Instructions to use diffusion-gif/any2ego with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use diffusion-gif/any2ego with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("fill-in-base-model", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("diffusion-gif/any2ego") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
data: attach pinned training dataset index and safe recovery helpers
Browse files- CHECKSUMS.sha256 +2 -2
- README.md +1 -1
- bundle_manifest.json +3 -3
CHECKSUMS.sha256
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4e0e37529fbf86ebef6bf7cadec4dda1b762f31ed6dba7815a79732bc6b7e836 checkpoints/caption_grpo/adapter_config.json
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290aacd2808639e3c3e10c81c69acfdd843100b8888ad183a2e03ba6e43f5d11 checkpoints/caption_grpo/adapter_model.safetensors
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67acb0f58a052ce7fd309e50cdaf473a9880bc31a8d1c7f68212d528088d275c tools/fetch_training_data.py
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README.md
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```bash
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# For old trajectory checkpoints; inspect other dataset IDs for Any2Ego:
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python tools/fetch_training_data.py --index TRAINING_DATA_INDEX.json --dataset
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# after owner login / dry-run inspection add --execute
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```
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```bash
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# For old trajectory checkpoints; inspect other dataset IDs for Any2Ego:
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python tools/fetch_training_data.py --index TRAINING_DATA_INDEX.json --dataset ego_exo_original_training_inputs --output data_download
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# after owner login / dry-run inspection add --execute
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```
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"path": "checkpoints/caption_grpo/adapter_config.json",
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"native_reader": "safetensors.safe_open(framework=pt, device=cpu), slices only",
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"validation": {
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