Upload README.md with huggingface_hub

README.md

@@ -1,3 +1,17 @@
+---
+title: SynLayers
+emoji: "🧩"
+colorFrom: blue
+colorTo: purple
+sdk: gradio
+python_version: "3.10"
+app_file: app.py
+suggested_hardware: a100-large
+models:
+  - SynLayers/Bbox-caption-8b
+pinned: false
+---
+
 # SynLayers Demo
 
 This folder now contains a unified real-world inference demo:
@@ -6,7 +20,6 @@ This folder now contains a unified real-world inference demo:
 2. `infer/infer.py` runs SynLayers decomposition with `infer/infer.yaml`.
 3. `demo/real_world_pipeline.py` stitches the two stages together for one uploaded image.
 4. `demo/app.py` provides a Gradio interface that can be used locally or adapted for a Hugging Face Space.
-5. `demo/upload_used_bundle_to_hf.py` uploads only the Python/config files actually used by the demo, plus the selected runtime assets.
 
 ## Local Run
 
@@ -23,104 +36,4 @@ python demo/real_world_pipeline.py \
   --image "/path/to/your/image.png"
 ```
 
-## Default Models
-
-The current local defaults are:
-
-- bbox-caption model:
-  `/project/llmsvgen/share/data/kmw_layered_checkpoint/Bbox-caption-8b`
-- SynLayers base checkpoints:
-  `/project/llmsvgen/share/data/kmw_layered_checkpoint/SynLayers_checkpoints`
-- SynLayers decomposition checkpoint:
-  `/project/llmsvgen/share/data/kmw_layered_checkpoint/SynLayers_ckpt/step_120000`
-- base config:
-  `infer/infer.yaml`
-
-## Hugging Face Space Notes
-
-The Gradio app is ready for a Hugging Face Space.
-After you upload the model/runtime bundle to `SynLayers/Bbox-caption-8b`, the Space can download
-those uploaded assets automatically and use them directly.
-
-The app supports overriding the local defaults with environment variables:
-
-- `SYNLAYERS_MODEL_REPO`
-- `SYNLAYERS_BBOX_MODEL`
-- `SYNLAYERS_BASE_MODEL`
-- `SYNLAYERS_ADAPTER_MODEL`
-- `SYNLAYERS_TRANSP_VAE`
-- `SYNLAYERS_PRETRAINED_LORA`
-- `SYNLAYERS_ARTPLUS_LORA`
-- `SYNLAYERS_DECOMP_CKPT_ROOT`
-- `SYNLAYERS_REAL_CONFIG`
-- `SYNLAYERS_DEMO_WORK_DIR`
-- `SYNLAYERS_EXAMPLE_DIR`
-
-In practice, for a real Hugging Face Space deployment you will want to:
-
-1. upload the required model/runtime assets to `SynLayers/Bbox-caption-8b`
-2. create a Gradio Space repo, for example `SynLayers/synlayers-real-world-demo`
-3. upload the Space scaffold with `demo/publish_space.py`
-4. set `SYNLAYERS_MODEL_REPO=SynLayers/Bbox-caption-8b` in the Space settings
-5. launch `app.py` as the Space entrypoint
-
-### Public interface flow
-
-1. Upload the model/runtime bundle:
-
-```bash
-python demo/upload_used_bundle_to_hf.py \
-  --repo-id SynLayers/Bbox-caption-8b
-```
-
-2. Create and upload the Space scaffold:
-
-```bash
-python demo/publish_space.py \
-  --repo-id SynLayers/synlayers-real-world-demo
-```
-
-3. In the Hugging Face Space settings, add:
-
-```text
-SYNLAYERS_MODEL_REPO=SynLayers/Bbox-caption-8b
-```
-
-Then the public Space interface will:
-
-- accept a user image upload
-- load the bbox-caption model from the uploaded model repo
-- download the SynLayers decomposition assets from that same repo
-- run the one-step decomposition pipeline
-- return the bbox visualization, merged output, per-layer outputs, and a downloadable archive
-
-## Upload Bundle
-
-To upload the minimal used demo bundle to a Hugging Face repo:
-
-```bash
-python demo/upload_used_bundle_to_hf.py \
-  --repo-id SynLayers/Bbox-caption-8b
-```
-
-This uploads:
-
-- the used `demo`, `infer`, `models`, and `tools` Python files
-- `demo/upload_used_bundle_to_hf.py`
-- `demo/publish_space.py`
-- `infer/infer.yaml`
-- `environment.yml`
-- `ckpt/trans_vae/0008000.pt`
-- `ckpt/pre_trained_LoRA/pytorch_lora_weights.safetensors`
-- `ckpt/prism_ft_LoRA/pytorch_lora_weights.safetensors`
-- `SynLayers_ckpt/step_120000`
-- `SynLayers_checkpoints/FLUX.1-dev`
-- `SynLayers_checkpoints/FLUX.1-dev-Controlnet-Inpainting-Alpha`
-
-## Fixed Prompt
-
-The bbox detector always uses the fixed prompt defined in:
-
-- `demo/infer/run_caption_bbox_infer.py`
 
-No extra user text prompt is required.