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| """ | |
| modified based on diffusion library from Huggingface: https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py | |
| """ | |
| import inspect | |
| import warnings | |
| from typing import List, Optional, Union | |
| import torch | |
| from tqdm.auto import tqdm | |
| from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer | |
| from diffusers.models import AutoencoderKL, UNet2DConditionModel | |
| from diffusers.pipeline_utils import DiffusionPipeline | |
| from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler | |
| from safety_checker import StableDiffusionSafetyChecker | |
| class ComposableStableDiffusionPipeline(DiffusionPipeline): | |
| def __init__( | |
| self, | |
| vae: AutoencoderKL, | |
| text_encoder: CLIPTextModel, | |
| tokenizer: CLIPTokenizer, | |
| unet: UNet2DConditionModel, | |
| scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler], | |
| 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, | |
| ) | |
| def __call__( | |
| self, | |
| prompt: Union[str, List[str]], | |
| height: Optional[int] = 512, | |
| width: Optional[int] = 512, | |
| 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", | |
| **kwargs, | |
| ): | |
| if "torch_device" in kwargs: | |
| device = kwargs.pop("torch_device") | |
| warnings.warn( | |
| "`torch_device` is deprecated as an input argument to `__call__` and will be removed in v0.3.0." | |
| " Consider using `pipe.to(torch_device)` instead." | |
| ) | |
| # Set device as before (to be removed in 0.3.0) | |
| if device is None: | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| self.to(device) | |
| 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 height % 8 != 0 or width % 8 != 0: | |
| raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") | |
| if '|' in prompt: | |
| prompt = [x.strip() for x in prompt.split('|')] | |
| print(prompt) | |
| # 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]) | |
| # get the intial random noise | |
| latents = torch.randn( | |
| (batch_size, self.unet.in_channels, height // 8, width // 8), | |
| generator=generator, | |
| device=self.device, | |
| ) | |
| # set timesteps | |
| accepts_offset = "offset" in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys()) | |
| extra_set_kwargs = {} | |
| if accepts_offset: | |
| extra_set_kwargs["offset"] = 1 | |
| self.scheduler.set_timesteps(num_inference_steps, **extra_set_kwargs) | |
| # if we use LMSDiscreteScheduler, let's make sure latents are mulitplied by sigmas | |
| if isinstance(self.scheduler, LMSDiscreteScheduler): | |
| latents = latents * self.scheduler.sigmas[0] | |
| # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature | |
| # eta (Ξ·) is only used with the DDIMScheduler, it will be ignored for other schedulers. | |
| # eta corresponds to Ξ· 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 | |
| for i, t in tqdm(enumerate(self.scheduler.timesteps)): | |
| # expand the latents if we are doing classifier free guidance | |
| latent_model_input = torch.cat([latents] * text_embeddings.shape[0]) if do_classifier_free_guidance else latents | |
| if isinstance(self.scheduler, LMSDiscreteScheduler): | |
| sigma = self.scheduler.sigmas[i] | |
| latent_model_input = latent_model_input / ((sigma**2 + 1) ** 0.5) | |
| # 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: | |
| pred_decomp = noise_pred.chunk(text_embeddings.shape[0]) | |
| noise_pred_uncond, noise_pred_text = pred_decomp[0], torch.cat(pred_decomp[1:], dim=0).mean(dim=0, keepdim=True) | |
| noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) | |
| # compute the previous noisy sample x_t -> x_t-1 | |
| if isinstance(self.scheduler, LMSDiscreteScheduler): | |
| latents = self.scheduler.step(noise_pred, i, latents, **extra_step_kwargs)["prev_sample"] | |
| else: | |
| latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs)["prev_sample"] | |
| # 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} |