qwen3-8b-classic-self-distill
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From language to images, open weights to APIs. Understand capabilities and compare real costs.
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EDSR model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the paper Enhanced Deep Residual Networks for Single Image Super-Resolution by Lim et al. (2017) and first released in this repository. The goal of image super resolution is to restore a high resolution (HR) image from a single low resolution (LR) image. The image below shows the ground truth (HR), the bicubic upscaling x2 and EDSR upscaling x2. This is a base model (~5mb vs ~100mb) that includes just 16 ResBlocks and 64 channels. You can use the pre-trained models for upscaling your images 2x, 3x and 4x. You can also use the trainer to train a model on your own dataset. The model can be used with the super_image library:
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此文本嵌入(text embedding)为 **负面文本嵌入**。它能够在对画风影响较小的前提下改善AI生成图片的手部细节。如果它让你的模型表现得比以前更糟,请勿使用它。您可与其他负面文本嵌入一同使用。虽然它是为 AnimeIllustDiffusion 模型设计的,但您也可以在其他模型上使用。 This text embedding is a **negative text embedding**. It can improve the hand details of AI-generated images with less impact on the style of painting. If it makes your model behave worse than before, please do not use it. You can use it with other negative text embeddings.
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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.