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Z-Image-Turbo-ComfyUI

The two single-file Z-Image assets that stable-diffusion.cpp needs, mirrored from Comfy-Org/z_image_turbo. Unsloth Studio fetches these on the no-GPU (native sd.cpp) path: both for Z-Image, and the Qwen3-4B conditioner again for FLUX.2 klein 4B, which shares it. They used to come straight from the community repack; a rename or a takedown there turns every CPU Z-Image and klein-4B load into a 404, so they are mirrored here. **The weights are unmodified:** byte for byte the files of the same name in the source repo. Only these two files are mirrored. The source repo also ships the Z-Image diffusion models, a distill LoRA and quantised conditioners; Studio does not use them from there, so they are not here.

agate-preview-003

**Agate Preview 003 renders at 512 × 512.** It is the first Agate to score above Stable Diffusion 1.5 on Qwen-Image-Bench: **32.6** against SD 1.5's 29.1 and Preview 002's 28.9, on all 1,000 prompts, with 261M parameters. On GenEval (official scorer) it scores **0.554**, level with Preview 001 (0.550) and below Preview 002 (0.577). It is Agate's 256 px network made multi-resolution (additions that start as exact no-ops), trained at 256 and 512 px, then fine-tuned with perceptual losses. It runs in Python, in ComfyUI (0.4.0) and in the browser (WebGPU demo, the default version there). **Why we built Agate:** it is the result of an architecture study for glyph and symbol generation; see Why we built Agate below.

AnyLoRA

`lykon/AnyLoRA` 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.

Cosmos-Tokenize1-CV8x8x8-720p

**Cosmos-Tokenize1** is a suite of visual tokenizers for images and videos that delivers various compression rates while maintaining high reconstruction quality. Cosmos Tokenizer can serve as an effective and efficient building block in both diffusion-based and autoregressive models for image and video generation. This model is ready for commercial use. Our tokenizers come in two types: **Continuous** (C) and **Discrete** (D), each with **Image** (I) and **Video** (V) variants: * Continuous tokenizers encode visual data into continuous latent embeddings, as shown in latent diffusion models like Stable Diffusion. These embeddings are suitable for models that generate data by sampling from continuous distributions. * Discrete tokenizers encode visual data into discrete latent codes, mapping them into quantized indices, as seen in autoregressive transformers such as VideoPoet. This discretization is required for models that generate data by optimizing the cross-entropy loss, such as the GPT models. **Model**: Cosmos-Tokenize1-CV8x8x8-360p, a continuous video tokenizer with 8x temporal compression, and 8x8 spatial compression rate.

stable-video-diffusion-img2vid-xt

(SVD) Image-to-Video is a latent diffusion model trained to generate short video clips from an image conditioning. This model was trained to generate 25 frames at resolution 576x1024 given a context frame of the same size, finetuned from [SVD Image-to-Video [14 frames]](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid). We also finetune the widely used f8-decoder for temporal consistency. For convenience, we additionally provide the model with the standard frame-wise decoder here. - **Developed by:** Stability AI - **Funded by:** Stability AI - **Model type:** Generative image-to-video model - **Finetuned from model:** SVD Image-to-Video [14 frames] 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-tensorrt

TIPO-500M-ft

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-2-1-base

controlnet-scribble-sdxl-1.0

The following five lines are using different control lines, from top to below, Scribble, Canny, HED, PIDI, Lineart Hello, I am very happy to announce the controlnet-scribble-sdxl-1.0 model, **a very powerful controlnet that can generate high resolution images visually comparable with midjourney**. The model was trained with large amount of high quality data(over 10000000 images), with carefully filtered and captioned(powerful vllm model). Besides, useful tricks are applied during the training, including date augmentation, mutiple loss and multi resolution. Note that this model can achieve higher aesthetic performance than our Controlnet-Canny-Sdxl-1.0 model, the model support any type of lines and any width of lines, the sketch can be very simple and so does the prompt. This model is more general and good at generate visual appealing images, The control ability is also strong, for example if you are unstatisfied with some local regions about the generated image, draw a more precise sketch and give a detail prompt will help a lot. **Note the model also support lineart or canny lines, you can try it and will get a surpurise!!!** - **Developed by:** xinsir - **Model type:** ControlNet_SDXL - **License:** apache-2.0 - **Finetuned from model [optional]:** stabilityai/stable-diffusion-xl-base-1.0

mlx-stable-diffusion-3-medium