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import asyncio
import json
import logging
import traceback
from pydantic import BaseModel
from fastapi import FastAPI, WebSocket, HTTPException, WebSocketDisconnect
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import StreamingResponse, JSONResponse
from fastapi.staticfiles import StaticFiles
from diffusers import DiffusionPipeline, AutoencoderTiny
from compel import Compel
import torch
from PIL import Image
import numpy as np
import gradio as gr
import io
import uuid
import os
import time
import psutil
MAX_QUEUE_SIZE = int(os.environ.get("MAX_QUEUE_SIZE", 0))
TIMEOUT = float(os.environ.get("TIMEOUT", 0))
SAFETY_CHECKER = os.environ.get("SAFETY_CHECKER", None)
# check if MPS is available OSX only M1/M2/M3 chips
mps_available = hasattr(torch.backends, "mps") and torch.backends.mps.is_available()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
torch_device = device
torch_dtype = torch.float16
print(f"TIMEOUT: {TIMEOUT}")
print(f"SAFETY_CHECKER: {SAFETY_CHECKER}")
print(f"MAX_QUEUE_SIZE: {MAX_QUEUE_SIZE}")
print(f"device: {device}")
if mps_available:
device = torch.device("mps")
torch_device = "cpu"
torch_dtype = torch.float32
if SAFETY_CHECKER == "True":
pipe = DiffusionPipeline.from_pretrained(
"SimianLuo/LCM_Dreamshaper_v7",
custom_pipeline="latent_consistency_txt2img.py",
custom_revision="main",
)
else:
pipe = DiffusionPipeline.from_pretrained(
"SimianLuo/LCM_Dreamshaper_v7",
safety_checker=None,
custom_pipeline="latent_consistency_txt2img.py",
custom_revision="main",
)
pipe.vae = AutoencoderTiny.from_pretrained(
"madebyollin/taesd", torch_dtype=torch_dtype, use_safetensors=True
)
pipe.set_progress_bar_config(disable=True)
pipe.to(torch_device=torch_device, torch_dtype=torch_dtype).to(device)
pipe.unet.to(memory_format=torch.channels_last)
# check if computer has less than 64GB of RAM using sys or os
if psutil.virtual_memory().total < 64 * 1024**3:
pipe.enable_attention_slicing()
if not mps_available:
pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
pipe(prompt="warmup", num_inference_steps=1, guidance_scale=8.0)
compel_proc = Compel(
tokenizer=pipe.tokenizer,
text_encoder=pipe.text_encoder,
truncate_long_prompts=False,
)
user_queue_map = {}
def predict(prompt, guidance_scale=8.0, seed=2159232):
generator = torch.manual_seed(seed)
prompt_embeds = compel_proc(prompt)
# Can be set to 1~50 steps. LCM support fast inference even <= 4 steps. Recommend: 1~8 steps.
num_inference_steps = 8
results = pipe(
prompt_embeds=prompt_embeds,
generator=generator,
num_inference_steps=num_inference_steps,
guidance_scale=guidance_scale,
lcm_origin_steps=50,
output_type="pil",
)
nsfw_content_detected = (
results.nsfw_content_detected[0]
if "nsfw_content_detected" in results
else False
)
if nsfw_content_detected:
return None
return results.images[0]
app = FastAPI()
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
class InputParams(BaseModel):
prompt: str
seed: int
guidance_scale: float
@app.websocket("/ws")
async def websocket_endpoint(websocket: WebSocket):
await websocket.accept()
if MAX_QUEUE_SIZE > 0 and len(user_queue_map) >= MAX_QUEUE_SIZE:
print("Server is full")
await websocket.send_json({"status": "error", "message": "Server is full"})
await websocket.close()
return
try:
uid = str(uuid.uuid4())
print(f"New user connected: {uid}")
await websocket.send_json(
{"status": "success", "message": "Connected", "userId": uid}
)
user_queue_map[uid] = {
"queue": asyncio.Queue(),
}
await websocket.send_json(
{"status": "start", "message": "Start Streaming", "userId": uid}
)
await handle_websocket_data(websocket, uid)
except WebSocketDisconnect as e:
logging.error(f"WebSocket Error: {e}, {uid}")
traceback.print_exc()
finally:
print(f"User disconnected: {uid}")
queue_value = user_queue_map.pop(uid, None)
queue = queue_value.get("queue", None)
if queue:
while not queue.empty():
try:
queue.get_nowait()
except asyncio.QueueEmpty:
continue
@app.get("/queue_size")
async def get_queue_size():
queue_size = len(user_queue_map)
return JSONResponse({"queue_size": queue_size})
@app.get("/stream/{user_id}")
async def stream(user_id: uuid.UUID):
uid = str(user_id)
try:
user_queue = user_queue_map[uid]
queue = user_queue["queue"]
async def generate():
while True:
params = await queue.get()
if params is None:
continue
image = predict(params.prompt, params.guidance_scale, params.seed)
if image is None:
continue
frame_data = io.BytesIO()
image.save(frame_data, format="JPEG")
frame_data = frame_data.getvalue()
if frame_data is not None and len(frame_data) > 0:
yield b"--frame\r\nContent-Type: image/jpeg\r\n\r\n" + frame_data + b"\r\n"
await asyncio.sleep(1.0 / 120.0)
return StreamingResponse(
generate(), media_type="multipart/x-mixed-replace;boundary=frame"
)
except Exception as e:
logging.error(f"Streaming Error: {e}, {user_queue_map}")
traceback.print_exc()
return HTTPException(status_code=404, detail="User not found")
async def handle_websocket_data(websocket: WebSocket, user_id: uuid.UUID):
uid = str(user_id)
user_queue = user_queue_map[uid]
queue = user_queue["queue"]
if not queue:
return HTTPException(status_code=404, detail="User not found")
last_time = time.time()
try:
while True:
params = await websocket.receive_json()
params = InputParams(**params)
while not queue.empty():
try:
queue.get_nowait()
except asyncio.QueueEmpty:
continue
await queue.put(params)
if TIMEOUT > 0 and time.time() - last_time > TIMEOUT:
await websocket.send_json(
{
"status": "timeout",
"message": "Your session has ended",
"userId": uid,
}
)
await websocket.close()
return
except Exception as e:
logging.error(f"Error: {e}")
traceback.print_exc()
app.mount("/", StaticFiles(directory="txt2img", html=True), name="public")