Instructions to use anonymousModelsTimeCSL/TimeCSL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use anonymousModelsTimeCSL/TimeCSL with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ali-vilab/In-Context-LoRA", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("anonymousModelsTimeCSL/TimeCSL") prompt = "Screenshot" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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Download README.md from anonymousModelsTimeCSL/TimeCSL: direct link, hf CLI and curl.
- Browser
- Download file 2.32 kB
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https://huggingface.co/anonymousModelsTimeCSL/TimeCSL/resolve/main/README.md
- Command line
-
hf download hf://anonymousModelsTimeCSL/TimeCSL/README.md
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curl -L -o README.md https://huggingface.co/anonymousModelsTimeCSL/TimeCSL/resolve/main/README.md
2.32 kB
| tags: | |
| - text-to-image | |
| - lora | |
| - diffusers | |
| - template:diffusion-lora | |
| widget: | |
| - text: Screenshot | |
| parameters: | |
| negative_prompt: Identifiability of the latent space | |
| output: | |
| url: images/Screenshot 2024-11-29 at 02.26.15.png | |
| base_model: ali-vilab/In-Context-LoRA | |
| instance_prompt: Time Series, Disentanglement, Identifiability | |
| license: apache-2.0 | |
| # TimeCSL | |
| <Gallery /> | |
| ## Model description | |
| # Identifiability Guarantees For Time Series Representation via Contrastive Sparsity-inducing | |
| Official code for the paper **Identifiability Guarantees For Time Series Representation via Contrastive Sparsity-inducing** | |
|  | |
| We define the identifiability problem for time series variable models, where factors are represented by latent slots. | |
| We are thrilled to announce the release of 221 models, now available in the `checkpoints` folder and downloadable from [https://huggingface.co/anonymousModelsTimeCSL/TimeCSL](https://huggingface.co/anonymousModelsTimeCSL/TimeCSL). These models are part of our commitment to advancing machine learning and equipping the community with state-of-the-art tools for time series analysis. Visit the repository to explore the models and seamlessly integrate them into your projects. | |
| Our code is available at https://anonymous.4open.science/r/TimeCSL-4320. | |
|  | |
| ### Some Results | |
|  | |
|  | |
| ## Trigger words | |
| You should use `Time Series` to trigger the image generation. | |
| You should use `Disentanglement` to trigger the image generation. | |
| You should use `Identifiability` to trigger the image generation. | |
| ## Download model | |
| [Download](/anonymousModelsTimeCSL/TimeCSL/tree/main) them in the Files & versions tab. | |