joseplcam's picture
Inline kernel setup: Diffusers downloads only each component's own module
c10a8f8 verified
Raw History Blame Contribute Delete
1.83 kB
Qwen is licensed under the Qwen RESEARCH LICENSE AGREEMENT, Copyright (c) 2026 Hangzhou Tongyi Laboratory Technology Co., Ltd. All Rights Reserved.
Built with Qwen.
This repository redistributes, for non-commercial research and evaluation use only, files derived from
Qwen/Qwen-Image-2.1 (revision 790c92633540aa0cb11d9abf19eb46d861714758).
Unmodified files: LICENSE, processor/, scheduler/, text_encoder/generation_config.json.
MODIFIED FILES (changed by joseplcam):
- transformer/diffusion_pytorch_model.safetensors, transformer/config.json
Quantized from the official BF16 transformer with SVDQuant + GPTQ (NVFP4 weights and activations,
rank-32 BF16 low-rank branch) using diffuse-compressor. Blocks 0, 1, 30, 31 and the global
modulation are the official BF16 tensors. config.json gained a `quantization_config` entry.
sha256 0304fc3e84b13663b439601380066754ef9b92025e159275abe163bc5f1ceeb4
- text_encoder/model.safetensors, text_encoder/config.json
Quantized from the official BF16 Qwen3-VL text encoder with the same method: the MLP projections
(gate/up/down) of decoder layers 4-31, rank-128 low-rank branch. All attention projections, decoder
layers 0-3 and 32-35, the vision tower, embeddings and lm_head are the official BF16 tensors.
config.json gained a `nunchaku_lite` entry.
sha256 83c4462b5ce6b98e2708ca53031e2f9a60a8e402c4c4a0ccd19cb2df8e817ae2
- vae/diffusion_pytorch_model.safetensors, vae/config.json
The official VAE weights cast from FP32 to BF16.
sha256 71879ffd5321e6d10c3c87513e2b474b1252efa7f3dec2969214a9bf06a6dd5c
- model_index.json
The `text_encoder` and `transformer` entries point to the custom classes below.
NEW FILES: text_encoder/modeling_nunchaku_qwen3vl.py, transformer/modeling_nunchaku_qwenimage21.py,
tools/ (the scripts that produced this repository).