Slim Aarons Generative Studio
A style-preservation and reinterpretation workflow using LoRA fine-tuning.
This project develops a reproducible pipeline for preserving and extending Slim Aarons’s mid-century photographic aesthetic through ethically transparent generative AI workflows.
A curated dataset of high-resolution Slim Aarons photographs was used to train a LoRA that captures compositional structure, color palette, lighting, and subject framing without replicating specific originals. The trained model was deployed through ComfyUI workflows and a Gradio web app, enabling accessible experimentation across text-to-image, image-to-image, editing, animation, and 2D-to-3D outputs.
Overview
Methodology and System Design
Gradio Web App
A browser-based interface provides:
Multiple generation modes
Sliders for LoRA strength, guidance scale, denoise strength
Seed control for reproducibility
This enables use by non-technical users in teaching and research contexts.
Additional tools generate:
GIFs and cinemagraphs
Ken Burns-style pan and zoom
Multi-frame storyboard sequences
These extend still imagery into short-form narrative outputs.
2D - 3D Pipeline
Ethics & Provenance
Outputs explicitly marked as AI-generated
Dataset provenance documented
Internal trigger token retained for traceability
Project framed as educational and preservational, not substitutive
Generated images were converted into textured 3D meshes using Hunyuan3D, producing lightweight assets suitable for VR, games, and spatial visualization.
Data Curation
1
Prompt and Trigger
2
Model Strategy
3
150 high-resolution images selected across eras and locations
Each image paired with a detailed descriptive caption
Captions encoded composition, color relationships, lighting, subject positioning, and mood
A standardized caption structure was introduced, along with a dedicated trigger token:
“SLMRNS A Slim Aarons photograph of”
This ensured consistent invocation of the learned style during generation and provided a clear marker of stylistic conditioning.
Training Setup
4
Quality Control
5
Resolution: 1024 × 1024
Hardware: single A100 GPU
~2,000 training steps
Tuned LoRA rank and text encoder parameters
Periodic checkpoints and sample review for quality assurance
Base model: FLUX.1-dev
Fine-tuning method: LoRA
Rationale: modular, lightweight style injection without altering the base model
Outputs were evaluated during training for:
Composition accuracy
Color fidelity
Architectural and clothing detail
Consistency across varied prompts
The final LoRA was exported as a compact safetensors file for reliable reuse.
The project delivers a reproducible Slim Aarons Generative Studio supporting research, teaching, and creative exploration. Planned extensions include usability testing, rights assessment for broader release, Hugging Face demo deployment, and expansion toward responsibly curated style-preservation toolkits.