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tsa18/q-arvd
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Python
License ·
Apache-2.0
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Using Q-ARVD, run the chunk-wise sensitivity measurement on my video model checkpoint and show me which frame ranges are most sensitive to quantization errors.
Walk me through the four-step Q-ARVD pipeline: sensitivity measurement, quantization training, sample generation, and metric evaluation for my video diffusion model.
I want to use Q-ARVD's adaptive two-scale outlier strategy. Show me how to configure and run the quantization training step on my model.
Compare video quality metrics between the original and Q-ARVD-quantized versions of my model using the provided evaluation scripts and interpret the results.
Full explanation on explaingit →
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