ainvfx-fluid
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From language to images, open weights to APIs. Understand capabilities and compare real costs.
Find the right engine for your next idea.
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This repository contains additional upscale models for SeedVR2 video upscaling, complementing the main numz/SeedVR2_comfyUI repository. SeedVR2 is a high-quality video upscaling model that uses diffusion techniques to enhance video resolution while maintaining temporal consistency. This implementation is optimized for ComfyUI and supports both standalone and multi-GPU operation. The GGUF format models in this repository are sourced from cmeka/SeedVR2-GGUF, where additional GGUF versions and quantizations are available. We acknowledge and appreciate cmeka's work in converting these models to GGUF format.
Aion-2.0 is a variant of DeepSeek V3.2 optimized for immersive roleplaying and storytelling. It is particularly strong at introducing tension, crises, and conflict into stories, making narratives feel more engaging....
Aion-3.0 is a multi-model roleplaying and storytelling system from AionLabs, built on the GLM family of models. It uses a collaborative generation process in which multiple specialized models each contribute...
Aion-3.0 Mini is a multi-model roleplaying and storytelling system from AionLabs, built on the DeepSeek family of models. It uses a collaborative generation process in which multiple specialized models each...
Aion 3.5 is a multi-model roleplaying and storytelling system from AionLabs, built on the GLM family of models. It uses a collaborative generation process in which multiple specialized models each...
Aion 3.5 Mini is a multi-model roleplaying and storytelling system from AionLabs, built on the GLM family of models. It is the smaller, lower-cost sibling of Aion 3.5 and uses...
Aion-RP-Llama-3.1-8B ranks the highest in the character evaluation portion of the RPBench-Auto benchmark, a roleplaying-specific variant of Arena-Hard-Auto, where LLMs evaluate each other’s responses. It is a fine-tuned base model...
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Recognises one handwritten Gurmukhi digit (੦ ੧ ੨ ੩ ੪ ੫ ੬ ੭ ੮ ੯) in an image. It is the model behind the digit recognition on AI Panjab. **Try it without code:** the Space: draw a digit and get the top three guesses. The Space also has a **Use via API** page (Python, JavaScript, curl). 1. a transparent background (RGBA) is put on white; any other image is converted to RGB; 2. grayscale with the ITU-R 601 weights: `0.2989 R + 0.5870 G + 0.1140 B`, kept in floating point; 3. resized to 28 x 28 with bilinear (antialiased) resizing.
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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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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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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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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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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。
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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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。
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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Pretrained RoBERTa BASE model on assorted Thai texts (78.5 GB). The script and documentation can be found at this repository. The architecture of the pretrained model is based on RoBERTa [[Liu et al., 2019]](https://arxiv.org/abs/1907.11692). You can use the pretrained model for masked language modeling (i.e. predicting a mask token in the input text). In addition, we also provide finetuned models for multiclass/multilabel text classification and token classification task.
Finetuning `wav2vec2-large-xlsr-53` on Thai Common Voice 7.0 We finetune wav2vec2-large-xlsr-53 based on Fine-tuning Wav2Vec2 for English ASR using Thai examples of Common Voice Corpus 7.0. The notebooks and scripts can be found in vistec-ai/wav2vec2-large-xlsr-53-th. The pretrained model and processor can be found at airesearch/wav2vec2-large-xlsr-53-th. Add `syllable_tokenize`, `word_tokenize` (PyThaiNLP) and deepcut tokenizers to `eval.py` from robust-speech-event
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Adjust provider, category or input/output filters. New models are added continuously.
Adjust monthly token usage to compare estimated API costs.
Uses collected, comparable USD-per-million-token rates. Snapshots exclude cache discounts, tiers, extra reasoning output and taxes. Check current source prices.
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.
Not necessarily. Check each version’s license, commercial restrictions and policies. Self-hosting also incurs compute and maintenance costs.
Rankings depend on tasks, versions and methods. Compare accuracy, latency, reliability and costs on your own workload.