Image-to-Video
Diffusers
Safetensors
video-generation
audio-video-generation
reference-to-video
long-video
multi-shot
dmd
Instructions to use jdopensource/JoyAI-Echo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use jdopensource/JoyAI-Echo 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("jdopensource/JoyAI-Echo", 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
JoyAI-Echo GGUFs
#4
by realrebelai - opened
I quanted some DiTs and encoders and pulled all the necessary files, i also forked and coded in the ggufs into a comfy node set. Hope someone gets some use out of it! 🫶
Nodes are linked in the repo description:
https://huggingface.co/realrebelai/JoyAI-Echo_GGUF