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Repository: bytedance/MoMA
Branch: main
Commit: 174853568da9
Files: 12
Total size: 3.9 MB
Directory structure:
gitextract_pyasb7u_/
├── README.md
├── checkpoints/
│ └── readme.txt
├── dataset_lib/
│ └── dataset_eval_MoMA.py
├── model_lib/
│ ├── __init__.py
│ ├── attention_processor.py
│ ├── moMA_generator.py
│ ├── modules.py
│ └── utils.py
├── requirements.txt
├── requirements_freeze.txt
├── run_MoMA_notebook.ipynb
└── run_evaluate_MoMA.py
================================================
FILE CONTENTS
================================================
================================================
FILE: README.md
================================================
# ___***MoMA: Multimodal LLM Adapter for Fast Personalized Image Generation***___
#### ECCV 2024 Accepted
<a href='https://moma-adapter.github.io/'><img src='https://img.shields.io/badge/Project-Page-green'></a>
<a href='https://arxiv.org/abs/2404.05674'><img src='https://img.shields.io/badge/Technique-Report-red'></a>
<a href='https://huggingface.co/KunpengSong/MoMA_llava_7b/tree/main'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Model-blue'></a>
<a href='https://huggingface.co/spaces/yizhezhu/MoMA_zeroGPU'><img src='https://img.shields.io/badge/Online-Demo-orange'></a>
---
## Introduction
we present MoMA: an open-vocabulary, training-free personalized image model that boasts flexible zero-shot capabilities. As foundational text-to-image models rapidly evolve, the demand for robust image-to-image translation grows. Addressing this need, MoMA specializes in subject-driven personalized image generation. Utilizing an open-source, Multimodal Large Language Model (MLLM), we train MoMA to serve a dual role as both a feature extractor and a generator. This approach effectively synergizes reference image and text prompt information to produce valuable image features, facilitating an image diffusion model. To better leverage the generated features, we further introduce a novel self-attention shortcut method that efficiently transfers image features to an image diffusion model, improving the resemblance of the target object in generated images. Remarkably, as a tuning-free plug-and-play module, our model requires only a single reference image and outperforms existing methods in generating images with high detail fidelity, enhanced identity-preservation, and prompt faithfulness. We commit to making our work open-source, thereby providing universal access to these advancements.
## Release
- [2024/04/20] 🔥 We release the model code on GitHub.
- [2024/04/22] 🔥 We add a HuggingFace repository and release the checkpoints.
- [2024/05/21] 🔥 We launch an [Online Demo](https://huggingface.co/spaces/yizhezhu/MoMA_zeroGPU) on HuggingFace Space! You don't need to provide masks. Our demo takes care of it!
## Installation
1. Install LlaVA:
Please install from its [official repository](https://github.com/haotian-liu/LLaVA#install)
2. Download our MoMA repository
```
cd ..
git clone https://github.com/bytedance/MoMA.git
cd MoMA
pip install -r requirements.txt
```
(we also provide a requirements_freeze.txt, generated by ```pip freeze```)
## Memory Requirements
We support 8-bit and 4-bit inferences which reduce memory consumptions:
+ If you have 22 GB or more GPU memory:
```args.load_8bit, args.load_4bit = False, False```
+ If you have 18 GB or more GPU memory:
```args.load_8bit, args.load_4bit = True, False```
+ If you have 14 GB or more GPU memory:
```args.load_8bit, args.load_4bit = False, True```
## Download Models
**You don't have to download any checkpoints**, our code will automatically download them from [HuggingFace repositories](https://huggingface.co/KunpengSong/MoMA_llava_7b/tree/main), which includes:
```
VAE: stabilityai--sd-vae-ft-mse
StableDiffusion: Realistic_Vision_V4.0_noVAE
MoMA:
Multi-modal LLM: MoMA_llava_7b (13 GB)
Attentions and mappings: attn_adapters_projectors.th (151 Mb)
```
## How to Use
### Jupyter-notebook
+ [**run_MoMA_notebook.ipynb**](run_MoMA_notebook.ipynb)
### Python code
+ [**run_evaluate_MoMA.py**](run_evaluate_MoMA.py)
run:
```CUDA_VISIBLE_DEVICES=0 python run_evaluate_MoMA.py```
(generated images will be saved in the output folder)
## Example Results
New context:
New texture:
**Hyper parameter:**
- In "changing context", you can increase the `strength` to get more accurate details. Mostly,`strength=1.0` is the best. It's recommended that `strength` is no greater than `1.2`.
- In "changing texture", you can change the `strength` to balance between detail accuracy and prompt fidelity. To get better prompt fidelity, just decrease `strength`. Mostly, `strength=0.4` is the best. It's recommended that `strength` is no greater than `0.6`.
## Citation
If you find our work useful for your research and applications, please consider citing us by:
```BibTeX
@article{song2024moma,
title={MoMA: Multimodal LLM Adapter for Fast Personalized Image Generation},
author={Song, Kunpeng and Zhu, Yizhe and Liu, Bingchen and Yan, Qing and Elgammal, Ahmed and Yang, Xiao},
journal={arXiv preprint arXiv:2404.05674},
year={2024}
}
```
================================================
FILE: checkpoints/readme.txt
================================================
the code will automatically download a pth file from huggingface.
name: attn_adapters_projectors.th
size: 150 MB
useage: weight for new self and cross attn layers
================================================
FILE: dataset_lib/dataset_eval_MoMA.py
================================================
from PIL import Image
import numpy as np
import torch
from torchvision import transforms
from llava.mm_utils import process_images, tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria
def Dataset_evaluate_MoMA(rgb_path, prompt,subject, mask_path, moMA_main_modal):
LLaVa_processor = moMA_main_modal.image_processor_llava
llava_config = moMA_main_modal.model_llava.config
transform = transforms.Compose([
transforms.Resize((512, 512)),
])
rgb_path, prompt,mask_path = rgb_path, prompt,mask_path
image_pil = Image.open(rgb_path)
mask_pil = Image.open(mask_path)
blip2_opt = prompt
if transform is not None:
image_pil = transform(image_pil)
mask_pil = transform(mask_pil)
mask_pil = np.array(mask_pil)
mask_pil = mask_pil[:,:,0] if len(mask_pil.shape)==3 else mask_pil
image = torch.from_numpy(np.array(image_pil)).permute(2,0,1)
mask = (torch.clamp((torch.from_numpy(mask_pil).unsqueeze(0)).float(),min=0.0,max=1.0)>0).float()
res = {'image': (image/127.5-1).unsqueeze(0),\
'mask': mask.unsqueeze(0), \
'text': [blip2_opt]}
image_wb = image * mask + torch.ones_like(image)* (1-mask)*255
image_pil = Image.fromarray(image_wb.permute(1,2,0).numpy().astype(np.uint8))
res['llava_processed'] = process_images([image_pil], LLaVa_processor, llava_config)
res['label'] = [subject]
return res
================================================
FILE: model_lib/__init__.py
================================================
================================================
FILE: model_lib/attention_processor.py
================================================
# modified from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
import math
from torchvision.utils import save_image
import torchvision.transforms as T
def get_mask_from_cross(attn_processors):
reference_masks = []
for attn_processor in attn_processors.values():
if isinstance(attn_processor, IPAttnProcessor):
reference_masks.append(attn_processor.mask_i)
mask = torch.cat(reference_masks,dim=1).mean(dim=1)
mask = (mask-mask.min())/(mask.max()-mask.min())
mask = (mask>0.2).to(torch.float32)*mask
mask = (mask-mask.min())/(mask.max()-mask.min())
return mask.unsqueeze(1)
class IPAttnProcessor(nn.Module):
r"""
Attention processor for IP-Adapater.
Args:
hidden_size (`int`):
The hidden size of the attention layer.
cross_attention_dim (`int`):
The number of channels in the `encoder_hidden_states`.
scale (`float`, defaults to 1.0):
the weight scale of image prompt.
num_tokens (`int`, defaults to 4 when do ip_adapter_plus it should be 16):
The context length of the image features.
"""
def __init__(self, hidden_size, cross_attention_dim=None, scale=1.0, num_tokens=4):
super().__init__()
self.hidden_size = hidden_size
self.cross_attention_dim = cross_attention_dim
self.scale = scale
self.num_tokens = num_tokens
self.to_k_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
self.to_v_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
self.store_attn = None
self.enabled = True
self.mode = 'inject'
self.subject_idxs = None
self.mask_i = None
self.mask_ig_prev = None
def __call__(
self,
attn,
hidden_states,
encoder_hidden_states=None,
attention_mask=None,
temb=None,
):
residual = hidden_states
input_ndim = hidden_states.ndim
if input_ndim == 4:
batch_size, channel, height, width = hidden_states.shape
hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
batch_size, sequence_length, _ = (
hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
)
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
if attn.group_norm is not None:
hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
query = attn.to_q(hidden_states)
if encoder_hidden_states is None:
encoder_hidden_states = hidden_states
else:
# get encoder_hidden_states, ip_hidden_states
end_pos = encoder_hidden_states.shape[1] - self.num_tokens
encoder_hidden_states, ip_hidden_states = encoder_hidden_states[:, :end_pos, :], encoder_hidden_states[:, end_pos:, :]
if attn.norm_cross:
encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_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)
hidden_states = torch.bmm(attention_probs, value)
hidden_states = attn.batch_to_head_dim(hidden_states)
# for ip-adapter
if self.enabled:
if self.mode == 'inject' or self.mode == 'masked_generation':
ip_key = self.to_k_ip(ip_hidden_states.to(torch.float16))
ip_value = self.to_v_ip(ip_hidden_states.to(torch.float16))
ip_key = attn.head_to_batch_dim(ip_key)
ip_value = attn.head_to_batch_dim(ip_value)
ip_attention_probs = attn.get_attention_scores(query, ip_key.to(torch.float32), None)
ip_hidden_states = torch.bmm(ip_attention_probs, ip_value.to(torch.float32))
ip_hidden_states = attn.batch_to_head_dim(ip_hidden_states)
if (self.mask_ig_prev is not None) and self.mode == 'masked_generation':
mask_ig_prev = rearrange(F.interpolate(self.mask_ig_prev,size=int(math.sqrt(query.shape[1]))),"b c h w -> b (h w) c")
if not mask_ig_prev.shape[0]==ip_hidden_states.shape[0]: mask_ig_prev = mask_ig_prev.repeat(2,1,1)
ip_hidden_states = ip_hidden_states * mask_ig_prev
hidden_states = hidden_states + self.scale * ip_hidden_states
if self.mode == 'extract' or self.mode == 'masked_generation':
subject_idxs = self.subject_idxs*2 if not (hidden_states.shape[0] == len(self.subject_idxs)) else self.subject_idxs
assert (hidden_states.shape[0] == len(subject_idxs))
attentions = rearrange(attention_probs, '(b h) n d -> b h n d', h=8).mean(1)
attn_extracted = [attentions[i, :, subject_idxs[i]].sum(-1) for i in range(hidden_states.shape[0])]
attn_extracted = [(atn-atn.min())/(atn.max()-atn.min()) for atn in attn_extracted]
attn_extracted = torch.stack(attn_extracted, dim=0)
attn_extracted = rearrange(attn_extracted, 'b (h w) -> b h w', h=int(math.sqrt(attention_probs.shape[1])))
attn_extracted = torch.clamp(F.interpolate(attn_extracted.unsqueeze(1),size=512),min=0,max=1)
self.mask_i = attn_extracted
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
if input_ndim == 4:
hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
return hidden_states
### added for self attention
class IPAttnProcessor_Self(nn.Module):
r"""
Attention processor for IP-Adapater. (But for self attention)
Args:
hidden_size (`int`):
The hidden size of the attention layer.
cross_attention_dim (`int`):
The number of channels in the `encoder_hidden_states`.
scale (`float`, defaults to 1.0):
the weight scale of image prompt.
num_tokens (`int`, defaults to 4 when do ip_adapter_plus it should be 16):
The context length of the image features.
"""
def __init__(self, hidden_size, cross_attention_dim=None, scale=1.0, num_tokens=4):
super().__init__()
self.hidden_size = hidden_size
self.cross_attention_dim = cross_attention_dim
self.scale = scale
self.num_tokens = num_tokens
self.to_k_ip = nn.Linear(hidden_size, hidden_size, bias=False)
self.to_v_ip = nn.Linear(hidden_size, hidden_size, bias=False)
self.scale_learnable = torch.nn.Parameter(torch.zeros(1),requires_grad=True)
self.enabled = True
self.mode = 'extract'
self.store_ks, self.store_vs = [], []
self.mask_id, self.mask_ig = None, None
def __call__(
self,
attn,
hidden_states,
encoder_hidden_states=None,
attention_mask=None,
temb=None,
):
input_ndim = hidden_states.ndim
if input_ndim == 4:
batch_size, channel, height, width = hidden_states.shape
hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
batch_size, sequence_length, _ = (
hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
)
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
if attn.group_norm is not None:
hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
query = attn.to_q(hidden_states)
if encoder_hidden_states is None:
encoder_hidden_states = hidden_states
else:
end_pos = encoder_hidden_states.shape[1] - self.num_tokens
encoder_hidden_states, ip_hidden_states = encoder_hidden_states[:, :end_pos, :], encoder_hidden_states[:, end_pos:, :]
if attn.norm_cross:
encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
key_0 = attn.to_k(encoder_hidden_states)
value_0 = attn.to_v(encoder_hidden_states)
query = attn.head_to_batch_dim(query)
key = attn.head_to_batch_dim(key_0)
value = attn.head_to_batch_dim(value_0)
attention_probs = attn.get_attention_scores(query, key, attention_mask)
hidden_states = torch.bmm(attention_probs, value)
hidden_states = attn.batch_to_head_dim(hidden_states)
if self.enabled:
if self.mode == 'extract':
ks, vs = attn.head_to_batch_dim(self.to_k_ip(key_0)), attn.head_to_batch_dim(self.to_v_ip(value_0))
self.store_ks, self.store_vs = self.store_ks+[ks], self.store_vs+[vs]
self.store_ks, self.store_vs = torch.cat(self.store_ks,dim=0), torch.cat(self.store_vs,dim=0)
if self.mode == 'masked_generation':
if not self.store_ks.shape[0]==query.shape[0]: self.store_ks,self.store_vs = self.store_ks.repeat(2,1,1), self.store_vs.repeat(2,1,1)
mask_id = self.mask_id.clone()
mask_id.masked_fill_(self.mask_id==False, -torch.finfo(mask_id.dtype).max)
mask_id = rearrange(F.interpolate(mask_id,size=int(math.sqrt(query.shape[1]))),"b c h w -> b c (h w)").repeat(1,query.shape[1],1)
mask_id = mask_id.repeat(8,1,1) # 8 is head dim
if not mask_id.shape[0]==int(query.shape[0]): mask_id = mask_id.repeat(2,1,1)
attention_probs_ref = attn.get_attention_scores(query, self.store_ks, mask_id.to(query.dtype))
hidden_states_ref = torch.bmm(attention_probs_ref, self.store_vs)
hidden_states_ref = attn.batch_to_head_dim(hidden_states_ref)
scale = self.scale.repeat(int(batch_size/self.scale.shape[0])).unsqueeze(-1).unsqueeze(-1) if type(self.scale)==torch.Tensor else self.scale
if self.mask_ig == None:
hidden_states = hidden_states + scale * hidden_states_ref * self.scale_learnable
else:
mask_ig = rearrange(F.interpolate(self.mask_ig,size=int(math.sqrt(query.shape[1]))),"b c h w -> b (h w) c")
if not mask_ig.shape[0]==hidden_states_ref.shape[0]: mask_ig = mask_ig.repeat(2,1,1)
hidden_states = hidden_states + scale * hidden_states_ref * mask_ig * self.scale_learnable
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
if input_ndim == 4:
hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
return hidden_states
================================================
FILE: model_lib/moMA_generator.py
================================================
from typing import List
import torch
from transformers import CLIPVisionModelWithProjection, CLIPImageProcessor
from PIL import Image
from model_lib.attention_processor import IPAttnProcessor, IPAttnProcessor_Self, get_mask_from_cross
from diffusers import StableDiffusionPipeline, DDIMScheduler, AutoencoderKL
import tqdm
def get_subject_idx(model,prompt,src_subject,device):
tokenized_prompt = model.tokenizer(prompt,padding="max_length",max_length=model.tokenizer.model_max_length,truncation=True,return_tensors="pt",).to(device)
input_ids = tokenized_prompt['input_ids']
src_subject_idxs = []
for subject,input_id in zip(src_subject,input_ids):
src_subject_token_id = [model.tokenizer.encode(i, add_special_tokens=False)[0] for i in subject.split(' ')]
src_subject_idxs = [i for i, x in enumerate(input_id.tolist()) if x in src_subject_token_id]
return [src_subject_idxs]
def add_function(model):
@torch.no_grad()
def generate_with_adapters(
model,
prompt_embeds,
num_inference_steps,
generator,
t_range=list(range(0,950)),
):
latents = model.prepare_latents(prompt_embeds.shape[0]//2,4,512,512,prompt_embeds.dtype,prompt_embeds.device,generator)
model.scheduler.set_timesteps(num_inference_steps)
iterator = tqdm.tqdm(model.scheduler.timesteps)
mask_ig_prev = None
for i, t in enumerate(iterator):
if not t in t_range:
model.moMA_generator.toggle_enable_flag('cross')
else:
model.moMA_generator.toggle_enable_flag('all')
latent_model_input = torch.cat([latents] * 2)
noise_pred = model.unet(
latent_model_input,
t,
encoder_hidden_states=prompt_embeds,
return_dict=False,
)[0]
# perform guidance
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + 7.5 * (noise_pred_text - noise_pred_uncond)
latents = model.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
mask_ig_prev = (get_mask_from_cross(model.unet.attn_processors))[latents.shape[0]:]
model.moMA_generator.set_self_mask('self','ig',mask_ig_prev)
model.moMA_generator.set_self_mask('cross',mask=mask_ig_prev.clone().detach())
image = model.vae.decode(latents / model.vae.config.scaling_factor, return_dict=False)[0]
return image ,mask_ig_prev.repeat(1,3,1,1) if (not mask_ig_prev==None) else None
model.generate_with_adapters = generate_with_adapters
class ImageProjModel(torch.nn.Module):
"""Projection Model"""
def __init__(self, cross_attention_dim=1024, clip_embeddings_dim=1024, clip_extra_context_tokens=4):
super().__init__()
self.cross_attention_dim = cross_attention_dim
self.clip_extra_context_tokens = clip_extra_context_tokens
self.proj = torch.nn.Linear(clip_embeddings_dim, self.clip_extra_context_tokens * cross_attention_dim)
self.norm = torch.nn.LayerNorm(cross_attention_dim)
def forward(self, image_embeds):
embeds = image_embeds
clip_extra_context_tokens = self.proj(embeds).reshape(-1, self.clip_extra_context_tokens, self.cross_attention_dim)
clip_extra_context_tokens = self.norm(clip_extra_context_tokens)
return clip_extra_context_tokens
class MoMA_generator:
def __init__(self, device,args):
self.args = args
self.device = device
noise_scheduler = DDIMScheduler(num_train_timesteps=1000,beta_start=0.00085,beta_end=0.012,beta_schedule="scaled_linear",clip_sample=False,set_alpha_to_one=False,steps_offset=1,)
print('Loading VAE: stabilityai--sd-vae-ft-mse...')
vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse")
print('Loading StableDiffusion: Realistic_Vision...')
self.pipe = StableDiffusionPipeline.from_pretrained(
"SG161222/Realistic_Vision_V4.0_noVAE",
torch_dtype=torch.bfloat16,
scheduler=noise_scheduler,
vae=vae,
feature_extractor=None,
safety_checker=None,
).to(self.device)
self.unet = self.pipe.unet
add_function(self.pipe)
self.pipe.moMA_generator = self
self.set_ip_adapter()
self.image_proj_model = self.init_proj()
def init_proj(self):
image_proj_model = ImageProjModel(
cross_attention_dim=768,
clip_embeddings_dim=1024,
clip_extra_context_tokens=4,
).to(self.device, dtype=torch.bfloat16)
return image_proj_model
def set_ip_adapter(self):
unet = self.unet
attn_procs = {}
for name in unet.attn_processors.keys():
cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim
if name.startswith("mid_block"):
hidden_size = unet.config.block_out_channels[-1]
elif name.startswith("up_blocks"):
block_id = int(name[len("up_blocks.")])
hidden_size = list(reversed(unet.config.block_out_channels))[block_id]
elif name.startswith("down_blocks"):
block_id = int(name[len("down_blocks.")])
hidden_size = unet.config.block_out_channels[block_id]
if cross_attention_dim is None:
attn_procs[name] = IPAttnProcessor_Self(hidden_size=hidden_size, cross_attention_dim=cross_attention_dim,scale=1.0,num_tokens=4).to(self.device, dtype=torch.float16)
else:
attn_procs[name] = IPAttnProcessor(hidden_size=hidden_size, cross_attention_dim=cross_attention_dim,scale=1.0,num_tokens=4).to(self.device, dtype=torch.float16)
unet.set_attn_processor(attn_procs)
@torch.inference_mode()
def get_image_embeds_CFG(self, llava_emb):
clip_image_embeds = llava_emb
image_prompt_embeds = self.image_proj_model(clip_image_embeds)
uncond_image_prompt_embeds = self.image_proj_model(torch.zeros_like(clip_image_embeds))
return image_prompt_embeds, uncond_image_prompt_embeds
def get_image_crossAttn_feature(
self,
llava_emb,
num_samples=1,
):
image_prompt_embeds, uncond_image_prompt_embeds = self.get_image_embeds_CFG(llava_emb)
bs_embed, seq_len, _ = image_prompt_embeds.shape
image_prompt_embeds = image_prompt_embeds.repeat(1, num_samples, 1)
image_prompt_embeds = image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)
uncond_image_prompt_embeds = uncond_image_prompt_embeds.repeat(1, num_samples, 1)
uncond_image_prompt_embeds = uncond_image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)
return image_prompt_embeds, uncond_image_prompt_embeds
# feature are from self-attention layers of Unet: feed reference image to Unet with t=0
def get_image_selfAttn_feature(
self,
pil_image,
prompt,
):
self.toggle_enable_flag('self')
self.toggle_extract_inject_flag('self', 'extract')
tokenized_prompt = self.pipe.tokenizer(prompt,padding="max_length",truncation=True,return_tensors="pt",).to(self.device)
text_embeddings = self.pipe.text_encoder(input_ids=tokenized_prompt.input_ids)[0]
ref_image = pil_image
ref_image.to(self.device)
with torch.no_grad(): latents = self.pipe.vae.encode(ref_image).latent_dist.sample()
latents = latents * self.pipe.vae.config.scaling_factor
noise = torch.randn_like(latents)
timesteps = torch.tensor([0],device=latents.device).long() # fixed to 0
noisy_latents = self.pipe.scheduler.add_noise(latents, noise, timesteps)
_ = self.unet(noisy_latents,timestep=timesteps,encoder_hidden_states=text_embeddings)["sample"]
# features are stored in attn_processors
return None
@torch.no_grad()
def generate_with_MoMA(
self,
batch,
llava_emb=None,
seed=None,
device='cuda',
):
self.reset_all()
img_ig,mask_id,subject,prompt = batch['image'].half().to(device),batch['mask'].half().to(device),batch['label'][0],batch['text'][0]
prompt = [f"photo of a {subject}. "+ prompt]
subject_idx = get_subject_idx(self.pipe,prompt,[subject],self.device)
negative_prompt = None
# get context-cross-attention feature (from MLLM decoder)
cond_llava_embeds, uncond_llava_embeds = self.get_image_crossAttn_feature(llava_emb,num_samples=1)
# get subject-cross-attention feature (from Unet)
self.get_image_selfAttn_feature(img_ig,subject) # features are stored in attn_processors
with torch.inference_mode():
prompt_embeds = self.pipe._encode_prompt(
prompt, device=self.device, num_images_per_prompt=1, do_classifier_free_guidance=True, negative_prompt=negative_prompt)
negative_prompt_embeds_, prompt_embeds_ = prompt_embeds.chunk(2)
prompt_embeds = torch.cat([prompt_embeds_, cond_llava_embeds], dim=1)
negative_prompt_embeds = torch.cat([negative_prompt_embeds_, uncond_llava_embeds], dim=1)
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
generator = torch.Generator(self.device).manual_seed(seed) if seed is not None else None
self.set_self_mask('eraseAll')
self.toggle_enable_flag('all')
self.toggle_extract_inject_flag('all','masked_generation')
self.set_self_mask('self','id',mask_id)
self.set_cross_subject_idxs(subject_idx)
images, mask = self.pipe.generate_with_adapters(
self.pipe,
prompt_embeds,
50,
generator,
)
images = torch.clip((images+1)/2.0,min=0.0,max=1.0)
return images.cpu(), mask.cpu()
def set_selfAttn_strength(self, strength):
for attn_processor in self.unet.attn_processors.values():
if isinstance(attn_processor, IPAttnProcessor):
attn_processor.scale = 1.0
if isinstance(attn_processor, IPAttnProcessor_Self):
attn_processor.scale = strength
def set_cross_subject_idxs(self, subject_idxs):
for attn_processor in self.unet.attn_processors.values():
if isinstance(attn_processor, IPAttnProcessor):
attn_processor.subject_idxs = subject_idxs
def set_self_mask(self,mode,id_ig='', mask=None): #only have effect on self attn of the generation process
for attn_processor in self.unet.attn_processors.values():
if mode == 'eraseAll':
if isinstance(attn_processor, IPAttnProcessor_Self):
attn_processor.mask_id,attn_processor.mask_ig = None,None
if isinstance(attn_processor, IPAttnProcessor):
attn_processor.mask_i, attn_processor.mask_ig_prev = None, None
if mode == 'self':
if isinstance(attn_processor, IPAttnProcessor_Self):
if id_ig == 'id':attn_processor.mask_id = mask
if id_ig == 'ig':attn_processor.mask_ig = mask
if mode == 'cross':
if isinstance(attn_processor, IPAttnProcessor):
attn_processor.mask_ig_prev = mask
def toggle_enable_flag(self, processor_enable_mode):
for attn_processor in self.unet.attn_processors.values():
if processor_enable_mode == 'cross':
if isinstance(attn_processor, IPAttnProcessor):attn_processor.enabled = True
if isinstance(attn_processor, IPAttnProcessor_Self):attn_processor.enabled = False
if processor_enable_mode == 'self':
if isinstance(attn_processor, IPAttnProcessor):attn_processor.enabled = False
if isinstance(attn_processor, IPAttnProcessor_Self):attn_processor.enabled = True
if processor_enable_mode == 'all':
attn_processor.enabled = True
if processor_enable_mode == 'none':
attn_processor.enabled = False
def toggle_extract_inject_flag(self, processor_name, mode): # mode: str, 'extract' or 'inject' or 'both'(cross only)
for attn_processor in self.unet.attn_processors.values():
if processor_name == 'cross':
if isinstance(attn_processor, IPAttnProcessor):attn_processor.mode = mode
if processor_name == 'self':
if isinstance(attn_processor, IPAttnProcessor_Self):attn_processor.mode = mode
if processor_name == 'all':
attn_processor.mode = mode
def reset_all(self,keep_self=False):
for attn_processor in self.unet.attn_processors.values():
if isinstance(attn_processor, IPAttnProcessor):
attn_processor.store_attn, attn_processor.subject_idxs, attn_processor.mask_i, attn_processor.mask_ig_prev, self.subject_idxs = None, None, None, None, None
if isinstance(attn_processor, IPAttnProcessor_Self):
attn_processor.mask_id, attn_processor.mask_ig = None, None
if not keep_self: attn_processor.store_ks, attn_processor.store_vs = [], []
================================================
FILE: model_lib/modules.py
================================================
import os
from PIL import Image
import torch
import torch.nn as nn
from typing import List, Optional
import torch.utils.checkpoint
from torchvision.transforms import ToPILImage
from model_lib.moMA_generator import MoMA_generator
from transformers.activations import ACT2FN
from huggingface_hub import hf_hub_download
from dataset_lib.dataset_eval_MoMA import Dataset_evaluate_MoMA
from llava.model.builder import load_pretrained_model
from llava.mm_utils import tokenizer_image_token, get_model_name_from_path
from llava.constants import IMAGE_TOKEN_INDEX
def add_function(model):
def my_llava_forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
images: Optional[torch.FloatTensor] = None,
return_dict: Optional[bool] = None,
):
(_,position_ids,attention_mask,_,inputs_embeds,_) = self.prepare_inputs_labels_for_multimodal(input_ids,position_ids,attention_mask,None,None,images)
outputs = self.model(
input_ids=None,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=None,
inputs_embeds=inputs_embeds,
use_cache=True,
output_attentions=False,
output_hidden_states=False,
return_dict=True,
)
return outputs[0]
model.my_llava_forward = my_llava_forward
class LlamaMLP_mapping(nn.Module):
def __init__(self, hidden_size,hidden_size_out):
super().__init__()
self.hidden_size, self.hidden_size_out = hidden_size,hidden_size_out
self.gate_proj = nn.Linear(self.hidden_size, self.hidden_size_out, bias=False)
self.up_proj = nn.Linear(self.hidden_size, self.hidden_size_out, bias=False)
self.down_proj = nn.Linear(self.hidden_size_out, self.hidden_size_out, bias=False)
self.act_fn = ACT2FN["silu"]
self.act_fn_output = ACT2FN["tanh"]
self.init_linear()
def forward(self, x):
down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
return down_proj
def init_linear(self):
torch.nn.init.xavier_normal_(self.gate_proj.weight)
self.gate_proj.weight.data=self.gate_proj.weight.data/4.0
torch.nn.init.xavier_normal_(self.up_proj.weight)
self.up_proj.weight.data=self.up_proj.weight.data/4.0
torch.nn.init.xavier_normal_(self.down_proj.weight)
self.down_proj.weight.data=self.down_proj.weight.data/4.0
class MoMA_main_modal(nn.Module):
def __init__(self,args):
super().__init__()
self.args = args
self.device = args.device
self.moMA_generator = MoMA_generator(self.device,args)
self.unet = self.moMA_generator.pipe.unet
self.vae = self.moMA_generator.pipe.vae
print('Loading MoMA: its Multi-modal LLM...')
model_name = get_model_name_from_path(args.model_path)
self.tokenizer_llava, self.model_llava, self.image_processor_llava, self.context_len_llava = load_pretrained_model(args.model_path, None, model_name, load_8bit=self.args.load_8bit, load_4bit=self.args.load_4bit, device=args.device)
add_function(self.model_llava)
self.mapping = LlamaMLP_mapping(4096,1024).to(self.device, dtype=torch.bfloat16)
self.load_saved_components()
self.freeze_modules()
def load_saved_components(self):
if not os.path.exists(self.args.load_attn_adapters):
print('Loading Attentions and LLM mappings...')
hf_hub_download(repo_id=self.args.model_path, filename="attn_adapters_projectors.th",local_dir='/'.join(self.args.load_attn_adapters.split('/')[:-1]))
#load attention adapters and self cross attentions
state_dict = torch.load(self.args.load_attn_adapters, map_location="cpu")
self.moMA_generator.image_proj_model.load_state_dict(state_dict["projectors"])
attn_layers = torch.nn.ModuleList(self.unet.attn_processors.values())
attn_layers.load_state_dict(state_dict["self_cross_attentions"],strict=False)
#load LLM projectors
self.load_state_dict(state_dict['llm_mapping'],strict=False)
def freeze_modules(self):
all_modules = [self.moMA_generator.pipe.vae,self.moMA_generator.pipe.text_encoder,self.unet,self.model_llava,self.mapping]
for module in all_modules:
module.train = False
module.requires_grad_(False)
def forward_MLLM(self,batch):
llava_processeds,subjects,prompts = batch['llava_processed'].half().to(self.device),batch['label'],batch['text']
input_ids,attention_masks,position_ids = [],[],[]
for subject,prompt in zip(subjects,prompts):
prompt_construct = f"USER: <image>\n A photo of a {subject}. Describe a new image of the same {subject} in: {prompt}. ASSISTANT: *"
input_id = tokenizer_image_token(prompt_construct, self.tokenizer_llava, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).to(self.device)
attention_mask = torch.ones(input_id.shape, dtype=torch.long, device=self.device)
position_id = torch.tensor(list(range(input_id.shape[-1])), device=self.device)
position_ids += [position_id]
attention_masks += [attention_mask[0]]
input_ids += [input_id[0]]
input_ids = torch.nn.utils.rnn.pad_sequence([i.flip(dims=[-1]) for i in input_ids],batch_first=True,padding_value=self.tokenizer_llava.pad_token_id).flip(dims=[1])
position_ids = torch.nn.utils.rnn.pad_sequence([i.flip(dims=[-1]) for i in position_ids],batch_first=True,padding_value=self.tokenizer_llava.pad_token_id).flip(dims=[1])
attention_masks = torch.nn.utils.rnn.pad_sequence([i.flip(dims=[-1]) for i in attention_masks],batch_first=True,padding_value=self.tokenizer_llava.pad_token_id).flip(dims=[1])
output = self.model_llava.my_llava_forward(self.model_llava,input_ids=input_ids,attention_mask=attention_masks,position_ids=position_ids,images=llava_processeds)
output = self.mapping(output)
return output[:,-1,:]
def reset(self):
self.moMA_generator.reset_all()
def generate_images(self, rgb_path, mask_path, subject, prompt, strength=1.0, num=1, seed=0, return_mask=False):
batch = Dataset_evaluate_MoMA(rgb_path, prompt, subject, mask_path,self)
self.moMA_generator.set_selfAttn_strength(strength)
num = 1 if return_mask else num
results = []
for sample_id in range(num):
with torch.cuda.amp.autocast(enabled=True, dtype=torch.bfloat16, cache_enabled=True):
with torch.no_grad():
### key steps
llava_emb = self.forward_MLLM(batch).clone().detach()
img,mask = self.moMA_generator.generate_with_MoMA(batch,llava_emb=llava_emb,seed=sample_id+seed,device=self.args.device)
self.reset()
###
if return_mask:
return torch.cat([(batch['image'].cpu()+1)/2.0,img,mask],dim=0)
else:
results += [img[0]]
to_pil = ToPILImage()
images = [to_pil(results[i]) for i in range(len(results))]
concatenated_image = Image.new('RGB', (images[0].width * num, images[0].height))
x_offset = 0
for img in images:
concatenated_image.paste(img, (x_offset, 0))
x_offset += img.width
return concatenated_image
================================================
FILE: model_lib/utils.py
================================================
import argparse
import torch
from torchvision.transforms import ToPILImage
from PIL import Image
def parse_args():
parser = argparse.ArgumentParser(description="Simple example of MoMA.")
parser.add_argument("--load_attn_adapters",type=str,default="checkpoints/attn_adapters_projectors.th",help="self_cross attentions and LLM projectors.")
parser.add_argument("--output_path",type=str,default="output",help="output directory.")
parser.add_argument("--device",type=str,default="cuda:0",help="device.")
parser.add_argument("--model_path",type=str,default="KunpengSong/MoMA_llava_7b",help="fine tuned llava (Multi-modal LLM decoder)")
args = parser.parse_known_args()[0]
return args
def show_PIL_image(tensor):
# tensor of shape [3, 3, 512, 512]
to_pil = ToPILImage()
images = [to_pil(tensor[i]) for i in range(tensor.shape[0])]
concatenated_image = Image.new('RGB', (images[0].width * 3, images[0].height))
x_offset = 0
for img in images:
concatenated_image.paste(img, (x_offset, 0))
x_offset += img.width
return concatenated_image
================================================
FILE: requirements.txt
================================================
diffusers
pytorch_lightning
einops
protobuf
chardet
Triton
peft
notebook
================================================
FILE: requirements_freeze.txt
================================================
accelerate==0.21.0
aiofiles==23.2.1
aiohttp==3.9.5
aiosignal==1.3.1
altair==5.3.0
annotated-types==0.6.0
anyio==4.3.0
argon2-cffi==23.1.0
argon2-cffi-bindings==21.2.0
arrow==1.3.0
asttokens==2.4.1
async-lru==2.0.4
async-timeout==4.0.3
attrs==23.2.0
Babel==2.14.0
beautifulsoup4==4.12.3
bitsandbytes==0.43.1
bleach==6.1.0
cffi==1.16.0
chardet==5.2.0
click==8.1.7
cmake==3.29.2
comm==0.2.2
contourpy==1.2.1
cycler==0.12.1
debugpy==1.8.1
decorator==5.1.1
defusedxml==0.7.1
diffusers==0.23.1
einops==0.6.1
einops-exts==0.0.4
exceptiongroup==1.2.1
executing==2.0.1
fastapi==0.110.2
fastjsonschema==2.19.1
ffmpy==0.3.2
fonttools==4.51.0
fqdn==1.5.1
frozenlist==1.4.1
fsspec==2024.3.1
gradio==4.16.0
gradio_client==0.8.1
h11==0.14.0
httpcore==1.0.5
httpx==0.27.0
huggingface-hub==0.22.2
importlib_metadata==7.1.0
importlib_resources==6.4.0
ipykernel==6.29.4
ipython==8.23.0
isoduration==20.11.0
jedi==0.19.1
joblib==1.4.0
json5==0.9.25
jsonpointer==2.4
jsonschema==4.21.1
jsonschema-specifications==2023.12.1
jupyter-events==0.10.0
jupyter-lsp==2.2.5
jupyter_client==8.6.1
jupyter_core==5.7.2
jupyter_server==2.14.0
jupyter_server_terminals==0.5.3
jupyterlab==4.1.6
jupyterlab_pygments==0.3.0
jupyterlab_server==2.27.0
kiwisolver==1.4.5
lightning-utilities==0.11.2
lit==18.1.3
markdown-it-py==3.0.0
markdown2==2.4.13
matplotlib==3.8.4
matplotlib-inline==0.1.7
mdurl==0.1.2
mistune==3.0.2
mkl-service==2.4.0
multidict==6.0.5
nbclient==0.10.0
nbconvert==7.16.3
nbformat==5.10.4
nest-asyncio==1.6.0
notebook==7.1.3
notebook_shim==0.2.4
nvidia-cublas-cu11==11.10.3.66
nvidia-cublas-cu12==12.1.3.1
nvidia-cuda-cupti-cu11==11.7.101
nvidia-cuda-cupti-cu12==12.1.105
nvidia-cuda-nvrtc-cu11==11.7.99
nvidia-cuda-nvrtc-cu12==12.1.105
nvidia-cuda-runtime-cu11==11.7.99
nvidia-cuda-runtime-cu12==12.1.105
nvidia-cudnn-cu11==8.5.0.96
nvidia-cudnn-cu12==8.9.2.26
nvidia-cufft-cu11==10.9.0.58
nvidia-cufft-cu12==11.0.2.54
nvidia-curand-cu11==10.2.10.91
nvidia-curand-cu12==10.3.2.106
nvidia-cusolver-cu11==11.4.0.1
nvidia-cusolver-cu12==11.4.5.107
nvidia-cusparse-cu11==11.7.4.91
nvidia-cusparse-cu12==12.1.0.106
nvidia-nccl-cu11==2.14.3
nvidia-nccl-cu12==2.18.1
nvidia-nvjitlink-cu12==12.4.127
nvidia-nvtx-cu11==11.7.91
nvidia-nvtx-cu12==12.1.105
orjson==3.10.1
overrides==7.7.0
packaging==24.0
pandas==2.2.2
pandocfilters==1.5.1
parso==0.8.4
peft==0.6.2
pexpect==4.9.0
platformdirs==4.2.0
prometheus_client==0.20.0
prompt-toolkit==3.0.43
protobuf==3.20.3
psutil==5.9.8
ptyprocess==0.7.0
pure-eval==0.2.2
pycparser==2.22
pydantic==2.7.0
pydantic_core==2.18.1
pydub==0.25.1
Pygments==2.17.2
pyparsing==3.1.2
python-dateutil==2.9.0.post0
python-json-logger==2.0.7
python-multipart==0.0.9
pytorch-lightning==2.2.1
pytz==2024.1
PyYAML==6.0.1
pyzmq==26.0.2
referencing==0.34.0
regex==2024.4.16
rfc3339-validator==0.1.4
rfc3986-validator==0.1.1
rich==13.7.1
rpds-py==0.18.0
ruff==0.4.1
safetensors==0.4.3
scikit-learn==1.2.2
scipy==1.13.0
semantic-version==2.10.0
Send2Trash==1.8.3
sentencepiece==0.1.99
shellingham==1.5.4
shortuuid==1.0.13
six==1.16.0
sniffio==1.3.1
soupsieve==2.5
stack-data==0.6.3
starlette==0.37.2
svgwrite==1.4.3
terminado==0.18.1
threadpoolctl==3.4.0
timm==0.6.13
tinycss2==1.2.1
tokenizers==0.15.1
tomli==2.0.1
tomlkit==0.12.0
toolz==0.12.1
torch==2.0.0
torchaudio==2.0.1
torchmetrics==1.3.2
torchvision==0.15.1
tornado==6.4
tqdm==4.66.2
traitlets==5.14.3
transformers==4.37.2
triton==2.0.0
typer==0.12.3
types-python-dateutil==2.9.0.20240316
typing_extensions==4.11.0
tzdata==2024.1
uri-template==1.3.0
uvicorn==0.29.0
wavedrom==2.0.3.post3
wcwidth==0.2.13
webcolors==1.13
webencodings==0.5.1
websocket-client==1.7.0
websockets==11.0.3
yarl==1.9.4
zipp==3.18.1
================================================
FILE: run_MoMA_notebook.ipynb
================================================
{
"cells": [
{
"cell_type": "code",
"execution_count": 8,
"id": "f29d70fa",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"\n",
"\n",
"import torch\n",
"from PIL import Image\n",
"from pytorch_lightning import seed_everything\n",
"import torch.utils.checkpoint\n",
"import warnings \n",
"\n",
"from model_lib.utils import parse_args\n",
"from model_lib.modules import MoMA_main_modal\n",
"warnings.filterwarnings('ignore') "
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "12a0837b",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Seed set to 0\n"
]
}
],
"source": [
"seed_everything(0)\n",
"args = parse_args()\n",
"\n",
"args.device = torch.device(\"cuda\", 0)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "fcbd152c",
"metadata": {},
"outputs": [],
"source": [
"# if you have 22 Gb GPU memory:\n",
"args.load_8bit, args.load_4bit = False, False\n",
"\n",
"# if you have 18 Gb GPU memory:\n",
"# args.load_8bit, args.load_4bit = True, False\n",
"\n",
"# if you have 14 Gb GPU memory:\n",
"# args.load_8bit, args.load_4bit = False, True"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "94c05459",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loading VAE: stabilityai--sd-vae-ft-mse...\n",
"Loading StableDiffusion: Realistic_Vision...\n",
"Fetching 9 files: 100%|██████████| 9/9 [01:01<00:00, 6.87s/it]\n",
"Loading pipeline components...: 100%|██████████| 5/5 [00:35<00:00, 7.07s/it]\n",
"Loading MoMA: its Multi-modal LLM...\n",
"Downloading shards: 100%|██████████| 2/2 [06:46<00:00, 203.36s/it]\n",
"Loading checkpoint shards: 100%|██████████| 2/2 [02:07<00:00, 63.51s/it]\n"
]
}
],
"source": [
"#load MoMA from HuggingFace. Auto download\n",
"moMA_main_modal = MoMA_main_modal(args).to(args.device, dtype=torch.bfloat16)\n",
"\n",
"# This will automatically download the following files:\n",
"# - VAE: stabilityai--sd-vae-ft-mse\n",
"# - StableDiffusion: Realistic_Vision_V4.0\n",
"# - MoMA: \n",
"# - the-Multi modal LLM: MoMA_llava_7b (based on LLaVA 7b)\n",
"# - the attentions and LLM mappings: attn_adapters_projectors.th\n",
"\n",
"\n",
"# It could take a while to download these models, if it is the first time running this code... \n",
"# so please be patient..."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "416b6747",
"metadata": {},
"outputs": [
{
"data": {
"image/jpeg": 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",
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
gitextract_pyasb7u_/ ├── README.md ├── checkpoints/ │ └── readme.txt ├── dataset_lib/ │ └── dataset_eval_MoMA.py ├── model_lib/ │ ├── __init__.py │ ├── attention_processor.py │ ├── moMA_generator.py │ ├── modules.py │ └── utils.py ├── requirements.txt ├── requirements_freeze.txt ├── run_MoMA_notebook.ipynb └── run_evaluate_MoMA.py
SYMBOL INDEX (41 symbols across 5 files)
FILE: dataset_lib/dataset_eval_MoMA.py
function Dataset_evaluate_MoMA (line 8) | def Dataset_evaluate_MoMA(rgb_path, prompt,subject, mask_path, moMA_main...
FILE: model_lib/attention_processor.py
function get_mask_from_cross (line 10) | def get_mask_from_cross(attn_processors):
class IPAttnProcessor (line 21) | class IPAttnProcessor(nn.Module):
method __init__ (line 35) | def __init__(self, hidden_size, cross_attention_dim=None, scale=1.0, n...
method __call__ (line 54) | def __call__(
class IPAttnProcessor_Self (line 137) | class IPAttnProcessor_Self(nn.Module):
method __init__ (line 151) | def __init__(self, hidden_size, cross_attention_dim=None, scale=1.0, n...
method __call__ (line 170) | def __call__(
FILE: model_lib/moMA_generator.py
function get_subject_idx (line 10) | def get_subject_idx(model,prompt,src_subject,device):
function add_function (line 20) | def add_function(model):
class ImageProjModel (line 66) | class ImageProjModel(torch.nn.Module):
method __init__ (line 68) | def __init__(self, cross_attention_dim=1024, clip_embeddings_dim=1024,...
method forward (line 76) | def forward(self, image_embeds):
class MoMA_generator (line 83) | class MoMA_generator:
method __init__ (line 84) | def __init__(self, device,args):
method init_proj (line 110) | def init_proj(self):
method set_ip_adapter (line 118) | def set_ip_adapter(self):
method get_image_embeds_CFG (line 138) | def get_image_embeds_CFG(self, llava_emb):
method get_image_crossAttn_feature (line 144) | def get_image_crossAttn_feature(
method get_image_selfAttn_feature (line 158) | def get_image_selfAttn_feature(
method generate_with_MoMA (line 184) | def generate_with_MoMA(
method set_selfAttn_strength (line 229) | def set_selfAttn_strength(self, strength):
method set_cross_subject_idxs (line 236) | def set_cross_subject_idxs(self, subject_idxs):
method set_self_mask (line 241) | def set_self_mask(self,mode,id_ig='', mask=None): #only have effect on...
method toggle_enable_flag (line 256) | def toggle_enable_flag(self, processor_enable_mode):
method toggle_extract_inject_flag (line 269) | def toggle_extract_inject_flag(self, processor_name, mode): # mode: st...
method reset_all (line 278) | def reset_all(self,keep_self=False):
FILE: model_lib/modules.py
function add_function (line 18) | def add_function(model):
class LlamaMLP_mapping (line 51) | class LlamaMLP_mapping(nn.Module):
method __init__ (line 52) | def __init__(self, hidden_size,hidden_size_out):
method forward (line 62) | def forward(self, x):
method init_linear (line 66) | def init_linear(self):
class MoMA_main_modal (line 74) | class MoMA_main_modal(nn.Module):
method __init__ (line 75) | def __init__(self,args):
method load_saved_components (line 94) | def load_saved_components(self):
method freeze_modules (line 108) | def freeze_modules(self):
method forward_MLLM (line 114) | def forward_MLLM(self,batch):
method reset (line 136) | def reset(self):
method generate_images (line 139) | def generate_images(self, rgb_path, mask_path, subject, prompt, streng...
FILE: model_lib/utils.py
function parse_args (line 6) | def parse_args():
function show_PIL_image (line 16) | def show_PIL_image(tensor):
Condensed preview — 12 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (4,062K chars).
[
{
"path": "README.md",
"chars": 4582,
"preview": "# ___***MoMA: Multimodal LLM Adapter for Fast Personalized Image Generation***___\n#### ECCV 2024 Accepted\n\n<a href='http"
},
{
"path": "checkpoints/readme.txt",
"chars": 164,
"preview": "the code will automatically download a pth file from huggingface.\n\nname: attn_adapters_projectors.th\nsize: 150 MB\nuseage"
},
{
"path": "dataset_lib/dataset_eval_MoMA.py",
"chars": 1451,
"preview": "from PIL import Image\nimport numpy as np\nimport torch\nfrom torchvision import transforms\nfrom llava.mm_utils import proc"
},
{
"path": "model_lib/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "model_lib/attention_processor.py",
"chars": 11331,
"preview": "# modified from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py\nimport to"
},
{
"path": "model_lib/moMA_generator.py",
"chars": 13528,
"preview": "from typing import List\nimport torch\nfrom transformers import CLIPVisionModelWithProjection, CLIPImageProcessor\nfrom PIL"
},
{
"path": "model_lib/modules.py",
"chars": 8089,
"preview": "import os\nfrom PIL import Image\nimport torch\nimport torch.nn as nn\nfrom typing import List, Optional\nimport torch.utils."
},
{
"path": "model_lib/utils.py",
"chars": 1109,
"preview": "import argparse\nimport torch\nfrom torchvision.transforms import ToPILImage\nfrom PIL import Image\n\ndef parse_args():\n "
},
{
"path": "requirements.txt",
"chars": 74,
"preview": "diffusers\npytorch_lightning\n\neinops\nprotobuf\nchardet\nTriton\npeft\nnotebook\n"
},
{
"path": "requirements_freeze.txt",
"chars": 3659,
"preview": "accelerate==0.21.0\naiofiles==23.2.1\naiohttp==3.9.5\naiosignal==1.3.1\naltair==5.3.0\nannotated-types==0.6.0\nanyio==4.3.0\nar"
},
{
"path": "run_MoMA_notebook.ipynb",
"chars": 4013247,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"id\": \"f29d70fa\",\n \"metadata\": {},\n \"outputs\":"
},
{
"path": "run_evaluate_MoMA.py",
"chars": 1578,
"preview": "import os\nimport torch\nfrom pytorch_lightning import seed_everything\nfrom torchvision.utils import save_image\nimport war"
}
]
About this extraction
This page contains the full source code of the bytedance/MoMA GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 12 files (3.9 MB), approximately 1.0M tokens, and a symbol index with 41 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.