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name: deployment description: "Guides Zero123Plus demo, serving, and Cog deployment workflows, including Gradio, Streamlit, checkpoints, UI options, and deployment failures." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Zero123Plus Deployment

Use this sub-skill when a task is about launching, adapting, packaging, or troubleshooting Zero123Plus demos and service surfaces: Streamlit, Gradio, Docker/Gitpod-style environments, Cog/Replicate predictors, checkpoint caches, ports, sharing, queues, and UI options.

Do not use this sub-skill as the main source for image-to-multiview algorithm details, ControlNets, camera poses, or batch generation code. Route those questions to the sibling generation sub-skill at ../generation/SKILL.md, then return here for serving and deployment decisions.

Read first

• references/deployment.md: deployment surfaces, dependency/bootstrap patterns, Docker/Gitpod/Cog behavior, and how the surfaces differ.
• references/demo-options.md: Streamlit and Gradio UI inputs, preprocessing options, examples, and output behavior.
• references/troubleshooting.md: deployment failure modes for checkpoints, rembg/SAM, ports, queues, Cog weights, pget, CUDA, and source demo side effects.

Bundled deployment templates

• scripts/launch_gradio_demo.py: a compact, self-contained Gradio launcher template. It avoids model downloads by default; pass --allow-download only when network/model fetching is approved.
• scripts/cog_predictor.py: a self-contained Cog predictor template with configurable cache/model paths and no model loading on import.

Routing procedure

1. Identify the requested surface: local Streamlit demo, Gradio demo, container image, Gitpod-style workspace, Cog/Replicate predictor, or a custom service.
2. Check whether the user has approved network downloads and whether the runtime has a CUDA GPU. Stop or ask before triggering Hugging Face model downloads, SAM checkpoint downloads, rembg/ONNX model downloads, or Cog weight archive fetches.
3. For a safer local Gradio launch, prefer the bundled launcher template over importing source UI modules. For Cog, adapt the bundled predictor template and configure its environment variables instead of relying on checkout-relative paths.
4. For UI behavior, input preprocessing, background removal, and output layout, read references/demo-options.md. For exact pipeline arguments and generated view semantics, route to ../generation/SKILL.md.
5. If a failure involves missing CUDA, unavailable weights, missing SAM/rembg components, blocked Gradio share tunnels, or Cog cache/download issues, use references/troubleshooting.md before proposing code changes.

Stop conditions

• No CUDA GPU is available for an actual generation-backed demo or Cog run.
• The required model/checkpoint/cache is absent and downloads are not approved.
• Background removal is required but rembg, segment_anything, or the SAM checkpoint is missing and the user has not approved installing/downloading it.
• The request is really about generation APIs, ControlNet variants, or camera outputs; route to the generation sub-skill instead of duplicating that logic here.