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    Models/Depth Estimation/jingheya/lotus-depth-d-v2-0-disparity
    Depth Estimationdiffusersapache-2.0

    lotus-depth-d-v2-0-disparity

    by jingheya

    Identifier
    Model ID
    jingheya/lotus-depth-d-v2-0-disparity

    Tags

    diffuserssafetensorsdepth-estimationarxiv:2409.18124license:apache-2.0diffusers:DirectRegressionPipelineregion:us

    Use lotus-depth-d-v2-0-disparity on Mixpeek

    Build multimodal processing pipelines with this model and others. Extract features, run inference, and set up retrieval in Mixpeek Studio.

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    How It Runs on Mixpeek

    On Mixpeek, lotus-depth-d-v2-0-disparity runs as a managed extractor inside a processing pipeline. Point a bucket of depth estimation data at it, and Mixpeek handles GPU provisioning, batching, retries, and writing the outputs into a vector store you can query.

    Extractor outputs land in the Mixpeek Vector Store (MVS), where you can combine them with retrieval, reranking, and filter stages to build end-to-end search and agent-perception pipelines, no model-serving infrastructure to maintain.