Instructions to use Lakonik/AsymFLUX.2-klein-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Lakonik/AsymFLUX.2-klein-9B with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Lakonik/AsymFLUX.2-klein-9B", 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
- Local Apps
- Draw Things
- DiffusionBee
| { | |
| "_class_name": "AsymFlux2Transformer2DModel", | |
| "_diffusers_version": "0.37.0", | |
| "attention_head_dim": 128, | |
| "axes_dims_rope": [ | |
| 32, | |
| 32, | |
| 32, | |
| 32 | |
| ], | |
| "base_rank": 128, | |
| "eps": 1e-06, | |
| "guidance_embeds": false, | |
| "in_channels": 3, | |
| "joint_attention_dim": 12288, | |
| "mlp_ratio": 3.0, | |
| "num_attention_heads": 32, | |
| "num_layers": 8, | |
| "num_single_layers": 24, | |
| "num_timesteps": 1, | |
| "patch_size": 16, | |
| "rope_theta": 2000, | |
| "sigma_min": 0.0001, | |
| "timestep_guidance_channels": 256 | |
| } | |