_loss_list,
latent=x_t,
num_inference_steps=self.num_ddim_steps,
guidance_scale=guidance_scale,
generator=None)
reconstruct_image = latent2image(model=self.ldm_stable.vae, latents=reconstruct_latent)[0]
########## edit ##########
cross_replace_steps = {
'default_': cross_replace_steps,
}
controller = make_controller(pipeline=self.ldm_stable,
prompts=prompts,
is_replace_controller=is_replace_controller,
cross_replace_steps=cross_replace_steps,
self_replace_steps=self_replace_steps,
blend_words=blend_word,
equilizer_params=eq_params,
num_ddim_steps=self.num_ddim_steps,
device=self.device)
latents, _ = direct_inversion_p2p_guidance_forward(model=self.ldm_stable,
prompt=prompts,
controller=controller,
noise_loss_list=noise_loss_list,
latent=x_t,
num_inference_steps=self.num_ddim_steps,
guidance_scale=guidance_scale,
generator=None)
images = latent2image(model=self.ldm_stable.vae, latents=latents)
image_instruct = txt_draw(f"source prompt: {prompt_src}\ntarget prompt: {prompt_tar}")
return Image.fromarray(np.concatenate((image_instruct, image_gt, reconstruct_image,images[-1]),axis=1))
def edit_image_directinversion_vary_guidance_scale(
self,
image_path,
prompt_src,
prompt_tar,
inverse_guidance_scale=1,
forward_guidance_scale=7.5,
cross_replace_steps=0.4,
self_replace_steps=0.6,
blend_word=None,
eq_params=None,
is_replace_controller=False,
):
image_gt = load_512(image_path)
prompts = [prompt_src, prompt_tar]
null_inversion = DirectInversion(model=self.ldm_stable,
num_ddim_steps=self.num_ddim_steps)
_, _, x_stars, noise_loss_list = null_inversion.invert_with_guidance_scale_vary_guidance(
image_gt=image_gt, prompt=prompts, inverse_guidance_scale=inverse_guidance_scale,
forward_guidance_scale=forward_guidance_scale)
x_t = x_stars[-1]
controller = AttentionStore()
reconstruct_latent, x_t = direct_inversion_p2p_guidance_forward(model=self.ldm_stable,
prompt=prompts,
controller=controller,
noise_loss_list=noise_loss_list,
latent=x_t,
num_inference_steps=self.num_ddim_steps,
guidance_scale=forward_guidance_scale,
generator=None)
reconstruct_image = latent2image(model=self.ldm_stable.vae, latents=reconstruct_latent)[0]
########## edit ##########
cross_replace_steps = {
'default_': cross_replace_steps,
}
controller = make_controller(pipeline=self.ldm_stable,
prompts=prompts,
is_replace_controller=is_replace_controller,
cross_replace_steps=cross_replace_steps,
self_replace_steps=self_replace_steps,
blend_words=blend_word,
equilizer_params=eq_params,
num_ddim_steps=self.num_ddim_steps,
device=self.device)
latents, _ = direct_inversion_p2p_guidance_forward(model=self.ldm_stable,
prompt=prompts,
controller=controller,
noise_loss_list=noise_loss_list,
latent=x_t,
num_inference_steps=self.num_ddim_steps,
guidance_scale=forward_guidance_scale,
generator=None)
images = latent2image(model=self.ldm_stable.vae, latents=latents)
image_instruct = txt_draw(f"source prompt: {prompt_src}\ntarget prompt: {prompt_tar}")
return Image.fromarray(np.concatenate((image_instruct, image_gt, reconstruct_image,images[-1]),axis=1))
def edit_image_null_text_inversion_proximal_guidanca(
self,
image_path,
prompt_src,
prompt_tar,
guidance_scale=7.5,
proximal=None,
quantile=0.7,
use_reconstruction_guidance=False,
recon_t=400,
recon_lr=0.1,
cross_replace_steps=0.4,
self_replace_steps=0.6,
blend_word=None,
eq_params=None,
is_replace_controller=False,
use_inversion_guidance=False,
dilate_mask=1,
):
image_gt = load_512(image_path)
prompts = [prompt_src, prompt_tar]
null_inversion = NullInversion(model=self.ldm_stable,
num_ddim_steps=self.num_ddim_steps)
_, image_enc_latent, x_stars, uncond_embeddings = null_inversion.invert(
image_gt=image_gt, prompt=prompt_src,guidance_scale=guidance_scale)
x_t = x_stars[-1]
controller = AttentionStore()
reconstruct_latent, x_t = proximal_guidance_forward(
model=self.ldm_stable,
prompt=[prompt_src],
controller=controller,
latent=x_t,
guidance_scale=guidance_scale,
generator=None,
uncond_embeddings=uncond_embeddings,
edit_stage=False,
prox=None,
quantile=quantile,
image_enc=None,
recon_lr=recon_lr,
recon_t=recon_t,
inversion_guidance=False,
x_stars=None,
dilate_mask=dilate_mask)
reconstruct_image = latent2image(model=self.ldm_stable.vae, latents=reconstruct_latent)[0]
image_instruct = txt_draw(f"source prompt: {prompt_src}\ntarget prompt: {prompt_tar}")
########## edit ##########
cross_replace_steps = {
'default_': cross_replace_steps,
}
controller = make_controller(pipeline=self.ldm_stable,
prompts=prompts,
is_replace_controller=is_replace_controller,
cross_replace_steps=cross_replace_steps,
self_replace_steps=self_replace_steps,
blend_words=blend_word,
equilizer_params=eq_params,
num_ddim_steps=self.num_ddim_steps,
device=self.device)
latents, _ = proximal_guidance_forward(
model=self.ldm_stable,
prompt=prompts,
controller=controller,
latent=x_t,
guidance_scale=guidance_scale,
generator=None,
uncond_embeddings=uncond_embeddings,
edit_stage=True,
prox=proximal,
quantile=quantile,
image_enc=image_enc_latent if use_reconstruction_guidance else None,
recon_lr=recon_lr
if use_reconstruction_guidance or use_inversion_guidance else 0,
recon_t=recon_t
if use_reconstruction_guidance or use_inversion_guidance else 1000,
x_stars=x_stars,
dilate_mask=dilate_mask)
images = latent2image(model=self.ldm_stable.vae, latents=latents)
return Image.fromarray(np.concatenate((image_instruct, image_gt, reconstruct_image,images[-1]),axis=1))
def edit_image_null_latent_inversion(
self,
image_path,
prompt_src,
prompt_tar,
guidance_scale=7.5,
cross_replace_steps=0.4,
self_replace_steps=0.6,
blend_word=None,
eq_params=None,
is_replace_controller=False,
):
image_gt = load_512(image_path)
prompts = [prompt_src, prompt_tar]
null_inversion = DirectInversion(model=self.ldm_stable,
num_ddim_steps=self.num_ddim_steps)
_, _, x_stars, noise_loss_list = null_inversion.invert_null_latent(
image_gt=image_gt, prompt=prompts,guidance_scale=guidance_scale)
x_t = x_stars[-1]
controller = AttentionStore()
reconstruct_latent, x_t = direct_inversion_p2p_guidance_forward(model=self.ldm_stable,
prompt=prompts,
controller=controller,
noise_loss_list=noise_loss_list,
latent=x_t,
num_inference_steps=self.num_ddim_steps,
guidance_scale=guidance_scale,
generator=None)
reconstruct_image = latent2image(model=self.ldm_stable.vae, latents=reconstruct_latent)[0]
########## edit ##########
cross_replace_steps = {
'default_': cross_replace_steps,
}
controller = make_controller(pipeline=self.ldm_stable,
prompts=prompts,
is_replace_controller=is_replace_controller,
cross_replace_steps=cross_replace_steps,
self_replace_steps=self_replace_steps,
blend_words=blend_word,
equilizer_params=eq_params,
num_ddim_steps=self.num_ddim_steps,
device=self.device)
latents, _ = direct_inversion_p2p_guidance_forward(model=self.ldm_stable,
prompt=prompts,
controller=controller,
noise_loss_list=noise_loss_list,
latent=x_t,
num_inference_steps=self.num_ddim_steps,
guidance_scale=guidance_scale,
generator=None)
images = latent2image(model=self.ldm_stable.vae, latents=latents)
image_instruct = txt_draw(f"source prompt: {prompt_src}\ntarget prompt: {prompt_tar}")
return Image.fromarray(np.concatenate((image_instruct, image_gt, reconstruct_image,images[-1]),axis=1))
def edit_image_directinversion_not_full(
self,
image_path,
prompt_src,
prompt_tar,
guidance_scale=7.5,
cross_replace_steps=0.4,
self_replace_steps=0.6,
blend_word=None,
eq_params=None,
is_replace_controller=False,
scale=1.
):
image_gt = load_512(image_path)
prompts = [prompt_src, prompt_tar]
null_inversion = DirectInversion(model=self.ldm_stable,
num_ddim_steps=self.num_ddim_steps)
_, _, x_stars, noise_loss_list = null_inversion.invert_not_full(
image_gt=image_gt, prompt=prompts,guidance_scale=guidance_scale,scale=scale)
x_t = x_stars[-1]
controller = AttentionStore()
reconstruct_latent, x_t = direct_inversion_p2p_guidance_forward(model=self.ldm_stable,
prompt=prompts,
controller=controller,
noise_loss_list=noise_loss_list,
latent=x_t,
num_inference_steps=self.num_ddim_steps,
guidance_scale=guidance_scale,
generator=None)
reconstruct_image = latent2image(model=self.ldm_stable.vae, latents=reconstruct_latent)[0]
########## edit ##########
cross_replace_steps = {
'default_': cross_replace_steps,
}
controller = make_controller(pipeline=self.ldm_stable,
prompts=prompts,
is_replace_controller=is_replace_controller,
cross_replace_steps=cross_replace_steps,
self_replace_steps=self_replace_steps,
blend_words=blend_word,
equilizer_params=eq_params,
num_ddim_steps=self.num_ddim_steps,
device=self.device)
latents, _ = direct_inversion_p2p_guidance_forward(model=self.ldm_stable,
prompt=prompts,
controller=controller,
noise_loss_list=noise_loss_list,
latent=x_t,
num_inference_steps=self.num_ddim_steps,
guidance_scale=guidance_scale,
generator=None)
images = latent2image(model=self.ldm_stable.vae, latents=latents)
image_instruct = txt_draw(f"source prompt: {prompt_src}\ntarget prompt: {prompt_tar}")
return Image.fromarray(np.concatenate((image_instruct, image_gt, reconstruct_image,images[-1]),axis=1))
def edit_image_directinversion_skip_step(
self,
image_path,
prompt_src,
prompt_tar,
skip_step,
guidance_scale=7.5,
cross_replace_steps=0.4,
self_replace_steps=0.6,
blend_word=None,
eq_params=None,
is_replace_controller=False,
):
image_gt = load_512(image_path)
prompts = [prompt_src, prompt_tar]
null_inversion = DirectInversion(model=self.ldm_stable,
num_ddim_steps=self.num_ddim_steps)
_, _, x_stars, noise_loss_list = null_inversion.invert_skip_step(
image_gt=image_gt, prompt=prompts,guidance_scale=guidance_scale,skip_step=skip_step)
x_t = x_stars[-1]
controller = AttentionStore()
reconstruct_latent, x_t = direct_inversion_p2p_guidance_forward(model=self.ldm_stable,
prompt=prompts,
controller=controller,
noise_loss_list=noise_loss_list,
latent=x_t,
num_inference_steps=self.num_ddim_steps,
guidance_scale=guidance_scale,
generator=None)
reconstruct_image = latent2image(model=self.ldm_stable.vae, latents=reconstruct_latent)[0]
########## edit ##########
cross_replace_steps = {
'default_': cross_replace_steps,
}
controller = make_controller(pipeline=self.ldm_stable,
prompts=prompts,
is_replace_controller=is_replace_controller,
cross_replace_steps=cross_replace_steps,
self_replace_steps=self_replace_steps,
blend_words=blend_word,
equilizer_params=eq_params,
num_ddim_steps=self.num_ddim_steps,
device=self.device)
latents, _ = direct_inversion_p2p_guidance_forward(model=self.ldm_stable,
prompt=prompts,
controller=controller,
noise_loss_list=noise_loss_list,
latent=x_t,
num_inference_steps=self.num_ddim_steps,
guidance_scale=guidance_scale,
generator=None)
images = latent2image(model=self.ldm_stable.vae, latents=latents)
image_instruct = txt_draw(f"source prompt: {prompt_src}\ntarget prompt: {prompt_tar}")
return Image.fromarray(np.concatenate((image_instruct, image_gt, reconstruct_image,images[-1]),axis=1))
def edit_image_directinversion_add_target(
self,
image_path,
prompt_src,
prompt_tar,
guidance_scale=7.5,
cross_replace_steps=0.4,
self_replace_steps=0.6,
blend_word=None,
eq_params=None,
is_replace_controller=False,
):
image_gt = load_512(image_path)
prompts = [prompt_src, prompt_tar]
null_inversion = DirectInversion(model=self.ldm_stable,
num_ddim_steps=self.num_ddim_steps)
_, _, x_stars, noise_loss_list = null_inversion.invert(
image_gt=image_gt, prompt=prompts,guidance_scale=guidance_scale)
x_t = x_stars[-1]
controller = AttentionStore()
reconstruct_latent, x_t = direct_inversion_p2p_guidance_forward_add_target(model=self.ldm_stable,
prompt=prompts,
controller=controller,
noise_loss_list=noise_loss_list,
latent=x_t,
num_inference_steps=self.num_ddim_steps,
guidance_scale=guidance_scale,
generator=None)
reconstruct_image = latent2image(model=self.ldm_stable.vae, latents=reconstruct_latent)[0]
########## edit ##########
cross_replace_steps = {
'default_': cross_replace_steps,
}
controller = make_controller(pipeline=self.ldm_stable,
prompts=prompts,
is_replace_controller=is_replace_controller,
cross_replace_steps=cross_replace_steps,
self_replace_steps=self_replace_steps,
blend_words=blend_word,
equilizer_params=eq_params,
num_ddim_steps=self.num_ddim_steps,
device=self.device)
latents, _ = direct_inversion_p2p_guidance_forward_add_target(model=self.ldm_stable,
prompt=prompts,
controller=controller,
noise_loss_list=noise_loss_list,
latent=x_t,
num_inference_steps=self.num_ddim_steps,
guidance_scale=guidance_scale,
generator=None)
images = latent2image(model=self.ldm_stable.vae, latents=latents)
image_instruct = txt_draw(f"source prompt: {prompt_src}\ntarget prompt: {prompt_tar}")
return Image.fromarray(np.concatenate((image_instruct, image_gt, reconstruct_image,images[-1]),axis=1))
def edit_image_directinversion_add_source(
self,
image_path,
prompt_src,
prompt_tar,
guidance_scale=7.5,
cross_replace_steps=0.4,
self_replace_steps=0.6,
blend_word=None,
eq_params=None,
is_replace_controller=False,
):
image_gt = load_512(image_path)
prompts = [prompt_src, prompt_tar]
null_inversion = DirectInversion(model=self.ldm_stable,
num_ddim_steps=self.num_ddim_steps)
_, _, x_stars, noise_loss_list = null_inversion.invert(
image_gt=image_gt, prompt=prompts,guidance_scale=guidance_scale)
x_t = x_stars[-1]
noise_loss_list_new=[]
for i in range(len(noise_loss_list)):
noise_loss_list_new.append(noise_loss_list[i][[0]].repeat(2,1,1,1))
controller = AttentionStore()
reconstruct_latent, x_t = direct_inversion_p2p_guidance_forward_add_target(model=self.ldm_stable,
prompt=prompts,
controller=controller,
noise_loss_list=noise_loss_list_new,
latent=x_t,
num_inference_steps=self.num_ddim_steps,
guidance_scale=guidance_scale,
generator=None)
reconstruct_image = latent2image(model=self.ldm_stable.vae, latents=reconstruct_latent)[0]
########## edit ##########
cross_replace_steps = {
'default_': cross_replace_steps,
}
controller = make_controller(pipeline=self.ldm_stable,
prompts=prompts,
is_replace_controller=is_replace_controller,
cross_replace_steps=cross_replace_steps,
self_replace_steps=self_replace_steps,
blend_words=blend_word,
equilizer_params=eq_params,
num_ddim_steps=self.num_ddim_steps,
device=self.device)
latents, _ = direct_inversion_p2p_guidance_forward_add_target(model=self.ldm_stable,
prompt=prompts,
controller=controller,
noise_loss_list=noise_loss_list_new,
latent=x_t,
num_inference_steps=self.num_ddim_steps,
guidance_scale=guidance_scale,
generator=None)
images = latent2image(model=self.ldm_stable.vae, latents=latents)
image_instruct = txt_draw(f"source prompt: {prompt_src}\ntarget prompt: {prompt_tar}")
return Image.fromarray(np.concatenate((image_instruct, image_gt, reconstruct_image,images[-1]),axis=1))
================================================
FILE: models/pix2pix_zero/base_pipeline.py
================================================
import torch
import inspect
from packaging import version
from typing import Any, Callable, Dict, List, Optional, Union
from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNet2DConditionModel
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import deprecate, is_accelerate_available, logging, randn_tensor, replace_example_docstring
from diffusers import StableDiffusionPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
class BasePipeline(DiffusionPipeline):
_optional_components = ["safety_checker", "feature_extractor"]
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet2DConditionModel,
scheduler: KarrasDiffusionSchedulers,
safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPFeatureExtractor,
requires_safety_checker: bool = True,
):
super().__init__()
if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
"to update the config accordingly as leaving `steps_offset` might led to incorrect results"
" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
" file"
)
deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(scheduler.config)
new_config["steps_offset"] = 1
scheduler._internal_dict = FrozenDict(new_config)
if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
" `clip_sample` should be set to False in the configuration file. Please make sure to update the"
" config accordingly as not setting `clip_sample` in the config might lead to incorrect results in"
" future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very"
" nice if you could open a Pull request for the `scheduler/scheduler_config.json` file"
)
deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(scheduler.config)
new_config["clip_sample"] = False
scheduler._internal_dict = FrozenDict(new_config)
if safety_checker is None and requires_safety_checker:
logger.warning(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
)
if safety_checker is not None and feature_extractor is None:
raise ValueError(
"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
)
is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
version.parse(unet.config._diffusers_version).base_version
) < version.parse("0.9.0.dev0")
is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
deprecation_message = (
"The configuration file of the unet has set the default `sample_size` to smaller than"
" 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
" following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
" CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
" \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
" in the config might lead to incorrect results in future versions. If you have downloaded this"
" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
" the `unet/config.json` file"
)
deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(unet.config)
new_config["sample_size"] = 64
unet._internal_dict = FrozenDict(new_config)
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
self.register_to_config(requires_safety_checker=requires_safety_checker)
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def _execution_device(self):
r"""
Returns the device on which the pipeline's models will be executed. After calling
`pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module
hooks.
"""
if self.device != torch.device("meta") or not hasattr(self.unet, "_hf_hook"):
return self.device
for module in self.unet.modules():
if (
hasattr(module, "_hf_hook")
and hasattr(module._hf_hook, "execution_device")
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device)
return self.device
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt
def _encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt=None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
):
r"""
Encodes the prompt into text encoder hidden states.
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
device: (`torch.device`):
torch device
num_images_per_prompt (`int`):
number of images that should be generated per prompt
do_classifier_free_guidance (`bool`):
whether to use classifier free guidance or not
negative_ prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead.
Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
"""
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
if prompt_embeds is None:
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
text_input_ids, untruncated_ids
):
removed_text = self.tokenizer.batch_decode(
untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
)
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = text_inputs.attention_mask.to(device)
else:
attention_mask = None
prompt_embeds = self.text_encoder(
text_input_ids.to(device),
attention_mask=attention_mask,
)
prompt_embeds = prompt_embeds[0]
prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
bs_embed, seq_len, _ = prompt_embeds.shape
# duplicate text embeddings for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance and negative_prompt_embeds is None:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
max_length = prompt_embeds.shape[1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = uncond_input.attention_mask.to(device)
else:
attention_mask = None
negative_prompt_embeds = self.text_encoder(
uncond_input.input_ids.to(device),
attention_mask=attention_mask,
)
negative_prompt_embeds = negative_prompt_embeds[0]
if do_classifier_free_guidance:
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
return prompt_embeds
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
def decode_latents(self, latents):
latents = 1 / 0.18215 * latents
image = self.vae.decode(latents).sample
image = (image / 2 + 0.5).clamp(0, 1)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
image = image.detach().cpu().permute(0, 2, 3, 1).float().numpy()
return image
def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
)
if latents is None:
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
else:
latents = latents.to(device)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
def run_safety_checker(self, image, device, dtype):
if self.safety_checker is not None:
safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device)
image, has_nsfw_concept = self.safety_checker(
images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
)
else:
has_nsfw_concept = None
return image, has_nsfw_concept
================================================
FILE: models/pix2pix_zero/cross_attention.py
================================================
import torch
from diffusers.models.attention import CrossAttention
class MyCrossAttnProcessor:
def __call__(self, attn: CrossAttention, hidden_states, encoder_hidden_states=None, attention_mask=None):
batch_size, sequence_length, _ = hidden_states.shape
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length)
query = attn.to_q(hidden_states)
encoder_hidden_states = encoder_hidden_states if encoder_hidden_states is not None else hidden_states
key = attn.to_k(encoder_hidden_states)
value = attn.to_v(encoder_hidden_states)
query = attn.head_to_batch_dim(query)
key = attn.head_to_batch_dim(key)
value = attn.head_to_batch_dim(value)
attention_probs = attn.get_attention_scores(query, key, attention_mask)
# new bookkeeping to save the attn probs
attn.attn_probs = attention_probs
hidden_states = torch.bmm(attention_probs, value)
hidden_states = attn.batch_to_head_dim(hidden_states)
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
return hidden_states
"""
A function that prepares a U-Net model for training by enabling gradient computation
for a specified set of parameters and setting the forward pass to be performed by a
custom cross attention processor.
Parameters:
unet: A U-Net model.
Returns:
unet: The prepared U-Net model.
"""
def prep_unet(unet):
# set the gradients for XA maps to be true
for name, params in unet.named_parameters():
if 'attn2' in name:
params.requires_grad = True
else:
params.requires_grad = False
# replace the fwd function
for name, module in unet.named_modules():
module_name = type(module).__name__
if module_name == "CrossAttention":
module.set_processor(MyCrossAttnProcessor())
return unet
================================================
FILE: models/pix2pix_zero/ddim_inv.py
================================================
import sys
import numpy as np
import torch
import torch.nn.functional as F
from random import randrange
from typing import Any, Callable, Dict, List, Optional, Union, Tuple
from diffusers import DDIMScheduler
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
sys.path.insert(0, "src/utils")
from models.pix2pix_zero.base_pipeline import BasePipeline
from models.pix2pix_zero.cross_attention import prep_unet
if torch.cuda.is_available():
device = "cuda"
else:
device = "cpu"
class DDIMInversion(BasePipeline):
def auto_corr_loss(self, x, random_shift=True):
B,C,H,W = x.shape
assert B==1
x = x.squeeze(0)
# x must be shape [C,H,W] now
reg_loss = 0.0
for ch_idx in range(x.shape[0]):
noise = x[ch_idx][None, None,:,:]
while True:
if random_shift: roll_amount = randrange(noise.shape[2]//2)
else: roll_amount = 1
reg_loss += (noise*torch.roll(noise, shifts=roll_amount, dims=2)).mean()**2
reg_loss += (noise*torch.roll(noise, shifts=roll_amount, dims=3)).mean()**2
if noise.shape[2] <= 8:
break
noise = F.avg_pool2d(noise, kernel_size=2)
return reg_loss
def kl_divergence(self, x):
_mu = x.mean()
_var = x.var()
return _var + _mu**2 - 1 - torch.log(_var+1e-7)
def __call__(
self,
prompt: Union[str, List[str]] = None,
num_inversion_steps: int = 50,
guidance_scale: float = 7.5,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
output_type: Optional[str] = "pil",
return_dict: bool = True,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
img=None, # the input image as a PIL image
torch_dtype=torch.float32,
# inversion regularization parameters
lambda_ac: float = 20.0,
lambda_kl: float = 20.0,
num_reg_steps: int = 5,
num_ac_rolls: int = 5,
):
# 0. modify the unet to be useful :D
self.unet = prep_unet(self.unet)
# set the scheduler to be the Inverse DDIM scheduler
# self.scheduler = MyDDIMScheduler.from_config(self.scheduler.config)
device = self._execution_device
do_classifier_free_guidance = guidance_scale > 1.0
self.scheduler.set_timesteps(num_inversion_steps, device=device)
timesteps = self.scheduler.timesteps
# Encode the input image with the first stage model
x0 = np.array(img)/255
x0 = torch.from_numpy(x0).type(torch_dtype).permute(2, 0, 1).unsqueeze(dim=0).repeat(1, 1, 1, 1).to(device)
x0 = (x0 - 0.5) * 2.
with torch.no_grad():
x0_enc = self.vae.encode(x0).latent_dist.sample().to(device, torch_dtype)
latents = x0_enc = 0.18215 * x0_enc
# Decode and return the image
with torch.no_grad():
x0_dec = self.decode_latents(x0_enc.detach())
image_x0_dec = self.numpy_to_pil(x0_dec)
with torch.no_grad():
prompt_embeds = self._encode_prompt(prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt).to(device)
extra_step_kwargs = self.prepare_extra_step_kwargs(None, eta)
latent_list=[latents]
# Do the inversion
num_warmup_steps = len(timesteps) - num_inversion_steps * self.scheduler.order # should be 0?
with self.progress_bar(total=num_inversion_steps) as progress_bar:
# noted: we edit the code here to solve the problem of zero-shot can only do 48 steps of inversion
for i, t in enumerate(timesteps.flip(0)):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# predict the noise residual
with torch.no_grad():
noise_pred = self.unet(latent_model_input,t,encoder_hidden_states=prompt_embeds,cross_attention_kwargs=cross_attention_kwargs,).sample
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# regularization of the noise prediction
e_t = noise_pred
for _outer in range(num_reg_steps):
if lambda_ac>0:
for _inner in range(num_ac_rolls):
_var = torch.autograd.Variable(e_t.detach().clone(), requires_grad=True)
l_ac = self.auto_corr_loss(_var)
l_ac.backward()
_grad = _var.grad.detach()/num_ac_rolls
e_t = e_t - lambda_ac*_grad
if lambda_kl>0:
_var = torch.autograd.Variable(e_t.detach().clone(), requires_grad=True)
l_kld = self.kl_divergence(_var)
l_kld.backward()
_grad = _var.grad.detach()
e_t = e_t - lambda_kl*_grad
e_t = e_t.detach()
noise_pred = e_t
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, reverse=True, **extra_step_kwargs).prev_sample
latent_list.append(latents)
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.update()
# 8. Post-processing
image = self.decode_latents(latents.detach())
image = self.numpy_to_pil(image)
return latent_list, image, image_x0_dec
================================================
FILE: models/pix2pix_zero/edit_directions.py
================================================
import os
import torch
if torch.cuda.is_available():
device = "cuda"
else:
device = "cpu"
"""
This function takes in a task name and returns the direction in the embedding space that transforms class A to class B for the given task.
Parameters:
task_name (str): name of the task for which direction is to be constructed.
Returns:
torch.Tensor: A tensor representing the direction in the embedding space that transforms class A to class B.
Examples:
>>> construct_direction("cat2dog")
"""
def construct_direction(task_name):
(src, dst) = task_name.split("2")
emb_dir = f"assets/embeddings_sd_1.4"
embs_a = torch.load(os.path.join(emb_dir, f"{src}.pt"), map_location=device)
embs_b = torch.load(os.path.join(emb_dir, f"{dst}.pt"), map_location=device)
return (embs_b.mean(0)-embs_a.mean(0)).unsqueeze(0)
================================================
FILE: models/pix2pix_zero/edit_pipeline.py
================================================
import pdb, sys
import numpy as np
import torch
from typing import Any, Callable, Dict, List, Optional, Union
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
sys.path.insert(0, "src/utils")
from models.pix2pix_zero.base_pipeline import BasePipeline
from models.pix2pix_zero.cross_attention import prep_unet
if torch.cuda.is_available():
device = "cuda"
else:
device = "cpu"
class EditingPipeline(BasePipeline):
def __call__(
self,
prompt: Union[str, List[str]] = None,
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
# pix2pix parameters
guidance_amount=0.1,
edit_dir=None,
x_in=None,
only_sample=False, # only perform sampling, and no editing
latent_list=None
):
x_in.to(dtype=self.unet.dtype, device=self._execution_device)
# 0. modify the unet to be useful :D
self.unet = prep_unet(self.unet)
# 1. setup all caching objects
d_ref_t2attn = {} # reference cross attention maps
# 2. Default height and width to unet
height = height or self.unet.config.sample_size * self.vae_scale_factor
width = width or self.unet.config.sample_size * self.vae_scale_factor
# 2. Define call parameters
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
device = self._execution_device
do_classifier_free_guidance = guidance_scale > 1.0
x_in = x_in.to(dtype=self.unet.dtype, device=self._execution_device)
# 3. Encode input prompt = 2x77x1024
prompt_embeds = self._encode_prompt( prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds,)
# 4. Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self.scheduler.timesteps
# 5. Prepare latent variables
num_channels_latents = self.unet.in_channels
# randomly sample a latent code if not provided
latents = self.prepare_latents(batch_size * num_images_per_prompt, num_channels_latents, height, width, prompt_embeds.dtype, device, generator, x_in,)
latents_init = latents.clone()
# 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
# 7. First Denoising loop for getting the reference cross attention maps
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
noise_loss_list=[]
with torch.no_grad():
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# predict the noise residual
noise_pred = self.unet(latent_model_input,t,encoder_hidden_states=prompt_embeds,cross_attention_kwargs=cross_attention_kwargs,).sample
# add the cross attention map to the dictionary
d_ref_t2attn[t.item()] = {}
for name, module in self.unet.named_modules():
module_name = type(module).__name__
if module_name == "CrossAttention" and 'attn2' in name:
attn_mask = module.attn_probs # size is num_channel,s*s,77
d_ref_t2attn[t.item()][name] = attn_mask.detach().cpu()
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
if latent_list is not None:
noise_loss_list.append(latent_list[-2-i]-latents)
latents=latents+noise_loss_list[-1]
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.update()
# make the reference image (reconstruction)
image_rec = self.numpy_to_pil(self.decode_latents(latents.detach()))
if only_sample:
return image_rec
prompt_embeds_edit = prompt_embeds.clone()
#add the edit only to the second prompt, idx 0 is the negative prompt
prompt_embeds_edit[1:2] += edit_dir
latents = latents_init
# Second denoising loop for editing the text prompt
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
x_in = latent_model_input.detach().clone()
x_in.requires_grad = True
opt = torch.optim.SGD([x_in], lr=guidance_amount)
# predict the noise residual
noise_pred = self.unet(x_in,t,encoder_hidden_states=prompt_embeds_edit.detach(),cross_attention_kwargs=cross_attention_kwargs,).sample
loss = 0.0
for name, module in self.unet.named_modules():
module_name = type(module).__name__
if module_name == "CrossAttention" and 'attn2' in name:
curr = module.attn_probs # size is num_channel,s*s,77
ref = d_ref_t2attn[t.item()][name].detach().to(device)
loss += ((curr-ref)**2).sum((1,2)).mean(0)
loss.backward(retain_graph=False)
opt.step()
# recompute the noise
with torch.no_grad():
noise_pred = self.unet(x_in.detach(),t,encoder_hidden_states=prompt_embeds_edit,cross_attention_kwargs=cross_attention_kwargs,).sample
latents = x_in.detach().chunk(2)[0]
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
if latent_list is not None:
latents=latents+noise_loss_list[i]
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.update()
# 8. Post-processing
image = self.decode_latents(latents.detach())
# 9. Run safety checker
# image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
# 10. Convert to PIL
image_edit = self.numpy_to_pil(image)
return image_rec, image_edit
================================================
FILE: models/pix2pix_zero/scheduler.py
================================================
# Copyright 2022 Stanford University Team and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import os, sys, pdb
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.utils import BaseOutput, randn_tensor
from diffusers.schedulers.scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class DDIMSchedulerOutput(BaseOutput):
"""
Output class for the scheduler's step function output.
Args:
prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the
denoising loop.
pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
The predicted denoised sample (x_{0}) based on the model output from the current timestep.
`pred_original_sample` can be used to preview progress or for guidance.
"""
prev_sample: torch.FloatTensor
pred_original_sample: Optional[torch.FloatTensor] = None
def betas_for_alpha_bar(num_diffusion_timesteps, max_beta=0.999) -> torch.Tensor:
"""
Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
(1-beta) over time from t = [0,1].
Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up
to that part of the diffusion process.
Args:
num_diffusion_timesteps (`int`): the number of betas to produce.
max_beta (`float`): the maximum beta to use; use values lower than 1 to
prevent singularities.
Returns:
betas (`np.ndarray`): the betas used by the scheduler to step the model outputs
"""
def alpha_bar(time_step):
return math.cos((time_step + 0.008) / 1.008 * math.pi / 2) ** 2
betas = []
for i in range(num_diffusion_timesteps):
t1 = i / num_diffusion_timesteps
t2 = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
return torch.tensor(betas)
class DDIMInverseScheduler(SchedulerMixin, ConfigMixin):
"""
Denoising diffusion implicit models is a scheduler that extends the denoising procedure introduced in denoising
diffusion probabilistic models (DDPMs) with non-Markovian guidance.
[`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__`
function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`.
[`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and
[`~SchedulerMixin.from_pretrained`] functions.
For more details, see the original paper: https://arxiv.org/abs/2010.02502
Args:
num_train_timesteps (`int`): number of diffusion steps used to train the model.
beta_start (`float`): the starting `beta` value of inference.
beta_end (`float`): the final `beta` value.
beta_schedule (`str`):
the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from
`linear`, `scaled_linear`, or `squaredcos_cap_v2`.
trained_betas (`np.ndarray`, optional):
option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc.
clip_sample (`bool`, default `True`):
option to clip predicted sample between -1 and 1 for numerical stability.
set_alpha_to_one (`bool`, default `True`):
each diffusion step uses the value of alphas product at that step and at the previous one. For the final
step there is no previous alpha. When this option is `True` the previous alpha product is fixed to `1`,
otherwise it uses the value of alpha at step 0.
steps_offset (`int`, default `0`):
an offset added to the inference steps. You can use a combination of `offset=1` and
`set_alpha_to_one=False`, to make the last step use step 0 for the previous alpha product, as done in
stable diffusion.
prediction_type (`str`, default `epsilon`, optional):
prediction type of the scheduler function, one of `epsilon` (predicting the noise of the diffusion
process), `sample` (directly predicting the noisy sample`) or `v_prediction` (see section 2.4
https://imagen.research.google/video/paper.pdf)
"""
_compatibles = [e.name for e in KarrasDiffusionSchedulers]
order = 1
@register_to_config
def __init__(
self,
num_train_timesteps: int = 1000,
beta_start: float = 0.0001,
beta_end: float = 0.02,
beta_schedule: str = "linear",
trained_betas: Optional[Union[np.ndarray, List[float]]] = None,
clip_sample: bool = True,
set_alpha_to_one: bool = True,
steps_offset: int = 0,
prediction_type: str = "epsilon",
):
if trained_betas is not None:
self.betas = torch.tensor(trained_betas, dtype=torch.float32)
elif beta_schedule == "linear":
self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)
elif beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
self.betas = (
torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2
)
elif beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
self.betas = betas_for_alpha_bar(num_train_timesteps)
else:
raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}")
self.alphas = 1.0 - self.betas
self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)
# At every step in ddim, we are looking into the previous alphas_cumprod
# For the final step, there is no previous alphas_cumprod because we are already at 0
# `set_alpha_to_one` decides whether we set this parameter simply to one or
# whether we use the final alpha of the "non-previous" one.
self.final_alpha_cumprod = torch.tensor(1.0) if set_alpha_to_one else self.alphas_cumprod[0]
# standard deviation of the initial noise distribution
self.init_noise_sigma = 1.0
# setable values
self.num_inference_steps = None
self.timesteps = torch.from_numpy(np.arange(0, num_train_timesteps)[::-1].copy().astype(np.int64))
def scale_model_input(self, sample: torch.FloatTensor, timestep: Optional[int] = None) -> torch.FloatTensor:
"""
Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
current timestep.
Args:
sample (`torch.FloatTensor`): input sample
timestep (`int`, optional): current timestep
Returns:
`torch.FloatTensor`: scaled input sample
"""
return sample
def _get_variance(self, timestep, prev_timestep):
alpha_prod_t = self.alphas_cumprod[timestep]
alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
beta_prod_t = 1 - alpha_prod_t
beta_prod_t_prev = 1 - alpha_prod_t_prev
variance = (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)
return variance
def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None):
"""
Sets the discrete timesteps used for the diffusion chain. Supporting function to be run before inference.
Args:
num_inference_steps (`int`):
the number of diffusion steps used when generating samples with a pre-trained model.
"""
if num_inference_steps > self.config.num_train_timesteps:
raise ValueError(
f"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"
f" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"
f" maximal {self.config.num_train_timesteps} timesteps."
)
self.num_inference_steps = num_inference_steps
step_ratio = self.config.num_train_timesteps // self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
timesteps = (np.arange(0, num_inference_steps) * step_ratio).round()[::-1].copy().astype(np.int64)
self.timesteps = torch.from_numpy(timesteps).to(device)
self.timesteps += self.config.steps_offset
def step(
self,
model_output: torch.FloatTensor,
timestep: int,
sample: torch.FloatTensor,
eta: float = 0.0,
use_clipped_model_output: bool = False,
generator=None,
variance_noise: Optional[torch.FloatTensor] = None,
return_dict: bool = True,
reverse=False
) -> Union[DDIMSchedulerOutput, Tuple]:
e_t = model_output
x = sample
prev_timestep = timestep + self.config.num_train_timesteps // self.num_inference_steps
# print(timestep, prev_timestep)
a_t = alpha_prod_t = self.alphas_cumprod[timestep-1]
# a_prev = alpha_t_prev = self.alphas_cumprod[prev_timestep-1] if prev_timestep >= 0 else self.final_alpha_cumprod
# noted: we edit the code here to solve the problem of zero-shot can only do 48 steps of inversion
if prev_timestep <= self.config.num_train_timesteps:
a_prev = alpha_t_prev = self.alphas_cumprod[prev_timestep-1]
else:
a_prev = alpha_t_prev = self.alphas_cumprod[-1]
beta_prod_t = 1 - alpha_prod_t
pred_x0 = (x - (1-a_t)**0.5 * e_t) / a_t.sqrt()
# direction pointing to x_t
dir_xt = (1. - a_prev).sqrt() * e_t
x = a_prev.sqrt()*pred_x0 + dir_xt
if not return_dict:
return (x,)
return DDIMSchedulerOutput(prev_sample=x, pred_original_sample=pred_x0)
def add_noise(
self,
original_samples: torch.FloatTensor,
noise: torch.FloatTensor,
timesteps: torch.IntTensor,
) -> torch.FloatTensor:
# Make sure alphas_cumprod and timestep have same device and dtype as original_samples
self.alphas_cumprod = self.alphas_cumprod.to(device=original_samples.device, dtype=original_samples.dtype)
timesteps = timesteps.to(original_samples.device)
sqrt_alpha_prod = self.alphas_cumprod[timesteps] ** 0.5
sqrt_alpha_prod = sqrt_alpha_prod.flatten()
while len(sqrt_alpha_prod.shape) < len(original_samples.shape):
sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)
sqrt_one_minus_alpha_prod = (1 - self.alphas_cumprod[timesteps]) ** 0.5
sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()
while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape):
sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)
noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
return noisy_samples
def get_velocity(
self, sample: torch.FloatTensor, noise: torch.FloatTensor, timesteps: torch.IntTensor
) -> torch.FloatTensor:
# Make sure alphas_cumprod and timestep have same device and dtype as sample
self.alphas_cumprod = self.alphas_cumprod.to(device=sample.device, dtype=sample.dtype)
timesteps = timesteps.to(sample.device)
sqrt_alpha_prod = self.alphas_cumprod[timesteps] ** 0.5
sqrt_alpha_prod = sqrt_alpha_prod.flatten()
while len(sqrt_alpha_prod.shape) < len(sample.shape):
sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)
sqrt_one_minus_alpha_prod = (1 - self.alphas_cumprod[timesteps]) ** 0.5
sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()
while len(sqrt_one_minus_alpha_prod.shape) < len(sample.shape):
sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)
velocity = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
return velocity
def __len__(self):
return self.config.num_train_timesteps
================================================
FILE: models/stylediffusion/clip_util.py
================================================
import torch
import torch.nn as nn
from clip.model import Transformer, LayerNorm
import os
import certifi
import urllib
os.environ["REQUESTS_CA_BUNDLE"] = certifi.where()
os.environ["SSL_CERT_FILE"] = certifi.where()
result = urllib.request.urlopen('https://www.example.com')
class VisionTransformer(nn.Module):
def __init__(self, input_resolution: int, patch_size: int, width: int, layers: int, heads: int, output_dim: int):
super().__init__()
self.input_resolution = input_resolution
self.output_dim = output_dim
self.conv1 = nn.Conv2d(in_channels=3, out_channels=width, kernel_size=patch_size, stride=patch_size, bias=False)
scale = width ** -0.5
self.class_embedding = nn.Parameter(scale * torch.randn(width))
self.positional_embedding = nn.Parameter(scale * torch.randn((input_resolution // patch_size) ** 2 + 1, width))
self.ln_pre = LayerNorm(width)
self.transformer = Transformer(width, layers, heads)
self.ln_post = LayerNorm(width)
self.proj = nn.Parameter(scale * torch.randn(width, output_dim))
def forward(self, x: torch.Tensor):
x = self.conv1(x) # shape = [*, width, grid, grid]
x = x.reshape(x.shape[0], x.shape[1], -1) # shape = [*, width, grid ** 2]
x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width]
x = torch.cat([self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), x], dim=1) # shape = [*, grid ** 2 + 1, width]
x = x + self.positional_embedding.to(x.dtype)
x = self.ln_pre(x)
x = x.permute(1, 0, 2) # NLD -> LND
x = self.transformer(x)
x = x.permute(1, 0, 2) # LND -> NLD
# x = self.ln_post(x[:, 0, :])
#
# if self.proj is not None:
# x = x @ self.proj
x = self.ln_post(x)
return x
================================================
FILE: models/stylediffusion/global_var.py
================================================
# -*- coding: utf-8 -*-
def _init():
global _global_dict
_global_dict = {}
def set_value(key, value):
_global_dict[key] = value
def get_value(key):
try:
return _global_dict[key]
except:
return None
================================================
FILE: models/stylediffusion/inversion.py
================================================
from typing import Optional, Union, Tuple, List, Callable, Dict
import torch
import numpy as np
from models.stylediffusion import global_var
from models.stylediffusion.utils import register_attention_control, image_grid,load_512,Trainer
from diffusers import DDIMScheduler
from PIL import Image
from tqdm import tqdm
from torch.optim.adam import Adam
import torch.nn.functional as nnf
from models.stylediffusion import ptp_utils_v
device=global_var.get_value("device")
NUM_DDIM_STEPS=global_var.get_value("NUM_DDIM_STEPS")
USE_INITIAL_INV=global_var.get_value("USE_INITIAL_INV")
class VaeInversion:
def prev_step(self, model_output: Union[torch.FloatTensor, np.ndarray], timestep: int,
sample: Union[torch.FloatTensor, np.ndarray]):
prev_timestep = timestep - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
alpha_prod_t = self.scheduler.alphas_cumprod[timestep]
alpha_prod_t_prev = self.scheduler.alphas_cumprod[
prev_timestep] if prev_timestep >= 0 else self.scheduler.final_alpha_cumprod
beta_prod_t = 1 - alpha_prod_t
pred_original_sample = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
pred_sample_direction = (1 - alpha_prod_t_prev) ** 0.5 * model_output
prev_sample = alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction
return prev_sample
def next_step(self, model_output: Union[torch.FloatTensor, np.ndarray], timestep: int,
sample: Union[torch.FloatTensor, np.ndarray]):
timestep, next_timestep = min(
timestep - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps, 999), timestep
alpha_prod_t = self.scheduler.alphas_cumprod[timestep] if timestep >= 0 else self.scheduler.final_alpha_cumprod
alpha_prod_t_next = self.scheduler.alphas_cumprod[next_timestep]
beta_prod_t = 1 - alpha_prod_t
next_original_sample = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
next_sample_direction = (1 - alpha_prod_t_next) ** 0.5 * model_output
next_sample = alpha_prod_t_next ** 0.5 * next_original_sample + next_sample_direction
return next_sample
def get_noise_pred_single(self, latents, t, context):
noise_pred = self.model.unet(latents, t, encoder_hidden_states=context)["sample"]
return noise_pred
def get_noise_pred(self, latents, t, is_forward=True, context=None, trainer=None):
if context is None:
context = self.context
uncond_embeddings, cond_embeddings = context
GUIDANCE_SCALE=global_var.get_value("GUIDANCE_SCALE")
guidance_scale = 1 if is_forward else GUIDANCE_SCALE
trainer.uncond = True
noise_pred_uncond = self.model.unet(latents, t, encoder_hidden_states=uncond_embeddings)["sample"]
trainer.uncond = False
noise_prediction_text = self.model.unet(latents, t, encoder_hidden_states=cond_embeddings)["sample"]
noise_pred = noise_pred_uncond + guidance_scale * (noise_prediction_text - noise_pred_uncond)
if is_forward:
latents = self.next_step(noise_pred, t, latents)
else:
latents = self.prev_step(noise_pred, t, latents)
return latents
@torch.no_grad()
def latent2image(self, latents, return_type='np'):
latents = 1 / 0.18215 * latents.detach()
image = self.model.vae.decode(latents)['sample']
if return_type == 'np':
image = (image / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).numpy()
image = (image * 255).astype(np.uint8)
return image
@torch.no_grad()
def image2latent(self, image):
with torch.no_grad():
if type(image) is Image:
image = np.array(image)
if type(image) is torch.Tensor and image.dim() == 4:
latents = image
else:
image = torch.from_numpy(image).float() / 127.5 - 1
image = image.permute(0, 3, 1, 2).to(device)
latents = self.model.vae.encode(image)['latent_dist'].mean
latents = latents * 0.18215
return latents
@torch.no_grad()
def init_prompt(self, prompt: List[str]):
uncond_input = self.model.tokenizer(
[""] * len(prompt), padding="max_length", max_length=self.model.tokenizer.model_max_length,
return_tensors="pt"
)
uncond_embeddings = self.model.text_encoder(uncond_input.input_ids.to(self.model.device))[0]
text_input = self.model.tokenizer(
prompt,
padding="max_length",
max_length=self.model.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_embeddings = self.model.text_encoder(text_input.input_ids.to(self.model.device))[0]
self.context = torch.cat([uncond_embeddings, text_embeddings])
self.prompt = prompt
@torch.no_grad()
def ddim_loop(self, latent, trainer=None):
# store cross-attn during the ddim inversion
if trainer:
register_attention_control(self.model, trainer, None)
uncond_embeddings, cond_embeddings = self.context.chunk(2)
all_latent = [latent]
latent = latent.clone().detach()
for i in range(NUM_DDIM_STEPS):
if trainer:
trainer.cur_att_layer = 32 # w=1, skip uncond attn layer
trainer.attention_store = {}
t = self.model.scheduler.timesteps[len(self.model.scheduler.timesteps) - i - 1]
noise_pred = self.get_noise_pred_single(latent, t, cond_embeddings)
latent = self.next_step(noise_pred, t, latent)
all_latent.append(latent)
if trainer:
attn_store = {}
for key, value in trainer.attention_store.items():
if 'down_cross' in key or 'up_cross' in key:
attn_store[key] = [v for v in value if v.shape[1]==16**2]
self.ddim_inv_attn += [attn_store] # A*(0), A*(1), ... A*(T-1)
# trainer.attention_store = sum(self.ddim_inv_attn)
if trainer:
trainer.attention_store = {}
for ddim_inv_attn in self.ddim_inv_attn:
if len(trainer.attention_store) == 0:
trainer.attention_store = ddim_inv_attn
else:
for key in trainer.attention_store:
for i in range(len(trainer.attention_store[key])):
trainer.attention_store[key][i] += ddim_inv_attn[key][i]
# A*(T) = A*(T-1)
self.ddim_inv_attn += [self.ddim_inv_attn[-1]]
return all_latent
@property
def scheduler(self):
return self.model.scheduler
@torch.no_grad()
def ddim_inversion(self, image, trainer=None):
latent = self.image2latent(image)
image_rec = self.latent2image(latent)
ddim_latents = self.ddim_loop(latent, trainer)
return image_rec, ddim_latents
def optimization(self, trainer, latents, image, num_inner_steps, num_epoch, epsilon):
# torch.cuda.empty_cache()
cross_attn_keys = self.ddim_inv_attn[0].keys()
register_attention_control(self.model, trainer, None)
image = torch.from_numpy(image).float() / 127.5 - 1
image = image.permute(0, 3, 1, 2).to(device)
trainer.image = image
uncond_embeddings, cond_embeddings = self.context.chunk(2)
x = np.linspace(0, NUM_DDIM_STEPS - 1, NUM_DDIM_STEPS)
NUM_INNER_STEPS = np.ceil(num_inner_steps * np.exp(-.1 * x))
bar = tqdm(total=int(np.sum(NUM_INNER_STEPS)), colour='red', ncols=100)
for epoch in range(num_epoch):
latent_cur = latents[-1]
for i in range(NUM_DDIM_STEPS):
num_inner_steps = int(NUM_INNER_STEPS[i])
trainer.i = i
if epoch == 0 and i > 0:
trainer.copy_params_and_buffers(trainer.embedding[i-1], trainer.embedding[i])
embedding_i = trainer.embedding[i]
optimizer = Adam(embedding_i.parameters(), lr=1e-2 * (1. - i / 100.))
embedding_i.requires_grad_(True)
latent_prev = latents[len(latents) - i - 2]
t = self.model.scheduler.timesteps[i]
with torch.no_grad():
trainer.uncond = True
noise_pred_uncond = self.get_noise_pred_single(latent_cur, t, uncond_embeddings)
for j in range(num_inner_steps):
trainer.uncond = False
trainer.cur_att_layer = trainer.num_uncond_att_layers
trainer.attention_store = {}
# latent loss
noise_pred_cond = self.get_noise_pred_single(latent_cur, t, cond_embeddings)
GUIDANCE_SCALE=global_var.get_value("GUIDANCE_SCALE")
noise_pred = noise_pred_uncond + GUIDANCE_SCALE * (noise_pred_cond - noise_pred_uncond)
latents_prev_rec = self.prev_step(noise_pred, t, latent_cur)
latent_loss = nnf.mse_loss(latents_prev_rec, latent_prev)
# cross-attn loss
for attn_key in list(trainer.attention_store.keys()):
if attn_key in cross_attn_keys:
trainer.attention_store[attn_key] = [attn for attn in trainer.attention_store[attn_key] if attn.shape[1]==16**2]
else:
del trainer.attention_store[attn_key]
attn_loss = torch.tensor(.0).to(device)
for key in cross_attn_keys:
if 'cross' in key:
for attn_gt, attn in zip(self.ddim_inv_attn[NUM_DDIM_STEPS - i][key], trainer.attention_store[key]):
attn_loss += nnf.mse_loss(attn_gt, attn)
# loss
loss = (latent_loss + attn_loss)
optimizer.zero_grad()
loss.backward()
optimizer.step()
loss_item = loss.item()
bar.desc = f"Epoch[{epoch+1}/{num_epoch}, t={i}, iter={num_inner_steps}]"
bar.set_postfix(loss=loss_item)
bar.update()
if loss_item < epsilon + i * 2e-5:
break
for j in range(j + 1, num_inner_steps):
bar.update()
with torch.no_grad():
trainer.attention_store = {}
context = (uncond_embeddings, cond_embeddings)
latent_cur = self.get_noise_pred(latent_cur, t, False, context, trainer)
embedding_i.requires_grad_(False)
with torch.no_grad():
image_inv = ptp_utils_v.latent2image(self.model.vae, latent_cur).squeeze()
if len(image_inv.shape) == 3:
image_inv = image_inv[np.newaxis, :]
image_inv = image_grid(image_inv, grid_size=(1, image_inv.shape[0]))
# Image.fromarray(image_inv).save(f'ptp-epoch{epoch}-{args.idx}.png')
bar.close()
return trainer
def invert(self, image_path: List[str], prompt: List[str], offsets=(0, 0, 0, 0), verbose=False, num_inner_steps=10, num_epoch=1, early_stop_epsilon=1e-5):
self.init_prompt(prompt)
image_gt = [load_512(path, *offsets) for path in image_path]
image_gt = np.array(image_gt)
# clip encoder and mapping-network
trainer = Trainer()
trainer.ddim_inv = True
if verbose:
print("DDIM inversion...")
image_rec, ddim_latents = self.ddim_inversion(image_gt, trainer)
trainer.ddim_inv = False
if trainer.attention_store:
# show_cross_attention(trainer, prompt, res=16, from_where=["up", "down"])
# show_hot_cross_attention(trainer, prompt, res=16, from_where=["up", "down"])
pass
trainer.attention_store = {}
if verbose:
print("StyleDiffusion optimization...")
trainer = self.optimization(trainer, ddim_latents, image_gt, num_inner_steps, num_epoch, early_stop_epsilon)
return (image_gt, image_rec), ddim_latents[-1], trainer
def eval_init(self, image_path: List[str], prompt_gt: List[str], offsets=(0, 0, 0, 0), verbose=True, trainer=None):
self.init_prompt(prompt_gt)
image_gt = [load_512(path, *offsets) for path in image_path]
image_gt = np.array(image_gt)
if verbose:
print("DDIM inversion...")
image_rec, ddim_latents = self.ddim_inversion(image_gt)
image = torch.from_numpy(image_gt).float() / 127.5 - 1
image = image.permute(0, 3, 1, 2).to(device)
trainer.image = image \
if not USE_INITIAL_INV else image.expand(2, *image.shape[1:])
return (image_gt, image_rec), ddim_latents[-1]
def __init__(self, model):
scheduler = DDIMScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", clip_sample=False,
set_alpha_to_one=False)
self.model = model
self.tokenizer = self.model.tokenizer
self.model.scheduler.set_timesteps(NUM_DDIM_STEPS)
self.prompt = None
self.context = None
self.ddim_inv_attn = []
================================================
FILE: models/stylediffusion/ptp_utils_v.py
================================================
# Copyright 2022 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import torch
from PIL import Image, ImageDraw, ImageFont
import cv2
from typing import Optional, Union, Tuple, List, Callable, Dict
from IPython.display import display
from tqdm.notebook import tqdm
from PIL import Image
def image_grid(imgs, rows, cols):
assert len(imgs) == rows * cols
w, h = imgs[0].size
grid = Image.new('RGB', size=(cols * w, rows * h))
grid_w, grid_h = grid.size
for i, img in enumerate(imgs):
grid.paste(img, box=(i % cols * w, i // cols * h))
return grid
def text_under_image(image: np.ndarray, text: str, text_color: Tuple[int, int, int] = (0, 0, 0)):
h, w, c = image.shape
offset = int(h * .2)
img = np.ones((h + offset, w, c), dtype=np.uint8) * 255
font = cv2.FONT_HERSHEY_SIMPLEX
# font = ImageFont.truetype("/usr/share/fonts/truetype/noto/NotoMono-Regular.ttf", font_size)
img[:h] = image
textsize = cv2.getTextSize(text, font, 1, 2)[0]
text_x, text_y = (w - textsize[0]) // 2, h + offset - textsize[1] // 2
cv2.putText(img, text, (text_x, text_y ), font, 1, text_color, 2)
return img
def view_images(images, num_rows=1, offset_ratio=0.02, save_name='ptp'):
if type(images) is list:
num_empty = len(images) % num_rows
elif images.ndim == 4:
num_empty = images.shape[0] % num_rows
else:
images = [images]
num_empty = 0
empty_images = np.ones(images[0].shape, dtype=np.uint8) * 255
images = [image.astype(np.uint8) for image in images] + [empty_images] * num_empty
num_items = len(images)
h, w, c = images[0].shape
offset = int(h * offset_ratio)
num_cols = num_items // num_rows
image_ = np.ones((h * num_rows + offset * (num_rows - 1),
w * num_cols + offset * (num_cols - 1), 3), dtype=np.uint8) * 255
for i in range(num_rows):
for j in range(num_cols):
image_[i * (h + offset): i * (h + offset) + h:, j * (w + offset): j * (w + offset) + w] = images[
i * num_cols + j]
Image.fromarray(image_).save(f'{save_name}.png')
def save_image_grid(img, fname, grid_size):
gw, gh = grid_size
_N, C, H, W = img.shape
assert gw * gh == _N
img = img.reshape(gh, gw, C, H, W)
img = img.transpose(0, 3, 1, 4, 2)
img = img.reshape(gh * H, gw * W, C)
assert C in [1, 3]
if C == 1:
Image.fromarray(img[:, :, 0], 'L').save(fname)
if C == 3:
Image.fromarray(img, 'RGB').save(fname)
def diffusion_step(model, controller, latents, context, t, guidance_scale, low_resource=False):
if low_resource:
controller.uncond = True
noise_pred_uncond = model.unet(latents, t, encoder_hidden_states=context[0])["sample"]
controller.uncond = False
noise_prediction_text = model.unet(latents, t, encoder_hidden_states=context[1])["sample"]
else:
latents_input = torch.cat([latents] * 2)
noise_pred = model.unet(latents_input, t, encoder_hidden_states=context)["sample"]
noise_pred_uncond, noise_prediction_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_prediction_text - noise_pred_uncond)
latents = model.scheduler.step(noise_pred, t, latents)["prev_sample"]
latents[:2] = controller.step_callback(latents[:2])
return latents
def latent2image(vae, latents):
latents = 1 / 0.18215 * latents
image = vae.decode(latents)['sample']
image = (image / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).numpy()
image = (image * 255).astype(np.uint8)
return image
def init_latent(latent, model, height, width, generator, batch_size):
if latent is None:
latent = torch.randn(
(1, model.unet.in_channels, height // 8, width // 8),
generator=generator,
)
latents = latent.expand(batch_size, model.unet.in_channels, height // 8, width // 8).to(model.device)
return latent, latents.clone()
@torch.no_grad()
def text2image_ldm(
model,
prompt: List[str],
controller,
num_inference_steps: int = 50,
guidance_scale: Optional[float] = 7.,
generator: Optional[torch.Generator] = None,
latent: Optional[torch.FloatTensor] = None,
):
register_attention_control(model, controller)
height = width = 256
batch_size = len(prompt)
uncond_input = model.tokenizer([""] * batch_size, padding="max_length", max_length=77, return_tensors="pt")
uncond_embeddings = model.bert(uncond_input.input_ids.to(model.device))[0]
text_input = model.tokenizer(prompt, padding="max_length", max_length=77, return_tensors="pt")
text_embeddings = model.bert(text_input.input_ids.to(model.device))[0]
latent, latents = init_latent(latent, model, height, width, generator, batch_size)
context = torch.cat([uncond_embeddings, text_embeddings])
model.scheduler.set_timesteps(num_inference_steps)
for t in tqdm(model.scheduler.timesteps):
latents = diffusion_step(model, controller, latents, context, t, guidance_scale)
image = latent2image(model.vqvae, latents)
return image, latent
@torch.no_grad()
def text2image_ldm_stable(
model,
prompt: List[str],
controller,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
generator: Optional[torch.Generator] = None,
latent: Optional[torch.FloatTensor] = None,
low_resource: bool = False,
):
register_attention_control(model, controller)
height = width = 512
batch_size = len(prompt)
text_input = model.tokenizer(
prompt,
padding="max_length",
max_length=model.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_embeddings = model.text_encoder(text_input.input_ids.to(model.device))[0]
max_length = text_input.input_ids.shape[-1]
uncond_input = model.tokenizer(
[""] * batch_size, padding="max_length", max_length=max_length, return_tensors="pt"
)
uncond_embeddings = model.text_encoder(uncond_input.input_ids.to(model.device))[0]
context = [uncond_embeddings, text_embeddings]
if not low_resource:
context = torch.cat(context)
latent, latents = init_latent(latent, model, height, width, generator, batch_size)
# set timesteps
extra_set_kwargs = {"offset": 1}
# model.scheduler.set_timesteps(num_inference_steps, **extra_set_kwargs)
model.scheduler.set_timesteps(num_inference_steps)
for t in tqdm(model.scheduler.timesteps):
latents = diffusion_step(model, controller, latents, context, t, guidance_scale, low_resource)
image = latent2image(model.vae, latents)
return image, latent
def register_attention_control(model, controller):
def ca_forward(self, place_in_unet):
to_out = self.to_out
if type(to_out) is torch.nn.modules.container.ModuleList:
to_out = self.to_out[0] # todo: ?
else:
to_out = self.to_out
def forward(x, context=None, mask=None):
batch_size, sequence_length, dim = x.shape
h = self.heads
q = self.to_q(x)
is_cross = context is not None
context = context if is_cross else x
k = self.to_k(context)
v = self.to_v(context)
q = self.reshape_heads_to_batch_dim(q)
k = self.reshape_heads_to_batch_dim(k)
v = self.reshape_heads_to_batch_dim(v)
sim = torch.einsum("b i d, b j d -> b i j", q, k) * self.scale
if mask is not None:
mask = mask.reshape(batch_size, -1)
max_neg_value = -torch.finfo(sim.dtype).max
mask = mask[:, None, :].repeat(h, 1, 1)
sim.masked_fill_(~mask, max_neg_value)
# attention, what we cannot get enough of
attn = sim.softmax(dim=-1)
attn = controller(attn, is_cross, place_in_unet)
out = torch.einsum("b i j, b j d -> b i d", attn, v)
out = self.reshape_batch_dim_to_heads(out)
return to_out(out)
return forward
class DummyController:
def __call__(self, *args):
return args[0]
def __init__(self):
self.num_att_layers = 0
if controller is None:
controller = DummyController()
def register_recr(net_, count, place_in_unet):
if net_.__class__.__name__ == 'CrossAttention':
net_.forward = ca_forward(net_, place_in_unet)
return count + 1
elif hasattr(net_, 'children'):
for net__ in net_.children():
count = register_recr(net__, count, place_in_unet)
return count
cross_att_count = 0
sub_nets = model.unet.named_children()
for net in sub_nets:
if "down" in net[0]:
cross_att_count += register_recr(net[1], 0, "down")
elif "up" in net[0]:
cross_att_count += register_recr(net[1], 0, "up")
elif "mid" in net[0]:
cross_att_count += register_recr(net[1], 0, "mid")
controller.num_att_layers = cross_att_count
def get_word_inds(text: str, word_place: int, tokenizer):
split_text = text.split(" ")
if type(word_place) is str:
word_place = [i for i, word in enumerate(split_text) if word_place == word]
elif type(word_place) is int:
word_place = [word_place]
out = []
if len(word_place) > 0:
words_encode = [tokenizer.decode([item]).strip("#") for item in tokenizer.encode(text)][1:-1]
cur_len, ptr = 0, 0
for i in range(len(words_encode)):
cur_len += len(words_encode[i])
if ptr in word_place:
out.append(i + 1)
if cur_len >= len(split_text[ptr]):
ptr += 1
cur_len = 0
return np.array(out)
def update_alpha_time_word(alpha, bounds: Union[float, Tuple[float, float]], prompt_ind: int,
word_inds: Optional[torch.Tensor]=None):
if type(bounds) is float:
bounds = 0, bounds
start, end = int(bounds[0] * alpha.shape[0]), int(bounds[1] * alpha.shape[0])
if word_inds is None:
word_inds = torch.arange(alpha.shape[2])
alpha[: start, prompt_ind, word_inds] = 0
alpha[start: end, prompt_ind, word_inds] = 1
alpha[end:, prompt_ind, word_inds] = 0
return alpha
def get_time_words_attention_alpha(prompts, num_steps,
cross_replace_steps: Union[float, Dict[str, Tuple[float, float]]],
tokenizer, max_num_words=77):
if type(cross_replace_steps) is not dict:
cross_replace_steps = {"default_": cross_replace_steps}
if "default_" not in cross_replace_steps:
cross_replace_steps["default_"] = (0., 1.)
alpha_time_words = torch.zeros(num_steps + 1, len(prompts) - 1, max_num_words)
for i in range(len(prompts) - 1):
alpha_time_words = update_alpha_time_word(alpha_time_words, cross_replace_steps["default_"],
i)
for key, item in cross_replace_steps.items():
if key != "default_":
inds = [get_word_inds(prompts[i], key, tokenizer) for i in range(1, len(prompts))]
for i, ind in enumerate(inds):
if len(ind) > 0:
alpha_time_words = update_alpha_time_word(alpha_time_words, item, i, ind)
alpha_time_words = alpha_time_words.reshape(num_steps + 1, len(prompts) - 1, 1, 1, max_num_words)
return alpha_time_words
================================================
FILE: models/stylediffusion/seq_aligner.py
================================================
# Copyright 2022 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
import numpy as np
class ScoreParams:
def __init__(self, gap, match, mismatch):
self.gap = gap
self.match = match
self.mismatch = mismatch
def mis_match_char(self, x, y):
if x != y:
return self.mismatch
else:
return self.match
def get_matrix(size_x, size_y, gap):
matrix = []
for i in range(len(size_x) + 1):
sub_matrix = []
for j in range(len(size_y) + 1):
sub_matrix.append(0)
matrix.append(sub_matrix)
for j in range(1, len(size_y) + 1):
matrix[0][j] = j*gap
for i in range(1, len(size_x) + 1):
matrix[i][0] = i*gap
return matrix
def get_matrix(size_x, size_y, gap):
matrix = np.zeros((size_x + 1, size_y + 1), dtype=np.int32)
matrix[0, 1:] = (np.arange(size_y) + 1) * gap
matrix[1:, 0] = (np.arange(size_x) + 1) * gap
return matrix
def get_traceback_matrix(size_x, size_y):
matrix = np.zeros((size_x + 1, size_y +1), dtype=np.int32)
matrix[0, 1:] = 1
matrix[1:, 0] = 2
matrix[0, 0] = 4
return matrix
def global_align(x, y, score):
matrix = get_matrix(len(x), len(y), score.gap)
trace_back = get_traceback_matrix(len(x), len(y))
for i in range(1, len(x) + 1):
for j in range(1, len(y) + 1):
left = matrix[i, j - 1] + score.gap
up = matrix[i - 1, j] + score.gap
diag = matrix[i - 1, j - 1] + score.mis_match_char(x[i - 1], y[j - 1])
matrix[i, j] = max(left, up, diag)
if matrix[i, j] == left:
trace_back[i, j] = 1
elif matrix[i, j] == up:
trace_back[i, j] = 2
else:
trace_back[i, j] = 3
return matrix, trace_back
def get_aligned_sequences(x, y, trace_back):
x_seq = []
y_seq = []
i = len(x)
j = len(y)
mapper_y_to_x = []
while i > 0 or j > 0:
if trace_back[i, j] == 3:
x_seq.append(x[i-1])
y_seq.append(y[j-1])
i = i-1
j = j-1
mapper_y_to_x.append((j, i))
elif trace_back[i][j] == 1:
x_seq.append('-')
y_seq.append(y[j-1])
j = j-1
mapper_y_to_x.append((j, -1))
elif trace_back[i][j] == 2:
x_seq.append(x[i-1])
y_seq.append('-')
i = i-1
elif trace_back[i][j] == 4:
break
mapper_y_to_x.reverse()
return x_seq, y_seq, torch.tensor(mapper_y_to_x, dtype=torch.int64)
def get_mapper(x: str, y: str, tokenizer, max_len=77):
x_seq = tokenizer.encode(x)
y_seq = tokenizer.encode(y)
score = ScoreParams(0, 1, -1)
matrix, trace_back = global_align(x_seq, y_seq, score)
mapper_base = get_aligned_sequences(x_seq, y_seq, trace_back)[-1]
alphas = torch.ones(max_len)
alphas[: mapper_base.shape[0]] = mapper_base[:, 1].ne(-1).float()
mapper = torch.zeros(max_len, dtype=torch.int64)
mapper[:mapper_base.shape[0]] = mapper_base[:, 1]
mapper[mapper_base.shape[0]:] = len(y_seq) + torch.arange(max_len - len(y_seq))
return mapper, alphas
def get_refinement_mapper(prompts, tokenizer, max_len=77):
x_seq = prompts[0]
mappers, alphas = [], []
for i in range(1, len(prompts)):
mapper, alpha = get_mapper(x_seq, prompts[i], tokenizer, max_len)
mappers.append(mapper)
alphas.append(alpha)
return torch.stack(mappers), torch.stack(alphas)
def get_word_inds(text: str, word_place: int, tokenizer):
split_text = text.split(" ")
if type(word_place) is str:
word_place = [i for i, word in enumerate(split_text) if word_place == word]
elif type(word_place) is int:
word_place = [word_place]
out = []
if len(word_place) > 0:
words_encode = [tokenizer.decode([item]).strip("#") for item in tokenizer.encode(text)][1:-1]
cur_len, ptr = 0, 0
for i in range(len(words_encode)):
cur_len += len(words_encode[i])
if ptr in word_place:
out.append(i + 1)
if cur_len >= len(split_text[ptr]):
ptr += 1
cur_len = 0
return np.array(out)
def get_replacement_mapper_(x: str, y: str, tokenizer, max_len=77):
words_x = x.split(' ')
words_y = y.split(' ')
if len(words_x) != len(words_y):
raise ValueError(f"attention replacement edit can only be applied on prompts with the same length"
f" but prompt A has {len(words_x)} words and prompt B has {len(words_y)} words.")
inds_replace = [i for i in range(len(words_y)) if words_y[i] != words_x[i]]
inds_source = [get_word_inds(x, i, tokenizer) for i in inds_replace]
inds_target = [get_word_inds(y, i, tokenizer) for i in inds_replace]
mapper = np.zeros((max_len, max_len))
i = j = 0
cur_inds = 0
while i < max_len and j < max_len:
if cur_inds < len(inds_source) and inds_source[cur_inds][0] == i:
inds_source_, inds_target_ = inds_source[cur_inds], inds_target[cur_inds]
if len(inds_source_) == len(inds_target_):
mapper[inds_source_, inds_target_] = 1
else:
ratio = 1 / len(inds_target_)
for i_t in inds_target_:
mapper[inds_source_, i_t] = ratio
cur_inds += 1
i += len(inds_source_)
j += len(inds_target_)
elif cur_inds < len(inds_source):
mapper[i, j] = 1
i += 1
j += 1
else:
mapper[j, j] = 1
i += 1
j += 1
return torch.from_numpy(mapper).float()
def get_replacement_mapper(prompts, tokenizer, max_len=77):
x_seq = prompts[0]
mappers = []
for i in range(1, len(prompts)):
mapper = get_replacement_mapper_(x_seq, prompts[i], tokenizer, max_len)
mappers.append(mapper)
return torch.stack(mappers)
================================================
FILE: models/stylediffusion/utils.py
================================================
from typing import Optional, Union, Tuple, List, Callable, Dict
from models.stylediffusion import global_var
from models.stylediffusion import ptp_utils_v
import torch
import torch.nn.functional as nnf
import abc
from models.stylediffusion import seq_aligner
import numpy as np
from PIL import Image
import clip
from models.stylediffusion.clip_util import VisionTransformer
clip.model.VisionTransformer = VisionTransformer
import torch.nn as nn
import torchvision.transforms as transforms
import copy
USE_INITIAL_INV=global_var.get_value("USE_INITIAL_INV")
MAX_NUM_WORDS=global_var.get_value("MAX_NUM_WORDS")
device=global_var.get_value("device")
NUM_DDIM_STEPS=global_var.get_value("NUM_DDIM_STEPS")
tokenizer=global_var.get_value("tokenizer")
LOW_RESOURCE=global_var.get_value("LOW_RESOURCE")
BLOCK_NUM=global_var.get_value("BLOCK_NUM")
class LocalBlend:
def get_mask(self, maps, alpha, use_pool):
k = 1
maps = (maps * alpha).sum(-1).mean(1)
if use_pool:
maps = nnf.max_pool2d(maps, (k * 2 + 1, k * 2 + 1), (1, 1), padding=(k, k))
mask = nnf.interpolate(maps, size=(64, 64))
mask = mask / mask.max(2, keepdims=True)[0].max(3, keepdims=True)[0]
mask = mask.gt(self.th[1 - int(use_pool)])
mask = mask[:1] + mask
return mask
def __call__(self, x_t, attention_store):
self.counter += 1
if self.counter > self.start_blend:
maps = attention_store["down_cross"][2:4] + attention_store["up_cross"][:3]
maps = [item.reshape(self.alpha_layers.shape[0], -1, 1, 16, 16, MAX_NUM_WORDS) for item in maps]
maps = torch.cat(maps, dim=1)
mask = self.get_mask(maps, self.alpha_layers, True)
if self.substruct_layers is not None:
maps_sub = ~self.get_mask(maps, self.substruct_layers, False)
mask = mask * maps_sub
mask = mask.float()
x_t = x_t[:1] + mask * (x_t - x_t[:1])
return x_t
def __init__(self, prompts: List[str], words: [List[List[str]]], substruct_words=None, start_blend=0.2,
th=(.3, .3)):
alpha_layers = torch.zeros(len(prompts), 1, 1, 1, 1, MAX_NUM_WORDS)
for i, (prompt, words_) in enumerate(zip(prompts, words)):
if type(words_) is str:
words_ = [words_]
for word in words_:
ind = ptp_utils_v.get_word_inds(prompt, word, tokenizer)
alpha_layers[i, :, :, :, :, ind] = 1
if substruct_words is not None:
substruct_layers = torch.zeros(len(prompts), 1, 1, 1, 1, MAX_NUM_WORDS)
for i, (prompt, words_) in enumerate(zip(prompts, substruct_words)):
if type(words_) is str:
words_ = [words_]
for word in words_:
ind = ptp_utils_v.get_word_inds(prompt, word, tokenizer)
substruct_layers[i, :, :, :, :, ind] = 1
self.substruct_layers = substruct_layers.to(device)
else:
self.substruct_layers = None
self.alpha_layers = alpha_layers.to(device)
self.start_blend = int(start_blend * NUM_DDIM_STEPS)
self.counter = 0
self.th = th
class EmptyControl:
def step_callback(self, x_t):
return x_t
def between_steps(self):
return
def __call__(self, attn, is_cross: bool, place_in_unet: str):
return attn
class AttentionControl(abc.ABC):
def step_callback(self, x_t):
return x_t
def between_steps(self):
return
@property
def num_uncond_att_layers(self):
return self.num_att_layers if LOW_RESOURCE else 0
@abc.abstractmethod
def forward(self, attn, is_cross: bool, place_in_unet: str):
raise NotImplementedError
@abc.abstractmethod
def replace_uncond(self, attn, is_cross: bool, place_in_unet: str):
raise NotImplementedError
def __call__(self, attn, is_cross: bool, place_in_unet: str):
if self.cur_att_layer >= self.num_uncond_att_layers:
if LOW_RESOURCE:
attn = self.forward(attn, is_cross, place_in_unet)
else:
h = attn.shape[0]
attn[h // 2:] = self.forward(attn[h // 2:], is_cross, place_in_unet)
else: # self-attn of unconditional branch
attn = self.replace_uncond(attn, is_cross, place_in_unet)
self.cur_att_layer += 1
if self.cur_att_layer == self.num_att_layers + self.num_uncond_att_layers:
self.cur_att_layer = 0
self.cur_step += 1
self.between_steps()
return attn
def reset(self):
self.cur_step = 0
self.cur_att_layer = 0
def __init__(self):
self.cur_step = 0
self.num_att_layers = -1
self.cur_att_layer = 0
class SpatialReplace(EmptyControl):
def step_callback(self, x_t):
if self.cur_step < self.stop_inject:
b = x_t.shape[0]
x_t = x_t[:1].expand(b, *x_t.shape[1:])
return x_t
def __init__(self, stop_inject: float):
super(SpatialReplace, self).__init__()
self.stop_inject = int((1 - stop_inject) * NUM_DDIM_STEPS)
class AttentionStore(AttentionControl):
@staticmethod
def get_empty_store():
return {"down_cross": [], "mid_cross": [], "up_cross": [],
"down_self": [], "mid_self": [], "up_self": []}
def forward(self, attn, is_cross: bool, place_in_unet: str):
key = f"{place_in_unet}_{'cross' if is_cross else 'self'}"
if attn.shape[1] <= 32 ** 2: # avoid memory overhead
self.step_store[key].append(attn)
return attn
def replace_uncond(self, attn, is_cross: bool, place_in_unet: str):
return attn
def between_steps(self):
if len(self.attention_store) == 0:
self.attention_store = self.step_store
else:
for key in self.attention_store:
for i in range(len(self.attention_store[key])):
self.attention_store[key][i] += self.step_store[key][i]
self.step_store = self.get_empty_store()
def get_average_attention(self):
average_attention = {key: [item / self.cur_step for item in self.attention_store[key]] for key in
self.attention_store}
return average_attention
def reset(self):
super(AttentionStore, self).reset()
self.step_store = self.get_empty_store()
self.attention_store = {}
def __init__(self, tau_neg=.0):
super(AttentionStore, self).__init__()
self.step_store = self.get_empty_store()
self.attention_store = {}
self.tau_neg = tau_neg
class AttentionControlEdit(AttentionStore, abc.ABC):
def step_callback(self, x_t):
if self.local_blend is not None:
x_t = self.local_blend(x_t, self.attention_store)
return x_t
def replace_self_attention(self, attn_base, att_replace, place_in_unet):
if att_replace.shape[2] <= 32 ** 2:
attn_base = attn_base.unsqueeze(0).expand(att_replace.shape[0], *attn_base.shape)
return attn_base
else:
return att_replace
@abc.abstractmethod
def replace_cross_attention(self, attn_base, att_replace):
raise NotImplementedError
def forward(self, attn, is_cross: bool, place_in_unet: str):
super(AttentionControlEdit, self).forward(attn, is_cross, place_in_unet)
if is_cross or (self.num_self_replace[0] <= self.cur_step < self.num_self_replace[1]):
h = attn.shape[0] // (self.batch_size)
attn = attn.reshape(self.batch_size, h, *attn.shape[1:])
attn_base, attn_repalce = attn[0], attn[1:]
if is_cross:
alpha_words = self.cross_replace_alpha[self.cur_step]
attn_repalce_new = self.replace_cross_attention(attn_base, attn_repalce) * alpha_words + (
1 - alpha_words) * attn_repalce
attn[1:] = attn_repalce_new
else:
attn[1:] = self.replace_self_attention(attn_base, attn_repalce, place_in_unet)
attn = attn.reshape(self.batch_size * h, *attn.shape[2:])
return attn
def replace_uncond(self, attn, is_cross: bool, place_in_unet: str):
super(AttentionControlEdit, self).replace_uncond(attn, is_cross, place_in_unet)
if not is_cross and self.num_uncond_self_replace[0] <= self.cur_step < self.num_uncond_self_replace[1]:
h = attn.shape[0] // (self.batch_size)
attn = attn.reshape(self.batch_size, h, *attn.shape[1:])
attn_base, attn_repalce = attn[0], attn[1:]
attn[1:] = self.replace_self_attention(attn_base, attn_repalce, place_in_unet)
attn = attn.reshape(self.batch_size * h, *attn.shape[2:])
return attn
def __init__(self, prompts, num_steps: int,
cross_replace_steps: Union[float, Tuple[float, float], Dict[str, Tuple[float, float]]],
self_replace_steps: Union[float, Tuple[float, float]],
uncond_self_replace_steps: Union[float, Tuple[float, float]],
local_blend: Optional[LocalBlend]):
super(AttentionControlEdit, self).__init__()
self.batch_size = len(prompts)
self.cross_replace_alpha = ptp_utils_v.get_time_words_attention_alpha(prompts, num_steps, cross_replace_steps,
tokenizer).to(device)
if type(self_replace_steps) is float:
self_replace_steps = 0, self_replace_steps
self.num_self_replace = int(num_steps * self_replace_steps[0]), int(num_steps * self_replace_steps[1])
if type(uncond_self_replace_steps) is float:
uncond_self_replace_steps = 0, uncond_self_replace_steps
self.num_uncond_self_replace = int(num_steps * uncond_self_replace_steps[0]), int(num_steps * uncond_self_replace_steps[1])
self.local_blend = local_blend
class AttentionReplace(AttentionControlEdit):
def replace_cross_attention(self, attn_base, att_replace):
return torch.einsum('hpw,bwn->bhpn', attn_base, self.mapper)
def __init__(self, prompts, num_steps: int, cross_replace_steps: float, self_replace_steps: float, uncond_self_replace_steps: float,
local_blend: Optional[LocalBlend] = None):
super(AttentionReplace, self).__init__(prompts, num_steps, cross_replace_steps, self_replace_steps, uncond_self_replace_steps, local_blend)
self.mapper = seq_aligner.get_replacement_mapper(prompts, tokenizer).to(device)
class AttentionRefine(AttentionControlEdit):
def replace_cross_attention(self, attn_base, att_replace):
attn_base_replace = attn_base[:, :, self.mapper].permute(2, 0, 1, 3)
attn_replace = attn_base_replace * self.alphas + att_replace * (1 - self.alphas)
# attn_replace = attn_replace / attn_replace.sum(-1, keepdims=True)
return attn_replace
def __init__(self, prompts, num_steps: int, cross_replace_steps: float, self_replace_steps: float, uncond_self_replace_steps: float,
local_blend: Optional[LocalBlend] = None):
super(AttentionRefine, self).__init__(prompts, num_steps, cross_replace_steps, self_replace_steps, uncond_self_replace_steps,
local_blend)
self.mapper, alphas = seq_aligner.get_refinement_mapper(prompts, tokenizer)
self.mapper, alphas = self.mapper.to(device), alphas.to(device)
self.alphas = alphas.reshape(alphas.shape[0], 1, 1, alphas.shape[1])
class AttentionReweight(AttentionControlEdit):
def replace_cross_attention(self, attn_base, att_replace):
if self.prev_controller is not None:
attn_base = self.prev_controller.replace_cross_attention(attn_base, att_replace)
attn_replace = attn_base[None, :, :, :] * self.equalizer[:, None, None, :]
# attn_replace = attn_replace / attn_replace.sum(-1, keepdims=True)
return attn_replace
def __init__(self, prompts, num_steps: int, cross_replace_steps: float, self_replace_steps: float, uncond_self_replace_steps: float, equalizer,
local_blend: Optional[LocalBlend] = None, controller: Optional[AttentionControlEdit] = None):
super(AttentionReweight, self).__init__(prompts, num_steps, cross_replace_steps, self_replace_steps, uncond_self_replace_steps,
local_blend)
self.equalizer = equalizer.to(device)
self.prev_controller = controller
def get_equalizer(text: str, word_select: Union[int, Tuple[int, ...]], values: Union[List[float],
Tuple[float, ...]]):
if type(word_select) is int or type(word_select) is str:
word_select = (word_select,)
equalizer = torch.ones(1, 77)
for word, val in zip(word_select, values):
inds = ptp_utils_v.get_word_inds(text, word, tokenizer)
equalizer[:, inds] = val
return equalizer
def aggregate_attention(attention_store: AttentionStore, prompts: List[str], res: int, from_where: List[str], is_cross: bool, select: int):
out = []
attention_maps = attention_store.get_average_attention()
num_pixels = res ** 2
for location in from_where:
for item in attention_maps[f"{location}_{'cross' if is_cross else 'self'}"]:
if item.shape[1] == num_pixels:
cross_maps = item.reshape(len(prompts), -1, res, res, item.shape[-1])[select]
out.append(cross_maps)
out = torch.cat(out, dim=0)
out = out.sum(0) / out.shape[0]
return out.cpu()
def make_controller(prompts: List[str], is_replace_controller: bool, cross_replace_steps: Dict[str, float],
self_replace_steps: float, uncond_self_replace_steps: float, blend_words=None, equilizer_params=None) -> AttentionControlEdit:
if blend_words is None:
lb = None
else:
lb = LocalBlend(prompts, blend_words)
if is_replace_controller:
controller = AttentionReplace(prompts, NUM_DDIM_STEPS, cross_replace_steps=cross_replace_steps,
self_replace_steps=self_replace_steps, uncond_self_replace_steps=uncond_self_replace_steps, local_blend=lb)
else:
controller = AttentionRefine(prompts, NUM_DDIM_STEPS, cross_replace_steps=cross_replace_steps,
self_replace_steps=self_replace_steps, uncond_self_replace_steps=uncond_self_replace_steps, local_blend=lb)
if equilizer_params is not None:
eq = get_equalizer(prompts[1], equilizer_params["words"], equilizer_params["values"])
controller = AttentionReweight(prompts, NUM_DDIM_STEPS, cross_replace_steps=cross_replace_steps,
self_replace_steps=self_replace_steps, uncond_self_replace_steps=uncond_self_replace_steps,
equalizer=eq, local_blend=lb, controller=controller)
return controller
def show_cross_attention(attention_store: AttentionStore, prompts: List[str], res: int, from_where: List[str], select: int = 0, save_name='cross-attn-map'):
tokens = tokenizer.encode(prompts[select])
decoder = tokenizer.decode
attention_maps = aggregate_attention(attention_store, prompts, res, from_where, True, select)
images = []
for i in range(len(tokens)):
image = attention_maps[:, :, i]
image = 255 * image / image.max()
image = image.unsqueeze(-1).expand(*image.shape, 3)
image = image.numpy().astype(np.uint8)
image = np.array(Image.fromarray(image).resize((256, 256)))
image = ptp_utils_v.text_under_image(image, decoder(int(tokens[i])))
images.append(image)
ptp_utils_v.view_images(np.stack(images, axis=0), save_name=save_name)
def show_hot_cross_attention(attention_store: AttentionStore, prompts: List[str], res: int, from_where: List[str], select: int = 0, save_name='cross-attn-map'):
import cv2
choice = 4
colormap_dict = {
1: cv2.COLORMAP_VIRIDIS,
2: cv2.COLORMAP_PLASMA,
3: cv2.COLORMAP_HOT,
4: cv2.COLORMAP_JET,
5: cv2.COLORMAP_INFERNO,
6: cv2.COLORMAP_AUTUMN,
7: cv2.COLORMAP_BONE,
8: cv2.COLORMAP_WINTER,
9: cv2.COLORMAP_RAINBOW,
10: cv2.COLORMAP_OCEAN,
11: cv2.COLORMAP_SUMMER,
12: cv2.COLORMAP_SPRING,
13: cv2.COLORMAP_COOL,
14: cv2.COLORMAP_HSV,
15: cv2.COLORMAP_PINK,
}
def gray_to_heatmap(gray_image, colormap):
colored_image = cv2.applyColorMap(gray_image, colormap)
return colored_image
if choice not in colormap_dict:
print("Invalid choice. Using the default colormap (viridis).")
choice = 1
tokens = tokenizer.encode(prompts[select])
decoder = tokenizer.decode
attention_maps = aggregate_attention(attention_store, prompts, res, from_where, True, select)
images = []
for i in range(len(tokens)):
image = attention_maps[:, :, i]
image = 255 * image / image.max()
image = image.unsqueeze(-1).expand(*image.shape, 3)
image = image.numpy().astype(np.uint8)
image = np.array(Image.fromarray(image).resize((256, 256)))
colored_image = gray_to_heatmap(image[:,:,0], colormap_dict[choice])
cv2.imwrite(f'{save_name}-{i}.png', colored_image)
def show_self_attention_comp(attention_store: AttentionStore, prompts: List[str], res: int, from_where: List[str],
max_com=10, select: int = 0):
attention_maps = aggregate_attention(attention_store, prompts, res, from_where, False, select).numpy().reshape(
(res ** 2, res ** 2))
u, s, vh = np.linalg.svd(attention_maps - np.mean(attention_maps, axis=1, keepdims=True))
images = []
for i in range(max_com):
image = vh[i].reshape(res, res)
image = image - image.min()
image = 255 * image / image.max()
image = np.repeat(np.expand_dims(image, axis=2), 3, axis=2).astype(np.uint8)
image = Image.fromarray(image).resize((256, 256))
image = np.array(image)
images.append(image)
ptp_utils_v.view_images(np.concatenate(images, axis=1), save_name='self-attn-map-comp')
def load_512(image_path, left=0, right=0, top=0, bottom=0):
if type(image_path) is str:
image = np.array(Image.open(image_path))[:, :, :3]
else:
image = image_path
h, w, c = image.shape
left = min(left, w - 1)
right = min(right, w - left - 1)
top = min(top, h - left - 1)
bottom = min(bottom, h - top - 1)
image = image[top:h - bottom, left:w - right]
h, w, c = image.shape
if h < w:
offset = (w - h) // 2
image = image[:, offset:offset + h]
elif w < h:
offset = (h - w) // 2
image = image[offset:offset + w]
image = np.array(Image.fromarray(image).resize((512, 512)))
return image
def register_attention_control(model, trainer, controller):
IS_TRAIN=global_var.get_value("IS_TRAIN")
assert IS_TRAIN is not None, print("must set True or False for args.is_train.")
assert controller is None if IS_TRAIN else (trainer and controller)
def ca_forward(self, place_in_unet):
to_out = self.to_out
if type(to_out) is torch.nn.modules.container.ModuleList:
to_out = self.to_out[0] # todo: ?
else:
to_out = self.to_out
def forward(x, context=None, mask=None):
batch_size, sequence_length, dim = x.shape
h = self.heads
q = self.to_q(x)
is_cross = context is not None
context = context if is_cross else x
k = self.to_k(context)
# image encoded to embedding for to_v() in cross-attn of conditional branch.
IS_TRAIN=global_var.get_value("IS_TRAIN")
if IS_TRAIN: # training phase
'''
skip when trainer.ddim_inv is True which means to store ground truth attn maps,
these attn maps are used as supervision during the training phase
'''
if (not trainer.uncond and is_cross) and (not trainer.ddim_inv):
context = trainer.forward_embed(context)
else: # editing phase
if not controller.uncond and is_cross:
if USE_INITIAL_INV:
context = trainer.forward_embed(context)
else:
i = trainer.i
cont = list(context.chunk(context.shape[0]))
for b in range(len(cont)):
trainer.i = trainer.I if b == 0 else i
cont[b] = trainer.forward_embed(cont[b])
context = cont[0] if len(cont) == 1 else torch.cat(cont)
trainer.i = i
v = self.to_v(context)
q = self.reshape_heads_to_batch_dim(q)
k = self.reshape_heads_to_batch_dim(k)
v = self.reshape_heads_to_batch_dim(v)
sim = torch.einsum("b i d, b j d -> b i j", q, k) * self.scale
if mask is not None:
mask = mask.reshape(batch_size, -1)
max_neg_value = -torch.finfo(sim.dtype).max
mask = mask[:, None, :].repeat(h, 1, 1)
sim.masked_fill_(~mask, max_neg_value)
# attention, what we cannot get enough of
attn = sim.softmax(dim=-1)
IS_TRAIN=global_var.get_value("IS_TRAIN")
if IS_TRAIN: # training phase
attn = trainer(attn, is_cross, place_in_unet)
else: # editing phase
attn = controller(attn, is_cross, place_in_unet)
out = torch.einsum("b i j, b j d -> b i d", attn, v)
out = self.reshape_batch_dim_to_heads(out)
return to_out(out)
return forward
class DummyController:
def __call__(self, *args):
return args[0]
def __init__(self):
self.num_att_layers = 0
IS_TRAIN=global_var.get_value("IS_TRAIN")
if IS_TRAIN:
if trainer is None:
trainer = DummyController()
else:
if controller is None:
controller = DummyController()
def register_recr(net_, count, place_in_unet):
if net_.__class__.__name__ == 'CrossAttention':
net_.forward = ca_forward(net_, place_in_unet)
return count + 1
elif hasattr(net_, 'children'):
for net__ in net_.children():
count = register_recr(net__, count, place_in_unet)
return count
cross_att_count = 0
sub_nets = model.unet.named_children()
for net in sub_nets:
if "down" in net[0]:
cross_att_count += register_recr(net[1], 0, "down")
elif "up" in net[0]:
cross_att_count += register_recr(net[1], 0, "up")
elif "mid" in net[0]:
cross_att_count += register_recr(net[1], 0, "mid")
IS_TRAIN=global_var.get_value("IS_TRAIN")
if IS_TRAIN:
trainer.num_att_layers = cross_att_count
else:
controller.num_att_layers = cross_att_count
def image_grid(img, grid_size):
gw, gh = grid_size
_N, H, W, C = img.shape
img = img.reshape(gh, gw, H, W, C)
img = img.transpose(0, 2, 1, 3, 4)
img = img.reshape(gh * H, gw * W, C)
return img
class Trainer(AttentionStore):
def __init__(self):
super(Trainer, self).__init__()
self.device = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu')
# clip image encoder
self.clip_model, clip_preprocess = clip.load('ViT-B/16', device=self.device)
self.clip_preprocess = clip_preprocess
self.preprocess = transforms.Compose([transforms.Normalize(mean=[-1.0, -1.0, -1.0], std=[2.0, 2.0, 2.0])] + # Un-normalize from [-1.0, 1.0] (GAN output) to [0, 1].
clip_preprocess.transforms[:2] + # to match CLIP input scale assumptions
clip_preprocess.transforms[4:]) # + skip convert PIL to tensor
self.image = None
self.embedding = []
# image embedding
scale = 2
self.embedding = []
self.convblock = nn.Sequential(nn.Conv1d(77 * scale, 77 * scale, kernel_size=1),
nn.BatchNorm1d(77 * scale, affine=True),
nn.LeakyReLU())
for _ in range(NUM_DDIM_STEPS):
self.embedding.append(nn.ModuleDict({
'conv_start': nn.Conv1d(197, 77 * scale, kernel_size=1), # (bs, 197, 768)->(bs, 77, 768)
'conv_block': nn.Sequential(*[copy.deepcopy(self.convblock) for _ in range(BLOCK_NUM)]),
'conv_end': nn.Conv1d(77 * scale, 77 * scale, kernel_size=1), # (bs, 77, 768)->(bs, 77, 768)
}).train().requires_grad_(False).to(device))
self.I = None # only for eval
self.i = None
self.uncond = False
self.ddim_inv = False
self.v_replace_steps = .5
def load_pretrained(self, pretrained_embedding):
for i, pre_embedding in enumerate(pretrained_embedding):
for pre_emb, emb in zip(pre_embedding.values(), self.embedding[i].values()):
self.copy_params_and_buffers(pre_emb, emb)
def named_params_and_buffers(self, module):
assert isinstance(module, torch.nn.Module)
return list(module.named_parameters()) + list(module.named_buffers())
def copy_params_and_buffers(self, src_vae, dst_vae, require_all=False):
assert isinstance(src_vae, torch.nn.Module)
assert isinstance(dst_vae, torch.nn.Module)
vae_tensors = dict(self.named_params_and_buffers(src_vae))
for name, tensor in self.named_params_and_buffers(dst_vae):
assert (name in vae_tensors) or not require_all
if name in vae_tensors and tensor.shape == vae_tensors[name].shape:
try:
tensor.copy_(vae_tensors[name].detach()).requires_grad_(tensor.requires_grad)
except Exception as e:
print(f'Error loading: {name} {vae_tensors[name].shape} {tensor.shape}')
raise e
# else:
# print(f'{name}: {tensor.shape}, {vae_tensors[name].shape}')
def encode_images(self, images: torch.Tensor) -> torch.Tensor:
images = self.preprocess(images).to(self.device)
return self.clip_model.encode_image(images)
def forward_embed(self, context):
if self.i is not None:
img_emb = self.encode_images(self.image).to(torch.float32)
for block in self.embedding[self.i].values():
img_emb = block(img_emb)
return (context * img_emb[:, :77, :] + img_emb[:, 77:, :]) if self.i is not None else context
# def forward_embed(self, context):
# if self.i is not None:
# context = self.encode_images(self.image).to(torch.float32)
# for block in self.embedding[self.i].values():
# context = block(context)
# return context
================================================
FILE: run_editing_blended_latent_diffusion.py
================================================
import numpy as np
from PIL import Image
import json
import os
import random
import argparse
from diffusers import DDIMScheduler, StableDiffusionPipeline
import torch
from utils.utils import txt_draw
def setup_seed(seed=1234):
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
random.seed(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
def mask_decode(encoded_mask,image_shape=[512,512]):
length=image_shape[0]*image_shape[1]
mask_array=np.zeros((length,))
for i in range(0,len(encoded_mask),2):
splice_len=min(encoded_mask[i+1],length-encoded_mask[i])
for j in range(splice_len):
mask_array[encoded_mask[i]+j]=1
mask_array=mask_array.reshape(image_shape[0], image_shape[1])
# to avoid annotation errors in boundary
mask_array[0,:]=1
mask_array[-1,:]=1
mask_array[:,0]=1
mask_array[:,-1]=1
return mask_array
class BlendedLatnetDiffusion:
def __init__(self,model_path="stabilityai/stable-diffusion-2-1-base",device="cuda"):
self.model_path = model_path
self.device = device
self.load_models()
def load_models(self):
pipe = StableDiffusionPipeline.from_pretrained(
self.model_path, torch_dtype=torch.float16
)
self.vae = pipe.vae.to(self.device)
self.tokenizer = pipe.tokenizer
self.text_encoder = pipe.text_encoder.to(self.device)
self.unet = pipe.unet.to(self.device)
self.scheduler = DDIMScheduler(
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
clip_sample=False,
set_alpha_to_one=False,
)
@torch.no_grad()
def edit_image(
self,
image_path,
mask,
prompts,
height=512,
width=512,
num_inference_steps=50,
guidance_scale=7.5,
generator=torch.manual_seed(42),
blending_percentage=0.25,
):
image_ori = Image.open(image_path)
image_ori = image_ori.resize((height, width), Image.BILINEAR)
image_ori = np.array(image_ori)[:, :, :3]
source_latents = self._image2latent(image_ori)
latent_mask, org_mask = self._read_mask(mask)
text_input = self.tokenizer(
prompts,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_embeddings = self.text_encoder(text_input.input_ids.to("cuda"))[0]
max_length = text_input.input_ids.shape[-1]
uncond_input = self.tokenizer(
[""],
padding="max_length",
max_length=max_length,
return_tensors="pt",
)
uncond_embeddings = self.text_encoder(uncond_input.input_ids.to("cuda"))[0]
text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
latents = torch.randn(
(1, self.unet.in_channels, height // 8, width // 8),
generator=generator,
)
latents = latents.to("cuda").half()
self.scheduler.set_timesteps(num_inference_steps)
for t in self.scheduler.timesteps[
int(len(self.scheduler.timesteps) * blending_percentage) :
]:
# expand the latents if we are doing classifier-free guidance to avoid doing two forward passes.
latent_model_input = torch.cat([latents] * 2)
latent_model_input = self.scheduler.scale_model_input(
latent_model_input, timestep=t
)
# predict the noise residual
with torch.no_grad():
noise_pred = self.unet(
latent_model_input, t, encoder_hidden_states=text_embeddings
).sample
# perform guidance
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (
noise_pred_text - noise_pred_uncond
)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents).prev_sample
# Blending
noise_source_latents = self.scheduler.add_noise(
source_latents, torch.randn_like(latents), t
)
latents = latents * latent_mask + noise_source_latents * (1 - latent_mask)
latents = 1 / 0.18215 * latents
with torch.no_grad():
image = self.vae.decode(latents).sample
image = (image / 2 + 0.5).clamp(0, 1)
image = image.detach().cpu().permute(0, 2, 3, 1).numpy()
images = (image * 255).round().astype("uint8")
image_instruct = txt_draw(f"edit prompt: {prompts}")
return [image_instruct,image_ori,np.zeros_like(image_instruct),images[0]]
@torch.no_grad()
def _image2latent(self, image):
image = torch.from_numpy(image).float() / 127.5 - 1
image = image.permute(2, 0, 1).unsqueeze(0).to("cuda")
image = image.half()
latents = self.vae.encode(image)["latent_dist"].mean
latents = latents * 0.18215
return latents
def _read_mask(self, mask, dest_size=(64, 64)):
org_mask = mask
mask = org_mask.resize(dest_size, Image.NEAREST)
mask = np.array(mask)
mask[mask < 0.5] = 0
mask[mask >= 0.5] = 1
mask = mask[np.newaxis, np.newaxis, ...]
mask = torch.from_numpy(mask).half().to(self.device)
return mask, org_mask
image_save_paths={
"blended-latent-diffusion":"blended-latent-diffusion"
}
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--rerun_exist_images', action= "store_true") # rerun existing images
parser.add_argument('--data_path', type=str, default="data") # the editing category that needed to run
parser.add_argument('--output_path', type=str, default="output") # the editing category that needed to run
parser.add_argument('--edit_category_list', nargs = '+', type=str, default=["0","1","2","3","4","5","6","7","8","9"]) # the editing category that needed to run
parser.add_argument('--edit_method_list', nargs = '+', type=str, default=["blended-latent-diffusion"]) # the editing methods that needed to run
args = parser.parse_args()
rerun_exist_images=args.rerun_exist_images
data_path=args.data_path
output_path=args.output_path
edit_category_list=args.edit_category_list
edit_method_list=args.edit_method_list
bld = BlendedLatnetDiffusion()
with open(f"{data_path}/mapping_file.json", "r") as f:
editing_instruction = json.load(f)
for key, item in editing_instruction.items():
if item["editing_type_id"] not in edit_category_list:
continue
original_prompt = item["original_prompt"].replace("[", "").replace("]", "")
editing_prompt = item["editing_prompt"].replace("[", "").replace("]", "")
image_path = os.path.join(f"{data_path}/annotation_images", item["image_path"])
editing_instruction = item["editing_instruction"]
blended_word = item["blended_word"].split(" ") if item["blended_word"] != "" else []
mask = Image.fromarray(np.uint8(mask_decode(item["mask"])[:,:,np.newaxis].repeat(3,2))).convert("L")
for edit_method in edit_method_list:
present_image_save_path=image_path.replace(data_path, os.path.join(output_path,image_save_paths[edit_method]))
if ((not os.path.exists(present_image_save_path)) or rerun_exist_images):
print(f"editing image [{image_path}] with [{edit_method}]")
setup_seed()
torch.cuda.empty_cache()
edited_image = Image.fromarray(np.concatenate(bld.edit_image(
image_path,
mask,
prompts=[editing_prompt] * 1,
blending_percentage=0.25,
),1))
if not os.path.exists(os.path.dirname(present_image_save_path)):
os.makedirs(os.path.dirname(present_image_save_path))
edited_image.save(present_image_save_path)
print(f"finish")
else:
print(f"skip image [{image_path}] with [{edit_method}]")
================================================
FILE: run_editing_edict.py
================================================
from models.edict.edict_functions import *
import argparse
from utils.utils import txt_draw,load_512
import json
def mask_decode(encoded_mask,image_shape=[512,512]):
length=image_shape[0]*image_shape[1]
mask_array=np.zeros((length,))
for i in range(0,len(encoded_mask),2):
splice_len=min(encoded_mask[i+1],length-encoded_mask[i])
for j in range(splice_len):
mask_array[encoded_mask[i]+j]=1
mask_array=mask_array.reshape(image_shape[0], image_shape[1])
# to avoid annotation errors in boundary
mask_array[0,:]=1
mask_array[-1,:]=1
mask_array[:,0]=1
mask_array[:,-1]=1
return mask_array
def setup_seed(seed=1234):
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
random.seed(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
def edit_image_edict_p2p(
image_path,
prompt_src,
prompt_tar,
use_p2p):
im = load_im_into_format_from_path(image_path)
latents = coupled_stablediffusion(prompt_src,
reverse=True,
init_image=im,
run_baseline=False,
)
recon = coupled_stablediffusion(prompt_src,
reverse=False,
fixed_starting_latent=latents,
run_baseline=False,
)
recon = recon[0]
edit=EDICT_editing(image_path,
prompt_src,
prompt_tar,
use_p2p=use_p2p)[0]
image_instruct = txt_draw(f"source prompt: {prompt_src}\ntarget prompt: {prompt_tar}")
image_gt = load_512(image_path)
out_image=Image.fromarray(np.concatenate((image_instruct,image_gt,np.array(recon),np.array(edit),),axis=1))
return out_image
image_save_paths={
"edict+direct_forward":"edict+direct_forward",
"edict+p2p":"edict+p2p",
}
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--rerun_exist_images', action= "store_true") # rerun existing images
parser.add_argument('--data_path', type=str, default="data") # the editing category that needed to run
parser.add_argument('--output_path', type=str, default="output") # the editing category that needed to run
parser.add_argument('--edit_category_list', nargs = '+', type=str, default=["0","1","2","3","4","5","6","7","8","9"]) # the editing category that needed to run
parser.add_argument('--edit_method_list', nargs = '+', type=str, default=["edict+p2p"]) # the editing methods that needed to run
args = parser.parse_args()
rerun_exist_images=args.rerun_exist_images
data_path=args.data_path
output_path=args.output_path
edit_category_list=args.edit_category_list
edit_method_list=args.edit_method_list
with open(f"{data_path}/mapping_file.json", "r") as f:
editing_instruction = json.load(f)
for key, item in editing_instruction.items():
if item["editing_type_id"] not in edit_category_list:
continue
original_prompt = item["original_prompt"].replace("[", "").replace("]", "")
editing_prompt = item["editing_prompt"].replace("[", "").replace("]", "")
image_path = os.path.join(f"{data_path}/annotation_images", item["image_path"])
editing_instruction = item["editing_instruction"]
blended_word = item["blended_word"].split(" ") if item["blended_word"] != "" else []
mask = Image.fromarray(np.uint8(mask_decode(item["mask"])[:,:,np.newaxis].repeat(3,2))).convert("L")
for edit_method in edit_method_list:
if edit_method=="edict+direct_forward":
use_p2p=False
elif edit_method=="edict+p2p":
use_p2p=True
present_image_save_path=image_path.replace(data_path, os.path.join(output_path,image_save_paths[edit_method]))
if ((not os.path.exists(present_image_save_path)) or rerun_exist_images):
print(f"editing image [{image_path}] with [{edit_method}]")
setup_seed()
torch.cuda.empty_cache()
edited_image = edit_image_edict_p2p(
image_path=image_path,
prompt_src=original_prompt,
prompt_tar=editing_prompt,
use_p2p=use_p2p)
if not os.path.exists(os.path.dirname(present_image_save_path)):
os.makedirs(os.path.dirname(present_image_save_path))
edited_image.save(present_image_save_path)
print(f"finish")
else:
print(f"skip image [{image_path}] with [{edit_method}]")
================================================
FILE: run_editing_edit_friendly_p2p.py
================================================
import torch
from diffusers import StableDiffusionPipeline
from diffusers import DDIMScheduler
import numpy as np
from PIL import Image
import os
from models.p2p.scheduler_dev import DDIMSchedulerDev
import json
import random
import argparse
from torch import autocast, inference_mode
from utils.utils import load_512,txt_draw
from models.edit_friendly_ddm.inversion_utils import inversion_forward_process, inversion_reverse_process
from models.edit_friendly_ddm.ptp_classes import AttentionReplace,AttentionRefine,AttentionStore
from models.edit_friendly_ddm.ptp_utils import register_attention_control
def mask_decode(encoded_mask,image_shape=[512,512]):
length=image_shape[0]*image_shape[1]
mask_array=np.zeros((length,))
for i in range(0,len(encoded_mask),2):
splice_len=min(encoded_mask[i+1],length-encoded_mask[i])
for j in range(splice_len):
mask_array[encoded_mask[i]+j]=1
mask_array=mask_array.reshape(image_shape[0], image_shape[1])
# to avoid annotation errors in boundary
mask_array[0,:]=1
mask_array[-1,:]=1
mask_array[:,0]=1
mask_array[:,-1]=1
return mask_array
def setup_seed(seed=1234):
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
random.seed(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
image_save_paths={
"edit-friendly-inversion+p2p":"edit-friendly-inversion+p2p",
}
device = torch.device('cuda') if torch.cuda.is_available() else torch.device(
'cpu')
NUM_DDIM_STEPS = 50
model_id="CompVis/stable-diffusion-v1-4"
ldm_stable = StableDiffusionPipeline.from_pretrained(
model_id).to(device)
ldm_stable.scheduler = DDIMScheduler.from_config(model_id, subfolder = "scheduler")
ldm_stable.scheduler.set_timesteps(NUM_DDIM_STEPS)
ETA=1
SKIP=12
def edit_image_EF(edit_method,
image_path,
prompt_src,
prompt_tar,
source_guidance_scale=1,
target_guidance_scale=7.5,cross_replace_steps=0.4,
self_replace_steps=0.6
):
if edit_method=="edit-friendly-inversion+p2p":
image_gt = load_512(image_path)
image_gt = torch.from_numpy(image_gt).float() / 127.5 - 1
image_gt = image_gt.permute(2, 0, 1).unsqueeze(0).to(device)
with autocast("cuda"), inference_mode():
w0 = (ldm_stable.vae.encode(image_gt).latent_dist.mode() * 0.18215).float()
controller = AttentionStore()
register_attention_control(ldm_stable, controller)
wt, zs, wts = inversion_forward_process(ldm_stable, w0, etas=ETA, prompt=prompt_src, cfg_scale=source_guidance_scale, prog_bar=True, num_inference_steps=NUM_DDIM_STEPS)
controller = AttentionStore()
register_attention_control(ldm_stable, controller)
x0_reconstruct, _ = inversion_reverse_process(ldm_stable, xT=wts[NUM_DDIM_STEPS-SKIP], etas=ETA, prompts=[prompt_tar], cfg_scales=[target_guidance_scale], prog_bar=True, zs=zs[:(NUM_DDIM_STEPS-SKIP)], controller=controller)
cfg_scale_list = [source_guidance_scale, target_guidance_scale]
prompts = [prompt_src, prompt_tar]
if (len(prompt_src.split(" ")) == len(prompt_tar.split(" "))):
controller = AttentionReplace(prompts, NUM_DDIM_STEPS, cross_replace_steps=cross_replace_steps, self_replace_steps=self_replace_steps, model=ldm_stable)
else:
# Should use Refine for target prompts with different number of tokens
controller = AttentionRefine(prompts, NUM_DDIM_STEPS, cross_replace_steps=cross_replace_steps, self_replace_steps=self_replace_steps, model=ldm_stable)
register_attention_control(ldm_stable, controller)
w0, _ = inversion_reverse_process(ldm_stable, xT=wts[NUM_DDIM_STEPS-SKIP], etas=ETA, prompts=prompts, cfg_scales=cfg_scale_list, prog_bar=True, zs=zs[:(NUM_DDIM_STEPS-SKIP)], controller=controller)
with autocast("cuda"), inference_mode():
x0_dec = ldm_stable.vae.decode(1 / 0.18215 * w0[1].unsqueeze(0)).sample
x0_reconstruct_edit = ldm_stable.vae.decode(1 / 0.18215 * w0[0].unsqueeze(0)).sample
x0_reconstruct = ldm_stable.vae.decode(1 / 0.18215 * x0_reconstruct[0].unsqueeze(0)).sample
image_instruct = txt_draw(f"source prompt: {prompt_src}\ntarget prompt: {prompt_tar}")
return Image.fromarray(np.concatenate(
(
image_instruct,
np.uint8((np.array(image_gt[0].permute(1,2,0).cpu().detach())/2+ 0.5)*255),
np.uint8((np.array(x0_reconstruct_edit[0].permute(1,2,0).cpu().detach())/2+ 0.5)*255),
np.uint8((np.array(x0_dec[0].permute(1,2,0).cpu().detach())/2+ 0.5)*255)
),
1
)
)
else:
raise NotImplementedError(f"No edit method named {edit_method}")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--rerun_exist_images', action= "store_true") # rerun existing images
parser.add_argument('--data_path', type=str, default="data") # the editing category that needed to run
parser.add_argument('--output_path', type=str, default="output") # the editing category that needed to run
parser.add_argument('--edit_category_list', nargs = '+', type=str, default=["0","1","2","3","4","5","6","7","8","9"]) # the editing category that needed to run
parser.add_argument('--edit_method_list', nargs = '+', type=str, default=["edit-friendly-inversion+p2p"]) # the editing methods that needed to run
args = parser.parse_args()
rerun_exist_images=args.rerun_exist_images
data_path=args.data_path
output_path=args.output_path
edit_category_list=args.edit_category_list
edit_method_list=args.edit_method_list
with open(f"{data_path}/mapping_file.json", "r") as f:
editing_instruction = json.load(f)
for key, item in editing_instruction.items():
if item["editing_type_id"] not in edit_category_list:
continue
original_prompt = item["original_prompt"].replace("[", "").replace("]", "")
editing_prompt = item["editing_prompt"].replace("[", "").replace("]", "")
image_path = os.path.join(f"{data_path}/annotation_images", item["image_path"])
editing_instruction = item["editing_instruction"]
blended_word = item["blended_word"].split(" ") if item["blended_word"] != "" else []
mask = Image.fromarray(np.uint8(mask_decode(item["mask"])[:,:,np.newaxis].repeat(3,2))).convert("L")
for edit_method in edit_method_list:
present_image_save_path=image_path.replace(data_path, os.path.join(output_path,image_save_paths[edit_method]))
if ((not os.path.exists(present_image_save_path)) or rerun_exist_images):
print(f"editing image [{image_path}] with [{edit_method}]")
setup_seed()
torch.cuda.empty_cache()
edited_image = edit_image_EF(
edit_method=edit_method,
image_path=image_path,
prompt_src=original_prompt,
prompt_tar=editing_prompt,
source_guidance_scale=1,
target_guidance_scale=7.5,
cross_replace_steps=0.4,
self_replace_steps=0.6
)
if not os.path.exists(os.path.dirname(present_image_save_path)):
os.makedirs(os.path.dirname(present_image_save_path))
edited_image.save(present_image_save_path)
print(f"finish")
else:
print(f"skip image [{image_path}] with [{edit_method}]")
================================================
FILE: run_editing_instructdiffusion.py
================================================
# --------------------------------------------------------
# InstructDiffusion
# Based on instruct-pix2pix (https://github.com/timothybrooks/instruct-pix2pix)
# Modified by Zigang Geng (zigang@mail.ustc.edu.cn)
# --------------------------------------------------------
from __future__ import annotations
import os
import math
import random
import sys
from argparse import ArgumentParser
import argparse
import einops
import k_diffusion as K
import numpy as np
import torch
import torch.nn as nn
from einops import rearrange
from omegaconf import OmegaConf
from PIL import Image, ImageOps
from torch import autocast
import json
from utils.utils import txt_draw
import requests
sys.path.append("models/InstructDiffusion/stable_diffusion")
from ldm.util import instantiate_from_config
class CFGDenoiser(nn.Module):
def __init__(self, model):
super().__init__()
self.inner_model = model
def forward(self, z, sigma, cond, uncond, text_cfg_scale, image_cfg_scale):
cfg_z = einops.repeat(z, "b ... -> (repeat b) ...", repeat=3)
cfg_sigma = einops.repeat(sigma, "b ... -> (repeat b) ...", repeat=3)
cfg_cond = {
"c_crossattn": [torch.cat([cond["c_crossattn"][0], uncond["c_crossattn"][0], cond["c_crossattn"][0]])],
"c_concat": [torch.cat([cond["c_concat"][0], cond["c_concat"][0], uncond["c_concat"][0]])],
}
out_cond, out_img_cond, out_txt_cond \
= self.inner_model(cfg_z, cfg_sigma, cond=cfg_cond).chunk(3)
return 0.5 * (out_img_cond + out_txt_cond) + \
text_cfg_scale * (out_cond - out_img_cond) + \
image_cfg_scale * (out_cond - out_txt_cond)
def load_model_from_config(config, ckpt, vae_ckpt=None, verbose=False):
model = instantiate_from_config(config.model)
print(f"Loading model from {ckpt}")
pl_sd = torch.load(ckpt, map_location="cpu")
if 'state_dict' in pl_sd:
pl_sd = pl_sd['state_dict']
m, u = model.load_state_dict(pl_sd, strict=False)
print(m, u)
return model
def mask_decode(encoded_mask,image_shape=[512,512]):
length=image_shape[0]*image_shape[1]
mask_array=np.zeros((length,))
for i in range(0,len(encoded_mask),2):
splice_len=min(encoded_mask[i+1],length-encoded_mask[i])
for j in range(splice_len):
mask_array[encoded_mask[i]+j]=1
mask_array=mask_array.reshape(image_shape[0], image_shape[1])
# to avoid annotation errors in boundary
mask_array[0,:]=1
mask_array[-1,:]=1
mask_array[:,0]=1
mask_array[:,-1]=1
return mask_array
def setup_seed(seed=1234):
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
random.seed(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
def instruct_diffusion_edit(edit_method, image_path,model, edit_prompt, resolution=512,steps=50,cfg_text=5.0,cfg_image=1.25):
if edit_method=="instruct-diffusion":
input_image_ = Image.open(image_path).convert("RGB")
width, height = input_image_.size
factor = resolution / max(width, height)
factor = math.ceil(min(width, height) * factor / 64) * 64 / min(width, height)
width_resize = int((width * factor) // 64) * 64
height_resize = int((height * factor) // 64) * 64
input_image = ImageOps.fit(input_image_, (width_resize, height_resize), method=Image.Resampling.LANCZOS)
with torch.no_grad(), autocast("cuda"):
cond = {}
cond["c_crossattn"] = [model.get_learned_conditioning([edit_prompt])]
input_image = 2 * torch.tensor(np.array(input_image)).float() / 255 - 1
input_image = rearrange(input_image, "h w c -> 1 c h w").to(next(model.parameters()).device)
cond["c_concat"] = [model.encode_first_stage(input_image).mode()]
uncond = {}
uncond["c_crossattn"] = [null_token]
uncond["c_concat"] = [torch.zeros_like(cond["c_concat"][0])]
sigmas = model_wrap.get_sigmas(steps)
extra_args = {
"cond": cond,
"uncond": uncond,
"text_cfg_scale": cfg_text,
"image_cfg_scale": cfg_image,
}
z = torch.randn_like(cond["c_concat"][0]) * sigmas[0]
z = K.sampling.sample_euler_ancestral(model_wrap_cfg, z, sigmas, extra_args=extra_args)
x = model.decode_first_stage(z)
x = torch.clamp((x + 1.0) / 2.0, min=0.0, max=1.0)
x = 255.0 * rearrange(x, "1 c h w -> h w c")
print(x.shape)
edited_image = Image.fromarray(x.type(torch.uint8).cpu().numpy())
edited_image = ImageOps.fit(edited_image, (width, height), method=Image.Resampling.LANCZOS)
image_instruct = txt_draw(f"edit prompt: {edit_prompt}")
return Image.fromarray(np.concatenate((image_instruct, np.array(input_image_), np.zeros_like(image_instruct), np.array(edited_image)),axis=1))
else:
raise NotImplementedError(f"No edit method named {edit_method}")
image_save_paths={
"instruct-diffusion":"instruct-diffusion",
}
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--rerun_exist_images', action= "store_true") # rerun existing images
parser.add_argument('--data_path', type=str, default="data") # the editing category that needed to run
parser.add_argument('--output_path', type=str, default="output") # the editing category that needed to run
parser.add_argument('--edit_category_list', nargs = '+', type=str, default=["0","1","2","3","4","5","6","7","8","9"]) # the editing category that needed to run
parser.add_argument('--edit_method_list', nargs = '+', type=str, default=["instruct-diffusion"]) # the editing methods that needed to run
parser.add_argument('--checkpoint', type=str, require=True, default="data/checkpoints/v1-5-pruned-emaonly-adaption.ckpt") # the editing methods that needed to run
args = parser.parse_args()
rerun_exist_images=args.rerun_exist_images
data_path=args.data_path
output_path=args.output_path
edit_category_list=args.edit_category_list
edit_method_list=args.edit_method_list
checkpoint=args.checkpoint
with open(f"{data_path}/mapping_file.json", "r") as f:
editing_instruction = json.load(f)
config = OmegaConf.load("models/InstructDiffusion/configs/instruct_diffusion.yaml")
model = load_model_from_config(config, checkpoint, None)
model.eval().cuda()
model_wrap = K.external.CompVisDenoiser(model)
model_wrap_cfg = CFGDenoiser(model_wrap)
null_token = model.get_learned_conditioning([""])
for key, item in editing_instruction.items():
if item["editing_type_id"] not in edit_category_list:
continue
original_prompt = item["original_prompt"].replace("[", "").replace("]", "")
editing_prompt = item["editing_prompt"].replace("[", "").replace("]", "")
image_path = os.path.join(f"{data_path}/annotation_images", item["image_path"])
editing_instruction = item["editing_instruction"]
blended_word = item["blended_word"].split(" ") if item["blended_word"] != "" else []
mask = Image.fromarray(np.uint8(mask_decode(item["mask"])[:,:,np.newaxis].repeat(3,2))).convert("L")
for edit_method in edit_method_list:
present_image_save_path=image_path.replace(data_path, os.path.join(output_path,image_save_paths[edit_method]))
if ((not os.path.exists(present_image_save_path)) or rerun_exist_images):
print(f"editing image [{image_path}] with [{edit_method}]")
setup_seed()
torch.cuda.empty_cache()
edited_image = instruct_diffusion_edit(edit_method,
image_path=image_path,
model=model,
edit_prompt=editing_instruction
)
if not os.path.exists(os.path.dirname(present_image_save_path)):
os.makedirs(os.path.dirname(present_image_save_path))
edited_image.save(present_image_save_path)
print(f"finish")
else:
print(f"skip image [{image_path}] with [{edit_method}]")
================================================
FILE: run_editing_instructpix2pix.py
================================================
from __future__ import annotations
import math
import random
import sys
from argparse import ArgumentParser
import einops
import k_diffusion as K
import numpy as np
import torch
import torch.nn as nn
from einops import rearrange
from omegaconf import OmegaConf
from PIL import Image, ImageOps
from torch import autocast
import json
import os
import argparse
from utils.utils import txt_draw
sys.path.append("models/instructpix2pix/stable_diffusion")
from ldm.util import instantiate_from_config
def setup_seed(seed=1234):
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
random.seed(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
class CFGDenoiser(nn.Module):
def __init__(self, model):
super().__init__()
self.inner_model = model
def forward(self, z, sigma, cond, uncond, text_cfg_scale, image_cfg_scale):
cfg_z = einops.repeat(z, "1 ... -> n ...", n=3)
cfg_sigma = einops.repeat(sigma, "1 ... -> n ...", n=3)
cfg_cond = {
"c_crossattn": [torch.cat([cond["c_crossattn"][0], uncond["c_crossattn"][0], uncond["c_crossattn"][0]])],
"c_concat": [torch.cat([cond["c_concat"][0], cond["c_concat"][0], uncond["c_concat"][0]])],
}
out_cond, out_img_cond, out_uncond = self.inner_model(cfg_z, cfg_sigma, cond=cfg_cond).chunk(3)
return out_uncond + text_cfg_scale * (out_cond - out_img_cond) + image_cfg_scale * (out_img_cond - out_uncond)
def load_model_from_config(config, ckpt, vae_ckpt=None, verbose=False):
print(f"Loading model from {ckpt}")
pl_sd = torch.load(ckpt, map_location="cpu")
if "global_step" in pl_sd:
print(f"Global Step: {pl_sd['global_step']}")
sd = pl_sd["state_dict"]
if vae_ckpt is not None:
print(f"Loading VAE from {vae_ckpt}")
vae_sd = torch.load(vae_ckpt, map_location="cpu")["state_dict"]
sd = {
k: vae_sd[k[len("first_stage_model.") :]] if k.startswith("first_stage_model.") else v
for k, v in sd.items()
}
model = instantiate_from_config(config.model)
m, u = model.load_state_dict(sd, strict=False)
if len(m) > 0 and verbose:
print("missing keys:")
print(m)
if len(u) > 0 and verbose:
print("unexpected keys:")
print(u)
return model
def mask_decode(encoded_mask,image_shape=[512,512]):
length=image_shape[0]*image_shape[1]
mask_array=np.zeros((length,))
for i in range(0,len(encoded_mask),2):
splice_len=min(encoded_mask[i+1],length-encoded_mask[i])
for j in range(splice_len):
mask_array[encoded_mask[i]+j]=1
mask_array=mask_array.reshape(image_shape[0], image_shape[1])
# to avoid annotation errors in boundary
mask_array[0,:]=1
mask_array[-1,:]=1
mask_array[:,0]=1
mask_array[:,-1]=1
return mask_array
def edit_instruct_pix2pix(
edit_method,
input,
edit,
resolution=512,
steps=50,
cfg_text=7.5,
cfg_image=1.5,
):
if edit_method=="instruct-pix2pix":
model_wrap = K.external.CompVisDenoiser(model)
model_wrap_cfg = CFGDenoiser(model_wrap)
null_token = model.get_learned_conditioning([""])
input_image_numpy = Image.open(input).convert("RGB")
width, height = input_image_numpy.size
factor = resolution / max(width, height)
factor = math.ceil(min(width, height) * factor / 64) * 64 / min(width, height)
width = int((width * factor) // 64) * 64
height = int((height * factor) // 64) * 64
input_image = ImageOps.fit(input_image_numpy, (width, height), method=Image.Resampling.LANCZOS)
with torch.no_grad(), autocast("cuda"), model.ema_scope():
cond = {}
cond["c_crossattn"] = [model.get_learned_conditioning([edit])]
input_image = 2 * torch.tensor(np.array(input_image)).float() / 255 - 1
input_image = rearrange(input_image, "h w c -> 1 c h w").to(model.device)
cond["c_concat"] = [model.encode_first_stage(input_image).mode()]
uncond = {}
uncond["c_crossattn"] = [null_token]
uncond["c_concat"] = [torch.zeros_like(cond["c_concat"][0])]
sigmas = model_wrap.get_sigmas(steps)
extra_args = {
"cond": cond,
"uncond": uncond,
"text_cfg_scale": cfg_text,
"image_cfg_scale": cfg_image,
}
z = torch.randn_like(cond["c_concat"][0]) * sigmas[0]
z = K.sampling.sample_euler_ancestral(model_wrap_cfg, z, sigmas, extra_args=extra_args)
x = model.decode_first_stage(z)
x = torch.clamp((x + 1.0) / 2.0, min=0.0, max=1.0)
x = 255.0 * rearrange(x, "1 c h w -> h w c")
edited_image = Image.fromarray(x.type(torch.uint8).cpu().numpy())
image_instruct = txt_draw(f"edit prompt: {edit}")
return Image.fromarray(np.concatenate((image_instruct, input_image_numpy, np.zeros_like(image_instruct), np.array(edited_image)),axis=1))
else:
raise NotImplementedError(f"No edit method named {edit_method}")
image_save_paths={
"instruct-pix2pix":"instruct-pix2pix",
}
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--rerun_exist_images', action= "store_true") # rerun existing images
parser.add_argument('--data_path', type=str, default="data") # the editing category that needed to run
parser.add_argument('--output_path', type=str, default="output") # the editing category that needed to run
parser.add_argument('--edit_category_list', nargs = '+', type=str, default=["0","1","2","3","4","5","6","7","8","9"]) # the editing category that needed to run
parser.add_argument('--edit_method_list', nargs = '+', type=str, default=["instruct-pix2pix"]) # the editing methods that needed to run
parser.add_argument('--checkpoint', type=str, default="data/checkpoints/instruct-pix2pix-00-22000.ckpt") # the editing methods that needed to run
args = parser.parse_args()
rerun_exist_images=args.rerun_exist_images
data_path=args.data_path
output_path=args.output_path
edit_category_list=args.edit_category_list
edit_method_list=args.edit_method_list
checkpoint=args.checkpoint
rerun_exist_images=False
with open("data/mapping_file.json", "r") as f:
editing_instruction = json.load(f)
config = OmegaConf.load("models/instructpix2pix/configs/generate.yaml")
model = load_model_from_config(config, checkpoint, None)
model.eval().cuda()
for key, item in editing_instruction.items():
if item["editing_type_id"] not in edit_category_list:
continue
original_prompt = item["original_prompt"].replace("[", "").replace("]", "")
editing_prompt = item["editing_prompt"].replace("[", "").replace("]", "")
image_path = os.path.join(f"{data_path}/annotation_images", item["image_path"])
editing_instruction = item["editing_instruction"]
blended_word = item["blended_word"].split(" ") if item["blended_word"] != "" else []
mask = Image.fromarray(np.uint8(mask_decode(item["mask"])[:,:,np.newaxis].repeat(3,2))).convert("L")
for edit_method in edit_method_list:
present_image_save_path=image_path.replace(data_path, os.path.join(output_path,image_save_paths[edit_method]))
if ((not os.path.exists(present_image_save_path)) or rerun_exist_images):
print(f"editing image [{image_path}] with [{edit_method}]")
setup_seed()
torch.cuda.empty_cache()
edited_image = edit_instruct_pix2pix(
edit_method=edit_method,
input=image_path,
edit=editing_instruction
)
if not os.path.exists(os.path.dirname(present_image_save_path)):
os.makedirs(os.path.dirname(present_image_save_path))
edited_image.save(present_image_save_path)
print(f"finish")
else:
print(f"skip image [{image_path}] with [{edit_method}]")
================================================
FILE: run_editing_masactrl.py
================================================
import argparse
import json
from PIL import Image
import numpy as np
import torch
import torch.nn.functional as F
import random
import os
from diffusers import DDIMScheduler
from models.p2p.inversion import DirectInversion
from models.masactrl.diffuser_utils import MasaCtrlPipeline
from models.masactrl.masactrl_utils import AttentionBase
from models.masactrl.masactrl_utils import regiter_attention_editor_diffusers
from models.masactrl.masactrl import MutualSelfAttentionControl
from utils.utils import load_512,txt_draw
from torchvision.io import read_image
def mask_decode(encoded_mask,image_shape=[512,512]):
length=image_shape[0]*image_shape[1]
mask_array=np.zeros((length,))
for i in range(0,len(encoded_mask),2):
splice_len=min(encoded_mask[i+1],length-encoded_mask[i])
for j in range(splice_len):
mask_array[encoded_mask[i]+j]=1
mask_array=mask_array.reshape(image_shape[0], image_shape[1])
# to avoid annotation errors in boundary
mask_array[0,:]=1
mask_array[-1,:]=1
mask_array[:,0]=1
mask_array[:,-1]=1
return mask_array
def setup_seed(seed=1234):
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
random.seed(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
def load_image(image_path, device):
image = read_image(image_path)
image = image[:3].unsqueeze_(0).float() / 127.5 - 1. # [-1, 1]
image = F.interpolate(image, (512, 512))
image = image.to(device)
return image
class MasaCtrlEditor:
def __init__(self, method_list, device, num_ddim_steps=50) -> None:
self.device=device
self.method_list=method_list
self.num_ddim_steps=num_ddim_steps
# init model
self.scheduler = DDIMScheduler(beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
clip_sample=False,
set_alpha_to_one=False)
self.model = MasaCtrlPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4", scheduler=self.scheduler).to(device)
self.model.scheduler.set_timesteps(self.num_ddim_steps)
def __call__(self,
edit_method,
image_path,
prompt_src,
prompt_tar,
guidance_scale,
step=4,
layper=10):
if edit_method=="ddim+masactrl":
return self.edit_image_ddim_MasaCtrl(image_path,prompt_src,prompt_tar,guidance_scale,step=step,layper=layper)
elif edit_method=="directinversion+masactrl":
return self.edit_image_directinversion_MasaCtrl(image_path,prompt_src,prompt_tar,guidance_scale,step=step,layper=layper)
else:
raise NotImplementedError(f"No edit method named {edit_method}")
def edit_image_directinversion_MasaCtrl(self,image_path,prompt_src,prompt_tar,guidance_scale,step=4,layper=10):
source_image=load_image(image_path, self.device)
image_gt = load_512(image_path)
prompts=["", prompt_tar]
null_inversion = DirectInversion(model=self.model,
num_ddim_steps=self.num_ddim_steps)
_, image_enc_latent, x_stars, noise_loss_list = null_inversion.invert(
image_gt=image_gt, prompt=prompts, guidance_scale=guidance_scale)
x_t = x_stars[-1]
# results of direct synthesis
editor = AttentionBase()
regiter_attention_editor_diffusers(self.model, editor)
image_fixed = self.model([prompt_tar],
latents=x_t,
num_inference_steps=self.num_ddim_steps,
guidance_scale=guidance_scale,
noise_loss_list=None)
# hijack the attention module
editor = MutualSelfAttentionControl(step, layper)
regiter_attention_editor_diffusers(self.model, editor)
# inference the synthesized image
image_masactrl = self.model(prompts,
latents= x_t.expand(len(prompts), -1, -1, -1),
guidance_scale=guidance_scale,
noise_loss_list=noise_loss_list)
image_instruct = txt_draw(f"source prompt: {prompt_src}\ntarget prompt: {prompt_tar}")
out_image=np.concatenate((
np.array(image_instruct),
((source_image[0].permute(1,2,0).detach().cpu().numpy() * 0.5 + 0.5)*255).astype(np.uint8),
(image_masactrl[0].permute(1,2,0).detach().cpu().numpy()*255).astype(np.uint8),
(image_masactrl[-1].permute(1,2,0).detach().cpu().numpy()*255).astype(np.uint8)),1)
return Image.fromarray(out_image)
def edit_image_ddim_MasaCtrl(self, image_path,prompt_src,prompt_tar,guidance_scale,step=4,layper=10):
source_image=load_image(image_path, self.device)
prompts=["", prompt_tar]
start_code, latents_list = self.model.invert(source_image,
"",
guidance_scale=guidance_scale,
num_inference_steps=self.num_ddim_steps,
return_intermediates=True)
start_code = start_code.expand(len(prompts), -1, -1, -1)
# results of direct synthesis
editor = AttentionBase()
regiter_attention_editor_diffusers(self.model, editor)
image_fixed = self.model([prompt_tar],
latents=start_code[-1:],
num_inference_steps=self.num_ddim_steps,
guidance_scale=guidance_scale)
# hijack the attention module
editor = MutualSelfAttentionControl(step, layper)
regiter_attention_editor_diffusers(self.model, editor)
# inference the synthesized image
image_masactrl = self.model(prompts,
latents=start_code,
guidance_scale=guidance_scale)
image_instruct = txt_draw(f"source prompt: {prompt_src}\ntarget prompt: {prompt_tar}")
out_image=np.concatenate((
np.array(image_instruct),
((source_image[0].permute(1,2,0).detach().cpu().numpy() * 0.5 + 0.5)*255).astype(np.uint8),
(image_masactrl[0].permute(1,2,0).detach().cpu().numpy()*255).astype(np.uint8),
(image_masactrl[-1].permute(1,2,0).detach().cpu().numpy()*255).astype(np.uint8)),1)
return Image.fromarray(out_image)
image_save_paths={
"ddim+masactrl":"ddim+masactrl",
"directinversion+masactrl":"directinversion+masactrl",
}
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--rerun_exist_images', action= "store_true") # rerun existing images
parser.add_argument('--data_path', type=str, default="data") # the editing category that needed to run
parser.add_argument('--output_path', type=str, default="output") # the editing category that needed to run
parser.add_argument('--edit_category_list', nargs = '+', type=str, default=["0","1","2","3","4","5","6","7","8","9"]) # the editing category that needed to run
parser.add_argument('--edit_method_list', nargs = '+', type=str, default=["ddim+masactrl","directinversion+masactrl"]) # the editing methods that needed to run
args = parser.parse_args()
rerun_exist_images=args.rerun_exist_images
data_path=args.data_path
output_path=args.output_path
edit_category_list=args.edit_category_list
edit_method_list=args.edit_method_list
masactrl_editor=MasaCtrlEditor(edit_method_list, torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu') )
with open(f"{data_path}/mapping_file.json", "r") as f:
editing_instruction = json.load(f)
for key, item in editing_instruction.items():
if item["editing_type_id"] not in edit_category_list:
continue
original_prompt = item["original_prompt"].replace("[", "").replace("]", "")
editing_prompt = item["editing_prompt"].replace("[", "").replace("]", "")
image_path = os.path.join(f"{data_path}/annotation_images", item["image_path"])
editing_instruction = item["editing_instruction"]
blended_word = item["blended_word"].split(" ") if item["blended_word"] != "" else []
mask = Image.fromarray(np.uint8(mask_decode(item["mask"])[:,:,np.newaxis].repeat(3,2))).convert("L")
for edit_method in edit_method_list:
present_image_save_path=image_path.replace(data_path, os.path.join(output_path,image_save_paths[edit_method]))
if ((not os.path.exists(present_image_save_path)) or rerun_exist_images):
print(f"editing image [{image_path}] with [{edit_method}]")
setup_seed()
torch.cuda.empty_cache()
edited_image = masactrl_editor(edit_method,
image_path=image_path,
prompt_src=original_prompt,
prompt_tar=editing_prompt,
guidance_scale=7.5,
step=4,
layper=10
)
if not os.path.exists(os.path.dirname(present_image_save_path)):
os.makedirs(os.path.dirname(present_image_save_path))
edited_image.save(present_image_save_path)
print(f"finish")
else:
print(f"skip image [{image_path}] with [{edit_method}]")
================================================
FILE: run_editing_p2p.py
================================================
import os
import numpy as np
import argparse
import json
from PIL import Image
import torch
import random
from models.p2p_editor import P2PEditor
def mask_decode(encoded_mask,image_shape=[512,512]):
length=image_shape[0]*image_shape[1]
mask_array=np.zeros((length,))
for i in range(0,len(encoded_mask),2):
splice_len=min(encoded_mask[i+1],length-encoded_mask[i])
for j in range(splice_len):
mask_array[encoded_mask[i]+j]=1
mask_array=mask_array.reshape(image_shape[0], image_shape[1])
# to avoid annotation errors in boundary
mask_array[0,:]=1
mask_array[-1,:]=1
mask_array[:,0]=1
mask_array[:,-1]=1
return mask_array
def setup_seed(seed=1234):
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
random.seed(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
image_save_paths={
"ddim+p2p":"ddim+p2p",
"null-text-inversion+p2p":"null-text-inversion+p2p",
"null-text-inversion+p2p_a800":"null-text-inversion+p2p_a800",
"null-text-inversion+p2p_3090":"null-text-inversion+p2p_3090",
"negative-prompt-inversion+p2p":"negative-prompt-inversion+p2p",
"directinversion+p2p":"directinversion+p2p",
"directinversion+p2p_guidance_0_1":"directinversion+p2p_guidance_0_1",
"directinversion+p2p_guidance_0_5":"directinversion+p2p_guidance_0_5",
"directinversion+p2p_guidance_0_25":"directinversion+p2p_guidance_0_25",
"directinversion+p2p_guidance_0_75":"directinversion+p2p_guidance_0_75",
"directinversion+p2p_guidance_1_1":"directinversion+p2p_guidance_1_1",
"directinversion+p2p_guidance_1_5":"directinversion+p2p_guidance_1_5",
"directinversion+p2p_guidance_1_25":"directinversion+p2p_guidance_1_25",
"directinversion+p2p_guidance_1_75":"directinversion+p2p_guidance_1_75",
"directinversion+p2p_guidance_25_1":"directinversion+p2p_guidance_25_1",
"directinversion+p2p_guidance_25_5":"directinversion+p2p_guidance_25_5",
"directinversion+p2p_guidance_25_25":"directinversion+p2p_guidance_25_25",
"directinversion+p2p_guidance_25_75":"directinversion+p2p_guidance_25_75",
"directinversion+p2p_guidance_5_1":"directinversion+p2p_guidance_5_1",
"directinversion+p2p_guidance_5_5":"directinversion+p2p_guidance_5_5",
"directinversion+p2p_guidance_5_25":"directinversion+p2p_guidance_5_25",
"directinversion+p2p_guidance_5_75":"directinversion+p2p_guidance_5_75",
"directinversion+p2p_guidance_75_1":"directinversion+p2p_guidance_75_1",
"directinversion+p2p_guidance_75_5":"directinversion+p2p_guidance_75_5",
"directinversion+p2p_guidance_75_25":"directinversion+p2p_guidance_75_25",
"directinversion+p2p_guidance_75_75":"directinversion+p2p_guidance_75_75",
"null-text-inversion+proximal-guidance":"null-text-inversion+proximal-guidance",
"negative-prompt-inversion+proximal-guidance":"negative-prompt-inversion+proximal-guidance",
"ablation_null-latent-inversion+p2p":"ablation_null-latent-inversion+p2p",
"ablation_directinversion_08+p2p":"ablation_directinversion_08+p2p",
"ablation_directinversion_04+p2p":"ablation_directinversion_04+p2p",
"ablation_directinversion_interval_2+p2p":"ablation_directinversion_interval_2+p2p",
"ablation_directinversion_interval_5+p2p":"ablation_directinversion_interval_5+p2p",
"ablation_directinversion_interval_10+p2p":"ablation_directinversion_interval_10+p2p",
"ablation_directinversion_interval_24+p2p":"ablation_directinversion_interval_24+p2p",
"ablation_directinversion_interval_49+p2p":"ablation_directinversion_interval_49+p2p",
"ablation_null-text-inversion_single_branch+p2p":"ablation_null-text-inversion_single_branch+p2p",
"ablation_directinversion_add-source+p2p":"ablation_directinversion_add-source+p2p",
"ablation_directinversion_add-target+p2p":"ablation_directinversion_add-target+p2p"
}
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--rerun_exist_images', action= "store_true") # rerun existing images
parser.add_argument('--data_path', type=str, default="data") # the editing category that needed to run
parser.add_argument('--output_path', type=str, default="output") # the editing category that needed to run
parser.add_argument('--edit_category_list', nargs = '+', type=str, default=["0","1","2","3","4","5","6","7","8","9"]) # the editing category that needed to run
parser.add_argument('--edit_method_list', nargs = '+', type=str, default=["ddim+p2p"]) # the editing methods that needed to run
args = parser.parse_args()
rerun_exist_images=args.rerun_exist_images
data_path=args.data_path
output_path=args.output_path
edit_category_list=args.edit_category_list
edit_method_list=args.edit_method_list
p2p_editor=P2PEditor(edit_method_list, torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu'),num_ddim_steps=50)
with open(f"{data_path}/mapping_file.json", "r") as f:
editing_instruction = json.load(f)
for key, item in editing_instruction.items():
if item["editing_type_id"] not in edit_category_list:
continue
original_prompt = item["original_prompt"].replace("[", "").replace("]", "")
editing_prompt = item["editing_prompt"].replace("[", "").replace("]", "")
image_path = os.path.join(f"{data_path}/annotation_images", item["image_path"])
editing_instruction = item["editing_instruction"]
blended_word = item["blended_word"].split(" ") if item["blended_word"] != "" else []
mask = Image.fromarray(np.uint8(mask_decode(item["mask"])[:,:,np.newaxis].repeat(3,2))).convert("L")
for edit_method in edit_method_list:
present_image_save_path=image_path.replace(data_path, os.path.join(output_path,image_save_paths[edit_method]))
if ((not os.path.exists(present_image_save_path)) or rerun_exist_images):
print(f"editing image [{image_path}] with [{edit_method}]")
setup_seed()
torch.cuda.empty_cache()
edited_image = p2p_editor(edit_method,
image_path=image_path,
prompt_src=original_prompt,
prompt_tar=editing_prompt,
guidance_scale=7.5,
cross_replace_steps=0.4,
self_replace_steps=0.6,
blend_word=(((blended_word[0], ),
(blended_word[1], ))) if len(blended_word) else None,
eq_params={
"words": (blended_word[1], ),
"values": (2, )
} if len(blended_word) else None,
proximal="l0",
quantile=0.75,
use_inversion_guidance=True,
recon_lr=1,
recon_t=400,
)
if not os.path.exists(os.path.dirname(present_image_save_path)):
os.makedirs(os.path.dirname(present_image_save_path))
edited_image.save(present_image_save_path)
print(f"finish")
else:
print(f"skip image [{image_path}] with [{edit_method}]")
================================================
FILE: run_editing_p2p_one_image.ipynb
================================================
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"\n",
"import os \n",
"import numpy as np\n",
"import argparse\n",
"import json\n",
"from PIL import Image\n",
"import torch\n",
"import random\n",
"\n",
"from models.p2p_editor import P2PEditor\n",
"\n",
"torch.cuda.set_device(3)\n",
"\n",
"def mask_decode(encoded_mask,image_shape=[512,512]):\n",
" length=image_shape[0]*image_shape[1]\n",
" mask_array=np.zeros((length,))\n",
" \n",
" for i in range(0,len(encoded_mask),2):\n",
" splice_len=min(encoded_mask[i+1],length-encoded_mask[i])\n",
" for j in range(splice_len):\n",
" mask_array[encoded_mask[i]+j]=1\n",
" \n",
" mask_array=mask_array.reshape(image_shape[0], image_shape[1])\n",
" # to avoid annotation errors in boundary\n",
" mask_array[0,:]=1\n",
" mask_array[-1,:]=1\n",
" mask_array[:,0]=1\n",
" mask_array[:,-1]=1\n",
" \n",
" return mask_array\n",
"\n",
"\n",
"def setup_seed(seed=1234):\n",
" torch.manual_seed(seed)\n",
" torch.cuda.manual_seed_all(seed)\n",
" np.random.seed(seed)\n",
" random.seed(seed)\n",
" torch.backends.cudnn.deterministic = True\n",
" torch.backends.cudnn.benchmark = False\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"safety_checker/pytorch_model.fp16.safetensors not found\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f6fa8dc8e4cb411780a0356c0170efaf",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Fetching 30 files: 0%| | 0/30 [00:00"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# null-text-inversion+p2p\n",
"\n",
"edited_image = p2p_editor(\"null-text-inversion+p2p\",\n",
" image_path=\"scripts/example_cake.jpg\",\n",
" prompt_src=\"a round cake with orange frosting on a wooden plate\",\n",
" prompt_tar=\"a square cake with orange frosting on a wooden plate\",\n",
" guidance_scale=7.5,\n",
" cross_replace_steps=0.4,\n",
" self_replace_steps=0.6,\n",
" blend_word=(((\"cake\", ),\n",
" (\"cake\", ))),\n",
" eq_params={\n",
" \"words\": (\"cake\", ),\n",
" \"values\": (2, )\n",
" },\n",
" proximal=\"l0\",\n",
" quantile=0.75,\n",
" use_inversion_guidance=True,\n",
" recon_lr=1,\n",
" recon_t=400,\n",
" )\n",
"\n",
"print(\"null-text-inversion+p2p\")\n",
"display(edited_image)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ours+p2p\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# directinversion(ours)+p2p\n",
"\n",
"edited_image = p2p_editor(\"directinversion+p2p\",\n",
" image_path=\"scripts/example_cake.jpg\",\n",
" prompt_src=\"a round cake with orange frosting on a wooden plate\",\n",
" prompt_tar=\"a square cake with orange frosting on a wooden plate\",\n",
" guidance_scale=7.5,\n",
" cross_replace_steps=0.4,\n",
" self_replace_steps=0.6,\n",
" blend_word=(((\"cake\", ),\n",
" (\"cake\", ))),\n",
" eq_params={\n",
" \"words\": (\"cake\", ),\n",
" \"values\": (2, )\n",
" },\n",
" proximal=\"l0\",\n",
" quantile=0.75,\n",
" use_inversion_guidance=True,\n",
" recon_lr=1,\n",
" recon_t=400,\n",
" )\n",
"\n",
"print(\"directinversion(ours)+p2p\")\n",
"display(edited_image)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"null-text-inversion+p2p\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# null-text-inversion+p2p\n",
"\n",
"edited_image = p2p_editor(\"null-text-inversion+p2p\",\n",
" image_path=\"scripts/example_cat.jpg\",\n",
" prompt_src=\"a cat sitting on a table with a green eyes\",\n",
" prompt_tar=\"a watercolor of a cat sitting on a table with a green eyes\",\n",
" guidance_scale=7.5,\n",
" cross_replace_steps=0.4,\n",
" self_replace_steps=0.6,\n",
" blend_word=(((\"cat\", ),\n",
" (\"cat\", ))),\n",
" eq_params={\n",
" \"words\": (\"watercolor\", ),\n",
" \"values\": (5, )\n",
" },\n",
" proximal=\"l0\",\n",
" quantile=0.75,\n",
" use_inversion_guidance=True,\n",
" recon_lr=1,\n",
" recon_t=400,\n",
" )\n",
"\n",
"print(\"null-text-inversion+p2p\")\n",
"display(edited_image)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ours+p2p\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# directinversion+p2p\n",
"\n",
"edited_image = p2p_editor(\"directinversion+p2p\",\n",
" image_path=\"scripts/example_cat.jpg\",\n",
" prompt_src=\"a cat sitting on a table with a green eyes\",\n",
" prompt_tar=\"a watercolor of a cat sitting on a table with a green eyes\",\n",
" guidance_scale=7.5,\n",
" cross_replace_steps=0.4,\n",
" self_replace_steps=0.6,\n",
" blend_word=(((\"cat\", ),\n",
" (\"cat\", ))),\n",
" eq_params={\n",
" \"words\": (\"watercolor\", ),\n",
" \"values\": (5, )\n",
" },\n",
" proximal=\"l0\",\n",
" quantile=0.75,\n",
" use_inversion_guidance=True,\n",
" recon_lr=1,\n",
" recon_t=400,\n",
" )\n",
"\n",
"print(\"directinversion(ours)+p2p\")\n",
"display(edited_image)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "p2p",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.18"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}
================================================
FILE: run_editing_p2p_one_image.py
================================================
import os
import numpy as np
import argparse
import json
from PIL import Image
import torch
import random
from models.p2p_editor import P2PEditor
def mask_decode(encoded_mask,image_shape=[512,512]):
length=image_shape[0]*image_shape[1]
mask_array=np.zeros((length,))
for i in range(0,len(encoded_mask),2):
splice_len=min(encoded_mask[i+1],length-encoded_mask[i])
for j in range(splice_len):
mask_array[encoded_mask[i]+j]=1
mask_array=mask_array.reshape(image_shape[0], image_shape[1])
# to avoid annotation errors in boundary
mask_array[0,:]=1
mask_array[-1,:]=1
mask_array[:,0]=1
mask_array[:,-1]=1
return mask_array
def setup_seed(seed=1234):
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
random.seed(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--image_path', type=str, default="scripts/example_cake.jpg") # the editing category that needed to run
parser.add_argument('--original_prompt', type=str, default="a round cake with orange frosting on a wooden plate") # the editing category that needed to run
parser.add_argument('--editing_prompt', type=str, default="a square cake with orange frosting on a wooden plate") # the editing category that needed to run
parser.add_argument('--blended_word', type=str, default="cake cake") # the editing category that needed to run
parser.add_argument('--output_path', nargs = '+',type=str, default=["ddim+p2p.jpg"]) # the editing category that needed to run
parser.add_argument('--edit_method_list', nargs = '+', type=str, default=["ddim+p2p"]) # the editing methods that needed to run
args = parser.parse_args()
output_path=args.output_path
edit_method_list=args.edit_method_list
p2p_editor=P2PEditor(edit_method_list, torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu') )
original_prompt = args.original_prompt
editing_prompt = args.editing_prompt
image_path = args.image_path
blended_word = args.blended_word.split(" ") if args.blended_word != "" else []
for edit_method_i in range(len(edit_method_list)):
edit_method=edit_method_list[edit_method_i]
present_image_save_path=output_path[edit_method_i]
print(f"editing image [{image_path}] with [{edit_method}]")
setup_seed()
torch.cuda.empty_cache()
edited_image = p2p_editor(edit_method,
image_path=image_path,
prompt_src=original_prompt,
prompt_tar=editing_prompt,
guidance_scale=7.5,
cross_replace_steps=0.4,
self_replace_steps=0.6,
blend_word=(((blended_word[0], ),
(blended_word[1], ))) if len(blended_word) else None,
eq_params={
"words": (blended_word[1], ),
"values": (2, )
} if len(blended_word) else None,
proximal="l0",
quantile=0.75,
use_inversion_guidance=True,
recon_lr=1,
recon_t=400,
)
edited_image.save(present_image_save_path)
print(f"finish")
================================================
FILE: run_editing_pix2pix_zero.py
================================================
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import random
import argparse
import json
from PIL import Image
from lavis.models import load_model_and_preprocess
from models.pix2pix_zero.ddim_inv import DDIMInversion
from models.pix2pix_zero.scheduler import DDIMInverseScheduler
from models.pix2pix_zero.edit_directions import construct_direction
from models.pix2pix_zero.edit_pipeline import EditingPipeline
from utils.utils import txt_draw
from diffusers import DDIMScheduler
NUM_DDIM_STEPS = 50
XA_GUIDANCE=0.1
device = torch.device('cuda') if torch.cuda.is_available() else torch.device(
'cpu')
# load the BLIP model
model_blip, vis_processors, _ = load_model_and_preprocess(name="blip_caption",
model_type="base_coco",
is_eval=True,
device=torch.device(device))
# make the DDIM inversion pipeline
pipe = DDIMInversion.from_pretrained('CompVis/stable-diffusion-v1-4').to(device)
pipe.scheduler = DDIMInverseScheduler.from_config(pipe.scheduler.config)
pipe.scheduler.num_inference_steps=NUM_DDIM_STEPS
edit_pipe = EditingPipeline.from_pretrained('CompVis/stable-diffusion-v1-4').to(device)
edit_pipe.scheduler = DDIMScheduler.from_config(edit_pipe.scheduler.config)
edit_pipe.scheduler.num_inference_steps=NUM_DDIM_STEPS
def setup_seed(seed=1234):
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
random.seed(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
## convert sentences to sentence embeddings
def load_sentence_embeddings(l_sentences, tokenizer, text_encoder, device=device):
with torch.no_grad():
l_embeddings = []
for sent in l_sentences:
text_inputs = tokenizer(
sent,
padding="max_length",
max_length=tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
prompt_embeds = text_encoder(text_input_ids.to(device), attention_mask=None)[0]
l_embeddings.append(prompt_embeds)
return torch.concat(l_embeddings, dim=0).mean(dim=0).unsqueeze(0)
def edit_image_ddim_pix2pix_zero(image_path,
prompt_src,
prompt_tar,
guidance_scale=7.5,
image_size=[512,512]):
image_gt = Image.open(image_path).resize(image_size, Image.Resampling.LANCZOS)
# generate the caption
prompt_str = model_blip.generate({"image": vis_processors["eval"](image_gt).unsqueeze(0).to(device)})[0]
latent_list, x_inv_image, x_dec_img = pipe(
prompt_str,
guidance_scale=1,
num_inversion_steps=NUM_DDIM_STEPS,
img=image_gt
)
inversion_latent=latent_list[-1].detach()
mean_emb_src = load_sentence_embeddings([prompt_src], edit_pipe.tokenizer, edit_pipe.text_encoder, device=device)
mean_emb_tar = load_sentence_embeddings([prompt_tar], edit_pipe.tokenizer, edit_pipe.text_encoder, device=device)
rec_pil, edit_pil = edit_pipe(prompt_str,
num_inference_steps=NUM_DDIM_STEPS,
x_in=inversion_latent,
edit_dir=(mean_emb_tar.mean(0)-mean_emb_src.mean(0)).unsqueeze(0),
guidance_amount=XA_GUIDANCE,
guidance_scale=guidance_scale,
negative_prompt=prompt_str # use the unedited prompt for the negative prompt
)
image_instruct = txt_draw(f"source prompt: {prompt_src}\ntarget prompt: {prompt_tar}")
out_image=np.concatenate((np.array(image_instruct),np.array(image_gt),np.array(rec_pil[0]),np.array(edit_pil[0])),1)
return Image.fromarray(out_image)
def edit_image_directinversion_pix2pix_zero(image_path,
prompt_src,
prompt_tar,
guidance_scale=7.5,
image_size=[512,512]):
image_gt = Image.open(image_path).resize(image_size, Image.Resampling.LANCZOS)
# generate the caption
prompt_str = model_blip.generate({"image": vis_processors["eval"](image_gt).unsqueeze(0).to(device)})[0]
latent_list, x_inv_image, x_dec_img = pipe(
prompt_str,
guidance_scale=1,
num_inversion_steps=NUM_DDIM_STEPS,
img=image_gt
)
inversion_latent=latent_list[-1].detach()
mean_emb_src = load_sentence_embeddings([prompt_src], edit_pipe.tokenizer, edit_pipe.text_encoder, device=device)
mean_emb_tar = load_sentence_embeddings([prompt_tar], edit_pipe.tokenizer, edit_pipe.text_encoder, device=device)
rec_pil, edit_pil = edit_pipe(prompt_str,
num_inference_steps=NUM_DDIM_STEPS,
x_in=inversion_latent,
edit_dir=(mean_emb_tar.mean(0)-mean_emb_src.mean(0)).unsqueeze(0),
guidance_amount=XA_GUIDANCE,
guidance_scale=guidance_scale,
negative_prompt=prompt_str, # use the unedited prompt for the negative prompt
latent_list=latent_list
)
image_instruct = txt_draw(f"source prompt: {prompt_src}\ntarget prompt: {prompt_tar}")
out_image=np.concatenate((np.array(image_instruct),np.array(image_gt),np.array(rec_pil[0]),np.array(edit_pil[0])),1)
return Image.fromarray(out_image)
def mask_decode(encoded_mask,image_shape=[512,512]):
length=image_shape[0]*image_shape[1]
mask_array=np.zeros((length,))
for i in range(0,len(encoded_mask),2):
splice_len=min(encoded_mask[i+1],length-encoded_mask[i])
for j in range(splice_len):
mask_array[encoded_mask[i]+j]=1
mask_array=mask_array.reshape(image_shape[0], image_shape[1])
# to avoid annotation errors in boundary
mask_array[0,:]=1
mask_array[-1,:]=1
mask_array[:,0]=1
mask_array[:,-1]=1
return mask_array
image_save_paths={
"ddim+pix2pix-zero":"ddim+pix2pix-zero",
"directinversion+pix2pix-zero":"directinversion+pix2pix-zero",
}
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--rerun_exist_images', action= "store_true") # rerun existing images
parser.add_argument('--data_path', type=str, default="data") # the editing category that needed to run
parser.add_argument('--output_path', type=str, default="output") # the editing category that needed to run
parser.add_argument('--edit_category_list', nargs = '+', type=str, default=["0","1","2","3","4","5","6","7","8","9"]) # the editing category that needed to run
parser.add_argument('--edit_method_list', nargs = '+', type=str, default=["ddim+pix2pix-zero","directinversion+pix2pix-zero"]) # the editing methods that needed to run
args = parser.parse_args()
rerun_exist_images=args.rerun_exist_images
data_path=args.data_path
output_path=args.output_path
edit_category_list=args.edit_category_list
edit_method_list=args.edit_method_list
with open(f"{data_path}/mapping_file.json", "r") as f:
editing_instruction = json.load(f)
for key, item in editing_instruction.items():
if item["editing_type_id"] not in edit_category_list:
continue
original_prompt = item["original_prompt"].replace("[", "").replace("]", "")
editing_prompt = item["editing_prompt"].replace("[", "").replace("]", "")
image_path = os.path.join(f"{data_path}/annotation_images", item["image_path"])
editing_instruction = item["editing_instruction"]
blended_word = item["blended_word"].split(" ") if item["blended_word"] != "" else []
mask = Image.fromarray(np.uint8(mask_decode(item["mask"])[:,:,np.newaxis].repeat(3,2))).convert("L")
for edit_method in edit_method_list:
present_image_save_path=image_path.replace(data_path, os.path.join(output_path,image_save_paths[edit_method]))
if ((not os.path.exists(present_image_save_path)) or rerun_exist_images):
print(f"editing image [{image_path}] with [{edit_method}]")
setup_seed()
torch.cuda.empty_cache()
if edit_method=="ddim+pix2pix-zero":
edited_image = edit_image_ddim_pix2pix_zero(
image_path=image_path,
prompt_src=original_prompt,
prompt_tar=editing_prompt,
guidance_scale=7.5,
)
elif edit_method=="directinversion+pix2pix-zero":
edited_image = edit_image_directinversion_pix2pix_zero(
image_path=image_path,
prompt_src=original_prompt,
prompt_tar=editing_prompt,
guidance_scale=7.5,
)
else:
raise NotImplementedError(f"No edit method named {edit_method}")
if not os.path.exists(os.path.dirname(present_image_save_path)):
os.makedirs(os.path.dirname(present_image_save_path))
edited_image.save(present_image_save_path)
print(f"finish")
else:
print(f"skip image [{image_path}] with [{edit_method}]")
================================================
FILE: run_editing_pnp.py
================================================
import torch
from diffusers import AutoencoderKL, UNet2DConditionModel, DDIMScheduler, StableDiffusionPipeline
import numpy as np
from PIL import Image
import os
import json
import random
import argparse
import torch.nn as nn
from transformers import CLIPTextModel, CLIPTokenizer
import torchvision.transforms as T
from utils.utils import txt_draw,load_512,latent2image
device = torch.device('cuda') if torch.cuda.is_available() else torch.device(
'cpu')
NUM_DDIM_STEPS = 50
def setup_seed(seed=1234):
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
random.seed(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
def get_timesteps(scheduler, num_inference_steps, strength, device):
# get the original timestep using init_timestep
init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
t_start = max(num_inference_steps - init_timestep, 0)
timesteps = scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start
class Preprocess(nn.Module):
def __init__(self, device,model_key):
super().__init__()
self.device = device
self.use_depth = False
print(f'[INFO] loading stable diffusion...')
# Create model
self.vae = AutoencoderKL.from_pretrained(model_key, subfolder="vae",
torch_dtype=torch.float16).to(self.device)
self.tokenizer = CLIPTokenizer.from_pretrained(model_key, subfolder="tokenizer")
self.text_encoder = CLIPTextModel.from_pretrained(model_key, subfolder="text_encoder", revision="fp16",
torch_dtype=torch.float16).to(self.device)
self.unet = UNet2DConditionModel.from_pretrained(model_key, subfolder="unet", revision="fp16",
torch_dtype=torch.float16).to(self.device)
self.scheduler = DDIMScheduler.from_pretrained(model_key, subfolder="scheduler")
print(f'[INFO] loaded stable diffusion!')
@torch.no_grad()
def get_text_embeds(self, prompt, negative_prompt, device="cuda"):
text_input = self.tokenizer(prompt, padding='max_length', max_length=self.tokenizer.model_max_length,
truncation=True, return_tensors='pt')
text_embeddings = self.text_encoder(text_input.input_ids.to(device))[0]
uncond_input = self.tokenizer(negative_prompt, padding='max_length', max_length=self.tokenizer.model_max_length,
return_tensors='pt')
uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(device))[0]
text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
return text_embeddings
@torch.no_grad()
def decode_latents(self, latents):
with torch.autocast(device_type='cuda', dtype=torch.float32):
latents = 1 / 0.18215 * latents
imgs = self.vae.decode(latents).sample
imgs = (imgs / 2 + 0.5).clamp(0, 1)
return imgs
def load_img(self, image_path):
image_pil = T.Resize(512)(Image.open(image_path).convert("RGB"))
image = T.ToTensor()(image_pil).unsqueeze(0).to(device)
return image
@torch.no_grad()
def encode_imgs(self, imgs):
with torch.autocast(device_type='cuda', dtype=torch.float32):
imgs = 2 * imgs - 1
posterior = self.vae.encode(imgs).latent_dist
latents = posterior.mean * 0.18215
return latents
@torch.no_grad()
def ddim_inversion(self, cond, latent):
latent_list=[latent]
timesteps = reversed(self.scheduler.timesteps)
with torch.autocast(device_type='cuda', dtype=torch.float32):
for i, t in enumerate(timesteps):
cond_batch = cond.repeat(latent.shape[0], 1, 1)
alpha_prod_t = self.scheduler.alphas_cumprod[t]
alpha_prod_t_prev = (
self.scheduler.alphas_cumprod[timesteps[i - 1]]
if i > 0 else self.scheduler.final_alpha_cumprod
)
mu = alpha_prod_t ** 0.5
mu_prev = alpha_prod_t_prev ** 0.5
sigma = (1 - alpha_prod_t) ** 0.5
sigma_prev = (1 - alpha_prod_t_prev) ** 0.5
eps = self.unet(latent, t, encoder_hidden_states=cond_batch).sample
pred_x0 = (latent - sigma_prev * eps) / mu_prev
latent = mu * pred_x0 + sigma * eps
latent_list.append(latent)
return latent_list
@torch.no_grad()
def ddim_sample(self, x, cond):
timesteps = self.scheduler.timesteps
latent_list=[]
with torch.autocast(device_type='cuda', dtype=torch.float32):
for i, t in enumerate(timesteps):
cond_batch = cond.repeat(x.shape[0], 1, 1)
alpha_prod_t = self.scheduler.alphas_cumprod[t]
alpha_prod_t_prev = (
self.scheduler.alphas_cumprod[timesteps[i + 1]]
if i < len(timesteps) - 1
else self.scheduler.final_alpha_cumprod
)
mu = alpha_prod_t ** 0.5
sigma = (1 - alpha_prod_t) ** 0.5
mu_prev = alpha_prod_t_prev ** 0.5
sigma_prev = (1 - alpha_prod_t_prev) ** 0.5
eps = self.unet(x, t, encoder_hidden_states=cond_batch).sample
pred_x0 = (x - sigma * eps) / mu
x = mu_prev * pred_x0 + sigma_prev * eps
latent_list.append(x)
return latent_list
@torch.no_grad()
def extract_latents(self, num_steps, data_path,
inversion_prompt=''):
self.scheduler.set_timesteps(num_steps)
cond = self.get_text_embeds(inversion_prompt, "")[1].unsqueeze(0)
image = self.load_img(data_path)
latent = self.encode_imgs(image)
inverted_x = self.ddim_inversion(cond, latent)
latent_reconstruction = self.ddim_sample(inverted_x[-1], cond)
rgb_reconstruction = self.decode_latents(latent_reconstruction[-1])
latent_reconstruction.reverse()
return inverted_x, rgb_reconstruction, latent_reconstruction
def register_time(model, t):
conv_module = model.unet.up_blocks[1].resnets[1]
setattr(conv_module, 't', t)
down_res_dict = {0: [0, 1], 1: [0, 1], 2: [0, 1]}
up_res_dict = {1: [0, 1, 2], 2: [0, 1, 2], 3: [0, 1, 2]}
for res in up_res_dict:
for block in up_res_dict[res]:
module = model.unet.up_blocks[res].attentions[block].transformer_blocks[0].attn1
setattr(module, 't', t)
for res in down_res_dict:
for block in down_res_dict[res]:
module = model.unet.down_blocks[res].attentions[block].transformer_blocks[0].attn1
setattr(module, 't', t)
module = model.unet.mid_block.attentions[0].transformer_blocks[0].attn1
setattr(module, 't', t)
def register_attention_control_efficient(model, injection_schedule):
def sa_forward(self):
to_out = self.to_out
if type(to_out) is torch.nn.modules.container.ModuleList:
to_out = self.to_out[0]
else:
to_out = self.to_out
def forward(x, encoder_hidden_states=None, attention_mask=None):
batch_size, sequence_length, dim = x.shape
h = self.heads
is_cross = encoder_hidden_states is not None
encoder_hidden_states = encoder_hidden_states if is_cross else x
if not is_cross and self.injection_schedule is not None and (
self.t in self.injection_schedule or self.t == 1000):
q = self.to_q(x)
k = self.to_k(encoder_hidden_states)
source_batch_size = int(q.shape[0] // 3)
# inject unconditional
q[source_batch_size:2 * source_batch_size] = q[:source_batch_size]
k[source_batch_size:2 * source_batch_size] = k[:source_batch_size]
# inject conditional
q[2 * source_batch_size:] = q[:source_batch_size]
k[2 * source_batch_size:] = k[:source_batch_size]
q = self.head_to_batch_dim(q)
k = self.head_to_batch_dim(k)
else:
q = self.to_q(x)
k = self.to_k(encoder_hidden_states)
q = self.head_to_batch_dim(q)
k = self.head_to_batch_dim(k)
v = self.to_v(encoder_hidden_states)
v = self.head_to_batch_dim(v)
sim = torch.einsum("b i d, b j d -> b i j", q, k) * self.scale
if attention_mask is not None:
attention_mask = attention_mask.reshape(batch_size, -1)
max_neg_value = -torch.finfo(sim.dtype).max
attention_mask = attention_mask[:, None, :].repeat(h, 1, 1)
sim.masked_fill_(~attention_mask, max_neg_value)
# attention, what we cannot get enough of
attn = sim.softmax(dim=-1)
out = torch.einsum("b i j, b j d -> b i d", attn, v)
out = self.batch_to_head_dim(out)
return to_out(out)
return forward
res_dict = {1: [1, 2], 2: [0, 1, 2], 3: [0, 1, 2]} # we are injecting attention in blocks 4 - 11 of the decoder, so not in the first block of the lowest resolution
for res in res_dict:
for block in res_dict[res]:
module = model.unet.up_blocks[res].attentions[block].transformer_blocks[0].attn1
module.forward = sa_forward(module)
setattr(module, 'injection_schedule', injection_schedule)
def register_conv_control_efficient(model, injection_schedule):
def conv_forward(self):
def forward(input_tensor, temb):
hidden_states = input_tensor
hidden_states = self.norm1(hidden_states)
hidden_states = self.nonlinearity(hidden_states)
if self.upsample is not None:
# upsample_nearest_nhwc fails with large batch sizes. see https://github.com/huggingface/diffusers/issues/984
if hidden_states.shape[0] >= 64:
input_tensor = input_tensor.contiguous()
hidden_states = hidden_states.contiguous()
input_tensor = self.upsample(input_tensor)
hidden_states = self.upsample(hidden_states)
elif self.downsample is not None:
input_tensor = self.downsample(input_tensor)
hidden_states = self.downsample(hidden_states)
hidden_states = self.conv1(hidden_states)
if temb is not None:
temb = self.time_emb_proj(self.nonlinearity(temb))[:, :, None, None]
if temb is not None and self.time_embedding_norm == "default":
hidden_states = hidden_states + temb
hidden_states = self.norm2(hidden_states)
if temb is not None and self.time_embedding_norm == "scale_shift":
scale, shift = torch.chunk(temb, 2, dim=1)
hidden_states = hidden_states * (1 + scale) + shift
hidden_states = self.nonlinearity(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.conv2(hidden_states)
if self.injection_schedule is not None and (self.t in self.injection_schedule or self.t == 1000):
source_batch_size = int(hidden_states.shape[0] // 3)
# inject unconditional
hidden_states[source_batch_size:2 * source_batch_size] = hidden_states[:source_batch_size]
# inject conditional
hidden_states[2 * source_batch_size:] = hidden_states[:source_batch_size]
if self.conv_shortcut is not None:
input_tensor = self.conv_shortcut(input_tensor)
output_tensor = (input_tensor + hidden_states) / self.output_scale_factor
return output_tensor
return forward
conv_module = model.unet.up_blocks[1].resnets[1]
conv_module.forward = conv_forward(conv_module)
setattr(conv_module, 'injection_schedule', injection_schedule)
class PNP(nn.Module):
def __init__(self, model_key,n_timesteps=NUM_DDIM_STEPS,device="cuda"):
super().__init__()
self.device = device
# Create SD models
print('Loading SD model')
pipe = StableDiffusionPipeline.from_pretrained(model_key, torch_dtype=torch.float16).to("cuda")
pipe.enable_xformers_memory_efficient_attention()
self.vae = pipe.vae
self.tokenizer = pipe.tokenizer
self.text_encoder = pipe.text_encoder
self.unet = pipe.unet
self.scheduler = DDIMScheduler.from_pretrained(model_key, subfolder="scheduler")
self.scheduler.set_timesteps(n_timesteps, device=self.device)
self.n_timesteps=NUM_DDIM_STEPS
print('SD model loaded')
@torch.no_grad()
def get_text_embeds(self, prompt, negative_prompt, batch_size=1):
# Tokenize text and get embeddings
text_input = self.tokenizer(prompt, padding='max_length', max_length=self.tokenizer.model_max_length,
truncation=True, return_tensors='pt')
text_embeddings = self.text_encoder(text_input.input_ids.to(self.device))[0]
# Do the same for unconditional embeddings
uncond_input = self.tokenizer(negative_prompt, padding='max_length', max_length=self.tokenizer.model_max_length,
return_tensors='pt')
uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
# Cat for final embeddings
text_embeddings = torch.cat([uncond_embeddings] * batch_size + [text_embeddings] * batch_size)
return text_embeddings
@torch.no_grad()
def decode_latent(self, latent):
with torch.autocast(device_type='cuda', dtype=torch.float32):
latent = 1 / 0.18215 * latent
img = self.vae.decode(latent).sample
img = (img / 2 + 0.5).clamp(0, 1)
return img
@torch.autocast(device_type='cuda', dtype=torch.float32)
def get_data(self,image_path):
# load image
image = Image.open(image_path).convert('RGB')
image = image.resize((512, 512), resample=Image.Resampling.LANCZOS)
image = T.ToTensor()(image).to(self.device)
return image
@torch.no_grad()
def denoise_step(self, x, t,guidance_scale,noisy_latent):
# register the time step and features in pnp injection modules
latent_model_input = torch.cat(([noisy_latent]+[x] * 2))
register_time(self, t.item())
# compute text embeddings
text_embed_input = torch.cat([self.pnp_guidance_embeds, self.text_embeds], dim=0)
# apply the denoising network
noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embed_input)['sample']
# perform guidance
_,noise_pred_uncond, noise_pred_cond = noise_pred.chunk(3)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_cond - noise_pred_uncond)
# compute the denoising step with the reference model
denoised_latent = self.scheduler.step(noise_pred, t, x)['prev_sample']
return denoised_latent
def init_pnp(self, conv_injection_t, qk_injection_t):
self.qk_injection_timesteps = self.scheduler.timesteps[:qk_injection_t] if qk_injection_t >= 0 else []
self.conv_injection_timesteps = self.scheduler.timesteps[:conv_injection_t] if conv_injection_t >= 0 else []
register_attention_control_efficient(self, self.qk_injection_timesteps)
register_conv_control_efficient(self, self.conv_injection_timesteps)
def run_pnp(self,image_path,noisy_latent,target_prompt,guidance_scale=7.5,pnp_f_t=0.8,pnp_attn_t=0.5):
# load image
self.image = self.get_data(image_path)
self.eps = noisy_latent[-1]
self.text_embeds = self.get_text_embeds(target_prompt, "ugly, blurry, black, low res, unrealistic")
self.pnp_guidance_embeds = self.get_text_embeds("", "").chunk(2)[0]
pnp_f_t = int(self.n_timesteps * pnp_f_t)
pnp_attn_t = int(self.n_timesteps * pnp_attn_t)
self.init_pnp(conv_injection_t=pnp_f_t, qk_injection_t=pnp_attn_t)
edited_img = self.sample_loop(self.eps,guidance_scale,noisy_latent)
return edited_img
def sample_loop(self, x,guidance_scale,noisy_latent):
with torch.autocast(device_type='cuda', dtype=torch.float32):
for i, t in enumerate(self.scheduler.timesteps, desc="Sampling"):
x = self.denoise_step(x, t,guidance_scale,noisy_latent[-1-i])
decoded_latent = self.decode_latent(x)
return decoded_latent
model_key = "runwayml/stable-diffusion-v1-5"
toy_scheduler = DDIMScheduler.from_pretrained(model_key, subfolder="scheduler")
toy_scheduler.set_timesteps(NUM_DDIM_STEPS)
timesteps_to_save, num_inference_steps = get_timesteps(toy_scheduler, num_inference_steps=NUM_DDIM_STEPS,
strength=1.0,
device=device)
model = Preprocess(device, model_key=model_key)
pnp = PNP(model_key)
def edit_image_ddim_PnP(
image_path,
prompt_src,
prompt_tar,
guidance_scale=7.5,
image_shape=[512,512]
):
torch.cuda.empty_cache()
image_gt = load_512(image_path)
_, rgb_reconstruction, latent_reconstruction = model.extract_latents(data_path=image_path,
num_steps=NUM_DDIM_STEPS,
inversion_prompt=prompt_src)
edited_image=pnp.run_pnp(image_path,latent_reconstruction,prompt_tar,guidance_scale)
image_instruct = txt_draw(f"source prompt: {prompt_src}\ntarget prompt: {prompt_tar}")
return Image.fromarray(np.concatenate((
image_instruct,
image_gt,
np.uint8(255*np.array(rgb_reconstruction[0].permute(1,2,0).cpu().detach())),
np.uint8(255*np.array(edited_image[0].permute(1,2,0).cpu().detach())),
),1))
def edit_image_directinversion_PnP(
image_path,
prompt_src,
prompt_tar,
guidance_scale=7.5,
image_shape=[512,512]
):
torch.cuda.empty_cache()
image_gt = load_512(image_path)
inverted_x, rgb_reconstruction, _ = model.extract_latents(data_path=image_path,
num_steps=NUM_DDIM_STEPS,
inversion_prompt=prompt_src)
edited_image=pnp.run_pnp(image_path,inverted_x,prompt_tar,guidance_scale)
image_instruct = txt_draw(f"source prompt: {prompt_src}\ntarget prompt: {prompt_tar}")
return Image.fromarray(np.concatenate((
image_instruct,
image_gt,
np.uint8(np.array(latent2image(model=pnp.vae, latents=inverted_x[1].to(pnp.vae.dtype))[0])),
np.uint8(255*np.array(edited_image[0].permute(1,2,0).cpu().detach())),
),1))
def mask_decode(encoded_mask,image_shape=[512,512]):
length=image_shape[0]*image_shape[1]
mask_array=np.zeros((length,))
for i in range(0,len(encoded_mask),2):
splice_len=min(encoded_mask[i+1],length-encoded_mask[i])
for j in range(splice_len):
mask_array[encoded_mask[i]+j]=1
mask_array=mask_array.reshape(image_shape[0], image_shape[1])
# to avoid annotation errors in boundary
mask_array[0,:]=1
mask_array[-1,:]=1
mask_array[:,0]=1
mask_array[:,-1]=1
return mask_array
image_save_paths={
"ddim+pnp":"ddim+pnp",
"directinversion+pnp":"directinversion+pnp",
}
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--rerun_exist_images', action= "store_true") # rerun existing images
parser.add_argument('--data_path', type=str, default="data") # the editing category that needed to run
parser.add_argument('--output_path', type=str, default="output") # the editing category that needed to run
parser.add_argument('--edit_category_list', nargs = '+', type=str, default=["0","1","2","3","4","5","6","7","8","9"]) # the editing category that needed to run
parser.add_argument('--edit_method_list', nargs = '+', type=str, default=["ddim+pnp","directinversion+pnp"]) # the editing methods that needed to run
args = parser.parse_args()
rerun_exist_images=args.rerun_exist_images
data_path=args.data_path
output_path=args.output_path
edit_category_list=args.edit_category_list
edit_method_list=args.edit_method_list
with open(f"{data_path}/mapping_file.json", "r") as f:
editing_instruction = json.load(f)
for key, item in editing_instruction.items():
if item["editing_type_id"] not in edit_category_list:
continue
original_prompt = item["original_prompt"].replace("[", "").replace("]", "")
editing_prompt = item["editing_prompt"].replace("[", "").replace("]", "")
image_path = os.path.join(f"{data_path}/annotation_images", item["image_path"])
editing_instruction = item["editing_instruction"]
blended_word = item["blended_word"].split(" ") if item["blended_word"] != "" else []
mask = Image.fromarray(np.uint8(mask_decode(item["mask"])[:,:,np.newaxis].repeat(3,2))).convert("L")
for edit_method in edit_method_list:
present_image_save_path=image_path.replace(data_path, os.path.join(output_path,image_save_paths[edit_method]))
if ((not os.path.exists(present_image_save_path)) or rerun_exist_images):
print(f"editing image [{image_path}] with [{edit_method}]")
setup_seed()
torch.cuda.empty_cache()
if edit_method=="ddim+pnp":
edited_image = edit_image_ddim_PnP(
image_path=image_path,
prompt_src=original_prompt,
prompt_tar=editing_prompt,
guidance_scale=7.5,
)
elif edit_method=="directinversion+pnp":
edited_image = edit_image_directinversion_PnP(
image_path=image_path,
prompt_src=original_prompt,
prompt_tar=editing_prompt,
guidance_scale=7.5,
)
else:
raise NotImplementedError(f"No edit method named {edit_method}")
if not os.path.exists(os.path.dirname(present_image_save_path)):
os.makedirs(os.path.dirname(present_image_save_path))
edited_image.save(present_image_save_path)
print(f"finish")
else:
print(f"skip image [{image_path}] with [{edit_method}]")
================================================
FILE: run_editing_stylediffusion.py
================================================
from typing import Optional, Union, Tuple, List, Callable, Dict
import os
import torch
import numpy as np
import random
import argparse
import json
from tqdm import tqdm
from diffusers import StableDiffusionPipeline, DDIMScheduler
from models.stylediffusion import global_var
from utils.utils import txt_draw
from PIL import Image
global_var._init()
global_var.set_value("USE_INITIAL_INV",False)
LOW_RESOURCE=True
global_var.set_value("LOW_RESOURCE",LOW_RESOURCE)
global_var.set_value("MAX_NUM_WORDS",77)
NUM_DDIM_STEPS = 50
global_var.set_value("NUM_DDIM_STEPS",NUM_DDIM_STEPS)
global_var.set_value("BLOCK_NUM",1)
global_var.set_value("IS_TRAIN",True)
device = torch.device('cuda') if torch.cuda.is_available() else torch.device(
'cpu')
global_var.set_value("device",device)
# make the DDIM inversion pipeline
scheduler = DDIMScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", clip_sample=False, set_alpha_to_one=False)
stable = StableDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4', scheduler=scheduler, local_files_only=True).to(device)
tokenizer = stable.tokenizer
global_var.set_value("tokenizer",tokenizer)
from models.stylediffusion.inversion import VaeInversion
from models.stylediffusion.utils import AttentionStore, EmptyControl, register_attention_control,make_controller
from models.stylediffusion import ptp_utils_v
def mask_decode(encoded_mask,image_shape=[512,512]):
length=image_shape[0]*image_shape[1]
mask_array=np.zeros((length,))
for i in range(0,len(encoded_mask),2):
splice_len=min(encoded_mask[i+1],length-encoded_mask[i])
for j in range(splice_len):
mask_array[encoded_mask[i]+j]=1
mask_array=mask_array.reshape(image_shape[0], image_shape[1])
# to avoid annotation errors in boundary
mask_array[0,:]=1
mask_array[-1,:]=1
mask_array[:,0]=1
mask_array[:,-1]=1
return mask_array
# Infernce Code
@torch.no_grad()
def text2image_ldm_stable(
model,
prompt: List[str],
trainer,
controller,
num_inference_steps: int = 50,
guidance_scale: Optional[float] = 7.5,
generator: Optional[torch.Generator] = None,
latent: Optional[torch.FloatTensor] = None,
start_time=50,
return_type='image'
):
batch_size = len(prompt)
register_attention_control(model, trainer, controller)
height = width = 512
text_input = model.tokenizer(
prompt,
padding="max_length",
max_length=model.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_embeddings = model.text_encoder(text_input.input_ids.to(model.device))[0]
max_length = text_input.input_ids.shape[-1]
uncond_input = model.tokenizer([""] * batch_size, padding="max_length", max_length=max_length, return_tensors="pt")
uncond_embeddings = model.text_encoder(uncond_input.input_ids.to(model.device))[0]
latent, latents = ptp_utils_v.init_latent(latent, model, height, width, generator, batch_size)
# image_latents = [vae_inversion.latent2image(latents[0].unsqueeze(dim=0))[0]]
model.scheduler.set_timesteps(num_inference_steps)
for i, t in enumerate(tqdm(model.scheduler.timesteps[-start_time:])):
trainer.I = i
trainer.i = i \
if i < NUM_DDIM_STEPS * trainer.v_replace_steps else None
context = (uncond_embeddings, text_embeddings)
latents = ptp_utils_v.diffusion_step(model, controller, latents, context, t, guidance_scale, low_resource=LOW_RESOURCE,)
# image_latents += [vae_inversion.latent2image(latents[0].unsqueeze(dim=0))[0]]
# os.makedirs('latent_save', exist_ok=True)
# for i, latent_i in enumerate(image_latents):
# Image.fromarray(latent_i).save(f'latent_save/Z{NUM_DDIM_STEPS - i}_bar.png')
if return_type == 'image':
image = ptp_utils_v.latent2image(model.vae, latents)
else:
image = latents
return image, latent
def run_and_display(stable, prompts, trainer, controller, latent=None, run_baseline=False, generator=None, verbose=True):
if run_baseline:
print("w.o. prompt-to-prompt")
images, latent = run_and_display(stable, prompts, trainer, EmptyControl(), latent=latent, run_baseline=False,
generator=generator)
print("with prompt-to-prompt")
images, x_t = text2image_ldm_stable(stable, prompts, trainer, controller, latent=latent,
num_inference_steps=NUM_DDIM_STEPS, guidance_scale=global_var.get_value("GUIDANCE_SCALE"),
generator=generator)
if verbose:
ptp_utils_v.view_images(images)
return images, x_t
def setup_seed(seed=1234):
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
random.seed(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
def edit_image_stylediffusion_p2p(
image_path,
prompt_src,
prompt_tar,
guidance_scale=7.5,
cross_replace_steps=0.4,
self_replace_steps=0.6,
blend_word=None,
eq_params=None,
is_replace_controller=False,
num_inner_steps=100,
num_epoch=1,
tau_v=.6,
tau_c=.6,
tau_s=.8,
tau_u=.5):
global_var.set_value("GUIDANCE_SCALE",guidance_scale)
global_var.set_value("IS_TRAIN",True)
vae_inversion = VaeInversion(stable)
(image_gt, image_rec), x_t, trainer = vae_inversion.invert(image_path, [prompt_src], verbose=True,
num_inner_steps=num_inner_steps,
num_epoch=num_epoch)
global_var.set_value("IS_TRAIN",False)
trainer.attention_store = {}
trainer.cur_step = 0
# (image_gt, image_rec), x_t = vae_inversion.eval_init(image_path, [prompt_src], trainer=trainer)
trainer.v_replace_steps = 1.0
controller = AttentionStore()
image_inv_recon, x_t_recon = run_and_display(stable, [prompt_src,prompt_tar], trainer, controller, run_baseline=False, latent=x_t,
verbose=False)
# edit
trainer.v_replace_steps = tau_v
cross_replace_steps = {'default_': tau_c,}
self_replace_steps = tau_s
uncond_self_replace_steps = tau_u
controller = make_controller([prompt_src,prompt_tar],
len(prompt_src.strip(" "))==len(prompt_tar.strip(" ")),
cross_replace_steps,
self_replace_steps,
uncond_self_replace_steps,
blend_word,
eq_params)
image_inv_edit, x_t_edit = run_and_display(stable,
[prompt_src,prompt_tar],
trainer,
controller,
run_baseline=False,
latent=x_t,
verbose=False)
image_instruct = txt_draw(f"source prompt: {prompt_src}\ntarget prompt: {prompt_tar}")
out_image=Image.fromarray(np.concatenate((image_instruct,image_gt[0],image_inv_recon[0],image_inv_edit[1],),axis=1))
return out_image
image_save_paths={
"stylediffusion+p2p":"styleidffusion+p2p",
}
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--rerun_exist_images', action= "store_true") # rerun existing images
parser.add_argument('--data_path', type=str, default="data") # the editing category that needed to run
parser.add_argument('--output_path', type=str, default="output") # the editing category that needed to run
parser.add_argument('--edit_category_list', nargs = '+', type=str, default=["0","1","2","3","4","5","6","7","8","9"]) # the editing category that needed to run
parser.add_argument('--edit_method_list', nargs = '+', type=str, default=["styleidffusion+p2p"]) # the editing methods that needed to run
args = parser.parse_args()
rerun_exist_images=args.rerun_exist_images
data_path=args.data_path
output_path=args.output_path
edit_category_list=args.edit_category_list
edit_method_list=args.edit_method_list
with open(f"{data_path}/mapping_file.json", "r") as f:
editing_instruction = json.load(f)
for key, item in editing_instruction.items():
if item["editing_type_id"] not in edit_category_list:
continue
original_prompt = item["original_prompt"].replace("[", "").replace("]", "")
editing_prompt = item["editing_prompt"].replace("[", "").replace("]", "")
image_path = os.path.join(f"{data_path}/annotation_images", item["image_path"])
editing_instruction = item["editing_instruction"]
blended_word = item["blended_word"].split(" ") if item["blended_word"] != "" else []
mask = Image.fromarray(np.uint8(mask_decode(item["mask"])[:,:,np.newaxis].repeat(3,2))).convert("L")
for edit_method in edit_method_list:
present_image_save_path=image_path.replace(data_path, os.path.join(output_path,image_save_paths[edit_method]))
if ((not os.path.exists(present_image_save_path)) or rerun_exist_images):
print(f"editing image [{image_path}] with [{edit_method}]")
setup_seed()
torch.cuda.empty_cache()
edited_image = edit_image_stylediffusion_p2p(
image_path=[image_path],
prompt_src=original_prompt,
prompt_tar=editing_prompt,
guidance_scale=7.5,
cross_replace_steps=0.4,
self_replace_steps=0.6,
blend_word=None,
eq_params=None,
is_replace_controller=False,
num_inner_steps=100,
num_epoch=1,
tau_v=.5,
tau_c=.6,
tau_s=.6,
tau_u=.0) # disable p2pro
if not os.path.exists(os.path.dirname(present_image_save_path)):
os.makedirs(os.path.dirname(present_image_save_path))
edited_image.save(present_image_save_path)
print(f"finish")
else:
print(f"skip image [{image_path}] with [{edit_method}]")
================================================
FILE: utils/utils.py
================================================
import matplotlib.pyplot as plt
from matplotlib.backends.backend_agg import FigureCanvasAgg
import numpy as np
import PIL.Image as Image
import torch
def slerp(val, low, high):
"""
taken from https://discuss.pytorch.org/t/help-regarding-slerp-function-for-generative-model-sampling/32475/4
"""
low_norm = low/torch.norm(low, dim=1, keepdim=True)
high_norm = high/torch.norm(high, dim=1, keepdim=True)
omega = torch.acos((low_norm*high_norm).sum(1))
so = torch.sin(omega)
res = (torch.sin((1.0-val)*omega)/so).unsqueeze(1)*low + (torch.sin(val*omega)/so).unsqueeze(1) * high
return res
def slerp_tensor(val, low, high):
"""
used in negtive prompt inversion
"""
shape = low.shape
res = slerp(val, low.flatten(1), high.flatten(1))
return res.reshape(shape)
def load_512(image_path, left=0, right=0, top=0, bottom=0):
if type(image_path) is str:
image = np.array(Image.open(image_path))[:, :, :3]
else:
image = image_path
h, w, c = image.shape
left = min(left, w-1)
right = min(right, w - left - 1)
top = min(top, h - left - 1)
bottom = min(bottom, h - top - 1)
image = image[top:h-bottom, left:w-right]
h, w, c = image.shape
if h < w:
offset = (w - h) // 2
image = image[:, offset:offset + h]
elif w < h:
offset = (h - w) // 2
image = image[offset:offset + w]
image = np.array(Image.fromarray(image).resize((512, 512)))
return image
def init_latent(latent, model, height, width, generator, batch_size):
if latent is None:
latent = torch.randn(
(1, model.unet.in_channels, height // 8, width // 8),
generator=generator,
)
latents = latent.expand(batch_size, model.unet.in_channels, height // 8, width // 8).to(model.device)
return latent, latents
@torch.no_grad()
def latent2image(model, latents, return_type='np'):
latents = 1 / 0.18215 * latents.detach()
image = model.decode(latents)['sample']
if return_type == 'np':
image = (image / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).numpy()
image = (image * 255).astype(np.uint8)
return image
@torch.no_grad()
def image2latent(model, image):
with torch.no_grad():
if type(image) is Image:
image = np.array(image)
if type(image) is torch.Tensor and image.dim() == 4:
latents = image
else:
image = torch.from_numpy(image).float() / 127.5 - 1
image = image.permute(2, 0, 1).unsqueeze(0).to(model.device)
latents = model.encode(image)['latent_dist'].mean
latents = latents * 0.18215
return latents
def get_word_inds(text: str, word_place: int, tokenizer):
split_text = text.split(" ")
if type(word_place) is str:
word_place = [i for i, word in enumerate(split_text) if word_place == word]
elif type(word_place) is int:
word_place = [word_place]
out = []
if len(word_place) > 0:
words_encode = [tokenizer.decode([item]).strip("#") for item in tokenizer.encode(text)][1:-1]
cur_len, ptr = 0, 0
for i in range(len(words_encode)):
cur_len += len(words_encode[i])
if ptr in word_place:
out.append(i + 1)
if cur_len >= len(split_text[ptr]):
ptr += 1
cur_len = 0
return np.array(out)
def update_alpha_time_word(alpha, bounds, prompt_ind,
word_inds=None):
if type(bounds) is float:
bounds = 0, bounds
start, end = int(bounds[0] * alpha.shape[0]), int(bounds[1] * alpha.shape[0])
if word_inds is None:
word_inds = torch.arange(alpha.shape[2])
alpha[: start, prompt_ind, word_inds] = 0
alpha[start: end, prompt_ind, word_inds] = 1
alpha[end:, prompt_ind, word_inds] = 0
return alpha
def get_time_words_attention_alpha(prompts, num_steps,
cross_replace_steps,
tokenizer, max_num_words=77):
if type(cross_replace_steps) is not dict:
cross_replace_steps = {"default_": cross_replace_steps}
if "default_" not in cross_replace_steps:
cross_replace_steps["default_"] = (0., 1.)
alpha_time_words = torch.zeros(num_steps + 1, len(prompts) - 1, max_num_words)
for i in range(len(prompts) - 1):
alpha_time_words = update_alpha_time_word(alpha_time_words, cross_replace_steps["default_"],
i)
for key, item in cross_replace_steps.items():
if key != "default_":
inds = [get_word_inds(prompts[i], key, tokenizer) for i in range(1, len(prompts))]
for i, ind in enumerate(inds):
if len(ind) > 0:
alpha_time_words = update_alpha_time_word(alpha_time_words, item, i, ind)
alpha_time_words = alpha_time_words.reshape(num_steps + 1, len(prompts) - 1, 1, 1, max_num_words)
return alpha_time_words
def txt_draw(text,
target_size=[512,512]):
plt.figure(dpi=300,figsize=(1,1))
plt.text(-0.1, 1.1, text,fontsize=3.5, wrap=True,verticalalignment="top",horizontalalignment="left")
plt.axis('off')
canvas = FigureCanvasAgg(plt.gcf())
canvas.draw()
w, h = canvas.get_width_height()
buf = np.fromstring(canvas.tostring_argb(), dtype=np.uint8)
buf.shape = (w, h, 4)
buf = np.roll(buf, 3, axis=2)
image = Image.frombytes("RGBA", (w, h), buf.tostring())
image = image.resize(target_size,Image.ANTIALIAS)
image = np.asarray(image)[:,:,:3]
plt.close('all')
return image
