app-txt2img.py

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

try:
    import intel_extension_for_pytorch as ipex
except:
    pass
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)
TORCH_COMPILE = os.environ.get("TORCH_COMPILE", None)   

WIDTH = 768
HEIGHT = 768
# disable tiny autoencoder for better quality speed tradeoff
USE_TINY_AUTOENCODER = False

# check if MPS is available OSX only M1/M2/M3 chips
mps_available = hasattr(torch.backends, "mps") and torch.backends.mps.is_available()
xpu_available = hasattr(torch, "xpu") and torch.xpu.is_available()
device = torch.device(
    "cuda" if torch.cuda.is_available() else "xpu" if xpu_available else "cpu"
)
torch_device = device
# change to torch.float16 to save GPU memory
torch_dtype = torch.float32

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",
    )
else:
    pipe = DiffusionPipeline.from_pretrained(
        "SimianLuo/LCM_Dreamshaper_v7",
        safety_checker=None,
    )
if USE_TINY_AUTOENCODER:
    pipe.vae = AutoencoderTiny.from_pretrained(
        "madebyollin/taesd", torch_dtype=torch_dtype, use_safetensors=True
    )
pipe.set_progress_bar_config(disable=True)
pipe.to(device=torch_device, 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 TORCH_COMPILE:
    pipe.text_encoder = torch.compile(pipe.text_encoder, mode="max-autotune", fullgraph=False)
    pipe.tokenizer = torch.compile(pipe.tokenizer, mode="max-autotune", fullgraph=False)
    pipe.unet = torch.compile(pipe.unet, mode="max-autotune", fullgraph=False)
    pipe.vae = torch.compile(pipe.vae, mode="max-autotune", fullgraph=False)

    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 = {}


class InputParams(BaseModel):
    seed: int = 2159232
    prompt: str
    guidance_scale: float = 8.0
    strength: float = 0.5
    steps: int = 4
    lcm_steps: int = 50
    width: int = WIDTH
    height: int = HEIGHT


def predict(params: InputParams):
    generator = torch.manual_seed(params.seed)
    prompt_embeds = compel_proc(params.prompt)
    # Can be set to 1~50 steps. LCM support fast inference even <= 4 steps. Recommend: 1~8 steps.
    results = pipe(
        prompt_embeds=prompt_embeds,
        generator=generator,
        num_inference_steps=params.steps,
        guidance_scale=params.guidance_scale,
        width=params.width,
        height=params.height,
        original_inference_steps=params.lcm_steps,
        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=["*"],
)


@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)
                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")