Instructions to use hohs/phd_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use hohs/phd_model with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("hohs/phd_model", 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": "PoseDiTTransformer2DModel", | |
| "_diffusers_version": "0.32.1", | |
| "_name_or_path": "checkpoints/trainvaltest-vitbb-square-vertex-augv2-rot90-drop20-heatmap-8gpu-rect-normalize-flow-shift3-shape_aug-pretrain-zeroshape/20250127-115851/checkpoint-9000", | |
| "activation_fn": "gelu-approximate", | |
| "attention_bias": true, | |
| "attention_head_dim": 32, | |
| "dropout": 0.0, | |
| "in_channels": 3, | |
| "norm_elementwise_affine": false, | |
| "norm_eps": 1e-05, | |
| "norm_num_groups": 32, | |
| "norm_type": "ada_norm_zero", | |
| "num_attention_heads": 16, | |
| "num_embeds_ada_norm": 10, | |
| "num_joints": 283, | |
| "num_layers": 20, | |
| "out_channels": null, | |
| "patch_size": 2, | |
| "sample_size": 32, | |
| "upcast_attention": false, | |
| "use_heatmap": true | |
| } | |