Update README.md

README.md

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 Interactive demo for [akrao9/Boomer-T2I](https://huggingface.co/akrao9/Boomer-T2I): a 1024×1024 text-to-image flow-matching model with Flash Linear Attention.
 
-## Space setup
 
-1. Create a new **Gradio** Space on Hugging Face.
-2. Upload the contents of this `Space/` folder (or point the Space repo here).
-3. In **Settings → Hardware**, select **ZeroGPU** (requires HF PRO for hosting). This allocates a shared **NVIDIA RTX Pro 6000 Blackwell** GPU (48 GB via `@spaces.GPU(size="large")`).
-4. Add a Space secret **`HF_TOKEN`** with a token that has accepted the [Gemma Terms of Use](https://ai.google.dev/gemma/terms).
-5. Restart the Space after the first build (model + VAE + text encoder download can take several minutes).
-
-## Local test
-
-```bash
-cd Space
-pip install -r requirements.txt
-export HF_TOKEN=hf_...
-python app.py
-```
-
-The `@spaces.GPU` decorator is a no-op locally; use a CUDA machine for full inference.
-
-Pinned deps for ZeroGPU: do **not** pin `torch` or `torchvision` in `requirements.txt` — the Space builder injects `torch<=2.11.0` automatically. Other pins: diffusers 0.38.0, transformers 5.9.0, flash-linear-attention 0.5.0.
-
-## Notes
-
-- Uses `@spaces.GPU(size="large", duration=150)` — RTX Pro 6000 Blackwell 48 GB tier.
-- Example prompts run generation on click (`run_on_click=True`).
-- VAE and Gemma 4 2B are fetched from their upstream repos on first startup.