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", torch_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
| { | |
| "_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 | |
| } | |