i can t solve this error.i never found tensor_format in text.what it should be?
noise_scheduler = DDPMScheduler(
beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000, tensor_format="pt")#, tensor_format="pt"text
DDPMScheduler
num_train_timesteps – number of diffusion steps used to train the model.
beta_start – the starting beta value of inference.
beta_end – the final beta value.
beta_schedule – the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from linear, scaled_linear, or squaredcos_cap_v2.
trained_betas – option to pass an array of betas directly to the constructor to bypass beta_start, beta_end etc.
variance_type – options to clip the variance used when adding noise to the denoised sample. Choose from fixed_small, fixed_small_log, fixed_large, fixed_large_log, learned or learned_range.
clip_sample – option to clip predicted sample between -1 and 1 for numerical stability.
prediction_type – prediction type of the scheduler function, one of epsilon (predicting the noise of the diffusion process), sample (directly predicting the noisy sample`) or v_prediction
i can t solve this error.i never found tensor_format in text.what it should be?
text
DDPMScheduler
num_train_timesteps – number of diffusion steps used to train the model.
beta_start – the starting beta value of inference.
beta_end – the final beta value.
beta_schedule – the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from linear, scaled_linear, or squaredcos_cap_v2.
trained_betas – option to pass an array of betas directly to the constructor to bypass beta_start, beta_end etc.
variance_type – options to clip the variance used when adding noise to the denoised sample. Choose from fixed_small, fixed_small_log, fixed_large, fixed_large_log, learned or learned_range.
clip_sample – option to clip predicted sample between -1 and 1 for numerical stability.
prediction_type – prediction type of the scheduler function, one of epsilon (predicting the noise of the diffusion process), sample (directly predicting the noisy sample`) or v_prediction