File size: 6,512 Bytes
3e47535 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 |
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
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)
print(f"TIMEOUT: {TIMEOUT}")
print(f"SAFETY_CHECKER: {SAFETY_CHECKER}")
print(f"MAX_QUEUE_SIZE: {MAX_QUEUE_SIZE}")
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.float16, use_safetensors=True
)
pipe.set_progress_bar_config(disable=True)
pipe.to(torch_device="cuda", torch_dtype=torch.float16)
pipe.unet.to(memory_format=torch.channels_last)
compel_proc = Compel(tokenizer=pipe.tokenizer, text_encoder=pipe.text_encoder, truncate_long_prompts=False)
pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
user_queue_map = {}
# warmup trigger compilation
pipe(prompt="warmup", num_inference_steps=1, guidance_scale=8.0)
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")
|