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import base64
import io
import time
import torch
import uvicorn
import numpy as np
import gc
import asyncio
from fastapi import FastAPI, HTTPException, Request
from accelerate import infer_auto_device_map, dispatch_model
from pydantic import BaseModel
from diffusers import (
Flux2Pipeline,
Flux2Transformer2DModel,
AutoencoderKLFlux2,
FlowMatchEulerDiscreteScheduler
)
from diffusers.pipelines.flux2.pipeline_flux2 import compute_empirical_mu, retrieve_timesteps
from diffusers.pipelines.flux2.image_processor import Flux2ImageProcessor
from transformers import Mistral3ForConditionalGeneration, AutoProcessor
# Argument parsing
parser = argparse.ArgumentParser(description="Flux2 Image Generation Server")
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 model")
args = parser.parse_args()
app = FastAPI()
# Global components
text_encoder = None
tokenizer = None
transformer = None
vae = None
scheduler = None
image_processor = None
request_lock = asyncio.Lock()
# Device maps
text_encoder_map = None
transformer_map = None
vae_map = None
GPU_MEMORY_FRACTION = 0.90
def load_model():
global text_encoder, tokenizer, transformer, vae, scheduler, image_processor
global text_encoder_map, transformer_map, vae_map
print(f"Loading model from {args.model}...")
try:
print("Loading Flux2 components...")
# Calculate max memory per GPU
#max_memory = {}
#if torch.cuda.is_available():
# for i in range(torch.cuda.device_count()):
# total_mem = torch.cuda.get_device_properties(i).total_memory
# max_memory[i] = int(total_mem * GPU_MEMORY_FRACTION)
max_memory = {
0: "5GB", # leave a little headroom
# 1: "10GB",
"cpu": "120GB" # your 128GB RAM minus OS
}
# Load Text Encoder (Mistral3) on CPU
print("Loading Text Encoder on CPU...")
text_encoder = Mistral3ForConditionalGeneration.from_pretrained(
args.model,
subfolder="text_encoder",
torch_dtype=torch.bfloat16,
device_map="cpu"
)
print("Calculating Text Encoder device map...")
text_encoder_map = infer_auto_device_map(text_encoder, max_memory=max_memory)
# Load Tokenizer on CPU
print("Loading Tokenizer on CPU...")
tokenizer = AutoProcessor.from_pretrained(
args.model,
subfolder="tokenizer",
device_map="cpu"
)
# Load Transformer on CPU
print("Loading Transformer on CPU...")
transformer = Flux2Transformer2DModel.from_pretrained(
args.model,
subfolder="transformer",
torch_dtype=torch.bfloat16,
device_map="cpu"
)
print("Calculating Transformer device map...")
transformer_map = infer_auto_device_map(transformer, max_memory=max_memory)
# Load VAE on CPU
print("Loading VAE on CPU...")
vae = AutoencoderKLFlux2.from_pretrained(
args.model,
subfolder="vae",
torch_dtype=torch.bfloat16,
device_map="cpu"
)
print("Calculating VAE device map...")
vae_map = infer_auto_device_map(vae, max_memory=max_memory)
# Initialize Scheduler
print("Initializing Scheduler...")
scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(
args.model,
subfolder="scheduler"
)
# Initialize Image Processor
print("Initializing Image Processor...")
# VAE scale factor logic from pipeline
vae_scale_factor = 2 ** (len(vae.config.block_out_channels) - 1)
image_processor = Flux2ImageProcessor(vae_scale_factor=vae_scale_factor * 2)
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"
@app.on_event("startup")
async def startup_event():
load_model()
@app.post("/v1/images/generations")
async def generate_image(request: ImageGenerationRequest):
if not transformer:
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
num_inference_steps = 28
guidance_scale = 4.0
max_sequence_length = 512
device = torch.device("cuda")
dtype = torch.bfloat16
images = []
# 1. Generate embeddings on CPU
print("Generating embeddings...")
flush()
prompt_embeds = Flux2Pipeline._get_mistral_3_small_prompt_embeds(
text_encoder=text_encoder,
tokenizer=tokenizer,
prompt=request.prompt,
# device=torch.device("cpu"),
max_sequence_length=max_sequence_length
)
# prompt_embeds = prompt_embeds.to("cuda")
# 2. Prepare Latents
# Flux latents are turned into 2x2 patches and packed.
# This means the latent width and height has to be divisible by the patch size.
# So the vae scale factor is multiplied by the patch size to account for this
vae_scale_factor = 2 ** (len(vae.config.block_out_channels) - 1)
height = height or 1024
width = width or 1024
# Resize to be divisible by vae_scale_factor * 2
height = 2 * (int(height) // (vae_scale_factor * 2))
width = 2 * (int(width) // (vae_scale_factor * 2))
num_channels_latents = transformer.config.in_channels // 4
shape = (1, num_channels_latents * 4, height // 2, width // 2)
# 3. Prepare IDs
# We need to prepare text_ids and latent_ids
# prompt_embeds shape: (batch_size, seq_len, hidden_dim)
batch_size, seq_len, _ = prompt_embeds.shape
# Repeat for num_images_per_prompt (assuming 1 for now per loop iteration)
# If request.n > 1, we loop outside or handle batching. Here we loop outside.
# Prepare text IDs
text_ids = Flux2Pipeline._prepare_text_ids(prompt_embeds).to(device)
for _ in range(request.n):
# Generate random latents
latents = torch.randn(shape, device=device, dtype=dtype)
# Prepare latent IDs
latent_ids = Flux2Pipeline._prepare_latent_ids(latents).to(device)
# Pack latents
packed_latents = Flux2Pipeline._pack_latents(latents)
# 4. Prepare Timesteps
sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps)
image_seq_len = packed_latents.shape[1]
mu = compute_empirical_mu(image_seq_len=image_seq_len, num_steps=num_inference_steps)
timesteps, num_inference_steps = retrieve_timesteps(
scheduler,
num_inference_steps,
device,
sigmas=sigmas,
mu=mu,
)
# --- SWAP TRANSFORMER TO CUDA ---
print("Moving Transformer to CUDA...")
flush()
dispatch_model(transformer, device_map=transformer_map)
# 5. Denoising Loop
print("Starting denoising loop on CUDA...")
scheduler.set_begin_index(0)
guidance = torch.full([1], guidance_scale, device=device, dtype=torch.float32)
guidance = guidance.expand(packed_latents.shape[0])
for i, t in enumerate(timesteps):
start_time = time.time()
# broadcast to batch dimension
timestep = t.expand(packed_latents.shape[0]).to(packed_latents.dtype)
noise_pred = transformer(
hidden_states=packed_latents,
timestep=timestep / 1000,
guidance=guidance,
encoder_hidden_states=prompt_embeds,
txt_ids=text_ids,
img_ids=latent_ids,
return_dict=False,
)[0]
# step
packed_latents = scheduler.step(noise_pred, t, packed_latents, return_dict=False)[0]
step_time = time.time() - start_time
print(f"Step {i+1}/{num_inference_steps}: {step_time:.2f}s")
# --- SWAP TRANSFORMER TO CPU ---
print("Moving Transformer to CPU...")
transformer.to("cpu")
flush()
# --- SWAP VAE TO CUDA ---
print("Moving VAE to CUDA...")
dispatch_model(vae, device_map=vae_map)
# 6. Decode
print("Decoding on CUDA...")
# Move packed_latents to CUDA for decoding (already there, but ensuring)
packed_latents = packed_latents.to(device)
latent_ids = latent_ids.to(device)
latents = Flux2Pipeline._unpack_latents_with_ids(packed_latents, latent_ids)
latents_bn_mean = vae.bn.running_mean.view(1, -1, 1, 1).to(latents.device, latents.dtype)
latents_bn_std = torch.sqrt(vae.bn.running_var.view(1, -1, 1, 1) + vae.config.batch_norm_eps).to(
latents.device, latents.dtype
)
latents = latents * latents_bn_std + latents_bn_mean
latents = Flux2Pipeline._unpatchify_latents(latents)
image = vae.decode(latents, return_dict=False)[0]
image = image_processor.postprocess(image, output_type="pil")[0]
# --- SWAP VAE TO CPU ---
print("Moving VAE to CPU...")
vae.to("cpu")
# Convert to base64
buffered = io.BytesIO()
image.save(buffered, format="PNG")
img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
images.append({"b64_json": img_str})
return {
"created": int(time.time()),
"data": images
}
if __name__ == "__main__":
uvicorn.run(app, host=args.host, port=args.port)
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