exported from PyTorch to ONNX (via optimum), then
dynamically quantized to int8 (quantize_dynamic, QInt8 weights) for the UNet and text encoder. The VAE is left in fp32 for output quality.

Nothing else was altered — no retraining, no merging. Shrinks a ~4.1 GB fp16 pipeline to ~1.3 GB so it fits comfortably in phone RAM.

Layout

unet/model.onnx (+ .onnx.data)
text_encoder/model.onnx (+ .onnx.data)
vae_decoder/model.onnx
vae_encoder/model.onnx
tokenizer/{vocab.json, merges.txt}

License

Inherited from the source model: mit. If that is CreativeML OpenRAIL-M, the Attachment A use-restrictions apply to you as well — they travel with the weights and cannot be removed by downstream redistribution. Please read them before use.

Credit for the underlying model belongs entirely to the original author.

latentdivergence/lcm-dreamshaper-int8-onnx

lcm-dreamshaper-int8-onnx

What was changed

Layout

License

latentdivergence/
lcm-dreamshaper-int8-onnx