Image-to-Video
Diffusers
Safetensors
Kandinsky6TI2VAPipeline
video
video-generation
text-to-video
audio-generation
kandinsky
Instructions to use kandinskylab/Kandinsky-6.0-Pro-pretrain-5s-Diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use kandinskylab/Kandinsky-6.0-Pro-pretrain-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("kandinskylab/Kandinsky-6.0-Pro-pretrain-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
Add split vocoder component (audio_vae unchanged, still usable by sglang's combined loader)
Browse files- model_index.json +4 -0
- vocoder/config.json +44 -0
- vocoder/diffusion_pytorch_model.safetensors +3 -0
model_index.json
CHANGED
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@@ -43,5 +43,9 @@
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"audio_vae": [
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"diffusers",
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"MMAudioVAE"
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]
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}
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"audio_vae": [
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"diffusers",
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"MMAudioVAE"
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],
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"vocoder": [
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"diffusers",
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"MMAudioVocoder"
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]
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}
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vocoder/config.json
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{
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"_class_name": "MMAudioVocoder",
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"_diffusers_version": "0.40.0.dev0",
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"num_mels": 128,
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"resblock_dilation_sizes": [
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[
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1,
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3,
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5
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],
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[
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1,
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3,
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5
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],
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[
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1,
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5
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]
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],
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"resblock_kernel_sizes": [
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3,
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7,
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11
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],
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"upsample_initial_channel": 1536,
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"upsample_kernel_sizes": [
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16,
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8,
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4,
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4,
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4
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],
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"upsample_rates": [
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8,
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4,
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2,
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2,
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2,
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2
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]
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}
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vocoder/diffusion_pytorch_model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:abf0536824bdce8dcf8ee10b815770111f4b5d05d0122e1bab4b5719324edca7
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size 488692656
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