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🤖 New and greatly improved version of the model, check out: - 🌑 https://huggingface.co/zer0int/CLIP-KO-LITE-TypoAttack-Attn-Dropout-ViT-L-14 ------- - Want to feed it yourself? All code for fine-tuning and much more is on my GitHub. ----- - Huggingface Transformers / Diffusers pipeline now implemented. - See here for an example script: Integrating my CLIP-L with Flux.1 - Otherwise, use as normal / any HF model: **: The "TEXT" model is the best for text. Full stop. But whether the "SMOOTH" model is better for your (text-free) scenario than the "TEXT" model really depends on the specific prompt. It might also be the case that the "TEXT" model leads to images that you prefer over "SMOOTH"; the only way to know is to experiment with both.

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🤖 New and greatly improved version of the model, check out: - 🌑 https://huggingface.co/zer0int/LongCLIP-KO-LITE-TypoAttack-Attn-ViT-L-14 ------- - Want to feed it yourself? All code for fine-tuning and much more is on my GitHub. ---- - # Note for using Long-CLIP as the Text Encoder with Flux.1, SDXL, Stable Diffusion: - Get the ComfyUI Long-CLIP nodes here: https://github.com/SeaArtLab/ComfyUI-Long-CLIP - If you don't use Comfy, it's at least a starting point for reverse engineering & applying it to your code! 🤗 ---- ---- New *BEST* model, custom loss with label smoothing. Small gain for a diverse and large good quality dataset, but big relative gains for an overfit-prone fine-tune (small batch size, 1 GPU, narrow dataset of e.g. 'sneakers', etc.) are possible! Fine-tune your model with the provided code for GmP-Smooth: https://github.com/zer0int/Long-CLIP

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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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Separate input, output and cache rates, then estimate the same workload. Image, audio and video rates may use items, duration or resolution rather than tokens.

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