[Stable Diffusion Inpainting] Allow standard text-to-img checkpoints to be...

[Stable Diffusion Inpainting] Allow standard text-to-img checkpoints to be useable for SD inpainting (#3533)

* Add default to inpaint

* Make sure controlnet also works with normal sd for inpaint

* Add tests

* improve

* Correct encode images function

* Correct inpaint controlnet

* Improve text2img inpanit

* make style

* up

* up

* up

* up

* fix more

src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint.py

@@ -50,49 +50,59 @@ logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 EXAMPLE_DOC_STRING = """
     Examples:
         ```py
-        >>> # !pip install opencv-python transformers accelerate
-        >>> from diffusers import StableDiffusionControlNetInpaintPipeline, ControlNetModel, UniPCMultistepScheduler
+        >>> # !pip install transformers accelerate
+        >>> from diffusers import StableDiffusionControlNetInpaintPipeline, ControlNetModel, DDIMScheduler
         >>> from diffusers.utils import load_image
         >>> import numpy as np
         >>> import torch
 
-        >>> import cv2
-        >>> from PIL import Image
+        >>> init_image = load_image(
+        ...     "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png"
+        ... )
+        >>> init_image = init_image.resize((512, 512))
+
+        >>> generator = torch.Generator(device="cpu").manual_seed(1)
+
+        >>> mask_image = load_image(
+        ...     "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png"
+        ... )
+        >>> mask_image = mask_image.resize((512, 512))
 
-        >>> 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).resize((512, 512))
-        >>> mask_image = load_image(mask_url).resize((512, 512))
+        >>> def make_inpaint_condition(image, image_mask):
+        ...     image = np.array(image.convert("RGB")).astype(np.float32) / 255.0
+        ...     image_mask = np.array(image_mask.convert("L")).astype(np.float32) / 255.0
 
-        >>> image = np.array(init_image)
+        ...     assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size"
+        ...     image[image_mask > 0.5] = -1.0  # set as masked pixel
+        ...     image = np.expand_dims(image, 0).transpose(0, 3, 1, 2)
+        ...     image = torch.from_numpy(image)
+        ...     return image
 
-        >>> # get canny image
-        >>> image = cv2.Canny(image, 100, 200)
-        >>> image = image[:, :, None]
-        >>> image = np.concatenate([image, image, image], axis=2)
-        >>> canny_image = Image.fromarray(image)
 
-        >>> # load control net and stable diffusion inpainting
-        >>> controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16)
+        >>> control_image = make_inpaint_condition(init_image, mask_image)
+
+        >>> controlnet = ControlNetModel.from_pretrained(
+        ...     "lllyasviel/control_v11p_sd15_inpaint", torch_dtype=torch.float16
+        ... )
         >>> pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained(
-        ...     "runwayml/stable-diffusion-inpainting", controlnet=controlnet, torch_dtype=torch.float16
+        ...     "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16
         ... )
 
         >>> # speed up diffusion process with faster scheduler and memory optimization
-        >>> pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
+        >>> pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
 
         >>> pipe.enable_model_cpu_offload()
 
         >>> # generate image
-        >>> generator = torch.manual_seed(0)
         >>> image = pipe(
-        ...     "spiderman",
-        ...     num_inference_steps=30,
+        ...     "a beautiful man",
+        ...     num_inference_steps=20,
         ...     generator=generator,
+        ...     eta=1.0,
         ...     image=init_image,
         ...     mask_image=mask_image,
-        ...     control_image=canny_image,
+        ...     control_image=control_image,
         ... ).images[0]
         ```
 """
@@ -226,6 +236,17 @@ class StableDiffusionControlNetInpaintPipeline(DiffusionPipeline, TextualInversi
     In addition the pipeline inherits the following loading methods:
         - *Textual-Inversion*: [`loaders.TextualInversionLoaderMixin.load_textual_inversion`]
 
+    <Tip>
+
+    This pipeline can be used both with checkpoints that have been specifically fine-tuned for inpainting, such as
+    [runwayml/stable-diffusion-inpainting](https://huggingface.co/runwayml/stable-diffusion-inpainting)
+     as well as default text-to-image stable diffusion checkpoints, such as
+     [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5).
+    Default text-to-image stable diffusion checkpoints might be preferable for controlnets that have been fine-tuned on
+    those, such as [lllyasviel/control_v11p_sd15_inpaint](https://huggingface.co/lllyasviel/control_v11p_sd15_inpaint).
+
+    </Tip>
+
     Args:
         vae ([`AutoencoderKL`]):
             Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
@@ -597,6 +618,16 @@ class StableDiffusionControlNetInpaintPipeline(DiffusionPipeline, TextualInversi
             extra_step_kwargs["generator"] = generator
         return extra_step_kwargs
 
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps
+    def get_timesteps(self, num_inference_steps, strength, device):
+        # get the original timestep using init_timestep
+        init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
+
+        t_start = max(num_inference_steps - init_timestep, 0)
+        timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]
+
+        return timesteps, num_inference_steps - t_start
+
     def check_inputs(
         self,
         prompt,
@@ -812,6 +843,8 @@ class StableDiffusionControlNetInpaintPipeline(DiffusionPipeline, TextualInversi
         image=None,
         timestep=None,
         is_strength_max=True,
+        return_noise=False,
+        return_image_latents=False,
     ):
         shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
         if isinstance(generator, list) and len(generator) != batch_size:
@@ -826,32 +859,28 @@ class StableDiffusionControlNetInpaintPipeline(DiffusionPipeline, TextualInversi
                 "However, either the image or the noise timestep has not been provided."
             )
 
-        if latents is None:
-            noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-            if is_strength_max:
-                # if strength is 100% then simply initialise the latents to noise
-                latents = noise
-            else:
-                # otherwise initialise latents as init image + noise
+        if return_image_latents or (latents is None and not is_strength_max):
             image = image.to(device=device, dtype=dtype)
-                if isinstance(generator, list):
-                    image_latents = [
-                        self.vae.encode(image[i : i + 1]).latent_dist.sample(generator=generator[i])
-                        for i in range(batch_size)
-                    ]
-                else:
-                    image_latents = self.vae.encode(image).latent_dist.sample(generator=generator)
-
-                image_latents = self.vae.config.scaling_factor * image_latents
+            image_latents = self._encode_vae_image(image=image, generator=generator)
 
-                latents = self.scheduler.add_noise(image_latents, noise, timestep)
+        if latents is None:
+            noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+            latents = noise if is_strength_max else self.scheduler.add_noise(image_latents, noise, timestep)
         else:
             latents = latents.to(device)
 
         # scale the initial noise by the standard deviation required by the scheduler
         latents = latents * self.scheduler.init_noise_sigma
 
-        return latents
+        outputs = (latents,)
+
+        if return_noise:
+            outputs += (noise,)
+
+        if return_image_latents:
+            outputs += (image_latents,)
+
+        return outputs
 
     def _default_height_width(self, height, width, image):
         # NOTE: It is possible that a list of images have different
@@ -891,17 +920,7 @@ class StableDiffusionControlNetInpaintPipeline(DiffusionPipeline, TextualInversi
         mask = mask.to(device=device, dtype=dtype)
 
         masked_image = masked_image.to(device=device, dtype=dtype)
-
-        # encode the mask image into latents space so we can concatenate it to the latents
-        if isinstance(generator, list):
-            masked_image_latents = [
-                self.vae.encode(masked_image[i : i + 1]).latent_dist.sample(generator=generator[i])
-                for i in range(batch_size)
-            ]
-            masked_image_latents = torch.cat(masked_image_latents, dim=0)
-        else:
-            masked_image_latents = self.vae.encode(masked_image).latent_dist.sample(generator=generator)
-        masked_image_latents = self.vae.config.scaling_factor * masked_image_latents
+        masked_image_latents = self._encode_vae_image(masked_image, generator=generator)
 
         # duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method
         if mask.shape[0] < batch_size:
@@ -930,6 +949,21 @@ class StableDiffusionControlNetInpaintPipeline(DiffusionPipeline, TextualInversi
         masked_image_latents = masked_image_latents.to(device=device, dtype=dtype)
         return mask, masked_image_latents
 
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_inpaint.StableDiffusionInpaintPipeline._encode_vae_image
+    def _encode_vae_image(self, image: torch.Tensor, generator: torch.Generator):
+        if isinstance(generator, list):
+            image_latents = [
+                self.vae.encode(image[i : i + 1]).latent_dist.sample(generator=generator[i])
+                for i in range(image.shape[0])
+            ]
+            image_latents = torch.cat(image_latents, dim=0)
+        else:
+            image_latents = self.vae.encode(image).latent_dist.sample(generator=generator)
+
+        image_latents = self.vae.config.scaling_factor * image_latents
+
+        return image_latents
+
     # override DiffusionPipeline
     def save_pretrained(
         self,
@@ -954,6 +988,7 @@ class StableDiffusionControlNetInpaintPipeline(DiffusionPipeline, TextualInversi
         ] = None,
         height: Optional[int] = None,
         width: Optional[int] = None,
+        strength: float = 1.0,
         num_inference_steps: int = 50,
         guidance_scale: float = 7.5,
         negative_prompt: Optional[Union[str, List[str]]] = None,
@@ -990,6 +1025,13 @@ class StableDiffusionControlNetInpaintPipeline(DiffusionPipeline, TextualInversi
                 The height in pixels of the generated image.
             width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
                 The width in pixels of the generated image.
+            strength (`float`, *optional*, defaults to 1.):
+                Conceptually, indicates how much to transform the masked portion of the reference `image`. Must be
+                between 0 and 1. `image` will be used as a starting point, adding more noise to it the larger the
+                `strength`. The number of denoising steps depends on the amount of noise initially added. When
+                `strength` is 1, added noise will be maximum and the denoising process will run for the full number of
+                iterations specified in `num_inference_steps`. A value of 1, therefore, essentially ignores the masked
+                portion of the reference `image`.
             num_inference_steps (`int`, *optional*, defaults to 50):
                 The number of denoising steps. More denoising steps usually lead to a higher quality image at the
                 expense of slower inference.
@@ -1145,13 +1187,25 @@ class StableDiffusionControlNetInpaintPipeline(DiffusionPipeline, TextualInversi
             assert False
 
         # 4. Preprocess mask and image - resizes image and mask w.r.t height and width
+        mask, masked_image, init_image = prepare_mask_and_masked_image(
+            image, mask_image, height, width, return_image=True
+        )
+
         # 5. Prepare timesteps
         self.scheduler.set_timesteps(num_inference_steps, device=device)
-        timesteps = self.scheduler.timesteps
+        timesteps, num_inference_steps = self.get_timesteps(
+            num_inference_steps=num_inference_steps, strength=strength, device=device
+        )
+        # at which timestep to set the initial noise (n.b. 50% if strength is 0.5)
+        latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
+        # create a boolean to check if the strength is set to 1. if so then initialise the latents with pure noise
+        is_strength_max = strength == 1.0
 
         # 6. Prepare latent variables
         num_channels_latents = self.vae.config.latent_channels
-        latents = self.prepare_latents(
+        num_channels_unet = self.unet.config.in_channels
+        return_image_latents = num_channels_unet == 4
+        latents_outputs = self.prepare_latents(
             batch_size * num_images_per_prompt,
             num_channels_latents,
             height,
@@ -1160,10 +1214,19 @@ class StableDiffusionControlNetInpaintPipeline(DiffusionPipeline, TextualInversi
             device,
             generator,
             latents,
+            image=init_image,
+            timestep=latent_timestep,
+            is_strength_max=is_strength_max,
+            return_noise=True,
+            return_image_latents=return_image_latents,
         )
 
+        if return_image_latents:
+            latents, noise, image_latents = latents_outputs
+        else:
+            latents, noise = latents_outputs
+
         # 7. Prepare mask latent variables
-        mask, masked_image = prepare_mask_and_masked_image(image, mask_image, height, width)
         mask, masked_image_latents = self.prepare_mask_latents(
             mask,
             masked_image,
@@ -1213,7 +1276,9 @@ class StableDiffusionControlNetInpaintPipeline(DiffusionPipeline, TextualInversi
                     mid_block_res_sample = torch.cat([torch.zeros_like(mid_block_res_sample), mid_block_res_sample])
 
                 # predict the noise residual
+                if num_channels_unet == 9:
                     latent_model_input = torch.cat([latent_model_input, mask, masked_image_latents], dim=1)
+
                 noise_pred = self.unet(
                     latent_model_input,
                     t,
@@ -1232,6 +1297,15 @@ class StableDiffusionControlNetInpaintPipeline(DiffusionPipeline, TextualInversi
                 # compute the previous noisy sample x_t -> x_t-1
                 latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
 
+                if num_channels_unet == 4:
+                    init_latents_proper = image_latents[:1]
+                    init_mask = mask[:1]
+
+                    if i < len(timesteps) - 1:
+                        init_latents_proper = self.scheduler.add_noise(init_latents_proper, noise, torch.tensor([t]))
+
+                    latents = (1 - init_mask) * init_latents_proper + init_mask * latents
+
                 # call the callback, if provided
                 if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                     progress_bar.update()

src/diffusers/pipelines/paint_by_example/pipeline_paint_by_example.py

@@ -328,17 +328,7 @@ class PaintByExamplePipeline(DiffusionPipeline):
         mask = mask.to(device=device, dtype=dtype)
 
         masked_image = masked_image.to(device=device, dtype=dtype)
-
-        # encode the mask image into latents space so we can concatenate it to the latents
-        if isinstance(generator, list):
-            masked_image_latents = [
-                self.vae.encode(masked_image[i : i + 1]).latent_dist.sample(generator=generator[i])
-                for i in range(batch_size)
-            ]
-            masked_image_latents = torch.cat(masked_image_latents, dim=0)
-        else:
-            masked_image_latents = self.vae.encode(masked_image).latent_dist.sample(generator=generator)
-        masked_image_latents = self.vae.config.scaling_factor * masked_image_latents
+        masked_image_latents = self._encode_vae_image(masked_image, generator=generator)
 
         # duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method
         if mask.shape[0] < batch_size:
@@ -367,6 +357,21 @@ class PaintByExamplePipeline(DiffusionPipeline):
         masked_image_latents = masked_image_latents.to(device=device, dtype=dtype)
         return mask, masked_image_latents
 
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_inpaint.StableDiffusionInpaintPipeline._encode_vae_image
+    def _encode_vae_image(self, image: torch.Tensor, generator: torch.Generator):
+        if isinstance(generator, list):
+            image_latents = [
+                self.vae.encode(image[i : i + 1]).latent_dist.sample(generator=generator[i])
+                for i in range(image.shape[0])
+            ]
+            image_latents = torch.cat(image_latents, dim=0)
+        else:
+            image_latents = self.vae.encode(image).latent_dist.sample(generator=generator)
+
+        image_latents = self.vae.config.scaling_factor * image_latents
+
+        return image_latents
+
     def _encode_image(self, image, device, num_images_per_prompt, do_classifier_free_guidance):
         dtype = next(self.image_encoder.parameters()).dtype
 

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py

@@ -155,7 +155,7 @@ def prepare_mask_and_masked_image(image, mask, height, width, return_image: bool
 
 class StableDiffusionInpaintPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin):
     r"""
-    Pipeline for text-guided image inpainting using Stable Diffusion. *This is an experimental feature*.
+    Pipeline for text-guided image inpainting using Stable Diffusion.
 
     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.)
@@ -167,6 +167,16 @@ class StableDiffusionInpaintPipeline(DiffusionPipeline, TextualInversionLoaderMi
     as well as the following saving methods:
         - *LoRA*: [`loaders.LoraLoaderMixin.save_lora_weights`]
 
+    <Tip>
+
+    It is recommended to use this pipeline with checkpoints that have been specifically fine-tuned for inpainting, such
+    as [runwayml/stable-diffusion-inpainting](https://huggingface.co/runwayml/stable-diffusion-inpainting). Default
+    text-to-image stable diffusion checkpoints, such as
+    [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) are also compatible with
+    this pipeline, but might be less performant.
+
+    </Tip>
+
     Args:
         vae ([`AutoencoderKL`]):
             Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
@@ -266,14 +276,10 @@ class StableDiffusionInpaintPipeline(DiffusionPipeline, TextualInversionLoaderMi
             new_config = dict(unet.config)
             new_config["sample_size"] = 64
             unet._internal_dict = FrozenDict(new_config)
+
         # Check shapes, assume num_channels_latents == 4, num_channels_mask == 1, num_channels_masked == 4
         if unet.config.in_channels != 9:
-            logger.warning(
-                f"You have loaded a UNet with {unet.config.in_channels} input channels, whereas by default,"
-                f" {self.__class__} assumes that `pipeline.unet` has 9 input channels: 4 for `num_channels_latents`,"
-                " 1 for `num_channels_mask`, and 4 for `num_channels_masked_image`. If you did not intend to modify"
-                " this behavior, please check whether you have loaded the right checkpoint."
-            )
+            logger.info(f"You have loaded a UNet with {unet.config.in_channels} input channels which.")
 
         self.register_modules(
             vae=vae,
@@ -620,6 +626,8 @@ class StableDiffusionInpaintPipeline(DiffusionPipeline, TextualInversionLoaderMi
         image=None,
         timestep=None,
         is_strength_max=True,
+        return_noise=False,
+        return_image_latents=False,
     ):
         shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
         if isinstance(generator, list) and len(generator) != batch_size:
@@ -634,32 +642,42 @@ class StableDiffusionInpaintPipeline(DiffusionPipeline, TextualInversionLoaderMi
                 "However, either the image or the noise timestep has not been provided."
             )
 
+        if return_image_latents or (latents is None and not is_strength_max):
+            image = image.to(device=device, dtype=dtype)
+            image_latents = self._encode_vae_image(image=image, generator=generator)
+
         if latents is None:
             noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-            if is_strength_max:
-                # if strength is 100% then simply initialise the latents to noise
-                latents = noise
+            latents = noise if is_strength_max else self.scheduler.add_noise(image_latents, noise, timestep)
         else:
-                # otherwise initialise latents as init image + noise
-                image = image.to(device=device, dtype=dtype)
+            latents = latents.to(device)
+
+        # scale the initial noise by the standard deviation required by the scheduler
+        latents = latents * self.scheduler.init_noise_sigma
+
+        outputs = (latents,)
+
+        if return_noise:
+            outputs += (noise,)
+
+        if return_image_latents:
+            outputs += (image_latents,)
+
+        return outputs
+
+    def _encode_vae_image(self, image: torch.Tensor, generator: torch.Generator):
         if isinstance(generator, list):
             image_latents = [
                 self.vae.encode(image[i : i + 1]).latent_dist.sample(generator=generator[i])
-                        for i in range(batch_size)
+                for i in range(image.shape[0])
             ]
+            image_latents = torch.cat(image_latents, dim=0)
         else:
             image_latents = self.vae.encode(image).latent_dist.sample(generator=generator)
 
         image_latents = self.vae.config.scaling_factor * image_latents
 
-                latents = self.scheduler.add_noise(image_latents, noise, timestep)
-        else:
-            latents = latents.to(device)
-
-        # scale the initial noise by the standard deviation required by the scheduler
-        latents = latents * self.scheduler.init_noise_sigma
-
-        return latents
+        return image_latents
 
     def prepare_mask_latents(
         self, mask, masked_image, batch_size, height, width, dtype, device, generator, do_classifier_free_guidance
@@ -673,17 +691,7 @@ class StableDiffusionInpaintPipeline(DiffusionPipeline, TextualInversionLoaderMi
         mask = mask.to(device=device, dtype=dtype)
 
         masked_image = masked_image.to(device=device, dtype=dtype)
-
-        # encode the mask image into latents space so we can concatenate it to the latents
-        if isinstance(generator, list):
-            masked_image_latents = [
-                self.vae.encode(masked_image[i : i + 1]).latent_dist.sample(generator=generator[i])
-                for i in range(batch_size)
-            ]
-            masked_image_latents = torch.cat(masked_image_latents, dim=0)
-        else:
-            masked_image_latents = self.vae.encode(masked_image).latent_dist.sample(generator=generator)
-        masked_image_latents = self.vae.config.scaling_factor * masked_image_latents
+        masked_image_latents = self._encode_vae_image(masked_image, generator=generator)
 
         # duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method
         if mask.shape[0] < batch_size:
@@ -916,7 +924,10 @@ class StableDiffusionInpaintPipeline(DiffusionPipeline, TextualInversionLoaderMi
 
         # 6. Prepare latent variables
         num_channels_latents = self.vae.config.latent_channels
-        latents = self.prepare_latents(
+        num_channels_unet = self.unet.config.in_channels
+        return_image_latents = num_channels_unet == 4
+
+        latents_outputs = self.prepare_latents(
             batch_size * num_images_per_prompt,
             num_channels_latents,
             height,
@@ -928,8 +939,15 @@ class StableDiffusionInpaintPipeline(DiffusionPipeline, TextualInversionLoaderMi
             image=init_image,
             timestep=latent_timestep,
             is_strength_max=is_strength_max,
+            return_noise=True,
+            return_image_latents=return_image_latents,
         )
 
+        if return_image_latents:
+            latents, noise, image_latents = latents_outputs
+        else:
+            latents, noise = latents_outputs
+
         # 7. Prepare mask latent variables
         mask, masked_image_latents = self.prepare_mask_latents(
             mask,
@@ -942,8 +960,12 @@ class StableDiffusionInpaintPipeline(DiffusionPipeline, TextualInversionLoaderMi
             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:
+            # default case for runwayml/stable-diffusion-inpainting
             num_channels_mask = mask.shape[1]
             num_channels_masked_image = masked_image_latents.shape[1]
             if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels:
@@ -954,6 +976,10 @@ class StableDiffusionInpaintPipeline(DiffusionPipeline, TextualInversionLoaderMi
                     f" = {num_channels_latents+num_channels_masked_image+num_channels_mask}. Please verify the config of"
                     " `pipeline.unet` or your `mask_image` or `image` input."
                 )
+        elif num_channels_unet != 4:
+            raise ValueError(
+                f"The unet {self.unet.__class__} should have either 4 or 9 input channels, not {self.unet.config.in_channels}."
+            )
 
         # 9. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
         extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
@@ -967,6 +993,8 @@ class StableDiffusionInpaintPipeline(DiffusionPipeline, TextualInversionLoaderMi
 
                 # concat latents, mask, masked_image_latents in the channel dimension
                 latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+
+                if num_channels_unet == 9:
                     latent_model_input = torch.cat([latent_model_input, mask, masked_image_latents], dim=1)
 
                 # predict the noise residual
@@ -986,6 +1014,15 @@ class StableDiffusionInpaintPipeline(DiffusionPipeline, TextualInversionLoaderMi
                 # compute the previous noisy sample x_t -> x_t-1
                 latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
 
+                if num_channels_unet == 4:
+                    init_latents_proper = image_latents[:1]
+                    init_mask = mask[:1]
+
+                    if i < len(timesteps) - 1:
+                        init_latents_proper = self.scheduler.add_noise(init_latents_proper, noise, torch.tensor([t]))
+
+                    latents = (1 - init_mask) * init_latents_proper + init_mask * latents
+
                 # call the callback, if provided
                 if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                     progress_bar.update()

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint_legacy.py

@@ -123,7 +123,6 @@ class StableDiffusionInpaintPipelineLegacy(
     """
     _optional_components = ["feature_extractor"]
 
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.__init__
     def __init__(
         self,
         vae: AutoencoderKL,
@@ -137,6 +136,13 @@ class StableDiffusionInpaintPipelineLegacy(
     ):
         super().__init__()
 
+        deprecation_message = (
+            f"The class {self.__class__} is deprecated and will be removed in v1.0.0. You can achieve exactly the same functionality"
+            "by loading your model into `StableDiffusionInpaintPipeline` instead. See https://github.com/huggingface/diffusers/pull/3533"
+            "for more information."
+        )
+        deprecate("legacy is outdated", "1.0.0", deprecation_message, standard_warn=False)
+
         if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
             deprecation_message = (
                 f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"

tests/pipelines/controlnet/test_controlnet_inpaint.py

@@ -163,6 +163,78 @@ class ControlNetInpaintPipelineFastTests(PipelineLatentTesterMixin, PipelineTest
         self._test_inference_batch_single_identical(expected_max_diff=2e-3)
 
 
+class ControlNetSimpleInpaintPipelineFastTests(ControlNetInpaintPipelineFastTests):
+    pipeline_class = StableDiffusionControlNetInpaintPipeline
+    params = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
+    batch_params = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
+    image_params = frozenset([])
+
+    def get_dummy_components(self):
+        torch.manual_seed(0)
+        unet = UNet2DConditionModel(
+            block_out_channels=(32, 64),
+            layers_per_block=2,
+            sample_size=32,
+            in_channels=4,
+            out_channels=4,
+            down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
+            up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
+            cross_attention_dim=32,
+        )
+        torch.manual_seed(0)
+        controlnet = ControlNetModel(
+            block_out_channels=(32, 64),
+            layers_per_block=2,
+            in_channels=4,
+            down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
+            cross_attention_dim=32,
+            conditioning_embedding_out_channels=(16, 32),
+        )
+        torch.manual_seed(0)
+        scheduler = DDIMScheduler(
+            beta_start=0.00085,
+            beta_end=0.012,
+            beta_schedule="scaled_linear",
+            clip_sample=False,
+            set_alpha_to_one=False,
+        )
+        torch.manual_seed(0)
+        vae = AutoencoderKL(
+            block_out_channels=[32, 64],
+            in_channels=3,
+            out_channels=3,
+            down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
+            up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
+            latent_channels=4,
+        )
+        torch.manual_seed(0)
+        text_encoder_config = CLIPTextConfig(
+            bos_token_id=0,
+            eos_token_id=2,
+            hidden_size=32,
+            intermediate_size=37,
+            layer_norm_eps=1e-05,
+            num_attention_heads=4,
+            num_hidden_layers=5,
+            pad_token_id=1,
+            vocab_size=1000,
+        )
+        text_encoder = CLIPTextModel(text_encoder_config)
+        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
+
+        components = {
+            "unet": unet,
+            "controlnet": controlnet,
+            "scheduler": scheduler,
+            "vae": vae,
+            "text_encoder": text_encoder,
+            "tokenizer": tokenizer,
+            "safety_checker": None,
+            "feature_extractor": None,
+        }
+        return components
+
+
 class MultiControlNetInpaintPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
     pipeline_class = StableDiffusionControlNetInpaintPipeline
     params = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
@@ -376,3 +448,60 @@ class ControlNetInpaintPipelineSlowTests(unittest.TestCase):
         )
 
         assert np.abs(expected_image - image).max() < 9e-2
+
+    def test_inpaint(self):
+        controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_inpaint")
+
+        pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained(
+            "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet
+        )
+        pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
+        pipe.enable_model_cpu_offload()
+        pipe.set_progress_bar_config(disable=None)
+
+        generator = torch.Generator(device="cpu").manual_seed(33)
+
+        init_image = load_image(
+            "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png"
+        )
+        init_image = init_image.resize((512, 512))
+
+        mask_image = load_image(
+            "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png"
+        )
+        mask_image = mask_image.resize((512, 512))
+
+        prompt = "a handsome man with ray-ban sunglasses"
+
+        def make_inpaint_condition(image, image_mask):
+            image = np.array(image.convert("RGB")).astype(np.float32) / 255.0
+            image_mask = np.array(image_mask.convert("L")).astype(np.float32) / 255.0
+
+            assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size"
+            image[image_mask > 0.5] = -1.0  # set as masked pixel
+            image = np.expand_dims(image, 0).transpose(0, 3, 1, 2)
+            image = torch.from_numpy(image)
+            return image
+
+        control_image = make_inpaint_condition(init_image, mask_image)
+
+        output = pipe(
+            prompt,
+            image=init_image,
+            mask_image=mask_image,
+            control_image=control_image,
+            guidance_scale=9.0,
+            eta=1.0,
+            generator=generator,
+            num_inference_steps=20,
+            output_type="np",
+        )
+        image = output.images[0]
+
+        assert image.shape == (512, 512, 3)
+
+        expected_image = load_numpy(
+            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/boy_ray_ban.npy"
+        )
+
+        assert np.abs(expected_image - image).max() < 9e-2

tests/pipelines/stable_diffusion/test_stable_diffusion_inpaint.py

@@ -232,6 +232,82 @@ class StableDiffusionInpaintPipelineFastTests(PipelineLatentTesterMixin, Pipelin
         super().test_inference_batch_single_identical(expected_max_diff=3e-3)
 
 
+class StableDiffusionSimpleInpaintPipelineFastTests(StableDiffusionInpaintPipelineFastTests):
+    pipeline_class = StableDiffusionInpaintPipeline
+    params = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
+    batch_params = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
+    image_params = frozenset([])
+    # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
+
+    def get_dummy_components(self):
+        torch.manual_seed(0)
+        unet = UNet2DConditionModel(
+            block_out_channels=(32, 64),
+            layers_per_block=2,
+            sample_size=32,
+            in_channels=4,
+            out_channels=4,
+            down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
+            up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
+            cross_attention_dim=32,
+        )
+        scheduler = PNDMScheduler(skip_prk_steps=True)
+        torch.manual_seed(0)
+        vae = AutoencoderKL(
+            block_out_channels=[32, 64],
+            in_channels=3,
+            out_channels=3,
+            down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
+            up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
+            latent_channels=4,
+        )
+        torch.manual_seed(0)
+        text_encoder_config = CLIPTextConfig(
+            bos_token_id=0,
+            eos_token_id=2,
+            hidden_size=32,
+            intermediate_size=37,
+            layer_norm_eps=1e-05,
+            num_attention_heads=4,
+            num_hidden_layers=5,
+            pad_token_id=1,
+            vocab_size=1000,
+        )
+        text_encoder = CLIPTextModel(text_encoder_config)
+        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
+
+        components = {
+            "unet": unet,
+            "scheduler": scheduler,
+            "vae": vae,
+            "text_encoder": text_encoder,
+            "tokenizer": tokenizer,
+            "safety_checker": None,
+            "feature_extractor": None,
+        }
+        return components
+
+    def test_stable_diffusion_inpaint(self):
+        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
+        components = self.get_dummy_components()
+        sd_pipe = StableDiffusionInpaintPipeline(**components)
+        sd_pipe = sd_pipe.to(device)
+        sd_pipe.set_progress_bar_config(disable=None)
+
+        inputs = self.get_dummy_inputs(device)
+        image = sd_pipe(**inputs).images
+        image_slice = image[0, -3:, -3:, -1]
+
+        assert image.shape == (1, 64, 64, 3)
+        expected_slice = np.array([0.4925, 0.4967, 0.4100, 0.5234, 0.5322, 0.4532, 0.5805, 0.5877, 0.4151])
+
+        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
+
+    @unittest.skip("skipped here because area stays unchanged due to mask")
+    def test_stable_diffusion_inpaint_lora(self):
+        ...
+
+
 @slow
 @require_torch_gpu
 class StableDiffusionInpaintPipelineSlowTests(unittest.TestCase):
@@ -403,6 +479,22 @@ class StableDiffusionInpaintPipelineSlowTests(unittest.TestCase):
         expected_slice = np.array([0.0021, 0.2350, 0.3712, 0.0575, 0.2485, 0.3451, 0.1857, 0.3156, 0.3943])
         assert np.abs(expected_slice - image_slice).max() < 3e-3
 
+    def test_stable_diffusion_simple_inpaint_ddim(self):
+        pipe = StableDiffusionInpaintPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", safety_checker=None)
+        pipe.to(torch_device)
+        pipe.set_progress_bar_config(disable=None)
+        pipe.enable_attention_slicing()
+
+        inputs = self.get_inputs(torch_device)
+        image = pipe(**inputs).images
+
+        image_slice = image[0, 253:256, 253:256, -1].flatten()
+
+        assert image.shape == (1, 512, 512, 3)
+        expected_slice = np.array([0.5157, 0.6858, 0.6873, 0.4619, 0.6416, 0.6898, 0.3702, 0.5960, 0.6935])
+
+        assert np.abs(expected_slice - image_slice).max() < 6e-4
+
 
 @nightly
 @require_torch_gpu