DarthAffe/stable-diffusion.cpp
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refactor: remove ununsed encode_video (#1332)

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leejet 2026-03-10 00:36:09 +08:00 committed by GitHub
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commit d6dd6d7b55
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1 changed files with 6 additions and 6 deletions
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src/stable-diffusion.cpp
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@ -2584,14 +2584,14 @@ public:
tile_size_y = get_tile_size(params.tile_size_y, params.rel_size_y, latent_y); tile_size_y = get_tile_size(params.tile_size_y, params.rel_size_y, latent_y);
} }
ggml_tensor* vae_encode(ggml_context* work_ctx, ggml_tensor* x, bool encode_video = false) { ggml_tensor* vae_encode(ggml_context* work_ctx, ggml_tensor* x) {
int64_t t0 = ggml_time_ms(); int64_t t0 = ggml_time_ms();
ggml_tensor* result = nullptr; ggml_tensor* result = nullptr;
const int vae_scale_factor = get_vae_scale_factor(); const int vae_scale_factor = get_vae_scale_factor();
int64_t W = x->ne[0] / vae_scale_factor; int64_t W = x->ne[0] / vae_scale_factor;
int64_t H = x->ne[1] / vae_scale_factor; int64_t H = x->ne[1] / vae_scale_factor;
int64_t C = get_latent_channel(); int64_t C = get_latent_channel();
if (vae_tiling_params.enabled && !encode_video) { if (vae_tiling_params.enabled) {
// TODO wan2.2 vae support? // TODO wan2.2 vae support?
int64_t ne2; int64_t ne2;
int64_t ne3; int64_t ne3;
@ -2619,7 +2619,7 @@ public:
if (!use_tiny_autoencoder) { if (!use_tiny_autoencoder) {
process_vae_input_tensor(x); process_vae_input_tensor(x);
if (vae_tiling_params.enabled && !encode_video) { if (vae_tiling_params.enabled) {
float tile_overlap; float tile_overlap;
int tile_size_x, tile_size_y; int tile_size_x, tile_size_y;
// multiply tile size for encode to keep the compute buffer size consistent // multiply tile size for encode to keep the compute buffer size consistent
@ -2636,7 +2636,7 @@ public:
} }
first_stage_model->free_compute_buffer(); first_stage_model->free_compute_buffer();
} else { } else {
if (vae_tiling_params.enabled && !encode_video) { if (vae_tiling_params.enabled) {
// split latent in 32x32 tiles and compute in several steps // split latent in 32x32 tiles and compute in several steps
auto on_tiling = [&](ggml_tensor* in, ggml_tensor* out, bool init) { auto on_tiling = [&](ggml_tensor* in, ggml_tensor* out, bool init) {
return tae_first_stage->compute(n_threads, in, false, &out, nullptr); return tae_first_stage->compute(n_threads, in, false, &out, nullptr);
@ -2712,8 +2712,8 @@ public:
return latent; return latent;
} }
ggml_tensor* encode_first_stage(ggml_context* work_ctx, ggml_tensor* x, bool encode_video = false) { ggml_tensor* encode_first_stage(ggml_context* work_ctx, ggml_tensor* x) {
ggml_tensor* vae_output = vae_encode(work_ctx, x, encode_video); ggml_tensor* vae_output = vae_encode(work_ctx, x);
return get_first_stage_encoding(work_ctx, vae_output); return get_first_stage_encoding(work_ctx, vae_output);
} }