Instructions to use BiliSakura/ADM-diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use BiliSakura/ADM-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("BiliSakura/ADM-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
- Draw Things
- DiffusionBee
File size: 356 Bytes
d717924 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 | {
"_class_name": "ADMClassifierModel",
"_diffusers_version": "0.36.0",
"classifier_attention_resolutions": "32,16,8",
"classifier_depth": 2,
"classifier_pool": "attention",
"classifier_resblock_updown": true,
"classifier_use_scale_shift_norm": true,
"classifier_width": 128,
"image_size": 512,
"num_classes": 1000,
"use_fp16": true
}
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