Instructions to use callgg/image-edit-lite with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use callgg/image-edit-lite 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("callgg/image-edit-lite", 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
Upload config.json
Browse files- transformer_1/config.json +17 -0
transformer_1/config.json
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{
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"_class_name": "QwenImageTransformer2DModel",
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"_diffusers_version": "0.36.0.dev0",
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"attention_head_dim": 128,
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"axes_dims_rope": [
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],
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"guidance_embeds": false,
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"in_channels": 64,
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"joint_attention_dim": 3584,
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"num_attention_heads": 24,
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"num_layers": 43,
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"out_channels": 16,
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"patch_size": 2
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}
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