PyTorch 3D Diffusion Model with Filename-Prompt Mapping
Develop a PyTorch-based simple diffusion neural network to generate 16x16x16 matrices. The implementation must include a custom dataset loader that reads .raw files from a 'dataset/' directory, extracts the text prompt from the filename, and saves generated results to an 'outputs/' directory.
npx skills add ECNU-ICALK/AutoSkill --skill pytorch-3d-diffusion-model-with-filename-prompt-mapping --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# PyTorch 3D Diffusion Model with Filename-Prompt Mapping Develop a PyTorch-based simple diffusion neural network to generate 16x16x16 matrices. The implementation must include a custom dataset loader that reads .raw files from a 'dataset/' directory, extracts the text prompt from the filename, and saves generated results to an 'outputs/' directory. ## Prompt # Role & Objective You are a PyTorch expert specializing in generative models. Write a Python script implementing a simple 3D diffusion neural network capable of generating 16x16x16 matrices based on text prompts derived from filenames. # Operational Rules & Constraints 1. **Model Architecture**: - Use a simplified UNet-like architecture. - Utilize `nn.Conv3d` and `nn.ConvTranspose3d` layers. - Input and output tensor shapes must be (1, 16, 16, 16). 2. **Data Loading**: - Create a custom `Dataset` class inheriting from `torch.utils.data.Dataset`. - **Source Directory**: Load data from `dataset/`. - **File Format**: Files have a `.raw` extension containing `float32` binary data. - **Prompt Extraction**: The text prompt is the filename stem (the part before the `.raw` extension). - **Data Shape**: Reshape loaded data to (1, 16,
- Prompt
- Triggers
What does the PyTorch 3D Diffusion Model with Filename-Prompt Mapping skill do?
Develop a PyTorch-based simple diffusion neural network to generate 16x16x16 matrices. The implementation must include a custom dataset loader that reads .raw files from a 'dataset/' directory, extracts the text prompt from the filename, and saves generated results to an 'outputs/' directory.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill pytorch-3d-diffusion-model-with-filename-prompt-mapping --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From ECNU-ICALK/AutoSkill, a repository with 539 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.