Instructions to use FlyingRoastDuck/I_DRUID with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use FlyingRoastDuck/I_DRUID with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("FlyingRoastDuck/I_DRUID", 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: 534 Bytes
96d051f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | {
"_class_name": "AdapterLayoutSD3Transformer2DModel",
"_diffusers_version": "0.33.0",
"_name_or_path": "./exps/adapter_IDM_FLUX_only_pos_IDM/checkpoint-01/tag00/checkpoint_2000",
"attention_head_dim": 64,
"attention_type": "layout",
"caption_projection_dim": 1536,
"in_channels": 16,
"joint_attention_dim": 4096,
"max_boxes_per_image": 10,
"num_attention_heads": 24,
"num_layers": 24,
"out_channels": 16,
"patch_size": 2,
"pooled_projection_dim": 2048,
"pos_embed_max_size": 192,
"sample_size": 128
}
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