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finegrain/
finegrain-product-placement-lora

Name: finegrain-product-placement-lora
Rating: 2.5 (42 reviews)

black-forest-labs/FLUX.1-Kontext-dev

adapter

other

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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.

👉 Try it on Hugging Face

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:

bash

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

bibtex

@misc{fg2025pplace,
  author = {The Finegrain Team},
  title = {Finegrain Product Placement LoRA},
  year = {2025},
  publisher = {HuggingFace},
}