from functools import partial from typing import Callable, List, Optional, Union, Tuple import torch from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer from diffusers.models import AutoencoderKL, UNet2DConditionModel # from diffusers import StableDiffusionPipeline from diffusers.pipelines.stable_diffusion.safety_checker import \ StableDiffusionSafetyChecker from diffusers.schedulers import DDIMScheduler,PNDMScheduler, LMSDiscreteScheduler from modified_stable_diffusion import ModifiedStableDiffusionPipeline from torchvision.transforms import ToPILImage import matplotlib.pyplot as plt ### credit to: https://github.com/cccntu/efficient-prompt-to-prompt def backward_ddim(x_t, alpha_t, alpha_tm1, eps_xt): """ from noise to image""" return ( alpha_tm1**0.5 * ( (alpha_t**-0.5 - alpha_tm1**-0.5) * x_t + ((1 / alpha_tm1 - 1) ** 0.5 - (1 / alpha_t - 1) ** 0.5) * eps_xt ) + x_t ) def forward_ddim(x_t, alpha_t, alpha_tp1, eps_xt): """ from image to noise, it's the same as backward_ddim""" return backward_ddim(x_t, alpha_t, alpha_tp1, eps_xt) class InversableStableDiffusionPipeline(ModifiedStableDiffusionPipeline): def __init__(self, vae, text_encoder, tokenizer, unet, scheduler, safety_checker, feature_extractor, requires_safety_checker: bool = False, ): super(InversableStableDiffusionPipeline, self).__init__(vae, text_encoder, tokenizer, unet, scheduler, safety_checker, feature_extractor, requires_safety_checker) self.forward_diffusion = partial(self.backward_diffusion, reverse_process=True) self.count = 0 def get_random_latents(self, latents=None, height=512, width=512, generator=None): height = height or self.unet.config.sample_size * self.vae_scale_factor width = width or self.unet.config.sample_size * self.vae_scale_factor batch_size = 1 device = self._execution_device num_channels_latents = self.unet.in_channels latents = self.prepare_latents( batch_size, num_channels_latents, height, width, self.text_encoder.dtype, device, generator, latents, ) return latents @torch.inference_mode() def get_text_embedding(self, prompt): text_input_ids = self.tokenizer( prompt, padding="max_length", truncation=True, max_length=self.tokenizer.model_max_length, return_tensors="pt", ).input_ids text_embeddings = self.text_encoder(text_input_ids.to(self.device))[0] return text_embeddings @torch.inference_mode() def get_image_latents(self, image, sample=True, rng_generator=None): encoding_dist = self.vae.encode(image).latent_dist if sample: encoding = encoding_dist.sample(generator=rng_generator) else: encoding = encoding_dist.mode() latents = encoding * 0.18215 return latents @torch.inference_mode() def backward_diffusion( self, use_old_emb_i=25, text_embeddings=None, old_text_embeddings=None, new_text_embeddings=None, latents: Optional[torch.FloatTensor] = None, num_inference_steps: int = 50, guidance_scale: float = 7.5, callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, callback_steps: Optional[int] = 1, reverse_process: True = False, **kwargs, ): """ Generate image from text prompt and latents """ # 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 # set timesteps self.scheduler.set_timesteps(num_inference_steps) # Some schedulers like PNDM have timesteps as arrays # It's more optimized to move all timesteps to correct device beforehand timesteps_tensor = self.scheduler.timesteps.to(self.device) # scale the initial noise by the standard deviation required by the scheduler latents = latents * self.scheduler.init_noise_sigma if old_text_embeddings is not None and new_text_embeddings is not None: prompt_to_prompt = True else: prompt_to_prompt = False for i, t in enumerate(self.progress_bar(timesteps_tensor if not reverse_process else reversed(timesteps_tensor))): if prompt_to_prompt: if i < use_old_emb_i: text_embeddings = old_text_embeddings else: text_embeddings = new_text_embeddings # expand the latents if we are doing classifier free guidance latent_model_input = ( torch.cat([latents] * 2) if do_classifier_free_guidance else latents ) latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) # 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 ) prev_timestep = ( t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps ) # call the callback, if provided if callback is not None and i % callback_steps == 0: callback(i, t, latents) # ddim alpha_prod_t = self.scheduler.alphas_cumprod[t] alpha_prod_t_prev = ( self.scheduler.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.scheduler.final_alpha_cumprod ) if reverse_process: alpha_prod_t, alpha_prod_t_prev = alpha_prod_t_prev, alpha_prod_t latents = backward_ddim( x_t=latents, alpha_t=alpha_prod_t, alpha_tm1=alpha_prod_t_prev, eps_xt=noise_pred, ) return latents @torch.inference_mode() def decode_image(self, latents: torch.FloatTensor, **kwargs): scaled_latents = 1 / 0.18215 * latents image = [ self.vae.decode(scaled_latents[i : i + 1]).sample for i in range(len(latents)) ] image = torch.cat(image, dim=0) return image @torch.inference_mode() def torch_to_numpy(self, image): image = (image / 2 + 0.5).clamp(0, 1) image = image.cpu().permute(0, 2, 3, 1).numpy() return image