The Artist (Chroma HD LoRA)
"Visualizing the Latent Space."
This LoRA was trained to generate abstract, geometric representations of what happens inside a Transformer model during inference. It translates mathematical concepts—vectors, attention heads, and token collapse—into visual metaphors using a vibrant, neon-noir aesthetic.
Trained for: Chroma HD (Flux Architecture)
The Concept
The imagery follows a specific logic based on LLM architecture:
The Tunnel: Represents the "Inference Tunnel" or the passage of data through layers.
The Lines: High-velocity vectors and input tokens.
The Colors: Attention heads firing (Cyan/Magenta/Orange).
The Center: The Singularity / EOS Token (End of Sequence).
Style: Atmospheric, ethereal, neon-circuitry, high contrast.
Best for: Artistic renders, abstract landscapes, glowing flora, "Tron-like" cityscapes.
Characteristics: The vectors behave like lightning or liquid; the floors look like circuit boards.
Usage Tips
Trigger Word: the_artist
Recommended Weight: 0.8 - 1.0
CFG: 3.5 - 5 (Chroma prefers lower guidance)
Prompting Strategy
While trained on natural language, this LoRA responds incredibly well to Danbooru-style tags for precision control.
Example Prompt using Bandoor tags: > the_artist, masterpiece, best quality, latent space, neon flowers, lightning vectors, blue and magenta, dark background, 3d render, abstract
Technical Specs
Base: Chroma HD (Flux-based)
Precision: BF16 (Brain Float 16)
Rank/Alpha: 2/16 (High-Efficiency Compressed Training)
License: CC BY 4.0
Disclaimer
This model is offered as is. It's just a LoRA model and it cannot answer for contents related to base model.
Recommended uses
Research and education
Dataset
Dataset is shared and it provides all images and descriptions used for training the model.
"This model is an artistic exploration of AI architecture."
Recipe
```
{
"engine": "kohya",
"unetLR": 0.0005,
"clipSkip": 1,
"loraType": "lora",
"keepTokens": 0,
"networkDim": 2,
"numRepeats": 9,
"resolution": 1024,
"lrScheduler": "cosine_with_restarts",
"minSnrGamma": 5,
"noiseOffset": 0.1,
"targetSteps": 259,
"enableBucket": true,
"networkAlpha": 16,
"optimizerType": "AdamW8Bit",
"textEncoderLR": 0,
"maxTrainEpochs": 5,
"shuffleCaption": false,
"trainBatchSize": 4,
"flipAugmentation": true,
"lrSchedulerNumCycles": 3
}