Spaces:
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txt2img
Browse files- Dockerfile +2 -1
- README.md +10 -0
- app-img2img.py +1 -1
- app-txt2img.py +205 -0
- latent_consistency_txt2img.py +836 -0
- txt2img/index.html +218 -0
- txt2img/tailwind.config.js +0 -0
Dockerfile
CHANGED
@@ -36,4 +36,5 @@ WORKDIR $HOME/app
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# Copy the current directory contents into the container at $HOME/app setting the owner to the user
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COPY --chown=user . $HOME/app
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-
CMD ["uvicorn", "app-img2img:app", "--host", "0.0.0.0", "--port", "7860"]
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# Copy the current directory contents into the container at $HOME/app setting the owner to the user
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COPY --chown=user . $HOME/app
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+
CMD ["uvicorn", "app-img2img:app", "--host", "0.0.0.0", "--port", "7860"]
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+
# CMD ["uvicorn", "app-txt2img:app", "--host", "0.0.0.0", "--port", "7860"]
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README.md
CHANGED
@@ -20,12 +20,22 @@ You need CUDA and Python
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`SAFETY_CHECKER`: disabled if you want NSFW filter off
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`MAX_QUEUE_SIZE`: limit number of users on current app instance
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```bash
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python -m venv venv
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source venv/bin/activate
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pip install -r requirements.txt
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uvicorn "app-img2img:app" --host 0.0.0.0 --port 7860 --reload
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```
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or with environment variables
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```bash
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TIMEOUT=120 SAFETY_CHECKER=True MAX_QUEUE_SIZE=4 uvicorn "app-img2img:app" --host 0.0.0.0 --port 7860 --reload
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`SAFETY_CHECKER`: disabled if you want NSFW filter off
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`MAX_QUEUE_SIZE`: limit number of users on current app instance
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+
### image to image
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```bash
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python -m venv venv
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source venv/bin/activate
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pip install -r requirements.txt
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uvicorn "app-img2img:app" --host 0.0.0.0 --port 7860 --reload
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```
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### text to image
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```bash
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python -m venv venv
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source venv/bin/activate
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pip install -r requirements.txt
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uvicorn "app-txt2img:app" --host 0.0.0.0 --port 7860 --reload
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```
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or with environment variables
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```bash
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TIMEOUT=120 SAFETY_CHECKER=True MAX_QUEUE_SIZE=4 uvicorn "app-img2img:app" --host 0.0.0.0 --port 7860 --reload
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app-img2img.py
CHANGED
@@ -67,7 +67,7 @@ def predict(input_image, prompt, guidance_scale=8.0, strength=0.5, seed=2159232)
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strength=strength,
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num_inference_steps=num_inference_steps,
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guidance_scale=guidance_scale,
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-
lcm_origin_steps=
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output_type="pil",
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)
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nsfw_content_detected = (
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strength=strength,
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num_inference_steps=num_inference_steps,
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guidance_scale=guidance_scale,
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+
lcm_origin_steps=50,
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output_type="pil",
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)
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nsfw_content_detected = (
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app-txt2img.py
ADDED
@@ -0,0 +1,205 @@
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1 |
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import asyncio
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import json
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import logging
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import traceback
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from pydantic import BaseModel
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from fastapi import FastAPI, WebSocket, HTTPException, WebSocketDisconnect
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from fastapi.middleware.cors import CORSMiddleware
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from fastapi.responses import StreamingResponse, JSONResponse
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from fastapi.staticfiles import StaticFiles
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from diffusers import DiffusionPipeline, AutoencoderTiny
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from compel import Compel
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import torch
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from PIL import Image
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import numpy as np
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import gradio as gr
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import io
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import uuid
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import os
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import time
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MAX_QUEUE_SIZE = int(os.environ.get("MAX_QUEUE_SIZE", 0))
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TIMEOUT = float(os.environ.get("TIMEOUT", 0))
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SAFETY_CHECKER = os.environ.get("SAFETY_CHECKER", None)
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print(f"TIMEOUT: {TIMEOUT}")
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print(f"SAFETY_CHECKER: {SAFETY_CHECKER}")
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print(f"MAX_QUEUE_SIZE: {MAX_QUEUE_SIZE}")
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if SAFETY_CHECKER == "True":
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pipe = DiffusionPipeline.from_pretrained(
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"SimianLuo/LCM_Dreamshaper_v7",
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custom_pipeline="latent_consistency_txt2img.py",
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custom_revision="main",
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)
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else:
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pipe = DiffusionPipeline.from_pretrained(
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"SimianLuo/LCM_Dreamshaper_v7",
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safety_checker=None,
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custom_pipeline="latent_consistency_txt2img.py",
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custom_revision="main",
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)
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pipe.vae = AutoencoderTiny.from_pretrained(
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"madebyollin/taesd", torch_dtype=torch.float16, use_safetensors=True
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)
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pipe.set_progress_bar_config(disable=True)
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pipe.to(torch_device="cuda", torch_dtype=torch.float16)
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49 |
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pipe.unet.to(memory_format=torch.channels_last)
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50 |
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compel_proc = Compel(tokenizer=pipe.tokenizer, text_encoder=pipe.text_encoder, truncate_long_prompts=False)
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51 |
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pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
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user_queue_map = {}
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+
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# warmup trigger compilation
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pipe(prompt="warmup", num_inference_steps=1, guidance_scale=8.0)
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+
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def predict(prompt, guidance_scale=8.0, seed=2159232):
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generator = torch.manual_seed(seed)
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prompt_embeds = compel_proc(prompt)
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# Can be set to 1~50 steps. LCM support fast inference even <= 4 steps. Recommend: 1~8 steps.
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num_inference_steps = 8
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results = pipe(
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prompt_embeds=prompt_embeds,
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generator=generator,
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num_inference_steps=num_inference_steps,
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guidance_scale=guidance_scale,
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lcm_origin_steps=50,
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output_type="pil",
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)
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nsfw_content_detected = (
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results.nsfw_content_detected[0]
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if "nsfw_content_detected" in results
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else False
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)
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if nsfw_content_detected:
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return None
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return results.images[0]
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+
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+
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app = FastAPI()
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"],
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allow_credentials=True,
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allow_methods=["*"],
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allow_headers=["*"],
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)
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+
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+
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class InputParams(BaseModel):
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prompt: str
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seed: int
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guidance_scale: float
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+
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95 |
+
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96 |
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@app.websocket("/ws")
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async def websocket_endpoint(websocket: WebSocket):
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await websocket.accept()
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if MAX_QUEUE_SIZE > 0 and len(user_queue_map) >= MAX_QUEUE_SIZE:
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print("Server is full")
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await websocket.send_json({"status": "error", "message": "Server is full"})
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await websocket.close()
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return
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+
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try:
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uid = str(uuid.uuid4())
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print(f"New user connected: {uid}")
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await websocket.send_json(
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{"status": "success", "message": "Connected", "userId": uid}
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)
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user_queue_map[uid] = {
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"queue": asyncio.Queue(),
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}
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await websocket.send_json(
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{"status": "start", "message": "Start Streaming", "userId": uid}
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)
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await handle_websocket_data(websocket, uid)
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except WebSocketDisconnect as e:
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logging.error(f"WebSocket Error: {e}, {uid}")
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traceback.print_exc()
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finally:
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print(f"User disconnected: {uid}")
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queue_value = user_queue_map.pop(uid, None)
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queue = queue_value.get("queue", None)
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if queue:
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while not queue.empty():
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try:
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queue.get_nowait()
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+
except asyncio.QueueEmpty:
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+
continue
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+
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+
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+
@app.get("/queue_size")
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async def get_queue_size():
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queue_size = len(user_queue_map)
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+
return JSONResponse({"queue_size": queue_size})
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+
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+
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+
@app.get("/stream/{user_id}")
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+
async def stream(user_id: uuid.UUID):
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uid = str(user_id)
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try:
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+
user_queue = user_queue_map[uid]
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+
queue = user_queue["queue"]
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+
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146 |
+
async def generate():
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while True:
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+
params = await queue.get()
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+
if params is None:
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+
continue
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+
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152 |
+
image = predict(params.prompt, params.guidance_scale, params.seed)
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153 |
+
if image is None:
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+
continue
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155 |
+
frame_data = io.BytesIO()
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156 |
+
image.save(frame_data, format="JPEG")
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+
frame_data = frame_data.getvalue()
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158 |
+
if frame_data is not None and len(frame_data) > 0:
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+
yield b"--frame\r\nContent-Type: image/jpeg\r\n\r\n" + frame_data + b"\r\n"
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+
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+
await asyncio.sleep(1.0 / 120.0)
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+
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+
return StreamingResponse(
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generate(), media_type="multipart/x-mixed-replace;boundary=frame"
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)
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166 |
+
except Exception as e:
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167 |
+
logging.error(f"Streaming Error: {e}, {user_queue_map}")
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168 |
+
traceback.print_exc()
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169 |
+
return HTTPException(status_code=404, detail="User not found")
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170 |
+
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171 |
+
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172 |
+
async def handle_websocket_data(websocket: WebSocket, user_id: uuid.UUID):
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173 |
+
uid = str(user_id)
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174 |
+
user_queue = user_queue_map[uid]
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175 |
+
queue = user_queue["queue"]
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176 |
+
if not queue:
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177 |
+
return HTTPException(status_code=404, detail="User not found")
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178 |
+
last_time = time.time()
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179 |
+
try:
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180 |
+
while True:
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181 |
+
params = await websocket.receive_json()
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182 |
+
params = InputParams(**params)
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183 |
+
while not queue.empty():
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184 |
+
try:
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185 |
+
queue.get_nowait()
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186 |
+
except asyncio.QueueEmpty:
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187 |
+
continue
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188 |
+
await queue.put(params)
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189 |
+
if TIMEOUT > 0 and time.time() - last_time > TIMEOUT:
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190 |
+
await websocket.send_json(
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191 |
+
{
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192 |
+
"status": "timeout",
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193 |
+
"message": "Your session has ended",
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194 |
+
"userId": uid,
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195 |
+
}
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196 |
+
)
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197 |
+
await websocket.close()
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198 |
+
return
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199 |
+
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200 |
+
except Exception as e:
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201 |
+
logging.error(f"Error: {e}")
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202 |
+
traceback.print_exc()
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203 |
+
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204 |
+
|
205 |
+
app.mount("/", StaticFiles(directory="txt2img", html=True), name="public")
|
latent_consistency_txt2img.py
ADDED
@@ -0,0 +1,836 @@
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|
1 |
+
# Copyright 2023 Stanford University Team and The HuggingFace Team. All rights reserved.
|
2 |
+
#
|
3 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
4 |
+
# you may not use this file except in compliance with the License.
|
5 |
+
# You may obtain a copy of the License at
|
6 |
+
#
|
7 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
8 |
+
#
|
9 |
+
# Unless required by applicable law or agreed to in writing, software
|
10 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
11 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
12 |
+
# See the License for the specific language governing permissions and
|
13 |
+
# limitations under the License.
|
14 |
+
|
15 |
+
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
|
16 |
+
# and https://github.com/hojonathanho/diffusion
|
17 |
+
|
18 |
+
import math
|
19 |
+
from dataclasses import dataclass
|
20 |
+
from typing import Any, Dict, List, Optional, Tuple, Union
|
21 |
+
|
22 |
+
import numpy as np
|
23 |
+
import torch
|
24 |
+
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
|
25 |
+
|
26 |
+
from diffusers import (
|
27 |
+
AutoencoderKL,
|
28 |
+
ConfigMixin,
|
29 |
+
DiffusionPipeline,
|
30 |
+
SchedulerMixin,
|
31 |
+
UNet2DConditionModel,
|
32 |
+
logging,
|
33 |
+
)
|
34 |
+
from diffusers.configuration_utils import register_to_config
|
35 |
+
from diffusers.image_processor import VaeImageProcessor
|
36 |
+
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
|
37 |
+
from diffusers.pipelines.stable_diffusion.safety_checker import (
|
38 |
+
StableDiffusionSafetyChecker,
|
39 |
+
)
|
40 |
+
from diffusers.utils import BaseOutput
|
41 |
+
|
42 |
+
|
43 |
+
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
|
44 |
+
|
45 |
+
|
46 |
+
class LatentConsistencyModelPipeline(DiffusionPipeline):
|
47 |
+
_optional_components = ["scheduler"]
|
48 |
+
|
49 |
+
def __init__(
|
50 |
+
self,
|
51 |
+
vae: AutoencoderKL,
|
52 |
+
text_encoder: CLIPTextModel,
|
53 |
+
tokenizer: CLIPTokenizer,
|
54 |
+
unet: UNet2DConditionModel,
|
55 |
+
scheduler: "LCMScheduler",
|
56 |
+
safety_checker: StableDiffusionSafetyChecker,
|
57 |
+
feature_extractor: CLIPImageProcessor,
|
58 |
+
requires_safety_checker: bool = True,
|
59 |
+
):
|
60 |
+
super().__init__()
|
61 |
+
|
62 |
+
scheduler = (
|
63 |
+
scheduler
|
64 |
+
if scheduler is not None
|
65 |
+
else LCMScheduler(
|
66 |
+
beta_start=0.00085,
|
67 |
+
beta_end=0.0120,
|
68 |
+
beta_schedule="scaled_linear",
|
69 |
+
prediction_type="epsilon",
|
70 |
+
)
|
71 |
+
)
|
72 |
+
|
73 |
+
self.register_modules(
|
74 |
+
vae=vae,
|
75 |
+
text_encoder=text_encoder,
|
76 |
+
tokenizer=tokenizer,
|
77 |
+
unet=unet,
|
78 |
+
scheduler=scheduler,
|
79 |
+
safety_checker=safety_checker,
|
80 |
+
feature_extractor=feature_extractor,
|
81 |
+
)
|
82 |
+
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
|
83 |
+
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
|
84 |
+
|
85 |
+
def _encode_prompt(
|
86 |
+
self,
|
87 |
+
prompt,
|
88 |
+
device,
|
89 |
+
num_images_per_prompt,
|
90 |
+
prompt_embeds: None,
|
91 |
+
):
|
92 |
+
r"""
|
93 |
+
Encodes the prompt into text encoder hidden states.
|
94 |
+
Args:
|
95 |
+
prompt (`str` or `List[str]`, *optional*):
|
96 |
+
prompt to be encoded
|
97 |
+
device: (`torch.device`):
|
98 |
+
torch device
|
99 |
+
num_images_per_prompt (`int`):
|
100 |
+
number of images that should be generated per prompt
|
101 |
+
prompt_embeds (`torch.FloatTensor`, *optional*):
|
102 |
+
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
|
103 |
+
provided, text embeddings will be generated from `prompt` input argument.
|
104 |
+
"""
|
105 |
+
|
106 |
+
if prompt is not None and isinstance(prompt, str):
|
107 |
+
pass
|
108 |
+
elif prompt is not None and isinstance(prompt, list):
|
109 |
+
len(prompt)
|
110 |
+
else:
|
111 |
+
prompt_embeds.shape[0]
|
112 |
+
|
113 |
+
if prompt_embeds is None:
|
114 |
+
text_inputs = self.tokenizer(
|
115 |
+
prompt,
|
116 |
+
padding="max_length",
|
117 |
+
max_length=self.tokenizer.model_max_length,
|
118 |
+
truncation=True,
|
119 |
+
return_tensors="pt",
|
120 |
+
)
|
121 |
+
text_input_ids = text_inputs.input_ids
|
122 |
+
untruncated_ids = self.tokenizer(
|
123 |
+
prompt, padding="longest", return_tensors="pt"
|
124 |
+
).input_ids
|
125 |
+
|
126 |
+
if untruncated_ids.shape[-1] >= text_input_ids.shape[
|
127 |
+
-1
|
128 |
+
] and not torch.equal(text_input_ids, untruncated_ids):
|
129 |
+
removed_text = self.tokenizer.batch_decode(
|
130 |
+
untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
|
131 |
+
)
|
132 |
+
logger.warning(
|
133 |
+
"The following part of your input was truncated because CLIP can only handle sequences up to"
|
134 |
+
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
|
135 |
+
)
|
136 |
+
|
137 |
+
if (
|
138 |
+
hasattr(self.text_encoder.config, "use_attention_mask")
|
139 |
+
and self.text_encoder.config.use_attention_mask
|
140 |
+
):
|
141 |
+
attention_mask = text_inputs.attention_mask.to(device)
|
142 |
+
else:
|
143 |
+
attention_mask = None
|
144 |
+
|
145 |
+
prompt_embeds = self.text_encoder(
|
146 |
+
text_input_ids.to(device),
|
147 |
+
attention_mask=attention_mask,
|
148 |
+
)
|
149 |
+
prompt_embeds = prompt_embeds[0]
|
150 |
+
|
151 |
+
if self.text_encoder is not None:
|
152 |
+
prompt_embeds_dtype = self.text_encoder.dtype
|
153 |
+
elif self.unet is not None:
|
154 |
+
prompt_embeds_dtype = self.unet.dtype
|
155 |
+
else:
|
156 |
+
prompt_embeds_dtype = prompt_embeds.dtype
|
157 |
+
|
158 |
+
prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)
|
159 |
+
|
160 |
+
bs_embed, seq_len, _ = prompt_embeds.shape
|
161 |
+
# duplicate text embeddings for each generation per prompt, using mps friendly method
|
162 |
+
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
|
163 |
+
prompt_embeds = prompt_embeds.view(
|
164 |
+
bs_embed * num_images_per_prompt, seq_len, -1
|
165 |
+
)
|
166 |
+
|
167 |
+
# Don't need to get uncond prompt embedding because of LCM Guided Distillation
|
168 |
+
return prompt_embeds
|
169 |
+
|
170 |
+
def run_safety_checker(self, image, device, dtype):
|
171 |
+
if self.safety_checker is None:
|
172 |
+
has_nsfw_concept = None
|
173 |
+
else:
|
174 |
+
if torch.is_tensor(image):
|
175 |
+
feature_extractor_input = self.image_processor.postprocess(
|
176 |
+
image, output_type="pil"
|
177 |
+
)
|
178 |
+
else:
|
179 |
+
feature_extractor_input = self.image_processor.numpy_to_pil(image)
|
180 |
+
safety_checker_input = self.feature_extractor(
|
181 |
+
feature_extractor_input, return_tensors="pt"
|
182 |
+
).to(device)
|
183 |
+
image, has_nsfw_concept = self.safety_checker(
|
184 |
+
images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
|
185 |
+
)
|
186 |
+
return image, has_nsfw_concept
|
187 |
+
|
188 |
+
def prepare_latents(
|
189 |
+
self,
|
190 |
+
batch_size,
|
191 |
+
num_channels_latents,
|
192 |
+
height,
|
193 |
+
width,
|
194 |
+
dtype,
|
195 |
+
device,
|
196 |
+
latents=None,
|
197 |
+
generator=None,
|
198 |
+
):
|
199 |
+
shape = (
|
200 |
+
batch_size,
|
201 |
+
num_channels_latents,
|
202 |
+
height // self.vae_scale_factor,
|
203 |
+
width // self.vae_scale_factor,
|
204 |
+
)
|
205 |
+
if generator is None:
|
206 |
+
generator = torch.Generator()
|
207 |
+
generator.manual_seed(torch.randint(0, 2 ** 32, (1,)).item())
|
208 |
+
|
209 |
+
if latents is None:
|
210 |
+
latents = torch.randn(shape, dtype=dtype, generator=generator).to(device)
|
211 |
+
else:
|
212 |
+
latents = latents.to(device)
|
213 |
+
# scale the initial noise by the standard deviation required by the scheduler
|
214 |
+
latents = latents * self.scheduler.init_noise_sigma
|
215 |
+
return latents
|
216 |
+
|
217 |
+
def get_w_embedding(self, w, embedding_dim=512, dtype=torch.float32):
|
218 |
+
"""
|
219 |
+
see https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298
|
220 |
+
Args:
|
221 |
+
timesteps: torch.Tensor: generate embedding vectors at these timesteps
|
222 |
+
embedding_dim: int: dimension of the embeddings to generate
|
223 |
+
dtype: data type of the generated embeddings
|
224 |
+
Returns:
|
225 |
+
embedding vectors with shape `(len(timesteps), embedding_dim)`
|
226 |
+
"""
|
227 |
+
assert len(w.shape) == 1
|
228 |
+
w = w * 1000.0
|
229 |
+
|
230 |
+
half_dim = embedding_dim // 2
|
231 |
+
emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)
|
232 |
+
emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)
|
233 |
+
emb = w.to(dtype)[:, None] * emb[None, :]
|
234 |
+
emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
|
235 |
+
if embedding_dim % 2 == 1: # zero pad
|
236 |
+
emb = torch.nn.functional.pad(emb, (0, 1))
|
237 |
+
assert emb.shape == (w.shape[0], embedding_dim)
|
238 |
+
return emb
|
239 |
+
|
240 |
+
@torch.no_grad()
|
241 |
+
def __call__(
|
242 |
+
self,
|
243 |
+
prompt: Union[str, List[str]] = None,
|
244 |
+
height: Optional[int] = 768,
|
245 |
+
width: Optional[int] = 768,
|
246 |
+
guidance_scale: float = 7.5,
|
247 |
+
num_images_per_prompt: Optional[int] = 1,
|
248 |
+
latents: Optional[torch.FloatTensor] = None,
|
249 |
+
generator: Optional[torch.Generator] = None,
|
250 |
+
num_inference_steps: int = 4,
|
251 |
+
lcm_origin_steps: int = 50,
|
252 |
+
prompt_embeds: Optional[torch.FloatTensor] = None,
|
253 |
+
output_type: Optional[str] = "pil",
|
254 |
+
return_dict: bool = True,
|
255 |
+
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
|
256 |
+
):
|
257 |
+
# 0. Default height and width to unet
|
258 |
+
height = height or self.unet.config.sample_size * self.vae_scale_factor
|
259 |
+
width = width or self.unet.config.sample_size * self.vae_scale_factor
|
260 |
+
|
261 |
+
# 2. Define call parameters
|
262 |
+
if prompt is not None and isinstance(prompt, str):
|
263 |
+
batch_size = 1
|
264 |
+
elif prompt is not None and isinstance(prompt, list):
|
265 |
+
batch_size = len(prompt)
|
266 |
+
else:
|
267 |
+
batch_size = prompt_embeds.shape[0]
|
268 |
+
|
269 |
+
device = self._execution_device
|
270 |
+
# do_classifier_free_guidance = guidance_scale > 0.0 # In LCM Implementation: cfg_noise = noise_cond + cfg_scale * (noise_cond - noise_uncond) , (cfg_scale > 0.0 using CFG)
|
271 |
+
|
272 |
+
# 3. Encode input prompt
|
273 |
+
prompt_embeds = self._encode_prompt(
|
274 |
+
prompt,
|
275 |
+
device,
|
276 |
+
num_images_per_prompt,
|
277 |
+
prompt_embeds=prompt_embeds,
|
278 |
+
)
|
279 |
+
|
280 |
+
# 4. Prepare timesteps
|
281 |
+
self.scheduler.set_timesteps(num_inference_steps, lcm_origin_steps)
|
282 |
+
timesteps = self.scheduler.timesteps
|
283 |
+
|
284 |
+
# 5. Prepare latent variable
|
285 |
+
num_channels_latents = self.unet.config.in_channels
|
286 |
+
latents = self.prepare_latents(
|
287 |
+
batch_size * num_images_per_prompt,
|
288 |
+
num_channels_latents,
|
289 |
+
height,
|
290 |
+
width,
|
291 |
+
prompt_embeds.dtype,
|
292 |
+
device,
|
293 |
+
latents,
|
294 |
+
generator
|
295 |
+
)
|
296 |
+
bs = batch_size * num_images_per_prompt
|
297 |
+
|
298 |
+
# 6. Get Guidance Scale Embedding
|
299 |
+
w = torch.tensor(guidance_scale).repeat(bs)
|
300 |
+
w_embedding = self.get_w_embedding(w, embedding_dim=256).to(
|
301 |
+
device=device, dtype=latents.dtype
|
302 |
+
)
|
303 |
+
|
304 |
+
# 7. LCM MultiStep Sampling Loop:
|
305 |
+
with self.progress_bar(total=num_inference_steps) as progress_bar:
|
306 |
+
for i, t in enumerate(timesteps):
|
307 |
+
ts = torch.full((bs,), t, device=device, dtype=torch.long)
|
308 |
+
latents = latents.to(prompt_embeds.dtype)
|
309 |
+
|
310 |
+
# model prediction (v-prediction, eps, x)
|
311 |
+
model_pred = self.unet(
|
312 |
+
latents,
|
313 |
+
ts,
|
314 |
+
timestep_cond=w_embedding,
|
315 |
+
encoder_hidden_states=prompt_embeds,
|
316 |
+
cross_attention_kwargs=cross_attention_kwargs,
|
317 |
+
return_dict=False,
|
318 |
+
)[0]
|
319 |
+
|
320 |
+
# compute the previous noisy sample x_t -> x_t-1
|
321 |
+
latents, denoised = self.scheduler.step(
|
322 |
+
model_pred, i, t, latents, return_dict=False
|
323 |
+
)
|
324 |
+
|
325 |
+
# # call the callback, if provided
|
326 |
+
# if i == len(timesteps) - 1:
|
327 |
+
progress_bar.update()
|
328 |
+
|
329 |
+
denoised = denoised.to(prompt_embeds.dtype)
|
330 |
+
if not output_type == "latent":
|
331 |
+
image = self.vae.decode(
|
332 |
+
denoised / self.vae.config.scaling_factor, return_dict=False
|
333 |
+
)[0]
|
334 |
+
image, has_nsfw_concept = self.run_safety_checker(
|
335 |
+
image, device, prompt_embeds.dtype
|
336 |
+
)
|
337 |
+
else:
|
338 |
+
image = denoised
|
339 |
+
has_nsfw_concept = None
|
340 |
+
|
341 |
+
if has_nsfw_concept is None:
|
342 |
+
do_denormalize = [True] * image.shape[0]
|
343 |
+
else:
|
344 |
+
do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
|
345 |
+
|
346 |
+
image = self.image_processor.postprocess(
|
347 |
+
image, output_type=output_type, do_denormalize=do_denormalize
|
348 |
+
)
|
349 |
+
|
350 |
+
if not return_dict:
|
351 |
+
return (image, has_nsfw_concept)
|
352 |
+
|
353 |
+
return StableDiffusionPipelineOutput(
|
354 |
+
images=image, nsfw_content_detected=has_nsfw_concept
|
355 |
+
)
|
356 |
+
|
357 |
+
|
358 |
+
@dataclass
|
359 |
+
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
|
360 |
+
class LCMSchedulerOutput(BaseOutput):
|
361 |
+
"""
|
362 |
+
Output class for the scheduler's `step` function output.
|
363 |
+
Args:
|
364 |
+
prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
|
365 |
+
Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the
|
366 |
+
denoising loop.
|
367 |
+
pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
|
368 |
+
The predicted denoised sample `(x_{0})` based on the model output from the current timestep.
|
369 |
+
`pred_original_sample` can be used to preview progress or for guidance.
|
370 |
+
"""
|
371 |
+
|
372 |
+
prev_sample: torch.FloatTensor
|
373 |
+
denoised: Optional[torch.FloatTensor] = None
|
374 |
+
|
375 |
+
|
376 |
+
# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar
|
377 |
+
def betas_for_alpha_bar(
|
378 |
+
num_diffusion_timesteps,
|
379 |
+
max_beta=0.999,
|
380 |
+
alpha_transform_type="cosine",
|
381 |
+
):
|
382 |
+
"""
|
383 |
+
Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
|
384 |
+
(1-beta) over time from t = [0,1].
|
385 |
+
Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up
|
386 |
+
to that part of the diffusion process.
|
387 |
+
Args:
|
388 |
+
num_diffusion_timesteps (`int`): the number of betas to produce.
|
389 |
+
max_beta (`float`): the maximum beta to use; use values lower than 1 to
|
390 |
+
prevent singularities.
|
391 |
+
alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar.
|
392 |
+
Choose from `cosine` or `exp`
|
393 |
+
Returns:
|
394 |
+
betas (`np.ndarray`): the betas used by the scheduler to step the model outputs
|
395 |
+
"""
|
396 |
+
if alpha_transform_type == "cosine":
|
397 |
+
|
398 |
+
def alpha_bar_fn(t):
|
399 |
+
return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2
|
400 |
+
|
401 |
+
elif alpha_transform_type == "exp":
|
402 |
+
|
403 |
+
def alpha_bar_fn(t):
|
404 |
+
return math.exp(t * -12.0)
|
405 |
+
|
406 |
+
else:
|
407 |
+
raise ValueError(f"Unsupported alpha_tranform_type: {alpha_transform_type}")
|
408 |
+
|
409 |
+
betas = []
|
410 |
+
for i in range(num_diffusion_timesteps):
|
411 |
+
t1 = i / num_diffusion_timesteps
|
412 |
+
t2 = (i + 1) / num_diffusion_timesteps
|
413 |
+
betas.append(min(1 - alpha_bar_fn(t2) / alpha_bar_fn(t1), max_beta))
|
414 |
+
return torch.tensor(betas, dtype=torch.float32)
|
415 |
+
|
416 |
+
|
417 |
+
def rescale_zero_terminal_snr(betas):
|
418 |
+
"""
|
419 |
+
Rescales betas to have zero terminal SNR Based on https://arxiv.org/pdf/2305.08891.pdf (Algorithm 1)
|
420 |
+
Args:
|
421 |
+
betas (`torch.FloatTensor`):
|
422 |
+
the betas that the scheduler is being initialized with.
|
423 |
+
Returns:
|
424 |
+
`torch.FloatTensor`: rescaled betas with zero terminal SNR
|
425 |
+
"""
|
426 |
+
# Convert betas to alphas_bar_sqrt
|
427 |
+
alphas = 1.0 - betas
|
428 |
+
alphas_cumprod = torch.cumprod(alphas, dim=0)
|
429 |
+
alphas_bar_sqrt = alphas_cumprod.sqrt()
|
430 |
+
|
431 |
+
# Store old values.
|
432 |
+
alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone()
|
433 |
+
alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone()
|
434 |
+
|
435 |
+
# Shift so the last timestep is zero.
|
436 |
+
alphas_bar_sqrt -= alphas_bar_sqrt_T
|
437 |
+
|
438 |
+
# Scale so the first timestep is back to the old value.
|
439 |
+
alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T)
|
440 |
+
|
441 |
+
# Convert alphas_bar_sqrt to betas
|
442 |
+
alphas_bar = alphas_bar_sqrt**2 # Revert sqrt
|
443 |
+
alphas = alphas_bar[1:] / alphas_bar[:-1] # Revert cumprod
|
444 |
+
alphas = torch.cat([alphas_bar[0:1], alphas])
|
445 |
+
betas = 1 - alphas
|
446 |
+
|
447 |
+
return betas
|
448 |
+
|
449 |
+
|
450 |
+
class LCMScheduler(SchedulerMixin, ConfigMixin):
|
451 |
+
"""
|
452 |
+
`LCMScheduler` extends the denoising procedure introduced in denoising diffusion probabilistic models (DDPMs) with
|
453 |
+
non-Markovian guidance.
|
454 |
+
This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic
|
455 |
+
methods the library implements for all schedulers such as loading and saving.
|
456 |
+
Args:
|
457 |
+
num_train_timesteps (`int`, defaults to 1000):
|
458 |
+
The number of diffusion steps to train the model.
|
459 |
+
beta_start (`float`, defaults to 0.0001):
|
460 |
+
The starting `beta` value of inference.
|
461 |
+
beta_end (`float`, defaults to 0.02):
|
462 |
+
The final `beta` value.
|
463 |
+
beta_schedule (`str`, defaults to `"linear"`):
|
464 |
+
The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from
|
465 |
+
`linear`, `scaled_linear`, or `squaredcos_cap_v2`.
|
466 |
+
trained_betas (`np.ndarray`, *optional*):
|
467 |
+
Pass an array of betas directly to the constructor to bypass `beta_start` and `beta_end`.
|
468 |
+
clip_sample (`bool`, defaults to `True`):
|
469 |
+
Clip the predicted sample for numerical stability.
|
470 |
+
clip_sample_range (`float`, defaults to 1.0):
|
471 |
+
The maximum magnitude for sample clipping. Valid only when `clip_sample=True`.
|
472 |
+
set_alpha_to_one (`bool`, defaults to `True`):
|
473 |
+
Each diffusion step uses the alphas product value at that step and at the previous one. For the final step
|
474 |
+
there is no previous alpha. When this option is `True` the previous alpha product is fixed to `1`,
|
475 |
+
otherwise it uses the alpha value at step 0.
|
476 |
+
steps_offset (`int`, defaults to 0):
|
477 |
+
An offset added to the inference steps. You can use a combination of `offset=1` and
|
478 |
+
`set_alpha_to_one=False` to make the last step use step 0 for the previous alpha product like in Stable
|
479 |
+
Diffusion.
|
480 |
+
prediction_type (`str`, defaults to `epsilon`, *optional*):
|
481 |
+
Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process),
|
482 |
+
`sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen
|
483 |
+
Video](https://imagen.research.google/video/paper.pdf) paper).
|
484 |
+
thresholding (`bool`, defaults to `False`):
|
485 |
+
Whether to use the "dynamic thresholding" method. This is unsuitable for latent-space diffusion models such
|
486 |
+
as Stable Diffusion.
|
487 |
+
dynamic_thresholding_ratio (`float`, defaults to 0.995):
|
488 |
+
The ratio for the dynamic thresholding method. Valid only when `thresholding=True`.
|
489 |
+
sample_max_value (`float`, defaults to 1.0):
|
490 |
+
The threshold value for dynamic thresholding. Valid only when `thresholding=True`.
|
491 |
+
timestep_spacing (`str`, defaults to `"leading"`):
|
492 |
+
The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and
|
493 |
+
Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information.
|
494 |
+
rescale_betas_zero_snr (`bool`, defaults to `False`):
|
495 |
+
Whether to rescale the betas to have zero terminal SNR. This enables the model to generate very bright and
|
496 |
+
dark samples instead of limiting it to samples with medium brightness. Loosely related to
|
497 |
+
[`--offset_noise`](https://github.com/huggingface/diffusers/blob/74fd735eb073eb1d774b1ab4154a0876eb82f055/examples/dreambooth/train_dreambooth.py#L506).
|
498 |
+
"""
|
499 |
+
|
500 |
+
# _compatibles = [e.name for e in KarrasDiffusionSchedulers]
|
501 |
+
order = 1
|
502 |
+
|
503 |
+
@register_to_config
|
504 |
+
def __init__(
|
505 |
+
self,
|
506 |
+
num_train_timesteps: int = 1000,
|
507 |
+
beta_start: float = 0.0001,
|
508 |
+
beta_end: float = 0.02,
|
509 |
+
beta_schedule: str = "linear",
|
510 |
+
trained_betas: Optional[Union[np.ndarray, List[float]]] = None,
|
511 |
+
clip_sample: bool = True,
|
512 |
+
set_alpha_to_one: bool = True,
|
513 |
+
steps_offset: int = 0,
|
514 |
+
prediction_type: str = "epsilon",
|
515 |
+
thresholding: bool = False,
|
516 |
+
dynamic_thresholding_ratio: float = 0.995,
|
517 |
+
clip_sample_range: float = 1.0,
|
518 |
+
sample_max_value: float = 1.0,
|
519 |
+
timestep_spacing: str = "leading",
|
520 |
+
rescale_betas_zero_snr: bool = False,
|
521 |
+
):
|
522 |
+
if trained_betas is not None:
|
523 |
+
self.betas = torch.tensor(trained_betas, dtype=torch.float32)
|
524 |
+
elif beta_schedule == "linear":
|
525 |
+
self.betas = torch.linspace(
|
526 |
+
beta_start, beta_end, num_train_timesteps, dtype=torch.float32
|
527 |
+
)
|
528 |
+
elif beta_schedule == "scaled_linear":
|
529 |
+
# this schedule is very specific to the latent diffusion model.
|
530 |
+
self.betas = (
|
531 |
+
torch.linspace(
|
532 |
+
beta_start**0.5,
|
533 |
+
beta_end**0.5,
|
534 |
+
num_train_timesteps,
|
535 |
+
dtype=torch.float32,
|
536 |
+
)
|
537 |
+
** 2
|
538 |
+
)
|
539 |
+
elif beta_schedule == "squaredcos_cap_v2":
|
540 |
+
# Glide cosine schedule
|
541 |
+
self.betas = betas_for_alpha_bar(num_train_timesteps)
|
542 |
+
else:
|
543 |
+
raise NotImplementedError(
|
544 |
+
f"{beta_schedule} does is not implemented for {self.__class__}"
|
545 |
+
)
|
546 |
+
|
547 |
+
# Rescale for zero SNR
|
548 |
+
if rescale_betas_zero_snr:
|
549 |
+
self.betas = rescale_zero_terminal_snr(self.betas)
|
550 |
+
|
551 |
+
self.alphas = 1.0 - self.betas
|
552 |
+
self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)
|
553 |
+
|
554 |
+
# At every step in ddim, we are looking into the previous alphas_cumprod
|
555 |
+
# For the final step, there is no previous alphas_cumprod because we are already at 0
|
556 |
+
# `set_alpha_to_one` decides whether we set this parameter simply to one or
|
557 |
+
# whether we use the final alpha of the "non-previous" one.
|
558 |
+
self.final_alpha_cumprod = (
|
559 |
+
torch.tensor(1.0) if set_alpha_to_one else self.alphas_cumprod[0]
|
560 |
+
)
|
561 |
+
|
562 |
+
# standard deviation of the initial noise distribution
|
563 |
+
self.init_noise_sigma = 1.0
|
564 |
+
|
565 |
+
# setable values
|
566 |
+
self.num_inference_steps = None
|
567 |
+
self.timesteps = torch.from_numpy(
|
568 |
+
np.arange(0, num_train_timesteps)[::-1].copy().astype(np.int64)
|
569 |
+
)
|
570 |
+
|
571 |
+
def scale_model_input(
|
572 |
+
self, sample: torch.FloatTensor, timestep: Optional[int] = None
|
573 |
+
) -> torch.FloatTensor:
|
574 |
+
"""
|
575 |
+
Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
|
576 |
+
current timestep.
|
577 |
+
Args:
|
578 |
+
sample (`torch.FloatTensor`):
|
579 |
+
The input sample.
|
580 |
+
timestep (`int`, *optional*):
|
581 |
+
The current timestep in the diffusion chain.
|
582 |
+
Returns:
|
583 |
+
`torch.FloatTensor`:
|
584 |
+
A scaled input sample.
|
585 |
+
"""
|
586 |
+
return sample
|
587 |
+
|
588 |
+
def _get_variance(self, timestep, prev_timestep):
|
589 |
+
alpha_prod_t = self.alphas_cumprod[timestep]
|
590 |
+
alpha_prod_t_prev = (
|
591 |
+
self.alphas_cumprod[prev_timestep]
|
592 |
+
if prev_timestep >= 0
|
593 |
+
else self.final_alpha_cumprod
|
594 |
+
)
|
595 |
+
beta_prod_t = 1 - alpha_prod_t
|
596 |
+
beta_prod_t_prev = 1 - alpha_prod_t_prev
|
597 |
+
|
598 |
+
variance = (beta_prod_t_prev / beta_prod_t) * (
|
599 |
+
1 - alpha_prod_t / alpha_prod_t_prev
|
600 |
+
)
|
601 |
+
|
602 |
+
return variance
|
603 |
+
|
604 |
+
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler._threshold_sample
|
605 |
+
def _threshold_sample(self, sample: torch.FloatTensor) -> torch.FloatTensor:
|
606 |
+
"""
|
607 |
+
"Dynamic thresholding: At each sampling step we set s to a certain percentile absolute pixel value in xt0 (the
|
608 |
+
prediction of x_0 at timestep t), and if s > 1, then we threshold xt0 to the range [-s, s] and then divide by
|
609 |
+
s. Dynamic thresholding pushes saturated pixels (those near -1 and 1) inwards, thereby actively preventing
|
610 |
+
pixels from saturation at each step. We find that dynamic thresholding results in significantly better
|
611 |
+
photorealism as well as better image-text alignment, especially when using very large guidance weights."
|
612 |
+
https://arxiv.org/abs/2205.11487
|
613 |
+
"""
|
614 |
+
dtype = sample.dtype
|
615 |
+
batch_size, channels, height, width = sample.shape
|
616 |
+
|
617 |
+
if dtype not in (torch.float32, torch.float64):
|
618 |
+
sample = (
|
619 |
+
sample.float()
|
620 |
+
) # upcast for quantile calculation, and clamp not implemented for cpu half
|
621 |
+
|
622 |
+
# Flatten sample for doing quantile calculation along each image
|
623 |
+
sample = sample.reshape(batch_size, channels * height * width)
|
624 |
+
|
625 |
+
abs_sample = sample.abs() # "a certain percentile absolute pixel value"
|
626 |
+
|
627 |
+
s = torch.quantile(abs_sample, self.config.dynamic_thresholding_ratio, dim=1)
|
628 |
+
s = torch.clamp(
|
629 |
+
s, min=1, max=self.config.sample_max_value
|
630 |
+
) # When clamped to min=1, equivalent to standard clipping to [-1, 1]
|
631 |
+
|
632 |
+
s = s.unsqueeze(1) # (batch_size, 1) because clamp will broadcast along dim=0
|
633 |
+
sample = (
|
634 |
+
torch.clamp(sample, -s, s) / s
|
635 |
+
) # "we threshold xt0 to the range [-s, s] and then divide by s"
|
636 |
+
|
637 |
+
sample = sample.reshape(batch_size, channels, height, width)
|
638 |
+
sample = sample.to(dtype)
|
639 |
+
|
640 |
+
return sample
|
641 |
+
|
642 |
+
def set_timesteps(
|
643 |
+
self,
|
644 |
+
num_inference_steps: int,
|
645 |
+
lcm_origin_steps: int,
|
646 |
+
device: Union[str, torch.device] = None,
|
647 |
+
):
|
648 |
+
"""
|
649 |
+
Sets the discrete timesteps used for the diffusion chain (to be run before inference).
|
650 |
+
Args:
|
651 |
+
num_inference_steps (`int`):
|
652 |
+
The number of diffusion steps used when generating samples with a pre-trained model.
|
653 |
+
"""
|
654 |
+
|
655 |
+
if num_inference_steps > self.config.num_train_timesteps:
|
656 |
+
raise ValueError(
|
657 |
+
f"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"
|
658 |
+
f" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"
|
659 |
+
f" maximal {self.config.num_train_timesteps} timesteps."
|
660 |
+
)
|
661 |
+
|
662 |
+
self.num_inference_steps = num_inference_steps
|
663 |
+
|
664 |
+
# LCM Timesteps Setting: # Linear Spacing
|
665 |
+
c = self.config.num_train_timesteps // lcm_origin_steps
|
666 |
+
lcm_origin_timesteps = (
|
667 |
+
np.asarray(list(range(1, lcm_origin_steps + 1))) * c - 1
|
668 |
+
) # LCM Training Steps Schedule
|
669 |
+
skipping_step = len(lcm_origin_timesteps) // num_inference_steps
|
670 |
+
timesteps = lcm_origin_timesteps[::-skipping_step][
|
671 |
+
:num_inference_steps
|
672 |
+
] # LCM Inference Steps Schedule
|
673 |
+
|
674 |
+
self.timesteps = torch.from_numpy(timesteps.copy()).to(device)
|
675 |
+
|
676 |
+
def get_scalings_for_boundary_condition_discrete(self, t):
|
677 |
+
self.sigma_data = 0.5 # Default: 0.5
|
678 |
+
|
679 |
+
# By dividing 0.1: This is almost a delta function at t=0.
|
680 |
+
c_skip = self.sigma_data**2 / ((t / 0.1) ** 2 + self.sigma_data**2)
|
681 |
+
c_out = (t / 0.1) / ((t / 0.1) ** 2 + self.sigma_data**2) ** 0.5
|
682 |
+
return c_skip, c_out
|
683 |
+
|
684 |
+
def step(
|
685 |
+
self,
|
686 |
+
model_output: torch.FloatTensor,
|
687 |
+
timeindex: int,
|
688 |
+
timestep: int,
|
689 |
+
sample: torch.FloatTensor,
|
690 |
+
eta: float = 0.0,
|
691 |
+
use_clipped_model_output: bool = False,
|
692 |
+
generator=None,
|
693 |
+
variance_noise: Optional[torch.FloatTensor] = None,
|
694 |
+
return_dict: bool = True,
|
695 |
+
) -> Union[LCMSchedulerOutput, Tuple]:
|
696 |
+
"""
|
697 |
+
Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion
|
698 |
+
process from the learned model outputs (most often the predicted noise).
|
699 |
+
Args:
|
700 |
+
model_output (`torch.FloatTensor`):
|
701 |
+
The direct output from learned diffusion model.
|
702 |
+
timestep (`float`):
|
703 |
+
The current discrete timestep in the diffusion chain.
|
704 |
+
sample (`torch.FloatTensor`):
|
705 |
+
A current instance of a sample created by the diffusion process.
|
706 |
+
eta (`float`):
|
707 |
+
The weight of noise for added noise in diffusion step.
|
708 |
+
use_clipped_model_output (`bool`, defaults to `False`):
|
709 |
+
If `True`, computes "corrected" `model_output` from the clipped predicted original sample. Necessary
|
710 |
+
because predicted original sample is clipped to [-1, 1] when `self.config.clip_sample` is `True`. If no
|
711 |
+
clipping has happened, "corrected" `model_output` would coincide with the one provided as input and
|
712 |
+
`use_clipped_model_output` has no effect.
|
713 |
+
generator (`torch.Generator`, *optional*):
|
714 |
+
A random number generator.
|
715 |
+
variance_noise (`torch.FloatTensor`):
|
716 |
+
Alternative to generating noise with `generator` by directly providing the noise for the variance
|
717 |
+
itself. Useful for methods such as [`CycleDiffusion`].
|
718 |
+
return_dict (`bool`, *optional*, defaults to `True`):
|
719 |
+
Whether or not to return a [`~schedulers.scheduling_lcm.LCMSchedulerOutput`] or `tuple`.
|
720 |
+
Returns:
|
721 |
+
[`~schedulers.scheduling_utils.LCMSchedulerOutput`] or `tuple`:
|
722 |
+
If return_dict is `True`, [`~schedulers.scheduling_lcm.LCMSchedulerOutput`] is returned, otherwise a
|
723 |
+
tuple is returned where the first element is the sample tensor.
|
724 |
+
"""
|
725 |
+
if self.num_inference_steps is None:
|
726 |
+
raise ValueError(
|
727 |
+
"Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"
|
728 |
+
)
|
729 |
+
|
730 |
+
# 1. get previous step value
|
731 |
+
prev_timeindex = timeindex + 1
|
732 |
+
if prev_timeindex < len(self.timesteps):
|
733 |
+
prev_timestep = self.timesteps[prev_timeindex]
|
734 |
+
else:
|
735 |
+
prev_timestep = timestep
|
736 |
+
|
737 |
+
# 2. compute alphas, betas
|
738 |
+
alpha_prod_t = self.alphas_cumprod[timestep]
|
739 |
+
alpha_prod_t_prev = (
|
740 |
+
self.alphas_cumprod[prev_timestep]
|
741 |
+
if prev_timestep >= 0
|
742 |
+
else self.final_alpha_cumprod
|
743 |
+
)
|
744 |
+
|
745 |
+
beta_prod_t = 1 - alpha_prod_t
|
746 |
+
beta_prod_t_prev = 1 - alpha_prod_t_prev
|
747 |
+
|
748 |
+
# 3. Get scalings for boundary conditions
|
749 |
+
c_skip, c_out = self.get_scalings_for_boundary_condition_discrete(timestep)
|
750 |
+
|
751 |
+
# 4. Different Parameterization:
|
752 |
+
parameterization = self.config.prediction_type
|
753 |
+
|
754 |
+
if parameterization == "epsilon": # noise-prediction
|
755 |
+
pred_x0 = (sample - beta_prod_t.sqrt() * model_output) / alpha_prod_t.sqrt()
|
756 |
+
|
757 |
+
elif parameterization == "sample": # x-prediction
|
758 |
+
pred_x0 = model_output
|
759 |
+
|
760 |
+
elif parameterization == "v_prediction": # v-prediction
|
761 |
+
pred_x0 = alpha_prod_t.sqrt() * sample - beta_prod_t.sqrt() * model_output
|
762 |
+
|
763 |
+
# 4. Denoise model output using boundary conditions
|
764 |
+
denoised = c_out * pred_x0 + c_skip * sample
|
765 |
+
|
766 |
+
# 5. Sample z ~ N(0, I), For MultiStep Inference
|
767 |
+
# Noise is not used for one-step sampling.
|
768 |
+
if len(self.timesteps) > 1:
|
769 |
+
noise = torch.randn(model_output.shape).to(model_output.device)
|
770 |
+
prev_sample = (
|
771 |
+
alpha_prod_t_prev.sqrt() * denoised + beta_prod_t_prev.sqrt() * noise
|
772 |
+
)
|
773 |
+
else:
|
774 |
+
prev_sample = denoised
|
775 |
+
|
776 |
+
if not return_dict:
|
777 |
+
return (prev_sample, denoised)
|
778 |
+
|
779 |
+
return LCMSchedulerOutput(prev_sample=prev_sample, denoised=denoised)
|
780 |
+
|
781 |
+
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.add_noise
|
782 |
+
def add_noise(
|
783 |
+
self,
|
784 |
+
original_samples: torch.FloatTensor,
|
785 |
+
noise: torch.FloatTensor,
|
786 |
+
timesteps: torch.IntTensor,
|
787 |
+
) -> torch.FloatTensor:
|
788 |
+
# Make sure alphas_cumprod and timestep have same device and dtype as original_samples
|
789 |
+
alphas_cumprod = self.alphas_cumprod.to(
|
790 |
+
device=original_samples.device, dtype=original_samples.dtype
|
791 |
+
)
|
792 |
+
timesteps = timesteps.to(original_samples.device)
|
793 |
+
|
794 |
+
sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5
|
795 |
+
sqrt_alpha_prod = sqrt_alpha_prod.flatten()
|
796 |
+
while len(sqrt_alpha_prod.shape) < len(original_samples.shape):
|
797 |
+
sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)
|
798 |
+
|
799 |
+
sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5
|
800 |
+
sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()
|
801 |
+
while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape):
|
802 |
+
sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)
|
803 |
+
|
804 |
+
noisy_samples = (
|
805 |
+
sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
|
806 |
+
)
|
807 |
+
return noisy_samples
|
808 |
+
|
809 |
+
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.get_velocity
|
810 |
+
def get_velocity(
|
811 |
+
self,
|
812 |
+
sample: torch.FloatTensor,
|
813 |
+
noise: torch.FloatTensor,
|
814 |
+
timesteps: torch.IntTensor,
|
815 |
+
) -> torch.FloatTensor:
|
816 |
+
# Make sure alphas_cumprod and timestep have same device and dtype as sample
|
817 |
+
alphas_cumprod = self.alphas_cumprod.to(
|
818 |
+
device=sample.device, dtype=sample.dtype
|
819 |
+
)
|
820 |
+
timesteps = timesteps.to(sample.device)
|
821 |
+
|
822 |
+
sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5
|
823 |
+
sqrt_alpha_prod = sqrt_alpha_prod.flatten()
|
824 |
+
while len(sqrt_alpha_prod.shape) < len(sample.shape):
|
825 |
+
sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)
|
826 |
+
|
827 |
+
sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5
|
828 |
+
sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()
|
829 |
+
while len(sqrt_one_minus_alpha_prod.shape) < len(sample.shape):
|
830 |
+
sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)
|
831 |
+
|
832 |
+
velocity = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
|
833 |
+
return velocity
|
834 |
+
|
835 |
+
def __len__(self):
|
836 |
+
return self.config.num_train_timesteps
|
txt2img/index.html
ADDED
@@ -0,0 +1,218 @@
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|
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|
|
|
|
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|
|
|
|
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|
|
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|
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|
|
|
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|
|
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|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
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|
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|
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|
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|
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|
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|
|
|
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|
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|
|
|
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|
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|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
<!doctype html>
|
2 |
+
<html>
|
3 |
+
|
4 |
+
<head>
|
5 |
+
<meta charset="UTF-8">
|
6 |
+
<title>Real-Time Latent Consistency Model</title>
|
7 |
+
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
8 |
+
<script
|
9 |
+
src="https://cdnjs.cloudflare.com/ajax/libs/iframe-resizer/4.3.1/iframeResizer.contentWindow.min.js"></script>
|
10 |
+
<script src="https://cdn.tailwindcss.com"></script>
|
11 |
+
<script type="module">
|
12 |
+
|
13 |
+
const seedEl = document.querySelector("#seed");
|
14 |
+
const promptEl = document.querySelector("#prompt");
|
15 |
+
const guidanceEl = document.querySelector("#guidance-scale");
|
16 |
+
const startBtn = document.querySelector("#start");
|
17 |
+
const stopBtn = document.querySelector("#stop");
|
18 |
+
const videoEl = document.querySelector("#webcam");
|
19 |
+
const imageEl = document.querySelector("#player");
|
20 |
+
const queueSizeEl = document.querySelector("#queue_size");
|
21 |
+
const errorEl = document.querySelector("#error");
|
22 |
+
|
23 |
+
function LCMLive(promptEl, liveImage, seedEl, guidanceEl) {
|
24 |
+
let websocket;
|
25 |
+
|
26 |
+
async function start() {
|
27 |
+
return new Promise((resolve, reject) => {
|
28 |
+
const websocketURL = `${window.location.protocol === "https:" ? "wss" : "ws"
|
29 |
+
}:${window.location.host}/ws`;
|
30 |
+
|
31 |
+
const socket = new WebSocket(websocketURL);
|
32 |
+
socket.onopen = () => {
|
33 |
+
console.log("Connected to websocket");
|
34 |
+
};
|
35 |
+
socket.onclose = () => {
|
36 |
+
console.log("Disconnected from websocket");
|
37 |
+
stop();
|
38 |
+
resolve({ "status": "disconnected" });
|
39 |
+
};
|
40 |
+
socket.onerror = (err) => {
|
41 |
+
console.error(err);
|
42 |
+
reject(err);
|
43 |
+
};
|
44 |
+
socket.onmessage = (event) => {
|
45 |
+
const data = JSON.parse(event.data);
|
46 |
+
switch (data.status) {
|
47 |
+
case "success":
|
48 |
+
break;
|
49 |
+
case "start":
|
50 |
+
const userId = data.userId;
|
51 |
+
initPromptStream(userId);
|
52 |
+
break;
|
53 |
+
case "timeout":
|
54 |
+
stop();
|
55 |
+
resolve({ "status": "timeout" });
|
56 |
+
case "error":
|
57 |
+
stop();
|
58 |
+
reject(data.message);
|
59 |
+
}
|
60 |
+
};
|
61 |
+
websocket = socket;
|
62 |
+
})
|
63 |
+
}
|
64 |
+
|
65 |
+
async function promptUpdateStream() {
|
66 |
+
const prompt = promptEl.value;
|
67 |
+
const seed = seedEl.value;
|
68 |
+
const guidance_scale = guidanceEl.value;
|
69 |
+
websocket.send(JSON.stringify({
|
70 |
+
prompt: prompt,
|
71 |
+
seed: seed,
|
72 |
+
guidance_scale: guidance_scale,
|
73 |
+
}));
|
74 |
+
}
|
75 |
+
|
76 |
+
function initPromptStream(userId) {
|
77 |
+
liveImage.src = `/stream/${userId}`;
|
78 |
+
promptEl.addEventListener("input", promptUpdateStream);
|
79 |
+
seedEl.addEventListener("input", promptUpdateStream);
|
80 |
+
guidanceEl.addEventListener("change", promptUpdateStream);
|
81 |
+
}
|
82 |
+
|
83 |
+
async function stop() {
|
84 |
+
websocket.close();
|
85 |
+
promptEl.removeEventListener("input", promptUpdateStream);
|
86 |
+
seedEl.removeEventListener("input", promptUpdateStream);
|
87 |
+
guidanceEl.removeEventListener("change", promptUpdateStream);
|
88 |
+
}
|
89 |
+
return {
|
90 |
+
start,
|
91 |
+
stop
|
92 |
+
}
|
93 |
+
}
|
94 |
+
function toggleMessage(type) {
|
95 |
+
errorEl.hidden = false;
|
96 |
+
switch (type) {
|
97 |
+
case "error":
|
98 |
+
errorEl.innerText = "To many users are using the same GPU, please try again later.";
|
99 |
+
errorEl.classList.toggle("bg-red-300", "text-red-900");
|
100 |
+
break;
|
101 |
+
case "success":
|
102 |
+
errorEl.innerText = "Your session has ended, please start a new one.";
|
103 |
+
errorEl.classList.toggle("bg-green-300", "text-green-900");
|
104 |
+
break;
|
105 |
+
}
|
106 |
+
setTimeout(() => {
|
107 |
+
errorEl.hidden = true;
|
108 |
+
}, 2000);
|
109 |
+
}
|
110 |
+
|
111 |
+
|
112 |
+
const lcmLive = LCMLive(promptEl, imageEl, seedEl, guidanceEl);
|
113 |
+
startBtn.addEventListener("click", async () => {
|
114 |
+
try {
|
115 |
+
startBtn.disabled = true;
|
116 |
+
const res = await lcmLive.start();
|
117 |
+
startBtn.disabled = false;
|
118 |
+
if (res.status === "timeout")
|
119 |
+
toggleMessage("success")
|
120 |
+
} catch (err) {
|
121 |
+
console.log(err);
|
122 |
+
toggleMessage("error")
|
123 |
+
}
|
124 |
+
});
|
125 |
+
stopBtn.addEventListener("click", () => {
|
126 |
+
lcmLive.stop();
|
127 |
+
});
|
128 |
+
window.addEventListener("beforeunload", () => {
|
129 |
+
lcmLive.stop();
|
130 |
+
});
|
131 |
+
setInterval(() =>
|
132 |
+
fetch("/queue_size")
|
133 |
+
.then((res) => res.json())
|
134 |
+
.then((data) => {
|
135 |
+
queueSizeEl.innerText = data.queue_size;
|
136 |
+
})
|
137 |
+
.catch((err) => {
|
138 |
+
console.log(err);
|
139 |
+
})
|
140 |
+
, 5000);
|
141 |
+
</script>
|
142 |
+
</head>
|
143 |
+
|
144 |
+
<body>
|
145 |
+
<div class="fixed right-2 top-2 p-4 font-bold text-sm rounded-lg max-w-xs text-center" id="error">
|
146 |
+
</div>
|
147 |
+
<main class="container mx-auto px-4 py-4 max-w-4xl flex flex-col gap-4">
|
148 |
+
<article class="text-center max-w-xl mx-auto">
|
149 |
+
<h1 class="text-3xl font-bold">Real-Time Latent Consistency Model</h1>
|
150 |
+
<h2 class="text-2xl font-bold mb-4">Text to Image</h2>
|
151 |
+
<p class="text-sm">
|
152 |
+
This demo showcases
|
153 |
+
<a href="https://huggingface.co/SimianLuo/LCM_Dreamshaper_v7" target="_blank"
|
154 |
+
class="text-blue-500 underline hover:no-underline">LCM</a> Text to Image model
|
155 |
+
using
|
156 |
+
<a href="https://github.com/huggingface/diffusers/tree/main/examples/community#latent-consistency-pipeline"
|
157 |
+
target="_blank" class="text-blue-500 underline hover:no-underline">Diffusers</a> with a MJPEG
|
158 |
+
stream server.
|
159 |
+
</p>
|
160 |
+
<p class="text-sm">
|
161 |
+
There are <span id="queue_size" class="font-bold">0</span> user(s) sharing the same GPU, affecting
|
162 |
+
real-time performance. Maximum queue size is 10. <a
|
163 |
+
href="https://huggingface.co/spaces/radames/Real-Time-Latent-Consistency-Model?duplicate=true"
|
164 |
+
target="_blank" class="text-blue-500 underline hover:no-underline">Duplicate</a> and run it on your own GPU.
|
165 |
+
</p>
|
166 |
+
</article>
|
167 |
+
<div>
|
168 |
+
<h2 class="font-medium">Prompt</h2>
|
169 |
+
<p class="text-sm text-gray-500">
|
170 |
+
Start your session and type your prompt here, accepts
|
171 |
+
<a href="https://github.com/damian0815/compel/blob/main/doc/syntax.md" target="_blank" class="text-blue-500 underline hover:no-underline">Compel</a> syntax.
|
172 |
+
</p>
|
173 |
+
<div class="flex text-normal px-1 py-1 border border-gray-700 rounded-md items-center">
|
174 |
+
<textarea type="text" id="prompt" class="font-light w-full px-3 py-2 mx-1 outline-none"
|
175 |
+
title="Start your session and type your prompt here, you can see the result in real-time."
|
176 |
+
placeholder="Add your prompt here...">Portrait of The Terminator with , glare pose, detailed, intricate, full of colour, cinematic lighting, trending on artstation, 8k, hyperrealistic, focused, extreme details, unreal engine 5, cinematic, masterpiece</textarea>
|
177 |
+
</div>
|
178 |
+
|
179 |
+
</div>
|
180 |
+
<div class="">
|
181 |
+
<details>
|
182 |
+
<summary class="font-medium cursor-pointer">Advanced Options</summary>
|
183 |
+
<div class="grid grid-cols-3 max-w-md items-center gap-3 py-3">
|
184 |
+
<label class="text-sm font-medium" for="guidance-scale">Guidance Scale
|
185 |
+
</label>
|
186 |
+
<input type="range" id="guidance-scale" name="guidance-scale" min="1" max="30" step="0.001"
|
187 |
+
value="8.0" oninput="this.nextElementSibling.value = Number(this.value).toFixed(2)">
|
188 |
+
<output class="text-xs w-[50px] text-center font-light px-1 py-1 border border-gray-700 rounded-md">
|
189 |
+
8.0</output>
|
190 |
+
<label class="text-sm font-medium" for="seed">Seed</label>
|
191 |
+
<input type="number" id="seed" name="seed" value="299792458"
|
192 |
+
class="font-light border border-gray-700 text-right rounded-md p-2">
|
193 |
+
<button
|
194 |
+
onclick="document.querySelector('#seed').value = Math.floor(Math.random() * Number.MAX_SAFE_INTEGER); document.querySelector('#seed').dispatchEvent(new Event('input'));"
|
195 |
+
class="bg-gray-700 hover:bg-gray-800 text-white font-normal py-1 w-[50px] rounded disabled:bg-gray-300 disabled:cursor-not-allowed text-sm">
|
196 |
+
Rand
|
197 |
+
</button>
|
198 |
+
</div>
|
199 |
+
</details>
|
200 |
+
</div>
|
201 |
+
<div>
|
202 |
+
<button id="start"
|
203 |
+
class="bg-gray-700 hover:bg-gray-800 text-white font-normal py-2 w-16 rounded disabled:bg-gray-300 disabled:cursor-not-allowed">
|
204 |
+
Start
|
205 |
+
</button>
|
206 |
+
<button id="stop"
|
207 |
+
class="bg-gray-700 hover:bg-gray-800 text-white font-normal py-2 w-16 rounded disabled:bg-gray-300 disabled:cursor-not-allowed">
|
208 |
+
Stop
|
209 |
+
</button>
|
210 |
+
</div>
|
211 |
+
<div class="relative rounded-lg border border-slate-300 overflow-hidden">
|
212 |
+
<img id="player" class="w-full aspect-square rounded-lg "
|
213 |
+
src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAQAAAC1HAwCAAAAC0lEQVR42mNkYAAAAAYAAjCB0C8AAAAASUVORK5CYII=">
|
214 |
+
</div>
|
215 |
+
</main>
|
216 |
+
</body>
|
217 |
+
|
218 |
+
</html>
|
txt2img/tailwind.config.js
ADDED
File without changes
|