Instructions to use fusing/ddpm_dummy_update with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use fusing/ddpm_dummy_update with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("fusing/ddpm_dummy_update", 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: 605 Bytes
a4672d2 2e1952b a4672d2 2e1952b a4672d2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | {
"_class_name": "UNet2DModel",
"_diffusers_version": "0.0.4",
"act_fn": "silu",
"attention_head_dim": 64,
"block_out_channels": [
32,
64
],
"center_input_sample": false,
"down_block_types": [
"DownBlock2D",
"AttnDownBlock2D"
],
"downsample_padding": 0,
"flip_sin_to_cos": false,
"freq_shift": 1,
"in_channels": 3,
"layers_per_block": 2,
"mid_block_scale_factor": 1,
"norm_eps": 1e-06,
"norm_num_groups": 32,
"out_channels": 3,
"sample_size": 32,
"time_embedding_type": "positional",
"up_block_types": [
"AttnUpBlock2D",
"UpBlock2D"
]
}
|