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Merge branch 'main' of https://github.com/huggingface/diffusers into main

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Patrick von Platen 2022-07-15 14:03:11 +00:00
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@ -78,7 +78,7 @@ Both models and schedulers should be load- and saveable from the Hub.
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)
#### **Example for [DDPM](https://arxiv.org/abs/2006.11239):**
#### **Example for Unconditonal Image generation [DDPM](https://arxiv.org/abs/2006.11239):**
```python
import torch
@ -129,8 +129,7 @@ image_pil = PIL.Image.fromarray(image_processed[0])
# 6. save image
image_pil.save("test.png")
```
#### **Example for [DDIM](https://arxiv.org/abs/2010.02502):**
#### **Example for Unconditonal Image generation [LDM](https://github.com/CompVis/latent-diffusion):**
```python
import torch
@ -185,157 +184,3 @@ image_pil = PIL.Image.fromarray(image_processed[0])
# 6. save image
image_pil.save("test.png")
```
#### **Examples for other modalities:**
[Diffuser](https://diffusion-planning.github.io/) for planning in reinforcement learning (currenlty only inference): [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1TmBmlYeKUZSkUZoJqfBmaicVTKx6nN1R?usp=sharing)
### 2. `diffusers` as a collection of popular Diffusion systems (Glide, Dalle, ...)
For more examples see [pipelines](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines).
#### **Example image generation with PNDM**
```python
from diffusers import PNDM, UNetModel, PNDMScheduler
import PIL.Image
import numpy as np
import torch
model_id = "fusing/ddim-celeba-hq"
model = UNetModel.from_pretrained(model_id)
scheduler = PNDMScheduler()
# load model and scheduler
pndm = PNDM(unet=model, noise_scheduler=scheduler)
# run pipeline in inference (sample random noise and denoise)
with torch.no_grad():
image = pndm()
# process image to PIL
image_processed = image.cpu().permute(0, 2, 3, 1)
image_processed = (image_processed + 1.0) / 2
image_processed = torch.clamp(image_processed, 0.0, 1.0)
image_processed = image_processed * 255
image_processed = image_processed.numpy().astype(np.uint8)
image_pil = PIL.Image.fromarray(image_processed[0])
# save image
image_pil.save("test.png")
```
#### **Example 1024x1024 image generation with SDE VE**
See [paper](https://arxiv.org/abs/2011.13456) for more information on SDE VE.
```python
from diffusers import DiffusionPipeline
import torch
import PIL.Image
import numpy as np
torch.manual_seed(32)
score_sde_sv = DiffusionPipeline.from_pretrained("fusing/ffhq_ncsnpp")
# Note this might take up to 3 minutes on a GPU
image = score_sde_sv(num_inference_steps=2000)
image = image.permute(0, 2, 3, 1).cpu().numpy()
image = np.clip(image * 255, 0, 255).astype(np.uint8)
image_pil = PIL.Image.fromarray(image[0])
# save image
image_pil.save("test.png")
```
#### **Example 32x32 image generation with SDE VP**
See [paper](https://arxiv.org/abs/2011.13456) for more information on SDE VP.
```python
from diffusers import DiffusionPipeline
import torch
import PIL.Image
import numpy as np
torch.manual_seed(32)
score_sde_sv = DiffusionPipeline.from_pretrained("fusing/cifar10-ddpmpp-deep-vp")
# Note this might take up to 3 minutes on a GPU
image = score_sde_sv(num_inference_steps=1000)
image = image.permute(0, 2, 3, 1).cpu().numpy()
image = np.clip(image * 255, 0, 255).astype(np.uint8)
image_pil = PIL.Image.fromarray(image[0])
# save image
image_pil.save("test.png")
```
#### **Text to Image generation with Latent Diffusion**
_Note: To use latent diffusion install transformers from [this branch](https://github.com/patil-suraj/transformers/tree/ldm-bert)._
```python
from diffusers import DiffusionPipeline
ldm = DiffusionPipeline.from_pretrained("fusing/latent-diffusion-text2im-large")
generator = torch.manual_seed(42)
prompt = "A painting of a squirrel eating a burger"
image = ldm([prompt], generator=generator, eta=0.3, guidance_scale=6.0, num_inference_steps=50)
image_processed = image.cpu().permute(0, 2, 3, 1)
image_processed = image_processed * 255.
image_processed = image_processed.numpy().astype(np.uint8)
image_pil = PIL.Image.fromarray(image_processed[0])
# save image
image_pil.save("test.png")
```
#### **Text to speech with GradTTS and BDDMPipeline**
```python
import torch
from diffusers import BDDMPipeline, GradTTSPipeline
torch_device = "cuda"
# load grad tts and bddm pipelines
grad_tts = GradTTSPipeline.from_pretrained("fusing/grad-tts-libri-tts")
bddm = BDDMPipeline.from_pretrained("fusing/diffwave-vocoder-ljspeech")
text = "Hello world, I missed you so much."
# generate mel spectograms using text
mel_spec = grad_tts(text, torch_device=torch_device)
# generate the speech by passing mel spectograms to BDDMPipeline pipeline
generator = torch.manual_seed(42)
audio = bddm(mel_spec, generator, torch_device=torch_device)
# save generated audio
from scipy.io.wavfile import write as wavwrite
sampling_rate = 22050
wavwrite("generated_audio.wav", sampling_rate, audio.squeeze().cpu().numpy())
```
## TODO
- [ ] Create common API for models
- [ ] Add tests for models
- [ ] Adapt schedulers for training
- [ ] Write google colab for training
- [ ] Write docs / Think about how to structure docs
- [ ] Add tests to circle ci
- [ ] Add [Diffusion LM models](https://arxiv.org/pdf/2205.14217.pdf)
- [ ] Add more vision models
- [ ] Add more speech models
- [ ] Add RL model
- [ ] Add FID and KID metrics