Instructions to use wty500/RL-RIG with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use wty500/RL-RIG with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("wty500/RL-RIG", 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 adapters/phase2_editor_config.json from wty500/RL-RIG: direct link, hf CLI and curl.
- Browser
- Download file 276 Bytes
-
https://huggingface.co/wty500/RL-RIG/resolve/main/adapters/phase2_editor_config.json
- Command line
-
hf download hf://wty500/RL-RIG/adapters/phase2_editor_config.json
-
curl -L -o phase2_editor_config.json https://huggingface.co/wty500/RL-RIG/resolve/main/adapters/phase2_editor_config.json
276 Bytes
| { | |
| "format": "RL-RIG native LoRA tensor names", | |
| "base_model": "Qwen/Qwen-Image-Edit-2511", | |
| "component": "transformer", | |
| "r": 64, | |
| "lora_alpha": 128, | |
| "lora_dropout": 0.0, | |
| "target_modules": [ | |
| "to_q", | |
| "to_k", | |
| "to_v", | |
| "to_out.0" | |
| ], | |
| "bias": "none" | |
| } | |