Upload custom_pipeline.py

custom_pipeline.py

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+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]