add inpainting example script (#241)
* add inpainting * added proper noising of init_latent as reccommened by jackloomen (https://github.com/huggingface/diffusers/pull/241#issuecomment-1226283542) * move image preprocessing inside pipeline and allow non 512x512 mask
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import inspect
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from typing import List, Optional, Union
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import numpy as np
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import torch
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import PIL
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from diffusers import AutoencoderKL, DDIMScheduler, DiffusionPipeline, PNDMScheduler, UNet2DConditionModel
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from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker
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from tqdm.auto import tqdm
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from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
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def preprocess(image):
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w, h = image.size
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w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32
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image = image.resize((w, h), resample=PIL.Image.LANCZOS)
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image = np.array(image).astype(np.float32) / 255.0
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image = image[None].transpose(0, 3, 1, 2)
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image = torch.from_numpy(image)
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return 2.0 * image - 1.0
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def preprocess_mask(mask):
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mask=mask.convert("L")
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w, h = mask.size
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w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32
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mask = mask.resize((w//8, h//8), resample=PIL.Image.NEAREST)
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mask = np.array(mask).astype(np.float32) / 255.0
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mask = np.tile(mask,(4,1,1))
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mask = mask[None].transpose(0, 1, 2, 3)#what does this step do?
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mask = 1 - mask #repaint white, keep black
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mask = torch.from_numpy(mask)
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return mask
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class StableDiffusionInpaintingPipeline(DiffusionPipeline):
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def __init__(
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self,
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vae: AutoencoderKL,
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text_encoder: CLIPTextModel,
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tokenizer: CLIPTokenizer,
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unet: UNet2DConditionModel,
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scheduler: Union[DDIMScheduler, PNDMScheduler],
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safety_checker: StableDiffusionSafetyChecker,
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feature_extractor: CLIPFeatureExtractor,
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):
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super().__init__()
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scheduler = scheduler.set_format("pt")
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self.register_modules(
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vae=vae,
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text_encoder=text_encoder,
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tokenizer=tokenizer,
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unet=unet,
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scheduler=scheduler,
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safety_checker=safety_checker,
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feature_extractor=feature_extractor,
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)
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@torch.no_grad()
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def __call__(
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self,
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prompt: Union[str, List[str]],
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init_image: torch.FloatTensor,
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mask_image: torch.FloatTensor,
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strength: float = 0.8,
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num_inference_steps: Optional[int] = 50,
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guidance_scale: Optional[float] = 7.5,
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eta: Optional[float] = 0.0,
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generator: Optional[torch.Generator] = None,
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output_type: Optional[str] = "pil",
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):
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if isinstance(prompt, str):
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batch_size = 1
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elif isinstance(prompt, list):
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batch_size = len(prompt)
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else:
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raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
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if strength < 0 or strength > 1:
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raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
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# set timesteps
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accepts_offset = "offset" in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys())
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extra_set_kwargs = {}
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offset = 0
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if accepts_offset:
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offset = 1
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extra_set_kwargs["offset"] = 1
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self.scheduler.set_timesteps(num_inference_steps, **extra_set_kwargs)
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#preprocess image
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init_image = preprocess(init_image).to(self.device)
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# encode the init image into latents and scale the latents
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init_latents = self.vae.encode(init_image).sample()
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init_latents = 0.18215 * init_latents
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init_latents_orig = init_latents
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# preprocess mask
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mask = preprocess_mask(mask_image).to(self.device)
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mask = torch.cat([mask] * batch_size)
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#check sizes
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if not mask.shape == init_latents.shape:
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raise ValueError(f"The mask and init_image should be the same size!")
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# prepare init_latents noise to latents
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init_latents = torch.cat([init_latents] * batch_size)
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# get the original timestep using init_timestep
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init_timestep = int(num_inference_steps * strength) + offset
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init_timestep = min(init_timestep, num_inference_steps)
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timesteps = self.scheduler.timesteps[-init_timestep]
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timesteps = torch.tensor([timesteps] * batch_size, dtype=torch.long, device=self.device)
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# add noise to latents using the timesteps
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noise = torch.randn(init_latents.shape, generator=generator, device=self.device)
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init_latents = self.scheduler.add_noise(init_latents, noise, timesteps)
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# get prompt text embeddings
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text_input = self.tokenizer(
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prompt,
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padding="max_length",
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max_length=self.tokenizer.model_max_length,
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truncation=True,
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return_tensors="pt",
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)
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text_embeddings = self.text_encoder(text_input.input_ids.to(self.device))[0]
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# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
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# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
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# corresponds to doing no classifier free guidance.
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do_classifier_free_guidance = guidance_scale > 1.0
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# get unconditional embeddings for classifier free guidance
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if do_classifier_free_guidance:
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max_length = text_input.input_ids.shape[-1]
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uncond_input = self.tokenizer(
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[""] * batch_size, padding="max_length", max_length=max_length, return_tensors="pt"
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)
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uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
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# For classifier free guidance, we need to do two forward passes.
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# Here we concatenate the unconditional and text embeddings into a single batch
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# to avoid doing two forward passes
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text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
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# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
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# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
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# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
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# and should be between [0, 1]
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accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
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extra_step_kwargs = {}
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if accepts_eta:
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extra_step_kwargs["eta"] = eta
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latents = init_latents
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t_start = max(num_inference_steps - init_timestep + offset, 0)
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for i, t in tqdm(enumerate(self.scheduler.timesteps[t_start:])):
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# expand the latents if we are doing classifier free guidance
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latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
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# predict the noise residual
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noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings)["sample"]
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# perform guidance
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if do_classifier_free_guidance:
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noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
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noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
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# compute the previous noisy sample x_t -> x_t-1
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latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs)["prev_sample"]
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#masking
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init_latents_proper = self.scheduler.add_noise(init_latents_orig, noise, t)
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latents = ( init_latents_proper * mask ) + ( latents * (1-mask) )
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# scale and decode the image latents with vae
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latents = 1 / 0.18215 * latents
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image = self.vae.decode(latents)
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image = (image / 2 + 0.5).clamp(0, 1)
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image = image.cpu().permute(0, 2, 3, 1).numpy()
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# run safety checker
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safety_cheker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(self.device)
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image, has_nsfw_concept = self.safety_checker(images=image, clip_input=safety_cheker_input.pixel_values)
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if output_type == "pil":
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image = self.numpy_to_pil(image)
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return {"sample": image, "nsfw_content_detected": has_nsfw_concept}
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