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https://github.com/ozanciga/diffusion-for-beginners
denoising diffusion models, as simple as possible
https://github.com/ozanciga/diffusion-for-beginners
dall-e diffusion imagen midjourney pytorch scheduler stable-diffusion
Last synced: about 2 months ago
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denoising diffusion models, as simple as possible
- Host: GitHub
- URL: https://github.com/ozanciga/diffusion-for-beginners
- Owner: ozanciga
- License: mit
- Created: 2022-10-06T22:19:16.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2022-11-08T11:43:02.000Z (almost 2 years ago)
- Last Synced: 2024-07-04T04:37:27.336Z (3 months ago)
- Topics: dall-e, diffusion, imagen, midjourney, pytorch, scheduler, stable-diffusion
- Language: Python
- Homepage:
- Size: 390 KB
- Stars: 152
- Watchers: 2
- Forks: 12
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- Awesome-Diffusion-Models - [Github
README
# diffusion for beginners
- implementation of _diffusion schedulers_ with minimal code & as faithful to the original work as i could. most recent work reuse or adopt code from previous work and build on it, or transcribe code from another framework - which is great! but i found it hard to follow at times. this is an attempt at simplifying below great papers. the trade-off is made between stability and correctness vs. brevity and simplicity.
$$\large{\mathbf{{\color{green}feel\ free\ to\ contribute\ to\ the\ list\ below!}}}$$
- [x] [dpm-solver++(2m)](samplers/dpm_solver_plus_plus.py) (lu et al. 2022), dpm-solver++: fast solver for guided sampling of diffusion probabilistic models, https://arxiv.org/abs/2211.01095
- [x] [exponential integrator](samplers/exponential_integrator.py) (zhang et al. 2022), fast sampling of diffusion models with exponential integrator, https://arxiv.org/abs/2204.13902
- [x] [dpm-solver](samplers/dpm_solver.py) (lu et al. 2022), dpm-solver: a fast ode solver for diffusion probabilistic model sampling in around 10 steps, https://arxiv.org/abs/2206.00927
- [x] [heun](samplers/heun.py) (karras et al. 2020), elucidating the design space of diffusion-based generative models, https://arxiv.org/abs/2206.00364
- [x] [pndm](samplers/pndm.py) (ho et al. 2020), pseudo numerical methods for diffusion models, https://arxiv.org/abs/2202.09778
- [x] [ddim](samplers/ddim.py) (song et al. 2020), denoising diffusion implicit models, https://arxiv.org/abs/2010.02502
- [x] [improved ddpm](samplers/improved_ddpm.py) (nichol and dhariwal 2021), improved denoising diffusion probabilistic models,https://arxiv.org/abs/2102.09672
- [x] [ddpm](samplers/ddpm.py) (ho et al. 2020), denoising diffusion probabilistic models, https://arxiv.org/abs/2006.11239
**prompt**: "a man eating an apple sitting on a bench"
dpm-solver++
exponential integrator
heun
dpm-solver
ddim
pndm
ddpm
improved ddpm
### * requirements *
while this repository is intended to be educational, if you wish to run and experiment, you'll need to obtain a [token from huggingface](https://huggingface.co/docs/hub/security-tokens) (and paste it to generate_sample.py), and install their excellent [diffusers library](https://github.com/huggingface/diffusers)
### * modification for heun sampler *
heun sampler uses two neural function evaluations per step, and modifies the input as well as the sigma. i wanted to be as faithful to the paper as much as possible, which necessitated changing the sampling code a little.
initiate the sampler as:
```python
sampler = HeunSampler(num_sample_steps=25, denoiser=pipe.unet, alpha_bar=pipe.scheduler.alphas_cumprod)
init_latents = torch.randn(batch_size, 4, 64, 64).to(device) * sampler.t0
```
and replace the inner loop for generate_sample.py as:
```python
for t in tqdm(sampler.timesteps):
latents = sampler(latents, t, text_embeddings, guidance_scale)
```
similarly, for dpm-solver-2,
```python
sampler = DPMSampler(num_sample_steps=20, denoiser=pipe.unet)
init_latents = torch.randn(batch_size, 4, 64, 64).to(device) * sampler.lmbd(1)[1]
```
and, for fast exponential integrator,
```python
sampler = ExponentialSampler(num_sample_steps=50, denoiser=pipe.unet)
init_latents = torch.randn(batch_size, 4, 64, 64).to(device)
```
and, for dpm-solver++ (2m),
```python
sampler = DPMPlusPlusSampler(denoiser=pipe.unet, num_sample_steps=20)
init_latents = torch.randn(batch_size, 4, 64, 64).to(device) * sampler.get_coeffs(sampler.t[0])[1]
```
## soft-diffusion
a sketch/draft of google's new paper, [soft diffusion: score matching for general corruptions](https://arxiv.org/abs/2209.05442), which achieves state-of-the-art results on celeba-64 dataset.
details can be found [here](soft_diffusion)