project setup

app.py

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+import gradio as gr
+from io import BytesIO
+
+from torch import autocast
+import requests
+import PIL
+import torch
+from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline
+
+pipe = StableDiffusionInpaintPipeline.from_pretrained(
+    "CompVis/stable-diffusion-v1-4",
+    revision="fp16",
+    torch_dtype=torch.float16,
+    use_auth_token=True,
+)
+
+
+def process_image(dict, prompt):
+    init_img = dict["image"].convert("RGB").resize((512, 512))
+    mask_img = dict["mask"].convert("RGB").resize((512, 512))
+    images = pipe(
+        prompt=prompt, init_image=init_img, mask_image=mask_img, strength=0.75
+    )["sample"]
+    return images[0]
+
+
+iface = gr.Interface(
+    fn=process_image,
+    title="Stable Diffusion In-Painting Tool on Colab with Gradio",
+    inputs=[
+        gr.Image(source="upload", tool="sketch", type="pil"),
+        gr.Textbox(label="prompt"),
+    ],
+    outputs=[gr.Image()],
+    description="Choose a feature and upload an image to see the processed result.",
+    article="<p style='text-align: center;'>Built with Gradio</p>",
+)
+
+
+iface.launch()

inpainting.py

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+# credit : Hugging Face Team
+import inspect
+from typing import List, Optional, Union
+
+import numpy as np
+import torch
+
+import PIL
+from diffusers import (
+    AutoencoderKL,
+    DDIMScheduler,
+    DiffusionPipeline,
+    PNDMScheduler,
+    UNet2DConditionModel,
+)
+from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker
+from tqdm.auto import tqdm
+from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
+
+
+def preprocess_image(image):
+    w, h = image.size
+    w, h = map(lambda x: x - x % 32, (w, h))  # resize to integer multiple of 32
+    image = image.resize((w, h), resample=PIL.Image.LANCZOS)
+    image = np.array(image).astype(np.float32) / 255.0
+    image = image[None].transpose(0, 3, 1, 2)
+    image = torch.from_numpy(image)
+    return 2.0 * image - 1.0
+
+
+def preprocess_mask(mask):
+    mask = mask.convert("L")
+    w, h = mask.size
+    w, h = map(lambda x: x - x % 32, (w, h))  # resize to integer multiple of 32
+    mask = mask.resize((w // 8, h // 8), resample=PIL.Image.NEAREST)
+    mask = np.array(mask).astype(np.float32) / 255.0
+    mask = np.tile(mask, (4, 1, 1))
+    mask = mask[None].transpose(0, 1, 2, 3)  # what does this step do?
+    mask = 1 - mask  # repaint white, keep black
+    mask = torch.from_numpy(mask)
+    return mask
+
+
+class StableDiffusionInpaintingPipeline(DiffusionPipeline):
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        tokenizer: CLIPTokenizer,
+        unet: UNet2DConditionModel,
+        scheduler: Union[DDIMScheduler, PNDMScheduler],
+        safety_checker: StableDiffusionSafetyChecker,
+        feature_extractor: CLIPFeatureExtractor,
+    ):
+        super().__init__()
+        scheduler = scheduler.set_format("pt")
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            unet=unet,
+            scheduler=scheduler,
+            safety_checker=safety_checker,
+            feature_extractor=feature_extractor,
+        )
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt: Union[str, List[str]],
+        init_image: torch.FloatTensor,
+        mask_image: torch.FloatTensor,
+        strength: float = 0.8,
+        num_inference_steps: Optional[int] = 50,
+        guidance_scale: Optional[float] = 7.5,
+        eta: Optional[float] = 0.0,
+        generator: Optional[torch.Generator] = None,
+        output_type: Optional[str] = "pil",
+    ):
+        if isinstance(prompt, str):
+            batch_size = 1
+        elif isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            raise ValueError(
+                f"`prompt` has to be of type `str` or `list` but is {type(prompt)}"
+            )
+
+        if strength < 0 or strength > 1:
+            raise ValueError(
+                f"The value of strength should in [0.0, 1.0] but is {strength}"
+            )
+
+        # set timesteps
+        accepts_offset = "offset" in set(
+            inspect.signature(self.scheduler.set_timesteps).parameters.keys()
+        )
+        extra_set_kwargs = {}
+        offset = 0
+        if accepts_offset:
+            offset = 1
+            extra_set_kwargs["offset"] = 1
+
+        self.scheduler.set_timesteps(num_inference_steps, **extra_set_kwargs)
+
+        # preprocess image
+        init_image = preprocess_image(init_image).to(self.device)
+
+        # encode the init image into latents and scale the latents
+        init_latents = self.vae.encode(init_image).sample()
+        init_latents = 0.18215 * init_latents
+
+        # prepare init_latents noise to latents
+        init_latents = torch.cat([init_latents] * batch_size)
+        init_latents_orig = init_latents
+
+        # preprocess mask
+        mask = preprocess_mask(mask_image).to(self.device)
+        mask = torch.cat([mask] * batch_size)
+
+        # check sizes
+        if not mask.shape == init_latents.shape:
+            raise ValueError(f"The mask and init_image should be the same size!")
+
+        # get the original timestep using init_timestep
+        init_timestep = int(num_inference_steps * strength) + offset
+        init_timestep = min(init_timestep, num_inference_steps)
+        timesteps = self.scheduler.timesteps[-init_timestep]
+        timesteps = torch.tensor(
+            [timesteps] * batch_size, dtype=torch.long, device=self.device
+        )
+
+        # add noise to latents using the timesteps
+        noise = torch.randn(init_latents.shape, generator=generator, device=self.device)
+        init_latents = self.scheduler.add_noise(init_latents, noise, timesteps)
+
+        # get prompt text embeddings
+        text_input = self.tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=self.tokenizer.model_max_length,
+            truncation=True,
+            return_tensors="pt",
+        )
+        text_embeddings = self.text_encoder(text_input.input_ids.to(self.device))[0]
+
+        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+        # corresponds to doing no classifier free guidance.
+        do_classifier_free_guidance = guidance_scale > 1.0
+        # get unconditional embeddings for classifier free guidance
+        if do_classifier_free_guidance:
+            max_length = text_input.input_ids.shape[-1]
+            uncond_input = self.tokenizer(
+                [""] * batch_size,
+                padding="max_length",
+                max_length=max_length,
+                return_tensors="pt",
+            )
+            uncond_embeddings = self.text_encoder(
+                uncond_input.input_ids.to(self.device)
+            )[0]
+
+            # For classifier free guidance, we need to do two forward passes.
+            # Here we concatenate the unconditional and text embeddings into a single batch
+            # to avoid doing two forward passes
+            text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
+
+        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
+        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
+        # and should be between [0, 1]
+        accepts_eta = "eta" in set(
+            inspect.signature(self.scheduler.step).parameters.keys()
+        )
+        extra_step_kwargs = {}
+        if accepts_eta:
+            extra_step_kwargs["eta"] = eta
+
+        latents = init_latents
+        t_start = max(num_inference_steps - init_timestep + offset, 0)
+        for i, t in tqdm(enumerate(self.scheduler.timesteps[t_start:])):
+            # expand the latents if we are doing classifier free guidance
+            latent_model_input = (
+                torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+            )
+
+            # predict the noise residual
+            noise_pred = self.unet(
+                latent_model_input, t, encoder_hidden_states=text_embeddings
+            )["sample"]
+
+            # perform guidance
+            if do_classifier_free_guidance:
+                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                noise_pred = noise_pred_uncond + guidance_scale * (
+                    noise_pred_text - noise_pred_uncond
+                )
+
+            # compute the previous noisy sample x_t -> x_t-1
+            latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs)[
+                "prev_sample"
+            ]
+
+            # masking
+            init_latents_proper = self.scheduler.add_noise(init_latents_orig, noise, t)
+            latents = (init_latents_proper * mask) + (latents * (1 - mask))
+
+        # scale and decode the image latents with vae
+        latents = 1 / 0.18215 * latents
+        image = self.vae.decode(latents)
+
+        image = (image / 2 + 0.5).clamp(0, 1)
+        image = image.cpu().permute(0, 2, 3, 1).numpy()
+
+        # run safety checker
+        safety_cheker_input = self.feature_extractor(
+            self.numpy_to_pil(image), return_tensors="pt"
+        ).to(self.device)
+        image, has_nsfw_concept = self.safety_checker(
+            images=image, clip_input=safety_cheker_input.pixel_values
+        )
+
+        if output_type == "pil":
+            image = self.numpy_to_pil(image)
+
+        return {"sample": image, "nsfw_content_detected": has_nsfw_concept}

requirements.txt

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+torch
+requests
+PIL
+diffusers
+gradio
+numpy
+tqdm
+typing
+inspect

test.py

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+import gradio as gr
+
+def greet(name, intensity):
+    return "Hello " * intensity + name + "!"
+
+demo = gr.Interface(
+    fn=greet,
+    inputs=["text", "slider"],
+    outputs=["text"],
+)
+
+demo.launch()

utils.py

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+def add_feature(image):
+    # inpainting features
+    pass
+
+
+def remove_feature(image):
+    # inpainting features
+    pass
+
+
+def enhance_feature(image):
+    # inpainting features
+    pass