Image-to-Image
Diffusers
Safetensors
Diffusion Single File
English
Flux2Pipeline
image-generation
image-editing
flux
Instructions to use unsloth/FLUX.2-dev with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use unsloth/FLUX.2-dev 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("unsloth/FLUX.2-dev", 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] - Diffusion Single File
How to use unsloth/FLUX.2-dev with Diffusion Single File:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
File size: 440 Bytes
351bbb0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | {
"_class_name": "Flux2Transformer2DModel",
"_diffusers_version": "0.36.0.dev0",
"attention_head_dim": 128,
"axes_dims_rope": [
32,
32,
32,
32
],
"eps": 1e-06,
"in_channels": 128,
"joint_attention_dim": 15360,
"mlp_ratio": 3.0,
"num_attention_heads": 48,
"num_layers": 8,
"num_single_layers": 48,
"out_channels": null,
"patch_size": 1,
"rope_theta": 2000,
"timestep_guidance_channels": 256
}
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