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MCSC Minecraft Skin Creator

7740°
MCSC   Minecraft Skin Creator
Created by
flowask
on Civitai
by Hex_Duck - MCSC - Minecraft Skin Creator
Creator notes
🌌 MCSC This LoRA is designed to generate high-quality Minecraft skins with exceptional accuracy, converting characters or people into clean and usable 64×64 texture maps. 🧩 Recommended Settings (Qwen) - Trigger Word: MCSC: - Strength: 0.75 - 1.3 - Resolution: 512x512 - Recommended Samplers: Euler Ancestral, DDIM 🧩 Recommended Settings (Flux) - Trigger Word: MCSC: - Strength: 1.2 - 1.5 - Resolution: 512x512 - Recommended Samplers: Euler Ancestral, lcm, Sa_solver, Er_sde 💡 Recommended Prompts: IMG2IMG MCSC: transform this into a 64x64 Minecraft skin texture with aligned UV layout, strict pixel grid, hard edges, consistent and coherent pixel placement across all body parts. TXT2IMG MCSC: 64x64 Minecraft skin texture with aligned UV layout, strict pixel grid, hard edges, consistent and coherent pixel placement across all body parts, 📅 Planned Releases & Roadmap ・V4 🟢 MCSC V4 IMG2IMG · QIE 2511 (Released) - Architecture: Full inner / Full outer layer training - Dataset: 200 image pairs - Note: Highly complex outer layer structures can occasionally produce scattered pixels, artifacts, or color bleeding, which can be removed using the MCSC viewer to achieve higher quality. 🟡 MCSC V4 TXT2IMG · QWEN (Coming Soon) - Architecture: Full inner / Head Only Outer layer training - Dataset: 200 image pairs - Note: Exclusive version for txt2img, likely less prone to errors. 🧑‍🎮 MCSC Viewer The generated skins need to be processed to correct imperfections, and they include a purple mask that must be removed to be used correctly in Minecraft. That said, I developed MCSC VIEWER, an all-in-one solution that gives you total control over skin processing, which outlayers to keep, and much more! Features - Automatic purple mask removal - Alpha transparency restoration - Precision 64×64 resizing - Interactive 3D preview - Steve and Alex model support - One-click PNG export - Outer Layer Control - And much more !! Links: Viewer (One click use - online): GitHub: 📋 DEV NOTES Two fresh new versions of MCSC are finally here for you all! These versions took a tremendous amount of work to develop and test. After roughly a week of experimentation, iteration, and troubleshooting, I finally reached a level of quality that I consider usable. Honestly, I still think these releases are quite far from what I originally envisioned for them, but given my current hardware limitations, I believe this is the best result I can realistically achieve for now. With that said, let's go over the technical changes introduced in this release. Flux Klein 9B VS Qwen Image Edit 2511 Overall, Qwen Image Edit performs better at generalization and detail generation, often producing skins with richer details and more complex visual features. On the other hand, Flux Klein frequently generates cleaner and simpler skins with fewer visible imperfections and artifacts. After extensive testing, I personally prefer the results produced by Qwen Image Edit. The additional detail, stronger concept understanding, and greater flexibility make it my preferred choice in many situations. However, this does not mean it is universally better than Flux Klein. In practice, the differences between the two models vary significantly depending on the character, prompt, and generation scenario. While Qwen Image Edit tends to produce more detailed results on average, Flux Klein often delivers cleaner, and simpler skins For that reason, I strongly recommend trying both versions and deciding for yourself which one better fits your workflow and preferred style. Despite their differences, both models are exceptionally capable at what they do. MCSC Viewer The real big changer of this release. While the new Qwen Image Edit versions bring interesting improvements and new possibilities, the MCSC Viewer is, in my opinion, the feature that truly transforms the overall user experience. The Python script I previously provided was extremely simple, difficult for non-technical users to operate, and often produced inconsistent or messy results because it lacked the fine-grained controls needed to properly process different skins. MCSC Viewer changes that completely. It provides a full set of tools and configuration options specifically designed for Minecraft skin post-processing, allowing the cleanup workflow to be performed quickly and largely automatically. Thanks to this tool, the vast majority of generated skins can now be converted into game-ready assets with little to no manual correction required. What previously required external scripts, manual editing, and trial-and-error can now be handled through a much more accessible and user-friendly workflow. More than the new LoRAs themselves, I believe MCSC Viewer is the feature that will have the biggest impact on the quality and usability of the final results. Future Development With the release of these two Qwen Image Edit LoRAs, I plan to enter another development hiatus for this project. Future work will likely resume only when either: - A new model worth training becomes available. - I gain access to better hardware. - I secure funding for more extensive training experiments. Commercial Usage This project was created as an open and freely accessible resource for the community. While I cannot prevent commercial usage, I strongly encourage anyone building upon this work to provide proper credit and include a link to this page. If your project directly relies on this LoRA, please acknowledge the original source and contributors. Final Notes With all that said, I truly hope you enjoy these new versions. They required an enormous amount of work, and I created them with a lot of care and passion for the community. I will continue releasing my usual LoRAs on my profile, and I already have plenty of exciting projects planned for the coming months. If you enjoy my work, I would greatly appreciate it if you could: - Share the project - Leave a like - Comment your thought
Trigger words
MCSC: 64x64 Minecraft skin texture with aligned UV layout, strict pixel grid, hard edges, consistent and coherent pixel placement across all body parts,
207 downloads · 16 likes on Civitai
SHA256
60EABC82799720CB1119B375C28D7E9D4D1D2B749F338B69BDC062561893E5B5
Ranking badge
Updated Oct 22, 2026
MCSC Minecraft Skin Creator on ModelTrends.ai: Heat 7,740°
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MCSC Minecraft Skin Creator Popularity Trend
Heat score since 6 Apr 2026
Heat score
7,740°
This week
n/a
Wasn't released/tracked
This month
n/a
Wasn't released/tracked
Past year
n/a
Wasn't released/tracked
MCSC Minecraft Skin Creator: Heat score since 6 Apr 20267,000°7,248°7,496°7,744°7,992°Apr 2026May 2026Jun 2026Aug 2026Sep 2026Oct 20267,740°ModelTrends.ai
7,000°
Floor
The scale runs from 0° to 9,999°. Everything below 7,000° is kept for models no source reports on yet, which carry a stand-in number there so lists stay complete. 7,000° is where measured data begins: a model with real data but almost no downloads sits at the floor, and the measurements fill the degrees above it. The floor says nothing about the model; only the degrees above it do, and they are what separates one model from another.
+698°
Civitai downloads
207 downloads of this exact version on Civitai. Downloads are the heaviest ingredient and are counted on a log scale: ten times the downloads adds the same 300°, so a model with a million downloads is not a thousand times hotter than one with a thousand. The six download sources share that one term, split between them in proportion to their counts.
+185°
Likes
16 likes and thumbs up across the sources. A like weighs half a download, on the same log scale.
+3°
Adoption rate
About 0 downloads a day, measured between two readings of the sources, never guessed from a lifetime average. Speed weighs a quarter of the download term, so a fresh surge shows without letting a one-day spike outrank years of use.
−146°
Age
−146° because the Flux2 weights came out in 2025. Every model inherits the age of the weights it is built on: 150° a year, up to 750°, and it keeps growing until then. Without it the top of the list would be 2022 and 2023 checkpoints on the strength of downloads that had years to pile up.
Show detailed breakdown
Every number on this site can be checked. This model's heat score is 7,740°, and here it is built one ingredient at a time, with the running total after each: the floor, the degrees each source adds, the bonus for the kind of model and what the age of its weights takes away. The colors are the bands of the chart above. The glossary at the end defines every term.
  1. 1
    Start at the floor.
    Why 7,000° and not zero: the scale runs from 0° to 9,999°, and the part below 7,000° is reserved for models no source reports on yet, which carry a stand-in number there so the lists stay complete and never outrank a model with data. 7,000° is where measured data begins. A model with real data but almost no downloads sits here; everything that follows is measurement, and the room above the floor holds ten tenfold steps in downloads at 300° each. The floor is the same for every model, so it decides nothing; the degrees above it do.
    7,000°
    running total
  2. 2
    Add the downloads.
    207 download-equivalents in all, from Civitai downloads. Ten times the downloads adds the same 300°, so size matters but never runs away with the score.
    +698°
    7,698°
    running total
  3. 3
    Add the likes.
    16 likes and thumbs up, at half the weight of a download.
    +185°
    7,883°
    running total
  4. 4
    Add the adoption rate.
    About 0 downloads a day right now, measured between two readings, at a quarter of the weight.
    +3°
    7,886°
    running total
  5. 5
    Take off the age.
    The Flux2 weights came out in 2025: 150° a year, up to 750°, growing until then.
    −146°
    7,740°
    running total
  6. =
    The heat score.
    Where this model stands on every list of this site. Nothing else adjusts it.
    7,740°
Glossary: the terms used here
Heat score
One number for how much a model is used and wanted right now, from 0° to 9,999°. Every list on this site that says hottest is sorted by it and nothing else.
Floor
The 7,000° every model with real data starts from. The scale runs from 0° to 9,999°; the part below the floor is reserved for stand-ins (models without data), so that a model with data always ranks above one without. The floor is the same for everyone and decides nothing; the degrees above it are the measurement.
Download-equivalents
Everything the score treats as a download, added up: downloads themselves, each torrent seeder as 1,000, each AI Horde image and each run on a generation site as one.
Log scale
Counting by tens, not by ones: going from 1,000 to 10,000 downloads adds the same degrees as going from 10,000 to 100,000. It keeps giants from crowding out everything else.
Likes
Thumbs up and likes from every source, worth half a download each on the log scale.
Adoption rate
How many downloads a day a model gets right now, from the difference between two readings of a source. A quarter of the weight of the download term.
Measured and estimated
Measured means two readings of a source exist and the difference is real. Estimated means only today's total is known and the past is spread over the model's life; the site says which one it is showing.
Seeder
Someone who keeps a copy of the weights online for others to download over a torrent, or a hosted mirror doing the same. Each one counts as 1,000 downloads.
AI Horde
A volunteer network that generates images for free on donated computers. The number of images it has made with a model is public, and each counts as one download.
Generation sites
Sites that host their own copies of a model and let people generate with it, such as PixAI, SeaArt and Yodayo. Their downloads count like Civitai's and each generation run as one download.
OpenRouter tokens
A text model served through an API, such as Claude or GPT, has no weights to download, so its usage is measured in tokens: the pieces of text it read and wrote through OpenRouter, a gateway many apps use, over the last 30 days. Every 10,000,000 tokens count as one download. Source: OpenRouter (openrouter.ai/rankings), CC BY 4.0.
Full model (checkpoint)
The weights themselves, the file that generates on its own. It gets a bonus because add-ons depend on it.
LoRA and embedding
Small add-on files that steer someone else's weights toward a style, a character or a concept. They cannot run alone.
Family and weights
The base a model is built on, such as SD 1.5, SDXL, Flux or Wan. Every model inherits the release date of its family's weights.
Age
Degrees taken off for how long ago the family's weights came out: 150° a year, up to 750°. It keeps old downloads that had years to pile up from freezing the top of the list.
Family boost
A small, bounded nudge for LoRAs of a family this site is pushing forward right now, always listed in the breakdown.
Stand-in score
A stable number below the floor given to a model no source reports on yet, so lists stay complete. It never ranks above a model with data.
New! (grace period)
A model less than 7 days old shows New! instead of a score and holds no place in the rankings. Its score is computed from day one, but a model one day old grows faster than anything, so without the wait the week's #1 would always be whatever came out yesterday rather than what held up for a week.
Trend
How a model's growth over a period compares with the typical model's growth over the same period. Faster than typical reads as a rise, slower as a fall.
Typical model
The middle of the catalog: half the models with a figure grew faster over the period, half slower.
How this model's popularity has moved against the rest of the catalog: its growth in downloads, likes and usage over each period, measured against the typical model's growth over the same period, with its heat score at the time. Above the typical growth reads as a rise, below it as a fall. The past is reconstructed from today's totals, the model's first day and its measured download speed where two readings exist; a model that did not exist at the start of a period shows n/a.
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Run Locally
MCSC_txt2img_v3.safetensors
332 MB · SafeTensor
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