Image-to-Video
Diffusers
Safetensors
MLX
cosmos3
video-generation
quantization
int4
w4a16
sdnq
apple-silicon
cuda
8-bit precision
Instructions to use JuliaML/Cosmos3-Super-Image2Video-4Step-INT4-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-INT4-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-INT4-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-INT4-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-INT4-G64-BF16 JuliaML/Cosmos3-Super-Image2Video-4Step-INT4-G64-BF16
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
File size: 901 Bytes
041ab66 | 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 26 27 28 29 | {
"format": "cosmos3-sdnq-uniform-community-v1",
"status": "complete",
"started_at": "2026-08-11T10:18:28+00:00",
"source": "models/Cosmos3-Super-Image2Video-4Step/transformer",
"output": "checkpoints/Cosmos3-Super-Image2Video-4Step-SDNQ-INT4-G64-BF16IO/transformer",
"weights_dtype": "int4",
"group_size": 64,
"activation_dtype": "bfloat16",
"bf16_io": [
"proj_in",
"proj_out"
],
"use_svd": false,
"use_hadamard": false,
"use_dynamic_quantization": false,
"use_quantized_matmul_by_default": false,
"quantization_device": "mps",
"quantization_seconds": 84.7199640829931,
"quantized_matrix_count": 896,
"parameter_bytes": 38334765440,
"finished_at": "2026-08-11T10:20:57+00:00",
"save_seconds": 63.97164558299119,
"total_seconds": 148.69399683299707,
"safetensor_bytes": 38335018664,
"safetensor_gib": 35.7022682800889,
"sdnq_version": "0.2.4"
}
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