ニュースとサイト内検索

stable-diffusion-xl-refiner-1.0-GGUF

control_v11p_sd15_openpose

**Controlnet v1.1** is the successor model of Controlnet v1.0 and was released in lllyasviel/ControlNet-v1-1 by Lvmin Zhang. 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. For more details, please also have a look at the 🧨 Diffusers docs.

sdxl-turbo

SDXL-Turbo is a fast generative text-to-image model that can synthesize photorealistic images from a text prompt in a single network evaluation. A real-time demo is available here: http://clipdrop.co/stable-diffusion-turbo SDXL-Turbo is a distilled version of SDXL 1.0, trained for real-time synthesis. SDXL-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. 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).

SD_PixelArt_SpriteSheet_Generator

This Stable diffusion checkpoint allows you to generate pixel art sprite sheets from four different angles. These first images are my results after merging this model with another model trained on my wife. merging another model with this one is the easiest way to get a consistent character with each view. still requires a bit of playing around with settings in img2img to get them how you want. for left and right, I suggest picking your best result and mirroring. after you are satisfied take your photo into photoshop or Krita, remove the background, and scale to the desired size. after this you can scale back up to display your results; this also clears up some of the color murkiness in the initial outputs. This model can be used just like any other Stable Diffusion model. For more information, please have a look at the Stable Diffusion. ___ ___ For the front view use "PixelartFSS" ___ ___ For the right view use "PixelartRSS" ___ ___ For the back view use "PixelartBSS" ___ ___ For the left view use "PixelartLSS" ___ ___ These are random results from the unmerged model ___ ___ here's a result from a merge with my Hermione model ___ ___ here's a result from a merge with my cat girl model

All-In-One-Pixel-Model

Stable Diffusion model trained using dreambooth to create pixel art, in 2 styles the sprite art can be used with the trigger word "pixelsprite" the scene art can be used with the trigger word "16bitscene" the art is not pixel perfect, but it can be fixed with pixelating tools like https://pinetools.com/pixelate-effect-image (they also have bulk pixelation)

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 RunwayML GitHub repository ( now deprecated ), ComfyUI, Automatic1111, SD.Next, InvokeAI .

Sakura-MiniMax-H3-Uncensored-GGUF

> For adults only; you are responsible for what you generate and for the laws that apply to you. The language weights come from a model whose refusals were removed and whose text generation breaks down on harmful prompts (see below); what this changes in the video we measured only for six non-explicit prompts (see below). Do not use it to depict minors in any sexual or abusive context, or to create sexual or defamatory images of real persons without their consent. Do not put it into a public service without your own moderation. The **text encoder** of MiniMax-H3 (the first 50 language layers of Qwen3-VL-32B plus its vision tower, passed to the video model with `--llm`) with the language weights of an **abliterated (uncensored)** Qwen3-VL-32B-Instruct, as GGUF for stable-diffusion.cpp. Independent work, not made, reviewed or endorsed by MiniMax, Comfy-Org, Qwen / Alibaba or llmfan46. - The text encoder shipped inside MiniMax-H3 is **the unmodified Qwen3-VL-32B-Instruct**: the sha256 of all 14 `text_encoder` shards of `MiniMaxAI/MiniMax-H3` equal those of `Qwen/Qwen3-VL-32B-Instruct`. MiniMax-H3 uses it truncated to 50 language layers and without the final language-model norm. - We built the encoder from an **abliterated checkpoint of that model made by a community member** (llmfan46's abliterated Qwen3-VL-32B-Instruct, Apache-2.0, revision `c44b949b30d111666a5ed9851c5cd633ed39b070`; sha256 of both shards checked against the Hub). **We did not run the ablation ourselves** for this repository. - Tensors were copied one by one into the layout of the Comfy-Org H3 encoder file (902 tensors; names, dtypes and shapes equal that file; the 14 layers 50 to 63, the final norm and the output head are dropped), then converted with `sd-cli -M convert`.

stable-diffusion-3.5-large-turbo

stable-diffusion-2-1-finetuned

stable-diffusion-v1-5-inpainting