folder renaming

Dockerfile

@@ -36,4 +36,4 @@ WORKDIR $HOME/app
 # Copy the current directory contents into the container at $HOME/app setting the owner to the user
 COPY --chown=user . $HOME/app
 
-CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]
+CMD ["uvicorn", "app-img2img:app", "--host", "0.0.0.0", "--port", "7860"]

README.md

@@ -24,18 +24,18 @@ You need CUDA and Python
 python -m venv venv 
 source venv/bin/activate 
 pip install -r requirements.txt
-uvicorn "app:app" --host 0.0.0.0 --port 7860 --reload
+uvicorn "app-img2img:app" --host 0.0.0.0 --port 7860 --reload
 ```
 or with environment variables
 ```bash
-TIMEOUT=120 SAFETY_CHECKER=True MAX_QUEUE_SIZE=4 uvicorn "app:app" --host 0.0.0.0 --port 7860 --reload
+TIMEOUT=120 SAFETY_CHECKER=True MAX_QUEUE_SIZE=4 uvicorn "app-img2img:app" --host 0.0.0.0 --port 7860 --reload
 ```
 
 If you're running locally and want to test it on Mobile Safari, the webserver needs to be served over HTTPS. 
 
 ```bash
 openssl req -newkey rsa:4096 -nodes -keyout key.pem -x509 -days 365 -out certificate.pem
-uvicorn "app:app" --host 0.0.0.0 --port 7860 --reload --log-level info --ssl-certfile=certificate.pem --ssl-keyfile=key.pem
+uvicorn "app-img2img:app" --host 0.0.0.0 --port 7860 --reload --log-level info --ssl-certfile=certificate.pem --ssl-keyfile=key.pem
 ```
 ## Docker
 You need NVIDIA Container Toolkit for Docker

app.py → app-img2img.py

@@ -32,6 +32,7 @@ if SAFETY_CHECKER == "True":
         "SimianLuo/LCM_Dreamshaper_v7",
         custom_pipeline="latent_consistency_img2img.py",
         custom_revision="main",
+        torch_dtype=torch.float32
     )
 else:
     pipe = DiffusionPipeline.from_pretrained(
@@ -39,17 +40,20 @@ else:
         safety_checker=None,
         custom_pipeline="latent_consistency_img2img.py",
         custom_revision="main",
+        torch_dtype=torch.float32
     )
 #TODO try to use tiny VAE
 # 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.to(torch_device="cuda", torch_dtype=torch.float32)
 pipe.unet.to(memory_format=torch.channels_last)
 pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
 user_queue_map = {}
 
+# for torch.compile
+pipe(prompt="warmup", image=[Image.new("RGB", (512, 512))])
 
 def predict(input_image, prompt, guidance_scale=8.0, strength=0.5, seed=2159232):
     generator = torch.manual_seed(seed)
@@ -210,4 +214,4 @@ async def handle_websocket_data(websocket: WebSocket, user_id: uuid.UUID):
         traceback.print_exc()
 
 
-app.mount("/", StaticFiles(directory="public", html=True), name="public")
+app.mount("/", StaticFiles(directory="img2img", html=True), name="public")