Instructions to use ovedrive/qwen-image-edit-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ovedrive/qwen-image-edit-4bit with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ovedrive/qwen-image-edit-4bit", dtype=torch.bfloat16, device_map="cuda") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Notebooks
- Google Colab
- Kaggle
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Model tested: Working perfectly even with 10 steps.
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Contact: [JustLab.ai](https://justlab.ai) for commercial support
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```python
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import os
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Model tested: Working perfectly even with 10 steps.
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Contact: [JustLab.ai](https://justlab.ai) for commercial support
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### Performance on rtx4090
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- 20 steps about 78 seconds.
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- 10 steps about 40 seconds.
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Interestingly I was under the impression that the Qwen-VL could not be quantized which is why several projects use the full 15Gb model.
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Here I have quantized it too and it seems to be workign fine.
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Sample script. (min 20GB VRAM)
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```python
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import os
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