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juggernaut-xl-v9-GGUF

GGUF conversions of Juggernaut XL v9 for **on-device** image generation with stable-diffusion.cpp. Converted with sd.cpp's `-M convert` so the tensors are correctly named and load directly — the community GGUF quants of this model are mis-exported and fail `get sd version from file`. `Q4_K_S` in a file name: stable-diffusion.cpp wrote the file with `--type q4_K`. Its weight tensors are Q4_K and the other tensors stay F16/BF16/F32. `Q4_K_S` is the closest standard GGUF label, so Hugging Face can list the file. This file was named `juggernaut-xl-v9-Q4_K.gguf` before 2026-10-21; the contents are unchanged, and links pinned to an earlier commit still work. **Original model:** https://huggingface.co/RunDiffusion/Juggernaut-XL-v9 — created by its respective authors. **License:** `creativeml-openrail-m` (carried over from the original; its use restrictions apply). This is a format conversion (quantization) only; all credit for the model belongs to the original creators.

stable-diffusion-3.5-large

control_v11p_sd15s2_lineart_anime

This checkpoint is a conversion of the original checkpoint into `diffusers` format. It can be used in combination with **Stable Diffusion**, such as runwayml/stable-diffusion-v1-5. @misc{zhang2023adding, title={Adding Conditional Control to Text-to-Image Diffusion Models}, author={Lvmin Zhang and Maneesh Agrawala}, year={2023}, eprint={2302.05543}, archivePrefix={arXiv}, primaryClass={cs.CV} } Controlnet was proposed in *Adding Conditional Control to Text-to-Image Diffusion Models* by Lvmin Zhang, Maneesh Agrawala.

stable-diffusion-v1-5

dreamshaper-xl-lightning

`lykon/dreamshaper-xl-lightning` is a Stable Diffusion model that has been fine-tuned on stabilityai/stable-diffusion-xl-base-1.0. Please consider supporting me: - on Patreon - or buy me a coffee For more general information on how to run text-to-image models with 🧨 Diffusers, see the docs.

tiny-sd

--- license: creativeml-openrail-m base_model: SG161222/Realistic_Vision_V4.0 datasets: - recastai/LAION-art-EN-improved-captions tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- This pipeline was distilled from **SG161222/Realistic_Vision_V4.0** on a Subset of **recastai/LAION-art-EN-improved-captions** dataset. Below are some example images generated with the tiny-sd model. We have observed that the distilled models are upto 80% faster than the Base SD1.5 Models. Below is a comparision on an A100 80GB.

stable-diffusion-v1-5-inpainting-GGUF

sd-vae-ft-ema

These weights are intended to be used with the 🧨 diffusers library. If you are looking for the model to use with the original CompVis Stable Diffusion codebase, come here. You can integrate this fine-tuned VAE decoder to your existing `diffusers` workflows, by including a `vae` argument to the `StableDiffusionPipeline` We publish two kl-f8 autoencoder versions, finetuned from the original kl-f8 autoencoder on a 1:1 ratio of LAION-Aesthetics and LAION-Humans, an unreleased subset containing only SFW images of humans. The intent was to fine-tune on the Stable Diffusion training set (the autoencoder was originally trained on OpenImages) but also enrich the dataset with images of humans to improve the reconstruction of faces. The first, _ft-EMA_, was resumed from the original checkpoint, trained for 313198 steps and uses EMA weights. It uses the same loss configuration as the original checkpoint (L1 + LPIPS). The second, _ft-MSE_, was resumed from _ft-EMA_ and uses EMA weights and was trained for another 280k steps using a different loss, with more emphasis on MSE reconstruction (MSE + 0.1 * LPIPS). It produces somewhat ``smoother'' outputs. The batch size for both versions was 192 (16 A100s, batch size 12 per GPU). To keep compatibility with existing models, only the decoder part was finetuned; the checkpoints can be used as a drop-in replacement for the existing autoencoder.

stable-diffusion-3-medium-diffusers

realistic-vision-v51