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I suddenly realized that I haven't published a checkpoint for ZIT. This checkpoint was created a month ago.therefore, many original images are no longer valid, and I'm not sure if I can post them. If you need to see...
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This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
Disclaimer: The model card is taken and modified from the official CLIP repository, it can be found here.
Same "style" as v2 but a bit more sharper.
Images are generated without LoRAs, using detailers. Example prompts can be found in the preview images.
Attempt to sloppify Mugen SDXL, was trained with 3.7k images actually handpicked (unlike other liars) for 150 epochs on 1536 target resolution.
Back after a couple of weeks' distraction working on other projects.
Disclaimer: The model card is taken and modified from the official CLIP repository, it can be found here.
This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
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Figure 1: ColBERT's late interaction, efficiently scoring the fine-grained similarity between a queries and a passage.
Update Jun 5, 2026: ☁️ Deploy Chronos-2 on AWS with AutoGluon-Cloud. Real-time, serverless, or batch inference in 3 lines of code — pandas DataFrames in, forecasts out. Check out the new deployment guide.
Using this open-source model in production? Consider switching to pyannoteAI for better and faster options.
