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https://github.com/leejet/stable-diffusion.cpp.git
synced 2026-11-11 19:36:33 +00:00
chore: silence narrowing conversion warnings (#1717)
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7b5f34d93e
commit
9f855c933b
4 files changed
+19
-22
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@@ -76,8 +76,8 @@ namespace SefiImage {
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auto semantic_embedder = std::dynamic_pointer_cast<SefiTimestepEmbedding>(blocks["semantic_embedder"]);
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auto texture_embedder = std::dynamic_pointer_cast<SefiTimestepEmbedding>(blocks["texture_embedder"]);
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auto sem_proj = ggml_ext_timestep_embedding(ctx->ggml_ctx, timestep_sem, timestep_guidance_in_dim, 10000, 1.f);
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auto tex_proj = ggml_ext_timestep_embedding(ctx->ggml_ctx, timestep_tex, timestep_guidance_in_dim, 10000, 1.f);
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auto sem_proj = ggml_ext_timestep_embedding(ctx->ggml_ctx, timestep_sem, (int)timestep_guidance_in_dim, 10000, 1.f);
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auto tex_proj = ggml_ext_timestep_embedding(ctx->ggml_ctx, timestep_tex, (int)timestep_guidance_in_dim, 10000, 1.f);
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auto sem_emb = semantic_embedder->forward(ctx, sem_proj);
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auto tex_emb = texture_embedder->forward(ctx, tex_proj);
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return ggml_concat(ctx->ggml_ctx, sem_emb, tex_emb, 0);
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+14
-16
@@ -113,26 +113,24 @@ namespace WAN {
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}
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};
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class Conv2dBut3d : public Conv2d {
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public:
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using Conv2d::Conv2d;
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ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) {
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ggml_tensor* x_swapped = ggml_permute(ctx->ggml_ctx, x, 0, 1, 3, 2);
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x_swapped = ggml_cont(ctx->ggml_ctx, x_swapped);
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x_swapped = ggml_cont(ctx->ggml_ctx, x_swapped);
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ggml_tensor* out = Conv2d::forward(ctx, x_swapped);
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ggml_tensor* out_swapped = ggml_permute(ctx->ggml_ctx, out, 0, 1, 3, 2);
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out_swapped = ggml_cont(ctx->ggml_ctx, out_swapped);
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return out_swapped;
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}
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};
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class Resample : public GGMLBlock {
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protected:
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int64_t dim;
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@@ -397,7 +395,7 @@ namespace WAN {
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ggml_tensor* h = x;
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if (in_dim != out_dim) {
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if (is_2D) {
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auto shortcut = std::dynamic_pointer_cast<Conv2dBut3d>(blocks["shortcut"]);
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auto shortcut = std::dynamic_pointer_cast<Conv2dBut3d>(blocks["shortcut"]);
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h = shortcut->forward(ctx, x);
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} else {
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@@ -843,9 +841,9 @@ namespace WAN {
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}
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// init block
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if(is_2D){
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if (is_2D) {
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blocks["conv1"] = std::shared_ptr<GGMLBlock>(new Conv2dBut3d(z_dim, dims[0], {3, 3}, {1, 1}, {1, 1}));
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}else{
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} else {
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blocks["conv1"] = std::shared_ptr<GGMLBlock>(new CausalConv3d(z_dim, dims[0], {3, 3, 3}, {1, 1, 1}, {1, 1, 1}));
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}
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@@ -890,7 +888,7 @@ namespace WAN {
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}
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// output blocks
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blocks["head.0"] = std::shared_ptr<GGMLBlock>(new RMS_norm(out_dim));
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blocks["head.0"] = std::shared_ptr<GGMLBlock>(new RMS_norm(out_dim));
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int64_t final_dim = wan2_2 ? 12 : 3;
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// head.1 is nn.SiLU()
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if (is_2D) {
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@@ -919,7 +917,7 @@ namespace WAN {
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// conv1
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if (is_2D) {
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auto conv1 = std::dynamic_pointer_cast<Conv2dBut3d>(blocks["conv1"]);
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x = conv1->forward(ctx, x);
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x = conv1->forward(ctx, x);
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} else if (feat_cache.size() > 0) {
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int idx = feat_idx;
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auto cache_x = ggml_ext_slice(ctx->ggml_ctx, x, 2, -CACHE_T, x->ne[2]);
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@@ -1011,7 +1009,7 @@ namespace WAN {
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int num_res_blocks = 2;
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std::vector<bool> temperal_upsample = {true, true, false};
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std::vector<bool> temperal_downsample = {false, true, true};
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bool is_2D = false;
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bool is_2D = false;
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int _conv_num = 33;
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int _conv_idx = 0;
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@@ -1040,8 +1038,8 @@ namespace WAN {
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_enc_conv_num = 26;
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}
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if(is_2D){
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temperal_upsample = {false, false, false};
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if (is_2D) {
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temperal_upsample = {false, false, false};
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temperal_downsample = {false, false, false};
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}
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@@ -1149,7 +1147,7 @@ namespace WAN {
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auto conv1 = std::dynamic_pointer_cast<Conv2dBut3d>(blocks["conv1"]);
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out = conv1->forward(ctx, out);
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} else {
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out = conv1->forward(ctx, out);
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out = conv1->forward(ctx, out);
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}
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auto mu = ggml_ext_chunk(ctx->ggml_ctx, out, 2, 3)[0];
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// sd::ggml_graph_cut::mark_graph_cut(mu, "wan_vae.encode.final", "mu");
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@@ -1170,9 +1168,9 @@ namespace WAN {
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int64_t iter_ = z->ne[2];
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auto x = z;
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if(is_2D){
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auto conv2 = std::dynamic_pointer_cast<Conv2dBut3d>(blocks["conv2"]);
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x = conv2->forward(ctx, z);
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if (is_2D) {
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auto conv2 = std::dynamic_pointer_cast<Conv2dBut3d>(blocks["conv2"]);
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x = conv2->forward(ctx, z);
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} else {
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x = conv2->forward(ctx, z);
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}
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