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 # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("PLAN-Lab/GraphVid", torch_dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Delete files vae/* with huggingface_hub
Browse files- vae/config.json +0 -81
- vae/diffusion_pytorch_model.safetensors +0 -3
vae/config.json
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{
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"_class_name": "AutoencoderKLLTXVideo",
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"_diffusers_version": "0.38.0.dev0",
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"_name_or_path": "GraphVid_1.0_VAE",
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"block_out_channels": [
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128,
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256,
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512,
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1024,
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2048
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],
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"decoder_block_out_channels": [
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256,
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512,
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1024
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],
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"decoder_causal": false,
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"decoder_inject_noise": [
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false,
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false,
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false,
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false
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],
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"decoder_layers_per_block": [
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5,
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5,
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5,
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5
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],
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"decoder_spatio_temporal_scaling": [
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true,
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true,
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true
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],
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"down_block_types": [
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"LTXVideo095DownBlock3D",
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"LTXVideo095DownBlock3D",
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"LTXVideo095DownBlock3D",
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"LTXVideo095DownBlock3D"
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],
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"downsample_type": [
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"spatial",
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"temporal",
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"spatiotemporal",
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"spatiotemporal"
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],
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"encoder_causal": true,
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"in_channels": 3,
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"latent_channels": 128,
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"layers_per_block": [
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4,
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6,
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6,
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2,
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2
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],
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"out_channels": 3,
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"patch_size": 4,
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"patch_size_t": 1,
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"resnet_norm_eps": 1e-06,
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"scaling_factor": 1.0,
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"spatial_compression_ratio": 32,
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"spatio_temporal_scaling": [
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true,
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true,
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true,
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true
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],
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"temporal_compression_ratio": 8,
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"timestep_conditioning": true,
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"upsample_factor": [
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2,
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2,
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2
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],
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"upsample_residual": [
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true,
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true,
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true
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]
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}
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vae/diffusion_pytorch_model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:3419989c7059923ee4134b858e0511d0b294a7c88b7745169763b37ebc4db7f0
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size 2493855612
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