Text-to-Speech
Diffusers
Safetensors
GGUF
docker
comfyui
talking-face
lip-sync
live-avatar
music-generation
image-generation
video-generation
skill-pilot
Instructions to use skill-pilot/media-mcp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use skill-pilot/media-mcp with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("skill-pilot/media-mcp", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f28688d3ab8266c9adcb94a707e43933491f3d9a99084729cfa8c7a27d2b9a7e
- Size of remote file:
- 476 kB
- SHA256:
- b2a5ce8090d32da3642cc4f81fdc996376bc6dd3f4cd5e3d165f71120d9f2bc8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.