Text-to-Image
Diffusers
Safetensors
QwenImage21Pipeline
qwen-image
qwen-image-2.1
nvfp4
svdquant
nunchaku
blackwell
image-editing
8-bit precision
Instructions to use joseplcam/Qwen-Image-2.1-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use joseplcam/Qwen-Image-2.1-NVFP4 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("joseplcam/Qwen-Image-2.1-NVFP4", 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
- Local Apps Settings
- Draw Things
- DiffusionBee
File size: 1,826 Bytes
3814de5 7cbf234 3814de5 7cbf234 3814de5 7cbf234 3814de5 7cbf234 3814de5 7cbf234 c10a8f8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 | 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).
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