Merge branch 'master' into t5_fix

README.md

@@ -286,7 +286,7 @@ usage: ./bin/sd [arguments]
 
 arguments:
   -h, --help                         show this help message and exit
-  -M, --mode [MODE]                  run mode, one of: [img_gen, vid_gen, convert], default: img_gen
+  -M, --mode [MODE]                  run mode, one of: [img_gen, vid_gen, upscale, convert], default: img_gen
   -t, --threads N                    number of threads to use during computation (default: -1)
                                      If threads <= 0, then threads will be set to the number of CPU physical cores
   --offload-to-cpu                   place the weights in RAM to save VRAM, and automatically load them into VRAM when needed
@@ -302,7 +302,7 @@ arguments:
   --taesd [TAESD_PATH]               path to taesd. Using Tiny AutoEncoder for fast decoding (low quality)
   --control-net [CONTROL_PATH]       path to control net model
   --embd-dir [EMBEDDING_PATH]        path to embeddings
-  --upscale-model [ESRGAN_PATH]      path to esrgan model. Upscale images after generate, just RealESRGAN_x4plus_anime_6B supported by now
+  --upscale-model [ESRGAN_PATH]      path to esrgan model. For img_gen mode, upscale images after generate, just RealESRGAN_x4plus_anime_6B supported by now
   --upscale-repeats                  Run the ESRGAN upscaler this many times (default 1)
   --type [TYPE]                      weight type (examples: f32, f16, q4_0, q4_1, q5_0, q5_1, q8_0, q2_K, q3_K, q4_K)
                                      If not specified, the default is the type of the weight file

esrgan.hpp

@@ -83,39 +83,44 @@ public:
 
 class RRDBNet : public GGMLBlock {
 protected:
-    int scale       = 4;  // default RealESRGAN_x4plus_anime_6B
+    int scale       = 4;
-    int num_block   = 6;  // default RealESRGAN_x4plus_anime_6B
+    int num_block   = 23;
     int num_in_ch   = 3;
     int num_out_ch  = 3;
-    int num_feat    = 64;  // default RealESRGAN_x4plus_anime_6B
+    int num_feat    = 64;
-    int num_grow_ch = 32;  // default RealESRGAN_x4plus_anime_6B
+    int num_grow_ch = 32;
 
 public:
-    RRDBNet() {
+    RRDBNet(int scale, int num_block, int num_in_ch, int num_out_ch, int num_feat, int num_grow_ch)
+        : scale(scale), num_block(num_block), num_in_ch(num_in_ch), num_out_ch(num_out_ch), num_feat(num_feat), num_grow_ch(num_grow_ch) {
         blocks["conv_first"] = std::shared_ptr<GGMLBlock>(new Conv2d(num_in_ch, num_feat, {3, 3}, {1, 1}, {1, 1}));
         for (int i = 0; i < num_block; i++) {
             std::string name = "body." + std::to_string(i);
             blocks[name]     = std::shared_ptr<GGMLBlock>(new RRDB(num_feat, num_grow_ch));
         }
         blocks["conv_body"] = std::shared_ptr<GGMLBlock>(new Conv2d(num_feat, num_feat, {3, 3}, {1, 1}, {1, 1}));
-        // upsample
+        if (scale >= 2) {
             blocks["conv_up1"] = std::shared_ptr<GGMLBlock>(new Conv2d(num_feat, num_feat, {3, 3}, {1, 1}, {1, 1}));
+        }
+        if (scale == 4) {
             blocks["conv_up2"] = std::shared_ptr<GGMLBlock>(new Conv2d(num_feat, num_feat, {3, 3}, {1, 1}, {1, 1}));
+        }
         blocks["conv_hr"]   = std::shared_ptr<GGMLBlock>(new Conv2d(num_feat, num_feat, {3, 3}, {1, 1}, {1, 1}));
         blocks["conv_last"] = std::shared_ptr<GGMLBlock>(new Conv2d(num_feat, num_out_ch, {3, 3}, {1, 1}, {1, 1}));
     }
 
+    int get_scale() { return scale; }
+    int get_num_block() { return num_block; }
+
     struct ggml_tensor* lrelu(struct ggml_context* ctx, struct ggml_tensor* x) {
         return ggml_leaky_relu(ctx, x, 0.2f, true);
     }
 
     struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x) {
         // x: [n, num_in_ch, h, w]
-        // return: [n, num_out_ch, h*4, w*4]
+        // return: [n, num_out_ch, h*scale, w*scale]
         auto conv_first = std::dynamic_pointer_cast<Conv2d>(blocks["conv_first"]);
         auto conv_body  = std::dynamic_pointer_cast<Conv2d>(blocks["conv_body"]);
-        auto conv_up1   = std::dynamic_pointer_cast<Conv2d>(blocks["conv_up1"]);
-        auto conv_up2   = std::dynamic_pointer_cast<Conv2d>(blocks["conv_up2"]);
         auto conv_hr    = std::dynamic_pointer_cast<Conv2d>(blocks["conv_hr"]);
         auto conv_last  = std::dynamic_pointer_cast<Conv2d>(blocks["conv_last"]);
 
@@ -130,15 +135,22 @@ public:
         body_feat = conv_body->forward(ctx, body_feat);
         feat      = ggml_add(ctx, feat, body_feat);
         // upsample
+        if (scale >= 2) {
+            auto conv_up1 = std::dynamic_pointer_cast<Conv2d>(blocks["conv_up1"]);
             feat          = lrelu(ctx, conv_up1->forward(ctx, ggml_upscale(ctx, feat, 2, GGML_SCALE_MODE_NEAREST)));
+            if (scale == 4) {
+                auto conv_up2 = std::dynamic_pointer_cast<Conv2d>(blocks["conv_up2"]);
                 feat          = lrelu(ctx, conv_up2->forward(ctx, ggml_upscale(ctx, feat, 2, GGML_SCALE_MODE_NEAREST)));
+            }
+        }
+        // for all scales
         auto out = conv_last->forward(ctx, lrelu(ctx, conv_hr->forward(ctx, feat)));
         return out;
     }
 };
 
 struct ESRGAN : public GGMLRunner {
-    RRDBNet rrdb_net;
+    std::unique_ptr<RRDBNet> rrdb_net;
     int scale     = 4;
     int tile_size = 128;  // avoid cuda OOM for 4gb VRAM
 
@@ -146,12 +158,14 @@ struct ESRGAN : public GGMLRunner {
            bool offload_params_to_cpu,
            const String2GGMLType& tensor_types = {})
         : GGMLRunner(backend, offload_params_to_cpu) {
-        rrdb_net.init(params_ctx, tensor_types, "");
+        // rrdb_net will be created in load_from_file
     }
 
     void enable_conv2d_direct() {
+        if (!rrdb_net)
+            return;
         std::vector<GGMLBlock*> blocks;
-        rrdb_net.get_all_blocks(blocks);
+        rrdb_net->get_all_blocks(blocks);
         for (auto block : blocks) {
             if (block->get_desc() == "Conv2d") {
                 auto conv_block = (Conv2d*)block;
@@ -167,31 +181,185 @@ struct ESRGAN : public GGMLRunner {
     bool load_from_file(const std::string& file_path, int n_threads) {
         LOG_INFO("loading esrgan from '%s'", file_path.c_str());
 
-        alloc_params_buffer();
-        std::map<std::string, ggml_tensor*> esrgan_tensors;
-        rrdb_net.get_param_tensors(esrgan_tensors);
-
         ModelLoader model_loader;
         if (!model_loader.init_from_file(file_path)) {
             LOG_ERROR("init esrgan model loader from file failed: '%s'", file_path.c_str());
             return false;
         }
 
-        bool success = model_loader.load_tensors(esrgan_tensors, {}, n_threads);
+        // Get tensor names
+        auto tensor_names = model_loader.get_tensor_names();
+
+        // Detect if it's ESRGAN format
+        bool is_ESRGAN = std::find(tensor_names.begin(), tensor_names.end(), "model.0.weight") != tensor_names.end();
+
+        // Detect parameters from tensor names
+        int detected_num_block = 0;
+        if (is_ESRGAN) {
+            for (const auto& name : tensor_names) {
+                if (name.find("model.1.sub.") == 0) {
+                    size_t first_dot = name.find('.', 12);
+                    if (first_dot != std::string::npos) {
+                        size_t second_dot = name.find('.', first_dot + 1);
+                        if (second_dot != std::string::npos && name.substr(first_dot + 1, 3) == "RDB") {
+                            try {
+                                int idx            = std::stoi(name.substr(12, first_dot - 12));
+                                detected_num_block = std::max(detected_num_block, idx + 1);
+                            } catch (...) {
+                            }
+                        }
+                    }
+                }
+            }
+        } else {
+            // Original format
+            for (const auto& name : tensor_names) {
+                if (name.find("body.") == 0) {
+                    size_t pos = name.find('.', 5);
+                    if (pos != std::string::npos) {
+                        try {
+                            int idx            = std::stoi(name.substr(5, pos - 5));
+                            detected_num_block = std::max(detected_num_block, idx + 1);
+                        } catch (...) {
+                        }
+                    }
+                }
+            }
+        }
+
+        int detected_scale = 4;  // default
+        if (is_ESRGAN) {
+            // For ESRGAN format, detect scale by highest model number
+            int max_model_num = 0;
+            for (const auto& name : tensor_names) {
+                if (name.find("model.") == 0) {
+                    size_t dot_pos = name.find('.', 6);
+                    if (dot_pos != std::string::npos) {
+                        try {
+                            int num       = std::stoi(name.substr(6, dot_pos - 6));
+                            max_model_num = std::max(max_model_num, num);
+                        } catch (...) {
+                        }
+                    }
+                }
+            }
+            if (max_model_num <= 4) {
+                detected_scale = 1;
+            } else if (max_model_num <= 7) {
+                detected_scale = 2;
+            } else {
+                detected_scale = 4;
+            }
+        } else {
+            // Original format
+            bool has_conv_up2 = std::any_of(tensor_names.begin(), tensor_names.end(), [](const std::string& name) {
+                return name == "conv_up2.weight";
+            });
+            bool has_conv_up1 = std::any_of(tensor_names.begin(), tensor_names.end(), [](const std::string& name) {
+                return name == "conv_up1.weight";
+            });
+            if (has_conv_up2) {
+                detected_scale = 4;
+            } else if (has_conv_up1) {
+                detected_scale = 2;
+            } else {
+                detected_scale = 1;
+            }
+        }
+
+        int detected_num_in_ch   = 3;
+        int detected_num_out_ch  = 3;
+        int detected_num_feat    = 64;
+        int detected_num_grow_ch = 32;
+
+        // Create RRDBNet with detected parameters
+        rrdb_net = std::make_unique<RRDBNet>(detected_scale, detected_num_block, detected_num_in_ch, detected_num_out_ch, detected_num_feat, detected_num_grow_ch);
+        rrdb_net->init(params_ctx, {}, "");
+
+        alloc_params_buffer();
+        std::map<std::string, ggml_tensor*> esrgan_tensors;
+        rrdb_net->get_param_tensors(esrgan_tensors);
+
+        bool success;
+        if (is_ESRGAN) {
+            // Build name mapping for ESRGAN format
+            std::map<std::string, std::string> expected_to_model;
+            expected_to_model["conv_first.weight"] = "model.0.weight";
+            expected_to_model["conv_first.bias"]   = "model.0.bias";
+
+            for (int i = 0; i < detected_num_block; i++) {
+                for (int j = 1; j <= 3; j++) {
+                    for (int k = 1; k <= 5; k++) {
+                        std::string expected_weight        = "body." + std::to_string(i) + ".rdb" + std::to_string(j) + ".conv" + std::to_string(k) + ".weight";
+                        std::string model_weight           = "model.1.sub." + std::to_string(i) + ".RDB" + std::to_string(j) + ".conv" + std::to_string(k) + ".0.weight";
+                        expected_to_model[expected_weight] = model_weight;
+
+                        std::string expected_bias        = "body." + std::to_string(i) + ".rdb" + std::to_string(j) + ".conv" + std::to_string(k) + ".bias";
+                        std::string model_bias           = "model.1.sub." + std::to_string(i) + ".RDB" + std::to_string(j) + ".conv" + std::to_string(k) + ".0.bias";
+                        expected_to_model[expected_bias] = model_bias;
+                    }
+                }
+            }
+
+            if (detected_scale == 1) {
+                expected_to_model["conv_body.weight"] = "model.1.sub." + std::to_string(detected_num_block) + ".weight";
+                expected_to_model["conv_body.bias"]   = "model.1.sub." + std::to_string(detected_num_block) + ".bias";
+                expected_to_model["conv_hr.weight"]   = "model.2.weight";
+                expected_to_model["conv_hr.bias"]     = "model.2.bias";
+                expected_to_model["conv_last.weight"] = "model.4.weight";
+                expected_to_model["conv_last.bias"]   = "model.4.bias";
+            } else {
+                expected_to_model["conv_body.weight"] = "model.1.sub." + std::to_string(detected_num_block) + ".weight";
+                expected_to_model["conv_body.bias"]   = "model.1.sub." + std::to_string(detected_num_block) + ".bias";
+                if (detected_scale >= 2) {
+                    expected_to_model["conv_up1.weight"] = "model.3.weight";
+                    expected_to_model["conv_up1.bias"]   = "model.3.bias";
+                }
+                if (detected_scale == 4) {
+                    expected_to_model["conv_up2.weight"]  = "model.6.weight";
+                    expected_to_model["conv_up2.bias"]    = "model.6.bias";
+                    expected_to_model["conv_hr.weight"]   = "model.8.weight";
+                    expected_to_model["conv_hr.bias"]     = "model.8.bias";
+                    expected_to_model["conv_last.weight"] = "model.10.weight";
+                    expected_to_model["conv_last.bias"]   = "model.10.bias";
+                } else if (detected_scale == 2) {
+                    expected_to_model["conv_hr.weight"]   = "model.5.weight";
+                    expected_to_model["conv_hr.bias"]     = "model.5.bias";
+                    expected_to_model["conv_last.weight"] = "model.7.weight";
+                    expected_to_model["conv_last.bias"]   = "model.7.bias";
+                }
+            }
+
+            std::map<std::string, ggml_tensor*> model_tensors;
+            for (auto& p : esrgan_tensors) {
+                auto it = expected_to_model.find(p.first);
+                if (it != expected_to_model.end()) {
+                    model_tensors[it->second] = p.second;
+                }
+            }
+
+            success = model_loader.load_tensors(model_tensors, {}, n_threads);
+        } else {
+            success = model_loader.load_tensors(esrgan_tensors, {}, n_threads);
+        }
 
         if (!success) {
             LOG_ERROR("load esrgan tensors from model loader failed");
             return false;
         }
 
-        LOG_INFO("esrgan model loaded");
+        scale = rrdb_net->get_scale();
+        LOG_INFO("esrgan model loaded with scale=%d, num_block=%d", scale, detected_num_block);
         return success;
     }
 
     struct ggml_cgraph* build_graph(struct ggml_tensor* x) {
-        struct ggml_cgraph* gf  = ggml_new_graph(compute_ctx);
+        if (!rrdb_net)
+            return nullptr;
+        constexpr int kGraphNodes = 1 << 16;  // 65k
+        struct ggml_cgraph* gf    = ggml_new_graph_custom(compute_ctx, kGraphNodes, /*grads*/ false);
         x                         = to_backend(x);
-        struct ggml_tensor* out = rrdb_net.forward(compute_ctx, x);
+        struct ggml_tensor* out   = rrdb_net->forward(compute_ctx, x);
         ggml_build_forward_expand(gf, out);
         return gf;
     }

examples/cli/main.cpp

@@ -41,13 +41,15 @@ const char* modes_str[] = {
     "img_gen",
     "vid_gen",
     "convert",
+    "upscale",
 };
-#define SD_ALL_MODES_STR "img_gen, vid_gen, convert"
+#define SD_ALL_MODES_STR "img_gen, vid_gen, convert, upscale"
 
 enum SDMode {
     IMG_GEN,
     VID_GEN,
     CONVERT,
+    UPSCALE,
     MODE_COUNT
 };
 
@@ -206,7 +208,7 @@ void print_usage(int argc, const char* argv[]) {
     printf("\n");
     printf("arguments:\n");
     printf("  -h, --help                         show this help message and exit\n");
-    printf("  -M, --mode [MODE]                  run mode, one of: [img_gen, vid_gen, convert], default: img_gen\n");
+    printf("  -M, --mode [MODE]                  run mode, one of: [img_gen, vid_gen, upscale, convert], default: img_gen\n");
     printf("  -t, --threads N                    number of threads to use during computation (default: -1)\n");
     printf("                                     If threads <= 0, then threads will be set to the number of CPU physical cores\n");
     printf("  --offload-to-cpu                   place the weights in RAM to save VRAM, and automatically load them into VRAM when needed\n");
@@ -222,7 +224,7 @@ void print_usage(int argc, const char* argv[]) {
     printf("  --taesd [TAESD_PATH]               path to taesd. Using Tiny AutoEncoder for fast decoding (low quality)\n");
     printf("  --control-net [CONTROL_PATH]       path to control net model\n");
     printf("  --embd-dir [EMBEDDING_PATH]        path to embeddings\n");
-    printf("  --upscale-model [ESRGAN_PATH]      path to esrgan model. Upscale images after generate, just RealESRGAN_x4plus_anime_6B supported by now\n");
+    printf("  --upscale-model [ESRGAN_PATH]      path to esrgan model. For img_gen mode, upscale images after generate, just RealESRGAN_x4plus_anime_6B supported by now\n");
     printf("  --upscale-repeats                  Run the ESRGAN upscaler this many times (default 1)\n");
     printf("  --type [TYPE]                      weight type (examples: f32, f16, q4_0, q4_1, q5_0, q5_1, q8_0, q2_K, q3_K, q4_K)\n");
     printf("                                     If not specified, the default is the type of the weight file\n");
@@ -821,13 +823,13 @@ void parse_args(int argc, const char** argv, SDParams& params) {
         params.n_threads = get_num_physical_cores();
     }
 
-    if (params.mode != CONVERT && params.mode != VID_GEN && params.prompt.length() == 0) {
+    if ((params.mode == IMG_GEN || params.mode == VID_GEN) && params.prompt.length() == 0) {
         fprintf(stderr, "error: the following arguments are required: prompt\n");
         print_usage(argc, argv);
         exit(1);
     }
 
-    if (params.model_path.length() == 0 && params.diffusion_model_path.length() == 0) {
+    if (params.mode != UPSCALE && params.model_path.length() == 0 && params.diffusion_model_path.length() == 0) {
         fprintf(stderr, "error: the following arguments are required: model_path/diffusion_model\n");
         print_usage(argc, argv);
         exit(1);
@@ -887,6 +889,17 @@ void parse_args(int argc, const char** argv, SDParams& params) {
         exit(1);
     }
 
+    if (params.mode == UPSCALE) {
+        if (params.esrgan_path.length() == 0) {
+            fprintf(stderr, "error: upscale mode needs an upscaler model (--upscale-model)\n");
+            exit(1);
+        }
+        if (params.init_image_path.length() == 0) {
+            fprintf(stderr, "error: upscale mode needs an init image (--init-img)\n");
+            exit(1);
+        }
+    }
+
     if (params.seed < 0) {
         srand((int)time(NULL));
         params.seed = rand();
@@ -897,14 +910,6 @@ void parse_args(int argc, const char** argv, SDParams& params) {
             params.output_path = "output.gguf";
         }
     }
-
-    if (!isfinite(params.sample_params.guidance.img_cfg)) {
-        params.sample_params.guidance.img_cfg = params.sample_params.guidance.txt_cfg;
-    }
-
-    if (!isfinite(params.high_noise_sample_params.guidance.img_cfg)) {
-        params.high_noise_sample_params.guidance.img_cfg = params.high_noise_sample_params.guidance.txt_cfg;
-    }
 }
 
 static std::string sd_basename(const std::string& path) {
@@ -1357,6 +1362,21 @@ int main(int argc, const char* argv[]) {
         params.flow_shift,
     };
 
+    sd_image_t* results = nullptr;
+    int num_results     = 0;
+
+    if (params.mode == UPSCALE) {
+        num_results = 1;
+        results     = (sd_image_t*)calloc(num_results, sizeof(sd_image_t));
+        if (results == NULL) {
+            printf("failed to allocate results array\n");
+            release_all_resources();
+            return 1;
+        }
+
+        results[0]      = init_image;
+        init_image.data = NULL;
+    } else {
         sd_ctx_t* sd_ctx = new_sd_ctx(&sd_ctx_params);
 
         if (sd_ctx == NULL) {
@@ -1369,8 +1389,6 @@ int main(int argc, const char* argv[]) {
             params.sample_params.sample_method = sd_get_default_sample_method(sd_ctx);
         }
 
-    sd_image_t* results;
-    int num_results = 1;
         if (params.mode == IMG_GEN) {
             sd_img_gen_params_t img_gen_params = {
                 params.prompt.c_str(),
@@ -1429,6 +1447,9 @@ int main(int argc, const char* argv[]) {
             return 1;
         }
 
+        free_sd_ctx(sd_ctx);
+    }
+
     int upscale_factor = 4;  // unused for RealESRGAN_x4plus_anime_6B.pth
     if (params.esrgan_path.size() > 0 && params.upscale_repeats > 0) {
         upscaler_ctx_t* upscaler_ctx = new_upscaler_ctx(params.esrgan_path.c_str(),
@@ -1439,7 +1460,7 @@ int main(int argc, const char* argv[]) {
         if (upscaler_ctx == NULL) {
             printf("new_upscaler_ctx failed\n");
         } else {
-            for (int i = 0; i < params.batch_count; i++) {
+            for (int i = 0; i < num_results; i++) {
                 if (results[i].data == NULL) {
                     continue;
                 }
@@ -1525,7 +1546,6 @@ int main(int argc, const char* argv[]) {
         results[i].data = NULL;
     }
     free(results);
-    free_sd_ctx(sd_ctx);
 
     release_all_resources();
 

ggml_extend.hpp

@@ -483,12 +483,15 @@ __STATIC_INLINE__ void ggml_split_tensor_2d(struct ggml_tensor* input,
     int64_t width    = output->ne[0];
     int64_t height   = output->ne[1];
     int64_t channels = output->ne[2];
+    int64_t ne3      = output->ne[3];
     GGML_ASSERT(input->type == GGML_TYPE_F32 && output->type == GGML_TYPE_F32);
     for (int iy = 0; iy < height; iy++) {
         for (int ix = 0; ix < width; ix++) {
             for (int k = 0; k < channels; k++) {
-                float value = ggml_tensor_get_f32(input, ix + x, iy + y, k);
+                for (int l = 0; l < ne3; l++) {
-                ggml_tensor_set_f32(output, value, ix, iy, k);
+                    float value = ggml_tensor_get_f32(input, ix + x, iy + y, k, l);
+                    ggml_tensor_set_f32(output, value, ix, iy, k, l);
+                }
             }
         }
     }
@@ -511,6 +514,7 @@ __STATIC_INLINE__ void ggml_merge_tensor_2d(struct ggml_tensor* input,
     int64_t width    = input->ne[0];
     int64_t height   = input->ne[1];
     int64_t channels = input->ne[2];
+    int64_t ne3      = input->ne[3];
 
     int64_t img_width  = output->ne[0];
     int64_t img_height = output->ne[1];
@@ -519,9 +523,10 @@ __STATIC_INLINE__ void ggml_merge_tensor_2d(struct ggml_tensor* input,
     for (int iy = y_skip; iy < height; iy++) {
         for (int ix = x_skip; ix < width; ix++) {
             for (int k = 0; k < channels; k++) {
-                float new_value = ggml_tensor_get_f32(input, ix, iy, k);
+                for (int l = 0; l < ne3; l++) {
+                    float new_value = ggml_tensor_get_f32(input, ix, iy, k, l);
                     if (overlap_x > 0 || overlap_y > 0) {  // blend colors in overlapped area
-                    float old_value = ggml_tensor_get_f32(output, x + ix, y + iy, k);
+                        float old_value = ggml_tensor_get_f32(output, x + ix, y + iy, k, l);
 
                         const float x_f_0 = (overlap_x > 0 && x > 0) ? (ix - x_skip) / float(overlap_x) : 1;
                         const float x_f_1 = (overlap_x > 0 && x < (img_width - width)) ? (width - ix) / float(overlap_x) : 1;
@@ -534,9 +539,10 @@ __STATIC_INLINE__ void ggml_merge_tensor_2d(struct ggml_tensor* input,
                         ggml_tensor_set_f32(
                             output,
                             old_value + new_value * ggml_smootherstep_f32(y_f) * ggml_smootherstep_f32(x_f),
-                        x + ix, y + iy, k);
+                            x + ix, y + iy, k, l);
                     } else {
-                    ggml_tensor_set_f32(output, new_value, x + ix, y + iy, k);
+                        ggml_tensor_set_f32(output, new_value, x + ix, y + iy, k, l);
+                    }
                 }
             }
         }
@@ -852,8 +858,8 @@ __STATIC_INLINE__ void sd_tiling_non_square(ggml_tensor* input,
     }
 
     struct ggml_init_params params = {};
-    params.mem_size += input_tile_size_x * input_tile_size_y * input->ne[2] * sizeof(float);     // input chunk
+    params.mem_size += input_tile_size_x * input_tile_size_y * input->ne[2] * input->ne[3] * sizeof(float);      // input chunk
-    params.mem_size += output_tile_size_x * output_tile_size_y * output->ne[2] * sizeof(float);  // output chunk
+    params.mem_size += output_tile_size_x * output_tile_size_y * output->ne[2] * output->ne[3] * sizeof(float);  // output chunk
     params.mem_size += 3 * ggml_tensor_overhead();
     params.mem_buffer = NULL;
     params.no_alloc   = false;
@@ -868,8 +874,8 @@ __STATIC_INLINE__ void sd_tiling_non_square(ggml_tensor* input,
     }
 
     // tiling
-    ggml_tensor* input_tile  = ggml_new_tensor_4d(tiles_ctx, GGML_TYPE_F32, input_tile_size_x, input_tile_size_y, input->ne[2], 1);
+    ggml_tensor* input_tile  = ggml_new_tensor_4d(tiles_ctx, GGML_TYPE_F32, input_tile_size_x, input_tile_size_y, input->ne[2], input->ne[3]);
-    ggml_tensor* output_tile = ggml_new_tensor_4d(tiles_ctx, GGML_TYPE_F32, output_tile_size_x, output_tile_size_y, output->ne[2], 1);
+    ggml_tensor* output_tile = ggml_new_tensor_4d(tiles_ctx, GGML_TYPE_F32, output_tile_size_x, output_tile_size_y, output->ne[2], output->ne[3]);
     int num_tiles            = num_tiles_x * num_tiles_y;
     LOG_INFO("processing %i tiles", num_tiles);
     pretty_progress(0, num_tiles, 0.0f);

model.h

@@ -269,6 +269,14 @@ public:
                       std::set<std::string> ignore_tensors = {},
                       int n_threads                        = 0);
 
+    std::vector<std::string> get_tensor_names() const {
+        std::vector<std::string> names;
+        for (const auto& ts : tensor_storages) {
+            names.push_back(ts.name);
+        }
+        return names;
+    }
+
     bool save_to_gguf_file(const std::string& file_path, ggml_type type, const std::string& tensor_type_rules);
     bool tensor_should_be_converted(const TensorStorage& tensor_storage, ggml_type type);
     int64_t get_params_mem_size(ggml_backend_t backend, ggml_type type = GGML_TYPE_COUNT);

stable-diffusion.cpp

@@ -1086,7 +1086,7 @@ public:
         std::vector<int> skip_layers(guidance.slg.layers, guidance.slg.layers + guidance.slg.layer_count);
 
         float cfg_scale     = guidance.txt_cfg;
-        float img_cfg_scale = guidance.img_cfg;
+        float img_cfg_scale = isfinite(guidance.img_cfg) ? guidance.img_cfg : guidance.txt_cfg;
         float slg_scale     = guidance.slg.scale;
 
         if (img_cfg_scale != cfg_scale && !sd_version_is_inpaint_or_unet_edit(version)) {
@@ -1430,10 +1430,23 @@ public:
         if (vae_tiling_params.enabled && !encode_video) {
             // TODO wan2.2 vae support?
             int C = sd_version_is_dit(version) ? 16 : 4;
+            int ne2;
+            int ne3;
+            if (sd_version_is_qwen_image(version)) {
+                ne2 = 1;
+                ne3 = C*x->ne[3];
+            } else {
                 if (!use_tiny_autoencoder) {
                     C *= 2;
                 }
-            result = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, W, H, C, x->ne[3]);
+                ne2 = C;
+                ne3 = x->ne[3];
+            }
+            result = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, W, H, ne2, ne3);
+        }
+
+        if (sd_version_is_qwen_image(version)) {
+            x = ggml_reshape_4d(work_ctx, x, x->ne[0], x->ne[1], 1, x->ne[2] * x->ne[3]);
         }
 
         if (sd_version_is_qwen_image(version)) {
@@ -1825,7 +1838,9 @@ char* sd_sample_params_to_str(const sd_sample_params_t* sample_params) {
              "eta: %.2f, "
              "shifted_timestep: %d)",
              sample_params->guidance.txt_cfg,
-             sample_params->guidance.img_cfg,
+             isfinite(sample_params->guidance.img_cfg)
+                 ? sample_params->guidance.img_cfg
+                 : sample_params->guidance.txt_cfg,
              sample_params->guidance.distilled_guidance,
              sample_params->guidance.slg.layer_count,
              sample_params->guidance.slg.layer_start,
@@ -1986,7 +2001,9 @@ sd_image_t* generate_image_internal(sd_ctx_t* sd_ctx,
         seed = rand();
     }
 
-    print_ggml_tensor(init_latent, true, "init");
+    if (!isfinite(guidance.img_cfg)) {
+        guidance.img_cfg = guidance.txt_cfg;
+    }
 
     // for (auto v : sigmas) {
     //     std::cout << v << " ";

stable-diffusion.h

@@ -284,6 +284,8 @@ SD_API sd_image_t upscale(upscaler_ctx_t* upscaler_ctx,
                           sd_image_t input_image,
                           uint32_t upscale_factor);
 
+SD_API int get_upscale_factor(upscaler_ctx_t* upscaler_ctx);
+
 SD_API bool convert(const char* input_path,
                     const char* vae_path,
                     const char* output_path,

upscaler.cpp

@@ -138,6 +138,13 @@ sd_image_t upscale(upscaler_ctx_t* upscaler_ctx, sd_image_t input_image, uint32_
     return upscaler_ctx->upscaler->upscale(input_image, upscale_factor);
 }
 
+int get_upscale_factor(upscaler_ctx_t* upscaler_ctx) {
+    if (upscaler_ctx == NULL || upscaler_ctx->upscaler == NULL || upscaler_ctx->upscaler->esrgan_upscaler == NULL) {
+        return 1;
+    }
+    return upscaler_ctx->upscaler->esrgan_upscaler->scale;
+}
+
 void free_upscaler_ctx(upscaler_ctx_t* upscaler_ctx) {
     if (upscaler_ctx->upscaler != NULL) {
         delete upscaler_ctx->upscaler;