Image-to-Image
Diffusers
Safetensors
Diffusion Single File
English
Flux2KleinPipeline
text-to-image
image-editing
flux
Instructions to use YuCollection/FLUX.2-klein-base-4B-Diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use YuCollection/FLUX.2-klein-base-4B-Diffusers 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("YuCollection/FLUX.2-klein-base-4B-Diffusers", 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 YuCollection/FLUX.2-klein-base-4B-Diffusers 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: 486 Bytes
8915af4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | {
"_class_name": "FlowMatchEulerDiscreteScheduler",
"_diffusers_version": "0.37.0.dev0",
"base_image_seq_len": 256,
"base_shift": 0.5,
"invert_sigmas": false,
"max_image_seq_len": 4096,
"max_shift": 1.15,
"num_train_timesteps": 1000,
"shift": 3.0,
"shift_terminal": null,
"stochastic_sampling": false,
"time_shift_type": "exponential",
"use_beta_sigmas": false,
"use_dynamic_shifting": true,
"use_exponential_sigmas": false,
"use_karras_sigmas": false
}
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