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README.md

@@ -1,3 +1,6 @@
+Here’s a cleaned, single README.md you can paste in as-is.
+(It includes the required YAML metadata at the very top and fixes your tags/list formatting.)
+
 title: 🎬 BackgroundFX Pro - SAM2 + MatAnyone
 emoji: 🎥
 colorFrom: indigo
@@ -22,19 +25,26 @@ MatAnyone
 🎬 BackgroundFX Pro — Professional Video Background Replacement
 
 BackgroundFX Pro is a GPU-accelerated app for Hugging Face Spaces (Docker) that replaces video backgrounds using:
-• SAM2 — high-quality object segmentation
-• MatAnyone — temporal video matting for stable alpha over time
+
+SAM2 — high-quality object segmentation
+
+MatAnyone — temporal video matting for stable alpha over time
 
 Built on: CUDA 12.1.1, PyTorch 2.5.1 (cu121), torchvision 0.20.1, Gradio 4.41.0.
 
 ✨ Features
 
-• Upload a video and replace its background with Solid color, AI-generated image, your own uploaded image, or an Unsplash search.
-• Optimized for T4 GPUs (HF Spaces).
-• Caching & logs stored in the repository volume for reliability:
-– HF cache: ./.hf
-– Torch cache: ./.torch
-– App data & logs: ./data (see data/run.log)
+Upload a video and replace its background with: solid color, AI-generated image, your own uploaded image, or an Unsplash search
+
+Optimized for T4 GPUs (HF Spaces)
+
+Caching & logs stored in the repository volume for reliability:
+
+HF cache: ./.hf
+
+Torch cache: ./.torch
+
+App data & logs: ./data (see data/run.log)
 
 🚀 Try It
 
@@ -57,23 +67,35 @@ Tip: Start with 720p/1080p on T4; 4K can exceed memory.
 
 🗂️ Project Structure (key files)
 
-• Dockerfile
-• requirements.txt
-• ui.py
-• ui_core_interface.py
-• ui_core_functionality.py
-• two_stage_pipeline.py
-• models/sam2_loader.py
-• models/matanyone_loader.py
-• utils/init.py
-• data/ (created at runtime for logs and outputs)
-• tmp/ (created at runtime for jobs and temp files)
+Dockerfile
+
+requirements.txt
+
+ui.py
+
+ui_core_interface.py
+
+ui_core_functionality.py
+
+two_stage_pipeline.py
+
+models/sam2_loader.py
+
+models/matanyone_loader.py
+
+utils/init.py
+
+data/ (created at runtime for logs and outputs)
+
+tmp/ (created at runtime for jobs and temp files)
 
 ⚙️ Runtime Notes
 
-• The app binds to the port from PORT or GRADIO_SERVER_PORT (defaults to 7860).
-• A heartbeat writes every ~2s to logs with memory and disk stats.
-• If the process dies without a final “PROCESS EXITING” line, it was likely an OOM or hard kill.
+The app binds to the port from PORT or GRADIO_SERVER_PORT (defaults to 7860).
+
+A heartbeat writes every ~2s to logs with memory and disk stats.
+
+If the process dies without a final “PROCESS EXITING” line, it was likely an OOM or hard kill.
 
 🧪 Local Development (Docker)
 
@@ -89,29 +111,39 @@ Run the container and map port 7860.
 
 🔧 Space Settings
 
-• SDK: docker
-• App port: 7860
-• Hardware: T4 GPU (or better)
+SDK: docker
+
+App port: 7860
+
+Hardware: T4 GPU (or better)
 
 📦 Key Versions
 
-• Gradio: 4.41.0
-• PyTorch: 2.5.1 (cu121)
-• torchvision: 0.20.1
-• SAM2: installed from source at build time in third_party/sam2
-• MatAnyone: installed from GitHub
+Gradio: 4.41.0
+
+PyTorch: 2.5.1 (cu121)
+
+torchvision: 0.20.1
+
+SAM2: installed from source at build time in third_party/sam2
+
+MatAnyone: installed from GitHub
 
 🧯 Troubleshooting
 
 Container exits after ~20s:
-• Check data/run.log — heartbeat shows RSS and cgroup memory limit.
-• If OOM is suspected: use lower-resolution videos, keep concurrency low (already set to max_threads=1 and queue(max_size=2)), and ensure GPU hardware is selected.
+
+Check data/run.log — heartbeat shows RSS and cgroup memory limit.
+
+If OOM is suspected: use lower-resolution videos, keep concurrency low (already set to max_threads=1 and queue(max_size=2)), and ensure GPU hardware is selected.
 
 “Could not open video” or empty output:
-• Verify the uploaded file codec/container. Re-encode to H.264/AAC in .mp4 if needed.
+
+Verify the uploaded file codec/container. Re-encode to H.264/AAC in .mp4 if needed.
 
 Weights not found / slow first run:
-• First run downloads model assets into ./.hf and ./.torch. Subsequent runs are faster.
+
+First run downloads model assets into ./.hf and ./.torch. Subsequent runs are faster.
 
 🔐 Privacy
 
@@ -123,6 +155,8 @@ MIT — see LICENSE or the Space’s license field.
 
 🙌 Credits
 
-• SAM2 by Meta FAIR
-• MatAnyone by Peiqing Yang et al.
-• Built with Gradio and PyTorch
+SAM2 by Meta FAIR
+
+MatAnyone by Peiqing Yang et al.
+
+Built with Gradio and PyTorch