mirror of
https://github.com/leejet/stable-diffusion.cpp.git
synced 2025-12-13 05:48:56 +00:00
add qwen image i2i pipline
This commit is contained in:
leejet
parent
477911fb20
commit
feb027958f
@ -1007,7 +1007,7 @@ public:
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ggml_tensor_scale(noise, augmentation_level);
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ggml_tensor_scale(noise, augmentation_level);
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ggml_tensor_add(init_img, noise);
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ggml_tensor_add(init_img, noise);
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}
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}
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ggml_tensor* moments = encode_first_stage(work_ctx, init_img);
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ggml_tensor* moments = vae_encode(work_ctx, init_img);
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c_concat = get_first_stage_encoding(work_ctx, moments);
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c_concat = get_first_stage_encoding(work_ctx, moments);
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}
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}
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}
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}
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@ -1316,116 +1316,6 @@ public:
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return x;
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return x;
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}
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}
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// ldm.models.diffusion.ddpm.LatentDiffusion.get_first_stage_encoding
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ggml_tensor* get_first_stage_encoding(ggml_context* work_ctx, ggml_tensor* moments) {
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// ldm.modules.distributions.distributions.DiagonalGaussianDistribution.sample
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ggml_tensor* latent = ggml_new_tensor_4d(work_ctx, moments->type, moments->ne[0], moments->ne[1], moments->ne[2] / 2, moments->ne[3]);
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struct ggml_tensor* noise = ggml_dup_tensor(work_ctx, latent);
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ggml_tensor_set_f32_randn(noise, rng);
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{
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float mean = 0;
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float logvar = 0;
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float value = 0;
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float std_ = 0;
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for (int i = 0; i < latent->ne[3]; i++) {
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for (int j = 0; j < latent->ne[2]; j++) {
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for (int k = 0; k < latent->ne[1]; k++) {
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for (int l = 0; l < latent->ne[0]; l++) {
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mean = ggml_tensor_get_f32(moments, l, k, j, i);
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logvar = ggml_tensor_get_f32(moments, l, k, j + (int)latent->ne[2], i);
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logvar = std::max(-30.0f, std::min(logvar, 20.0f));
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std_ = std::exp(0.5f * logvar);
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value = mean + std_ * ggml_tensor_get_f32(noise, l, k, j, i);
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value = value * scale_factor;
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// printf("%d %d %d %d -> %f\n", i, j, k, l, value);
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ggml_tensor_set_f32(latent, value, l, k, j, i);
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}
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}
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}
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}
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}
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return latent;
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}
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void get_tile_sizes(int& tile_size_x,
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int& tile_size_y,
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float& tile_overlap,
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const sd_tiling_params_t& params,
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int latent_x,
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int latent_y,
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float encoding_factor = 1.0f) {
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tile_overlap = std::max(std::min(params.target_overlap, 0.5f), 0.0f);
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auto get_tile_size = [&](int requested_size, float factor, int latent_size) {
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const int default_tile_size = 32;
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const int min_tile_dimension = 4;
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int tile_size = default_tile_size;
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// factor <= 1 means simple fraction of the latent dimension
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// factor > 1 means number of tiles across that dimension
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if (factor > 0.f) {
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if (factor > 1.0)
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factor = 1 / (factor - factor * tile_overlap + tile_overlap);
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tile_size = std::round(latent_size * factor);
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} else if (requested_size >= min_tile_dimension) {
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tile_size = requested_size;
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}
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tile_size *= encoding_factor;
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return std::max(std::min(tile_size, latent_size), min_tile_dimension);
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};
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tile_size_x = get_tile_size(params.tile_size_x, params.rel_size_x, latent_x);
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tile_size_y = get_tile_size(params.tile_size_y, params.rel_size_y, latent_y);
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}
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ggml_tensor* encode_first_stage(ggml_context* work_ctx, ggml_tensor* x, bool encode_video = false) {
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int64_t t0 = ggml_time_ms();
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ggml_tensor* result = NULL;
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int W = x->ne[0] / 8;
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int H = x->ne[1] / 8;
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if (vae_tiling_params.enabled && !encode_video) {
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// TODO wan2.2 vae support?
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int C = sd_version_is_dit(version) ? 16 : 4;
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if (!use_tiny_autoencoder) {
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C *= 2;
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}
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result = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, W, H, C, x->ne[3]);
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}
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if (!use_tiny_autoencoder) {
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process_vae_input_tensor(x);
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if (vae_tiling_params.enabled && !encode_video) {
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float tile_overlap;
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int tile_size_x, tile_size_y;
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// multiply tile size for encode to keep the compute buffer size consistent
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get_tile_sizes(tile_size_x, tile_size_y, tile_overlap, vae_tiling_params, W, H, 1.30539f);
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LOG_DEBUG("VAE Tile size: %dx%d", tile_size_x, tile_size_y);
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auto on_tiling = [&](ggml_tensor* in, ggml_tensor* out, bool init) {
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first_stage_model->compute(n_threads, in, false, &out, work_ctx);
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};
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sd_tiling_non_square(x, result, 8, tile_size_x, tile_size_y, tile_overlap, on_tiling);
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} else {
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first_stage_model->compute(n_threads, x, false, &result, work_ctx);
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}
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first_stage_model->free_compute_buffer();
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} else {
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if (vae_tiling_params.enabled && !encode_video) {
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// split latent in 32x32 tiles and compute in several steps
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auto on_tiling = [&](ggml_tensor* in, ggml_tensor* out, bool init) {
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tae_first_stage->compute(n_threads, in, false, &out, NULL);
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};
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sd_tiling(x, result, 8, 64, 0.5f, on_tiling);
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} else {
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tae_first_stage->compute(n_threads, x, false, &result, work_ctx);
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}
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tae_first_stage->free_compute_buffer();
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}
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int64_t t1 = ggml_time_ms();
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LOG_DEBUG("computing vae encode graph completed, taking %.2fs", (t1 - t0) * 1.0f / 1000);
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return result;
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}
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void process_latent_in(ggml_tensor* latent) {
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void process_latent_in(ggml_tensor* latent) {
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if (sd_version_is_wan(version) || sd_version_is_qwen_image(version)) {
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if (sd_version_is_wan(version) || sd_version_is_qwen_image(version)) {
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GGML_ASSERT(latent->ne[3] == 16 || latent->ne[3] == 48);
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GGML_ASSERT(latent->ne[3] == 16 || latent->ne[3] == 48);
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@ -1506,6 +1396,146 @@ public:
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}
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}
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}
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}
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void get_tile_sizes(int& tile_size_x,
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int& tile_size_y,
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float& tile_overlap,
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const sd_tiling_params_t& params,
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int latent_x,
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int latent_y,
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float encoding_factor = 1.0f) {
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tile_overlap = std::max(std::min(params.target_overlap, 0.5f), 0.0f);
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auto get_tile_size = [&](int requested_size, float factor, int latent_size) {
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const int default_tile_size = 32;
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const int min_tile_dimension = 4;
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int tile_size = default_tile_size;
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// factor <= 1 means simple fraction of the latent dimension
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// factor > 1 means number of tiles across that dimension
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if (factor > 0.f) {
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if (factor > 1.0)
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factor = 1 / (factor - factor * tile_overlap + tile_overlap);
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tile_size = std::round(latent_size * factor);
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} else if (requested_size >= min_tile_dimension) {
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tile_size = requested_size;
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}
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tile_size *= encoding_factor;
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return std::max(std::min(tile_size, latent_size), min_tile_dimension);
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};
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tile_size_x = get_tile_size(params.tile_size_x, params.rel_size_x, latent_x);
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tile_size_y = get_tile_size(params.tile_size_y, params.rel_size_y, latent_y);
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}
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ggml_tensor* vae_encode(ggml_context* work_ctx, ggml_tensor* x, bool encode_video = false) {
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int64_t t0 = ggml_time_ms();
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ggml_tensor* result = NULL;
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int W = x->ne[0] / 8;
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int H = x->ne[1] / 8;
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if (vae_tiling_params.enabled && !encode_video) {
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// TODO wan2.2 vae support?
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int C = sd_version_is_dit(version) ? 16 : 4;
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if (!use_tiny_autoencoder) {
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C *= 2;
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}
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result = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, W, H, C, x->ne[3]);
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}
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if (sd_version_is_qwen_image(version)) {
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x = ggml_reshape_4d(work_ctx, x, x->ne[0], x->ne[1], 1, x->ne[2] * x->ne[3]);
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}
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if (!use_tiny_autoencoder) {
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process_vae_input_tensor(x);
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if (vae_tiling_params.enabled && !encode_video) {
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float tile_overlap;
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int tile_size_x, tile_size_y;
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// multiply tile size for encode to keep the compute buffer size consistent
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get_tile_sizes(tile_size_x, tile_size_y, tile_overlap, vae_tiling_params, W, H, 1.30539f);
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LOG_DEBUG("VAE Tile size: %dx%d", tile_size_x, tile_size_y);
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auto on_tiling = [&](ggml_tensor* in, ggml_tensor* out, bool init) {
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first_stage_model->compute(n_threads, in, false, &out, work_ctx);
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};
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sd_tiling_non_square(x, result, 8, tile_size_x, tile_size_y, tile_overlap, on_tiling);
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} else {
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first_stage_model->compute(n_threads, x, false, &result, work_ctx);
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}
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first_stage_model->free_compute_buffer();
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} else {
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if (vae_tiling_params.enabled && !encode_video) {
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// split latent in 32x32 tiles and compute in several steps
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auto on_tiling = [&](ggml_tensor* in, ggml_tensor* out, bool init) {
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tae_first_stage->compute(n_threads, in, false, &out, NULL);
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};
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sd_tiling(x, result, 8, 64, 0.5f, on_tiling);
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} else {
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tae_first_stage->compute(n_threads, x, false, &result, work_ctx);
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}
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tae_first_stage->free_compute_buffer();
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}
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int64_t t1 = ggml_time_ms();
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LOG_DEBUG("computing vae encode graph completed, taking %.2fs", (t1 - t0) * 1.0f / 1000);
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return result;
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}
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ggml_tensor* gaussian_latent_sample(ggml_context* work_ctx, ggml_tensor* moments) {
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// ldm.modules.distributions.distributions.DiagonalGaussianDistribution.sample
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ggml_tensor* latent = ggml_new_tensor_4d(work_ctx, moments->type, moments->ne[0], moments->ne[1], moments->ne[2] / 2, moments->ne[3]);
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struct ggml_tensor* noise = ggml_dup_tensor(work_ctx, latent);
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ggml_tensor_set_f32_randn(noise, rng);
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{
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float mean = 0;
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float logvar = 0;
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float value = 0;
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float std_ = 0;
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for (int i = 0; i < latent->ne[3]; i++) {
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for (int j = 0; j < latent->ne[2]; j++) {
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for (int k = 0; k < latent->ne[1]; k++) {
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for (int l = 0; l < latent->ne[0]; l++) {
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mean = ggml_tensor_get_f32(moments, l, k, j, i);
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logvar = ggml_tensor_get_f32(moments, l, k, j + (int)latent->ne[2], i);
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logvar = std::max(-30.0f, std::min(logvar, 20.0f));
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std_ = std::exp(0.5f * logvar);
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value = mean + std_ * ggml_tensor_get_f32(noise, l, k, j, i);
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// printf("%d %d %d %d -> %f\n", i, j, k, l, value);
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ggml_tensor_set_f32(latent, value, l, k, j, i);
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}
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}
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}
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}
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}
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return latent;
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}
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ggml_tensor* get_first_stage_encoding(ggml_context* work_ctx, ggml_tensor* vae_output) {
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ggml_tensor* latent;
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if (use_tiny_autoencoder || sd_version_is_qwen_image(version)) {
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latent = vae_output;
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} else if (version == VERSION_SD1_PIX2PIX) {
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latent = ggml_view_3d(work_ctx,
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vae_output,
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vae_output->ne[0],
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vae_output->ne[1],
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vae_output->ne[2] / 2,
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vae_output->nb[1],
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vae_output->nb[2],
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0);
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} else {
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latent = gaussian_latent_sample(work_ctx, vae_output);
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}
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process_latent_in(latent);
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if (sd_version_is_qwen_image(version)) {
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latent = ggml_reshape_4d(work_ctx, latent, latent->ne[0], latent->ne[1], latent->ne[3], 1);
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}
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return latent;
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}
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ggml_tensor* encode_first_stage(ggml_context* work_ctx, ggml_tensor* x, bool encode_video = false) {
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ggml_tensor* vae_output = vae_encode(work_ctx, x, encode_video);
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return get_first_stage_encoding(work_ctx, vae_output);
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}
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ggml_tensor* decode_first_stage(ggml_context* work_ctx, ggml_tensor* x, bool decode_video = false) {
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ggml_tensor* decode_first_stage(ggml_context* work_ctx, ggml_tensor* x, bool decode_video = false) {
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int64_t W = x->ne[0] * 8;
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int64_t W = x->ne[0] * 8;
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int64_t H = x->ne[1] * 8;
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int64_t H = x->ne[1] * 8;
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@ -1959,6 +1989,8 @@ sd_image_t* generate_image_internal(sd_ctx_t* sd_ctx,
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seed = rand();
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seed = rand();
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}
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}
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print_ggml_tensor(init_latent, true, "init");
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// for (auto v : sigmas) {
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// for (auto v : sigmas) {
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// std::cout << v << " ";
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// std::cout << v << " ";
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// }
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// }
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@ -2352,12 +2384,9 @@ sd_image_t* generate_image(sd_ctx_t* sd_ctx, const sd_img_gen_params_t* sd_img_g
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ggml_tensor* masked_img = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, width, height, 3, 1);
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ggml_tensor* masked_img = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, width, height, 3, 1);
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sd_apply_mask(init_img, mask_img, masked_img);
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sd_apply_mask(init_img, mask_img, masked_img);
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ggml_tensor* masked_latent = NULL;
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ggml_tensor* masked_latent = NULL;
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if (!sd_ctx->sd->use_tiny_autoencoder) {
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ggml_tensor* moments = sd_ctx->sd->encode_first_stage(work_ctx, masked_img);
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masked_latent = sd_ctx->sd->get_first_stage_encoding(work_ctx, moments);
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} else {
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masked_latent = sd_ctx->sd->encode_first_stage(work_ctx, masked_img);
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masked_latent = sd_ctx->sd->encode_first_stage(work_ctx, masked_img);
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}
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concat_latent = ggml_new_tensor_4d(work_ctx,
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concat_latent = ggml_new_tensor_4d(work_ctx,
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GGML_TYPE_F32,
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GGML_TYPE_F32,
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masked_latent->ne[0],
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masked_latent->ne[0],
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@ -2407,12 +2436,7 @@ sd_image_t* generate_image(sd_ctx_t* sd_ctx, const sd_img_gen_params_t* sd_img_g
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}
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}
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}
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}
|
||||||
|
|
||||||
if (!sd_ctx->sd->use_tiny_autoencoder) {
|
|
||||||
ggml_tensor* moments = sd_ctx->sd->encode_first_stage(work_ctx, init_img);
|
|
||||||
init_latent = sd_ctx->sd->get_first_stage_encoding(work_ctx, moments);
|
|
||||||
} else {
|
|
||||||
init_latent = sd_ctx->sd->encode_first_stage(work_ctx, init_img);
|
init_latent = sd_ctx->sd->encode_first_stage(work_ctx, init_img);
|
||||||
}
|
|
||||||
} else {
|
} else {
|
||||||
LOG_INFO("TXT2IMG");
|
LOG_INFO("TXT2IMG");
|
||||||
if (sd_version_is_inpaint(sd_ctx->sd->version)) {
|
if (sd_version_is_inpaint(sd_ctx->sd->version)) {
|
||||||
@ -2451,23 +2475,7 @@ sd_image_t* generate_image(sd_ctx_t* sd_ctx, const sd_img_gen_params_t* sd_img_g
|
|||||||
1);
|
1);
|
||||||
sd_image_to_tensor(*ref_images[i], img);
|
sd_image_to_tensor(*ref_images[i], img);
|
||||||
|
|
||||||
ggml_tensor* latent = NULL;
|
ggml_tensor* latent = sd_ctx->sd->encode_first_stage(work_ctx, img);
|
||||||
if (sd_ctx->sd->use_tiny_autoencoder) {
|
|
||||||
latent = sd_ctx->sd->encode_first_stage(work_ctx, img);
|
|
||||||
} else if (sd_ctx->sd->version == VERSION_SD1_PIX2PIX) {
|
|
||||||
latent = sd_ctx->sd->encode_first_stage(work_ctx, img);
|
|
||||||
latent = ggml_view_3d(work_ctx,
|
|
||||||
latent,
|
|
||||||
latent->ne[0],
|
|
||||||
latent->ne[1],
|
|
||||||
latent->ne[2] / 2,
|
|
||||||
latent->nb[1],
|
|
||||||
latent->nb[2],
|
|
||||||
0);
|
|
||||||
} else {
|
|
||||||
ggml_tensor* moments = sd_ctx->sd->encode_first_stage(work_ctx, img);
|
|
||||||
latent = sd_ctx->sd->get_first_stage_encoding(work_ctx, moments);
|
|
||||||
}
|
|
||||||
ref_latents.push_back(latent);
|
ref_latents.push_back(latent);
|
||||||
}
|
}
|
||||||
|
|
||||||
@ -2629,8 +2637,6 @@ SD_API sd_image_t* generate_video(sd_ctx_t* sd_ctx, const sd_vid_gen_params_t* s
|
|||||||
int64_t t2 = ggml_time_ms();
|
int64_t t2 = ggml_time_ms();
|
||||||
LOG_INFO("encode_first_stage completed, taking %" PRId64 " ms", t2 - t1);
|
LOG_INFO("encode_first_stage completed, taking %" PRId64 " ms", t2 - t1);
|
||||||
|
|
||||||
sd_ctx->sd->process_latent_in(concat_latent);
|
|
||||||
|
|
||||||
ggml_tensor* concat_mask = ggml_new_tensor_4d(work_ctx,
|
ggml_tensor* concat_mask = ggml_new_tensor_4d(work_ctx,
|
||||||
GGML_TYPE_F32,
|
GGML_TYPE_F32,
|
||||||
concat_latent->ne[0],
|
concat_latent->ne[0],
|
||||||
@ -2656,7 +2662,7 @@ SD_API sd_image_t* generate_video(sd_ctx_t* sd_ctx, const sd_vid_gen_params_t* s
|
|||||||
sd_image_to_tensor(sd_vid_gen_params->init_image, init_img);
|
sd_image_to_tensor(sd_vid_gen_params->init_image, init_img);
|
||||||
init_img = ggml_reshape_4d(work_ctx, init_img, width, height, 1, 3);
|
init_img = ggml_reshape_4d(work_ctx, init_img, width, height, 1, 3);
|
||||||
|
|
||||||
auto init_image_latent = sd_ctx->sd->encode_first_stage(work_ctx, init_img); // [b*c, 1, h/16, w/16]
|
auto init_image_latent = sd_ctx->sd->vae_encode(work_ctx, init_img); // [b*c, 1, h/16, w/16]
|
||||||
|
|
||||||
init_latent = generate_init_latent(sd_ctx, work_ctx, width, height, frames, true);
|
init_latent = generate_init_latent(sd_ctx, work_ctx, width, height, frames, true);
|
||||||
denoise_mask = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, init_latent->ne[0], init_latent->ne[1], init_latent->ne[2], 1);
|
denoise_mask = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, init_latent->ne[0], init_latent->ne[1], init_latent->ne[2], 1);
|
||||||
@ -2687,7 +2693,6 @@ SD_API sd_image_t* generate_video(sd_ctx_t* sd_ctx, const sd_vid_gen_params_t* s
|
|||||||
ref_img = ggml_reshape_4d(work_ctx, ref_img, width, height, 1, 3);
|
ref_img = ggml_reshape_4d(work_ctx, ref_img, width, height, 1, 3);
|
||||||
|
|
||||||
ref_image_latent = sd_ctx->sd->encode_first_stage(work_ctx, ref_img); // [b*c, 1, h/16, w/16]
|
ref_image_latent = sd_ctx->sd->encode_first_stage(work_ctx, ref_img); // [b*c, 1, h/16, w/16]
|
||||||
sd_ctx->sd->process_latent_in(ref_image_latent);
|
|
||||||
auto zero_latent = ggml_dup_tensor(work_ctx, ref_image_latent);
|
auto zero_latent = ggml_dup_tensor(work_ctx, ref_image_latent);
|
||||||
ggml_set_f32(zero_latent, 0.f);
|
ggml_set_f32(zero_latent, 0.f);
|
||||||
ref_image_latent = ggml_tensor_concat(work_ctx, ref_image_latent, zero_latent, 3); // [b*2*c, 1, h/16, w/16]
|
ref_image_latent = ggml_tensor_concat(work_ctx, ref_image_latent, zero_latent, 3); // [b*2*c, 1, h/16, w/16]
|
||||||
@ -2719,9 +2724,6 @@ SD_API sd_image_t* generate_video(sd_ctx_t* sd_ctx, const sd_vid_gen_params_t* s
|
|||||||
inactive = sd_ctx->sd->encode_first_stage(work_ctx, inactive); // [b*c, t, h/8, w/8]
|
inactive = sd_ctx->sd->encode_first_stage(work_ctx, inactive); // [b*c, t, h/8, w/8]
|
||||||
reactive = sd_ctx->sd->encode_first_stage(work_ctx, reactive); // [b*c, t, h/8, w/8]
|
reactive = sd_ctx->sd->encode_first_stage(work_ctx, reactive); // [b*c, t, h/8, w/8]
|
||||||
|
|
||||||
sd_ctx->sd->process_latent_in(inactive);
|
|
||||||
sd_ctx->sd->process_latent_in(reactive);
|
|
||||||
|
|
||||||
int64_t length = inactive->ne[2];
|
int64_t length = inactive->ne[2];
|
||||||
if (ref_image_latent) {
|
if (ref_image_latent) {
|
||||||
length += 1;
|
length += 1;
|
||||||
|
|||||||
Loading…
x
Reference in New Issue
Block a user