Repository: facebookresearch/DiT
Branch: main
Commit: ed81ce222909
Files: 16
Total size: 124.8 KB
Directory structure:
gitextract_h_w8e40c/
├── CODE_OF_CONDUCT.md
├── CONTRIBUTING.md
├── LICENSE.txt
├── README.md
├── diffusion/
│ ├── __init__.py
│ ├── diffusion_utils.py
│ ├── gaussian_diffusion.py
│ ├── respace.py
│ └── timestep_sampler.py
├── download.py
├── environment.yml
├── models.py
├── run_DiT.ipynb
├── sample.py
├── sample_ddp.py
└── train.py
================================================
FILE CONTENTS
================================================
================================================
FILE: CODE_OF_CONDUCT.md
================================================
# Code of Conduct
## Our Pledge
In the interest of fostering an open and welcoming environment, we as
contributors and maintainers pledge to make participation in our project and
our community a harassment-free experience for everyone, regardless of age, body
size, disability, ethnicity, sex characteristics, gender identity and expression,
level of experience, education, socio-economic status, nationality, personal
appearance, race, religion, or sexual identity and orientation.
## Our Standards
Examples of behavior that contributes to creating a positive environment
include:
* Using welcoming and inclusive language
* Being respectful of differing viewpoints and experiences
* Gracefully accepting constructive criticism
* Focusing on what is best for the community
* Showing empathy towards other community members
Examples of unacceptable behavior by participants include:
* The use of sexualized language or imagery and unwelcome sexual attention or
advances
* Trolling, insulting/derogatory comments, and personal or political attacks
* Public or private harassment
* Publishing others' private information, such as a physical or electronic
address, without explicit permission
* Other conduct which could reasonably be considered inappropriate in a
professional setting
## Our Responsibilities
Project maintainers are responsible for clarifying the standards of acceptable
behavior and are expected to take appropriate and fair corrective action in
response to any instances of unacceptable behavior.
Project maintainers have the right and responsibility to remove, edit, or
reject comments, commits, code, wiki edits, issues, and other contributions
that are not aligned to this Code of Conduct, or to ban temporarily or
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## Scope
This Code of Conduct applies within all project spaces, and it also applies when
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Examples of representing a project or community include using an official
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as an appointed representative at an online or offline event. Representation of
a project may be further defined and clarified by project maintainers.
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reasonable belief that an individual's behavior may have a negative impact on
the project or its community.
## Enforcement
Instances of abusive, harassing, or otherwise unacceptable behavior may be
reported by contacting the project team at <opensource-conduct@meta.com>. All
complaints will be reviewed and investigated and will result in a response that
is deemed necessary and appropriate to the circumstances. The project team is
obligated to maintain confidentiality with regard to the reporter of an incident.
Further details of specific enforcement policies may be posted separately.
Project maintainers who do not follow or enforce the Code of Conduct in good
faith may face temporary or permanent repercussions as determined by other
members of the project's leadership.
## Attribution
This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4,
available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html
[homepage]: https://www.contributor-covenant.org
For answers to common questions about this code of conduct, see
https://www.contributor-covenant.org/faq
================================================
FILE: CONTRIBUTING.md
================================================
# Contributing to DiT
We want to make contributing to this project as easy and transparent as
possible.
## Our Development Process
Work on the `DiT` repo has mostly concluded.
## Pull Requests
We actively welcome your pull requests.
1. Fork the repo and create your branch from `main`.
2. If you've added code that should be tested, add tests.
3. If you've changed APIs, update the documentation.
4. Ensure the test suite passes.
5. Make sure your code lints.
6. If you haven't already, complete the Contributor License Agreement ("CLA").
## Contributor License Agreement ("CLA")
In order to accept your pull request, we need you to submit a CLA. You only need
to do this once to work on any of Meta's open source projects.
Complete your CLA here: <https://code.facebook.com/cla>
## Issues
We use GitHub issues to track public bugs. Please ensure your description is
clear and has sufficient instructions to be able to reproduce the issue.
Meta has a [bounty program](https://www.facebook.com/whitehat/) for the safe
disclosure of security bugs. In those cases, please go through the process
outlined on that page and do not file a public issue.
## License
By contributing to `DiT`, you agree that your contributions will be licensed
under the LICENSE file in the root directory of this source tree.
================================================
FILE: LICENSE.txt
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FILE: README.md
================================================
## Scalable Diffusion Models with Transformers (DiT)<br><sub>Official PyTorch Implementation</sub>
### [Paper](http://arxiv.org/abs/2212.09748) | [Project Page](https://www.wpeebles.com/DiT) | Run DiT-XL/2 [](https://huggingface.co/spaces/wpeebles/DiT) [](http://colab.research.google.com/github/facebookresearch/DiT/blob/main/run_DiT.ipynb) <a href="https://replicate.com/arielreplicate/scalable_diffusion_with_transformers"><img src="https://replicate.com/arielreplicate/scalable_diffusion_with_transformers/badge"></a>
This repo contains PyTorch model definitions, pre-trained weights and training/sampling code for our paper exploring
diffusion models with transformers (DiTs). You can find more visualizations on our [project page](https://www.wpeebles.com/DiT).
> [**Scalable Diffusion Models with Transformers**](https://www.wpeebles.com/DiT)<br>
> [William Peebles](https://www.wpeebles.com), [Saining Xie](https://www.sainingxie.com)
> <br>UC Berkeley, New York University<br>
We train latent diffusion models, replacing the commonly-used U-Net backbone with a transformer that operates on
latent patches. We analyze the scalability of our Diffusion Transformers (DiTs) through the lens of forward pass
complexity as measured by Gflops. We find that DiTs with higher Gflops---through increased transformer depth/width or
increased number of input tokens---consistently have lower FID. In addition to good scalability properties, our
DiT-XL/2 models outperform all prior diffusion models on the class-conditional ImageNet 512×512 and 256×256 benchmarks,
achieving a state-of-the-art FID of 2.27 on the latter.
This repository contains:
* 🪐 A simple PyTorch [implementation](models.py) of DiT
* ⚡️ Pre-trained class-conditional DiT models trained on ImageNet (512x512 and 256x256)
* 💥 A self-contained [Hugging Face Space](https://huggingface.co/spaces/wpeebles/DiT) and [Colab notebook](http://colab.research.google.com/github/facebookresearch/DiT/blob/main/run_DiT.ipynb) for running pre-trained DiT-XL/2 models
* 🛸 A DiT [training script](train.py) using PyTorch DDP
An implementation of DiT directly in Hugging Face `diffusers` can also be found [here](https://github.com/huggingface/diffusers/blob/main/docs/source/en/api/pipelines/dit.mdx).
## Setup
First, download and set up the repo:
```bash
git clone https://github.com/facebookresearch/DiT.git
cd DiT
```
We provide an [`environment.yml`](environment.yml) file that can be used to create a Conda environment. If you only want
to run pre-trained models locally on CPU, you can remove the `cudatoolkit` and `pytorch-cuda` requirements from the file.
```bash
conda env create -f environment.yml
conda activate DiT
```
## Sampling [](https://huggingface.co/spaces/wpeebles/DiT) [](http://colab.research.google.com/github/facebookresearch/DiT/blob/main/run_DiT.ipynb)
**Pre-trained DiT checkpoints.** You can sample from our pre-trained DiT models with [`sample.py`](sample.py). Weights for our pre-trained DiT model will be
automatically downloaded depending on the model you use. The script has various arguments to switch between the 256x256
and 512x512 models, adjust sampling steps, change the classifier-free guidance scale, etc. For example, to sample from
our 512x512 DiT-XL/2 model, you can use:
```bash
python sample.py --image-size 512 --seed 1
```
For convenience, our pre-trained DiT models can be downloaded directly here as well:
| DiT Model | Image Resolution | FID-50K | Inception Score | Gflops |
|---------------|------------------|---------|-----------------|--------|
| [XL/2](https://dl.fbaipublicfiles.com/DiT/models/DiT-XL-2-256x256.pt) | 256x256 | 2.27 | 278.24 | 119 |
| [XL/2](https://dl.fbaipublicfiles.com/DiT/models/DiT-XL-2-512x512.pt) | 512x512 | 3.04 | 240.82 | 525 |
**Custom DiT checkpoints.** If you've trained a new DiT model with [`train.py`](train.py) (see [below](#training-dit)), you can add the `--ckpt`
argument to use your own checkpoint instead. For example, to sample from the EMA weights of a custom
256x256 DiT-L/4 model, run:
```bash
python sample.py --model DiT-L/4 --image-size 256 --ckpt /path/to/model.pt
```
## Training DiT
We provide a training script for DiT in [`train.py`](train.py). This script can be used to train class-conditional
DiT models, but it can be easily modified to support other types of conditioning. To launch DiT-XL/2 (256x256) training with `N` GPUs on
one node:
```bash
torchrun --nnodes=1 --nproc_per_node=N train.py --model DiT-XL/2 --data-path /path/to/imagenet/train
```
### PyTorch Training Results
We've trained DiT-XL/2 and DiT-B/4 models from scratch with the PyTorch training script
to verify that it reproduces the original JAX results up to several hundred thousand training iterations. Across our experiments, the PyTorch-trained models give
similar (and sometimes slightly better) results compared to the JAX-trained models up to reasonable random variation. Some data points:
| DiT Model | Train Steps | FID-50K<br> (JAX Training) | FID-50K<br> (PyTorch Training) | PyTorch Global Training Seed |
|------------|-------------|----------------------------|--------------------------------|------------------------------|
| XL/2 | 400K | 19.5 | **18.1** | 42 |
| B/4 | 400K | **68.4** | 68.9 | 42 |
| B/4 | 400K | 68.4 | **68.3** | 100 |
These models were trained at 256x256 resolution; we used 8x A100s to train XL/2 and 4x A100s to train B/4. Note that FID
here is computed with 250 DDPM sampling steps, with the `mse` VAE decoder and without guidance (`cfg-scale=1`).
**TF32 Note (important for A100 users).** When we ran the above tests, TF32 matmuls were disabled per PyTorch's defaults.
We've enabled them at the top of `train.py` and `sample.py` because it makes training and sampling way way way faster on
A100s (and should for other Ampere GPUs too), but note that the use of TF32 may lead to some differences compared to
the above results.
### Enhancements
Training (and sampling) could likely be sped-up significantly by:
- [ ] using [Flash Attention](https://github.com/HazyResearch/flash-attention) in the DiT model
- [ ] using `torch.compile` in PyTorch 2.0
Basic features that would be nice to add:
- [ ] Monitor FID and other metrics
- [ ] Generate and save samples from the EMA model periodically
- [ ] Resume training from a checkpoint
- [ ] AMP/bfloat16 support
**🔥 Feature Update** Check out this repository at https://github.com/chuanyangjin/fast-DiT to preview a selection of training speed acceleration and memory saving features including gradient checkpointing, mixed precision training and pre-extrated VAE features. With these advancements, we have achieved a training speed of 0.84 steps/sec for DiT-XL/2 using just a single A100 GPU.
## Evaluation (FID, Inception Score, etc.)
We include a [`sample_ddp.py`](sample_ddp.py) script which samples a large number of images from a DiT model in parallel. This script
generates a folder of samples as well as a `.npz` file which can be directly used with [ADM's TensorFlow
evaluation suite](https://github.com/openai/guided-diffusion/tree/main/evaluations) to compute FID, Inception Score and
other metrics. For example, to sample 50K images from our pre-trained DiT-XL/2 model over `N` GPUs, run:
```bash
torchrun --nnodes=1 --nproc_per_node=N sample_ddp.py --model DiT-XL/2 --num-fid-samples 50000
```
There are several additional options; see [`sample_ddp.py`](sample_ddp.py) for details.
## Differences from JAX
Our models were originally trained in JAX on TPUs. The weights in this repo are ported directly from the JAX models.
There may be minor differences in results stemming from sampling with different floating point precisions. We re-evaluated
our ported PyTorch weights at FP32, and they actually perform marginally better than sampling in JAX (2.21 FID
versus 2.27 in the paper).
## BibTeX
```bibtex
@article{Peebles2022DiT,
title={Scalable Diffusion Models with Transformers},
author={William Peebles and Saining Xie},
year={2022},
journal={arXiv preprint arXiv:2212.09748},
}
```
## Acknowledgments
We thank Kaiming He, Ronghang Hu, Alexander Berg, Shoubhik Debnath, Tim Brooks, Ilija Radosavovic and Tete Xiao for helpful discussions.
William Peebles is supported by the NSF Graduate Research Fellowship.
This codebase borrows from OpenAI's diffusion repos, most notably [ADM](https://github.com/openai/guided-diffusion).
## License
The code and model weights are licensed under CC-BY-NC. See [`LICENSE.txt`](LICENSE.txt) for details.
================================================
FILE: diffusion/__init__.py
================================================
# Modified from OpenAI's diffusion repos
# GLIDE: https://github.com/openai/glide-text2im/blob/main/glide_text2im/gaussian_diffusion.py
# ADM: https://github.com/openai/guided-diffusion/blob/main/guided_diffusion
# IDDPM: https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py
from . import gaussian_diffusion as gd
from .respace import SpacedDiffusion, space_timesteps
def create_diffusion(
timestep_respacing,
noise_schedule="linear",
use_kl=False,
sigma_small=False,
predict_xstart=False,
learn_sigma=True,
rescale_learned_sigmas=False,
diffusion_steps=1000
):
betas = gd.get_named_beta_schedule(noise_schedule, diffusion_steps)
if use_kl:
loss_type = gd.LossType.RESCALED_KL
elif rescale_learned_sigmas:
loss_type = gd.LossType.RESCALED_MSE
else:
loss_type = gd.LossType.MSE
if timestep_respacing is None or timestep_respacing == "":
timestep_respacing = [diffusion_steps]
return SpacedDiffusion(
use_timesteps=space_timesteps(diffusion_steps, timestep_respacing),
betas=betas,
model_mean_type=(
gd.ModelMeanType.EPSILON if not predict_xstart else gd.ModelMeanType.START_X
),
model_var_type=(
(
gd.ModelVarType.FIXED_LARGE
if not sigma_small
else gd.ModelVarType.FIXED_SMALL
)
if not learn_sigma
else gd.ModelVarType.LEARNED_RANGE
),
loss_type=loss_type
# rescale_timesteps=rescale_timesteps,
)
================================================
FILE: diffusion/diffusion_utils.py
================================================
# Modified from OpenAI's diffusion repos
# GLIDE: https://github.com/openai/glide-text2im/blob/main/glide_text2im/gaussian_diffusion.py
# ADM: https://github.com/openai/guided-diffusion/blob/main/guided_diffusion
# IDDPM: https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py
import torch as th
import numpy as np
def normal_kl(mean1, logvar1, mean2, logvar2):
"""
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, th.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 th.exp().
logvar1, logvar2 = [
x if isinstance(x, th.Tensor) else th.tensor(x).to(tensor)
for x in (logvar1, logvar2)
]
return 0.5 * (
-1.0
+ logvar2
- logvar1
+ th.exp(logvar1 - logvar2)
+ ((mean1 - mean2) ** 2) * th.exp(-logvar2)
)
def approx_standard_normal_cdf(x):
"""
A fast approximation of the cumulative distribution function of the
standard normal.
"""
return 0.5 * (1.0 + th.tanh(np.sqrt(2.0 / np.pi) * (x + 0.044715 * th.pow(x, 3))))
def continuous_gaussian_log_likelihood(x, *, means, log_scales):
"""
Compute the log-likelihood of a continuous Gaussian distribution.
:param x: the targets
:param means: the Gaussian mean Tensor.
:param log_scales: the Gaussian log stddev Tensor.
:return: a tensor like x of log probabilities (in nats).
"""
centered_x = x - means
inv_stdv = th.exp(-log_scales)
normalized_x = centered_x * inv_stdv
log_probs = th.distributions.Normal(th.zeros_like(x), th.ones_like(x)).log_prob(normalized_x)
return log_probs
def discretized_gaussian_log_likelihood(x, *, means, log_scales):
"""
Compute the log-likelihood of a Gaussian distribution discretizing to a
given image.
:param x: the target images. It is assumed that this was uint8 values,
rescaled to the range [-1, 1].
:param means: the Gaussian mean Tensor.
:param log_scales: the Gaussian log stddev Tensor.
:return: a tensor like x of log probabilities (in nats).
"""
assert x.shape == means.shape == log_scales.shape
centered_x = x - means
inv_stdv = th.exp(-log_scales)
plus_in = inv_stdv * (centered_x + 1.0 / 255.0)
cdf_plus = approx_standard_normal_cdf(plus_in)
min_in = inv_stdv * (centered_x - 1.0 / 255.0)
cdf_min = approx_standard_normal_cdf(min_in)
log_cdf_plus = th.log(cdf_plus.clamp(min=1e-12))
log_one_minus_cdf_min = th.log((1.0 - cdf_min).clamp(min=1e-12))
cdf_delta = cdf_plus - cdf_min
log_probs = th.where(
x < -0.999,
log_cdf_plus,
th.where(x > 0.999, log_one_minus_cdf_min, th.log(cdf_delta.clamp(min=1e-12))),
)
assert log_probs.shape == x.shape
return log_probs
================================================
FILE: diffusion/gaussian_diffusion.py
================================================
# Modified from OpenAI's diffusion repos
# GLIDE: https://github.com/openai/glide-text2im/blob/main/glide_text2im/gaussian_diffusion.py
# ADM: https://github.com/openai/guided-diffusion/blob/main/guided_diffusion
# IDDPM: https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py
import math
import numpy as np
import torch as th
import enum
from .diffusion_utils import discretized_gaussian_log_likelihood, normal_kl
def mean_flat(tensor):
"""
Take the mean over all non-batch dimensions.
"""
return tensor.mean(dim=list(range(1, len(tensor.shape))))
class ModelMeanType(enum.Enum):
"""
Which type of output the model predicts.
"""
PREVIOUS_X = enum.auto() # the model predicts x_{t-1}
START_X = enum.auto() # the model predicts x_0
EPSILON = enum.auto() # the model predicts epsilon
class ModelVarType(enum.Enum):
"""
What is used as the model's output variance.
The LEARNED_RANGE option has been added to allow the model to predict
values between FIXED_SMALL and FIXED_LARGE, making its job easier.
"""
LEARNED = enum.auto()
FIXED_SMALL = enum.auto()
FIXED_LARGE = enum.auto()
LEARNED_RANGE = enum.auto()
class LossType(enum.Enum):
MSE = enum.auto() # use raw MSE loss (and KL when learning variances)
RESCALED_MSE = (
enum.auto()
) # use raw MSE loss (with RESCALED_KL when learning variances)
KL = enum.auto() # use the variational lower-bound
RESCALED_KL = enum.auto() # like KL, but rescale to estimate the full VLB
def is_vb(self):
return self == LossType.KL or self == LossType.RESCALED_KL
def _warmup_beta(beta_start, beta_end, num_diffusion_timesteps, warmup_frac):
betas = beta_end * np.ones(num_diffusion_timesteps, dtype=np.float64)
warmup_time = int(num_diffusion_timesteps * warmup_frac)
betas[:warmup_time] = np.linspace(beta_start, beta_end, warmup_time, dtype=np.float64)
return betas
def get_beta_schedule(beta_schedule, *, beta_start, beta_end, num_diffusion_timesteps):
"""
This is the deprecated API for creating beta schedules.
See get_named_beta_schedule() for the new library of schedules.
"""
if beta_schedule == "quad":
betas = (
np.linspace(
beta_start ** 0.5,
beta_end ** 0.5,
num_diffusion_timesteps,
dtype=np.float64,
)
** 2
)
elif beta_schedule == "linear":
betas = np.linspace(beta_start, beta_end, num_diffusion_timesteps, dtype=np.float64)
elif beta_schedule == "warmup10":
betas = _warmup_beta(beta_start, beta_end, num_diffusion_timesteps, 0.1)
elif beta_schedule == "warmup50":
betas = _warmup_beta(beta_start, beta_end, num_diffusion_timesteps, 0.5)
elif beta_schedule == "const":
betas = beta_end * np.ones(num_diffusion_timesteps, dtype=np.float64)
elif beta_schedule == "jsd": # 1/T, 1/(T-1), 1/(T-2), ..., 1
betas = 1.0 / np.linspace(
num_diffusion_timesteps, 1, num_diffusion_timesteps, dtype=np.float64
)
else:
raise NotImplementedError(beta_schedule)
assert betas.shape == (num_diffusion_timesteps,)
return betas
def get_named_beta_schedule(schedule_name, num_diffusion_timesteps):
"""
Get a pre-defined beta schedule for the given name.
The beta schedule library consists of beta schedules which remain similar
in the limit of num_diffusion_timesteps.
Beta schedules may be added, but should not be removed or changed once
they are committed to maintain backwards compatibility.
"""
if schedule_name == "linear":
# Linear schedule from Ho et al, extended to work for any number of
# diffusion steps.
scale = 1000 / num_diffusion_timesteps
return get_beta_schedule(
"linear",
beta_start=scale * 0.0001,
beta_end=scale * 0.02,
num_diffusion_timesteps=num_diffusion_timesteps,
)
elif schedule_name == "squaredcos_cap_v2":
return betas_for_alpha_bar(
num_diffusion_timesteps,
lambda t: math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2,
)
else:
raise NotImplementedError(f"unknown beta schedule: {schedule_name}")
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)
class GaussianDiffusion:
"""
Utilities for training and sampling diffusion models.
Original ported from this codebase:
https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/diffusion_utils_2.py#L42
:param betas: a 1-D numpy array of betas for each diffusion timestep,
starting at T and going to 1.
"""
def __init__(
self,
*,
betas,
model_mean_type,
model_var_type,
loss_type
):
self.model_mean_type = model_mean_type
self.model_var_type = model_var_type
self.loss_type = loss_type
# Use float64 for accuracy.
betas = np.array(betas, dtype=np.float64)
self.betas = betas
assert len(betas.shape) == 1, "betas must be 1-D"
assert (betas > 0).all() and (betas <= 1).all()
self.num_timesteps = int(betas.shape[0])
alphas = 1.0 - betas
self.alphas_cumprod = np.cumprod(alphas, axis=0)
self.alphas_cumprod_prev = np.append(1.0, self.alphas_cumprod[:-1])
self.alphas_cumprod_next = np.append(self.alphas_cumprod[1:], 0.0)
assert self.alphas_cumprod_prev.shape == (self.num_timesteps,)
# calculations for diffusion q(x_t | x_{t-1}) and others
self.sqrt_alphas_cumprod = np.sqrt(self.alphas_cumprod)
self.sqrt_one_minus_alphas_cumprod = np.sqrt(1.0 - self.alphas_cumprod)
self.log_one_minus_alphas_cumprod = np.log(1.0 - self.alphas_cumprod)
self.sqrt_recip_alphas_cumprod = np.sqrt(1.0 / self.alphas_cumprod)
self.sqrt_recipm1_alphas_cumprod = np.sqrt(1.0 / self.alphas_cumprod - 1)
# calculations for posterior q(x_{t-1} | x_t, x_0)
self.posterior_variance = (
betas * (1.0 - self.alphas_cumprod_prev) / (1.0 - self.alphas_cumprod)
)
# below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
self.posterior_log_variance_clipped = np.log(
np.append(self.posterior_variance[1], self.posterior_variance[1:])
) if len(self.posterior_variance) > 1 else np.array([])
self.posterior_mean_coef1 = (
betas * np.sqrt(self.alphas_cumprod_prev) / (1.0 - self.alphas_cumprod)
)
self.posterior_mean_coef2 = (
(1.0 - self.alphas_cumprod_prev) * np.sqrt(alphas) / (1.0 - self.alphas_cumprod)
)
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 q_sample(self, x_start, t, noise=None):
"""
Diffuse the data for a given number of diffusion steps.
In other words, sample from q(x_t | x_0).
:param x_start: the initial data batch.
:param t: the number of diffusion steps (minus 1). Here, 0 means one step.
:param noise: if specified, the split-out normal noise.
:return: A noisy version of x_start.
"""
if noise is None:
noise = th.randn_like(x_start)
assert noise.shape == x_start.shape
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 q_posterior_mean_variance(self, x_start, x_t, t):
"""
Compute the mean and variance of the diffusion posterior:
q(x_{t-1} | x_t, x_0)
"""
assert x_start.shape == x_t.shape
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
)
assert (
posterior_mean.shape[0]
== posterior_variance.shape[0]
== posterior_log_variance_clipped.shape[0]
== x_start.shape[0]
)
return posterior_mean, posterior_variance, posterior_log_variance_clipped
def p_mean_variance(self, model, x, t, clip_denoised=True, denoised_fn=None, model_kwargs=None):
"""
Apply the model to get p(x_{t-1} | x_t), as well as a prediction of
the initial x, x_0.
:param model: the model, which takes a signal and a batch of timesteps
as input.
:param x: the [N x C x ...] tensor at time t.
:param t: a 1-D Tensor of timesteps.
:param clip_denoised: if True, clip the denoised signal into [-1, 1].
:param denoised_fn: if not None, a function which applies to the
x_start prediction before it is used to sample. Applies before
clip_denoised.
:param model_kwargs: if not None, a dict of extra keyword arguments to
pass to the model. This can be used for conditioning.
:return: a dict with the following keys:
- 'mean': the model mean output.
- 'variance': the model variance output.
- 'log_variance': the log of 'variance'.
- 'pred_xstart': the prediction for x_0.
"""
if model_kwargs is None:
model_kwargs = {}
B, C = x.shape[:2]
assert t.shape == (B,)
model_output = model(x, t, **model_kwargs)
if isinstance(model_output, tuple):
model_output, extra = model_output
else:
extra = None
if self.model_var_type in [ModelVarType.LEARNED, ModelVarType.LEARNED_RANGE]:
assert model_output.shape == (B, C * 2, *x.shape[2:])
model_output, model_var_values = th.split(model_output, C, dim=1)
min_log = _extract_into_tensor(self.posterior_log_variance_clipped, t, x.shape)
max_log = _extract_into_tensor(np.log(self.betas), t, x.shape)
# The model_var_values is [-1, 1] for [min_var, max_var].
frac = (model_var_values + 1) / 2
model_log_variance = frac * max_log + (1 - frac) * min_log
model_variance = th.exp(model_log_variance)
else:
model_variance, model_log_variance = {
# for fixedlarge, we set the initial (log-)variance like so
# to get a better decoder log likelihood.
ModelVarType.FIXED_LARGE: (
np.append(self.posterior_variance[1], self.betas[1:]),
np.log(np.append(self.posterior_variance[1], self.betas[1:])),
),
ModelVarType.FIXED_SMALL: (
self.posterior_variance,
self.posterior_log_variance_clipped,
),
}[self.model_var_type]
model_variance = _extract_into_tensor(model_variance, t, x.shape)
model_log_variance = _extract_into_tensor(model_log_variance, t, x.shape)
def process_xstart(x):
if denoised_fn is not None:
x = denoised_fn(x)
if clip_denoised:
return x.clamp(-1, 1)
return x
if self.model_mean_type == ModelMeanType.START_X:
pred_xstart = process_xstart(model_output)
else:
pred_xstart = process_xstart(
self._predict_xstart_from_eps(x_t=x, t=t, eps=model_output)
)
model_mean, _, _ = self.q_posterior_mean_variance(x_start=pred_xstart, x_t=x, t=t)
assert model_mean.shape == model_log_variance.shape == pred_xstart.shape == x.shape
return {
"mean": model_mean,
"variance": model_variance,
"log_variance": model_log_variance,
"pred_xstart": pred_xstart,
"extra": extra,
}
def _predict_xstart_from_eps(self, x_t, t, eps):
assert x_t.shape == eps.shape
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) * eps
)
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 condition_mean(self, cond_fn, p_mean_var, x, t, model_kwargs=None):
"""
Compute the mean for the previous step, given a function cond_fn that
computes the gradient of a conditional log probability with respect to
x. In particular, cond_fn computes grad(log(p(y|x))), and we want to
condition on y.
This uses the conditioning strategy from Sohl-Dickstein et al. (2015).
"""
gradient = cond_fn(x, t, **model_kwargs)
new_mean = p_mean_var["mean"].float() + p_mean_var["variance"] * gradient.float()
return new_mean
def condition_score(self, cond_fn, p_mean_var, x, t, model_kwargs=None):
"""
Compute what the p_mean_variance output would have been, should the
model's score function be conditioned by cond_fn.
See condition_mean() for details on cond_fn.
Unlike condition_mean(), this instead uses the conditioning strategy
from Song et al (2020).
"""
alpha_bar = _extract_into_tensor(self.alphas_cumprod, t, x.shape)
eps = self._predict_eps_from_xstart(x, t, p_mean_var["pred_xstart"])
eps = eps - (1 - alpha_bar).sqrt() * cond_fn(x, t, **model_kwargs)
out = p_mean_var.copy()
out["pred_xstart"] = self._predict_xstart_from_eps(x, t, eps)
out["mean"], _, _ = self.q_posterior_mean_variance(x_start=out["pred_xstart"], x_t=x, t=t)
return out
def p_sample(
self,
model,
x,
t,
clip_denoised=True,
denoised_fn=None,
cond_fn=None,
model_kwargs=None,
):
"""
Sample x_{t-1} from the model at the given timestep.
:param model: the model to sample from.
:param x: the current tensor at x_{t-1}.
:param t: the value of t, starting at 0 for the first diffusion step.
:param clip_denoised: if True, clip the x_start prediction to [-1, 1].
:param denoised_fn: if not None, a function which applies to the
x_start prediction before it is used to sample.
:param cond_fn: if not None, this is a gradient function that acts
similarly to the model.
:param model_kwargs: if not None, a dict of extra keyword arguments to
pass to the model. This can be used for conditioning.
:return: a dict containing the following keys:
- 'sample': a random sample from the model.
- 'pred_xstart': a prediction of x_0.
"""
out = self.p_mean_variance(
model,
x,
t,
clip_denoised=clip_denoised,
denoised_fn=denoised_fn,
model_kwargs=model_kwargs,
)
noise = th.randn_like(x)
nonzero_mask = (
(t != 0).float().view(-1, *([1] * (len(x.shape) - 1)))
) # no noise when t == 0
if cond_fn is not None:
out["mean"] = self.condition_mean(cond_fn, out, x, t, model_kwargs=model_kwargs)
sample = out["mean"] + nonzero_mask * th.exp(0.5 * out["log_variance"]) * noise
return {"sample": sample, "pred_xstart": out["pred_xstart"]}
def p_sample_loop(
self,
model,
shape,
noise=None,
clip_denoised=True,
denoised_fn=None,
cond_fn=None,
model_kwargs=None,
device=None,
progress=False,
):
"""
Generate samples from the model.
:param model: the model module.
:param shape: the shape of the samples, (N, C, H, W).
:param noise: if specified, the noise from the encoder to sample.
Should be of the same shape as `shape`.
:param clip_denoised: if True, clip x_start predictions to [-1, 1].
:param denoised_fn: if not None, a function which applies to the
x_start prediction before it is used to sample.
:param cond_fn: if not None, this is a gradient function that acts
similarly to the model.
:param model_kwargs: if not None, a dict of extra keyword arguments to
pass to the model. This can be used for conditioning.
:param device: if specified, the device to create the samples on.
If not specified, use a model parameter's device.
:param progress: if True, show a tqdm progress bar.
:return: a non-differentiable batch of samples.
"""
final = None
for sample in self.p_sample_loop_progressive(
model,
shape,
noise=noise,
clip_denoised=clip_denoised,
denoised_fn=denoised_fn,
cond_fn=cond_fn,
model_kwargs=model_kwargs,
device=device,
progress=progress,
):
final = sample
return final["sample"]
def p_sample_loop_progressive(
self,
model,
shape,
noise=None,
clip_denoised=True,
denoised_fn=None,
cond_fn=None,
model_kwargs=None,
device=None,
progress=False,
):
"""
Generate samples from the model and yield intermediate samples from
each timestep of diffusion.
Arguments are the same as p_sample_loop().
Returns a generator over dicts, where each dict is the return value of
p_sample().
"""
if device is None:
device = next(model.parameters()).device
assert isinstance(shape, (tuple, list))
if noise is not None:
img = noise
else:
img = th.randn(*shape, device=device)
indices = list(range(self.num_timesteps))[::-1]
if progress:
# Lazy import so that we don't depend on tqdm.
from tqdm.auto import tqdm
indices = tqdm(indices)
for i in indices:
t = th.tensor([i] * shape[0], device=device)
with th.no_grad():
out = self.p_sample(
model,
img,
t,
clip_denoised=clip_denoised,
denoised_fn=denoised_fn,
cond_fn=cond_fn,
model_kwargs=model_kwargs,
)
yield out
img = out["sample"]
def ddim_sample(
self,
model,
x,
t,
clip_denoised=True,
denoised_fn=None,
cond_fn=None,
model_kwargs=None,
eta=0.0,
):
"""
Sample x_{t-1} from the model using DDIM.
Same usage as p_sample().
"""
out = self.p_mean_variance(
model,
x,
t,
clip_denoised=clip_denoised,
denoised_fn=denoised_fn,
model_kwargs=model_kwargs,
)
if cond_fn is not None:
out = self.condition_score(cond_fn, out, x, t, model_kwargs=model_kwargs)
# Usually our model outputs epsilon, but we re-derive it
# in case we used x_start or x_prev prediction.
eps = self._predict_eps_from_xstart(x, t, out["pred_xstart"])
alpha_bar = _extract_into_tensor(self.alphas_cumprod, t, x.shape)
alpha_bar_prev = _extract_into_tensor(self.alphas_cumprod_prev, t, x.shape)
sigma = (
eta
* th.sqrt((1 - alpha_bar_prev) / (1 - alpha_bar))
* th.sqrt(1 - alpha_bar / alpha_bar_prev)
)
# Equation 12.
noise = th.randn_like(x)
mean_pred = (
out["pred_xstart"] * th.sqrt(alpha_bar_prev)
+ th.sqrt(1 - alpha_bar_prev - sigma ** 2) * eps
)
nonzero_mask = (
(t != 0).float().view(-1, *([1] * (len(x.shape) - 1)))
) # no noise when t == 0
sample = mean_pred + nonzero_mask * sigma * noise
return {"sample": sample, "pred_xstart": out["pred_xstart"]}
def ddim_reverse_sample(
self,
model,
x,
t,
clip_denoised=True,
denoised_fn=None,
cond_fn=None,
model_kwargs=None,
eta=0.0,
):
"""
Sample x_{t+1} from the model using DDIM reverse ODE.
"""
assert eta == 0.0, "Reverse ODE only for deterministic path"
out = self.p_mean_variance(
model,
x,
t,
clip_denoised=clip_denoised,
denoised_fn=denoised_fn,
model_kwargs=model_kwargs,
)
if cond_fn is not None:
out = self.condition_score(cond_fn, out, x, t, model_kwargs=model_kwargs)
# Usually our model outputs epsilon, but we re-derive it
# in case we used x_start or x_prev prediction.
eps = (
_extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x.shape) * x
- out["pred_xstart"]
) / _extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x.shape)
alpha_bar_next = _extract_into_tensor(self.alphas_cumprod_next, t, x.shape)
# Equation 12. reversed
mean_pred = out["pred_xstart"] * th.sqrt(alpha_bar_next) + th.sqrt(1 - alpha_bar_next) * eps
return {"sample": mean_pred, "pred_xstart": out["pred_xstart"]}
def ddim_sample_loop(
self,
model,
shape,
noise=None,
clip_denoised=True,
denoised_fn=None,
cond_fn=None,
model_kwargs=None,
device=None,
progress=False,
eta=0.0,
):
"""
Generate samples from the model using DDIM.
Same usage as p_sample_loop().
"""
final = None
for sample in self.ddim_sample_loop_progressive(
model,
shape,
noise=noise,
clip_denoised=clip_denoised,
denoised_fn=denoised_fn,
cond_fn=cond_fn,
model_kwargs=model_kwargs,
device=device,
progress=progress,
eta=eta,
):
final = sample
return final["sample"]
def ddim_sample_loop_progressive(
self,
model,
shape,
noise=None,
clip_denoised=True,
denoised_fn=None,
cond_fn=None,
model_kwargs=None,
device=None,
progress=False,
eta=0.0,
):
"""
Use DDIM to sample from the model and yield intermediate samples from
each timestep of DDIM.
Same usage as p_sample_loop_progressive().
"""
if device is None:
device = next(model.parameters()).device
assert isinstance(shape, (tuple, list))
if noise is not None:
img = noise
else:
img = th.randn(*shape, device=device)
indices = list(range(self.num_timesteps))[::-1]
if progress:
# Lazy import so that we don't depend on tqdm.
from tqdm.auto import tqdm
indices = tqdm(indices)
for i in indices:
t = th.tensor([i] * shape[0], device=device)
with th.no_grad():
out = self.ddim_sample(
model,
img,
t,
clip_denoised=clip_denoised,
denoised_fn=denoised_fn,
cond_fn=cond_fn,
model_kwargs=model_kwargs,
eta=eta,
)
yield out
img = out["sample"]
def _vb_terms_bpd(
self, model, x_start, x_t, t, clip_denoised=True, model_kwargs=None
):
"""
Get a term for the variational lower-bound.
The resulting units are bits (rather than nats, as one might expect).
This allows for comparison to other papers.
:return: a dict with the following keys:
- 'output': a shape [N] tensor of NLLs or KLs.
- 'pred_xstart': the x_0 predictions.
"""
true_mean, _, true_log_variance_clipped = self.q_posterior_mean_variance(
x_start=x_start, x_t=x_t, t=t
)
out = self.p_mean_variance(
model, x_t, t, clip_denoised=clip_denoised, model_kwargs=model_kwargs
)
kl = normal_kl(
true_mean, true_log_variance_clipped, out["mean"], out["log_variance"]
)
kl = mean_flat(kl) / np.log(2.0)
decoder_nll = -discretized_gaussian_log_likelihood(
x_start, means=out["mean"], log_scales=0.5 * out["log_variance"]
)
assert decoder_nll.shape == x_start.shape
decoder_nll = mean_flat(decoder_nll) / np.log(2.0)
# At the first timestep return the decoder NLL,
# otherwise return KL(q(x_{t-1}|x_t,x_0) || p(x_{t-1}|x_t))
output = th.where((t == 0), decoder_nll, kl)
return {"output": output, "pred_xstart": out["pred_xstart"]}
def training_losses(self, model, x_start, t, model_kwargs=None, noise=None):
"""
Compute training losses for a single timestep.
:param model: the model to evaluate loss on.
:param x_start: the [N x C x ...] tensor of inputs.
:param t: a batch of timestep indices.
:param model_kwargs: if not None, a dict of extra keyword arguments to
pass to the model. This can be used for conditioning.
:param noise: if specified, the specific Gaussian noise to try to remove.
:return: a dict with the key "loss" containing a tensor of shape [N].
Some mean or variance settings may also have other keys.
"""
if model_kwargs is None:
model_kwargs = {}
if noise is None:
noise = th.randn_like(x_start)
x_t = self.q_sample(x_start, t, noise=noise)
terms = {}
if self.loss_type == LossType.KL or self.loss_type == LossType.RESCALED_KL:
terms["loss"] = self._vb_terms_bpd(
model=model,
x_start=x_start,
x_t=x_t,
t=t,
clip_denoised=False,
model_kwargs=model_kwargs,
)["output"]
if self.loss_type == LossType.RESCALED_KL:
terms["loss"] *= self.num_timesteps
elif self.loss_type == LossType.MSE or self.loss_type == LossType.RESCALED_MSE:
model_output = model(x_t, t, **model_kwargs)
if self.model_var_type in [
ModelVarType.LEARNED,
ModelVarType.LEARNED_RANGE,
]:
B, C = x_t.shape[:2]
assert model_output.shape == (B, C * 2, *x_t.shape[2:])
model_output, model_var_values = th.split(model_output, C, dim=1)
# Learn the variance using the variational bound, but don't let
# it affect our mean prediction.
frozen_out = th.cat([model_output.detach(), model_var_values], dim=1)
terms["vb"] = self._vb_terms_bpd(
model=lambda *args, r=frozen_out: r,
x_start=x_start,
x_t=x_t,
t=t,
clip_denoised=False,
)["output"]
if self.loss_type == LossType.RESCALED_MSE:
# Divide by 1000 for equivalence with initial implementation.
# Without a factor of 1/1000, the VB term hurts the MSE term.
terms["vb"] *= self.num_timesteps / 1000.0
target = {
ModelMeanType.PREVIOUS_X: self.q_posterior_mean_variance(
x_start=x_start, x_t=x_t, t=t
)[0],
ModelMeanType.START_X: x_start,
ModelMeanType.EPSILON: noise,
}[self.model_mean_type]
assert model_output.shape == target.shape == x_start.shape
terms["mse"] = mean_flat((target - model_output) ** 2)
if "vb" in terms:
terms["loss"] = terms["mse"] + terms["vb"]
else:
terms["loss"] = terms["mse"]
else:
raise NotImplementedError(self.loss_type)
return terms
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 = th.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 calc_bpd_loop(self, model, x_start, clip_denoised=True, model_kwargs=None):
"""
Compute the entire variational lower-bound, measured in bits-per-dim,
as well as other related quantities.
:param model: the model to evaluate loss on.
:param x_start: the [N x C x ...] tensor of inputs.
:param clip_denoised: if True, clip denoised samples.
:param model_kwargs: if not None, a dict of extra keyword arguments to
pass to the model. This can be used for conditioning.
:return: a dict containing the following keys:
- total_bpd: the total variational lower-bound, per batch element.
- prior_bpd: the prior term in the lower-bound.
- vb: an [N x T] tensor of terms in the lower-bound.
- xstart_mse: an [N x T] tensor of x_0 MSEs for each timestep.
- mse: an [N x T] tensor of epsilon MSEs for each timestep.
"""
device = x_start.device
batch_size = x_start.shape[0]
vb = []
xstart_mse = []
mse = []
for t in list(range(self.num_timesteps))[::-1]:
t_batch = th.tensor([t] * batch_size, device=device)
noise = th.randn_like(x_start)
x_t = self.q_sample(x_start=x_start, t=t_batch, noise=noise)
# Calculate VLB term at the current timestep
with th.no_grad():
out = self._vb_terms_bpd(
model,
x_start=x_start,
x_t=x_t,
t=t_batch,
clip_denoised=clip_denoised,
model_kwargs=model_kwargs,
)
vb.append(out["output"])
xstart_mse.append(mean_flat((out["pred_xstart"] - x_start) ** 2))
eps = self._predict_eps_from_xstart(x_t, t_batch, out["pred_xstart"])
mse.append(mean_flat((eps - noise) ** 2))
vb = th.stack(vb, dim=1)
xstart_mse = th.stack(xstart_mse, dim=1)
mse = th.stack(mse, dim=1)
prior_bpd = self._prior_bpd(x_start)
total_bpd = vb.sum(dim=1) + prior_bpd
return {
"total_bpd": total_bpd,
"prior_bpd": prior_bpd,
"vb": vb,
"xstart_mse": xstart_mse,
"mse": mse,
}
def _extract_into_tensor(arr, timesteps, broadcast_shape):
"""
Extract values from a 1-D numpy array for a batch of indices.
:param arr: the 1-D numpy array.
:param timesteps: a tensor of indices into the array to extract.
:param broadcast_shape: a larger shape of K dimensions with the batch
dimension equal to the length of timesteps.
:return: a tensor of shape [batch_size, 1, ...] where the shape has K dims.
"""
res = th.from_numpy(arr).to(device=timesteps.device)[timesteps].float()
while len(res.shape) < len(broadcast_shape):
res = res[..., None]
return res + th.zeros(broadcast_shape, device=timesteps.device)
================================================
FILE: diffusion/respace.py
================================================
# Modified from OpenAI's diffusion repos
# GLIDE: https://github.com/openai/glide-text2im/blob/main/glide_text2im/gaussian_diffusion.py
# ADM: https://github.com/openai/guided-diffusion/blob/main/guided_diffusion
# IDDPM: https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py
import numpy as np
import torch as th
from .gaussian_diffusion import GaussianDiffusion
def space_timesteps(num_timesteps, section_counts):
"""
Create a list of timesteps to use from an original diffusion process,
given the number of timesteps we want to take from equally-sized portions
of the original process.
For example, if there's 300 timesteps and the section counts are [10,15,20]
then the first 100 timesteps are strided to be 10 timesteps, the second 100
are strided to be 15 timesteps, and the final 100 are strided to be 20.
If the stride is a string starting with "ddim", then the fixed striding
from the DDIM paper is used, and only one section is allowed.
:param num_timesteps: the number of diffusion steps in the original
process to divide up.
:param section_counts: either a list of numbers, or a string containing
comma-separated numbers, indicating the step count
per section. As a special case, use "ddimN" where N
is a number of steps to use the striding from the
DDIM paper.
:return: a set of diffusion steps from the original process to use.
"""
if isinstance(section_counts, str):
if section_counts.startswith("ddim"):
desired_count = int(section_counts[len("ddim") :])
for i in range(1, num_timesteps):
if len(range(0, num_timesteps, i)) == desired_count:
return set(range(0, num_timesteps, i))
raise ValueError(
f"cannot create exactly {num_timesteps} steps with an integer stride"
)
section_counts = [int(x) for x in section_counts.split(",")]
size_per = num_timesteps // len(section_counts)
extra = num_timesteps % len(section_counts)
start_idx = 0
all_steps = []
for i, section_count in enumerate(section_counts):
size = size_per + (1 if i < extra else 0)
if size < section_count:
raise ValueError(
f"cannot divide section of {size} steps into {section_count}"
)
if section_count <= 1:
frac_stride = 1
else:
frac_stride = (size - 1) / (section_count - 1)
cur_idx = 0.0
taken_steps = []
for _ in range(section_count):
taken_steps.append(start_idx + round(cur_idx))
cur_idx += frac_stride
all_steps += taken_steps
start_idx += size
return set(all_steps)
class SpacedDiffusion(GaussianDiffusion):
"""
A diffusion process which can skip steps in a base diffusion process.
:param use_timesteps: a collection (sequence or set) of timesteps from the
original diffusion process to retain.
:param kwargs: the kwargs to create the base diffusion process.
"""
def __init__(self, use_timesteps, **kwargs):
self.use_timesteps = set(use_timesteps)
self.timestep_map = []
self.original_num_steps = len(kwargs["betas"])
base_diffusion = GaussianDiffusion(**kwargs) # pylint: disable=missing-kwoa
last_alpha_cumprod = 1.0
new_betas = []
for i, alpha_cumprod in enumerate(base_diffusion.alphas_cumprod):
if i in self.use_timesteps:
new_betas.append(1 - alpha_cumprod / last_alpha_cumprod)
last_alpha_cumprod = alpha_cumprod
self.timestep_map.append(i)
kwargs["betas"] = np.array(new_betas)
super().__init__(**kwargs)
def p_mean_variance(
self, model, *args, **kwargs
): # pylint: disable=signature-differs
return super().p_mean_variance(self._wrap_model(model), *args, **kwargs)
def training_losses(
self, model, *args, **kwargs
): # pylint: disable=signature-differs
return super().training_losses(self._wrap_model(model), *args, **kwargs)
def condition_mean(self, cond_fn, *args, **kwargs):
return super().condition_mean(self._wrap_model(cond_fn), *args, **kwargs)
def condition_score(self, cond_fn, *args, **kwargs):
return super().condition_score(self._wrap_model(cond_fn), *args, **kwargs)
def _wrap_model(self, model):
if isinstance(model, _WrappedModel):
return model
return _WrappedModel(
model, self.timestep_map, self.original_num_steps
)
def _scale_timesteps(self, t):
# Scaling is done by the wrapped model.
return t
class _WrappedModel:
def __init__(self, model, timestep_map, original_num_steps):
self.model = model
self.timestep_map = timestep_map
# self.rescale_timesteps = rescale_timesteps
self.original_num_steps = original_num_steps
def __call__(self, x, ts, **kwargs):
map_tensor = th.tensor(self.timestep_map, device=ts.device, dtype=ts.dtype)
new_ts = map_tensor[ts]
# if self.rescale_timesteps:
# new_ts = new_ts.float() * (1000.0 / self.original_num_steps)
return self.model(x, new_ts, **kwargs)
================================================
FILE: diffusion/timestep_sampler.py
================================================
# Modified from OpenAI's diffusion repos
# GLIDE: https://github.com/openai/glide-text2im/blob/main/glide_text2im/gaussian_diffusion.py
# ADM: https://github.com/openai/guided-diffusion/blob/main/guided_diffusion
# IDDPM: https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py
from abc import ABC, abstractmethod
import numpy as np
import torch as th
import torch.distributed as dist
def create_named_schedule_sampler(name, diffusion):
"""
Create a ScheduleSampler from a library of pre-defined samplers.
:param name: the name of the sampler.
:param diffusion: the diffusion object to sample for.
"""
if name == "uniform":
return UniformSampler(diffusion)
elif name == "loss-second-moment":
return LossSecondMomentResampler(diffusion)
else:
raise NotImplementedError(f"unknown schedule sampler: {name}")
class ScheduleSampler(ABC):
"""
A distribution over timesteps in the diffusion process, intended to reduce
variance of the objective.
By default, samplers perform unbiased importance sampling, in which the
objective's mean is unchanged.
However, subclasses may override sample() to change how the resampled
terms are reweighted, allowing for actual changes in the objective.
"""
@abstractmethod
def weights(self):
"""
Get a numpy array of weights, one per diffusion step.
The weights needn't be normalized, but must be positive.
"""
def sample(self, batch_size, device):
"""
Importance-sample timesteps for a batch.
:param batch_size: the number of timesteps.
:param device: the torch device to save to.
:return: a tuple (timesteps, weights):
- timesteps: a tensor of timestep indices.
- weights: a tensor of weights to scale the resulting losses.
"""
w = self.weights()
p = w / np.sum(w)
indices_np = np.random.choice(len(p), size=(batch_size,), p=p)
indices = th.from_numpy(indices_np).long().to(device)
weights_np = 1 / (len(p) * p[indices_np])
weights = th.from_numpy(weights_np).float().to(device)
return indices, weights
class UniformSampler(ScheduleSampler):
def __init__(self, diffusion):
self.diffusion = diffusion
self._weights = np.ones([diffusion.num_timesteps])
def weights(self):
return self._weights
class LossAwareSampler(ScheduleSampler):
def update_with_local_losses(self, local_ts, local_losses):
"""
Update the reweighting using losses from a model.
Call this method from each rank with a batch of timesteps and the
corresponding losses for each of those timesteps.
This method will perform synchronization to make sure all of the ranks
maintain the exact same reweighting.
:param local_ts: an integer Tensor of timesteps.
:param local_losses: a 1D Tensor of losses.
"""
batch_sizes = [
th.tensor([0], dtype=th.int32, device=local_ts.device)
for _ in range(dist.get_world_size())
]
dist.all_gather(
batch_sizes,
th.tensor([len(local_ts)], dtype=th.int32, device=local_ts.device),
)
# Pad all_gather batches to be the maximum batch size.
batch_sizes = [x.item() for x in batch_sizes]
max_bs = max(batch_sizes)
timestep_batches = [th.zeros(max_bs).to(local_ts) for bs in batch_sizes]
loss_batches = [th.zeros(max_bs).to(local_losses) for bs in batch_sizes]
dist.all_gather(timestep_batches, local_ts)
dist.all_gather(loss_batches, local_losses)
timesteps = [
x.item() for y, bs in zip(timestep_batches, batch_sizes) for x in y[:bs]
]
losses = [x.item() for y, bs in zip(loss_batches, batch_sizes) for x in y[:bs]]
self.update_with_all_losses(timesteps, losses)
@abstractmethod
def update_with_all_losses(self, ts, losses):
"""
Update the reweighting using losses from a model.
Sub-classes should override this method to update the reweighting
using losses from the model.
This method directly updates the reweighting without synchronizing
between workers. It is called by update_with_local_losses from all
ranks with identical arguments. Thus, it should have deterministic
behavior to maintain state across workers.
:param ts: a list of int timesteps.
:param losses: a list of float losses, one per timestep.
"""
class LossSecondMomentResampler(LossAwareSampler):
def __init__(self, diffusion, history_per_term=10, uniform_prob=0.001):
self.diffusion = diffusion
self.history_per_term = history_per_term
self.uniform_prob = uniform_prob
self._loss_history = np.zeros(
[diffusion.num_timesteps, history_per_term], dtype=np.float64
)
self._loss_counts = np.zeros([diffusion.num_timesteps], dtype=np.int)
def weights(self):
if not self._warmed_up():
return np.ones([self.diffusion.num_timesteps], dtype=np.float64)
weights = np.sqrt(np.mean(self._loss_history ** 2, axis=-1))
weights /= np.sum(weights)
weights *= 1 - self.uniform_prob
weights += self.uniform_prob / len(weights)
return weights
def update_with_all_losses(self, ts, losses):
for t, loss in zip(ts, losses):
if self._loss_counts[t] == self.history_per_term:
# Shift out the oldest loss term.
self._loss_history[t, :-1] = self._loss_history[t, 1:]
self._loss_history[t, -1] = loss
else:
self._loss_history[t, self._loss_counts[t]] = loss
self._loss_counts[t] += 1
def _warmed_up(self):
return (self._loss_counts == self.history_per_term).all()
================================================
FILE: download.py
================================================
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""
Functions for downloading pre-trained DiT models
"""
from torchvision.datasets.utils import download_url
import torch
import os
pretrained_models = {'DiT-XL-2-512x512.pt', 'DiT-XL-2-256x256.pt'}
def find_model(model_name):
"""
Finds a pre-trained DiT model, downloading it if necessary. Alternatively, loads a model from a local path.
"""
if model_name in pretrained_models: # Find/download our pre-trained DiT checkpoints
return download_model(model_name)
else: # Load a custom DiT checkpoint:
assert os.path.isfile(model_name), f'Could not find DiT checkpoint at {model_name}'
checkpoint = torch.load(model_name, map_location=lambda storage, loc: storage)
if "ema" in checkpoint: # supports checkpoints from train.py
checkpoint = checkpoint["ema"]
return checkpoint
def download_model(model_name):
"""
Downloads a pre-trained DiT model from the web.
"""
assert model_name in pretrained_models
local_path = f'pretrained_models/{model_name}'
if not os.path.isfile(local_path):
os.makedirs('pretrained_models', exist_ok=True)
web_path = f'https://dl.fbaipublicfiles.com/DiT/models/{model_name}'
download_url(web_path, 'pretrained_models')
model = torch.load(local_path, map_location=lambda storage, loc: storage)
return model
if __name__ == "__main__":
# Download all DiT checkpoints
for model in pretrained_models:
download_model(model)
print('Done.')
================================================
FILE: environment.yml
================================================
name: DiT
channels:
- pytorch
- nvidia
dependencies:
- python >= 3.8
- pytorch >= 1.13
- torchvision
- pytorch-cuda=11.7
- pip:
- timm
- diffusers
- accelerate
================================================
FILE: models.py
================================================
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# --------------------------------------------------------
# References:
# GLIDE: https://github.com/openai/glide-text2im
# MAE: https://github.com/facebookresearch/mae/blob/main/models_mae.py
# --------------------------------------------------------
import torch
import torch.nn as nn
import numpy as np
import math
from timm.models.vision_transformer import PatchEmbed, Attention, Mlp
def modulate(x, shift, scale):
return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
#################################################################################
# Embedding Layers for Timesteps and Class Labels #
#################################################################################
class TimestepEmbedder(nn.Module):
"""
Embeds scalar timesteps into vector representations.
"""
def __init__(self, hidden_size, frequency_embedding_size=256):
super().__init__()
self.mlp = nn.Sequential(
nn.Linear(frequency_embedding_size, hidden_size, bias=True),
nn.SiLU(),
nn.Linear(hidden_size, hidden_size, bias=True),
)
self.frequency_embedding_size = frequency_embedding_size
@staticmethod
def timestep_embedding(t, dim, max_period=10000):
"""
Create sinusoidal timestep embeddings.
:param t: 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, D) Tensor of positional embeddings.
"""
# https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py
half = dim // 2
freqs = torch.exp(
-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
).to(device=t.device)
args = t[:, 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)
return embedding
def forward(self, t):
t_freq = self.timestep_embedding(t, self.frequency_embedding_size)
t_emb = self.mlp(t_freq)
return t_emb
class LabelEmbedder(nn.Module):
"""
Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance.
"""
def __init__(self, num_classes, hidden_size, dropout_prob):
super().__init__()
use_cfg_embedding = dropout_prob > 0
self.embedding_table = nn.Embedding(num_classes + use_cfg_embedding, hidden_size)
self.num_classes = num_classes
self.dropout_prob = dropout_prob
def token_drop(self, labels, force_drop_ids=None):
"""
Drops labels to enable classifier-free guidance.
"""
if force_drop_ids is None:
drop_ids = torch.rand(labels.shape[0], device=labels.device) < self.dropout_prob
else:
drop_ids = force_drop_ids == 1
labels = torch.where(drop_ids, self.num_classes, labels)
return labels
def forward(self, labels, train, force_drop_ids=None):
use_dropout = self.dropout_prob > 0
if (train and use_dropout) or (force_drop_ids is not None):
labels = self.token_drop(labels, force_drop_ids)
embeddings = self.embedding_table(labels)
return embeddings
#################################################################################
# Core DiT Model #
#################################################################################
class DiTBlock(nn.Module):
"""
A DiT block with adaptive layer norm zero (adaLN-Zero) conditioning.
"""
def __init__(self, hidden_size, num_heads, mlp_ratio=4.0, **block_kwargs):
super().__init__()
self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
self.attn = Attention(hidden_size, num_heads=num_heads, qkv_bias=True, **block_kwargs)
self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
mlp_hidden_dim = int(hidden_size * mlp_ratio)
approx_gelu = lambda: nn.GELU(approximate="tanh")
self.mlp = Mlp(in_features=hidden_size, hidden_features=mlp_hidden_dim, act_layer=approx_gelu, drop=0)
self.adaLN_modulation = nn.Sequential(
nn.SiLU(),
nn.Linear(hidden_size, 6 * hidden_size, bias=True)
)
def forward(self, x, c):
shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(c).chunk(6, dim=1)
x = x + gate_msa.unsqueeze(1) * self.attn(modulate(self.norm1(x), shift_msa, scale_msa))
x = x + gate_mlp.unsqueeze(1) * self.mlp(modulate(self.norm2(x), shift_mlp, scale_mlp))
return x
class FinalLayer(nn.Module):
"""
The final layer of DiT.
"""
def __init__(self, hidden_size, patch_size, out_channels):
super().__init__()
self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True)
self.adaLN_modulation = nn.Sequential(
nn.SiLU(),
nn.Linear(hidden_size, 2 * hidden_size, bias=True)
)
def forward(self, x, c):
shift, scale = self.adaLN_modulation(c).chunk(2, dim=1)
x = modulate(self.norm_final(x), shift, scale)
x = self.linear(x)
return x
class DiT(nn.Module):
"""
Diffusion model with a Transformer backbone.
"""
def __init__(
self,
input_size=32,
patch_size=2,
in_channels=4,
hidden_size=1152,
depth=28,
num_heads=16,
mlp_ratio=4.0,
class_dropout_prob=0.1,
num_classes=1000,
learn_sigma=True,
):
super().__init__()
self.learn_sigma = learn_sigma
self.in_channels = in_channels
self.out_channels = in_channels * 2 if learn_sigma else in_channels
self.patch_size = patch_size
self.num_heads = num_heads
self.x_embedder = PatchEmbed(input_size, patch_size, in_channels, hidden_size, bias=True)
self.t_embedder = TimestepEmbedder(hidden_size)
self.y_embedder = LabelEmbedder(num_classes, hidden_size, class_dropout_prob)
num_patches = self.x_embedder.num_patches
# Will use fixed sin-cos embedding:
self.pos_embed = nn.Parameter(torch.zeros(1, num_patches, hidden_size), requires_grad=False)
self.blocks = nn.ModuleList([
DiTBlock(hidden_size, num_heads, mlp_ratio=mlp_ratio) for _ in range(depth)
])
self.final_layer = FinalLayer(hidden_size, patch_size, self.out_channels)
self.initialize_weights()
def initialize_weights(self):
# Initialize transformer layers:
def _basic_init(module):
if isinstance(module, nn.Linear):
torch.nn.init.xavier_uniform_(module.weight)
if module.bias is not None:
nn.init.constant_(module.bias, 0)
self.apply(_basic_init)
# Initialize (and freeze) pos_embed by sin-cos embedding:
pos_embed = get_2d_sincos_pos_embed(self.pos_embed.shape[-1], int(self.x_embedder.num_patches ** 0.5))
self.pos_embed.data.copy_(torch.from_numpy(pos_embed).float().unsqueeze(0))
# Initialize patch_embed like nn.Linear (instead of nn.Conv2d):
w = self.x_embedder.proj.weight.data
nn.init.xavier_uniform_(w.view([w.shape[0], -1]))
nn.init.constant_(self.x_embedder.proj.bias, 0)
# Initialize label embedding table:
nn.init.normal_(self.y_embedder.embedding_table.weight, std=0.02)
# Initialize timestep embedding MLP:
nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02)
nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02)
# Zero-out adaLN modulation layers in DiT blocks:
for block in self.blocks:
nn.init.constant_(block.adaLN_modulation[-1].weight, 0)
nn.init.constant_(block.adaLN_modulation[-1].bias, 0)
# Zero-out output layers:
nn.init.constant_(self.final_layer.adaLN_modulation[-1].weight, 0)
nn.init.constant_(self.final_layer.adaLN_modulation[-1].bias, 0)
nn.init.constant_(self.final_layer.linear.weight, 0)
nn.init.constant_(self.final_layer.linear.bias, 0)
def unpatchify(self, x):
"""
x: (N, T, patch_size**2 * C)
imgs: (N, H, W, C)
"""
c = self.out_channels
p = self.x_embedder.patch_size[0]
h = w = int(x.shape[1] ** 0.5)
assert h * w == x.shape[1]
x = x.reshape(shape=(x.shape[0], h, w, p, p, c))
x = torch.einsum('nhwpqc->nchpwq', x)
imgs = x.reshape(shape=(x.shape[0], c, h * p, h * p))
return imgs
def forward(self, x, t, y):
"""
Forward pass of DiT.
x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
t: (N,) tensor of diffusion timesteps
y: (N,) tensor of class labels
"""
x = self.x_embedder(x) + self.pos_embed # (N, T, D), where T = H * W / patch_size ** 2
t = self.t_embedder(t) # (N, D)
y = self.y_embedder(y, self.training) # (N, D)
c = t + y # (N, D)
for block in self.blocks:
x = block(x, c) # (N, T, D)
x = self.final_layer(x, c) # (N, T, patch_size ** 2 * out_channels)
x = self.unpatchify(x) # (N, out_channels, H, W)
return x
def forward_with_cfg(self, x, t, y, cfg_scale):
"""
Forward pass of DiT, but also batches the unconditional forward pass for classifier-free guidance.
"""
# https://github.com/openai/glide-text2im/blob/main/notebooks/text2im.ipynb
half = x[: len(x) // 2]
combined = torch.cat([half, half], dim=0)
model_out = self.forward(combined, t, y)
# For exact reproducibility reasons, we apply classifier-free guidance on only
# three channels by default. The standard approach to cfg applies it to all channels.
# This can be done by uncommenting the following line and commenting-out the line following that.
# eps, rest = model_out[:, :self.in_channels], model_out[:, self.in_channels:]
eps, rest = model_out[:, :3], model_out[:, 3:]
cond_eps, uncond_eps = torch.split(eps, len(eps) // 2, dim=0)
half_eps = uncond_eps + cfg_scale * (cond_eps - uncond_eps)
eps = torch.cat([half_eps, half_eps], dim=0)
return torch.cat([eps, rest], dim=1)
#################################################################################
# Sine/Cosine Positional Embedding Functions #
#################################################################################
# https://github.com/facebookresearch/mae/blob/main/util/pos_embed.py
def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False, extra_tokens=0):
"""
grid_size: int of the grid height and width
return:
pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
"""
grid_h = np.arange(grid_size, dtype=np.float32)
grid_w = np.arange(grid_size, dtype=np.float32)
grid = np.meshgrid(grid_w, grid_h) # here w goes first
grid = np.stack(grid, axis=0)
grid = grid.reshape([2, 1, grid_size, grid_size])
pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
if cls_token and extra_tokens > 0:
pos_embed = np.concatenate([np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0)
return pos_embed
def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
assert embed_dim % 2 == 0
# use half of dimensions to encode grid_h
emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2)
emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2)
emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D)
return emb
def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
"""
embed_dim: output dimension for each position
pos: a list of positions to be encoded: size (M,)
out: (M, D)
"""
assert embed_dim % 2 == 0
omega = np.arange(embed_dim // 2, dtype=np.float64)
omega /= embed_dim / 2.
omega = 1. / 10000**omega # (D/2,)
pos = pos.reshape(-1) # (M,)
out = np.einsum('m,d->md', pos, omega) # (M, D/2), outer product
emb_sin = np.sin(out) # (M, D/2)
emb_cos = np.cos(out) # (M, D/2)
emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D)
return emb
#################################################################################
# DiT Configs #
#################################################################################
def DiT_XL_2(**kwargs):
return DiT(depth=28, hidden_size=1152, patch_size=2, num_heads=16, **kwargs)
def DiT_XL_4(**kwargs):
return DiT(depth=28, hidden_size=1152, patch_size=4, num_heads=16, **kwargs)
def DiT_XL_8(**kwargs):
return DiT(depth=28, hidden_size=1152, patch_size=8, num_heads=16, **kwargs)
def DiT_L_2(**kwargs):
return DiT(depth=24, hidden_size=1024, patch_size=2, num_heads=16, **kwargs)
def DiT_L_4(**kwargs):
return DiT(depth=24, hidden_size=1024, patch_size=4, num_heads=16, **kwargs)
def DiT_L_8(**kwargs):
return DiT(depth=24, hidden_size=1024, patch_size=8, num_heads=16, **kwargs)
def DiT_B_2(**kwargs):
return DiT(depth=12, hidden_size=768, patch_size=2, num_heads=12, **kwargs)
def DiT_B_4(**kwargs):
return DiT(depth=12, hidden_size=768, patch_size=4, num_heads=12, **kwargs)
def DiT_B_8(**kwargs):
return DiT(depth=12, hidden_size=768, patch_size=8, num_heads=12, **kwargs)
def DiT_S_2(**kwargs):
return DiT(depth=12, hidden_size=384, patch_size=2, num_heads=6, **kwargs)
def DiT_S_4(**kwargs):
return DiT(depth=12, hidden_size=384, patch_size=4, num_heads=6, **kwargs)
def DiT_S_8(**kwargs):
return DiT(depth=12, hidden_size=384, patch_size=8, num_heads=6, **kwargs)
DiT_models = {
'DiT-XL/2': DiT_XL_2, 'DiT-XL/4': DiT_XL_4, 'DiT-XL/8': DiT_XL_8,
'DiT-L/2': DiT_L_2, 'DiT-L/4': DiT_L_4, 'DiT-L/8': DiT_L_8,
'DiT-B/2': DiT_B_2, 'DiT-B/4': DiT_B_4, 'DiT-B/8': DiT_B_8,
'DiT-S/2': DiT_S_2, 'DiT-S/4': DiT_S_4, 'DiT-S/8': DiT_S_8,
}
================================================
FILE: run_DiT.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "355UKMUQJxFd"
},
"source": [
"# Scalable Diffusion Models with Transformer (DiT)\n",
"\n",
"This notebook samples from pre-trained DiT models. DiTs are class-conditional latent diffusion models trained on ImageNet that use transformers in place of U-Nets as the DDPM backbone. DiT outperforms all prior diffusion models on the ImageNet benchmarks.\n",
"\n",
"[Project Page](https://www.wpeebles.com/DiT) | [HuggingFace Space](https://huggingface.co/spaces/wpeebles/DiT) | [Paper](http://arxiv.org/abs/2212.09748) | [GitHub](github.com/facebookresearch/DiT)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "zJlgLkSaKn7u"
},
"source": [
"# 1. Setup\n",
"\n",
"We recommend using GPUs (Runtime > Change runtime type > Hardware accelerator > GPU). Run this cell to clone the DiT GitHub repo and setup PyTorch. You only have to run this once."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!git clone https://github.com/facebookresearch/DiT.git\n",
"import DiT, os\n",
"os.chdir('DiT')\n",
"os.environ['PYTHONPATH'] = '/env/python:/content/DiT'\n",
"!pip install diffusers timm --upgrade\n",
"# DiT imports:\n",
"import torch\n",
"from torchvision.utils import save_image\n",
"from diffusion import create_diffusion\n",
"from diffusers.models import AutoencoderKL\n",
"from download import find_model\n",
"from models import DiT_XL_2\n",
"from PIL import Image\n",
"from IPython.display import display\n",
"torch.set_grad_enabled(False)\n",
"device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
"if device == \"cpu\":\n",
" print(\"GPU not found. Using CPU instead.\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "AXpziRkoOvV9"
},
"source": [
"# Download DiT-XL/2 Models\n",
"\n",
"You can choose between a 512x512 model and a 256x256 model. You can swap-out the LDM VAE, too."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "EWG-WNimO59K"
},
"outputs": [],
"source": [
"image_size = 256 #@param [256, 512]\n",
"vae_model = \"stabilityai/sd-vae-ft-ema\" #@param [\"stabilityai/sd-vae-ft-mse\", \"stabilityai/sd-vae-ft-ema\"]\n",
"latent_size = int(image_size) // 8\n",
"# Load model:\n",
"model = DiT_XL_2(input_size=latent_size).to(device)\n",
"state_dict = find_model(f\"DiT-XL-2-{image_size}x{image_size}.pt\")\n",
"model.load_state_dict(state_dict)\n",
"model.eval() # important!\n",
"vae = AutoencoderKL.from_pretrained(vae_model).to(device)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "5JTNyzNZKb9E"
},
"source": [
"# 2. Sample from Pre-trained DiT Models\n",
"\n",
"You can customize several sampling options. For the full list of ImageNet classes, [check out this](https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "-Hw7B5h4Kk4p"
},
"outputs": [],
"source": [
"# Set user inputs:\n",
"seed = 0 #@param {type:\"number\"}\n",
"torch.manual_seed(seed)\n",
"num_sampling_steps = 250 #@param {type:\"slider\", min:0, max:1000, step:1}\n",
"cfg_scale = 4 #@param {type:\"slider\", min:1, max:10, step:0.1}\n",
"class_labels = 207, 360, 387, 974, 88, 979, 417, 279 #@param {type:\"raw\"}\n",
"samples_per_row = 4 #@param {type:\"number\"}\n",
"\n",
"# Create diffusion object:\n",
"diffusion = create_diffusion(str(num_sampling_steps))\n",
"\n",
"# Create sampling noise:\n",
"n = len(class_labels)\n",
"z = torch.randn(n, 4, latent_size, latent_size, device=device)\n",
"y = torch.tensor(class_labels, device=device)\n",
"\n",
"# Setup classifier-free guidance:\n",
"z = torch.cat([z, z], 0)\n",
"y_null = torch.tensor([1000] * n, device=device)\n",
"y = torch.cat([y, y_null], 0)\n",
"model_kwargs = dict(y=y, cfg_scale=cfg_scale)\n",
"\n",
"# Sample images:\n",
"samples = diffusion.p_sample_loop(\n",
" model.forward_with_cfg, z.shape, z, clip_denoised=False, \n",
" model_kwargs=model_kwargs, progress=True, device=device\n",
")\n",
"samples, _ = samples.chunk(2, dim=0) # Remove null class samples\n",
"samples = vae.decode(samples / 0.18215).sample\n",
"\n",
"# Save and display images:\n",
"save_image(samples, \"sample.png\", nrow=int(samples_per_row), \n",
" normalize=True, value_range=(-1, 1))\n",
"samples = Image.open(\"sample.png\")\n",
"display(samples)"
]
}
],
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"display_name": "Python 3.8.10 64-bit",
"language": "python",
"name": "python3"
},
"language_info": {
"name": "python",
"version": "3.8.10"
},
"vscode": {
"interpreter": {
"hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
}
}
},
"nbformat": 4,
"nbformat_minor": 0
}
================================================
FILE: sample.py
================================================
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""
Sample new images from a pre-trained DiT.
"""
import torch
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True
from torchvision.utils import save_image
from diffusion import create_diffusion
from diffusers.models import AutoencoderKL
from download import find_model
from models import DiT_models
import argparse
def main(args):
# Setup PyTorch:
torch.manual_seed(args.seed)
torch.set_grad_enabled(False)
device = "cuda" if torch.cuda.is_available() else "cpu"
if args.ckpt is None:
assert args.model == "DiT-XL/2", "Only DiT-XL/2 models are available for auto-download."
assert args.image_size in [256, 512]
assert args.num_classes == 1000
# Load model:
latent_size = args.image_size // 8
model = DiT_models[args.model](
input_size=latent_size,
num_classes=args.num_classes
).to(device)
# Auto-download a pre-trained model or load a custom DiT checkpoint from train.py:
ckpt_path = args.ckpt or f"DiT-XL-2-{args.image_size}x{args.image_size}.pt"
state_dict = find_model(ckpt_path)
model.load_state_dict(state_dict)
model.eval() # important!
diffusion = create_diffusion(str(args.num_sampling_steps))
vae = AutoencoderKL.from_pretrained(f"stabilityai/sd-vae-ft-{args.vae}").to(device)
# Labels to condition the model with (feel free to change):
class_labels = [207, 360, 387, 974, 88, 979, 417, 279]
# Create sampling noise:
n = len(class_labels)
z = torch.randn(n, 4, latent_size, latent_size, device=device)
y = torch.tensor(class_labels, device=device)
# Setup classifier-free guidance:
z = torch.cat([z, z], 0)
y_null = torch.tensor([1000] * n, device=device)
y = torch.cat([y, y_null], 0)
model_kwargs = dict(y=y, cfg_scale=args.cfg_scale)
# Sample images:
samples = diffusion.p_sample_loop(
model.forward_with_cfg, z.shape, z, clip_denoised=False, model_kwargs=model_kwargs, progress=True, device=device
)
samples, _ = samples.chunk(2, dim=0) # Remove null class samples
samples = vae.decode(samples / 0.18215).sample
# Save and display images:
save_image(samples, "sample.png", nrow=4, normalize=True, value_range=(-1, 1))
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--model", type=str, choices=list(DiT_models.keys()), default="DiT-XL/2")
parser.add_argument("--vae", type=str, choices=["ema", "mse"], default="mse")
parser.add_argument("--image-size", type=int, choices=[256, 512], default=256)
parser.add_argument("--num-classes", type=int, default=1000)
parser.add_argument("--cfg-scale", type=float, default=4.0)
parser.add_argument("--num-sampling-steps", type=int, default=250)
parser.add_argument("--seed", type=int, default=0)
parser.add_argument("--ckpt", type=str, default=None,
help="Optional path to a DiT checkpoint (default: auto-download a pre-trained DiT-XL/2 model).")
args = parser.parse_args()
main(args)
================================================
FILE: sample_ddp.py
================================================
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""
Samples a large number of images from a pre-trained DiT model using DDP.
Subsequently saves a .npz file that can be used to compute FID and other
evaluation metrics via the ADM repo: https://github.com/openai/guided-diffusion/tree/main/evaluations
For a simple single-GPU/CPU sampling script, see sample.py.
"""
import torch
import torch.distributed as dist
from models import DiT_models
from download import find_model
from diffusion import create_diffusion
from diffusers.models import AutoencoderKL
from tqdm import tqdm
import os
from PIL import Image
import numpy as np
import math
import argparse
def create_npz_from_sample_folder(sample_dir, num=50_000):
"""
Builds a single .npz file from a folder of .png samples.
"""
samples = []
for i in tqdm(range(num), desc="Building .npz file from samples"):
sample_pil = Image.open(f"{sample_dir}/{i:06d}.png")
sample_np = np.asarray(sample_pil).astype(np.uint8)
samples.append(sample_np)
samples = np.stack(samples)
assert samples.shape == (num, samples.shape[1], samples.shape[2], 3)
npz_path = f"{sample_dir}.npz"
np.savez(npz_path, arr_0=samples)
print(f"Saved .npz file to {npz_path} [shape={samples.shape}].")
return npz_path
def main(args):
"""
Run sampling.
"""
torch.backends.cuda.matmul.allow_tf32 = args.tf32 # True: fast but may lead to some small numerical differences
assert torch.cuda.is_available(), "Sampling with DDP requires at least one GPU. sample.py supports CPU-only usage"
torch.set_grad_enabled(False)
# Setup DDP:
dist.init_process_group("nccl")
rank = dist.get_rank()
device = rank % torch.cuda.device_count()
seed = args.global_seed * dist.get_world_size() + rank
torch.manual_seed(seed)
torch.cuda.set_device(device)
print(f"Starting rank={rank}, seed={seed}, world_size={dist.get_world_size()}.")
if args.ckpt is None:
assert args.model == "DiT-XL/2", "Only DiT-XL/2 models are available for auto-download."
assert args.image_size in [256, 512]
assert args.num_classes == 1000
# Load model:
latent_size = args.image_size // 8
model = DiT_models[args.model](
input_size=latent_size,
num_classes=args.num_classes
).to(device)
# Auto-download a pre-trained model or load a custom DiT checkpoint from train.py:
ckpt_path = args.ckpt or f"DiT-XL-2-{args.image_size}x{args.image_size}.pt"
state_dict = find_model(ckpt_path)
model.load_state_dict(state_dict)
model.eval() # important!
diffusion = create_diffusion(str(args.num_sampling_steps))
vae = AutoencoderKL.from_pretrained(f"stabilityai/sd-vae-ft-{args.vae}").to(device)
assert args.cfg_scale >= 1.0, "In almost all cases, cfg_scale be >= 1.0"
using_cfg = args.cfg_scale > 1.0
# Create folder to save samples:
model_string_name = args.model.replace("/", "-")
ckpt_string_name = os.path.basename(args.ckpt).replace(".pt", "") if args.ckpt else "pretrained"
folder_name = f"{model_string_name}-{ckpt_string_name}-size-{args.image_size}-vae-{args.vae}-" \
f"cfg-{args.cfg_scale}-seed-{args.global_seed}"
sample_folder_dir = f"{args.sample_dir}/{folder_name}"
if rank == 0:
os.makedirs(sample_folder_dir, exist_ok=True)
print(f"Saving .png samples at {sample_folder_dir}")
dist.barrier()
# Figure out how many samples we need to generate on each GPU and how many iterations we need to run:
n = args.per_proc_batch_size
global_batch_size = n * dist.get_world_size()
# To make things evenly-divisible, we'll sample a bit more than we need and then discard the extra samples:
total_samples = int(math.ceil(args.num_fid_samples / global_batch_size) * global_batch_size)
if rank == 0:
print(f"Total number of images that will be sampled: {total_samples}")
assert total_samples % dist.get_world_size() == 0, "total_samples must be divisible by world_size"
samples_needed_this_gpu = int(total_samples // dist.get_world_size())
assert samples_needed_this_gpu % n == 0, "samples_needed_this_gpu must be divisible by the per-GPU batch size"
iterations = int(samples_needed_this_gpu // n)
pbar = range(iterations)
pbar = tqdm(pbar) if rank == 0 else pbar
total = 0
for _ in pbar:
# Sample inputs:
z = torch.randn(n, model.in_channels, latent_size, latent_size, device=device)
y = torch.randint(0, args.num_classes, (n,), device=device)
# Setup classifier-free guidance:
if using_cfg:
z = torch.cat([z, z], 0)
y_null = torch.tensor([1000] * n, device=device)
y = torch.cat([y, y_null], 0)
model_kwargs = dict(y=y, cfg_scale=args.cfg_scale)
sample_fn = model.forward_with_cfg
else:
model_kwargs = dict(y=y)
sample_fn = model.forward
# Sample images:
samples = diffusion.p_sample_loop(
sample_fn, z.shape, z, clip_denoised=False, model_kwargs=model_kwargs, progress=False, device=device
)
if using_cfg:
samples, _ = samples.chunk(2, dim=0) # Remove null class samples
samples = vae.decode(samples / 0.18215).sample
samples = torch.clamp(127.5 * samples + 128.0, 0, 255).permute(0, 2, 3, 1).to("cpu", dtype=torch.uint8).numpy()
# Save samples to disk as individual .png files
for i, sample in enumerate(samples):
index = i * dist.get_world_size() + rank + total
Image.fromarray(sample).save(f"{sample_folder_dir}/{index:06d}.png")
total += global_batch_size
# Make sure all processes have finished saving their samples before attempting to convert to .npz
dist.barrier()
if rank == 0:
create_npz_from_sample_folder(sample_folder_dir, args.num_fid_samples)
print("Done.")
dist.barrier()
dist.destroy_process_group()
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--model", type=str, choices=list(DiT_models.keys()), default="DiT-XL/2")
parser.add_argument("--vae", type=str, choices=["ema", "mse"], default="ema")
parser.add_argument("--sample-dir", type=str, default="samples")
parser.add_argument("--per-proc-batch-size", type=int, default=32)
parser.add_argument("--num-fid-samples", type=int, default=50_000)
parser.add_argument("--image-size", type=int, choices=[256, 512], default=256)
parser.add_argument("--num-classes", type=int, default=1000)
parser.add_argument("--cfg-scale", type=float, default=1.5)
parser.add_argument("--num-sampling-steps", type=int, default=250)
parser.add_argument("--global-seed", type=int, default=0)
parser.add_argument("--tf32", action=argparse.BooleanOptionalAction, default=True,
help="By default, use TF32 matmuls. This massively accelerates sampling on Ampere GPUs.")
parser.add_argument("--ckpt", type=str, default=None,
help="Optional path to a DiT checkpoint (default: auto-download a pre-trained DiT-XL/2 model).")
args = parser.parse_args()
main(args)
================================================
FILE: train.py
================================================
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""
A minimal training script for DiT using PyTorch DDP.
"""
import torch
# the first flag below was False when we tested this script but True makes A100 training a lot faster:
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True
import torch.distributed as dist
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.utils.data import DataLoader
from torch.utils.data.distributed import DistributedSampler
from torchvision.datasets import ImageFolder
from torchvision import transforms
import numpy as np
from collections import OrderedDict
from PIL import Image
from copy import deepcopy
from glob import glob
from time import time
import argparse
import logging
import os
from models import DiT_models
from diffusion import create_diffusion
from diffusers.models import AutoencoderKL
#################################################################################
# Training Helper Functions #
#################################################################################
@torch.no_grad()
def update_ema(ema_model, model, decay=0.9999):
"""
Step the EMA model towards the current model.
"""
ema_params = OrderedDict(ema_model.named_parameters())
model_params = OrderedDict(model.named_parameters())
for name, param in model_params.items():
# TODO: Consider applying only to params that require_grad to avoid small numerical changes of pos_embed
ema_params[name].mul_(decay).add_(param.data, alpha=1 - decay)
def requires_grad(model, flag=True):
"""
Set requires_grad flag for all parameters in a model.
"""
for p in model.parameters():
p.requires_grad = flag
def cleanup():
"""
End DDP training.
"""
dist.destroy_process_group()
def create_logger(logging_dir):
"""
Create a logger that writes to a log file and stdout.
"""
if dist.get_rank() == 0: # real logger
logging.basicConfig(
level=logging.INFO,
format='[\033[34m%(asctime)s\033[0m] %(message)s',
datefmt='%Y-%m-%d %H:%M:%S',
handlers=[logging.StreamHandler(), logging.FileHandler(f"{logging_dir}/log.txt")]
)
logger = logging.getLogger(__name__)
else: # dummy logger (does nothing)
logger = logging.getLogger(__name__)
logger.addHandler(logging.NullHandler())
return logger
def center_crop_arr(pil_image, image_size):
"""
Center cropping implementation from ADM.
https://github.com/openai/guided-diffusion/blob/8fb3ad9197f16bbc40620447b2742e13458d2831/guided_diffusion/image_datasets.py#L126
"""
while min(*pil_image.size) >= 2 * image_size:
pil_image = pil_image.resize(
tuple(x // 2 for x in pil_image.size), resample=Image.BOX
)
scale = image_size / min(*pil_image.size)
pil_image = pil_image.resize(
tuple(round(x * scale) for x in pil_image.size), resample=Image.BICUBIC
)
arr = np.array(pil_image)
crop_y = (arr.shape[0] - image_size) // 2
crop_x = (arr.shape[1] - image_size) // 2
return Image.fromarray(arr[crop_y: crop_y + image_size, crop_x: crop_x + image_size])
#################################################################################
# Training Loop #
#################################################################################
def main(args):
"""
Trains a new DiT model.
"""
assert torch.cuda.is_available(), "Training currently requires at least one GPU."
# Setup DDP:
dist.init_process_group("nccl")
assert args.global_batch_size % dist.get_world_size() == 0, f"Batch size must be divisible by world size."
rank = dist.get_rank()
device = rank % torch.cuda.device_count()
seed = args.global_seed * dist.get_world_size() + rank
torch.manual_seed(seed)
torch.cuda.set_device(device)
print(f"Starting rank={rank}, seed={seed}, world_size={dist.get_world_size()}.")
# Setup an experiment folder:
if rank == 0:
os.makedirs(args.results_dir, exist_ok=True) # Make results folder (holds all experiment subfolders)
experiment_index = len(glob(f"{args.results_dir}/*"))
model_string_name = args.model.replace("/", "-") # e.g., DiT-XL/2 --> DiT-XL-2 (for naming folders)
experiment_dir = f"{args.results_dir}/{experiment_index:03d}-{model_string_name}" # Create an experiment folder
checkpoint_dir = f"{experiment_dir}/checkpoints" # Stores saved model checkpoints
os.makedirs(checkpoint_dir, exist_ok=True)
logger = create_logger(experiment_dir)
logger.info(f"Experiment directory created at {experiment_dir}")
else:
logger = create_logger(None)
# Create model:
assert args.image_size % 8 == 0, "Image size must be divisible by 8 (for the VAE encoder)."
latent_size = args.image_size // 8
model = DiT_models[args.model](
input_size=latent_size,
num_classes=args.num_classes
)
# Note that parameter initialization is done within the DiT constructor
ema = deepcopy(model).to(device) # Create an EMA of the model for use after training
requires_grad(ema, False)
model = DDP(model.to(device), device_ids=[rank])
diffusion = create_diffusion(timestep_respacing="") # default: 1000 steps, linear noise schedule
vae = AutoencoderKL.from_pretrained(f"stabilityai/sd-vae-ft-{args.vae}").to(device)
logger.info(f"DiT Parameters: {sum(p.numel() for p in model.parameters()):,}")
# Setup optimizer (we used default Adam betas=(0.9, 0.999) and a constant learning rate of 1e-4 in our paper):
opt = torch.optim.AdamW(model.parameters(), lr=1e-4, weight_decay=0)
# Setup data:
transform = transforms.Compose([
transforms.Lambda(lambda pil_image: center_crop_arr(pil_image, args.image_size)),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True)
])
dataset = ImageFolder(args.data_path, transform=transform)
sampler = DistributedSampler(
dataset,
num_replicas=dist.get_world_size(),
rank=rank,
shuffle=True,
seed=args.global_seed
)
loader = DataLoader(
dataset,
batch_size=int(args.global_batch_size // dist.get_world_size()),
shuffle=False,
sampler=sampler,
num_workers=args.num_workers,
pin_memory=True,
drop_last=True
)
logger.info(f"Dataset contains {len(dataset):,} images ({args.data_path})")
# Prepare models for training:
update_ema(ema, model.module, decay=0) # Ensure EMA is initialized with synced weights
model.train() # important! This enables embedding dropout for classifier-free guidance
ema.eval() # EMA model should always be in eval mode
# Variables for monitoring/logging purposes:
train_steps = 0
log_steps = 0
running_loss = 0
start_time = time()
logger.info(f"Training for {args.epochs} epochs...")
for epoch in range(args.epochs):
sampler.set_epoch(epoch)
logger.info(f"Beginning epoch {epoch}...")
for x, y in loader:
x = x.to(device)
y = y.to(device)
with torch.no_grad():
# Map input images to latent space + normalize latents:
x = vae.encode(x).latent_dist.sample().mul_(0.18215)
t = torch.randint(0, diffusion.num_timesteps, (x.shape[0],), device=device)
model_kwargs = dict(y=y)
loss_dict = diffusion.training_losses(model, x, t, model_kwargs)
loss = loss_dict["loss"].mean()
opt.zero_grad()
loss.backward()
opt.step()
update_ema(ema, model.module)
# Log loss values:
running_loss += loss.item()
log_steps += 1
train_steps += 1
if train_steps % args.log_every == 0:
# Measure training speed:
torch.cuda.synchronize()
end_time = time()
steps_per_sec = log_steps / (end_time - start_time)
# Reduce loss history over all processes:
avg_loss = torch.tensor(running_loss / log_steps, device=device)
dist.all_reduce(avg_loss, op=dist.ReduceOp.SUM)
avg_loss = avg_loss.item() / dist.get_world_size()
logger.info(f"(step={train_steps:07d}) Train Loss: {avg_loss:.4f}, Train Steps/Sec: {steps_per_sec:.2f}")
# Reset monitoring variables:
running_loss = 0
log_steps = 0
start_time = time()
# Save DiT checkpoint:
if train_steps % args.ckpt_every == 0 and train_steps > 0:
if rank == 0:
checkpoint = {
"model": model.module.state_dict(),
"ema": ema.state_dict(),
"opt": opt.state_dict(),
"args": args
}
checkpoint_path = f"{checkpoint_dir}/{train_steps:07d}.pt"
torch.save(checkpoint, checkpoint_path)
logger.info(f"Saved checkpoint to {checkpoint_path}")
dist.barrier()
model.eval() # important! This disables randomized embedding dropout
# do any sampling/FID calculation/etc. with ema (or model) in eval mode ...
logger.info("Done!")
cleanup()
if __name__ == "__main__":
# Default args here will train DiT-XL/2 with the hyperparameters we used in our paper (except training iters).
parser = argparse.ArgumentParser()
parser.add_argument("--data-path", type=str, required=True)
parser.add_argument("--results-dir", type=str, default="results")
parser.add_argument("--model", type=str, choices=list(DiT_models.keys()), default="DiT-XL/2")
parser.add_argument("--image-size", type=int, choices=[256, 512], default=256)
parser.add_argument("--num-classes", type=int, default=1000)
parser.add_argument("--epochs", type=int, default=1400)
parser.add_argument("--global-batch-size", type=int, default=256)
parser.add_argument("--global-seed", type=int, default=0)
parser.add_argument("--vae", type=str, choices=["ema", "mse"], default="ema") # Choice doesn't affect training
parser.add_argument("--num-workers", type=int, default=4)
parser.add_argument("--log-every", type=int, default=100)
parser.add_argument("--ckpt-every", type=int, default=50_000)
args = parser.parse_args()
main(args)
gitextract_h_w8e40c/ ├── CODE_OF_CONDUCT.md ├── CONTRIBUTING.md ├── LICENSE.txt ├── README.md ├── diffusion/ │ ├── __init__.py │ ├── diffusion_utils.py │ ├── gaussian_diffusion.py │ ├── respace.py │ └── timestep_sampler.py ├── download.py ├── environment.yml ├── models.py ├── run_DiT.ipynb ├── sample.py ├── sample_ddp.py └── train.py
SYMBOL INDEX (110 symbols across 10 files)
FILE: diffusion/__init__.py
function create_diffusion (line 10) | def create_diffusion(
FILE: diffusion/diffusion_utils.py
function normal_kl (line 10) | def normal_kl(mean1, logvar1, mean2, logvar2):
function approx_standard_normal_cdf (line 39) | def approx_standard_normal_cdf(x):
function continuous_gaussian_log_likelihood (line 47) | def continuous_gaussian_log_likelihood(x, *, means, log_scales):
function discretized_gaussian_log_likelihood (line 62) | def discretized_gaussian_log_likelihood(x, *, means, log_scales):
FILE: diffusion/gaussian_diffusion.py
function mean_flat (line 16) | def mean_flat(tensor):
class ModelMeanType (line 23) | class ModelMeanType(enum.Enum):
class ModelVarType (line 33) | class ModelVarType(enum.Enum):
class LossType (line 46) | class LossType(enum.Enum):
method is_vb (line 54) | def is_vb(self):
function _warmup_beta (line 58) | def _warmup_beta(beta_start, beta_end, num_diffusion_timesteps, warmup_f...
function get_beta_schedule (line 65) | def get_beta_schedule(beta_schedule, *, beta_start, beta_end, num_diffus...
function get_named_beta_schedule (line 98) | def get_named_beta_schedule(schedule_name, num_diffusion_timesteps):
function betas_for_alpha_bar (line 125) | def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.9...
class GaussianDiffusion (line 144) | class GaussianDiffusion:
method __init__ (line 153) | def __init__(
method q_mean_variance (line 203) | def q_mean_variance(self, x_start, t):
method q_sample (line 215) | def q_sample(self, x_start, t, noise=None):
method q_posterior_mean_variance (line 232) | def q_posterior_mean_variance(self, x_start, x_t, t):
method p_mean_variance (line 254) | def p_mean_variance(self, model, x, t, clip_denoised=True, denoised_fn...
method _predict_xstart_from_eps (line 334) | def _predict_xstart_from_eps(self, x_t, t, eps):
method _predict_eps_from_xstart (line 341) | def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
method condition_mean (line 346) | def condition_mean(self, cond_fn, p_mean_var, x, t, model_kwargs=None):
method condition_score (line 358) | def condition_score(self, cond_fn, p_mean_var, x, t, model_kwargs=None):
method p_sample (line 376) | def p_sample(
method p_sample_loop (line 419) | def p_sample_loop(
method p_sample_loop_progressive (line 464) | def p_sample_loop_progressive(
method ddim_sample (line 513) | def ddim_sample(
method ddim_reverse_sample (line 562) | def ddim_reverse_sample(
method ddim_sample_loop (line 600) | def ddim_sample_loop(
method ddim_sample_loop_progressive (line 633) | def ddim_sample_loop_progressive(
method _vb_terms_bpd (line 682) | def _vb_terms_bpd(
method training_losses (line 715) | def training_losses(self, model, x_start, t, model_kwargs=None, noise=...
method _prior_bpd (line 789) | def _prior_bpd(self, x_start):
method calc_bpd_loop (line 805) | def calc_bpd_loop(self, model, x_start, clip_denoised=True, model_kwar...
function _extract_into_tensor (line 861) | def _extract_into_tensor(arr, timesteps, broadcast_shape):
FILE: diffusion/respace.py
function space_timesteps (line 12) | def space_timesteps(num_timesteps, section_counts):
class SpacedDiffusion (line 65) | class SpacedDiffusion(GaussianDiffusion):
method __init__ (line 73) | def __init__(self, use_timesteps, **kwargs):
method p_mean_variance (line 89) | def p_mean_variance(
method training_losses (line 94) | def training_losses(
method condition_mean (line 99) | def condition_mean(self, cond_fn, *args, **kwargs):
method condition_score (line 102) | def condition_score(self, cond_fn, *args, **kwargs):
method _wrap_model (line 105) | def _wrap_model(self, model):
method _scale_timesteps (line 112) | def _scale_timesteps(self, t):
class _WrappedModel (line 117) | class _WrappedModel:
method __init__ (line 118) | def __init__(self, model, timestep_map, original_num_steps):
method __call__ (line 124) | def __call__(self, x, ts, **kwargs):
FILE: diffusion/timestep_sampler.py
function create_named_schedule_sampler (line 13) | def create_named_schedule_sampler(name, diffusion):
class ScheduleSampler (line 27) | class ScheduleSampler(ABC):
method weights (line 38) | def weights(self):
method sample (line 44) | def sample(self, batch_size, device):
class UniformSampler (line 62) | class UniformSampler(ScheduleSampler):
method __init__ (line 63) | def __init__(self, diffusion):
method weights (line 67) | def weights(self):
class LossAwareSampler (line 71) | class LossAwareSampler(ScheduleSampler):
method update_with_local_losses (line 72) | def update_with_local_losses(self, local_ts, local_losses):
method update_with_all_losses (line 106) | def update_with_all_losses(self, ts, losses):
class LossSecondMomentResampler (line 120) | class LossSecondMomentResampler(LossAwareSampler):
method __init__ (line 121) | def __init__(self, diffusion, history_per_term=10, uniform_prob=0.001):
method weights (line 130) | def weights(self):
method update_with_all_losses (line 139) | def update_with_all_losses(self, ts, losses):
method _warmed_up (line 149) | def _warmed_up(self):
FILE: download.py
function find_model (line 18) | def find_model(model_name):
function download_model (line 32) | def download_model(model_name):
FILE: models.py
function modulate (line 19) | def modulate(x, shift, scale):
class TimestepEmbedder (line 27) | class TimestepEmbedder(nn.Module):
method __init__ (line 31) | def __init__(self, hidden_size, frequency_embedding_size=256):
method timestep_embedding (line 41) | def timestep_embedding(t, dim, max_period=10000):
method forward (line 61) | def forward(self, t):
class LabelEmbedder (line 67) | class LabelEmbedder(nn.Module):
method __init__ (line 71) | def __init__(self, num_classes, hidden_size, dropout_prob):
method token_drop (line 78) | def token_drop(self, labels, force_drop_ids=None):
method forward (line 89) | def forward(self, labels, train, force_drop_ids=None):
class DiTBlock (line 101) | class DiTBlock(nn.Module):
method __init__ (line 105) | def __init__(self, hidden_size, num_heads, mlp_ratio=4.0, **block_kwar...
method forward (line 118) | def forward(self, x, c):
class FinalLayer (line 125) | class FinalLayer(nn.Module):
method __init__ (line 129) | def __init__(self, hidden_size, patch_size, out_channels):
method forward (line 138) | def forward(self, x, c):
class DiT (line 145) | class DiT(nn.Module):
method __init__ (line 149) | def __init__(
method initialize_weights (line 182) | def initialize_weights(self):
method unpatchify (line 218) | def unpatchify(self, x):
method forward (line 233) | def forward(self, x, t, y):
method forward_with_cfg (line 250) | def forward_with_cfg(self, x, t, y, cfg_scale):
function get_2d_sincos_pos_embed (line 274) | def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False, extra...
function get_2d_sincos_pos_embed_from_grid (line 292) | def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
function get_1d_sincos_pos_embed_from_grid (line 303) | def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
function DiT_XL_2 (line 328) | def DiT_XL_2(**kwargs):
function DiT_XL_4 (line 331) | def DiT_XL_4(**kwargs):
function DiT_XL_8 (line 334) | def DiT_XL_8(**kwargs):
function DiT_L_2 (line 337) | def DiT_L_2(**kwargs):
function DiT_L_4 (line 340) | def DiT_L_4(**kwargs):
function DiT_L_8 (line 343) | def DiT_L_8(**kwargs):
function DiT_B_2 (line 346) | def DiT_B_2(**kwargs):
function DiT_B_4 (line 349) | def DiT_B_4(**kwargs):
function DiT_B_8 (line 352) | def DiT_B_8(**kwargs):
function DiT_S_2 (line 355) | def DiT_S_2(**kwargs):
function DiT_S_4 (line 358) | def DiT_S_4(**kwargs):
function DiT_S_8 (line 361) | def DiT_S_8(**kwargs):
FILE: sample.py
function main (line 21) | def main(args):
FILE: sample_ddp.py
function create_npz_from_sample_folder (line 28) | def create_npz_from_sample_folder(sample_dir, num=50_000):
function main (line 45) | def main(args):
FILE: train.py
function update_ema (line 40) | def update_ema(ema_model, model, decay=0.9999):
function requires_grad (line 52) | def requires_grad(model, flag=True):
function cleanup (line 60) | def cleanup():
function create_logger (line 67) | def create_logger(logging_dir):
function center_crop_arr (line 85) | def center_crop_arr(pil_image, image_size):
function main (line 110) | def main(args):
Condensed preview — 16 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (133K chars).
[
{
"path": "CODE_OF_CONDUCT.md",
"chars": 3537,
"preview": "# Code of Conduct\n\n## Our Pledge\n\nIn the interest of fostering an open and welcoming environment, we as\ncontributors and"
},
{
"path": "CONTRIBUTING.md",
"chars": 1307,
"preview": "# Contributing to DiT\nWe want to make contributing to this project as easy and transparent as\npossible.\n\n## Our Developm"
},
{
"path": "LICENSE.txt",
"chars": 19330,
"preview": "\nAttribution-NonCommercial 4.0 International\n\n=======================================================================\n\nC"
},
{
"path": "README.md",
"chars": 9285,
"preview": "## Scalable Diffusion Models with Transformers (DiT)<br><sub>Official PyTorch Implementation</sub>\n\n### [Paper](http://a"
},
{
"path": "diffusion/__init__.py",
"chars": 1622,
"preview": "# Modified from OpenAI's diffusion repos\n# GLIDE: https://github.com/openai/glide-text2im/blob/main/glide_text2im/ga"
},
{
"path": "diffusion/diffusion_utils.py",
"chars": 3189,
"preview": "# Modified from OpenAI's diffusion repos\n# GLIDE: https://github.com/openai/glide-text2im/blob/main/glide_text2im/ga"
},
{
"path": "diffusion/gaussian_diffusion.py",
"chars": 34326,
"preview": "# Modified from OpenAI's diffusion repos\n# GLIDE: https://github.com/openai/glide-text2im/blob/main/glide_text2im/ga"
},
{
"path": "diffusion/respace.py",
"chars": 5485,
"preview": "# Modified from OpenAI's diffusion repos\n# GLIDE: https://github.com/openai/glide-text2im/blob/main/glide_text2im/ga"
},
{
"path": "diffusion/timestep_sampler.py",
"chars": 6013,
"preview": "# Modified from OpenAI's diffusion repos\n# GLIDE: https://github.com/openai/glide-text2im/blob/main/glide_text2im/ga"
},
{
"path": "download.py",
"chars": 1713,
"preview": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the li"
},
{
"path": "environment.yml",
"chars": 186,
"preview": "name: DiT\nchannels:\n - pytorch\n - nvidia\ndependencies:\n - python >= 3.8\n - pytorch >= 1.13\n - torchvision\n - pytor"
},
{
"path": "models.py",
"chars": 14995,
"preview": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the li"
},
{
"path": "run_DiT.ipynb",
"chars": 5222,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"355UKMUQJxFd\"\n },\n \"source\": [\n \"# Scal"
},
{
"path": "sample.py",
"chars": 3269,
"preview": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the li"
},
{
"path": "sample_ddp.py",
"chars": 7411,
"preview": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the li"
},
{
"path": "train.py",
"chars": 10949,
"preview": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the li"
}
]
About this extraction
This page contains the full source code of the facebookresearch/DiT GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 16 files (124.8 KB), approximately 31.2k tokens, and a symbol index with 110 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.