Instructions to use blanchon/dc_flux_krea_diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use blanchon/dc_flux_krea_diffusers with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("blanchon/dc_flux_krea_diffusers", 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 Settings
- Draw Things
- DiffusionBee
File size: 1,600 Bytes
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"_class_name": "AutoencoderDC",
"_diffusers_version": "0.36.0",
"_name_or_path": "mit-han-lab/dc-ae-f64c128-in-1.0-diffusers",
"attention_head_dim": 32,
"decoder_act_fns": [
"relu",
"relu",
"relu",
"silu",
"silu",
"silu",
"silu"
],
"decoder_block_out_channels": [
128,
256,
512,
512,
1024,
1024,
2048
],
"decoder_block_types": [
"ResBlock",
"ResBlock",
"ResBlock",
"EfficientViTBlock",
"EfficientViTBlock",
"EfficientViTBlock",
"EfficientViTBlock"
],
"decoder_conv_act_fn": "relu",
"decoder_in_shortcut": true,
"decoder_layers_per_block": [
0,
5,
10,
2,
2,
2,
2
],
"decoder_norm_types": [
"batch_norm",
"batch_norm",
"batch_norm",
"rms_norm",
"rms_norm",
"rms_norm",
"rms_norm"
],
"decoder_qkv_multiscales": [
[],
[],
[],
[],
[],
[],
[]
],
"downsample_block_type": "pixel_unshuffle",
"encoder_block_out_channels": [
128,
256,
512,
512,
1024,
1024,
2048
],
"encoder_block_types": [
"ResBlock",
"ResBlock",
"ResBlock",
"EfficientViTBlock",
"EfficientViTBlock",
"EfficientViTBlock",
"EfficientViTBlock"
],
"encoder_layers_per_block": [
0,
4,
8,
2,
2,
2,
2
],
"encoder_out_shortcut": true,
"encoder_qkv_multiscales": [
[],
[],
[],
[],
[],
[],
[]
],
"in_channels": 3,
"latent_channels": 128,
"scaling_factor": 0.2889,
"upsample_block_type": "pixel_shuffle"
}
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