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stable-diffusion-3.5-large-turbo_amdgpu

control_v11p_sd15_scribble

**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. @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} }

sdols-2.0-fast

MiniMax-H3-GGUF

The license of the quantized files follows the license of the original model: - MiniMax-H3: https://huggingface.co/MiniMaxAI/MiniMax-H3/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.

beautiful-realistic-asian

Coding in PHP/Node/Java etc? Have a look at docs for more code examples: View docs Try model for free: Generate Images Model link: View model

FLUX-3-Image-Klein-GGUF

Black Forest Labs' FLUX 3 Image Klein as GGUF for stable-diffusion.cpp, built from the published BF16 weights with our own importance matrix and measured on one stand against every other Klein GGUF we could find. The renders and measurements are public. Built by the team behind Atomic Chat , the app that runs open-weight models locally and offline. Everything to run Klein in stable-diffusion.cpp is here: the image model, the Qwen3-VL-4B text encoder ( text_encoders/ ) and the autoencoder ( vae/ ). The image model files also load in ComfyUI through ComfyUI-GGUF; see In ComfyUI . The prompt upsampler Klein expects is in FLUX-3-Image-Klein-pup-GGUF . AD-Q5_K stays 14% closer to the BF16 picture than unsloth's UD-Q5_K_XL at a smaller size, and AD-Q4_K 21% closer than their Q4_K_M . Where their file is better, the table below says so. The text encoder moves the picture as much as the image model does. Our AD-Q4_K encoder is 21% closer than the Q4_K_M most people use, at the same size. Looking for uncensored? The open Klein pipeline has no content filter. Our text encoder and prompt upsampler without refusals are in FLUX-3-Image-Klein-TE-Uncensored-GGUF and FLUX-3-Image-Klein-pup-Uncensored-GGUF ; what they change on the pictures is in Content filtering . FLUX 3 Image Klein is Black Forest Labs' 5.6B text-to-image and editing model: one model for generation, single-reference edits and up to ten reference images, distilled to **16 steps at CFG 1**. This repo has the image model as GGUF for stable-diffusion.cpp, with the text encoder and the autoencoder next to it.

animatediff-motion-adapter-v1-5-2

AnimateDiff is a method that allows you to create videos using pre-existing Stable Diffusion Text to Image models. It achieves this by inserting motion module layers into a frozen text to image model and training it on video clips to extract a motion prior. These motion modules are applied after the ResNet and Attention blocks in the Stable Diffusion UNet. Their purpose is to introduce coherent motion across image frames. To support these modules we introduce the concepts of a MotionAdapter and UNetMotionModel. These serve as a convenient way to use these motion modules with existing Stable Diffusion models. The following example demonstrates how you can utilize the motion modules with an existing Stable Diffusion text to image model.

small-stable-diffusion-v0

Ar4ikov_-_gpt2-medium-650k-stable-diffusion-prompt-generator-gguf

LongCLIP-GmP-ViT-L-14

🤖 New and greatly improved version of the model, check out: - 🌑 https://huggingface.co/zer0int/LongCLIP-KO-LITE-TypoAttack-Attn-ViT-L-14 ------- - Want to feed it yourself? All code for fine-tuning and much more is on my GitHub. ---- - # Note for using Long-CLIP as the Text Encoder with Flux.1, SDXL, Stable Diffusion: - Get the ComfyUI Long-CLIP nodes here: https://github.com/SeaArtLab/ComfyUI-Long-CLIP - If you don't use Comfy, it's at least a starting point for reverse engineering & applying it to your code! 🤗 ---- ---- New *BEST* model, custom loss with label smoothing. Small gain for a diverse and large good quality dataset, but big relative gains for an overfit-prone fine-tune (small batch size, 1 GPU, narrow dataset of e.g. 'sneakers', etc.) are possible! Fine-tune your model with the provided code for GmP-Smooth: https://github.com/zer0int/Long-CLIP