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Diffusion models for 2D/3D generation
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Module 11
Model Optimization and Deployment
1
Quantization and Mixed-Precision for Optimized Inference
Apply quantization and mixed-precision techniques to optimize inference speed and memory
2
Progressive Distillation for Faster Student Models
Implement progressive distillation to train a faster student model from a multi-step teacher
3
Distilled Models vs. Multi-Step Samplers: Performance Comparison
Compare the performance (speed vs. quality) of distilled models against multi-step samplers
4
Optimizing Inference Throughput with Batching Strategies
Implement and evaluate batching strategies to maximize inference throughput
5
Optimizing Memory in Diffusion Models
Profile and debug memory bottlenecks in diffusion training and inference pipelines
6
Evaluating Generative Models: Metrics and Trade-offs
Compare standard evaluation metrics (e.g., FID, IS, CLIP Score) and analyze their respective strengths and weaknesses
7
Automated Evaluation Pipeline for Generated Assets
Implement an automated evaluation pipeline to compute quality and alignment scores for generated assets
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Advanced 3D Generation Techniques
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