Image-to-Video
Diffusers
Safetensors
English
video-generation
controllable-generation
graph-conditioning
computer-vision
Instructions to use PLAN-Lab/GraphVid with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use PLAN-Lab/GraphVid with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image, export_to_video # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("PLAN-Lab/GraphVid", dtype=torch.bfloat16, device_map="cuda") pipe.to("cuda") prompt = "A man with short gray hair plays a red electric guitar." image = load_image( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png" ) output = pipe(image=image, prompt=prompt).frames[0] export_to_video(output, "output.mp4") - Notebooks
- Google Colab
- Kaggle
Delete files model_index.json with huggingface_hub
Browse files- model_index.json +0 -25
model_index.json
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{
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"_class_name": "LTXConditionPipeline",
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"_diffusers_version": "0.38.0.dev0",
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"_name_or_path": "GraphVid_1.0",
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"scheduler": [
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"diffusers",
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"FlowMatchEulerDiscreteScheduler"
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],
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"text_encoder": [
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"transformers",
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"T5EncoderModel"
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],
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"tokenizer": [
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"transformers",
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"T5Tokenizer"
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],
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"transformer": [
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"GraphVid_transformer",
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"GraphVidLTXTransformer3DModel"
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],
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"vae": [
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"diffusers",
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"AutoencoderKLLTXVideo"
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]
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}
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