Education Application Implementation Program
DiffSynth-Engine is suitable for use:
- Principle Demonstration: visualize the diffusion process by modifying the num_inference_steps parameter
- comparison experiment: Comparison of generation quality vs. video memory usage with different quant parameters
- secondary development: Explanation of LoRA and other extension techniques based on a clear code structure
Teaching Proposal Program::
- Basic course: using FLUX to generate a single map, demo prompt project
- Advanced Lesson: Debugging Video Parameters of Wan2.1 and Analyzing Inter-frame Continuity
- Lab session: comparing technical implementation of CPU offload and quantization mode
Instructional Configuration Elements::
- Lab equipment recommends 8GB of video memory to start
- Prepare pre-downloaded model files to avoid classroom download delays
- Provide Jupyter Notebook templates to lower the threshold of getting started
This answer comes from the articleDiffSynth-Engine: Open Source Engine for Low-Existing Deployments of FLUX, Wan 2.1The
May not be reproduced without permission:AI productivity tools " How to apply DiffSynth-Engine for AI teaching in educational scenarios?
