Image-to-Image
Diffusers
Safetensors
Sana
English
image-editing
text-guided-editing
diffusion
qwen-vl
multimodal
Instructions to use iitolstykh/VIBE-Image-Edit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use iitolstykh/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("iitolstykh/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] - Sana
How to use iitolstykh/VIBE-Image-Edit with Sana:
# Load the model and infer image from text import torch from app.sana_pipeline import SanaPipeline from torchvision.utils import save_image sana = SanaPipeline("configs/sana_config/1024ms/Sana_1600M_img1024.yaml") sana.from_pretrained("hf://iitolstykh/VIBE-Image-Edit") image = sana( prompt='a cyberpunk cat with a neon sign that says "Sana"', height=1024, width=1024, guidance_scale=5.0, pag_guidance_scale=2.0, num_inference_steps=18, ) - Notebooks
- Google Colab
- Kaggle
Upload model_index.json
Browse files- model_index.json +24 -0
model_index.json
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{
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"_class_name": "VIBESanaEditingPipeline",
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"_diffusers_version": "0.33.1",
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"scheduler": [
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"diffusers",
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"DPMSolverMultistepScheduler"
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],
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"text_encoder": [
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"transformers",
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"Qwen3VLForConditionalGeneration"
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],
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"tokenizer": [
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"transformers",
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"Qwen3VLProcessor"
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],
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"transformer": [
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"vibe.transformer.vibe_sana_editing",
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"VIBESanaEditingModel"
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],
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"vae": [
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"diffusers",
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"AutoencoderDC"
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]
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}
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