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fix: adjust timestep calculations for DDIM and TCD #665

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fix: adjust timestep calculations for DDIM and TCD #665

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18 changes: 12 additions & 6 deletions denoiser.hpp
View file
Original file line number Diff line number Diff line change
@@ -474,6 +474,7 @@ static void sample_k_diffusion(sample_method_t method,
ggml_context* work_ctx,
ggml_tensor* x,
std::vector<float> sigmas,
int initial_step,
std::shared_ptr<RNG> rng,
float eta) {
size_t steps = sigmas.size() - 1;
@@ -1060,10 +1061,14 @@ static void sample_k_diffusion(sample_method_t method,
// x_t"
// - pred_prev_sample -> "x_t-1"
int timestep =
roundf(TIMESTEPS -
i * ((float)TIMESTEPS / steps)) - 1;
TIMESTEPS - 1 -
(int)roundf((initial_step + i) *
(TIMESTEPS / float(initial_step + steps)));
// 1. get previous step value (=t-1)
int prev_timestep = timestep - TIMESTEPS / steps;
int prev_timestep =
TIMESTEPS - 1 -
(int)roundf((initial_step + i + 1) *
(TIMESTEPS / float(initial_step + steps)));
// The sigma here is chosen to cause the
// CompVisDenoiser to produce t = timestep
float sigma = compvis_sigmas[timestep];
@@ -1236,12 +1241,13 @@ static void sample_k_diffusion(sample_method_t method,
// Analytic form for TCD timesteps
int timestep = TIMESTEPS - 1 -
(TIMESTEPS / original_steps) *
(int)floor(i * ((float)original_steps / steps));
(int)floor((initial_step + i) *
((float)original_steps / (initial_step + steps)));
// 1. get previous step value
int prev_timestep = i >= steps - 1 ? 0 :
TIMESTEPS - 1 - (TIMESTEPS / original_steps) *
(int)floor((i + 1) *
((float)original_steps / steps));
(int)floor((initial_step + i + 1) *
((float)original_steps / (initial_step + steps)));
// Here timestep_s is tau_n' in Algorithm 4. The _s
// notation appears to be that from C. Lu,
// "DPM-Solver: A Fast ODE Solver for Diffusion
11 changes: 9 additions & 2 deletions stable-diffusion.cpp
View file
Original file line number Diff line number Diff line change
@@ -798,6 +798,7 @@ class StableDiffusionGGML {
float eta,
sample_method_t method,
const std::vector<float>& sigmas,
int initial_step,
int start_merge_step,
SDCondition id_cond,
std::vector<int> skip_layers = {},
@@ -991,7 +992,7 @@ class StableDiffusionGGML {
return denoised;
};

sample_k_diffusion(method, denoise, work_ctx, x, sigmas, rng, eta);
sample_k_diffusion(method, denoise, work_ctx, x, sigmas, initial_step, rng, eta);

x = denoiser->inverse_noise_scaling(sigmas[sigmas.size() - 1], x);

@@ -1202,6 +1203,7 @@ sd_image_t* generate_image(sd_ctx_t* sd_ctx,
int height,
enum sample_method_t sample_method,
const std::vector<float>& sigmas,
int initial_step,
int64_t seed,
int batch_count,
const sd_image_t* control_cond,
@@ -1464,6 +1466,7 @@ sd_image_t* generate_image(sd_ctx_t* sd_ctx,
eta,
sample_method,
sigmas,
initial_step,
start_merge_step,
id_cond,
skip_layers,
@@ -1611,6 +1614,7 @@ sd_image_t* txt2img(sd_ctx_t* sd_ctx,
height,
sample_method,
sigmas,
0,
seed,
batch_count,
control_cond,
@@ -1775,8 +1779,9 @@ sd_image_t* img2img(sd_ctx_t* sd_ctx,
if (t_enc == sample_steps)
t_enc--;
LOG_INFO("target t_enc is %zu steps", t_enc);
int initial_step = sample_steps - t_enc - 1;
std::vector<float> sigma_sched;
sigma_sched.assign(sigmas.begin() + sample_steps - t_enc - 1, sigmas.end());
sigma_sched.assign(sigmas.begin() + initial_step, sigmas.end());

sd_image_t* result_images = generate_image(sd_ctx,
work_ctx,
@@ -1791,6 +1796,7 @@ sd_image_t* img2img(sd_ctx_t* sd_ctx,
height,
sample_method,
sigma_sched,
initial_step,
seed,
batch_count,
control_cond,
@@ -1903,6 +1909,7 @@ SD_API sd_image_t* img2vid(sd_ctx_t* sd_ctx,
0.f,
sample_method,
sigmas,
0,
-1,
SDCondition(NULL, NULL, NULL));