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