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: 509 Bytes
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"_class_name": "JoyImageEditPipeline",
"_diffusers_version": "0.38.0.dev0",
"processor": [
"transformers",
"Qwen3VLProcessor"
],
"scheduler": [
"diffusers",
"FlowMatchEulerDiscreteScheduler"
],
"text_encoder": [
"transformers",
"Qwen3VLForConditionalGeneration"
],
"tokenizer": [
"transformers",
"Qwen2TokenizerFast"
],
"transformer": [
"diffusers",
"JoyImageEditTransformer3DModel"
],
"vae": [
"diffusers",
"AutoencoderKLWan"
]
}
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