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mlx-stable-diffusion-3.5-large-4bit-quantized

TokForge-DreamShaper-LCM-GGUF-q4

A **Q4_0-quantized** single self-contained **GGUF** for `stable-diffusion.cpp`, packaging **DreamShaper-7** (SD1.5, realistic finetune) with the **LCM-LoRA** fused into the UNet for fast, guidance-free **few-step** sampling. This is the **6 GB-tier fast image route** for the TokForge apps, the smaller sibling of `TokForge-DreamShaper-LCM-GGUF` (f16, ~2.1 GB). It renders the same coherent people and hands (no SD-Turbo body-horror) at the same few-step LCM speed class, but at a smaller download + RAM footprint so it fits the 6 GB device tier where the f16 build is gated out. This GGUF was quantized with **stable-diffusion.cpp's own `-M convert --type q4_0`**, whose `tensor_should_be_converted` rule **protects embeddings and norms**; it keeps every tensor whose name contains `embedding` (and all `.bias` / `.scale` / norm tensors) at **F16**. So the CLIP text encoder's `token_embedding.weight` stays **F16**.

sd-vae-ft-mse

These weights are intended to be used with the 🧨 diffusers library. If you are looking for the model to use with the original CompVis Stable Diffusion codebase, come here. You can integrate this fine-tuned VAE decoder to your existing `diffusers` workflows, by including a `vae` argument to the `StableDiffusionPipeline` We publish two kl-f8 autoencoder versions, finetuned from the original kl-f8 autoencoder on a 1:1 ratio of LAION-Aesthetics and LAION-Humans, an unreleased subset containing only SFW images of humans. The intent was to fine-tune on the Stable Diffusion training set (the autoencoder was originally trained on OpenImages) but also enrich the dataset with images of humans to improve the reconstruction of faces. The first, _ft-EMA_, was resumed from the original checkpoint, trained for 313198 steps and uses EMA weights. It uses the same loss configuration as the original checkpoint (L1 + LPIPS). The second, _ft-MSE_, was resumed from _ft-EMA_ and uses EMA weights and was trained for another 280k steps using a different loss, with more emphasis on MSE reconstruction (MSE + 0.1 * LPIPS). It produces somewhat ``smoother'' outputs. The batch size for both versions was 192 (16 A100s, batch size 12 per GPU). To keep compatibility with existing models, only the decoder part was finetuned; the checkpoints can be used as a drop-in replacement for the existing autoencoder.

control_v11f1p_sd15_depth

**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.

animagine-xl-4.0

**Animagine XL 4.0**, also stylized as **Anim4gine**, is the ultimate anime-themed finetuned SDXL model and the latest installment of Animagine XL series. Despite being a continuation, the model was retrained from Stable Diffusion XL 1.0 with a massive dataset of 8.4M diverse anime-style images from various sources with the knowledge cut-off of January 7th 2025 and finetuned for approximately 2650 GPU hours. Similar to the previous version, this model was trained using tag ordering method for the identity and style training. With the release of **Animagine XL 4.0 Opt (Optimized)**, the model has been further refined with an additional dataset, improving **stability**, **anatomy accuracy**, **noise reduction**, **color saturation**, and **overall color accuracy**. These enhancements make **Animagine XL 4.0 Opt** more consistent and visually appealing while maintaining the signature quality of the series. - 2025-02-13 – Added Animagine XL 4.0 Opt - Better stability for more consistent outputs - Enhanced anatomy with more accurate proportions - Reduced noise and artifacts in generations - Fixed low saturation issues, resulting in richer colors - Improved color accuracy for more visually appealing results - 2025-01-24 – Initial release - **Developed by**: Cagliostro Research Lab - **Model type**: Diffusion-based text-to-image generative model - **License**: CreativeML Open RAIL++-M - **Model Description**: This is a model that can be used to generate and modify specifically anime-themed images based on text prompt - **Fine-tuned from**: Stable Diffusion XL 1.0

Sakura-FLUX.3-Image-Klein-GGUF

**Built with FLUX.** Community quantization of FLUX.3 Image Klein (distilled, 16 steps, no CFG) with the official, unmodified Qwen3-VL-4B-Instruct text encoder. Independent work, not made, reviewed or endorsed by Black Forest Labs. The image model files, the VAE and the workflow are under the **FLUX Kommunity License** (copy in `LICENSE.md`: non-commercial use, or commercial use of outputs only as a Qualifying User under US$5 million revenue with the filtering or review the license requires); the unmodified text encoder is Apache-2.0 (`LICENSE-Apache-2.0.txt`). See `NOTICE`. An uncensored version of the **text encoder** (only the encoder, the image model is not changed) is available separately: Sakura-FLUX.3-Image-Klein-TextEncoder-Uncensored-GGUF. It works with the image model files of this repository. Q8_0 image model and the official text encoder, ComfyUI 0.41.1 with ComfyUI-GGUF (Radeon 8060S), 1024x1024, 16 steps, CFG 1.0, `euler`/`simple`. These are the **same nine prompts and seeds** (1202, 1302, 1305, 1306, 1105, 1103, 1503, 1506, 1406 in the order shown) as in the repositories Sakura-Qwen-Image-2.1-Turbo-GGUF and Sakura-Iris-3B-GGUF, so that the models can be compared. One seed per prompt, no re-rolls, all nine images are shown; the prompts were chosen for those repositories from a larger set, not for FLUX. The prompts are the captions of the images above. The same prompts and seeds with our uncensored text encoder are in Sakura-FLUX.3-Image-Klein-TextEncoder-Uncensored-GGUF. The FLUX images were made with ComfyUI, the Qwen and Iris images with stable-diffusion.cpp, and models, steps and samplers differ, so differences between the sets are not only differences of the text encoder. Everything except the runtime is in this repository. Pick the column of the program you use (🟦 stable-diffusion.cpp, 🟧 ComfyUI):

stable-diffusion-3-medium-gguf

stable-diffusion-2.1

MMaDA-8B-MixCoT

We introduce MMaDA, a novel class of multimodal diffusion foundation models designed to achieve superior performance across diverse domains such as textual reasoning, multimodal understanding, and text-to-image generation. MMaDA is distinguished by three key innovations: 1. MMaDA adopts a unified diffusion architecture with a shared probabilistic formulation and a modality-agnostic design, eliminating the need for modality-specific components. 2. MMaDA introduces a mixed long chain-of-thought (CoT) fine-tuning strategy that curates a unified CoT format across modalities. 3. MMaDA adopts a unified policy-gradient-based RL algorithm, which we call UniGRPO, tailored for diffusion foundation models. Utilizing diversified reward modeling, UniGRPO unifies post-training across both reasoning and generation tasks, ensuring consistent performance improvements. Compared to MMaDA-8B-Base, MMaDA-8B-MixCoT exhibits better instruction-following capabilities and more stable CoT generation performance.

4x_foolhardy_Remacri

Copy the file 4x_foolhardy_Remacri.safetensors inside the folder: stable-diffusion/models/ESRGAN or ComfyUI/models/upscale_models