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MagicPrompt-Stable-Diffusion-i1-GGUF

Krea-2-Turbo-GGUF

This repository contains GGUF format quantizations of **Krea 2 Turbo**, a state-of-the-art 12-billion parameter Diffusion Transformer (DiT) text-to-image model developed by Krea.ai. GGUF weights allow for efficient, low-memory inference across consumer hardware, CPUs, and Apple Silicon. You can run these GGUF text-to-image files directly using compatible implementations such as `stable-diffusion.cpp`.

FLUX.2-VAE

The single-file FLUX.2 VAE that stable-diffusion.cpp needs for both FLUX.2 [dev] and FLUX.2 klein, mirrored from Comfy-Org/flux2-dev. Unsloth Studio fetches this on the no-GPU (native sd.cpp) path. It used to come straight from the community repack; a rename or a takedown there turns every CPU FLUX.2 load into a 404, so it is mirrored here. **The weights are unmodified:** byte for byte the file of the same name in the source repo. The rest of FLUX.2 [dev] is under the FLUX Non-Commercial License. The **autoencoder is not**. Black Forest Labs released it separately and says so in their own repository: "The autoencoder is released under Apache 2.0" (github.com/black-forest-labs/flux2).

Taiyi-Stable-Diffusion-1B-Chinese-v0.1

Moss-image-nsfw-uncensored-lora-GGUF

Separate F16 GGUF conversions of the adult-photography LoRAs from axiomofmind/Moss-image-nsfw-uncensored-lora. They are designed to be applied at inference time to axiomofmind/Moss-Image-SFT-GGUF, keeping the adapter strength adjustable without creating a merged model for every base quantization or scale. This repository contains the **LoRA adapter only**. It does not contain the base diffusion transformer, Qwen3.5 text encoder, tokenizer, or VAE. > **Adult-content notice:** The source adapter is intended for lawful creative generation involving clearly adult fictional subjects and can produce explicit sexual imagery. It is for adults only in jurisdictions where such content is lawful. Do not use it for minors or age-ambiguous subjects, abuse, exploitation, coercion, non-consensual imagery, or sexualized depictions of identifiable real people without explicit consent.

tiny-random-stable-diffusion-xl

stable-diffusion-v2-1-turbo-GGUF

marigold-normals-v1-1

This is a model card for the `marigold-normals-v1-1` model for monocular normals estimation from a single image. The model is fine-tuned from the `stable-diffusion-2` model as described in our papers: - CVPR'2024 paper titled "Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation" - Journal extension titled "Marigold: Affordable Adaptation of Diffusion-Based Image Generators for Image Analysis"

Qwen-Image-2.1-GGUF

This is a GGUF quantized version of Qwen-Image-2.1. unsloth/Qwen-Image-2.1-GGUF uses Unsloth Dynamic 2.0 methodology for SOTA performance. - Important layers are upcasted to higher precision, per tensor, from a measured sensitivity scan. - Run these with Unsloth Desktop, stable-diffusion.cpp and more. A GGUF is the denoiser only, so it needs the VAE and the Qwen3-VL text encoder alongside it. - VAE: unsloth/Qwen-Image-2.1-FP8 `vae/qwen_image_2.1_vae_bf16.safetensors`. Text encoder: unsloth/Qwen3-VL-8B-Instruct-GGUF `Qwen3-VL-8B-Instruct-UD-Q4_K_XL.gguf`, the Dynamic 2.0 4-bit rung rather than the uniform `Q4_K_M`. Measured against the `Q4_K_M` encoder at a shared seed, with the denoiser and VAE held fixed: LPIPS 0.029, SSIM 0.959, 5.15 GB vs 5.03 GB, 36.5 s vs 39.0 s. Rendered with the Q4_K_M denoiser and the Q4_K_M text encoder, 1024x1024, 20 steps, cfg 6.0, euler.

stable-diffusion-xl-base-0.9