HerbstPhoto_v4_Flux2
A LoRA model for Flux 2 Dev trained exclusively on analog photography I own the rights to. Produces intensely imperfect images that feel candid and alive.
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Model Description
HerbstPhoto_v4_Flux2 breaks past the plastic look of AI-generated images by introducing authentic analog characteristics: filmic softness, emulsion bloom & halation, optical artifacts (lens flares, light leaks, chromatic aberration, barrel distortion), and grain that behaves naturally across exposure levels. The contrast curve is aggressively low latitude, embracing clipped highlights and crushed shadows.
This model represents my belief that we can take control of AI's potential by training on our own material—and that we can bring an empowering version of image generation into reality through tools made by individuals, accessible to anyone with a laptop.
Intended Use
Creative image generation with an authentic analog film aesthetic. Ideal for photographers, filmmakers, and artists looking to move beyond the sterile perfection of typical AI outputs.
Usage
Trigger Word
Include HerbstPhoto in your prompt.
LoRA Strength
| Strength | Effect |
|---|---|
| 0.4 - 0.75 | Balanced analog aesthetic with good prompt adherence |
| 0.73 | Sweet spot |
| 0.8 - 1.0 | Maximum texture/degradation, reduced prompt adherence |
Resolution
- Recommended: 2048x1152 (16:9) or 2488x2048
- Produces good results across aspect ratios and sizes up to 2K
Schedulers & Samplers
Tested every combination. These work best:
dpmpp_2s_a+sgm_uniformer_sde+ddim_uniformdpmpp_sde+simpledpmpp_3m_sde_gpu+simpleipndm+simpledpmpp_sde+ddim_uni
Prompting
Flux 2's incorporation of the mistral_3_small_fp8 text encoder handles long, complex prompts well—but I tuned this LoRA to produce dramatic effects even with simple language. You don't need style, texture, or lighting tokens.
Training
- Base Model: Flux 2 Dev (Black Forest Labs)
- Training Framework: AI Toolkit (Ostris, LLC)
- Hardware: H200 GPU cluster via Runpod
- Methodology: 100+ training runs, changing one parameter per run for clean A/B testing
- Dataset: My own analog photography (full rights ownership)
Ethical Training
This model is trained exclusively on photographs I created and own. No scraped data, no unlicensed material.
Resources
- Model Download: Patreon (Free)
- ComfyUI Template & Tutorial: Link
Upcoming Releases
Versions for Flux 1 Dev, Z-image, and SDXL coming soon for faster generation and lower compute requirements.
Author
Calvin — Filmmaker and creative technologist Patreon
License
Please check the Patreon page for license terms.