README.md

metadata

title: Linarix V2 T2I
emoji: 🎨
colorFrom: indigo
colorTo: purple
sdk: gradio
sdk_version: 6.20.0
app_file: app.py
python_version: 3.11
pinned: false
license: other
short_description: 1024px text-to-image with Linarix-v2 (GDN-2) on ZeroGPU
models:
  - Akrao9/Linarix-v2
  - Qwen/Qwen3.5-4B
  - mit-han-lab/dc-ae-f32c32-sana-1.1-diffusers

Linarix-v2 — Text to Image Demo

Interactive demo for Akrao9/Linarix-v2: a Boomer v8 flow-matching model with a bidirectional GatedDeltaNet-2 semantic encoder, substitutive joint-attention refresh layers, Block AttnRes, and a full-attention DDT velocity decoder. The app reads the resolution and inference defaults from the exported checkpoint instead of duplicating them here.

Capabilities: strong on landscapes, scenic/architectural scenes, rigid man-made structures (lighthouses, locomotives, bridges), and composed portraits. Text conditioning uses Qwen3.5-4B layers 9/18/27 with per-layer RMS normalization and feature concatenation; decode uses DC-AE f32c32; sampling uses STORK-4. Step count, CFG scale/rescale, guidance interval, RKG substeps and flow shift all come from the checkpoint's own metadata, so they are deliberately not restated here — the Status line under each generation reports what actually ran.

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. Required secret: add HF_TOKEN (Settings → Secrets) with a token that can read gated Akrao9/Linarix-v2. Spaces preload_from_hub cannot auth gated/private repos, so the model is downloaded at runtime via snapshot_download(token=...).
5. Restart / Factory rebuild the Space after setting the secret (model + VAE + text encoder download can take a few minutes on first boot).

Local test

cd Space
pip install -r requirements.txt
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 gradio in requirements.txt — the Space builder injects torch<=2.11.0 and gradio[oauth,mcp]==<sdk_version from this README> into the same pip install, so a gradio pin that differs from sdk_version makes the build fail with ResolutionImpossible. Include torchvision (unpinned) for the Qwen3.5 AutoProcessor. Other pins: diffusers 0.38.0, transformers 5.9.0, and flash-linear-attention at the git SHA in requirements.txt — a released version tag is not enough, since Block AttnRes needs the same FLA build the checkpoint was exported against.

app.py redirects Hugging Face caches to /tmp/huggingface (ZeroGPU ~/.cache is often read-only).

Set python_version: 3.11 in this README (Spaces default is 3.10). After changing it, Factory rebuild the Space so the runtime upgrades.

Startup warnings (usually harmless)

Message	Cause	Action
Python 3.10 below recommended 3.11	Spaces default until python_version: 3.11 is applied	Factory rebuild after README change
Triton is not supported… roll back to CPU	FLA probes Triton at import; on some builders/GPUs it falls back to non-Triton CUDA kernels	Safe if generation works; rebuild on 3.11
torch.compile is not available in Python 3.10	FLA training helper stub; inference does not use torch.compile	Fixed by Python 3.11 rebuild
local_dir_use_symlinks deprecated	Older huggingface_hub call inside a dependency	Cosmetic; ignore or upgrade hub later

Notes

• Uses @spaces.GPU(size="large", duration=150) — RTX Pro 6000 Blackwell 48 GB tier — for STORK-4 generation (Tan et al., 2025).
• Example prompts run generation on click (run_on_click=True).
• The CFG scale, rescale and guidance interval defaults come from the exported checkpoint; the sliders start at those values. For exact object counts, sweep a few seeds.
• VAE and Qwen3.5-4B are fetched from their upstream repos on first startup.