Instructions to use AbstractPhil/sd15-flow-lune-json-vit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use AbstractPhil/sd15-flow-lune-json-vit with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("AbstractPhil/sd15-flow-lune-json-vit", 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
| { | |
| "run": "vit", | |
| "condition_column": "vit_json_prompt", | |
| "len_column": "vit_json_token_len", | |
| "hf_repo_id": "AbstractPhil/sd15-flow-lune-json-vit", | |
| "dataset_name": "AbstractPhil/synthetic-object-relations-json", | |
| "sd_base": "stable-diffusion-v1-5/stable-diffusion-v1-5", | |
| "unet_repo": "AbstractPhil/sd15-flow-lune-flux", | |
| "unet_subfolder": "flux_t2_6_pose_t4_6_port_t1_4/checkpoint-00018765/unet", | |
| "output_dir": "./outputs", | |
| "max_clip_tokens": 225, | |
| "seed": 42, | |
| "batch_size": 8, | |
| "base_lr": 1e-05, | |
| "shift": 2.0, | |
| "dropout": 0.1, | |
| "num_train_epochs": 4, | |
| "warmup_epochs": 1, | |
| "checkpointing_steps": 1000, | |
| "num_workers": 0, | |
| "vae_scale": 0.18215, | |
| "save_optimizer": false, | |
| "upload_to_hub": true | |
| } |