upstream-archive/stable-diffusion.cpp
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fix: avoid narrowing conversion in SigVQ patch embedding and format code

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leejet
2026-09-21 01:27:31 +08:00
parent 187b2561ea
commit b56c68617d
4 changed files with 19 additions and 19 deletions
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src/model/diffusion/qwen_image_2_1.hpp
+12 -12
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@@ -193,16 +193,16 @@ namespace Qwen {
class QwenImage21TransformerBlock : public GGMLBlock {
public:
QwenImage21TransformerBlock(const QwenImage21Config& config) {
blocks["img_norm1"] = std::make_shared<LayerNorm>(config.hidden_size, 1e-6f, false);
blocks["img_norm2"] = std::make_shared<LayerNorm>(config.hidden_size, 1e-6f, false);
blocks["attn"] = std::make_shared<QwenImage21Attention>(config);
blocks["img_norm1"] = std::make_shared<LayerNorm>(config.hidden_size, 1e-6f, false);
blocks["img_norm2"] = std::make_shared<LayerNorm>(config.hidden_size, 1e-6f, false);
blocks["attn"] = std::make_shared<QwenImage21Attention>(config);
if (config.fused_mlp) {
blocks["img_mlp.gate_up"] = std::make_shared<Linear>(config.hidden_size, 2 * config.intermediate_size, false);
} else {
blocks["img_mlp.proj"] = std::make_shared<Linear>(config.hidden_size, config.intermediate_size, false);
blocks["img_mlp.gate_layer"] = std::make_shared<Linear>(config.hidden_size, config.intermediate_size, false);
}
blocks["img_mlp.out"] = std::make_shared<Linear>(config.intermediate_size, config.hidden_size, false);
blocks["img_mlp.out"] = std::make_shared<Linear>(config.intermediate_size, config.hidden_size, false);
}
static ggml_tensor* modulate(ggml_context* ctx, ggml_tensor* x, ggml_tensor* params, int64_t prefix_length, bool gate = false) {
@@ -220,12 +220,12 @@ namespace Qwen {
}
ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x, const std::vector<ggml_tensor*>& modulation, ggml_tensor* pe, const QwenImage21Layout& layout, const std::vector<ggml_tensor*>& masks) {
auto h = std::dynamic_pointer_cast<LayerNorm>(blocks["img_norm1"])->forward(ctx, x);
h = modulate(ctx->ggml_ctx, h, modulation[0], layout.prefix_length);
h = std::dynamic_pointer_cast<QwenImage21Attention>(blocks["attn"])->forward(ctx, h, pe, layout.segments, masks);
x = ggml_add(ctx->ggml_ctx, x, modulate(ctx->ggml_ctx, h, modulation[1], layout.prefix_length, true));
h = std::dynamic_pointer_cast<LayerNorm>(blocks["img_norm2"])->forward(ctx, x);
h = modulate(ctx->ggml_ctx, h, modulation[2], layout.prefix_length);
auto h = std::dynamic_pointer_cast<LayerNorm>(blocks["img_norm1"])->forward(ctx, x);
h = modulate(ctx->ggml_ctx, h, modulation[0], layout.prefix_length);
h = std::dynamic_pointer_cast<QwenImage21Attention>(blocks["attn"])->forward(ctx, h, pe, layout.segments, masks);
x = ggml_add(ctx->ggml_ctx, x, modulate(ctx->ggml_ctx, h, modulation[1], layout.prefix_length, true));
h = std::dynamic_pointer_cast<LayerNorm>(blocks["img_norm2"])->forward(ctx, x);
h = modulate(ctx->ggml_ctx, h, modulation[2], layout.prefix_length);
ggml_tensor* gate;
auto fused = blocks.find("img_mlp.gate_up");
if (fused != blocks.end()) {
@@ -237,8 +237,8 @@ namespace Qwen {
gate = std::dynamic_pointer_cast<Linear>(blocks["img_mlp.gate_layer"])->forward(ctx, h);
h = std::dynamic_pointer_cast<Linear>(blocks["img_mlp.proj"])->forward(ctx, h);
}
h = ggml_mul(ctx->ggml_ctx, h, ggml_silu(ctx->ggml_ctx, gate));
h = std::dynamic_pointer_cast<Linear>(blocks["img_mlp.out"])->forward(ctx, h);
h = ggml_mul(ctx->ggml_ctx, h, ggml_silu(ctx->ggml_ctx, gate));
h = std::dynamic_pointer_cast<Linear>(blocks["img_mlp.out"])->forward(ctx, h);
return ggml_add(ctx->ggml_ctx, x, modulate(ctx->ggml_ctx, h, modulation[3], layout.prefix_length, true));
}
};