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Browse deepinfra models:

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stabilityai/sd3.5 cover image
$0.06 / img
  • text-to-image

At 8 billion parameters, with superior quality and prompt adherence, this base model is the most powerful in the Stable Diffusion family. This model is ideal for professional use cases at 1 megapixel resolution

stabilityai/sd3.5-medium cover image
bf16
$0.03 / img
  • text-to-image

At 2.5 billion parameters, with improved MMDiT-X architecture and training methods, this model is designed to run “out of the box” on consumer hardware, striking a balance between quality and ease of customization. It is capable of generating images ranging between 0.25 and 2 megapixel resolution.

stabilityai/sdxl-turbo cover image
$0.0002 x (width / 1024) x (height / 1024) x (iters / 5)
  • text-to-image

The SDXL Turbo model, developed by Stability AI, is an optimized, fast text-to-image generative model. It is a distilled version of SDXL 1.0, leveraging Adversarial Diffusion Distillation (ADD) to generate high-quality images in less steps.

stabilityai/stable-diffusion-2-1 cover image
Replaced
  • text-to-image

Stable Diffusion is a latent text-to-image diffusion model. Generate realistic images given text description

thenlper/gte-base cover image
512
$0.005 / Mtoken
  • embeddings

The GTE models are trained by Alibaba DAMO Academy. They are mainly based on the BERT framework and currently offer three different sizes of models, including GTE-large, GTE-base, and GTE-small. The GTE models are trained on a large-scale corpus of relevance text pairs, covering a wide range of domains and scenarios. This enables the GTE models to be applied to various downstream tasks of text embeddings, including information retrieval, semantic textual similarity, text reranking, etc.

thenlper/gte-large cover image
512
$0.010 / Mtoken
  • embeddings

The GTE models are trained by Alibaba DAMO Academy. They are mainly based on the BERT framework and currently offer three different sizes of models, including GTE-large, GTE-base, and GTE-small. The GTE models are trained on a large-scale corpus of relevance text pairs, covering a wide range of domains and scenarios. This enables the GTE models to be applied to various downstream tasks of text embeddings, including information retrieval, semantic textual similarity, text reranking, etc.

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