Image-to-Image
Diffusers
Safetensors
English
FluxKleinPipeline
image-editing
instruction-based-image-editing
4k
distillation
dcae
Instructions to use openvivo/BluePixel-Turbo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use openvivo/BluePixel-Turbo 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("openvivo/BluePixel-Turbo", 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
File size: 1,622 Bytes
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"_class_name": "AutoencoderDC",
"_diffusers_version": "0.39.0",
"_name_or_path": "assets/pretrained_models/fluxklein/sft-lora-v3-4k-step_35000/vae",
"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": 0.4538,
"upsample_block_type": "pixel_shuffle"
}
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