[SD-XL] Add inpainting

docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_xl.mdx

@@ -57,6 +57,50 @@ prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
 image = pipe(prompt=prompt).images[0]
 ```
 
+### Image-to-image 
+
+You can use SDXL as follows for *image-to-image*:
+
+```py 
+import torch
+from diffusers import StableDiffusionXLImg2ImgPipeline
+from diffusers.utils import load_image
+
+pipe = StableDiffusionXLImg2ImgPipeline.from_pretrained(
+    "stabilityai/stable-diffusion-xl-refiner-0.9", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
+)
+pipe = pipe.to("cuda")
+url = "https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/aa_xl/000000009.png"
+
+init_image = load_image(url).convert("RGB")
+prompt = "a photo of an astronaut riding a horse on mars"
+image = pipe(prompt, image=init_image).images[0]
+```
+
+### Inpainting
+
+You can use SDXL as follows for *inpainting*
+
+```py 
+import torch
+from diffusers import StableDiffusionXLInpaintPipeline
+from diffusers.utils import load_image
+
+pipe = StableDiffusionXLInpaintPipeline.from_pretrained(
+    "stabilityai/stable-diffusion-xl-base-0.9", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
+)
+pipe.to("cuda")
+
+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 = load_image(img_url).convert("RGB")
+mask_image = load_image(mask_url).convert("RGB")
+
+prompt = "A majestic tiger sitting on a bench"
+image = pipe(prompt=prompt, image=init_image, mask_image=mask_image, num_inference_steps=50, strength=0.80).images[0]
+```
+
 ### Refining the image output
 
 In addition to the [base model checkpoint](https://huggingface.co/stabilityai/stable-diffusion-xl-base-0.9), 
@@ -183,24 +227,65 @@ image = refiner(prompt=prompt, image=image[None, :]).images[0]
 |---|---|
 | ![](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/sd_xl/init_image.png) | ![](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/sd_xl/refined_image.png) |
 
-### Image-to-image 
+<Tip>
 
-```py 
-import torch
-from diffusers import StableDiffusionXLImg2ImgPipeline
+The refiner can also very well be used in an in-painting setting. To do so just make
+  sure you use the [`StableDiffusionXLInpaintPipeline`] classes as shown below
+
+</Tip>
+
+To use the refiner for inpainting in the Ensemble of Expert Denoisers setting you can do the following:
+
+```py
+from diffusers import StableDiffusionXLInpaintPipeline
 from diffusers.utils import load_image
 
-pipe = StableDiffusionXLImg2ImgPipeline.from_pretrained(
-    "stabilityai/stable-diffusion-xl-refiner-0.9", torch_dtype=torch.float16
+pipe = StableDiffusionXLInpaintPipeline.from_pretrained(
+    "stabilityai/stable-diffusion-xl-base-0.9", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
 )
-pipe = pipe.to("cuda")
-url = "https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/aa_xl/000000009.png"
+pipe.to("cuda")
 
-init_image = load_image(url).convert("RGB")
-prompt = "a photo of an astronaut riding a horse on mars"
-image = pipe(prompt, image=init_image).images[0]
+refiner = StableDiffusionXLInpaintPipeline.from_pretrained(
+    "stabilityai/stable-diffusion-xl-refiner-0.9",
+    text_encoder_2=pipe.text_encoder_2,
+    vae=pipe.vae,
+    torch_dtype=torch.float16,
+    use_safetensors=True,
+    variant="fp16",
+)
+refiner.to("cuda")
+
+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 = load_image(img_url).convert("RGB")
+mask_image = load_image(mask_url).convert("RGB")
+
+prompt = "A majestic tiger sitting on a bench"
+num_inference_steps = 75
+high_noise_frac = 0.7
+
+image = pipe(
+    prompt=prompt,
+    image=init_image,
+    mask_image=mask_image,
+    num_inference_steps=num_inference_steps,
+    strength=0.80,
+    denoising_start=high_noise_frac,
+    output_type="latent",
+).images
+image = refiner(
+    prompt=prompt,
+    image=image,
+    mask_image=mask_image,
+    num_inference_steps=num_inference_steps,
+    denoising_start=high_noise_frac,
+).images[0]
 ```
 
+To use the refiner for inpainting in the standard SDE-style setting, simply remove `denoising_end` and `denoising_start` and choose a smaller
+number of inference steps for the refiner.
+
 ### Loading single file checkpoints / original file format
 
 By making use of [`~diffusers.loaders.FromSingleFileMixin.from_single_file`] you can also load the 
@@ -271,3 +356,9 @@ pip install xformers
 [[autodoc]] StableDiffusionXLImg2ImgPipeline
 	- all
 	- __call__
+
+## StableDiffusionXLInpaintPipeline
+
+[[autodoc]] StableDiffusionXLInpaintPipeline
+	- all
+	- __call__

src/diffusers/__init__.py

@@ -195,7 +195,11 @@
 except OptionalDependencyNotAvailable:
     from .utils.dummy_torch_and_transformers_and_invisible_watermark_objects import *  # noqa F403
 else:
-    from .pipelines import StableDiffusionXLImg2ImgPipeline, StableDiffusionXLPipeline
+    from .pipelines import (
+        StableDiffusionXLImg2ImgPipeline,
+        StableDiffusionXLInpaintPipeline,
+        StableDiffusionXLPipeline,
+    )
 
 try:
     if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):

src/diffusers/pipelines/__init__.py

@@ -119,7 +119,11 @@
 except OptionalDependencyNotAvailable:
     from ..utils.dummy_torch_and_transformers_and_invisible_watermark_objects import *  # noqa F403
 else:
-    from .stable_diffusion_xl import StableDiffusionXLImg2ImgPipeline, StableDiffusionXLPipeline
+    from .stable_diffusion_xl import (
+        StableDiffusionXLImg2ImgPipeline,
+        StableDiffusionXLInpaintPipeline,
+        StableDiffusionXLPipeline,
+    )
 
 try:
     if not is_onnx_available():

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py

@@ -981,8 +981,6 @@ def __call__(
             generator,
             do_classifier_free_guidance,
         )
-        init_image = init_image.to(device=device, dtype=masked_image_latents.dtype)
-        init_image = self._encode_vae_image(init_image, generator=generator)
 
         # 8. Check that sizes of mask, masked image and latents match
         if num_channels_unet == 9:

src/diffusers/pipelines/stable_diffusion_xl/__init__.py

@@ -24,3 +24,4 @@ class StableDiffusionXLPipelineOutput(BaseOutput):
 if is_transformers_available() and is_torch_available() and is_invisible_watermark_available():
     from .pipeline_stable_diffusion_xl import StableDiffusionXLPipeline
     from .pipeline_stable_diffusion_xl_img2img import StableDiffusionXLImg2ImgPipeline
+    from .pipeline_stable_diffusion_xl_inpaint import StableDiffusionXLInpaintPipeline

src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl.py

@@ -59,6 +59,7 @@
 """
 
 
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg
 def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
     """
     Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and
@@ -75,7 +76,7 @@ def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
 
 class StableDiffusionXLPipeline(DiffusionPipeline, FromSingleFileMixin, LoraLoaderMixin):
     r"""
-    Pipeline for text-to-image generation using Stable Diffusion.
+    Pipeline for text-to-image generation using Stable Diffusion XL.
 
     This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
     library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
@@ -92,12 +93,21 @@ class StableDiffusionXLPipeline(DiffusionPipeline, FromSingleFileMixin, LoraLoad
         vae ([`AutoencoderKL`]):
             Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
         text_encoder ([`CLIPTextModel`]):
-            Frozen text-encoder. Stable Diffusion uses the text portion of
+            Frozen text-encoder. Stable Diffusion XL uses the text portion of
             [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
             the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
+        text_encoder_2 ([` CLIPTextModelWithProjection`]):
+            Second frozen text-encoder. Stable Diffusion XL uses the text and pool portion of
+            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection),
+            specifically the
+            [laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)
+            variant.
         tokenizer (`CLIPTokenizer`):
             Tokenizer of class
             [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
+        tokenizer_2 (`CLIPTokenizer`):
+            Second Tokenizer of class
+            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
         unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
         scheduler ([`SchedulerMixin`]):
             A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of

src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_img2img.py

@@ -64,6 +64,7 @@
 """
 
 
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg
 def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
     """
     Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and
@@ -80,7 +81,7 @@ def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
 
 class StableDiffusionXLImg2ImgPipeline(DiffusionPipeline, FromSingleFileMixin, LoraLoaderMixin):
     r"""
-    Pipeline for text-to-image generation using Stable Diffusion.
+    Pipeline for text-to-image generation using Stable Diffusion XL.
 
     This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
     library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
@@ -97,12 +98,21 @@ class StableDiffusionXLImg2ImgPipeline(DiffusionPipeline, FromSingleFileMixin, L
         vae ([`AutoencoderKL`]):
             Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
         text_encoder ([`CLIPTextModel`]):
-            Frozen text-encoder. Stable Diffusion uses the text portion of
+            Frozen text-encoder. Stable Diffusion XL uses the text portion of
             [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
             the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
+        text_encoder_2 ([` CLIPTextModelWithProjection`]):
+            Second frozen text-encoder. Stable Diffusion XL uses the text and pool portion of
+            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection),
+            specifically the
+            [laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)
+            variant.
         tokenizer (`CLIPTokenizer`):
             Tokenizer of class
             [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
+        tokenizer_2 (`CLIPTokenizer`):
+            Second Tokenizer of class
+            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
         unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
         scheduler ([`SchedulerMixin`]):
             A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of