Instructions to use jaxmetaverse/all_models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use jaxmetaverse/all_models with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("jaxmetaverse/all_models", 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
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- BiRefNet
- CogVideo
- DiffuEraser
- FILM
- InfiniteYou
- Janus-Pro
- Joy_caption
- LLM
- OmniGen
- RMBG
- SEEDVR2
- SVFR
- TTS
- VQA
- animatediff_models
- animatediff_motion_lora
- audio_encoders
- blip
- checkpoints
- clip
- clip_interrogator
- clip_vision
- clipseg
- configs
- controlnet
- deepbump
- depthanything
- diffusers
- diffusion_models
- echo_mimic
- embeddings
- emotion2vec
- face_parsing
- face_restore
- facedetection
- facerestore_models
- float
- florence2
- gligen
- grounding-dino
- hypernetworks
- inpaint
- insightface
- instantid
- interpolation
- ipadapter
- lama
- liveportrait
- loras
- mediapipe