Update custom_pipeline.py

custom_pipeline.py

@@ -1,16 +1,8 @@
-import numpy as np
 import torch
-from diffusers.pipelines.flux.pipeline_output import FluxPipeline, FluxPipelineOutput
-from typing import List, Union, Optional, Dict, Any, Callable
-from diffusers.pipelines.flux.pipeline_flux import calculate_shift, retrieve_timesteps
-from diffusers.utils import is_torch_xla_available
-
-if is_torch_xla_available():
-    import torch_xla.core.xla_model as xm
-
-    XLA_AVAILABLE = True
-else:
-    XLA_AVAILABLE = False
+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
@@ -27,7 +19,7 @@ def calculate_timestep_shift(image_seq_len: int) -> float:
     return mu
 
 def prepare_timesteps(
-    scheduler,
+    scheduler: FlowMatchEulerDiscreteScheduler,
     num_inference_steps: Optional[int] = None,
     device: Optional[Union[str, torch.device]] = None,
     timesteps: Optional[List[int]] = None,
@@ -49,23 +41,24 @@ def prepare_timesteps(
     num_inference_steps = len(timesteps)
     return timesteps, num_inference_steps
 
-# FLUX pipeline with CFG and intermediate outputs
+# FLUX pipeline function
 class FluxWithCFGPipeline(FluxPipeline):
     """
-    Flux pipeline with Classifier-Free Guidance and the ability to yield 
-    intermediate images during the denoising process with progressively 
-    increasing resolution for faster generation.
+    Extends the FluxPipeline to yield intermediate images during the denoising process 
+    with progressively increasing resolution for faster generation.
     """
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.default_sample_size = 512  # Default sample size from the first pipeline
+
     @torch.inference_mode()
-    def __call__(
+    def generate_images(
         self,
         prompt: Union[str, List[str]] = None,
         prompt_2: Optional[Union[str, List[str]]] = None,
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        negative_prompt_2: Optional[Union[str, List[str]]] = None,
         height: Optional[int] = None,
         width: Optional[int] = None,
-        num_inference_steps: int = 28,
+        num_inference_steps: int = 4,
         timesteps: List[int] = None,
         guidance_scale: float = 3.5,
         num_images_per_prompt: Optional[int] = 1,
@@ -73,21 +66,16 @@ class FluxWithCFGPipeline(FluxPipeline):
         latents: Optional[torch.FloatTensor] = None,
         prompt_embeds: Optional[torch.FloatTensor] = None,
         pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
-        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
-        negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
         output_type: Optional[str] = "pil",
         return_dict: bool = True,
         joint_attention_kwargs: Optional[Dict[str, Any]] = None,
-        callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
-        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
-        max_sequence_length: int = 512,
-        yield_intermediates: bool = False,  # New parameter for yielding intermediates
+        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. Raise error if not correct
+        # 1. Check inputs
         self.check_inputs(
             prompt,
             prompt_2,
@@ -95,7 +83,6 @@ class FluxWithCFGPipeline(FluxPipeline):
             width,
             prompt_embeds=prompt_embeds,
             pooled_prompt_embeds=pooled_prompt_embeds,
-            callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
             max_sequence_length=max_sequence_length,
         )
 
@@ -104,23 +91,12 @@ class FluxWithCFGPipeline(FluxPipeline):
         self._interrupt = False
 
         # 2. Define call parameters
-        if prompt is not None and isinstance(prompt, str):
-            batch_size = 1
-        elif prompt is not None and isinstance(prompt, list):
-            batch_size = len(prompt)
-        else:
-            batch_size = prompt_embeds.shape[0]
-
+        batch_size = 1 if isinstance(prompt, str) else len(prompt)
         device = self._execution_device
 
-        lora_scale = (
-            self.joint_attention_kwargs.get("scale", None) if self.joint_attention_kwargs is not None else None
-        )
-        (
-            prompt_embeds,
-            pooled_prompt_embeds,
-            text_ids,
-        ) = self.encode_prompt(
+        # 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,
@@ -130,20 +106,6 @@ class FluxWithCFGPipeline(FluxPipeline):
             max_sequence_length=max_sequence_length,
             lora_scale=lora_scale,
         )
-        (
-            negative_prompt_embeds,
-            negative_pooled_prompt_embeds,
-            negative_text_ids,
-        ) = self.encode_prompt(
-            prompt=negative_prompt,
-            prompt_2=negative_prompt_2,
-            prompt_embeds=negative_prompt_embeds,
-            pooled_prompt_embeds=negative_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
@@ -170,97 +132,41 @@ class FluxWithCFGPipeline(FluxPipeline):
             sigmas,
             mu=mu,
         )
-        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
         self._num_timesteps = len(timesteps)
 
-        # 6. Denoising loop
-        with self.progress_bar(total=num_inference_steps) as progress_bar:
-            for i, t in enumerate(timesteps):
-                if self.interrupt:
-                    continue
-
-                # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
-                timestep = t.expand(latents.shape[0]).to(latents.dtype)
-
-                # handle guidance
-                if self.transformer.config.guidance_embeds:
-                    guidance = torch.tensor([guidance_scale], device=device)
-                    guidance = guidance.expand(latents.shape[0])
-                else:
-                    guidance = None
+        # 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
 
-                noise_pred_text = 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]
-
-                noise_pred_uncond = self.transformer(
-                    hidden_states=latents,
-                    timestep=timestep / 1000,
-                    guidance=guidance,
-                    pooled_projections=negative_pooled_prompt_embeds,
-                    encoder_hidden_states=negative_prompt_embeds,
-                    txt_ids=negative_text_ids,
-                    img_ids=latent_image_ids,
-                    joint_attention_kwargs=self.joint_attention_kwargs,
-                    return_dict=False,
-                )[0]
-
-                noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
-
-                # compute the previous noisy sample x_t -> x_t-1
-                latents_dtype = latents.dtype
-                latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
-
-                if latents.dtype != latents_dtype:
-                    if torch.backends.mps.is_available():
-                        # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
-                        latents = latents.to(latents_dtype)
-
-                if callback_on_step_end is not None:
-                    callback_kwargs = {}
-                    for k in callback_on_step_end_tensor_inputs:
-                        callback_kwargs[k] = locals()[k]
-                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
-
-                    latents = callback_outputs.pop("latents", latents)
-                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
-
-                # call the callback, if provided
-                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
-                    progress_bar.update()
-
-                    # Yield intermediate images if requested
-                    if yield_intermediates:
-                        yield self._decode_latents_to_image(latents, height, width, output_type)
-
-                if XLA_AVAILABLE:
-                    xm.mark_step()
-
-        # Final image decoding 
-        if output_type == "latent":
-            image = latents
-        else:
-            image = self._decode_latents_to_image(latents, height, width, output_type)
-
-        # Offload all models
-        self.maybe_free_model_hooks()
-
-        if not return_dict:
-            return (image,)
-
-        return FluxPipelineOutput(images=image)
-
-    def _decode_latents_to_image(self, latents, height, width, output_type):
+        # 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)
+
+    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 / self.vae.config.scaling_factor) + self.vae.config.shift_factor
-        image = self.vae.decode(latents, return_dict=False)[0]
+        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]