Instructions to use KevinHuang/OmniX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use KevinHuang/OmniX with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("KevinHuang/OmniX", 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 folder using huggingface_hub
Browse files
v2/image_text_to_pano/MixTraining_RGB-npu_8/checkpoints_lora/filled_rgb/pytorch_lora_weights.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:dfb31099b0b9d7bfd157a45d29168fedbda8159042af495df94c01723d4096c5
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size 224194520
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v2/image_text_to_pano/MixTraining_RGB-npu_8/checkpoints_lora/masked_rgb/pytorch_lora_weights.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:e65d187679eda3afa76d5356ddd29826392a5f40529bbac0fefb019053a70f3f
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size 224194520
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v2/image_text_to_pano/MixTraining_RGB-npu_8/checkpoints_lora/record.txt
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step=50000
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Training Dataset: Pano360, Structured3D, HDR360-UHD, PanoX
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