text stringlengths 1 93.6k |
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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(torch_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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has_nsfw_concept = []
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for hni in range(0, len(image)):
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has_nsfw_concept.append(0)
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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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class StableDiffusionImg2ImgPipeline(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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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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# encode the init image into latents and scale the latents
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init_latents = self.vae.encode(init_image.to(self.device)).sample()
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init_latents = 0.18215 * init_latents
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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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