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controlnet-canny-sdxl-1.0-small

These are small controlnet weights trained on stabilityai/stable-diffusion-xl-base-1.0 with canny conditioning. This checkpoint is 7x smaller than the original XL controlnet checkpoint. You can find some example images in the following. prompt: a woman, close up, detailed, beautiful, street photography, photorealistic, detailed, Kodak ektar 100, natural, candid shot 🚨 Please note that this checkpoint is experimental and there's a lot of room for improvement. We encourage the community to build on top of it, improve it, and provide us with feedback. 🚨

stable-diffusion-xl-1.0-turbo-GGUF

stable-diffusion-2-1-base-custom

These are Custom Diffusion adaption weights for stabilityai/stable-diffusion-2-1-base. The weights were trained on None using Custom Diffusion. You can find some example images in the following.

stable-diffusion-3.5-medium-GGUF

dreamlike-photoreal-2.0

You can add **photo** to your prompt to make your gens look more photorealistic. Non-square aspect ratios work better for some prompts. If you want a portrait photo, try using a vertical aspect ratio. If you want a landscape photo, try using a horizontal aspect ratio. This model was trained on 768x768px images, so use 768x768px, 640x896px, 896x640px, etc. It also works pretty good with higher resolutions such as 768x1024px or 1024x768px. This model can be used just like any other Stable Diffusion model. For more information, please have a look at the Stable Diffusion Pipeline.

Hunyuan3D-2.1

We would like to thank the contributors to the TripoSG, DINOv2, Stable Diffusion, FLUX, diffusers and HuggingFace repositories, for their open research and exploration.

Qwen-Image-2.1-GGUF

The license of the quantized files follows the license of the original model: - Qwen RESEARCH LICENSE AGREEMENT: https://huggingface.co/Qwen/Qwen-Image-2.1/blob/main/LICENSE This model can be used with `stable-diffusion.cpp`. > [!IMPORTANT] > Please use the **leejet** version of `ComfyUI-GGUF`, rather than the version maintained by `city96`. > The `city96` repository appears to no longer be actively maintained.

stable-diffusion-2-inpainting

This `stable-diffusion-2-inpainting` model is resumed from stable-diffusion-2-base (`512-base-ema.ckpt`) and trained for another 200k steps. Follows the mask-generation strategy presented in LAMA which, in combination with the latent VAE representations of the masked image, are used as an additional conditioning. @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } Using the 🤗's Diffusers library to run Stable Diffusion 2 inpainting in a simple and efficient manner.

stable-diffusion-v1-5

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. You can use this both with the 🧨Diffusers library and the RunwayML GitHub repository.

beksinski-style-stable-diffusion