""" 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, ) @torch.no_grad() 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 (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 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}