Instructions to use ChuuniZ/models_collection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ChuuniZ/models_collection with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ChuuniZ/models_collection", 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
- Joy_caption
- LLM
- RMBG
- checkpoints
- ckpts
- clip
- clip_vision
- configs
- controlnet
- diffusers
- diffusion_models
- dz_facedetailer
- embeddings
- facedetection
- gligen
- grounding-dino
- hypernetworks
- inpaint
- insightface
- instantid
- ipadapter-flux
- ipadapter
- lama
- loras
- mediapipe
- onnx
- photomaker
- pulid
- rembg
- sams
- sapiens
- segformer_b2_clothes
- segformer_b3_clothes
- segformer_b3_fashion
- stablesr
- style_models
- text_encoders
- ultralytics
- unet
- upscale_models
- vae
- vae_approx
- vitmatte
- 2.37 kB
- 29 Bytes
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