Instructions to use kr-cen/Qwen-Image-MICo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use kr-cen/Qwen-Image-MICo with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("kr-cen/Qwen-Image-MICo", 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
Gguf version please
Please add gguf version like q4 or q5 for consumer gpus
Thanks a lot for the feedback and for trying out the model. I agree that the ~60GB VRAM peak is a significant bottleneck for consumer hardware. While GGUF quantization is technically feasible and highly effective for dropping VRAM, my current focus has been shifted to other projects, so I don't have the bandwidth to maintain GGUF quants right now.
Since the model weights are fully open source, I highly encourage and welcome anyone in the community to quantize it. If you or anyone else manages to create a GGUF version (e.g., using city96's conversion tools), please let me know, I would be more than happy to feature it and link to your repo in our page.