Total runs:
14.5K
Run Growth:
1.5K
Growth Rate:
10.04%
Sponsored by
AdsCreator.com
finegrain / finegrain-product-placement-lora
Model's Last Updated: September 11 2025
image-to-image
Introduction of finegrain-product-placement-lora
Model Details of finegrain-product-placement-lora
Finegrain Product Placement LoRA
A lightweight LoRA (rank=8) for Flux Kontext, enabling realistic product placement in photos.
- Built on EditNet , our pixel-perfect image editing dataset for supervised fine-tuning.
- Designed for product photography.
- Trained to support bounding-box control for predictable placement (position and scale).
- Distributed under the same license as Flux Kontext.
Features
- Products are relighted according to the scene.
- Perspective is adjusted to remain as close as possible to the reference image, minimizing hallucinations.
- Shadows and reflections are added for realism.
- Can generate at any resolution, with a maximum short side of 1024px.
Limitations
- Occlusion ✅ — as long as the product stays the main focus.
- Virtual try-on ⚠️ — only for hats, watches, and glasses.
- Scene leakage ⚠️ — the product may occasionally be transformed into another element of the scene.
- Small text ⚠️ — fine details, like small text on products, may not be fully preserved.
- Textures ⚠️ — May look washed out at times.
- Occasional failures ⚠️ — Sometimes the model does not generate anything.
- Products in hands ❌ — not supported.
Usage
This is a Flux Kontext LoRA but it uses a non-standard formulation and requires specific input preparation.
For that reason, it cannot be used with the official Diffusers pipeline. Here is how you can use it with uv instead:
HF_TOKEN=hf_aBcD1234 \
uv run --with git+https://github.com/finegrain-ai/finegrain-toolbox \
python foo.py
from pathlib import Path
import torch
from finegrain_toolbox.flux import Model, TextEncoder
from finegrain_toolbox.processors import product_placement
from huggingface_hub import hf_hub_download
from PIL import Image
device = torch.device("cuda")
dtype = torch.bfloat16
model = Model.from_pretrained(
"black-forest-labs/FLUX.1-Kontext-dev",
device=device,
dtype=dtype,
)
text_encoder = TextEncoder.from_pretrained(
"black-forest-labs/FLUX.1-Kontext-dev",
device=device,
dtype=dtype,
)
lora_path = Path(
hf_hub_download(
repo_id="finegrain/finegrain-product-placement-lora",
filename="finegrain-placement-v1-rank8.safetensors",
)
)
prompt = text_encoder.encode("Add this in the box")
model.transformer.load_lora_adapter(lora_path, adapter_name="inserter")
scene_image = Image.open("scene.webp")
reference = Image.open("reference.webp")
bbox = (1085, 1337, 1737, 3077)
result = product_placement.process(
model=model,
scene=scene_image,
reference=reference,
bbox=bbox,
prompt=prompt,
)
result.output.save("output.webp")
Citation
@misc{fg2025pplace,
author = {The Finegrain Team},
title = {Finegrain Product Placement LoRA},
year = {2025},
publisher = {HuggingFace},
}
Runs of finegrain finegrain-product-placement-lora on huggingface.co
143
Total runs
0
24-hour runs
-4
3-day runs
-22
7-day runs
1
30-day runs
More Information About finegrain-product-placement-lora huggingface.co Model
Url of finegrain-product-placement-lora
finegrain-product-placement-lora huggingface.co Url
Provider of finegrain-product-placement-lora huggingface.co
Other API from finegrain
Updated:September 11 2024