Update app.py

app.py

@@ -14,170 +14,12 @@ from tqdm.auto import tqdm
 from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
 import gradio as gr
 import random
+import StableDiffusionInpaintingPipelineCustom
 
 device = "cuda"
 model_path = "CompVis/stable-diffusion-v1-4"
 
-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}
-
+StableDiffusionInpaintingPipeline = StableDiffusionInpaintingPipelineCustom.StableDiffusionInpaintingPipeline
 pipe = StableDiffusionInpaintingPipeline.from_pretrained(
       model_path,
       revision="fp16",