Instructions to use vladmandic/VIBE-Image-Edit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use vladmandic/VIBE-Image-Edit 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("vladmandic/VIBE-Image-Edit", 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: 423 Bytes
7c8053f 3e0cf6a 7c8053f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | {
"_class_name": "VIBESanaEditingPipeline",
"_diffusers_version": "0.33.1",
"scheduler": [
"diffusers",
"DPMSolverMultistepScheduler"
],
"text_encoder": [
"transformers",
"Qwen3VLForConditionalGeneration"
],
"tokenizer": [
"transformers",
"Qwen3VLProcessor"
],
"transformer": [
"diffusers",
"VIBESanaEditingModel"
],
"vae": [
"diffusers",
"AutoencoderDC"
]
}
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