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stable-diffusion-3.5-large-controlnet-canny

Please note: This model is released under the Stability Community License. Visit Stability AI to learn or contact us for commercial licensing details. Here are the key components of the license: * Free for non-commercial use: Individuals and organizations can use the model free of charge for non-commercial use, including scientific research. * Free for commercial use (up to $1M in annual revenue): Startups, small to medium-sized businesses, and creators can use the model for commercial purposes at no cost, as long as their total annual revenue is less than $1M. * Ownership of outputs: Retain ownership of the media generated without restrictive licensing implications. For organizations with annual revenue more than $1M, please contact us here to inquire about an Enterprise License.

stable-diffusion-3.5

Original model repository: https://huggingface.co/stabilityai/stable-diffusion-3.5-large This is a smaller checkpoint for SD3.5 Large that contains the text encoders/CLIP built in. Read our blog for instructions on how to use SD3.5 with ComfyUI.

sd-turbo

SD-Turbo is a distilled version of Stable Diffusion 2.1, trained for real-time synthesis. SD-Turbo is based on a novel training method called Adversarial Diffusion Distillation (ADD) (see the technical report), which allows sampling large-scale foundational image diffusion models in 1 to 4 steps at high image quality. This approach uses score distillation to leverage large-scale off-the-shelf image diffusion models as a teacher signal and combines this with an adversarial loss to ensure high image fidelity even in the low-step regime of one or two sampling steps. - **Developed by:** Stability AI - **Funded by:** Stability AI - **Model type:** Generative text-to-image model - **Finetuned from model:** Stable Diffusion 2.1 For research purposes, we recommend our `generative-models` Github repository (https://github.com/Stability-AI/generative-models), which implements the most popular diffusion frameworks (both training and inference).

stable-diffusion-3.5-controlnets

SD3.5 Large Depth Before After SD3.5 Large Canny ControlNet Before After SD3.5 Large Blur Before After

yoso-normal-v1-8-1

This repository contains the weights of StableNormal: Reducing Diffusion Variance for Stable and Sharp Normal See the Github repository: https://github.com/Stable-X/StableNormal regarding installation instructions.

MiniMax-H3-GGUF

Instructions further below. GGUF for MiniMax-H3, compatible on most platforms including stablediffusion.cpp and Unsloth. GGUF quantizations of MiniMaxAI/MiniMax-H3 MiniMax H3 is an omni-modal generative system that produces video with native stereo audio, up to 15 seconds at 24 FPS with 32 kHz stereo audio. Both halves of the runtime are in this repo: the denoisers and the Qwen3-VL text encoder they need.

Sakura-Qwen-Image-2.1-Turbo-Uncensored-GGUF

**Built with Qwen.** Community quantization of Qwen/Qwen-Image-2.1-Turbo (8-step distilled, 7B DiT) with an **abliterated text encoder**. Independent work, not made or endorsed by Alibaba/Qwen. **Non-commercial use only** (Qwen RESEARCH LICENSE AGREEMENT, copy in `LICENSE`). For adults only; you are responsible for what you generate and for the laws that apply to you. Q8_0 image model and our 8bit uncensored text encoder, stable-diffusion.cpp (Vulkan, Radeon 8060S), 1024x1024, 8 steps, CFG 1.0, `euler`, official Turbo sigmas. One seed per prompt and no re-rolls; the prompts themselves were chosen from a larger set of candidates. About 77 s per image including model loading; the GPU memory in use on our machine peaked at 18.3 GiB while generating (Windows counter for the whole GPU, 1.5 GiB of it were in use before the run). The images were made with stable-diffusion.cpp, not with ComfyUI. The prompts are neutral. These images say nothing about what the ablated text encoder changes for restricted content; they only show that ordinary prompts still work. The same nine prompts and seeds are used in the sibling repository Sakura-Qwen-Image-2.1-Turbo-GGUF with the official encoder, so the two sets can be compared.

dreamshaper-8

`lykon/dreamshaper-8` is a Stable Diffusion model that has been fine-tuned on runwayml/stable-diffusion-v1-5. 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.

stable-diffusion-3-2b

stable-diffusion-2-1-GGUF