Instructions to use tiny-random/flux.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use tiny-random/flux.2 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("tiny-random/flux.2", dtype=torch.bfloat16, device_map="cuda") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Notebooks
- Google Colab
- Kaggle
| { | |
| "_class_name": "Flux2Transformer2DModel", | |
| "_diffusers_version": "0.36.0.dev0", | |
| "attention_head_dim": 32, | |
| "axes_dims_rope": [ | |
| 8, | |
| 12, | |
| 12 | |
| ], | |
| "eps": 1e-06, | |
| "in_channels": 32, | |
| "joint_attention_dim": 8, | |
| "mlp_ratio": 3.0, | |
| "num_attention_heads": 2, | |
| "num_layers": 2, | |
| "num_single_layers": 2, | |
| "out_channels": null, | |
| "patch_size": 1, | |
| "rope_theta": 2000, | |
| "timestep_guidance_channels": 256 | |
| } | |