Instructions to use Moqi27/FLUX.2-small-decoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Moqi27/FLUX.2-small-decoder 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("Moqi27/FLUX.2-small-decoder", 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 Moqi27/FLUX.2-small-decoder 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: 842 Bytes
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"_class_name": "AutoencoderKLFlux2",
"_diffusers_version": "0.32.0.dev0",
"in_channels": 3,
"out_channels": 3,
"down_block_types": [
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"DownEncoderBlock2D"
],
"up_block_types": [
"UpDecoderBlock2D",
"UpDecoderBlock2D",
"UpDecoderBlock2D",
"UpDecoderBlock2D"
],
"block_out_channels": [
128,
256,
512,
512
],
"decoder_block_out_channels": [
96,
192,
384,
384
],
"layers_per_block": 2,
"act_fn": "silu",
"latent_channels": 32,
"norm_num_groups": 32,
"sample_size": 1024,
"force_upcast": true,
"use_quant_conv": true,
"use_post_quant_conv": true,
"mid_block_add_attention": true,
"batch_norm_eps": 0.0001,
"batch_norm_momentum": 0.1,
"patch_size": [
2,
2
]
} |