Update README.md

README.md

@@ -36,27 +36,53 @@ Existing literature typically treats style-driven and subject-driven generation
 
 ### 🔧 Requirements and Installation
 
-Clone our [Github repo](https://github.com/bytedance/UNO)
-
-
 Install the requirements
 ```bash
 ## create a virtual environment with python >= 3.10 <= 3.12, like
-# python -m venv uso_env
-# source uso_env/bin/activate
-# then install
-pip install -r requirements.txt
+python -m venv uso_env
+source uso_env/bin/activate
+## or
+conda create -n uso_env python=3.10 -y
+conda activate uso_env
+## then install the requirements by you need
+pip install -r requirements.txt # legacy installation command
 ```
 
-then download checkpoints in one of the three ways:
-1. Directly run the inference scripts, the checkpoints will be downloaded automatically by the `hf_hub_download` function in the code to your `$HF_HOME`(the default value is `~/.cache/huggingface`).
-2. use `huggingface-cli download <repo name>` to download `black-forest-labs/FLUX.1-dev`, `xlabs-ai/xflux_text_encoders`, `openai/clip-vit-large-patch14`, `TODO UNO hf model`, then run the inference scripts.
-3. use `huggingface-cli download <repo name> --local-dir <LOCAL_DIR>` to download all the checkpoints menthioned in 2. to the directories your want. Then set the environment variable `TODO`. Finally, run the inference scripts.
+Then download checkpoints in one of the following ways:
+- **Suppose you already have some of the checkpoints**
+```bash
+# 1. download USO official checkpoints
+pip install huggingface_hub
+huggingface-cli download bytedance-research/USO --local-dir <YOU_SAVE_DIR> --local-dir-use-symlinks False
+
+# 2. Then set the environment variable for FLUX.1 base model
+export AE="YOUR_AE_PATH"
+export FLUX_DEV="YOUR_FLUX_DEV_PATH"
+export T5="YOUR_T5_PATH"
+export CLIP="YOUR_CLIP_PATH"
+# or export HF_HOME="YOUR_HF_HOME"
 
-### 🌟 Gradio Demo
+# 3. Then set the environment variable for USO
+export LORA="<YOU_SAVE_DIR>/uso_flux_v1.0/dit_lora.safetensors"
+export PROJECTION_MODEL="<YOU_SAVE_DIR>/uso_flux_v1.0/projector.safetensors"
+```
+- Directly run the inference scripts, the checkpoints will be downloaded automatically by the `hf_hub_download` function in the code.
 
+### ✍️ Inference
+Start from the examples below to explore and spark your creativity. ✨
 ```bash
-python app.py
+# the first image is a content reference, and the rest are style references.
+
+# for subject-driven generation
+python inference.py --prompt "The man in flower shops carefully match bouquets, conveying beautiful emotions and blessings with flowers. " --image_paths "assets/gradio_examples/identity1.jpg" --width 1024 --height 1024
+# for style-driven generation
+# please keep the first image path empty
+python inference.py --prompt "A cat sleeping on a chair." --image_paths "" "assets/gradio_examples/style1.webp" --width 1024 --height 1024
+# for ip-style generation
+python inference.py --prompt "The woman gave an impassioned speech on the podium." --image_paths "assets/gradio_examples/identity2.webp" "assets/gradio_examples/style2.webp" --width 1024 --height 1024
+# for multi-style generation
+# please keep the first image path empty
+python inference.py --prompt "A handsome man." --image_paths "" "assets/gradio_examples/style3.webp" "assets/gradio_examples/style4.webp" --width 1024 --height 1024
 ```
 
 ## 📄 Disclaimer