stopmotion-consistency-v1-lora

Model · Image to image

FLUX.2-klein-4B-GGUF

Unsloth AI

This is a GGUF quantized version of FLUX.2-klein-4B. unsloth/FLUX.2-klein-4B-GGUF uses Unsloth Dynamic 2.0 methodology for SOTA performance. - Important layers are upcasted to higher precision. - Uses tooling from ComfyUI-GGUF by city96. The FLUX.2 [klein] model family are our fastest image models to date. FLUX.2 [klein] unifies generation and editing in a single compact architecture, delivering state-of-the-art quality with end-to-end inference in as low as under a second. Built for applications that require real-time image generation without sacrificing quality, and runs on consumer hardware, with as little as 13GB VRAM. FLUX.2 [klein] 4B is a 4 billion parameter rectified flow…

Model · Image to image

FLUX.2-klein-4b-fp8

Black Forest Labs

FLUX.2 [klein] 4B is a 4 billion parameter rectified flow transformer capable of generating images from text descriptions and supports multi-reference editing capabilities. For more information, please read our blog post. This repository holds an FP8 version of FLUX.2 [klein] 4B. The main repository of this model (full BF16 weights) can be found here. Limitations - This model is not intended or able to provide factual information. - While the model can output text, text rendered may be inaccurate or subject to distortion. - As a statistical model, this checkpoint may represent or amplify biases observed in the training data. - The model may fail to generate output that matches the prompts.…

Model · Image to image

qwen-image-edit-plus-nsfw-lora

ScottzillaSystems

A LoRA adapter for the Qwen-Image-Edit-2511 diffusion pipeline, enabling NSFW content generation and editing capabilities. The easiest way to use this LoRA is through the ScottzillaSystems Image Editor — select " MCNL-NSFW-v1" from the adapter dropdown. Use these concepts in your prompts to activate specific capabilities: nsfw nipples vagina penis missionary cowgirlout reversecowgirlpov blowjob cumonface creamp1e l1ck

Model · Image to image

FLUX.2-klein-base-4b-fp8

Black Forest Labs

FLUX.2 [klein] 4B Base is a 4 billion parameter rectified flow transformer capable of generating images from text descriptions and supports multi-reference editing capabilities. For more information, please read our blog post. This repository holds an FP8 version of FLUX.2 [klein] 4B Base. The main repository of this model (full BF16 weights) can be found here. Limitations - This model is not intended or able to provide factual information. - While the model can output text, text rendered may be inaccurate or subject to distortion. - As a statistical model, this checkpoint may represent or amplify biases observed in the training data. - The model may fail to generate output that matches the…

Model · Image to image

safetensored-upscalers

Ashtaka

compact is quick and decent i guess - dat is very slow but great quality (dark go to) - drct similar quality to dat but slightly faster - esrgan is old and mediocre - nafnet is an image restoration architecture - omnisr is quick and good quality (nub go to) - rcan is old but good - real-esrgan is esrgan but real (mediocre) - span is quick and good quality (my go to) I do not own any models in this repo. These are just converted pickles to safetensors. The script used is modified from https://github.com/OpenModelDB/model-hub/blob/main/tools/convert-pth.py with Gemini 2.5 Pro.

Release package for the paper's single-pass super-resolution models: Both models follow the paper protocol: RGB image → bicubic resize to 512×512 → one forward pass → mean-only color matching in the float domain, before PNG quantization: per RGB channel, clamp(sr - mean(sr,H,W) + mean(lr,H,W), 0, 1), where lr is the same 512×512 bicubic-resized tensor the model consumed. The formula is recorded in every run manifest. There is no hidden color alignment, post-processing, normalization, or implicit fallback model. The released RAESR decoder is the paper's Stage 3, seed 42, step-600 EMA checkpoint: The encoder layers are named 0-indexed in code and manifests (layers 1..23 for RAESR, layers…