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 phase2_editor/transformer/config.json from wty500/RL-RIG: direct link, hf CLI and curl.
- Browser
- Download file 362 Bytes
-
https://huggingface.co/wty500/RL-RIG/resolve/main/phase2_editor/transformer/config.json
- Command line
-
hf download hf://wty500/RL-RIG/phase2_editor/transformer/config.json
-
curl -L -o config.json https://huggingface.co/wty500/RL-RIG/resolve/main/phase2_editor/transformer/config.json
362 Bytes
| { | |
| "_class_name": "QwenImageTransformer2DModel", | |
| "_diffusers_version": "0.36.0.dev0", | |
| "attention_head_dim": 128, | |
| "axes_dims_rope": [ | |
| 16, | |
| 56, | |
| 56 | |
| ], | |
| "guidance_embeds": false, | |
| "in_channels": 64, | |
| "joint_attention_dim": 3584, | |
| "num_attention_heads": 24, | |
| "num_layers": 60, | |
| "out_channels": 16, | |
| "patch_size": 2, | |
| "zero_cond_t": true | |
| } | |