update readme

README.md

@@ -27,38 +27,39 @@ You need CUDA and Python 3.10, Node > 19, Mac with an M1/M2/M3 chip or Intel Arc
 ```bash
 python -m venv venv
 source venv/bin/activate
-pip3 install -r requirements.txt
+pip3 install -r server/requirements.txt
 cd frontend && npm install && npm run build && cd ..
-# fastest pipeline
-python run.py --reload --pipeline img2imgSD21Turbo 
+python server/main.py --reload --pipeline img2imgSDTurbo 
  ```
 
-# Pipelines
-You can build your own pipeline following examples here [here](pipelines),
-don't forget to fuild the frontend first 
+Don't forget to fuild the frontend!!! 
+
 ```bash
 cd frontend && npm install && npm run build && cd ..
 ```
 
+# Pipelines
+You can build your own pipeline following examples here [here](pipelines),
+
+
 # LCM
 ### Image to Image
 
 ```bash
-python run.py --reload --pipeline img2img 
+python server/main.py --reload --pipeline img2img 
 ```
 
 # LCM
 ### Text to Image
 
 ```bash
-python run.py --reload --pipeline txt2img 
+python server/main.py --reload --pipeline txt2img 
 ```
 
 ### Image to Image ControlNet Canny
 
-
 ```bash
-python run.py --reload --pipeline controlnet 
+python server/main.py --reload --pipeline controlnet 
 ```
 
 
@@ -67,39 +68,73 @@ python run.py --reload --pipeline controlnet
 Using LCM-LoRA, giving it the super power of doing inference in as little as 4 steps. [Learn more here](https://huggingface.co/blog/lcm_lora) or [technical report](https://huggingface.co/papers/2311.05556)
 
 
-
 ### Image to Image ControlNet Canny LoRa
 
 ```bash
-python run.py --reload --pipeline controlnetLoraSD15
+python server/main.py --reload --pipeline controlnetLoraSD15
 ```
 or SDXL, note that SDXL is slower than SD15 since the inference runs on 1024x1024 images
 
 ```bash
-python run.py --reload --pipeline controlnetLoraSDXL
+python server/main.py --reload --pipeline controlnetLoraSDXL
 ```
 
 ### Text to Image
 
 ```bash
-python run.py --reload --pipeline txt2imgLora
+python server/main.py --reload --pipeline txt2imgLora
 ```
 
-or 
-
 ```bash
-python run.py --reload --pipeline txt2imgLoraSDXL
+python server/main.py --reload --pipeline txt2imgLoraSDXL
 ```
+# Available Pipelines
+
+#### [LCM](https://huggingface.co/SimianLuo/LCM_Dreamshaper_v7)
+
+`img2img`  
+`txt2img`   
+`controlnet`   
+`txt2imgLora`   
+`controlnetLoraSD15` 
+
+#### [SD15](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
+`controlnetLoraSDXL`   
+`txt2imgLoraSDXL`   
+
+#### [SDXL Turbo](https://huggingface.co/stabilityai/sd-xl-turbo)
+
+`img2imgSDXLTurbo`    
+`controlnetSDXLTurbo`   
+
+
+#### [SDTurbo](https://huggingface.co/stabilityai/sd-turbo)
+`img2imgSDTurbo`   
+`controlnetSDTurbo`   
+
+#### [Segmind-Vega](https://huggingface.co/segmind/Segmind-Vega)
+`controlnetSegmindVegaRT`   
+`img2imgSegmindVegaRT`   
 
 
 ### Setting environment variables
 
 
-`TIMEOUT`: limit user session timeout  
-`SAFETY_CHECKER`: disabled if you want NSFW filter off  
-`MAX_QUEUE_SIZE`: limit number of users on current app instance  
-`TORCH_COMPILE`: enable if you want to use torch compile for faster inference works well on A100 GPUs
-`USE_TAESD`: enable if you want to use Autoencoder Tiny
+* `--host`: Host address (default: 0.0.0.0)  
+* `--port`: Port number (default: 7860)  
+* `--reload`: Reload code on change  
+* `--max-queue-size`: Maximum queue size (optional)
+* `--timeout`: Timeout period (optional)
+* `--safety-checker`: Enable Safety Checker (optional) 
+* `--torch-compile`: Use Torch Compile
+* `--use-taesd` / `--no-taesd`: Use Tiny Autoencoder  
+* `--pipeline`: Pipeline to use (default: "txt2img")  
+* `--ssl-certfile`: SSL Certificate File (optional)
+* `--ssl-keyfile`: SSL Key File (optional)
+* `--debug`: Print Inference time  
+* `--compel`: Compel option  
+* `--sfast`: Enable Stable Fast   
+* `--oneflow`: Enable OneFlow    
 
 If you run using `bash build-run.sh` you can set `PIPELINE` variables to choose the pipeline you want to run
 
@@ -110,14 +145,14 @@ PIPELINE=txt2imgLoraSDXL bash build-run.sh
 and setting environment variables
 
 ```bash
-TIMEOUT=120 SAFETY_CHECKER=True MAX_QUEUE_SIZE=4 python run.py --reload --pipeline txt2imgLoraSDXL
+TIMEOUT=120 SAFETY_CHECKER=True MAX_QUEUE_SIZE=4 python server/main.py --reload --pipeline txt2imgLoraSDXL
 ```
 
 If you're running locally and want to test it on Mobile Safari, the webserver needs to be served over HTTPS, or follow this instruction on my [comment](https://github.com/radames/Real-Time-Latent-Consistency-Model/issues/17#issuecomment-1811957196)
 
 ```bash
 openssl req -newkey rsa:4096 -nodes -keyout key.pem -x509 -days 365 -out certificate.pem
-python run.py --reload --ssl-certfile=certificate.pem --ssl-keyfile=key.pem
+python server/main.py --reload --ssl-certfile=certificate.pem --ssl-keyfile=key.pem
 ```
 
 ## Docker
@@ -141,15 +176,18 @@ or with environment variables
 ```bash
 docker run -ti -e PIPELINE=txt2imgLoraSDXL -p 7860:7860 --gpus all lcm-live
 ```
-# Development Mode
-
 
-```bash
-python run.py --reload  
-```
 
 # Demo on Hugging Face
 
-https://huggingface.co/spaces/radames/Real-Time-Latent-Consistency-Model
+
+* [radames/Real-Time-Latent-Consistency-Model](https://huggingface.co/spaces/radames/Real-Time-Latent-Consistency-Model)  
+* [radames/Real-Time-SD-Turbo](https://huggingface.co/spaces/radames/Real-Time-SD-Turbo)  
+* [latent-consistency/Real-Time-LCM-ControlNet-Lora-SD1.5](https://huggingface.co/spaces/latent-consistency/Real-Time-LCM-ControlNet-Lora-SD1.5)  
+* [latent-consistency/Real-Time-LCM-Text-to-Image-Lora-SD1.5](https://huggingface.co/spaces/latent-consistency/Real-Time-LCM-Text-to-Image-Lora-SD1.5)  
+* [radames/Real-Time-Latent-Consistency-Model-Text-To-Image](https://huggingface.co/spaces/radames/Real-Time-Latent-Consistency-Model-Text-To-Image)  
+
+
+
 
 https://github.com/radames/Real-Time-Latent-Consistency-Model/assets/102277/c4003ac5-e7ff-44c0-97d3-464bb659de70

server/config.py

@@ -7,7 +7,6 @@ class Args(NamedTuple):
     host: str
     port: int
     reload: bool
-    mode: str
     max_queue_size: int
     timeout: float
     safety_checker: bool
@@ -35,15 +34,11 @@ TORCH_COMPILE = os.environ.get("TORCH_COMPILE", None) == "True"
 USE_TAESD = os.environ.get("USE_TAESD", "True") == "True"
 default_host = os.getenv("HOST", "0.0.0.0")
 default_port = int(os.getenv("PORT", "7860"))
-default_mode = os.getenv("MODE", "default")
 
 parser = argparse.ArgumentParser(description="Run the app")
 parser.add_argument("--host", type=str, default=default_host, help="Host address")
 parser.add_argument("--port", type=int, default=default_port, help="Port number")
 parser.add_argument("--reload", action="store_true", help="Reload code on change")
-parser.add_argument(
-    "--mode", type=str, default=default_mode, help="App Inferece Mode: txt2img, img2img"
-)
 parser.add_argument(
     "--max-queue-size",
     dest="max_queue_size",