Create pipeline.py

pipeline.py

@@ -0,0 +1,386 @@
+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"
+        print("decice", device)
+
+        vae.to(device)
+        unet.to(device)
+        text_encoder.to(device)
+        text_encoder_2.to(device)
+        #dtype = torch.float16
+        #vae = AutoencoderKL.from_pretrained(model_path, subfolder="vae").to(device)
+        #tokenizer = CLIPTokenizer.from_pretrained(model_path, subfolder="tokenizer")
+        #tokenizer_2 = CLIPTokenizer.from_pretrained(model_path, subfolder="tokenizer_2")
+        #text_encoder = CLIPTextModel.from_pretrained(model_path, subfolder="text_encoder").to(device, dtype=dtype)
+        #text_encoder_2 = CLIPTextModelWithProjection.from_pretrained(model_path, subfolder="text_encoder_2").to(device, dtype=dtype)
+        #unet = UNet2DConditionModel.from_pretrained(model_path, subfolder="unet").to(device, dtype=dtype)
+        #vae.eval()
+        #unet.eval()
+
+        self.register_modules(unet=unet,
+                              vae=vae,
+                              text_encoder=text_encoder,
+                              text_encoder_2=text_encoder_2,
+                              tokenizer=tokenizer,
+                              tokenizer_2=tokenizer_2,
+                              )
+        print("decice2", self.device)
+    def prepare_prompt_input(self, prompt_o, prompt_b, batch_size, height, width):
+        print("self.device", self.device)
+        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 = 512,
+        width: int = 512,
+        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