Instructions to use stdstu123/LynnReal-Onmi-flash-beta-0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use stdstu123/LynnReal-Onmi-flash-beta-0.1 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("stdstu123/LynnReal-Onmi-flash-beta-0.1", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
LynnReal Flash
Code: LynnReal-AI/LynnReal-Omni
A 42-block joint video/audio DiT with three denoiser evaluations. Supports text-to-video and native first/last keyframes. Middle blocks use spatial stride-two token selection with full-resolution residual restoration.
The complete transformer uses trained W8A8 projections: INT8 weights, per-token INT8 activations, INT32 accumulation, and BF16 outputs. Sensitive projections remain BF16. Packed weights and per-channel scales are stored without requantization in indexed Hugging Face safetensors shards.
Load using the release model.flash_int8.load_transformer or the launchers
in script/sample/flash/int8. This custom packed format requires that loader;
a generic Diffusers from_pretrained does not install the INT8 operators.
Shared conditioning and codec components are included in this model bundle.
See LICENSE and NOTICE for upstream terms.
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