Pytorch 3d Diffusion Model With Filename Prompt Mapping

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Five audit layers

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Audit 0.4.1 · source sha 04e912cff54b · 14 Oct 2026Report an issue or request a re-scan

• PublisherECNU-ICALK GitHub organizationOwner of the repository this skill is read from
• Source folderECNU-ICALK/AutoSkill/SkillBank/ConvSkill/english_gpt4_8_GLM4.7/pytorch-3d-diffusion-model-with-filename-prompt-mappingThe exact folder we audited
• LicenceNot declaredAs the repository states it
• Commit pinned for this verdict04e912cff54bA new audit runs when the source changes
• Repository statusNot archivedLast push 157 days ago
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Its description: 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…

---
id: "f2fc9b30-5e3c-40e1-a3c0-a0587e11d0aa"
name: "PyTorch 3D Diffusion Model with Filename-Prompt Mapping"
description: "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."
version: "0.1.0"
tags:
  - "pytorch"
  - "diffusion"
  - "3d-matrix"
  - "raw-files"
  - "data-loading"
triggers:
  - "write pytorch 3d diffusion model"
  - "generate 16x16x16 matrices from text"
  - "load raw files as prompts pytorch"
  - "simple diffusion network python"
  - "filename as text prompt dataset"
---

# 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, 16, 16).

3. **Transform Handling**:
   - Ensure data is converted to a tensor (e.g., using `torch.from_numpy`).
   - **Critical**: Do not apply `torchvision.transforms.ToTensor()` to data that is already a PyTorch tensor. Use a custom transform or conditional logic to avoid `AttributeError: 'Tensor' object has no attribute 'tobytes'`.

4. **Output Handling**:
   - Save generated matrices to an `outputs/` directory.
   - Create the directory if it does not exist.
   - Use the text prompt to name the output file (e.g., `{prompt}.raw`).

5. **Functions**:
   - Implement a `train(model, data_loader, optimizer, epochs)` function.
   - Implement a `generate(model, seed_matrix, prompt_embedding)` function.
   - Include a `save_generated` utility function.

# Anti-Patterns
- Do not use complex NLP models for text embedding unless explicitly requested; treat the filename string as the prompt identifier.
- Do not apply `ToTensor` transform on already tensorized data.
- Do not hardcode specific file paths other than `dataset/` and `outputs/`.

## Triggers

- write pytorch 3d diffusion model
- generate 16x16x16 matrices from text
- load raw files as prompts pytorch
- simple diffusion network python
- filename as text prompt dataset

Excerpt, first 3,206 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

git clone https://github.com/ECNU-ICALK/AutoSkill
git -C AutoSkill checkout 04e912cff54b

Works in

1. 2026-10-14
   ✓SAFE B 89 / 100

   first audit · source 04e912cff54b

Questions about Pytorch 3d Diffusion Model With Filename Prompt Mapping

• github.com/ECNU-ICALK/AutoSkill/tree/main/SkillBank/ConvSkill/en — the audited source at commit 04e912cff54b and every finding aboveObserved · 14 Oct 2026

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