initial files from sdcpp (not working)

otherarch/sdcpp/denoiser.hpp

@@ -0,0 +1,125 @@
+#ifndef __DENOISER_HPP__
+#define __DENOISER_HPP__
+
+#include "ggml_extend.hpp"
+
+/*================================================= CompVisDenoiser ==================================================*/
+
+// Ref: https://github.com/crowsonkb/k-diffusion/blob/master/k_diffusion/external.py
+
+#define TIMESTEPS 1000
+
+struct SigmaSchedule {
+    float alphas_cumprod[TIMESTEPS];
+    float sigmas[TIMESTEPS];
+    float log_sigmas[TIMESTEPS];
+
+    virtual std::vector<float> get_sigmas(uint32_t n) = 0;
+
+    float sigma_to_t(float sigma) {
+        float log_sigma = std::log(sigma);
+        std::vector<float> dists;
+        dists.reserve(TIMESTEPS);
+        for (float log_sigma_val : log_sigmas) {
+            dists.push_back(log_sigma - log_sigma_val);
+        }
+
+        int low_idx = 0;
+        for (size_t i = 0; i < TIMESTEPS; i++) {
+            if (dists[i] >= 0) {
+                low_idx++;
+            }
+        }
+        low_idx      = std::min(std::max(low_idx - 1, 0), TIMESTEPS - 2);
+        int high_idx = low_idx + 1;
+
+        float low  = log_sigmas[low_idx];
+        float high = log_sigmas[high_idx];
+        float w    = (low - log_sigma) / (low - high);
+        w          = std::max(0.f, std::min(1.f, w));
+        float t    = (1.0f - w) * low_idx + w * high_idx;
+
+        return t;
+    }
+
+    float t_to_sigma(float t) {
+        int low_idx     = static_cast<int>(std::floor(t));
+        int high_idx    = static_cast<int>(std::ceil(t));
+        float w         = t - static_cast<float>(low_idx);
+        float log_sigma = (1.0f - w) * log_sigmas[low_idx] + w * log_sigmas[high_idx];
+        return std::exp(log_sigma);
+    }
+};
+
+struct DiscreteSchedule : SigmaSchedule {
+    std::vector<float> get_sigmas(uint32_t n) {
+        std::vector<float> result;
+
+        int t_max = TIMESTEPS - 1;
+
+        if (n == 0) {
+            return result;
+        } else if (n == 1) {
+            result.push_back(t_to_sigma((float)t_max));
+            result.push_back(0);
+            return result;
+        }
+
+        float step = static_cast<float>(t_max) / static_cast<float>(n - 1);
+        for (uint32_t i = 0; i < n; ++i) {
+            float t = t_max - step * i;
+            result.push_back(t_to_sigma(t));
+        }
+        result.push_back(0);
+        return result;
+    }
+};
+
+struct KarrasSchedule : SigmaSchedule {
+    std::vector<float> get_sigmas(uint32_t n) {
+        // These *COULD* be function arguments here,
+        // but does anybody ever bother to touch them?
+        float sigma_min = 0.1f;
+        float sigma_max = 10.f;
+        float rho       = 7.f;
+
+        std::vector<float> result(n + 1);
+
+        float min_inv_rho = pow(sigma_min, (1.f / rho));
+        float max_inv_rho = pow(sigma_max, (1.f / rho));
+        for (uint32_t i = 0; i < n; i++) {
+            // Eq. (5) from Karras et al 2022
+            result[i] = pow(max_inv_rho + (float)i / ((float)n - 1.f) * (min_inv_rho - max_inv_rho), rho);
+        }
+        result[n] = 0.;
+        return result;
+    }
+};
+
+struct Denoiser {
+    std::shared_ptr<SigmaSchedule> schedule              = std::make_shared<DiscreteSchedule>();
+    virtual std::vector<float> get_scalings(float sigma) = 0;
+};
+
+struct CompVisDenoiser : public Denoiser {
+    float sigma_data = 1.0f;
+
+    std::vector<float> get_scalings(float sigma) {
+        float c_out = -sigma;
+        float c_in  = 1.0f / std::sqrt(sigma * sigma + sigma_data * sigma_data);
+        return {c_out, c_in};
+    }
+};
+
+struct CompVisVDenoiser : public Denoiser {
+    float sigma_data = 1.0f;
+
+    std::vector<float> get_scalings(float sigma) {
+        float c_skip = sigma_data * sigma_data / (sigma * sigma + sigma_data * sigma_data);
+        float c_out  = -sigma * sigma_data / std::sqrt(sigma * sigma + sigma_data * sigma_data);
+        float c_in   = 1.0f / std::sqrt(sigma * sigma + sigma_data * sigma_data);
+        return {c_skip, c_out, c_in};
+    }
+};
+
+#endif  // __DENOISER_HPP__