File size: 6,521 Bytes
1e103b7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 | import argparse
import base64
import io
import time
import torch
import uvicorn
import gc
import asyncio
from typing import Optional
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from diffusers import FluxPipeline, FluxKontextPipeline
from nunchaku import NunchakuFluxTransformer2dModel
from PIL import Image
# Argument parsing
parser = argparse.ArgumentParser(description="Flux Image Generation Server with Nunchaku")
parser.add_argument("--host", type=str, default="0.0.0.0", help="Host to bind to")
parser.add_argument("--port", type=int, default=8000, help="Port to bind to")
parser.add_argument("--model", type=str, default="black-forest-labs/FLUX.1-dev", help="Path or Repo ID of the base model")
parser.add_argument("--optimized-model", type=str, required=True, help="Path to the optimized Nunchaku model safetensors file")
args = parser.parse_args()
app = FastAPI()
# Global components
pipeline = None
img2img_pipeline = None
request_lock = asyncio.Lock()
def load_model():
global pipeline, img2img_pipeline
print(f"Loading base model from {args.model}...")
print(f"Loading optimized transformer from {args.optimized_model}...")
try:
# Load the optimized transformer
# Ensuring transformer is in bfloat16 to match the pipeline expectation
transformer = NunchakuFluxTransformer2dModel.from_pretrained(args.optimized_model)
# Load the pipeline with the optimized transformer
pipeline = FluxPipeline.from_pretrained(
args.model,
transformer=transformer,
torch_dtype=torch.bfloat16,
).to("cuda")
# Load the Img2Img/Context pipeline sharing the same components
# We use strict component sharing to avoid VRAM duplication
print("Initializing FluxKontextPipeline for image inputs...")
# Since FluxKontextPipeline shares architecture with FluxPipeline, we can initialize it with the same components
img2img_pipeline = FluxKontextPipeline.from_pretrained(
args.model,
transformer=pipeline.transformer,
vae=pipeline.vae,
text_encoder=pipeline.text_encoder,
text_encoder_2=pipeline.text_encoder_2,
tokenizer=pipeline.tokenizer,
tokenizer_2=pipeline.tokenizer_2,
scheduler=pipeline.scheduler,
torch_dtype=torch.bfloat16
).to("cuda")
# Enable CPU offload for the main pipeline.
# Since components are shared, this should handle memory management for both.
pipeline.enable_model_cpu_offload()
# img2img_pipeline.enable_model_cpu_offload() # Avoid double hook registration
except Exception as e:
print(f"Error loading model: {e}")
raise e
print("Model loaded successfully!")
def flush():
gc.collect()
torch.cuda.empty_cache()
class ImageGenerationRequest(BaseModel):
prompt: str
n: int = 1
size: str = "1024x1024"
response_format: str = "b64_json"
quality: str = "standard"
style: str = "vivid"
image: Optional[str] = None # Base64 encoded image
@app.on_event("startup")
async def startup_event():
load_model()
@app.post("/v1/images/generations")
async def generate_image(request: ImageGenerationRequest):
if not pipeline:
raise HTTPException(status_code=500, detail="Model not loaded")
async with request_lock:
print(f"Received request: {request.prompt}")
# Parse size
try:
width, height = map(int, request.size.split("x"))
except ValueError:
width, height = 1024, 1024
# Flux requires dimensions to be multiples of 16 (or 8 depending on VAE)
# Standard Flux dev usually works well with 1024x1024
# We'll ensure they are divisible by 16 just in case
width = (width // 16) * 16
height = (height // 16) * 16
images = []
try:
input_image = None
if request.image:
try:
# Handle data URI if present
img_data = request.image
if "," in img_data:
img_data = img_data.split(",")[1]
input_bytes = base64.b64decode(img_data)
input_image = Image.open(io.BytesIO(input_bytes)).convert("RGB")
# Resize input image to match request size
input_image = input_image.resize((width, height), Image.LANCZOS)
print(f"Processed input image of size {input_image.size}")
except Exception as e:
print(f"Failed to decode input image: {e}")
raise HTTPException(status_code=400, detail="Invalid image data")
# Generate images
if input_image:
# Use FluxKontextPipeline
print("Running FluxKontextPipeline...")
generated_images = pipeline(
image=input_image,
prompt=request.prompt,
height=height,
width=width,
num_inference_steps=28,
guidance_scale=2.5, # Recommended for Kontext
num_images_per_prompt=request.n
).images
else:
# Use standard FluxPipeline
print("Running FluxPipeline...")
generated_images = pipeline(
request.prompt,
height=height,
width=width,
num_inference_steps=28, # Standard for Flux Dev
guidance_scale=3.5, # Nunchaku example uses 3.5
num_images_per_prompt=request.n
).images
for image in generated_images:
buffered = io.BytesIO()
image.save(buffered, format="PNG")
img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
images.append({"b64_json": img_str})
except Exception as e:
print(f"Error during generation: {e}")
raise HTTPException(status_code=500, detail=str(e))
finally:
flush()
return {
"created": int(time.time()),
"data": images
}
if __name__ == "__main__":
uvicorn.run(app, host=args.host, port=args.port)
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