Update custome_pipeline.py

custome_pipeline.py

@@ -1,168 +1 @@
-import torch
-import numpy as np
-from diffusers import FluxPipeline, FlowMatchEulerDiscreteScheduler
-from typing import Any, Dict, List, Optional, Union
-from PIL import Image
-
-# Constants for shift calculation
-BASE_SEQ_LEN = 256
-MAX_SEQ_LEN = 4096
-BASE_SHIFT = 0.5
-MAX_SHIFT = 1.2
-
-# Helper functions
-def calculate_timestep_shift(image_seq_len: int) -> float:
-    """Calculates the timestep shift (mu) based on the image sequence length."""
-    m = (MAX_SHIFT - BASE_SHIFT) / (MAX_SEQ_LEN - BASE_SEQ_LEN)
-    b = BASE_SHIFT - m * BASE_SEQ_LEN
-    mu = image_seq_len * m + b
-    return mu
-
-def prepare_timesteps(
-    scheduler: FlowMatchEulerDiscreteScheduler,
-    num_inference_steps: Optional[int] = None,
-    device: Optional[Union[str, torch.device]] = None,
-    timesteps: Optional[List[int]] = None,
-    sigmas: Optional[List[float]] = None,
-    mu: Optional[float] = None,
-) -> (torch.Tensor, int):
-    """Prepares the timesteps for the diffusion process."""
-    if timesteps is not None and sigmas is not None:
-        raise ValueError("Only one of `timesteps` or `sigmas` can be passed.")
-
-    if timesteps is not None:
-        scheduler.set_timesteps(timesteps=timesteps, device=device)
-    elif sigmas is not None:
-        scheduler.set_timesteps(sigmas=sigmas, device=device)
-    else:
-        scheduler.set_timesteps(num_inference_steps, device=device, mu=mu)
-
-    timesteps = scheduler.timesteps
-    num_inference_steps = len(timesteps)
-    return timesteps, num_inference_steps
-
-# FLUX pipeline function
-class FluxWithCFGPipeline(FluxPipeline):
-    """
-    Extends the FluxPipeline to yield intermediate images during the denoising process 
-    with progressively increasing resolution for faster generation.
-    """
-    @torch.inference_mode()
-    def generate_images(
-        self,
-        prompt: Union[str, List[str]] = None,
-        prompt_2: Optional[Union[str, List[str]]] = None,
-        height: Optional[int] = None,
-        width: Optional[int] = None,
-        num_inference_steps: int = 4,
-        timesteps: List[int] = None,
-        guidance_scale: float = 3.5,
-        num_images_per_prompt: Optional[int] = 1,
-        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
-        latents: Optional[torch.FloatTensor] = None,
-        prompt_embeds: Optional[torch.FloatTensor] = None,
-        pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        joint_attention_kwargs: Optional[Dict[str, Any]] = None,
-        max_sequence_length: int = 300,
-    ):
-        """Generates images and yields intermediate results during the denoising process."""
-        height = height or self.default_sample_size * self.vae_scale_factor
-        width = width or self.default_sample_size * self.vae_scale_factor
-
-        # 1. Check inputs
-        self.check_inputs(
-            prompt,
-            prompt_2,
-            height,
-            width,
-            prompt_embeds=prompt_embeds,
-            pooled_prompt_embeds=pooled_prompt_embeds,
-            max_sequence_length=max_sequence_length,
-        )
-
-        self._guidance_scale = guidance_scale
-        self._joint_attention_kwargs = joint_attention_kwargs
-        self._interrupt = False
-
-        # 2. Define call parameters
-        batch_size = 1 if isinstance(prompt, str) else len(prompt)
-        device = self._execution_device
-
-        # 3. Encode prompt
-        lora_scale = joint_attention_kwargs.get("scale", None) if joint_attention_kwargs is not None else None
-        prompt_embeds, pooled_prompt_embeds, text_ids = self.encode_prompt(
-            prompt=prompt,
-            prompt_2=prompt_2,
-            prompt_embeds=prompt_embeds,
-            pooled_prompt_embeds=pooled_prompt_embeds,
-            device=device,
-            num_images_per_prompt=num_images_per_prompt,
-            max_sequence_length=max_sequence_length,
-            lora_scale=lora_scale,
-        )
-        # 4. Prepare latent variables
-        num_channels_latents = self.transformer.config.in_channels // 4
-        latents, latent_image_ids = self.prepare_latents(
-            batch_size * num_images_per_prompt,
-            num_channels_latents,
-            height,
-            width,
-            prompt_embeds.dtype,
-            device,
-            generator,
-            latents,
-        )
-        # 5. Prepare timesteps
-        sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps)
-        image_seq_len = latents.shape[1]
-        mu = calculate_timestep_shift(image_seq_len)
-        timesteps, num_inference_steps = prepare_timesteps(
-            self.scheduler,
-            num_inference_steps,
-            device,
-            timesteps,
-            sigmas,
-            mu=mu,
-        )
-        self._num_timesteps = len(timesteps)
-
-        # Handle guidance
-        guidance = torch.full([1], guidance_scale, device=device, dtype=torch.float16).expand(latents.shape[0]) if self.transformer.config.guidance_embeds else None
-
-        # 6. Denoising loop
-        for i, t in enumerate(timesteps):
-            if self.interrupt:
-                continue
-
-            timestep = t.expand(latents.shape[0]).to(latents.dtype)
-
-            noise_pred = self.transformer(
-                hidden_states=latents,
-                timestep=timestep / 1000,
-                guidance=guidance,
-                pooled_projections=pooled_prompt_embeds,
-                encoder_hidden_states=prompt_embeds,
-                txt_ids=text_ids,
-                img_ids=latent_image_ids,
-                joint_attention_kwargs=self.joint_attention_kwargs,
-                return_dict=False,
-            )[0]
-
-             # Yield intermediate result
-            latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
-            torch.cuda.empty_cache()
-
-        # Final image
-        return self._decode_latents_to_image(latents, height, width, output_type)
-        self.maybe_free_model_hooks()
-        torch.cuda.empty_cache()
-
-    def _decode_latents_to_image(self, latents, height, width, output_type, vae=None):
-        """Decodes the given latents into an image."""
-        vae = vae or self.vae
-        latents = self._unpack_latents(latents, height, width, self.vae_scale_factor)
-        latents = (latents / vae.config.scaling_factor) + vae.config.shift_factor
-        image = vae.decode(latents, return_dict=False)[0]
-        return self.image_processor.postprocess(image, output_type=output_type)[0]
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