Image-to-Video
Diffusers
Safetensors
Kandinsky6TI2VAPipeline
video
video-generation
text-to-video
audio-generation
kandinsky
Instructions to use ml-resources/Kandinsky-6.0-Pro-5s-Diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ml-resources/Kandinsky-6.0-Pro-5s-Diffusers 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("ml-resources/Kandinsky-6.0-Pro-5s-Diffusers", 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
File size: 718 Bytes
86885f5 | 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 30 31 32 33 | {
"_class_name": "AutoencoderKLHunyuanVideo",
"_diffusers_version": "0.32.0.dev0",
"act_fn": "silu",
"block_out_channels": [
128,
256,
512,
512
],
"down_block_types": [
"HunyuanVideoDownBlock3D",
"HunyuanVideoDownBlock3D",
"HunyuanVideoDownBlock3D",
"HunyuanVideoDownBlock3D"
],
"in_channels": 3,
"latent_channels": 16,
"layers_per_block": 2,
"mid_block_add_attention": true,
"norm_num_groups": 32,
"out_channels": 3,
"scaling_factor": 0.476986,
"spatial_compression_ratio": 8,
"temporal_compression_ratio": 4,
"up_block_types": [
"HunyuanVideoUpBlock3D",
"HunyuanVideoUpBlock3D",
"HunyuanVideoUpBlock3D",
"HunyuanVideoUpBlock3D"
]
}
|