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The current random generator is fixed by the designated seed number (default: 42). Although this is fine with generating a latent image, it is quite not right for ancestral sampling. The ancestral sampling adds a random noise at every step, but due to the fixed seed, every execution generates the same image, which should not happen.
The text was updated successfully, but these errors were encountered:
Looking at rng.hpp and the de-noising code I believe this is already implemented correctly. Euler_a and dpm++2s_a get their random noise using the ggml_tensor_set_f32_randn function which sources it's rng from the RNG randn class method which in turn uses the base seed. Therefore, each de-noising step is "random" relative to each other de-noising step, but deterministic to the overall seed which I believe is the desired behavior.
The current random generator is fixed by the designated seed number (default: 42). Although this is fine with generating a latent image, it is quite not right for ancestral sampling. The ancestral sampling adds a random noise at every step, but due to the fixed seed, every execution generates the same image, which should not happen.
The text was updated successfully, but these errors were encountered: