LunaR is a specialized LoRA designed to enhance [mention the style, e.g., feminine features / cinematic lighting / specific character details]. It focuses on delivering high-quality textures and consistent results for [mention the subject, e.g., portrait photography / stylized characters].<h3 id="key-features:">Key Features:</h3><ul><li>Style: [e.g., Realistic, Semirealistic, Stylized]</li><li>Best used for: [e.g., Close-up portraits, dramatic lighting, fashion shots]</li><li>Trigger Word: <code>LunaR</code></li></ul><hr /><h2 id="usage-tips">Usage Tips</h2><ul><li>Recommended Weight: <code>0.6 - 0.8</code> (Adjust based on your checkpoint)</li><li>Sampling Method: [e.g., Euler a, DPM++ 2M Karras]</li><li>CFG Scale: <code>5 - 7</code></li><li>Base Model: Recommended for use with [mention your base model, e.g., Pony Diffusion V6 / SDXL 1.0].</li></ul><hr /><h2 id="notes">Notes</h2><ul><li>What it’s good at: [e.g., Capturing intricate skin details and soft lighting.]</li><li>What to avoid: [e.g., Not intended for complex landscapes or heavy architectural work.]</li></ul>If you enjoy using this model, please consider leaving a review and sharing your generations! Your feedback helps me improve future iterations.

LunaR

Version changes or notesInitial release of the LunaR LoRA. This model is fine-tuned to generate high-quality, photorealistic portraits with a signature vibrant pink and yellow ombre hair style. It handles both soft, natural indoor lighting and dramatic, cinematic night scenes with excellent skin texture and facial consistency.Trigger Words <code>LunaR</code> (Note: If you trained it with specific tags for the hair or glasses, you can add <code>multicolored hair, round glasses</code> as well, but <code>LunaR</code> is your main one.)Recommended Settings <ul><li>Clip Skip: <code>2</code></li><li>Min Strength: <code>0.5</code></li><li>Max Strength: <code>1.0</code></li><li>Strength (Default): <code>0.8</code></li></ul>Training Parameters If you remember your training data, you can fill these in, but they are optional:<ul><li>Epochs (Korszakok): <code>10</code> (standard guess)</li><li>Steps (Lépések): <code>2000</code> (standard guess)</li></ul>Quick Tip: Once you fill these out and click Next (Következő), you will be able to upload your <code>.safetensors</code> file and then the showcase images we wrote the prompts for earlier!

image_z_image_turbo (1) (1) (1).json

decoder (1).pt

clip_l (1) (3).safetensors

ultra_ae.safetensors

TCTR18QW73ACEKD4Z97SXSE490.safetensors

v1.0.1

zImageClearVae_halfNatural (1).safetensors

Do-neg.safetensors

KponyStandardVAE_v1 (1).safetensors

ultrafluxVAEImproved_v10 (1).safetensors

v1.0

v1.01

UltraFlux VAE is a high-performance Variational Autoencoder specifically engineered to solve the "softness" and memory bottlenecks associated with native 4K image generation in the Flux ecosystem. While standard Flux models typically utilize an F8 VAE that results in massive latent grids and slow processing times at high resolutions, UltraFlux adopts a more efficient F16 (16x downsampling) architecture. This significantly reduces the computational load and increases throughput, but because aggressive compression often risks losing micro-details, the developers implemented a specialized non-adversarial post-training scheme. During this phase, the decoder is fine-tuned on a massive 1-million-image 4K dataset using a unique SNR-Aware Huber Wavelet objective, which specifically prioritizes high-frequency fidelity. This allows the VAE to reconstruct sharp textures—such as hair, skin pores, and fine text—that are typically blurred by traditional compression methods. Beyond its technical efficiency, the UltraFlux VAE is designed as a "plug-and-play" solution for high-fidelity workflows like ComfyUI, where it is often used to instantly sharpen images without the need for time-consuming high-res fix steps or external upscalers. By shifting the heavy lifting to a more compressed F16 latent space while maintaining an ultra-detailed reconstruction through its fine-tuned decoder, it effectively bridges the gap between speed and perceptual quality. This makes it a cornerstone of the broader UltraFlux project, which aims to provide a unified framework for generating high-quality images across diverse aspect ratios (wide, square, and portrait) while matching or even exceeding the clarity of proprietary 4K models. UltraFlux VAE is a specialized Variational Autoencoder designed to enable high-fidelity, native 4K image generation within the Flux architecture. This VAE significantly improves the quality of your Flux and Z-Image Turbo outputs. I stumbled upon this when I was looking through stuffs on Hugging Face and after seeing how good this is I felt it should be known, used and praised by more folks so I mirrored it here on CivitAI.While standard Flux &amp; Z-Image Turbo models typically struggle at 4K resolution due to memory constraints and loss of detail, the UltraFlux VAE addresses these issues through several key innovations:1. High-Resolution Optimization - * F16 Compression: Unlike the standard Flux VAE (which often uses F8 downsampling), UltraFlux adopts an F16 VAE. This reduces the latent grid size by half (e.g., from 512×512 to 256×256), making the 4K generation process significantly faster and more memory-efficient. * 4K Post-Training: To compensate for the aggressive F16 compression, the decoder underwent a non-adversarial post-training phase using a high-detail subset of the MultiAspect-4K-1M dataset (a corpus of 1 million 4K images).2. Detail Preservation * Wavelet Reconstruction Loss: The VAE was fine-tuned using a "wavelet loss" objective that specifically targets high-frequency information. This ensures that micro-details—such as skin texture, hair, and fine environmental elements—remain sharp during the decoding process. * Micro-Contrast Enhancement: Users have noted that this VAE acts almost like an "unsharp mask" or a high-end sharpening filter, resolving softened details that standard VAEs might blur at high resolutions.3. Practical Usage * Plug-and-Play: It is often used as a standalone replacement for the standard Flux VAE in workflows like ComfyUI to instantly "sharpen" images without needing complex high-res fix steps. * Native 4K Focus: It is part of the broader UltraFlux project, which co-designs the data, architecture (using Resonance 2D RoPE), and the VAE to maintain consistent quality across diverse aspect ratios (wide, tall, and square).This is a reupload of resource from this Hugging Face Repo -<a target="_blank" rel="ugc" href="https://huggingface.co/Owen777/UltraFlux-v1/tree/main/vae">https://huggingface.co/Owen777/UltraFlux-v1/tree/main/vae</a>

UltraFlux VAE (Mirrored from Hugging Face Repo)

ultrafluxVAEMirroredFrom_ultrafluxV1VAE.safetensors

UltraFlux-v1_VAE

kam1.safetensors

letsdo.safetensors

asset.zip

ultrafluxVAEImproved_v10 (1) (2).safetensors

1Kam.1

kam.jpg.zip

PL1.0

llm01

ultrafluxVAEImproved_v10.safetensors

KponyStandardVAE_v1.safetensors

v1.01.1

KKD1

kkd1_v10.safetensors

kkd1_v10_trainingData.zip

kkd1_v10_txt.safetensors

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