Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
日本語の概要は準備中です。原文の説明を表示しています。
Use and troubleshoot the Dream Textures Blender add-on for Stable Diffusion image generation, texture projection, render passes, model setup, and backend APIs.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Dream Textures is a Blender add-on that integrates Stable Diffusion/Diffusers workflows into Blender for prompt-based image generation, seamless textures, inpainting/outpainting, AI upscaling, 3D texture projection, Cycles render-pass processing, and node-based scene generation.
Load this root skill when a task names Dream Textures, Blender Stable Diffusion, Dream panel generation, Project Dream Texture, Dream Textures render pass, dream_textures backend/API objects, or the repository's model/dependency setup. Then route to the smallest sub-skill below.
dream_textures.0.4.1 from the source snapshot in repo-provenance.md.bl_info: 3.1.0.dream_textures, then install exactly one matching platform/backend requirements file into the add-on's .python_dependencies directory using Blender's Python or the add-on Developer Tools; the setup sub-skill has the step-by-step workflow.Minimal visibility checks:
python scripts/check_dream_textures_environment.py --help
python scripts/check_dream_textures_environment.py --addon-dir /path/to/dream_textures
The helper is a safe diagnostic only; it does not prove full Blender generation.
| User task or symptom | Read this |
|---|---|
Install/enable the add-on, decide release vs source install, choose CUDA/ROCm/MPS/DirectML requirements, diagnose .python_dependencies, manage Hugging Face or DreamStudio credentials, download/link/import models, or fix checkpoint/model-type mismatch | setup-and-models |
| Generate images/textures from prompts, use negative prompts, source-image modify/depth modes, inpaint/outpaint, seamless axes, ControlNet from images, prompt history JSON, or AI upscaling | generation-workflows |
| Project a generated texture onto selected mesh faces, use viewport depth/color, bake projected UVs, enable the Cycles Dream Textures render pass, connect the compositor socket, use the Dream Textures render engine/node tree, or create scene annotation maps | scene-integration |
Implement or debug a custom backend, inspect GenerationArguments, task dataclasses, Backend.generate, DiffusersBackend, schedulers, model-type compatibility, generator Future callbacks, or source-level import errors | backend-and-api |
| The symptom spans several areas or you need first-triage guidance | root troubleshooting |
generation-workflows; 3D Viewport projection, Cycles pass, and render-engine node trees route to scene-integration.setup-and-models; model/task validation and scheduler/source internals route to backend-and-api only when the user asks about API behavior or code.まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
日本語の概要は準備中です。原文の説明を表示しています。
Guide 3DDFA Python inference, geometry rendering, training/evaluation, and optional C++ ONNX workflows for 3D dense face alignment.
日本語の概要は準備中です。原文の説明を表示しています。
Routes 3DDFA_V2 face-alignment setup, still-image demos, video tracking, and ONNX benchmarking workflows.
日本語の概要は準備中です。原文の説明を表示しています。
Operate AB3DMOT 3D multi-object tracking workflows for KITTI and nuScenes data, tracking, evaluation, and visualization.
日本語の概要は準備中です。原文の説明を表示しています。
Use Hugging Face Accelerate for PyTorch training-loop migration, distributed launch/configuration, DeepSpeed/FSDP/TPU backend setup, big-model inference/offload, checkpointing, tracking, and troubleshooting.
日本語の概要は準備中です。原文の説明を表示しています。
Route Acme reinforcement-learning framework tasks across core loops, replay/data, JAX agents, and TensorFlow/Sonnet agents.
日本語の概要は準備中です。原文の説明を表示しています。