LucasFang/FLUX-Reason-6M
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A small text-to-image model (343M parameters) that generates 256×256 images. Trained from scratch in ~5 hours on a single RTX PRO 6000 (96 GB).
| Architecture | DiT with rectified flow (flow matching) |
| Parameters | 343M |
| Hidden size / depth / heads | 1024 / 20 / 16 |
| Resolution | 256×256 |
| Latent space | DC-AE f32c32 (mit-han-lab/dc-ae-f32c32-sana-1.0-diffusers), 32×8×8 = 64 tokens |
| Text encoder | google/flan-t5-base (frozen, 96 tokens) |
| Details | 2D RoPE, QK-norm, adaLN-single, SwiGLU, cross-attention, EMA weights |
These weights continue from the base model (revision v1.0-base, 32,748 steps).
The sample grid above is regenerated from these weights.
pip install torch diffusers transformers safetensors huggingface_hub sentencepiece
from huggingface_hub import hf_hub_download
import importlib.util
path = hf_hub_download("caikybaldo999/zyi-256-flash", "zyi256.py")
spec = importlib.util.spec_from_file_location("zyi256", path)
zyi256 = importlib.util.module_from_spec(spec); spec.loader.exec_module(zyi256)
pipe = zyi256.ZyiPipeline("caikybaldo999/zyi-256-flash")
images = pipe("a misty mountain lake at sunrise with pine trees", n=4, steps=28, cfg=4.5, seed=0)
images[0].save("out.png")
English prompts work best (the training captions are in English).