python fast_ddpm_main.py --config {DATASET}.yml --dataset {DATASET_NAME} --exp {PROJECT_PATH} --doc {MODEL_NAME} --scheduler_type {SAMPLING STRATEGY} --timesteps {STEPS} python fast_ddpm_main.py --config {DATASET}.yml --dataset {DATASET_NAME} --exp {PROJECT_PATH} --doc {MODEL_...
python fast_ddpm_main.py --config {DATASET}.yml --dataset {DATASET_NAME} --exp {PROJECT_PATH} --doc {MODEL_NAME} --scheduler_type {SAMPLING STRATEGY} --timesteps {STEPS} python fast_ddpm_main.py --config {DATASET}.yml --dataset {DATASET_NAME} --exp {PROJECT_PATH} --doc {MODEL_...
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虽然DDPM 本质上是基于梯度的模型,但要保证高样本质量,通常需要花费数百到数千个去噪步骤。当减少采样步骤时,会观察到由于可感知的背景噪声导致的质量明显下降。 在这项工作中,我们提出了 FastDiff,一种用于高质量语音合成的快速条件扩散模型。为了提高音频质量,FastDiff 采用了一系列不同感受野的模式的时间感知位置变...
在今年的IJCAI 2022和ACM-MM 2022上,浙江大学的两篇论文提出了全新的语音合成条件扩散模型 FastDiff和ProDiff,能大幅减少 DDPM 每步降噪所需时间在解决其原来的速度瓶颈的同时,仍能生成出与人声难以分辨的高质量语音, 并挖掘其应用在语音合成上的潜力。FastDiff, ProDiff分别在声码器和语音合成上得到验证,揭示了扩...
Note that FID here is computed with 250 DDPM sampling steps, with the mse VAE decoder and without guidance (cfg-scale=1). Improved Training Performance In comparison to the original implementation, we implement a selection of training speed acceleration and memory saving features including gradient ...
generation.sampler_name 调度名(unpic, euler_a,euler,ddim,ddpm,deis,dpm2,dpm2-a,dpm++_2m,dpm++_2m_karras,heun,heun_karras,lms,pndm:w) euler_a generation.sampling_steps 生成的步骤数 25 generation.batch_size 一次生成几张 1 generation.batch_count 生成几次 1 generation.cfg_scale 随机或贴合...
Note that DPM-Solver only needs the noise prediction model (the ϵ θ ( x t , t ) model, also as known as the "mean" model), so for diffusion models which predict both "mean" and "variance" (such as improved-DDPM), you need to firstly define another function by yourself to ...
Notes and plans for fastdiffusion course. Contribute to fastai/fastdiffusion development by creating an account on GitHub.