XXXpilar/Fastfill-test

FastFill (v4, base): object-conditioned indoor room layout

FastFill lays out the furniture of one room in a single forward pass. Given:

• the room's floor polygon, doors and windows, and fixed objects;
• the list of objects to place, with categories and optional descriptions;
• optional constraints;

it predicts, for every object:

• a size [depth, width, height] in metres;
• a bottom-centre position [x, y, z] in metres;
• a yaw: the direction the object's front faces, counter-clockwise from +x.

This repository holds what you need beyond the base model:

• the LoRA adapter on Qwen3-8B;
• the geometry decoder and heads;
• the tokenizer;
• the exact FastFill code the model was trained with.

Download Qwen/Qwen3-8B and run it. Nothing else is needed.

Use

pip install torch              # the build for your CUDA / platform
pip install -r requirements.txt
python predict.py example_condition.json                          # fetches Qwen/Qwen3-8B from the Hub
python predict.py example_condition.json --qwen /path/to/Qwen3-8B  # or a local copy

In Python:

import json, predict
model, tokenizer, max_length = predict.load(qwen="Qwen/Qwen3-8B", device="cuda")
condition = json.load(open("example_condition.json"))
layout = predict.predict_layout(model, tokenizer, condition, max_length=max_length, device="cuda",
                                grid_decode="spread")

Hardware: the bf16 base model needs about 16 GB of GPU memory. One room takes well under a second on a GH200.

Input and output

• Input: a fastfill.v2 condition. See example_condition.json and code/fastfill/v2/schema.py.
  • room: floor_polygon_xy_m, floor_z_m, height_m, openings (doors and windows), fixed_objects.
  • objects: id, category, optional description and support_parent.
  • constraints: against_wall, near, faces, between, ...
• Output: {"schema_version": "fastfill.v2", "objects": [{"id", "target_size_local_m", "bottom_center_m", "yaw_rad"}, ...]}, one entry per requested object.

Model

Base model	Qwen3-8B, frozen, bf16
Adapter	LoRA rank 8, alpha 16, on q/k/v/o projections
Decoder and heads	2-layer object decoder (dim 128, 4 heads) size head (log-space) yaw: 12 bins plus a residual position: 16×16 grid cell plus a residual
Training	main4-cell05-v4fixed-e5-base: 6,552 steps (5 epochs) effective batch 24, lr 1e-4 with cosine schedule max length 8,192 tokens, up to 256 objects per room
Data	FastFill v4 layout data (v2-fixed build) indoor rooms from about 20 public sources
Code	code/fastfill: training commit 26939bc the model, batch, evaluate, io and schema modules are byte-identical to it

checkpoint/training_manifest.json records the training run (data hashes, full configuration).

Results (test300: 300 held-out rooms, stratified by source)

Decoding	Position error (m)	Size error (log)	Yaw error (rad)	Out of bounds	Blocks a door	Collisions per room	Constraints met	Clean rooms
spread	3.11	0.321	0.593	10.0%	11.7%	3.65	37.7%	11.1%

Read the raw output as a draft. It is not a guaranteed-valid layout. Sizes are the model's strongest output, and they are what our downstream workflow uses. Positions often collide, block doors or break constraints. Pair the model with a geometric checker or repair step before using a layout in simulation.

Limitations

• One room at a time. The model reads furniture boxes only, not meshes or appearance.
• No validity guarantee. Collisions, out-of-bounds objects and blocked doors occur, as shown in the table above.
• Licence. The training data mixes sources released for non-commercial research; use this model under the same terms. The base model, Qwen3-8B, is Apache-2.0.