Text-to-Image
Diffusers
Safetensors
English
Chinese
QwenImage21Pipeline
sdnq
int4
uint4
image-generation
image-editing
apple-silicon
8-bit precision
Instructions to use ixim/Image21-INT4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ixim/Image21-INT4 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ixim/Image21-INT4", 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
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Download CHANGES.md from ixim/Image21-INT4: direct link, hf CLI and curl.
- Browser
- Download file 653 Bytes
-
https://huggingface.co/ixim/Image21-INT4/resolve/9116984e195059685457c97a94f01c45fc532fdf/CHANGES.md
- Command line
-
hf download hf://ixim/Image21-INT4@9116984e195059685457c97a94f01c45fc532fdf/CHANGES.md
-
curl -L -o CHANGES.md https://huggingface.co/ixim/Image21-INT4/resolve/9116984e195059685457c97a94f01c45fc532fdf/CHANGES.md
653 Bytes
Modifications
Modified by ixim / iximbox: eligible linear weights converted from Qwen-Image-2.1 to SDNQ UINT4 with SVD rank 32. Built with Qwen. Non-commercial research/evaluation under the accompanying Qwen Research License.
- Converted eligible transformer and text-encoder linear layers to SDNQ UINT4.
- Stored a rank-32 SVD residual of the quantization error with the weights.
- Left the requested sensitive projections, normalization, embeddings, vision tower, output head and VAE in floating point.
- Did not use a calibration set or fine-tuning.
- Quantized matmul is off so CUDA and Apple Silicon use the same eager dequantization.