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
Upload config.json with huggingface_hub
Browse files- config.json +24 -0
config.json
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{
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"run": "vit",
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"condition_column": "vit_json_prompt",
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"len_column": "vit_json_token_len",
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"hf_repo_id": "AbstractPhil/sd15-flow-lune-json-vit",
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"dataset_name": "AbstractPhil/synthetic-object-relations-json",
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"sd_base": "stable-diffusion-v1-5/stable-diffusion-v1-5",
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"unet_repo": "AbstractPhil/sd15-flow-lune-flux",
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"unet_subfolder": "flux_t2_6_pose_t4_6_port_t1_4/checkpoint-00018765/unet",
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"output_dir": "./outputs",
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"max_clip_tokens": 225,
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"seed": 42,
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"batch_size": 8,
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"base_lr": 1e-05,
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"shift": 2.0,
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"dropout": 0.1,
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"num_train_epochs": 4,
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"warmup_epochs": 1,
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"checkpointing_steps": 1000,
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"num_workers": 0,
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"vae_scale": 0.18215,
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"save_optimizer": false,
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"upload_to_hub": true
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}
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