Update custom_pipeline.py

custom_pipeline.py

@@ -3,6 +3,7 @@ import numpy as np
 from diffusers import FluxPipeline, FlowMatchEulerDiscreteScheduler
 from typing import Any, Dict, List, Optional, Union
 from PIL import Image
+from collections import OrderedDict
 
 # Constants for shift calculation
 BASE_SEQ_LEN = 256
@@ -47,6 +48,169 @@ class FluxWithCFGPipeline(FluxPipeline):
     Extends the FluxPipeline to yield intermediate images during the denoising process 
     with progressively increasing resolution for faster generation.
     """
+    def __init__(
+        self,
+        vae,
+        text_encoder,
+        text_encoder_2,
+        tokenizer,
+        tokenizer_2,
+        transformer,
+        scheduler: FlowMatchEulerDiscreteScheduler,
+    ):
+        super().__init__(vae, text_encoder, text_encoder_2, tokenizer, tokenizer_2, transformer, scheduler)
+        self.cuda_graphs = {}
+
+    def capture_cuda_graph(
+        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,
+        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,
+        **kwargs,
+    ):
+        """
+        Captures a static CUDA Graph for the generation process given static inputs.
+        """
+        # Use a static size for all inputs
+        static_height = height
+        static_width = width
+
+        # 1. Check inputs
+        self.check_inputs(
+            prompt,
+            prompt_2,
+            static_height,
+            static_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
+        device = self._execution_device
+
+        # 3. Encode prompt (with static inputs)
+        lora_scale = joint_attention_kwargs.get("scale", None) if joint_attention_kwargs is not None else None
+
+        # Use a static prompt for capture
+        static_prompt = "static prompt" if isinstance(prompt, str) else ["static prompt"]
+        prompt_embeds, pooled_prompt_embeds, text_ids = self.encode_prompt(
+            prompt=static_prompt,
+            prompt_2=prompt_2,
+            prompt_embeds=None,
+            pooled_prompt_embeds=None,
+            device=device,
+            num_images_per_prompt=num_images_per_prompt,
+            max_sequence_length=max_sequence_length,
+            lora_scale=lora_scale,
+        )
+
+        # 4. Prepare latent variables (with static inputs)
+        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,
+            static_height,
+            static_width,
+            prompt_embeds.dtype,
+            device,
+            generator,
+            None,
+        )
+
+        # 5. Prepare timesteps (with static inputs)
+        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,
+            None,
+            sigmas,
+            mu=mu,
+        )
+        self._num_timesteps = len(timesteps)
+
+        guidance = torch.full([1], guidance_scale, device=device, dtype=torch.float16).expand(latents.shape[0]) if self.transformer.config.guidance_embeds else None
+
+        # Capture the graph
+        torch.cuda.synchronize()
+        stream = torch.cuda.Stream()
+        stream.wait_stream(torch.cuda.current_stream())
+        with torch.cuda.stream(stream):
+            for i, t in enumerate(timesteps):
+                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]
+                latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
+        
+        torch.cuda.current_stream().wait_stream(stream)
+        torch.cuda.synchronize()
+
+        # Capture the CUDA graph
+        graph = torch.cuda.CUDAGraph()
+        with torch.cuda.graph(graph, stream=stream):
+            # Create static inputs
+            static_inputs = OrderedDict()
+            static_inputs["hidden_states"] = latents.clone()
+            static_inputs["timestep"] = timesteps[0].expand(latents.shape[0]).to(latents.dtype)
+            static_inputs["guidance"] = guidance.clone() if guidance is not None else None
+            static_inputs["pooled_projections"] = pooled_prompt_embeds.clone()
+            static_inputs["encoder_hidden_states"] = prompt_embeds.clone()
+            static_inputs["txt_ids"] = text_ids
+            static_inputs["img_ids"] = latent_image_ids.clone()
+            static_inputs["joint_attention_kwargs"] = self.joint_attention_kwargs
+
+            # Run the static graph
+            for i, t in enumerate(timesteps):
+                timestep = static_inputs["timestep"].clone()
+                noise_pred = self.transformer(
+                    hidden_states=static_inputs["hidden_states"],
+                    timestep=timestep / 1000,
+                    guidance=static_inputs["guidance"],
+                    pooled_projections=static_inputs["pooled_projections"],
+                    encoder_hidden_states=static_inputs["encoder_hidden_states"],
+                    txt_ids=static_inputs["txt_ids"],
+                    img_ids=static_inputs["img_ids"],
+                    joint_attention_kwargs=static_inputs["joint_attention_kwargs"],
+                    return_dict=False,
+                )[0]
+                static_inputs["hidden_states"] = self.scheduler.step(noise_pred, t, static_inputs["hidden_states"], return_dict=False)[0]
+
+            # Decode the latents after the loop
+            final_latents = static_inputs["hidden_states"]
+            final_image = self._decode_latents_to_image(final_latents, static_height, static_width, output_type)
+
+        # Store the graph and static inputs in the dictionary
+        self.cuda_graphs[(static_height, static_width, num_inference_steps)] = (graph, static_inputs, final_image)
+
     @torch.inference_mode()
     def generate_images(
         self,
@@ -71,6 +235,34 @@ class FluxWithCFGPipeline(FluxPipeline):
         height = height or self.default_sample_size * self.vae_scale_factor
         width = width or self.default_sample_size * self.vae_scale_factor
 
+        # 0. Check if a CUDA graph can be used
+        if (height, width, num_inference_steps) in self.cuda_graphs:
+            graph, static_inputs, final_image = self.cuda_graphs[(height, width, num_inference_steps)]
+
+            # Update dynamic inputs (like prompt) in static_inputs
+            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=self._execution_device,
+                num_images_per_prompt=num_images_per_prompt,
+                max_sequence_length=max_sequence_length,
+                lora_scale=lora_scale,
+            )
+
+            # Update only the dynamic parts of static_inputs
+            static_inputs["pooled_projections"].copy_(pooled_prompt_embeds)
+            static_inputs["encoder_hidden_states"].copy_(prompt_embeds)
+            static_inputs["txt_ids"] = text_ids
+
+            # Replay the graph
+            graph.replay()
+            torch.cuda.empty_cache()
+
+            return final_image
+
         # 1. Check inputs
         self.check_inputs(
             prompt,