Instructions to use fusing/latent-diffusion-text2im-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fusing/latent-diffusion-text2im-large with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("fusing/latent-diffusion-text2im-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download unet/config.json from fusing/latent-diffusion-text2im-large: direct link, hf CLI and curl.
- Browser
- Download file 745 Bytes
-
https://huggingface.co/fusing/latent-diffusion-text2im-large/resolve/85cbe9699f34a107b12e0bc20455f62f712a398e/unet/config.json
- Command line
-
hf download hf://fusing/latent-diffusion-text2im-large@85cbe9699f34a107b12e0bc20455f62f712a398e/unet/config.json
-
curl -L -o config.json https://huggingface.co/fusing/latent-diffusion-text2im-large/resolve/85cbe9699f34a107b12e0bc20455f62f712a398e/unet/config.json
745 Bytes
| { | |
| "_class_name": "UNetLDMModel", | |
| "_diffusers_version": "0.0.1", | |
| "attention_resolutions": [ | |
| 4, | |
| 2, | |
| 1 | |
| ], | |
| "channel_mult": [ | |
| 1, | |
| 2, | |
| 4, | |
| 4 | |
| ], | |
| "context_dim": 1280, | |
| "conv_resample": true, | |
| "dims": 2, | |
| "dropout": 0, | |
| "image_size": 32, | |
| "in_channels": 4, | |
| "legacy": false, | |
| "model_channels": 320, | |
| "n_embed": null, | |
| "name_or_path": "../fusing-models/unet/", | |
| "num_classes": null, | |
| "num_head_channels": -1, | |
| "num_heads": 8, | |
| "num_heads_upsample": -1, | |
| "num_res_blocks": 2, | |
| "out_channels": 4, | |
| "resblock_updown": false, | |
| "transformer_depth": 1, | |
| "use_checkpoint": false, | |
| "use_fp16": false, | |
| "use_new_attention_order": false, | |
| "use_scale_shift_norm": false, | |
| "use_spatial_transformer": true | |
| } | |