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GOD infinity – for Danbooru/e621 artist styles

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GOD infinity – for Danbooru/e621 artist styles - AI Model cover image
Uploaded at
Jun 7, 2026, 1:24 PM
Uses
52
Reviews
No reviews
Sampling Steps

32

Sampling Method

Euler a

CFG Scale

7

Negative

low quality, missing fingers, watermark, username, artist name, copyright_name, signature, warm filter, jpeg artifacts, frame, distorted, mutation,

Permissions
  • Allow image generation & sharing
  • Allow users to download this model
  • Allow commercial use of generated images

Description

https://civitai.red/models/2539936/god-infinity-for-danboorue621-artist-styles

GOD infinity V_PRED (v10.2) – personal merge for Danbooru/e621 artist styles

What is this? An evolution of my personal merge, now updated to version 10.2. Like the previous version, this model was created for personal use but is shared here for those who are interested in experimenting with these specific styles.

Important note I am still using AMD hardware. This means you may not get an exact 1:1 bit-level replica of the demo images if you are on Nvidia, but the outputs will be very close using the same parameters and the embedded workflows.

Who is it for? This model is designed for creators who prefer diverse artist styles, textures, sketches, manga, and expressive anime representations over standardized, highly polished outputs. It is built to interpret a wide variety of tags from Danbooru and e621.

Technical details & V-Prediction Unlike the previous 9.1 (eps) version, GOD infinity V_PRED is built on a completely different set of fine-tuned models trained on the v-prediction (v_pred) target.

Crucial Setup Note: Proper configuration is mandatory. Without the correct v-prediction settings, the model will not generate properly and will produce a broken, noisy mess.

In ComfyUI: You must use the ModelSamplingDiscrete node and set the sampling parameter to v_prediction.

Other WebUIs (Forge, Automatic1111, etc.): If you are unsure how to run v-prediction models on your preferred platform, please look up the instructions independently. It requires specific settings, but the result is well worth the effort.

Why V-Prediction? Transitioning to this architecture offers several distinct characteristics:

Better, more in-depth prompt adherence.

Deeper, richer black tones.

Consequently, improved overall contrast on average.

Useful Tips for Creators:

Quality Tags Caution: Standard quality/aesthetic tags (such as "masterpiece", etc.) have a strong influence on the rendering characteristics of specific artist styles. They can significantly alter shadow realism, color correction, and contrast. Use them sparingly, or leverage them intentionally to customize and tweak the output style.

Handling Monochrome: Some artist styles integrated into this merge have a tendency to produce monochrome, two-tone, or three-tone images by default. If you want to force color, adding the tag monochrome to your negative prompt is usually effective.

How to use: The demo images use a straightforward setup:

Initial generation pass

Upscaler Workflows are embedded in the metadata of the demo images.

You can find me here: Telegram: https://t.me/eternitykek Twitter/X: https://x.com/eternity_heh

Note on the previous version (GOD 9.1): If you are looking for the previous GOD 9.1 (eps) version — it is a standard epsilon-target model. It is fully compatible with standard SDXL LoRAs and regular workflows without requiring any v-prediction configuration.

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