Image-to-Video
Diffusers
Safetensors
MLX
cosmos3
video-generation
quantization
int8
w8a16
sdnq
apple-silicon
cuda
8-bit precision
Instructions to use JuliaML/Cosmos3-Super-Image2Video-4Step-INT8-G64-BF16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use JuliaML/Cosmos3-Super-Image2Video-4Step-INT8-G64-BF16 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("JuliaML/Cosmos3-Super-Image2Video-4Step-INT8-G64-BF16", 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") - MLX
How to use JuliaML/Cosmos3-Super-Image2Video-4Step-INT8-G64-BF16 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Cosmos3-Super-Image2Video-4Step-INT8-G64-BF16 JuliaML/Cosmos3-Super-Image2Video-4Step-INT8-G64-BF16
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
File size: 2,604 Bytes
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"format": "cosmos3-sdnq-verification-v1",
"source": "models/Cosmos3-Super-Image2Video-4Step/transformer",
"checkpoint": "checkpoints/Cosmos3-Super-Image2Video-4Step-SDNQ-INT8-G64-BF16IO/transformer",
"selection_report": "benchmarks/i2v/verification-selection.json",
"weights_dtype": "int8",
"group_size": 64,
"selected_tensors": [
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"layers.42.self_attn.add_v_proj.weight",
"layers.42.mlp.gate_proj.weight",
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"norm.weight",
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],
"packing_exact": true,
"global_relative_mse": 4.091530100791147e-05,
"core_global_relative_mse": 4.091530100791147e-05,
"elapsed_seconds": 2.059502917000003,
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"exact": true
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{
"name": "norm.weight",
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},
{
"name": "proj_in.weight",
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},
{
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]
}
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