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manideep63/aj1-chicago-lora
aj1-chicago-lora is a text-to-image model from manideep63. Use it when you need an image from a text prompt. It is set up for diffusers. The card lists the license as other.
A DreamBooth LoRA for FLUX.2 [klein] 9B that learns a single specific product — the Air Jordan 1 Retro High OG "Chicago" (style 555088-101) — and renders it into new photographic scenes from a text prompt while keepin…
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From the Hugging Face model README
A DreamBooth LoRA for FLUX.2 [klein] 9B that learns a single specific product — the Air Jordan 1 Retro High OG "Chicago" (style 555088-101) — and renders it into new photographic scenes from a text prompt while keeping its silhouette, colourway and logo placement consistent.
It was trained as part of a product-imagery workflow: fit a small adapter on a handful of reference photos of one product, then generate unlimited on-brand studio and lifestyle shots of that product without a photoshoot.
Refer to the shoe in your prompt with the full phrase:
tjkzx Air Jordan 1 Chicago sneaker
The phrase pairs a rare token (tjkzx) with the real product name. The token gives
the adapter a clean, collision-free handle; the name lets the base model contribute
its existing knowledge of the shoe. Everything else in the prompt describes the
scene — background, lighting, lens, mood.
import torch
from diffusers import Flux2KleinPipeline
pipe = Flux2KleinPipeline.from_pretrained(
"black-forest-labs/FLUX.2-klein-9B", torch_dtype=torch.bfloat16
).to("cuda")
pipe.load_lora_weights("manideep63/aj1-chicago-lora")
prompt = (
"a product photo of tjkzx Air Jordan 1 Chicago sneaker on a white marble "
"podium, soft studio softbox lighting, subtle shadow, e-commerce hero shot, "
"85mm, sharp focus"
)
image = pipe(prompt, num_inference_steps=8, guidance_scale=1.0).images[0]
image.save("out.png")
FLUX.2 klein is a distilled model: keep num_inference_steps between 4 and 8 and
guidance_scale around 1.0. Higher step counts add time without much benefit.
| Path | Description |
|---|---|
pytorch_lora_weights.safetensors | Final adapter, rank 96 (fp32) |
checkpoint-250 … checkpoint-2500 | Intermediate checkpoints, saved every 250 steps |
inference/ | Ten sample generations (default scene set) |
sophisticated/ | Twelve sample generations (detailed campaign-style prompts) |
logs/ | TensorBoard training logs |
The final adapter at the repository root is the recommended weight. The numbered checkpoints are provided so you can trade a little identity strength for a little more scene flexibility if a particular prompt needs it.
| Base model | FLUX.2 [klein] 9B |
| Method | DreamBooth LoRA, rank 96 (alpha 96) |
| Reference set | 16 images of the one shoe — 12 studio angles from a 360° set plus 4 lifestyle shots |
| Captions | Scene-only: each caption describes the surroundings and never the shoe's colours or materials, so identity is carried by the trigger phrase and the images |
| Optimiser | AdamW, learning rate 1e-4, constant schedule, 100 warmup steps |
| Precision | bf16 training, fp32 saved weights |
| Steps / batch / resolution | 2500 / 4 / 1024 |
| Guidance (training) | 1.0 |
This adapter is for product-photography style image generation of one specific sneaker. Identity is most faithful on the canonical white / black / red colourway in clean studio-to-lifestyle compositions. Very small, heavily occluded, or extreme-angle renders can lose fine stitching and panel detail. As a distilled model, klein follows the overall mood and composition of a prompt well but is less literal on very long prompts than the full FLUX.2 dev model.
This adapter is built on FLUX.2 [klein] 9B and is distributed under the same FLUX.2 Non-Commercial License — non-commercial use only. See the linked license for full terms.
"Air Jordan", "Jordan", the Jumpman logo and "Nike" are trademarks of Nike, Inc. This is an independent research and educational demonstration of product-imagery fine-tuning. It is not affiliated with, authorised by, or endorsed by Nike, Inc. or Jordan Brand, and all rights in the product and its branding remain with their respective owners.