OriginLab Game-Depth
Collection
Dense depth from game engines: dataset + Lotus-recipe pretrained and NYU-fine-tuned models. • 2 items • Updated • 2
pip install -U diffusers transformers accelerate
import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("originlab/lotus-game-depth", dtype=torch.bfloat16, device_map="cuda")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]Request access to OriginLab Lotus Game-Depth models
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OriginLab Lotus Game-Depth (pretrained + NYU fine-tuned)
Website: originlab.ai
Two Lotus-recipe latent-diffusion depth checkpoints (SD2-base UNet, 8-channel conv_in, single-step x0 at t=999, trunc_disparity), in one repo:
pretrained/- trained from scratch on the OriginLab Game-Depth dataset (game-engine z-buffers), no real data. Zero-shot KITTI AbsRel 0.191 (Lotus 0.224, Marigold 0.244).nyu-ft/- the above fine-tuned on real NYU Depth V2. NYU AbsRel 0.116, on par with a fairly-tuned Lotus baseline (0.115) using 0% indoor pretraining data and ~4x fewer frames.
Dataset: https://huggingface.co/datasets/originlab/game-depth
Load
from diffusers import UNet2DConditionModel
# game-pretrained
unet = UNet2DConditionModel.from_pretrained("originlab/lotus-game-depth", subfolder="pretrained/unet")
# NYU fine-tuned
unet = UNet2DConditionModel.from_pretrained("originlab/lotus-game-depth", subfolder="nyu-ft/unet")
Each subfolder contains the full pipeline (unet, vae, text_encoder, scheduler, ...); run with the Lotus single-step depth pipeline.
License
Non-commercial research use with a model-release clause (see LICENSE).
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