Image-to-Video
Diffusers
Safetensors
English
Chinese
ImageToVideoPipeline
video generation
conversational video generation
talking human video generation
Instructions to use ssbtech/models-part1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ssbtech/models-part1 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("ssbtech/models-part1", 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
Upload model_index.json with huggingface_hub
Browse files- model_index.json +18 -0
model_index.json
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{
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"_class_name": "DiffusionPipeline",
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"_diffusers_version": "0.31.0",
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"model_type": "image-to-video",
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"pipeline_tag": "image-to-video",
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"scheduler": [
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"scheduler"
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],
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"components": {
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"vae": ["AutoencoderKL"],
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"image_encoder": ["CLIPVisionModelWithProjection"],
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"text_encoder": ["CLIPTextModel"],
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"tokenizer": ["CLIPTokenizer"],
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"unet": ["UNet3DConditionModel"],
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"scheduler": ["DDIMScheduler"],
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"feature_extractor": ["CLIPImageProcessor"]
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}
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}
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