Herbst Photo - analog film

Aesthetic Properties of the model: 

HerbstPhoto_v4_Flux2 produces intensely imperfect images that feel candid and alive. The model creates analog degradation micro-textures that break past the plastic look by introducing filmic softness, emulsion bloom & hailation, optical artifacts - such as lens flares, light leaks, chromatic aberration, barrel distortion - and grain that behaves naturally across exposure levels. Compositions are moody and take form in chiaroscuro light, with dark regions that blanket the frame to create asymmetry and bright slivers that form hotspots to maintain balance. The contrast curve is aggressively low latitude, embracing clipped highlights and crushed shadows, while preserving a high black point to feel true to the celluloid nature of the images the model was trained on. 

Version 4 is trained for Flux 2 Dev from @Black Forest Labs because I beleive it’s the best image diffusion model, however it’s a heavy and can take several minutes to generate a single high-res image, so I will also be releasing an updated version for Z-image, Flux 1 Dev, and SDXL in the coming weeks for those who are looking to use less compute or create faster. 

Best Practices using the model:

• Prompts: Include “HerbstPhoto” in the prompt. Though the Flux 2 Model can handle prompts that are long and complex thanks to its incorporation of the minstral_3_small_fp8 text encoder from @Minstral AI I tuned this LoRA to produce dramatic effects even with simple language writing that does not include style, texture, and lighting tokens. 

• LoRA strength: 0.4 - 0.75. (0.73 sweet spot) 0.8-1.0 for less prompt adherence and max image texture/degradation. 

• Resolution: 2048x1152, though the model also produces good results across aspect ratios and sizes up to 2k. 

• Schedulers and Samplers: I tested every combination of Schedulers and Samplers for Flux 2 (378 total) and can recommend a handful of combinations that I tested on a Pro 6000 WK GPU @ 1024x1024 @ 20 steps that each have different aesthetics and render speeds.

1. dpmpp_2s_ancestarl + sgm_uniform: Best balance of texture & fidelity. 160 sec. Render 

2. er_sde + ddim_uniform: Good balance of texture & fidelity. 60 sec. render

3. dpmpp_sde + simple: Softer focus, lower contrast, less artifacts, brighter. 130 sec. Render

4. dpmpp_3m_sde_gpu + simple: higher contrast, brighter, more chromatic aberrations. 60 sec. render

5. Ipndm + simple: Higher clarity, less softness, fewer artifacts, cooler. 60 sec. render

6. dpmpp_sde + ddim_uni: higher saturation, color shifting. 130 sec. Render

Training Process Overview:

I used AI Toolkit from Ostris on an H200 GPU cluster from Runpod to train over 100 versions of the model, all using the same dataset + simple captions. For each run, I changed one parameter to get a clean A/B tests and figure out what actually moves the needle. I’ll share the full research soon :) After lots of testing, I am happy to finally release HerbstPhoto_v4_Flux2.

Coming soon:

HerbstPhoto_v4.1_Flux1Dev

HerbstPhoto_v4.2_ZImage

HerbstPhoto_v4.3_SDXL

HerbstPhoto_v4.4_Flux2_DarkAbyss

HerbstPhoto_v4.5_Flux2_FishEye

HerbstPhoto_v4.6_Qwen_ImageEnhancer