# PyTorch 3D Diffusion Model with Raw File I/O

> Implement a simple PyTorch diffusion neural network to generate 16x16x16 matrices based on text prompts derived from filenames, including dataset loading from .raw files and saving outputs.

- Skill: `ecnu-icalk/pytorch-3d-diffusion-model-with-raw-file-i-o` (Agent Skill)
- Install (CLI): `npx skillmds@latest add ecnu-icalk/pytorch-3d-diffusion-model-with-raw-file-i-o`
- Raw SKILL.md: https://api.skillmd.com/api/skills/ecnu-icalk/pytorch-3d-diffusion-model-with-raw-file-i-o/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: ECNU-ICALK (https://skillmd.com/u/ecnu-icalk)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/ecnu-icalk/pytorch-3d-diffusion-model-with-raw-file-i-o

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# PyTorch 3D Diffusion Model with Raw File I/O

Implement a simple PyTorch diffusion neural network to generate 16x16x16 matrices based on text prompts derived from filenames, including dataset loading from .raw files and saving outputs.

## Prompt

# Role & Objective
Act as a Python/PyTorch developer. Write a simple diffusion neural network to generate 16x16x16 3D matrices based on text prompts.

# Operational Rules & Constraints
- Use PyTorch for the implementation.
- The network must be able to receive a 16x16x16 noise or input matrix paired with a text prompt.
- Provide two specific functions: `train` and `generate`.
- Implement dataset uploading from a "dataset/" folder.
- Save generated results to an "outputs/" directory.
- Matrix files must use the .raw extension.
- The text prompt for a matrix is defined as the filename (the part before the .raw extension).
- Include a script to generate pseudo datapoints for training (e.g., 500 random matrices with random word filenames).

# Anti-Patterns
- Do not use complex architectures unless requested; keep the model simple as per the initial request.
- Do not ignore the specific file extension (.raw) or the filename-to-prompt mapping logic.

## Triggers

- Write simple diffusion neural network on Python
- generate 16x16x16 matrixes by text prompt
- PyTorch diffusion model raw files
- dataset uploading from dataset folder
- generate pseudo datapoints for diffusion network