Text-to-image (T2I) models contain rich spatial priors. Synthesizing photorealistic, cluttered scenes requires an understanding of geometry, including perspective and relative scale. Prior works adapt T2I models to leverage this prior for depth prediction, but they require dense depth data and involve complex recipes. We propose Modality Forcing, a simple, scalable post-training recipe for joint image-depth generation using a single DiT trained on sparse depth data. Modality Forcing enables conditional and joint generation of image and depth in any permutation by assigning separate noise levels per modality. Per-modality decoders let us train on sparse, real-world depth and achieve strong, generalizable depth prediction. We further show that Modality Forcing inherits the scalability of T2I pre-training: by training a set of T2I models from scratch (300M to 3B parameters), we find that larger models trained on more image data produce more accurate depth. Our strongest model is competitive with state-of-the-art monocular depth estimators and reduces AbsRel by 57% relative to existing joint image-depth generative models. These results provide strong evidence that image generation is a scalable pre-training objective for spatial perception.

Text → RGB + depth, built on FLUX.2. Modality Forcing turns a pretrained text-to-image diffusion transformer into a joint image–depth generator with a simple post-training recipe: each modality is noised independently during training, so at inference any permutation of conditional and joint generation falls out of a single model.

The released model is a single diffusion transformer with three streams — text, image, and depth — that supports three generation modes:

• Abstract
• Overview
• Installation
• Model Weights
• Quick Start
• Usage
  • Joint — text → RGB + depth
  • Image-to-depth — text + image → depth
  • Depth-to-image — text + depth → RGB
• Interactive Demo
• How It Works
• License
• Citation

Requires Python 3.10+ and a CUDA GPU with at least 48 GB of memory (in bf16 the DiT is ~24 GB and the Qwen3-8B text encoder ~16 GB, plus activations — an A100/H100-class card).

Option A — uv (recommended):

git clone https://github.com/Duisterhof/modality-forcing.git
cd modality-forcing

# Pick the torch extra matching your driver (`nvidia-smi` → "CUDA Version")
uv sync --extra cu128

This creates .venv/ from the committed uv.lock, so you get the exact dependency set the release was tested with. Run scripts through uv run (no activation needed) or activate the env:

uv run scripts/joint.py --prompt "a cozy sunlit kitchen with wooden cabinets"
# or: source .venv/bin/activate && python scripts/joint.py ...

Option B — conda / pip:

git clone https://github.com/Duisterhof/modality-forcing.git
cd modality-forcing

conda create -n mofo python=3.12 -y && conda activate mofo
# (a plain venv works the same: python -m venv .venv && source .venv/bin/activate)

# Install a PyTorch build matching your driver's CUDA FIRST — pick the index
# for your CUDA: cu130, cu128, cu126, cu121, cu118. A cu12x wheel runs on any
# >= that 12.x driver, so it need not match exactly.
pip install torch --index-url https://download.pytorch.org/whl/cu128
pip install -r requirements.txt

In both cases, verify the GPU is visible — this must print True:

python -c "import torch; print(torch.cuda.is_available())"   # uv: prefix with `uv run`

The model weights and the FLUX.2 autoencoder are downloaded automatically from the Hugging Face Hub on first run. By default the scripts pull bartduis/modality_forcing and the Qwen3-8B text encoder Qwen/Qwen3-8B. Point --model at a different repo id or a local checkpoint directory (containing config.json and model.safetensors) to override.

python scripts/joint.py --prompt "a cozy sunlit kitchen with wooden cabinets"

This generates an RGB image, a depth map, and a colored 3D point cloud from a single text prompt, and writes them to ./outputs/. (uv users can run any command in this README without activating the env: uv run scripts/joint.py ….)

All three scripts share these options: --prompt, --model, --text-encoder, --num-steps (default 50), --seed, --device, --resolution (default 512; must match the checkpoint's training resolution), --output-dir (default ./outputs), and --compile. Inference runs in bfloat16 (the depth head in fp32).

--compile runs the DiT under torch.compile(mode="reduce-overhead") (CUDA graphs) for a ~1.4× faster sampling loop. The first run spends a few minutes compiling (about a minute once Triton's kernel cache is warm); kernels are cached at ~/.cache/modality-forcing/torchinductor (override with TORCHINDUCTOR_CACHE_DIR), so subsequent runs warm-start in seconds. Worth it for repeated generations; a single one-off run is faster without it.

Each run writes a timestamped subdirectory containing rgb.png, depth_raw.npy (raw depth, relative scale — unit-mean normalized), depth_magma.png (disparity visualization, near = bright), and metadata.json.

python scripts/joint.py --prompt "a cozy sunlit kitchen with wooden cabinets"

Every run also exports a colored point cloud as cloud.glb (for 3D viewers) and cloud.ply (for point-cloud tools).

• --cfg-scale (default 4.0) — classifier-free guidance for the RGB stream.
• --log2-alpha (default 5.0) — tilts the RGB/depth denoising trajectory. > 0 is rgb-first (RGB resolves before depth, giving cleaner depth), 0 is the diagonal joint schedule, < 0 is depth-first.
• --refine-depth — run a second image→depth pass on the generated RGB for sharper, more RGB-consistent geometry (matches the online demo).
• --sor — also apply statistical outlier removal to the point cloud (off by default; can over-trim fine structures).
• --edge-rtol (default 0.04) — depth-edge mask for the point cloud: drops pixels at depth jumps larger than this fraction (removes occlusion-boundary floaters). Lower = more aggressive; 0 disables it.
• --fov-deg (default 65) — vertical field of view used to back-project the cloud.

python scripts/i2d.py --prompt "" --image photo.jpg

The RGB is held fixed and resized to 512×512 (non-square inputs are stretched; the hosted demo letterboxes instead), and guidance is disabled (CFG = 1.0). Leave --prompt empty unless you want to nudge the depth with a caption. It also writes the point cloud (GLB + PLY); --edge-rtol and --fov-deg work the same as in joint mode.

python scripts/d2i.py \
    --prompt "a cozy sunlit kitchen with wooden cabinets" \
    --depth depth.npy

--depth accepts a .npy float array or a 16-bit single-channel PNG/TIFF — e.g. the depth_raw.npy written by a previous joint run. The depth is normalized scale-invariantly before conditioning, so it can be metric or relative. --cfg-scale (default 4.0) guides the RGB stream.

Try the model without installing anything in the online demo. app.py is the same Gradio app; to run it locally:

python app.py        # or: uv run app.py

For a long-lived local demo, COMPILE=1 python app.py torch.compiles the DiT (one-time cost on the first generation, then every later generation benefits).

On HF Spaces, ZeroGPU cannot use torch.compile (it forks a fresh process per GPU call); the app instead compiles the DiT ahead of time with the spaces AoT path, on by default. The compiled package is cached on disk (/data if the Space has persistent storage) and reloads in milliseconds on later boots; set ZEROGPU_AOTI=0 to opt out, or ZEROGPU_AOTI=1 to exercise the same path locally.

• 512×512 generation; depth is a peer stream patchified into the model's depth tokens (16×16 patches), decoded back to a depth map.
• The text encoder draws multi-layer hidden states from Qwen3-8B (mirroring the FLUX.2 [klein] recipe).
• Depth normalization (unit_mean + mip-NeRF 360 contract) is invertible up to the per-sample scale, which is why i2d/d2i depth is relative.

• Code is licensed under Apache-2.0 (see LICENSE). Files derived from the FLUX.2 reference implementation (flux_rgbd/_flux2/, flux_rgbd/dit.py) are Apache-2.0, Copyright Black Forest Labs, with modifications by World Labs.
• Model weights are licensed under CC BY-NC 4.0 (see LICENSE-WEIGHTS) — non-commercial use only.

If you find Modality Forcing useful, please consider giving the repo a star and citing our paper:

@article{duisterhof2026mofo,
  title   = {Modality Forcing for Scalable Spatial Generation},
  author  = {Duisterhof, Bardienus Pieter and Ramanan, Deva and Ichnowski, Jeffrey and Johnson, Justin and Park, Keunhong},
  journal = {arXiv preprint arXiv:2606.13676},
  year    = {2026}
}