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stable-diffusion-v1-5-GGUF
gpustack**Model creator**: Runway ML **Original model**: stable-diffusion-v1-5 **GGUF quantization**: based on stable-diffusion.cpp ac54e that patched by llama-box. Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. For more information about how Stable Diffusion functions, please have a look at 🤗's Stable Diffusion blog. The **Stable-Diffusion-v1-5** checkpoint was initialized with the weights of the Stable-Diffusion-v1-2 checkpoint and subsequently fine-tuned on 595k steps at resolution 512x512 on "laion-aesthetics v2 5+" and 10% dropping of the text-conditioning to improve classifier-free guidance sampling.
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stable-diffusion-v2-1-GGUF
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stable-diffusion-v2-1-turbo-GGUF
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stable-diffusion-v3-5-large-GGUF
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**Model creator**: Stability AI **Original model**: stable-diffusion-xl-base-1.0 **GGUF quantization**: based on stable-diffusion.cpp ac54e that patched by llama-box. **VAE From**: madebyollin/sdxl-vae-fp16-fix. SDXL consists of an ensemble of experts pipeline for latent diffusion: In a first step, the base model is used to generate (noisy) latents, which are then further processed with a refinement model (available here: https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0/) specialized for the final denoising steps. Note that the base model can be used as a standalone module. Alternatively, we can use a two-stage pipeline as follows: First, the base model is used to generate latents of the desired output size. In the second step, we use a specialized high-resolution model and apply a technique called SDEdit (https://arxiv.org/abs/2108.01073, also known as "img2img") to the latents generated in the first step, using the same prompt. This technique is slightly slower than the first one, as it requires more function evaluations.
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GQI-Eval-135M
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The image conditioned version of TRELLIS, a large 3D genetive model. It was introduced in the paper Structured 3D Latents for Scalable and Versatile 3D Generation.
grabbe-ai
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ug-cppo-finai-2025
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gradient-tinystories-20m
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Llama-3-8B-Instruct-262k
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Llama-3-8B-Instruct-Gradient-1048k
gradientaiJoin our custom agent and long context (262k-1M+) waitlist: https://forms.gle/L6TDY7dozx8TuoUv7 Gradient incorporates your data to deploy autonomous assistants that power critical operations across your business. If you're looking to build custom AI models or agents, email us a message contact@gradient.ai. For more info see our end-to-end development service for custom LLMs and AI systems This model extends LLama-3 8B's context length from 8k to > 1040K, developed by Gradient, sponsored by compute from Crusoe Energy. It demonstrates that SOTA LLMs can learn to operate on long context with minimal training by appropriately adjusting RoPE theta. We trained on 830M tokens for this stage, and 1.4B tokens total for all stages, which is
augmented-04c4ba89c4193579
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augmented-187d41b80680d946
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augmented-2383ac808a357447
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augmented-2e64076e1e3d549d
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augmented-705e4129a1c14a7b
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augmented-8df8d945ca945d7e
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