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

…main

docs/source/_toctree.yml

@@ -106,6 +106,8 @@
       title: "Score SDE VE"
     - local: api/pipelines/stable_diffusion
       title: "Stable Diffusion"
+    - local: api/pipelines/stable_diffusion_2
+      title: "Stable Diffusion 2"
     - local: api/pipelines/stable_diffusion_safe
       title: "Safe Stable Diffusion"
     - local: api/pipelines/stochastic_karras_ve

docs/source/api/pipelines/alt_diffusion.mdx

@@ -51,7 +51,7 @@ To use a different scheduler, you can either change it via the [`ConfigMixin.fro
 ```
 
 
-- *How to conver all use cases with multiple or single pipeline*
+- *How to convert all use cases with multiple or single pipeline*
 
 If you want to use all possible use cases in a single `DiffusionPipeline` we recommend using the `components` functionality to instantiate all components in the most memory-efficient way:
 

docs/source/api/pipelines/overview.mdx

@@ -58,6 +58,9 @@ available a colab notebook to directly try them out.
 | [stable_diffusion](./api/pipelines/stable_diffusion) | [**Stable Diffusion**](https://stability.ai/blog/stable-diffusion-public-release) | Text-to-Image Generation | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb)
 | [stable_diffusion](./api/pipelines/stable_diffusion) | [**Stable Diffusion**](https://stability.ai/blog/stable-diffusion-public-release) | Image-to-Image Text-Guided Generation | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/image_2_image_using_diffusers.ipynb)
 | [stable_diffusion](./api/pipelines/stable_diffusion) | [**Stable Diffusion**](https://stability.ai/blog/stable-diffusion-public-release) | Text-Guided Image Inpainting | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/in_painting_with_stable_diffusion_using_diffusers.ipynb)
+| [stable_diffusion_2](./api/pipelines/stable_diffusion_2) | [**Stable Diffusion 2**](https://stability.ai/blog/stable-diffusion-v2-release) | Text-to-Image Generation | 
+| [stable_diffusion_2](./api/pipelines/stable_diffusion_2) | [**Stable Diffusion 2**](https://stability.ai/blog/stable-diffusion-v2-release) | Text-Guided Image Inpainting | 
+| [stable_diffusion_2](./api/pipelines/stable_diffusion_2) | [**Stable Diffusion 2**](https://stability.ai/blog/stable-diffusion-v2-release) | Text-Guided Super Resolution Image-to-Image |
 | [stable_diffusion_safe](./api/pipelines/stable_diffusion_safe) | [**Safe Stable Diffusion**](https://arxiv.org/abs/2211.05105) | Text-Guided Generation | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ml-research/safe-latent-diffusion/blob/main/examples/Safe%20Latent%20Diffusion.ipynb)
 | [stochastic_karras_ve](./api/pipelines/stochastic_karras_ve) | [**Elucidating the Design Space of Diffusion-Based Generative Models**](https://arxiv.org/abs/2206.00364) | Unconditional Image Generation |
 | [versatile_diffusion](./api/pipelines/versatile_diffusion) | [Versatile Diffusion: Text, Images and Variations All in One Diffusion Model](https://arxiv.org/abs/2211.08332) | Text-to-Image Generation | 

docs/source/api/pipelines/stable_diffusion.mdx

@@ -48,7 +48,7 @@ To use a different scheduler, you can either change it via the [`ConfigMixin.fro
 ```
 
 
-### How to conver all use cases with multiple or single pipeline
+### How to convert all use cases with multiple or single pipeline
 
 If you want to use all possible use cases in a single `DiffusionPipeline` you can either:
 - Make use of the [Stable Diffusion Mega Pipeline](https://github.com/huggingface/diffusers/tree/main/examples/community#stable-diffusion-mega) or 
@@ -95,3 +95,10 @@ If you want to use all possible use cases in a single `DiffusionPipeline` you ca
 	- __call__
 	- enable_attention_slicing
 	- disable_attention_slicing
+
+
+## StableDiffusionUpscalePipeline
+[[autodoc]] StableDiffusionUpscalePipeline
+	- __call__
+	- enable_attention_slicing
+	- disable_attention_slicing

docs/source/api/pipelines/stable_diffusion_2.mdx

@@ -0,0 +1,142 @@
+<!--Copyright 2022 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
+# Stable diffusion 2
+
+Stable Diffusion 2 is a text-to-image _latent diffusion_ model built upon the work of [Stable Diffusion 1](https://stability.ai/blog/stable-diffusion-public-release). 
+The project to train Stable Diffusion 2 was led by Robin Rombach and Katherine Crowson from [Stability AI](https://stability.ai/) and [LAION](https://laion.ai/).
+
+*The Stable Diffusion 2.0 release includes robust text-to-image models trained using a brand new text encoder (OpenCLIP), developed by LAION with support from Stability AI, which greatly improves the quality of the generated images compared to earlier V1 releases. The text-to-image models in this release can generate images with default resolutions of both 512x512 pixels and 768x768 pixels. 
+These models are trained on an aesthetic subset of the [LAION-5B dataset](https://laion.ai/blog/laion-5b/) created by the DeepFloyd team at Stability AI, which is then further filtered to remove adult content using [LAION’s NSFW filter](https://openreview.net/forum?id=M3Y74vmsMcY).*
+
+For more details about how Stable Diffusion 2 works and how it differs from Stable Diffusion 1, please refer to the official [launch announcement post](https://stability.ai/blog/stable-diffusion-v2-release).
+
+## Tips
+
+### Available checkpoints:
+
+Note that the architecture is more or less identical to [Stable Diffusion 1](./api/pipelines/stable_diffusion) so please refer to [this page](./api/pipelines/stable_diffusion) for API documentation.
+
+- *Text-to-Image (512x512 resolution)*: [stabilityai/stable-diffusion-2-base](https://huggingface.co/stabilityai/stable-diffusion-2-base) with [`StableDiffusionPipeline`]
+- *Text-to-Image (768x768 resolution)*: [stabilityai/stable-diffusion-2](https://huggingface.co/stabilityai/stable-diffusion-2) with [`StableDiffusionPipeline`]
+- *Image Inpainting (512x512 resolution)*: [stabilityai/stable-diffusion-2-inpainting](https://huggingface.co/stabilityai/stable-diffusion-2-inpainting) with [`StableDiffusionInpaintPipeline`]
+- *Image Upscaling (x4 resolution resolution)*: [stable-diffusion-x4-upscaler](https://huggingface.co/stabilityai/stable-diffusion-x4-upscaler) [`StableDiffusionUpscalePipeline`]
+
+We recommend using the [`DPMSolverMultistepScheduler`] as it's currently the fastest scheduler there is.
+
+- *Text-to-Image (512x512 resolution)*:
+
+```python
+from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
+import torch
+
+repo_id = "stabilityai/stable-diffusion-2-base"
+pipe = DiffusionPipeline.from_pretrained(repo_id, torch_dtype=torch.float16, revision="fp16")
+
+pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
+pipe = pipe.to("cuda")
+
+prompt = "High quality photo of an astronaut riding a horse in space"
+image = pipe(prompt, num_inference_steps=25).images[0]
+image.save("astronaut.png")
+```
+
+- *Text-to-Image (768x768 resolution)*:
+
+```python
+from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
+import torch
+
+repo_id = "stabilityai/stable-diffusion-2"
+pipe = DiffusionPipeline.from_pretrained(repo_id, torch_dtype=torch.float16, revision="fp16")
+
+pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
+pipe = pipe.to("cuda")
+
+prompt = "High quality photo of an astronaut riding a horse in space"
+image = pipe(prompt, guidance_scale=9, num_inference_steps=25).images[0]
+image.save("astronaut.png")
+```
+
+- *Image Inpainting (512x512 resolution)*:
+
+```python
+import PIL
+import requests
+import torch
+from io import BytesIO
+
+from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
+
+
+def download_image(url):
+    response = requests.get(url)
+    return PIL.Image.open(BytesIO(response.content)).convert("RGB")
+
+
+img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
+mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"
+
+init_image = download_image(img_url).resize((512, 512))
+mask_image = download_image(mask_url).resize((512, 512))
+
+repo_id = "stabilityai/stable-diffusion-2-inpainting"
+pipe = DiffusionPipeline.from_pretrained(repo_id, torch_dtype=torch.float16, revision="fp16")
+
+pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
+pipe = pipe.to("cuda")
+
+prompt = "Face of a yellow cat, high resolution, sitting on a park bench"
+image = pipe(prompt=prompt, image=init_image, mask_image=mask_image, num_inference_steps=25).images[0]
+
+image.save("yellow_cat.png")
+```
+
+- *Image Upscaling (x4 resolution resolution)*: [stable-diffusion-x4-upscaler](https://huggingface.co/stabilityai/stable-diffusion-x4-upscaler) [`StableDiffusionUpscalePipeline`]
+
+```python
+import requests
+from PIL import Image
+from io import BytesIO
+from diffusers import StableDiffusionUpscalePipeline
+import torch
+
+# load model and scheduler
+model_id = "stabilityai/stable-diffusion-x4-upscaler"
+pipeline = StableDiffusionUpscalePipeline.from_pretrained(model_id, revision="fp16", torch_dtype=torch.float16)
+pipeline = pipeline.to("cuda")
+
+# let's download an  image
+url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale/low_res_cat.png"
+response = requests.get(url)
+low_res_img = Image.open(BytesIO(response.content)).convert("RGB")
+low_res_img = low_res_img.resize((128, 128))
+prompt = "a white cat"
+upscaled_image = pipeline(prompt=prompt, image=low_res_img).images[0]
+upscaled_image.save("upsampled_cat.png")
+```
+
+### How to load and use different schedulers.
+
+The stable diffusion pipeline uses [`DDIMScheduler`] scheduler by default. But `diffusers` provides many other schedulers that can be used with the stable diffusion pipeline such as [`PNDMScheduler`], [`LMSDiscreteScheduler`], [`EulerDiscreteScheduler`], [`EulerAncestralDiscreteScheduler`] etc.
+To use a different scheduler, you can either change it via the [`ConfigMixin.from_config`] method or pass the `scheduler` argument to the `from_pretrained` method of the pipeline. For example, to use the [`EulerDiscreteScheduler`], you can do the following:
+
+```python
+>>> from diffusers import StableDiffusionPipeline, EulerDiscreteScheduler
+
+>>> pipeline = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2")
+>>> pipeline.scheduler = EulerDiscreteScheduler.from_config(pipeline.scheduler.config)
+
+>>> # or
+>>> euler_scheduler = EulerDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-2", subfolder="scheduler")
+>>> pipeline = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2", scheduler=euler_scheduler)
+```

docs/source/index.mdx

@@ -48,6 +48,9 @@ available a colab notebook to directly try them out.
 | [stable_diffusion](./api/pipelines/stable_diffusion) | [**Stable Diffusion**](https://stability.ai/blog/stable-diffusion-public-release) | Text-to-Image Generation | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb)
 | [stable_diffusion](./api/pipelines/stable_diffusion) | [**Stable Diffusion**](https://stability.ai/blog/stable-diffusion-public-release) | Image-to-Image Text-Guided Generation | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/image_2_image_using_diffusers.ipynb)
 | [stable_diffusion](./api/pipelines/stable_diffusion) | [**Stable Diffusion**](https://stability.ai/blog/stable-diffusion-public-release) | Text-Guided Image Inpainting | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/in_painting_with_stable_diffusion_using_diffusers.ipynb)
+| [stable_diffusion_2](./api/pipelines/stable_diffusion_2) | [**Stable Diffusion 2**](https://stability.ai/blog/stable-diffusion-v2-release) | Text-to-Image Generation | 
+| [stable_diffusion_2](./api/pipelines/stable_diffusion_2) | [**Stable Diffusion 2**](https://stability.ai/blog/stable-diffusion-v2-release) | Text-Guided Image Inpainting | 
+| [stable_diffusion_2](./api/pipelines/stable_diffusion_2) | [**Stable Diffusion 2**](https://stability.ai/blog/stable-diffusion-v2-release) | Text-Guided Super Resolution Image-to-Image |
 | [stable_diffusion_safe](./api/pipelines/stable_diffusion_safe) | [**Safe Stable Diffusion**](https://arxiv.org/abs/2211.05105) | Text-Guided Generation | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ml-research/safe-latent-diffusion/blob/main/examples/Safe%20Latent%20Diffusion.ipynb)
 | [stochastic_karras_ve](./api/pipelines/stochastic_karras_ve) | [**Elucidating the Design Space of Diffusion-Based Generative Models**](https://arxiv.org/abs/2206.00364) | Unconditional Image Generation | 
 | [versatile_diffusion](./api/pipelines/versatile_diffusion) | [Versatile Diffusion: Text, Images and Variations All in One Diffusion Model](https://arxiv.org/abs/2211.08332) | Text-to-Image Generation | 

examples/community/bit_diffusion.py

@@ -138,7 +138,7 @@ def ddpm_bit_scheduler_step(
     model_output: torch.FloatTensor,
     timestep: int,
     sample: torch.FloatTensor,
-    predict_epsilon=True,
+    prediction_type="epsilon",
     generator=None,
     return_dict: bool = True,
 ) -> Union[DDPMSchedulerOutput, Tuple]:
@@ -150,8 +150,8 @@ def ddpm_bit_scheduler_step(
         timestep (`int`): current discrete timestep in the diffusion chain.
         sample (`torch.FloatTensor`):
             current instance of sample being created by diffusion process.
-        predict_epsilon (`bool`):
-            optional flag to use when model predicts the samples directly instead of the noise, epsilon.
+        prediction_type (`str`, default `epsilon`):
+            indicates whether the model predicts the noise (epsilon), or the samples (`sample`).
         generator: random number generator.
         return_dict (`bool`): option for returning tuple rather than DDPMSchedulerOutput class
     Returns:
@@ -174,10 +174,12 @@ def ddpm_bit_scheduler_step(
 
     # 2. compute predicted original sample from predicted noise also called
     # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
-    if predict_epsilon:
+    if prediction_type == "epsilon":
         pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)
-    else:
+    elif prediction_type == "sample":
         pred_original_sample = model_output
+    else:
+        raise ValueError(f"Unsupported prediction_type {prediction_type}.")
 
     # 3. Clip "predicted x_0"
     scale = self.bit_scale

examples/community/clip_guided_stable_diffusion.py

@@ -78,7 +78,12 @@ def __init__(
         )
 
         self.normalize = transforms.Normalize(mean=feature_extractor.image_mean, std=feature_extractor.image_std)
-        self.make_cutouts = MakeCutouts(feature_extractor.size)
+        cut_out_size = (
+            feature_extractor.size
+            if isinstance(feature_extractor.size, int)
+            else feature_extractor.size["shortest_edge"]
+        )
+        self.make_cutouts = MakeCutouts(cut_out_size)
 
         set_requires_grad(self.text_encoder, False)
         set_requires_grad(self.clip_model, False)

examples/unconditional_image_generation/train_unconditional.py

@@ -194,9 +194,10 @@ def parse_args():
     )
 
     parser.add_argument(
-        "--predict_epsilon",
-        action="store_true",
-        default=True,
+        "--prediction_type",
+        type=str,
+        default="epsilon",
+        choices=["epsilon", "sample"],
         help="Whether the model should predict the 'epsilon'/noise error or directly the reconstructed image 'x0'.",
     )
 
@@ -256,13 +257,13 @@ def main(args):
             "UpBlock2D",
         ),
     )
-    accepts_predict_epsilon = "predict_epsilon" in set(inspect.signature(DDPMScheduler.__init__).parameters.keys())
+    accepts_prediction_type = "prediction_type" in set(inspect.signature(DDPMScheduler.__init__).parameters.keys())
 
-    if accepts_predict_epsilon:
+    if accepts_prediction_type:
         noise_scheduler = DDPMScheduler(
             num_train_timesteps=args.ddpm_num_steps,
             beta_schedule=args.ddpm_beta_schedule,
-            predict_epsilon=args.predict_epsilon,
+            prediction_type=args.prediction_type,
         )
     else:
         noise_scheduler = DDPMScheduler(num_train_timesteps=args.ddpm_num_steps, beta_schedule=args.ddpm_beta_schedule)
@@ -365,9 +366,9 @@ def transforms(examples):
                 # Predict the noise residual
                 model_output = model(noisy_images, timesteps).sample
 
-                if args.predict_epsilon:
+                if args.prediction_type == "epsilon":
                     loss = F.mse_loss(model_output, noise)  # this could have different weights!
-                else:
+                elif args.prediction_type == "sample":
                     alpha_t = _extract_into_tensor(
                         noise_scheduler.alphas_cumprod, timesteps, (clean_images.shape[0], 1, 1, 1)
                     )
@@ -376,6 +377,8 @@ def transforms(examples):
                         model_output, clean_images, reduction="none"
                     )  # use SNR weighting from distillation paper
                     loss = loss.mean()
+                else:
+                    raise ValueError(f"Unsupported prediction type: {args.prediction_type}")
 
                 accelerator.backward(loss)
 

scripts/convert_original_stable_diffusion_to_diffusers.py

@@ -211,6 +211,7 @@ def create_unet_diffusers_config(original_config):
     """
     Creates a config for the diffusers based on the config of the LDM model.
     """
+    model_params = original_config.model.params
     unet_params = original_config.model.params.unet_config.params
 
     block_out_channels = [unet_params.model_channels * mult for mult in unet_params.channel_mult]
@@ -230,7 +231,7 @@ def create_unet_diffusers_config(original_config):
         resolution //= 2
 
     config = dict(
-        sample_size=unet_params.image_size,
+        sample_size=model_params.image_size,
         in_channels=unet_params.in_channels,
         out_channels=unet_params.out_channels,
         down_block_types=tuple(down_block_types),

scripts/convert_stable_diffusion_checkpoint_to_onnx.py

@@ -215,8 +215,10 @@ def convert_models(model_path: str, output_path: str, opset: int, fp16: bool = F
         )
         del pipeline.safety_checker
         safety_checker = OnnxRuntimeModel.from_pretrained(output_path / "safety_checker")
+        feature_extractor = pipeline.feature_extractor
     else:
         safety_checker = None
+        feature_extractor = None
 
     onnx_pipeline = OnnxStableDiffusionPipeline(
         vae_encoder=OnnxRuntimeModel.from_pretrained(output_path / "vae_encoder"),
@@ -226,7 +228,8 @@ def convert_models(model_path: str, output_path: str, opset: int, fp16: bool = F
         unet=OnnxRuntimeModel.from_pretrained(output_path / "unet"),
         scheduler=pipeline.scheduler,
         safety_checker=safety_checker,
-        feature_extractor=pipeline.feature_extractor,
+        feature_extractor=feature_extractor,
+        requires_safety_checker=safety_checker is not None,
     )
 
     onnx_pipeline.save_pretrained(output_path)