Image-to-Image
Diffusers
Safetensors
English
Image-to-Image
ControlNet
Diffusers
QwenImageControlNetPipeline
Qwen-Image
Instructions to use InstantX/Qwen-Image-ControlNet-Union with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use InstantX/Qwen-Image-ControlNet-Union 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("InstantX/Qwen-Image-ControlNet-Union", 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
Fuvk
#5
by Frank1223 - opened
README.md
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@@ -7,9 +7,6 @@ pipeline_tag: image-to-image
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tags:
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- Image-to-Image
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- ControlNet
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- Diffusers
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- QwenImageControlNetPipeline
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- Qwen-Image
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base_model: Qwen/Qwen-Image
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We find that the model was unable to preserve some details without explicit 'TEXT' in prompt, such as small font text.
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# Acknowledgements
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This model is developed by InstantX Team. All copyright reserved.
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tags:
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- Image-to-Image
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- ControlNet
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base_model: Qwen/Qwen-Image
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---
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We find that the model was unable to preserve some details without explicit 'TEXT' in prompt, such as small font text.
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# Acknowledgements
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This model is developed by InstantX Team. All copyright reserved.
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