Instructions to use scikit-plots/consistency-decoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use scikit-plots/consistency-decoder with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("scikit-plots/consistency-decoder", 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
File size: 1,320 Bytes
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"_class_name": "ConsistencyDecoderVAE",
"_diffusers_version": "0.23.0.dev0",
"_name_or_path": "openai/consistency-decoder",
"decoder_add_attention": false,
"decoder_block_out_channels": [
320,
640,
1024,
1024
],
"decoder_down_block_types": [
"ResnetDownsampleBlock2D",
"ResnetDownsampleBlock2D",
"ResnetDownsampleBlock2D",
"ResnetDownsampleBlock2D"
],
"decoder_downsample_padding": 1,
"decoder_in_channels": 7,
"decoder_layers_per_block": 3,
"decoder_norm_eps": 1e-05,
"decoder_norm_num_groups": 32,
"decoder_num_train_timesteps": 1024,
"decoder_out_channels": 6,
"decoder_resnet_time_scale_shift": "scale_shift",
"decoder_time_embedding_type": "learned",
"decoder_up_block_types": [
"ResnetUpsampleBlock2D",
"ResnetUpsampleBlock2D",
"ResnetUpsampleBlock2D",
"ResnetUpsampleBlock2D"
],
"encoder_act_fn": "silu",
"encoder_block_out_channels": [
128,
256,
512,
512
],
"encoder_double_z": true,
"encoder_down_block_types": [
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"DownEncoderBlock2D"
],
"encoder_in_channels": 3,
"encoder_layers_per_block": 2,
"encoder_norm_num_groups": 32,
"encoder_out_channels": 4,
"latent_channels": 4,
"scaling_factor": 0.18215
} |