pipeline.py

import random
from typing import Callable, Dict, List, Optional

import torch
from tqdm import tqdm
from diffusers import DiffusionPipeline
from diffusers.configuration_utils import ConfigMixin

def get_scaled_coeffs():
    beta_min = 0.85
    beta_max = 12.0
    return beta_min**0.5, beta_max**0.5-beta_min**0.5
def beta(t):
    a, b = get_scaled_coeffs()
    return (a+t*b)**2
def int_beta(t):
  a, b = get_scaled_coeffs()
  return ((a+b*t)**3-a**3)/(3*b)
def sigma(t):
  return torch.expm1(int_beta(t))**0.5
def sigma_orig(t):
  return (-torch.expm1(-int_beta(t)))**0.5

class SuperDiffSDXLPipeline(DiffusionPipeline, ConfigMixin):
    """SuperDiffSDXLPipeline."""

    def __init__(self, unet: Callable, vae: Callable, text_encoder: Callable, text_encoder_2: Callable, tokenizer: Callable, tokenizer_2: Callable) -> None:

        """__init__.

        Parameters
        ----------
        model : Callable
            model
        vae : Callable
            vae
        text_encoder : Callable
            text_encoder
        scheduler : Callable
            scheduler
        tokenizer : Callable
            tokenizer
        kwargs :
            kwargs

        Returns
        -------
        None

        """
        super().__init__()
        device = "cuda" if torch.cuda.is_available() else "cpu"

        vae.to(device)
        unet.to(device)
        text_encoder.to(device)
        text_encoder_2.to(device)


        self.register_modules(unet=unet,
                              vae=vae,
                              text_encoder=text_encoder,
                              text_encoder_2=text_encoder_2,
                              tokenizer=tokenizer,
                              tokenizer_2=tokenizer_2,
                              )
        
    def prepare_prompt_input(self, prompt_o, prompt_b, batch_size, height, width):
        text_input = self.tokenizer(prompt_o* batch_size, padding="max_length", max_length=self.tokenizer.model_max_length, truncation=True, return_tensors="pt")
        text_input_2 = self.tokenizer_2(prompt_o* batch_size, padding="max_length", max_length=self.tokenizer_2.model_max_length, truncation=True, return_tensors="pt")
        with torch.no_grad():
            text_embeddings = self.text_encoder(text_input.input_ids.to(self.device), output_hidden_states=True)
            text_embeddings_2 = self.text_encoder_2(text_input_2.input_ids.to(self.device), output_hidden_states=True)
        prompt_embeds_o = torch.concat((text_embeddings.hidden_states[-2], text_embeddings_2.hidden_states[-2]), dim=-1)
        pooled_prompt_embeds_o = text_embeddings_2[0]
        negative_prompt_embeds = torch.zeros_like(prompt_embeds_o)
        negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds_o)

        text_input = self.tokenizer(prompt_b* batch_size, padding="max_length", max_length=self.tokenizer.model_max_length, truncation=True, return_tensors="pt")
        text_input_2 = self.tokenizer_2(prompt_b* batch_size, padding="max_length", max_length=self.tokenizer_2.model_max_length, truncation=True, return_tensors="pt")
        with torch.no_grad():
            text_embeddings = self.text_encoder(text_input.input_ids.to(self.device), output_hidden_states=True)
            text_embeddings_2 = self.text_encoder_2(text_input_2.input_ids.to(self.device), output_hidden_states=True)
        prompt_embeds_b = torch.concat((text_embeddings.hidden_states[-2], text_embeddings_2.hidden_states[-2]), dim=-1)
        pooled_prompt_embeds_b = text_embeddings_2[0]
        add_time_ids_o = torch.tensor([(height,width,0,0,height,width)])
        add_time_ids_b = torch.tensor([(height,width,0,0,height,width)])
        negative_add_time_ids = torch.tensor([(height,width,0,0,height,width)])
        prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds_o, prompt_embeds_b], dim=0)
        add_text_embeds = torch.cat([negative_pooled_prompt_embeds, pooled_prompt_embeds_o, pooled_prompt_embeds_b], dim=0)
        add_time_ids = torch.cat([negative_add_time_ids, add_time_ids_o, add_time_ids_b], dim=0)

        prompt_embeds = prompt_embeds.to(self.device)
        add_text_embeds = add_text_embeds.to(self.device)
        add_time_ids = add_time_ids.to(self.device).repeat(batch_size, 1)
        added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
        return prompt_embeds, added_cond_kwargs
    @torch.no_grad
    def get_batch(self, latents: Callable, nrow: int, ncol: int) -> Callable:
        """get_batch.

        Parameters
        ----------
        latents : Callable
            latents
        nrow : int
            nrow
        ncol : int
            ncol

        Returns
        -------
        Callable

        """
        image = self.vae.decode(
            latents / self.vae.config.scaling_factor, return_dict=False
        )[0]
        image = (image / 2 + 0.5).clamp(0, 1).squeeze()
        if len(image.shape) < 4:
            image = image.unsqueeze(0)
        image = (image.permute(0, 2, 3, 1) * 255).to(torch.uint8)
        return image

    @torch.no_grad
    def get_text_embedding(self, prompt: str) -> Callable:
        """get_text_embedding.

        Parameters
        ----------
        prompt : str
            prompt

        Returns
        -------
        Callable

        """
        text_input = self.tokenizer(
            prompt,
            padding="max_length",
            max_length=self.tokenizer.model_max_length,
            truncation=True,
            return_tensors="pt",
        )
        return self.text_encoder(text_input.input_ids.to(self.device))[0]

    @torch.no_grad
    def get_vel(self, t: float, sigma: float, latents: Callable, embeddings: Callable):
        """get_vel.

        Parameters
        ----------
        t : float
            t
        sigma : float
            sigma
        latents : Callable
            latents
        embeddings : Callable
            embeddings
        """
        def v(_x, _e): return self.model(
            _x / ((sigma**2 + 1) ** 0.5), t, encoder_hidden_states=_e
        ).sample
        embeds = torch.cat(embeddings)
        latent_input = latents
        vel = v(latent_input, embeds)
        return vel

    def preprocess(
        self,
        prompt_1: str,
        prompt_2: str,
        seed: int = None,
        num_inference_steps: int = 1000,
        batch_size: int = 1,
        lift: int = 0.0,
        height: int = 512,
        width: int = 512,
        guidance_scale: int = 7.5,
    ) -> Callable:
        """preprocess.

        Parameters
        ----------
        prompt_1 : str
            prompt_1
        prompt_2 : str
            prompt_2
        seed : int
            seed
        num_inference_steps : int
            num_inference_steps
        batch_size : int
            batch_size
        lift : int
            lift
        height : int
            height
        width : int
            width
        guidance_scale : int
            guidance_scale

        Returns
        -------
        Callable

        """
        # Tokenize the input
        self.batch_size = batch_size
        self.num_inference_steps = num_inference_steps
        self.guidance_scale = guidance_scale
        self.lift = lift
        self.seed = seed
        if self.seed is None:
            self.seed = random.randint(0, 2**32 - 1)

        #obj_prompt = [prompt_1]
        #bg_prompt = [prompt_2]
        #obj_embeddings = self.get_text_embedding(obj_prompt * batch_size)
        #bg_embeddings = self.get_text_embedding(bg_prompt * batch_size)

        #uncond_embeddings = self.get_text_embedding([""] * batch_size)

        generator = torch.cuda.manual_seed(
            self.seed
        )  # Seed generator to create the initial latent noise
        latents = torch.randn((batch_size, self.unet.in_channels, height // 8, width // 8), generator=generator, dtype=self.dtype, device=self.device,)
        prompt_embeds, added_cond_kwargs = self.prepare_prompt_input(prompt_1, prompt_2, batch_size, height, width)
        #latents = torch.randn(
        #    (batch_size, self.model.config.in_channels, height // 8, width // 8),
        #    generator=generator,
        #    device=self.device,
        #)

        #latents_og = latents.clone().detach()
        #latents_uncond_og = latents.clone().detach()

        #self.scheduler.set_timesteps(num_inference_steps)
        #latents = latents * self.scheduler.init_noise_sigma

        #latents_uncond = latents.clone().detach()
        return {
            "latents": latents,
            "prompt_embeds": prompt_embeds,
            "added_cond_kwargs": added_cond_kwargs,
        }

    def _forward(self, model_inputs: Dict) -> Callable:
        """_forward.

        Parameters
        ----------
        model_inputs : Dict
            model_inputs

        Returns
        -------
        Callable

        """
        latents = model_inputs["latents"]
        prompt_embeds = model_inputs["prompt_embeds"]
        added_cond_kwargs = model_inputs["added_cond_kwargs"]

        t = torch.tensor(1.0)
        dt = 1.0/self.num_inference_steps
        train_number_steps = 1000
        latents = latents * (sigma(t)**2+1)**0.5
        with torch.no_grad():
            for i in tqdm(range(self.num_inference_steps)):
                latent_model_input = torch.cat([latents] * 3)
                sigma_t = sigma(t)
                dsigma = sigma(t-dt) - sigma_t
                latent_model_input /= (sigma_t**2+1)**0.5
                with torch.no_grad():
                    noise_pred = self.unet(latent_model_input, t*train_number_steps, encoder_hidden_states=prompt_embeds, added_cond_kwargs=added_cond_kwargs, return_dict=False)[0]

                noise_pred_uncond, noise_pred_text_o, noise_pred_text_b = noise_pred.chunk(3)

                noise = torch.sqrt(2*torch.abs(dsigma)*sigma_t)*torch.randn_like(latents)


                dx_ind = 2*dsigma*(noise_pred_uncond + self.guidance_scale*(noise_pred_text_b - noise_pred_uncond)) + noise
                kappa = (torch.abs(dsigma)*(noise_pred_text_b-noise_pred_text_o)*(noise_pred_text_b+noise_pred_text_o)).sum((1,2,3))-(dx_ind*((noise_pred_text_o-noise_pred_text_b))).sum((1,2,3))
                kappa /= 2*dsigma*self.guidance_scale*((noise_pred_text_o-noise_pred_text_b)**2).sum((1,2,3))
                noise_pred = noise_pred_uncond + self.guidance_scale*((noise_pred_text_b - noise_pred_uncond) + kappa[:,None,None,None]*(noise_pred_text_o-noise_pred_text_b))

                latents +=  2*dsigma * noise_pred + noise
                t -= dt
            return latents

    def postprocess(self, latents: Callable) -> Callable:
        """postprocess.

        Parameters
        ----------
        latents : Callable
            latents

        Returns
        -------
        Callable

        """
        latents = latents/self.vae.config.scaling_factor
        latents = latents.to(torch.float32)
        with torch.no_grad():
            image = self.vae.decode(latents, return_dict=False)[0]

        image = (image / 2 + 0.5).clamp(0, 1)
        image = image.detach().cpu().permute(0, 2, 3, 1).numpy()
        images = (image * 255).round().astype("uint8")
        return images

    def __call__(
        self,
        prompt_1: str,
        prompt_2: str,
        seed: int = None,
        num_inference_steps: int = 1000,
        batch_size: int = 1,
        lift: int = 0.0,
        height: int = 1024,
        width: int = 1024,
        guidance_scale: int = 7.5,
    ) -> Callable:
        """__call__.

        Parameters
        ----------
        prompt_1 : str
            prompt_1
        prompt_2 : str
            prompt_2
        seed : int
            seed
        num_inference_steps : int
            num_inference_steps
        batch_size : int
            batch_size
        lift : int
            lift
        height : int
            height
        width : int
            width
        guidance_scale : int
            guidance_scale

        Returns
        -------
        Callable

        """
        # Preprocess inputs
        model_inputs = self.preprocess(
            prompt_1,
            prompt_2,
            seed,
            num_inference_steps,
            batch_size,
            lift,
            height,
            width,
            guidance_scale,
        )

        # Forward pass through the pipeline
        latents = self._forward(model_inputs)

        # Postprocess to generate the final output
        images = self.postprocess(latents)
        return images