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462b4edd31
* EDICT pipeline initial commit - Starting point taking from https://github.com/Joqsan/edict-diffusion * refactor __init__() method * minor refactoring * refactor scheduler code - remove scheduler and move its methods to the EDICTPipeline class * make CFG optional - refactor encode_prompt(). - include optional generator for sampling with vae. - minor variable renaming * add EDICT pipeline description to README.md * replace preprocess() with VaeImageProcessor * run make style and make quality commands --------- Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
265 lines
9.3 KiB
Python
265 lines
9.3 KiB
Python
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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# as self.alphas_cumprod is always in cpu
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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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# j - model_input index, k - base index
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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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# j - model_input index, k - base index
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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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# either one is fine
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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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