Instructions to use ShimbaLLM/Dupe-R-IMG with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ShimbaLLM/Dupe-R-IMG with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ShimbaLLM/Dupe-R-IMG", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download vae_config.json from ShimbaLLM/Dupe-R-IMG: direct link, hf CLI and curl.
- Browser
- Download file 547 Bytes
-
https://huggingface.co/ShimbaLLM/Dupe-R-IMG/resolve/main/vae_config.json
- Command line
-
hf download hf://ShimbaLLM/Dupe-R-IMG/vae_config.json
-
curl -L -o vae_config.json https://huggingface.co/ShimbaLLM/Dupe-R-IMG/resolve/main/vae_config.json
547 Bytes
| { | |
| "_class_name": "AutoencoderKL", | |
| "_diffusers_version": "0.4.2", | |
| "act_fn": "silu", | |
| "block_out_channels": [ | |
| 128, | |
| 256, | |
| 512, | |
| 512 | |
| ], | |
| "down_block_types": [ | |
| "DownEncoderBlock2D", | |
| "DownEncoderBlock2D", | |
| "DownEncoderBlock2D", | |
| "DownEncoderBlock2D" | |
| ], | |
| "in_channels": 3, | |
| "latent_channels": 4, | |
| "layers_per_block": 2, | |
| "norm_num_groups": 32, | |
| "out_channels": 3, | |
| "sample_size": 256, | |
| "up_block_types": [ | |
| "UpDecoderBlock2D", | |
| "UpDecoderBlock2D", | |
| "UpDecoderBlock2D", | |
| "UpDecoderBlock2D" | |
| ] | |
| } | |