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
| { | |
| "_class_name": "ImageToVideoPipeline", | |
| "_diffusers_version": "0.31.0", | |
| "model_type": "image-to-video", | |
| "pipeline_tag": "image-to-video", | |
| "components": { | |
| "vae": ["AutoencoderKL"], | |
| "image_encoder": ["CLIPVisionModelWithProjection"], | |
| "text_encoder": ["CLIPTextModel"], | |
| "tokenizer": ["CLIPTokenizer"], | |
| "unet": ["UNet3DConditionModel"], | |
| "scheduler": ["DPMSolverMultistepScheduler"], | |
| "feature_extractor": ["CLIPImageProcessor"] | |
| } | |
| } | |