292 lines
12 KiB
Markdown
292 lines
12 KiB
Markdown
<p align="center">
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<br>
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<img src="docs/source/imgs/diffusers_library.jpg" width="400"/>
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<br>
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<p>
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<p align="center">
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<a href="https://github.com/huggingface/diffusers/blob/main/LICENSE">
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<img alt="GitHub" src="https://img.shields.io/github/license/huggingface/datasets.svg?color=blue">
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</a>
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<a href="https://github.com/huggingface/diffusers/releases">
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<img alt="GitHub release" src="https://img.shields.io/github/release/huggingface/diffusers.svg">
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</a>
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<a href="CODE_OF_CONDUCT.md">
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<img alt="Contributor Covenant" src="https://img.shields.io/badge/Contributor%20Covenant-2.0-4baaaa.svg">
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</a>
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</p>
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🤗 Diffusers provides pretrained diffusion models across multiple modalities, such as vision and audio, and serves
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as a modular toolbox for inference and training of diffusion models.
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More precisely, 🤗 Diffusers offers:
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- State-of-the-art diffusion pipelines that can be run in inference with just a couple of lines of code (see [src/diffusers/pipelines](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines)).
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- Various noise schedulers that can be used interchangeably for the prefered speed vs. quality trade-off in inference (see [src/diffusers/schedulers](https://github.com/huggingface/diffusers/tree/main/src/diffusers/schedulers)).
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- Multiple types of models, such as UNet, that can be used as building blocks in an end-to-end diffusion system (see [src/diffusers/models](https://github.com/huggingface/diffusers/tree/main/src/diffusers/models)).
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- Training examples to show how to train the most popular diffusion models (see [examples](https://github.com/huggingface/diffusers/tree/main/examples)).
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## Definitions
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**Models**: Neural network that models $p_\theta(\mathbf{x}_{t-1}|\mathbf{x}_t)$ (see image below) and is trained end-to-end to *denoise* a noisy input to an image.
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*Examples*: UNet, Conditioned UNet, 3D UNet, Transformer UNet
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<p align="center">
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<img src="https://user-images.githubusercontent.com/10695622/174349667-04e9e485-793b-429a-affe-096e8199ad5b.png" width="800"/>
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<br>
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<em> Figure from DDPM paper (https://arxiv.org/abs/2006.11239). </em>
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<p>
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**Schedulers**: Algorithm class for both **inference** and **training**.
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The class provides functionality to compute previous image according to alpha, beta schedule as well as predict noise for training.
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*Examples*: [DDPM](https://arxiv.org/abs/2006.11239), [DDIM](https://arxiv.org/abs/2010.02502), [PNDM](https://arxiv.org/abs/2202.09778), [DEIS](https://arxiv.org/abs/2204.13902)
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<p align="center">
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<img src="https://user-images.githubusercontent.com/10695622/174349706-53d58acc-a4d1-4cda-b3e8-432d9dc7ad38.png" width="800"/>
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<br>
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<em> Sampling and training algorithms. Figure from DDPM paper (https://arxiv.org/abs/2006.11239). </em>
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<p>
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**Diffusion Pipeline**: End-to-end pipeline that includes multiple diffusion models, possible text encoders, ...
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*Examples*: Glide, Latent-Diffusion, Imagen, DALL-E 2
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<p align="center">
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<img src="https://user-images.githubusercontent.com/10695622/174348898-481bd7c2-5457-4830-89bc-f0907756f64c.jpeg" width="550"/>
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<br>
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<em> Figure from ImageGen (https://imagen.research.google/). </em>
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<p>
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## Philosophy
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- Readability and clarity is prefered over highly optimized code. A strong importance is put on providing readable, intuitive and elementary code design. *E.g.*, the provided [schedulers](https://github.com/huggingface/diffusers/tree/main/src/diffusers/schedulers) are separated from the provided [models](https://github.com/huggingface/diffusers/tree/main/src/diffusers/models) and provide well-commented code that can be read alongside the original paper.
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- Diffusers is **modality independent** and focusses on providing pretrained models and tools to build systems that generate **continous outputs**, *e.g.* vision and audio.
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- Diffusion models and schedulers are provided as consise, elementary building blocks whereas diffusion pipelines are a collection of end-to-end diffusion systems that can be used out-of-the-box, should stay as close as possible to their original implementation and can include components of other library, such as text-encoders. Examples for diffusion pipelines are [Glide](https://github.com/openai/glide-text2im) and [Latent Diffusion](https://github.com/CompVis/latent-diffusion).
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## Quickstart
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### Installation
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```
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pip install diffusers # should install diffusers 0.0.4
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```
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### 1. `diffusers` as a toolbox for schedulers and models
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`diffusers` is more modularized than `transformers`. The idea is that researchers and engineers can use only parts of the library easily for the own use cases.
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It could become a central place for all kinds of models, schedulers, training utils and processors that one can mix and match for one's own use case.
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Both models and schedulers should be load- and saveable from the Hub.
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For more examples see [schedulers](https://github.com/huggingface/diffusers/tree/main/src/diffusers/schedulers) and [models](https://github.com/huggingface/diffusers/tree/main/src/diffusers/models)
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#### **Example for [DDPM](https://arxiv.org/abs/2006.11239):**
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```python
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import torch
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from diffusers import UNetModel, DDPMScheduler
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import PIL
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import numpy as np
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import tqdm
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generator = torch.manual_seed(0)
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torch_device = "cuda" if torch.cuda.is_available() else "cpu"
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# 1. Load models
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noise_scheduler = DDPMScheduler.from_config("fusing/ddpm-lsun-church", tensor_format="pt")
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unet = UNetModel.from_pretrained("fusing/ddpm-lsun-church").to(torch_device)
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# 2. Sample gaussian noise
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image = torch.randn(
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(1, unet.in_channels, unet.resolution, unet.resolution),
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generator=generator,
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)
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image = image.to(torch_device)
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# 3. Denoise
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num_prediction_steps = len(noise_scheduler)
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for t in tqdm.tqdm(reversed(range(num_prediction_steps)), total=num_prediction_steps):
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# predict noise residual
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with torch.no_grad():
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residual = unet(image, t)
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# predict previous mean of image x_t-1
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pred_prev_image = noise_scheduler.step(residual, image, t)
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# optionally sample variance
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variance = 0
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if t > 0:
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noise = torch.randn(image.shape, generator=generator).to(image.device)
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variance = noise_scheduler.get_variance(t).sqrt() * noise
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# set current image to prev_image: x_t -> x_t-1
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image = pred_prev_image + variance
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# 5. process image to PIL
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image_processed = image.cpu().permute(0, 2, 3, 1)
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image_processed = (image_processed + 1.0) * 127.5
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image_processed = image_processed.numpy().astype(np.uint8)
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image_pil = PIL.Image.fromarray(image_processed[0])
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# 6. save image
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image_pil.save("test.png")
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```
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#### **Example for [DDIM](https://arxiv.org/abs/2010.02502):**
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```python
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import torch
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from diffusers import UNetModel, DDIMScheduler
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import PIL
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import numpy as np
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import tqdm
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generator = torch.manual_seed(0)
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torch_device = "cuda" if torch.cuda.is_available() else "cpu"
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# 1. Load models
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noise_scheduler = DDIMScheduler.from_config("fusing/ddpm-celeba-hq", tensor_format="pt")
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unet = UNetModel.from_pretrained("fusing/ddpm-celeba-hq").to(torch_device)
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# 2. Sample gaussian noise
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image = torch.randn(
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(1, unet.in_channels, unet.resolution, unet.resolution),
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generator=generator,
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)
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image = image.to(torch_device)
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# 3. Denoise
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num_inference_steps = 50
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eta = 0.0 # <- deterministic sampling
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for t in tqdm.tqdm(reversed(range(num_inference_steps)), total=num_inference_steps):
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# 1. predict noise residual
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orig_t = len(noise_scheduler) // num_inference_steps * t
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with torch.inference_mode():
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residual = unet(image, orig_t)
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# 2. predict previous mean of image x_t-1
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pred_prev_image = noise_scheduler.step(residual, image, t, num_inference_steps, eta)
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# 3. optionally sample variance
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variance = 0
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if eta > 0:
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noise = torch.randn(image.shape, generator=generator).to(image.device)
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variance = noise_scheduler.get_variance(t).sqrt() * eta * noise
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# 4. set current image to prev_image: x_t -> x_t-1
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image = pred_prev_image + variance
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# 5. process image to PIL
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image_processed = image.cpu().permute(0, 2, 3, 1)
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image_processed = (image_processed + 1.0) * 127.5
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image_processed = image_processed.numpy().astype(np.uint8)
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image_pil = PIL.Image.fromarray(image_processed[0])
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# 6. save image
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image_pil.save("test.png")
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```
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#### **Examples for other modalities:**
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[Diffuser](https://diffusion-planning.github.io/) for planning in reinforcement learning: [](https://colab.research.google.com/drive/1TmBmlYeKUZSkUZoJqfBmaicVTKx6nN1R?usp=sharing)
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### 2. `diffusers` as a collection of popular Diffusion systems (Glide, Dalle, ...)
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For more examples see [pipelines](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines).
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#### **Example image generation with PNDM**
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```python
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from diffusers import PNDM, UNetModel, PNDMScheduler
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import PIL.Image
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import numpy as np
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import torch
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model_id = "fusing/ddim-celeba-hq"
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model = UNetModel.from_pretrained(model_id)
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scheduler = PNDMScheduler()
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# load model and scheduler
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pndm = PNDM(unet=model, noise_scheduler=scheduler)
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# run pipeline in inference (sample random noise and denoise)
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with torch.no_grad():
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image = pndm()
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# process image to PIL
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image_processed = image.cpu().permute(0, 2, 3, 1)
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image_processed = (image_processed + 1.0) / 2
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image_processed = torch.clamp(image_processed, 0.0, 1.0)
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image_processed = image_processed * 255
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image_processed = image_processed.numpy().astype(np.uint8)
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image_pil = PIL.Image.fromarray(image_processed[0])
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# save image
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image_pil.save("test.png")
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```
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#### **Text to Image generation with Latent Diffusion**
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_Note: To use latent diffusion install transformers from [this branch](https://github.com/patil-suraj/transformers/tree/ldm-bert)._
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```python
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from diffusers import DiffusionPipeline
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ldm = DiffusionPipeline.from_pretrained("fusing/latent-diffusion-text2im-large")
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generator = torch.manual_seed(42)
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prompt = "A painting of a squirrel eating a burger"
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image = ldm([prompt], generator=generator, eta=0.3, guidance_scale=6.0, num_inference_steps=50)
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image_processed = image.cpu().permute(0, 2, 3, 1)
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image_processed = image_processed * 255.
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image_processed = image_processed.numpy().astype(np.uint8)
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image_pil = PIL.Image.fromarray(image_processed[0])
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# save image
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image_pil.save("test.png")
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```
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#### **Text to speech with GradTTS and BDDMPipeline**
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```python
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import torch
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from diffusers import BDDMPipeline, GradTTSPipeline
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torch_device = "cuda"
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# load grad tts and bddm pipelines
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grad_tts = GradTTSPipeline.from_pretrained("fusing/grad-tts-libri-tts")
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bddm = BDDMPipeline.from_pretrained("fusing/diffwave-vocoder-ljspeech")
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text = "Hello world, I missed you so much."
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# generate mel spectograms using text
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mel_spec = grad_tts(text, torch_device=torch_device)
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# generate the speech by passing mel spectograms to BDDMPipeline pipeline
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generator = torch.manual_seed(42)
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audio = bddm(mel_spec, generator, torch_device=torch_device)
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# save generated audio
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from scipy.io.wavfile import write as wavwrite
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sampling_rate = 22050
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wavwrite("generated_audio.wav", sampling_rate, audio.squeeze().cpu().numpy())
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```
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## TODO
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- [ ] Create common API for models
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- [ ] Add tests for models
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- [ ] Adapt schedulers for training
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- [ ] Write google colab for training
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- [ ] Write docs / Think about how to structure docs
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- [ ] Add tests to circle ci
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- [ ] Add [Diffusion LM models](https://arxiv.org/pdf/2205.14217.pdf)
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- [ ] Add more vision models
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- [ ] Add more speech models
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- [ ] Add RL model
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- [ ] Add FID and KID metrics
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