Instructions to use Runware/JoyAI-Image-Edit-Diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Runware/JoyAI-Image-Edit-Diffusers 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("Runware/JoyAI-Image-Edit-Diffusers", 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
File size: 387 Bytes
a7258e1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | {
"_class_name": "JoyImageEditTransformer3DModel",
"_diffusers_version": "0.38.0.dev0",
"hidden_size": 4096,
"in_channels": 16,
"mlp_width_ratio": 4.0,
"num_attention_heads": 32,
"num_layers": 40,
"out_channels": 16,
"patch_size": [
1,
2,
2
],
"rope_dim_list": [
16,
56,
56
],
"rope_type": "rope",
"text_dim": 4096,
"theta": 10000
}
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