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README.md

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+---
+license: other
+license_name: flux-1-dev-non-commercial-license
+license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.
+language:
+- en
+inference: true
+---

handler.py

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+# https://github.com/sayakpaul/diffusers-torchao
+
+import os
+from typing import Any, Dict
+
+from diffusers import FluxPipeline, FluxTransformer2DModel, AutoencoderKL, TorchAoConfig
+from PIL import Image
+import torch
+from torchao.quantization import quantize_, autoquant, int8_dynamic_activation_int8_weight, int8_dynamic_activation_int4_weight
+from huggingface_hub import hf_hub_download
+import gc
+
+import subprocess
+subprocess.run("pip list", shell=True)
+
+IS_COMPILE = True
+IS_TURBO = False
+IS_4BIT = True
+
+#if IS_COMPILE:
+#    import torch._dynamo
+#    torch._dynamo.config.suppress_errors = True
+
+from huggingface_inference_toolkit.logging import logger
+
+def load_pipeline_stable(repo_id: str, dtype: torch.dtype) -> Any:
+    quantization_config = TorchAoConfig("int4dq" if IS_4BIT else "int8dq")
+    vae = AutoencoderKL.from_pretrained(repo_id, subfolder="vae", torch_dtype=dtype)
+    pipe = FluxPipeline.from_pretrained(repo_id, vae=vae, torch_dtype=dtype, quantization_config=quantization_config)
+    pipe.transformer.fuse_qkv_projections()
+    pipe.vae.fuse_qkv_projections()
+    pipe.to("cuda")
+    return pipe
+
+def load_pipeline_compile(repo_id: str, dtype: torch.dtype) -> Any:
+    quantization_config = TorchAoConfig("int4dq" if IS_4BIT else "int8dq")
+    vae = AutoencoderKL.from_pretrained(repo_id, subfolder="vae", torch_dtype=dtype)
+    pipe = FluxPipeline.from_pretrained(repo_id, vae=vae, torch_dtype=dtype, quantization_config=quantization_config)
+    pipe.transformer.fuse_qkv_projections()
+    pipe.vae.fuse_qkv_projections()
+    pipe.transformer.to(memory_format=torch.channels_last)
+    pipe.transformer = torch.compile(pipe.transformer, mode="reduce-overhead", fullgraph=False, dynamic=False)
+    pipe.vae.to(memory_format=torch.channels_last)
+    pipe.vae = torch.compile(pipe.vae, mode="reduce-overhead", fullgraph=False, dynamic=False)
+    pipe.to("cuda")
+    return pipe
+
+def load_pipeline_autoquant(repo_id: str, dtype: torch.dtype) -> Any:
+    pipe = FluxPipeline.from_pretrained(repo_id, torch_dtype=dtype).to("cuda")
+    pipe.transformer.fuse_qkv_projections()
+    pipe.vae.fuse_qkv_projections()
+    pipe.transformer.to(memory_format=torch.channels_last)
+    pipe.transformer = torch.compile(pipe.transformer, mode="max-autotune", fullgraph=True)
+    pipe.vae.to(memory_format=torch.channels_last)
+    pipe.vae = torch.compile(pipe.vae, mode="max-autotune", fullgraph=True)
+    pipe.transformer = autoquant(pipe.transformer, error_on_unseen=False)
+    pipe.vae = autoquant(pipe.vae, error_on_unseen=False)
+    pipe.to("cuda")
+    return pipe
+
+def load_pipeline_turbo(repo_id: str, dtype: torch.dtype) -> Any:
+    pipe = FluxPipeline.from_pretrained(repo_id, torch_dtype=dtype).to("cuda")
+    pipe.load_lora_weights(hf_hub_download("ByteDance/Hyper-SD", "Hyper-FLUX.1-dev-8steps-lora.safetensors"), adapter_name="hyper-sd")
+    pipe.set_adapters(["hyper-sd"], adapter_weights=[0.125])
+    pipe.fuse_lora()
+    pipe.unload_lora_weights()
+    pipe.transformer.fuse_qkv_projections()
+    pipe.vae.fuse_qkv_projections()
+    weight = int8_dynamic_activation_int4_weight() if IS_4BIT else int8_dynamic_activation_int8_weight()
+    quantize_(pipe.transformer, weight, device="cuda")
+    quantize_(pipe.vae, weight, device="cuda")
+    quantize_(pipe.text_encoder_2, weight, device="cuda")
+    pipe.to("cuda")
+    return pipe
+
+def load_pipeline_turbo_compile(repo_id: str, dtype: torch.dtype) -> Any:
+    pipe = FluxPipeline.from_pretrained(repo_id, torch_dtype=dtype).to("cuda")
+    pipe.load_lora_weights(hf_hub_download("ByteDance/Hyper-SD", "Hyper-FLUX.1-dev-8steps-lora.safetensors"), adapter_name="hyper-sd")
+    pipe.set_adapters(["hyper-sd"], adapter_weights=[0.125])
+    pipe.fuse_lora()
+    pipe.unload_lora_weights()
+    pipe.transformer.fuse_qkv_projections()
+    pipe.vae.fuse_qkv_projections()
+    weight = int8_dynamic_activation_int4_weight() if IS_4BIT else int8_dynamic_activation_int8_weight()
+    quantize_(pipe.transformer, weight, device="cuda")
+    quantize_(pipe.vae, weight, device="cuda")
+    quantize_(pipe.text_encoder_2, weight, device="cuda")
+    pipe.transformer.to(memory_format=torch.channels_last)
+    pipe.transformer = torch.compile(pipe.transformer, mode="reduce-overhead", fullgraph=False, dynamic=False)
+    pipe.vae.to(memory_format=torch.channels_last)
+    pipe.vae = torch.compile(pipe.vae, mode="reduce-overhead", fullgraph=False, dynamic=False)
+    pipe.to("cuda")
+    return pipe
+
+class EndpointHandler:
+    def __init__(self, path=""):
+        repo_id = "NoMoreCopyrightOrg/flux-dev-8step" if IS_TURBO else "NoMoreCopyrightOrg/flux-dev"
+        dtype = torch.bfloat16
+        #dtype = torch.float16 # for older nVidia GPUs
+        if IS_COMPILE: load_pipeline_compile(repo_id, dtype)
+        else: self.pipeline = load_pipeline_stable(repo_id, dtype)
+        gc.collect()
+        torch.cuda.empty_cache()
+
+    def __call__(self, data: Dict[str, Any]) -> Image.Image:
+        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", 8 if IS_TURBO else 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)
+
+        return self.pipeline(  # type: ignore
+            prompt,
+            height=height,
+            width=width,
+            guidance_scale=guidance_scale,
+            num_inference_steps=num_inference_steps,
+            generator=generator,
+            output_type="pil",
+        ).images[0]
+
+

requirements.txt

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+--extra-index-url https://download.pytorch.org/whl/cu121
+torch==2.6.0+cu121
+torchvision
+torchaudio
+huggingface_hub
+torchao==0.9.0
+diffusers==0.32.2
+peft
+transformers
+numpy
+scipy
+Pillow
+sentencepiece
+protobuf
+triton