Real-Time-Latent-Consistency-Model / app-controlnetlora.py
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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,
HTMLResponse,
FileResponse,
)
from diffusers import (
StableDiffusionControlNetImg2ImgPipeline,
ControlNetModel,
LCMScheduler,
)
from compel import Compel
import torch
from canny_gpu import SobelOperator
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 = 512
HEIGHT = 512
# 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"
)
# change to torch.float16 to save GPU memory
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")
device = "cpu"
torch_dtype = torch.float32
controlnet_canny = ControlNetModel.from_pretrained(
"lllyasviel/control_v11p_sd15_canny", torch_dtype=torch_dtype
).to(device)
canny_torch = SobelOperator(device=device)
models_id = [
"plasmo/woolitize",
"nitrosocke/Ghibli-Diffusion",
"nitrosocke/mo-di-diffusion",
]
lcm_lora_id = "latent-consistency/lcm-lora-sdv1-5"
if SAFETY_CHECKER == "True":
pipes = {}
for model_id in models_id:
pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(
model_id,
controlnet=controlnet_canny,
)
pipes[model_id] = pipe
else:
pipes = {}
for model_id in models_id:
pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(
model_id,
safety_checker=None,
controlnet=controlnet_canny,
)
pipes[model_id] = pipe
for pipe in pipes.values():
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
pipe.set_progress_bar_config(disable=True)
pipe.to(device=device, dtype=torch_dtype).to(device)
if psutil.virtual_memory().total < 64 * 1024**3:
pipe.enable_attention_slicing()
# Load LCM LoRA
pipe.load_lora_weights(lcm_lora_id, adapter_name="lcm")
compel_proc = Compel(
tokenizer=pipe.tokenizer,
text_encoder=pipe.text_encoder,
truncate_long_prompts=False,
)
if TORCH_COMPILE:
pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
pipe.vae = torch.compile(pipe.vae, mode="reduce-overhead", fullgraph=True)
pipe(
prompt="warmup",
image=[Image.new("RGB", (768, 768))],
control_image=[Image.new("RGB", (768, 768))],
)
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
controlnet_scale: float = 0.8
controlnet_start: float = 0.0
controlnet_end: float = 1.0
canny_low_threshold: float = 0.31
canny_high_threshold: float = 0.78
debug_canny: bool = False
model_id: str = "nitrosocke/Ghibli-Diffusion"
def predict(input_image: Image.Image, params: InputParams):
generator = torch.manual_seed(params.seed)
control_image = canny_torch(
input_image, params.canny_low_threshold, params.canny_high_threshold
)
prompt_embeds = compel_proc(params.prompt)
pipe = pipes[params.model_id]
results = pipe(
control_image=control_image,
prompt_embeds=prompt_embeds,
generator=generator,
image=input_image,
strength=params.strength,
num_inference_steps=params.steps,
guidance_scale=params.guidance_scale,
width=params.width,
height=params.height,
output_type="pil",
controlnet_conditioning_scale=params.controlnet_scale,
control_guidance_start=params.controlnet_start,
control_guidance_end=params.controlnet_end,
)
nsfw_content_detected = (
results.nsfw_content_detected[0]
if "nsfw_content_detected" in results
else False
)
if nsfw_content_detected:
return None
result_image = results.images[0]
if params.debug_canny:
# paste control_image on top of result_image
w0, h0 = (200, 200)
control_image = control_image.resize((w0, h0))
w1, h1 = result_image.size
result_image.paste(control_image, (w1 - w0, h1 - h0))
return result_image
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():
last_prompt: str = None
while True:
data = await queue.get()
input_image = data["image"]
params = data["params"]
if input_image is None:
continue
image = predict(
input_image,
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:
data = await websocket.receive_bytes()
params = await websocket.receive_json()
params = InputParams(**params)
pil_image = Image.open(io.BytesIO(data))
while not queue.empty():
try:
queue.get_nowait()
except asyncio.QueueEmpty:
continue
await queue.put({"image": pil_image, "params": 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.get("/", response_class=HTMLResponse)
async def root():
return FileResponse("./static/controlnetlora.html")