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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
from huggingface_hub import hf_hub_download
IS_COMPILE = False
IS_TURBO = False
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("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("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, 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.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()
quantize_(pipe.transformer, int8_dynamic_activation_int8_weight(), device="cuda")
quantize_(pipe.vae, int8_dynamic_activation_int8_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.transformer.fuse_qkv_projections()
pipe.vae.fuse_qkv_projections()
quantize_(pipe.transformer, int8_dynamic_activation_int8_weight(), device="cuda")
quantize_(pipe.vae, int8_dynamic_activation_int8_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.to("cuda")
return pipe
class EndpointHandler:
def __init__(self, path=""):
repo_id = "camenduru/FLUX.1-dev-diffusers"
dtype = torch.bfloat16
if IS_COMPILE:
if IS_TURBO: self.pipeline = load_pipeline_turbo_compile(repo_id, dtype)
else: self.pipeline = load_pipeline_compile(repo_id, dtype)
else:
if IS_TURBO: self.pipeline = load_pipeline_turbo(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=}")
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]
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