from typing import Optional import torch from PIL import Image from tqdm.auto import tqdm from transformers import CLIPTextModel, CLIPTokenizer from diffusers import AutoencoderKL, DDIMScheduler, DiffusionPipeline, UNet2DConditionModel from diffusers.image_processor import VaeImageProcessor from diffusers.utils import ( deprecate, ) class EDICTPipeline(DiffusionPipeline): def __init__( self, vae: AutoencoderKL, text_encoder: CLIPTextModel, tokenizer: CLIPTokenizer, unet: UNet2DConditionModel, scheduler: DDIMScheduler, mixing_coeff: float = 0.93, leapfrog_steps: bool = True, ): self.mixing_coeff = mixing_coeff self.leapfrog_steps = leapfrog_steps super().__init__() self.register_modules( vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, scheduler=scheduler, ) self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) def _encode_prompt( self, prompt: str, negative_prompt: Optional[str] = None, do_classifier_free_guidance: bool = False ): text_inputs = self.tokenizer( prompt, padding="max_length", max_length=self.tokenizer.model_max_length, truncation=True, return_tensors="pt", ) prompt_embeds = self.text_encoder(text_inputs.input_ids.to(self.device)).last_hidden_state prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=self.device) if do_classifier_free_guidance: uncond_tokens = "" if negative_prompt is None else negative_prompt uncond_input = self.tokenizer( uncond_tokens, padding="max_length", max_length=self.tokenizer.model_max_length, truncation=True, return_tensors="pt", ) negative_prompt_embeds = self.text_encoder(uncond_input.input_ids.to(self.device)).last_hidden_state prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) return prompt_embeds def denoise_mixing_layer(self, x: torch.Tensor, y: torch.Tensor): x = self.mixing_coeff * x + (1 - self.mixing_coeff) * y y = self.mixing_coeff * y + (1 - self.mixing_coeff) * x return [x, y] def noise_mixing_layer(self, x: torch.Tensor, y: torch.Tensor): y = (y - (1 - self.mixing_coeff) * x) / self.mixing_coeff x = (x - (1 - self.mixing_coeff) * y) / self.mixing_coeff return [x, y] def _get_alpha_and_beta(self, t: torch.Tensor): # as self.alphas_cumprod is always in cpu t = int(t) alpha_prod = self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod return alpha_prod, 1 - alpha_prod def noise_step( self, base: torch.Tensor, model_input: torch.Tensor, model_output: torch.Tensor, timestep: torch.Tensor, ): prev_timestep = timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps alpha_prod_t, beta_prod_t = self._get_alpha_and_beta(timestep) alpha_prod_t_prev, beta_prod_t_prev = self._get_alpha_and_beta(prev_timestep) a_t = (alpha_prod_t_prev / alpha_prod_t) ** 0.5 b_t = -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5 next_model_input = (base - b_t * model_output) / a_t return model_input, next_model_input.to(base.dtype) def denoise_step( self, base: torch.Tensor, model_input: torch.Tensor, model_output: torch.Tensor, timestep: torch.Tensor, ): prev_timestep = timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps alpha_prod_t, beta_prod_t = self._get_alpha_and_beta(timestep) alpha_prod_t_prev, beta_prod_t_prev = self._get_alpha_and_beta(prev_timestep) a_t = (alpha_prod_t_prev / alpha_prod_t) ** 0.5 b_t = -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5 next_model_input = a_t * base + b_t * model_output return model_input, next_model_input.to(base.dtype) @torch.no_grad() def decode_latents(self, latents: torch.Tensor): latents = 1 / self.vae.config.scaling_factor * latents image = self.vae.decode(latents).sample image = (image / 2 + 0.5).clamp(0, 1) return image @torch.no_grad() def prepare_latents( self, image: Image.Image, text_embeds: torch.Tensor, timesteps: torch.Tensor, guidance_scale: float, generator: Optional[torch.Generator] = None, ): do_classifier_free_guidance = guidance_scale > 1.0 image = image.to(device=self.device, dtype=text_embeds.dtype) latent = self.vae.encode(image).latent_dist.sample(generator) latent = self.vae.config.scaling_factor * latent coupled_latents = [latent.clone(), latent.clone()] for i, t in tqdm(enumerate(timesteps), total=len(timesteps)): coupled_latents = self.noise_mixing_layer(x=coupled_latents[0], y=coupled_latents[1]) # j - model_input index, k - base index for j in range(2): k = j ^ 1 if self.leapfrog_steps: if i % 2 == 0: k, j = j, k model_input = coupled_latents[j] base = coupled_latents[k] latent_model_input = torch.cat([model_input] * 2) if do_classifier_free_guidance else model_input noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeds).sample 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) base, model_input = self.noise_step( base=base, model_input=model_input, model_output=noise_pred, timestep=t, ) coupled_latents[k] = model_input return coupled_latents @torch.no_grad() def __call__( self, base_prompt: str, target_prompt: str, image: Image.Image, guidance_scale: float = 3.0, num_inference_steps: int = 50, strength: float = 0.8, negative_prompt: Optional[str] = None, generator: Optional[torch.Generator] = None, output_type: Optional[str] = "pil", ): do_classifier_free_guidance = guidance_scale > 1.0 image = self.image_processor.preprocess(image) base_embeds = self._encode_prompt(base_prompt, negative_prompt, do_classifier_free_guidance) target_embeds = self._encode_prompt(target_prompt, negative_prompt, do_classifier_free_guidance) self.scheduler.set_timesteps(num_inference_steps, self.device) t_limit = num_inference_steps - int(num_inference_steps * strength) fwd_timesteps = self.scheduler.timesteps[t_limit:] bwd_timesteps = fwd_timesteps.flip(0) coupled_latents = self.prepare_latents(image, base_embeds, bwd_timesteps, guidance_scale, generator) for i, t in tqdm(enumerate(fwd_timesteps), total=len(fwd_timesteps)): # j - model_input index, k - base index for k in range(2): j = k ^ 1 if self.leapfrog_steps: if i % 2 == 1: k, j = j, k model_input = coupled_latents[j] base = coupled_latents[k] latent_model_input = torch.cat([model_input] * 2) if do_classifier_free_guidance else model_input noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=target_embeds).sample 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) base, model_input = self.denoise_step( base=base, model_input=model_input, model_output=noise_pred, timestep=t, ) coupled_latents[k] = model_input coupled_latents = self.denoise_mixing_layer(x=coupled_latents[0], y=coupled_latents[1]) # either one is fine final_latent = coupled_latents[0] if output_type not in ["latent", "pt", "np", "pil"]: deprecation_message = ( f"the output_type {output_type} is outdated. Please make sure to set it to one of these instead: " "`pil`, `np`, `pt`, `latent`" ) deprecate("Unsupported output_type", "1.0.0", deprecation_message, standard_warn=False) output_type = "np" if output_type == "latent": image = final_latent else: image = self.decode_latents(final_latent) image = self.image_processor.postprocess(image, output_type=output_type) return image