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handler.py

@@ -1,42 +1,101 @@
+# 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
 
 IS_COMPILE = False
+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
+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.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 compile_pipeline(pipe) -> Any:
+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.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, backend="inductor")
-    #pipe.vae.to(memory_format=torch.channels_last)
-    #pipe.vae = torch.compile(pipe.vae, mode="reduce-overhead", fullgraph=False, dynamic=False, backend="inductor")
+    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 = "camenduru/FLUX.1-dev-diffusers"
-        #repo_id = "NoMoreCopyright/FLUX.1-dev-test"
-        dtype = torch.bfloat16
-        quantization_config = TorchAoConfig("int8dq")
-        vae = AutoencoderKL.from_pretrained(repo_id, subfolder="vae", torch_dtype=dtype)
-        #transformer = FluxTransformer2DModel.from_pretrained(repo_id, subfolder="transformer", torch_dtype=dtype, quantization_config=quantization_config).to("cuda")
-        self.pipeline = FluxPipeline.from_pretrained(repo_id, vae=vae, torch_dtype=dtype, quantization_config=quantization_config)
-        self.pipeline.transformer.fuse_qkv_projections()
-        self.pipeline.vae.fuse_qkv_projections()
-        if IS_COMPILE: self.pipeline = compile_pipeline(self.pipeline)
-        self.pipeline.to("cuda")
-
-    @torch.inference_mode()
+        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)
+
     def __call__(self, data: Dict[str, Any]) -> Image.Image:
-        #logger.info(f"Received incoming request with {data=}")
+        logger.info(f"Received incoming request with {data=}")
 
         if "inputs" in data and isinstance(data["inputs"], str):
             prompt = data.pop("inputs")
@@ -50,7 +109,7 @@ class EndpointHandler:
 
         parameters = data.pop("parameters", {})
 
-        num_inference_steps = parameters.get("num_inference_steps", 28)
+        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)

requirements.txt

@@ -1,15 +1,14 @@
 huggingface_hub
 torch==2.4.0
 torchvision
+torchaudio
 torchao==0.9.0
-diffusers
+diffusers==0.32.2
 peft
-accelerate
 transformers
 numpy
 scipy
 Pillow
 sentencepiece
 protobuf
-pytorch-lightning
 triton