Image-to-Image
Diffusers
Safetensors
English
Image-to-Image
ControlNet
Diffusers
QwenImageControlNetInpaintPipeline
Qwen-Image
Instructions to use InstantX/Qwen-Image-ControlNet-Inpainting with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use InstantX/Qwen-Image-ControlNet-Inpainting 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-Inpainting", 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
| { | |
| "_class_name": "QwenImageControlNetModel", | |
| "_diffusers_version": "0.35.0.dev0", | |
| "_name_or_path": "qwenimage-controlnet-inpaint-v2/checkpoint-25000/controlnet", | |
| "attention_head_dim": 128, | |
| "axes_dims_rope": [ | |
| 16, | |
| 56, | |
| 56 | |
| ], | |
| "extra_condition_channels": 4, | |
| "guidance_embeds": false, | |
| "in_channels": 64, | |
| "joint_attention_dim": 3584, | |
| "num_attention_heads": 24, | |
| "num_layers": 6, | |
| "out_channels": 16, | |
| "patch_size": 2, | |
| "pooled_projection_dim": 768 | |
| } | |