Instructions to use Baptlem/UCDR-Net_models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Baptlem/UCDR-Net_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("Baptlem/UCDR-Net_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
Download coyo1M-bridge2M/config.json from Baptlem/UCDR-Net_models: direct link, hf CLI and curl.
- Browser
- Download file 665 Bytes
-
https://huggingface.co/Baptlem/UCDR-Net_models/resolve/main/coyo1M-bridge2M/config.json
- Command line
-
hf download hf://Baptlem/UCDR-Net_models/coyo1M-bridge2M/config.json
-
curl -L -o config.json https://huggingface.co/Baptlem/UCDR-Net_models/resolve/main/coyo1M-bridge2M/config.json
665 Bytes
| { | |
| "_class_name": "FlaxControlNetModel", | |
| "_diffusers_version": "0.16.0.dev0", | |
| "attention_head_dim": 8, | |
| "block_out_channels": [ | |
| 320, | |
| 640, | |
| 1280, | |
| 1280 | |
| ], | |
| "conditioning_embedding_out_channels": [ | |
| 16, | |
| 32, | |
| 96, | |
| 256 | |
| ], | |
| "controlnet_conditioning_channel_order": "rgb", | |
| "cross_attention_dim": 768, | |
| "down_block_types": [ | |
| "CrossAttnDownBlock2D", | |
| "CrossAttnDownBlock2D", | |
| "CrossAttnDownBlock2D", | |
| "DownBlock2D" | |
| ], | |
| "dropout": 0.0, | |
| "flip_sin_to_cos": true, | |
| "freq_shift": 0, | |
| "in_channels": 4, | |
| "layers_per_block": 2, | |
| "only_cross_attention": false, | |
| "sample_size": 32, | |
| "use_linear_projection": false | |
| } | |