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stable-diffusion-v1-1

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 with D🧨iffusers blog. The **Stable-Diffusion-v1-1** was trained on 237,000 steps at resolution `256x256` on laion2B-en, followed by 194,000 steps at resolution `512x512` on laion-high-resolution (170M examples from LAION-5B with resolution `>= 1024x1024`). For more information, please refer to Training. This weights here are intended to be used with the D🧨iffusers library. If you are looking for the weights to be loaded into the CompVis Stable Diffusion codebase, come here

dreamshaper-xl-1-0

`lykon-models/dreamshaper-xl-1-0` is a Stable Diffusion model that has been fine-tuned on stabilityai/stable-diffusion-xl-base-1.0.

TIPO-500M

500M LLaMA arch model trained for TIPO. Tech Report: https://arxiv.org/abs/2411.08127 In this project, we introduce "TIPO" (**T**ext to **I**mage with text presampling for **P**rompt **O**ptimization), an innovative framework designed to significantly enhance the quality and usability of Text-to-Image (T2I) generative models. TIPO utilizes the Large Language Models (LLMs) to perform "Text Presampling" within the inference pipeline of text-to-image generative modeling. By refining and extending user input prompts, TIPO enables generative models to produce superior results with minimal user effort, making T2I systems more accessible and effective for a wider range of users. Use updated version of DTG extension (renamed to z-tipo-extension), current version of z-tipo-extension support stable-diffusion-webui, stable-diffusion-webui-forge and ComfyUI. SD-Next haven't been tested. https://github.com/KohakuBlueleaf/z-tipo-extension

stable-diffusion-v3-5-large-turbo-GGUF

stable-diffusion-3-medium-amdnpu

controlnet-openpose-sdxl-1.0

- **Developed by:** xinsir - **Model type:** ControlNet_SDXL - **License:** apache-2.0 - **Finetuned from model [optional]:** stabilityai/stable-diffusion-xl-base-1.0 thanks feiyuuu for report the problem. When using the default pose line the performance may be unstable, this is because the pose label use more thick line in training to have a better look. This difference can be fix by using the following method: Find the util.py in controlnet_aux python package, usually the path is like: /your anaconda3 path/envs/your env name/lib/python3.8/site-packages/controlnet_aux/open_pose/util.py Replace the draw_bodypose function with the following code:

stable-diffusion-inpainting

tiny-stable-diffusion-torch

stable-diffusion-3.5-large-DF11

CyberRealisticXL

**CyberRealistic XL** is a photorealistic image generation model based on **Stable Diffusion XL 1.0 (SDXL)**. It is designed to produce highly realistic, cinematic-quality outputs with minimal prompt engineering. This checkpoint is optimized for portraits, fashion, and editorial-style scenes, but also handles a wide range of creative prompts with remarkable fidelity. - **Model Type**: Image generation (Text-to-Image) - **Base Model**: Stable Diffusion XL 1.0 - **Format**: `safetensors` - **Latest Version**: v5.7 - **Creator**: Cyberdelia - **File Size**: ~6.31 GB - **License**: CreativeML Open RAIL++-M License - ⚡ **Ultra-Photorealism** – Exceptional detail in faces, lighting, and textures. - 🧩 **Effortless Prompting** – Works well with short prompts, but supports complex ones too. - 🪞 **Skin & Eye Fidelity** – Improved clarity in eyes, skin detail, and facial structure. - 🔧 **Built-in VAE** – No need for an external VAE; optimized out of the box.