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handler.py

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+import os
+from typing import Any, Dict
+from PIL import Image
+import torch
+from diffusers import FluxPipeline
+from huggingface_inference_toolkit.logging import logger
+from para_attn.first_block_cache.diffusers_adapters import apply_cache_on_pipe
+import time
+import torch.distributed as dist
+from para_attn.context_parallel import init_context_parallel_mesh
+from para_attn.context_parallel.diffusers_adapters import parallelize_pipe
+from para_attn.parallel_vae.diffusers_adapters import parallelize_vae
+dist.init_process_group()
+
+torch.cuda.set_device(dist.get_rank())
+
+class EndpointHandler:
+    def __init__(self, path=""):
+        self.pipe = FluxPipeline.from_pretrained(
+            "NoMoreCopyrightOrg/flux-dev",
+            torch_dtype=torch.bfloat16,
+        ).to("cuda")
+        mesh = init_context_parallel_mesh(
+            self.pipe.device.type,
+            max_ring_dim_size=2,
+        )
+        parallelize_pipe(
+            self.pipe,
+            mesh=mesh,
+        )
+        parallelize_vae(self.pipe.vae, mesh=mesh._flatten())
+        apply_cache_on_pipe(self.pipe, residual_diff_threshold=0.12)
+        torch._inductor.config.reorder_for_compute_comm_overlap = True
+        self.pipe.transformer = torch.compile(
+            self.pipe.transformer, mode="max-autotune-no-cudagraphs",
+        )
+        self.pipe.vae = torch.compile(
+            self.pipe.vae, mode="max-autotune-no-cudagraphs",
+        )
+
+    def __call__(self, data: Dict[str, Any]) -> str:
+        logger.info(f"Received incoming request with {data=}")
+
+        if "inputs" in data and isinstance(data["inputs"], str):
+            prompt = data.pop("inputs")
+        elif "prompt" in data and isinstance(data["prompt"], str):
+            prompt = data.pop("prompt")
+        else:
+            raise ValueError(
+                "Provided input body must contain either the key `inputs` or `prompt` with the"
+                " prompt to use for the image generation, and it needs to be a non-empty string."
+            )
+
+        parameters = data.pop("parameters", {})
+
+        num_inference_steps = parameters.get("num_inference_steps", 28)
+        width = parameters.get("width", 1024)
+        height = parameters.get("height", 1024)
+        guidance_scale = parameters.get("guidance_scale", 3.5)
+
+        # seed generator (seed cannot be provided as is but via a generator)
+        seed = parameters.get("seed", 0)
+        generator = torch.manual_seed(seed)
+        start_time = time.time()
+        result = self.pipe(  # type: ignore
+            prompt,
+            height=height,
+            width=width,
+            guidance_scale=guidance_scale,
+            num_inference_steps=num_inference_steps,
+            generator=generator,
+            output_type="pil" if dist.get_rank() == 0 else "pt",
+        ).images[0]
+        end_time = time.time()
+        if dist.get_rank() == 0:
+            time_taken = end_time - start_time
+            print(f"Time taken: {time_taken:.2f} seconds")
+            return result
+        return "123"

requirements.txt

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+--extra-index-url https://download.pytorch.org/whl/cu126
+torch==2.6.0+cu126
+torchvision
+torchaudio
+huggingface_hub
+torchao==0.9.0
+diffusers==0.32.2
+peft
+transformers
+numpy
+scipy
+Pillow
+sentencepiece
+protobuf
+triton
+schedule
+para-attn