diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp
index 480be8639ff95..ba06381dfb13d 100644
--- a/examples/llava/clip.cpp
+++ b/examples/llava/clip.cpp
@@ -554,7 +554,7 @@ struct clip_ctx {
     ggml_gallocr_t compute_alloc = NULL;
 };
 
-static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32_batch * imgs, std::pair<int, int> load_image_size = {448, 448}) {
+static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32_batch * imgs, std::pair<int, int> load_image_size = {448, 448}, bool is_inf = false) {
     if (!ctx->has_vision_encoder) {
         LOG_TEE("This gguf file seems to have no vision encoder\n");
         return nullptr;
@@ -569,6 +569,10 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
     if (ctx->has_minicpmv_projector) {
         image_size_width     = load_image_size.first;
         image_size_height    = load_image_size.second;
+        if (is_inf){
+            image_size_width = imgs->data->nx;
+            image_size_height = imgs->data->ny;
+        }
     }
     const int patch_size    = hparams.patch_size;
     const int num_patches   = ((image_size_width / patch_size) * (image_size_height / patch_size));
@@ -762,7 +766,8 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
             embeddings = ggml_mul_mat(ctx0, model.mm_2_w, embeddings);
             embeddings = ggml_add(ctx0, embeddings, model.mm_2_b);
 
-        } else if (ctx->proj_type == PROJECTOR_TYPE_MLP_NORM) {
+        } 
+        else if (ctx->proj_type == PROJECTOR_TYPE_MLP_NORM) {
             embeddings = ggml_mul_mat(ctx0, model.mm_0_w, embeddings);
             embeddings = ggml_add(ctx0, embeddings, model.mm_0_b);
             // ggml_tensor_printf(embeddings, "mm_0_w",0,true,false);
@@ -1450,7 +1455,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1, s
         new_clip->compute_alloc = ggml_gallocr_new(ggml_backend_get_default_buffer_type(new_clip->backend));
         clip_image_f32_batch batch;
         batch.size = 1;
-        ggml_cgraph * gf = clip_image_build_graph(new_clip, &batch, load_image_size);
+        ggml_cgraph * gf = clip_image_build_graph(new_clip, &batch, load_image_size, false);
         ggml_gallocr_reserve(new_clip->compute_alloc, gf);
         size_t compute_memory_buffer_size = ggml_gallocr_get_buffer_size(new_clip->compute_alloc, 0);
         LOG_TEE("%s: compute allocated memory: %.2f MB\n", __func__, compute_memory_buffer_size /1024.0/1024.0);
@@ -2080,7 +2085,7 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
     }
 
     // build the inference graph
-    ggml_cgraph * gf = clip_image_build_graph(ctx, imgs, load_image_size);
+    ggml_cgraph * gf = clip_image_build_graph(ctx, imgs, load_image_size, true);
     ggml_gallocr_alloc_graph(ctx->compute_alloc, gf);
 
     // set inputs
@@ -2091,8 +2096,8 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
     int image_size_width     = image_size;
     int image_size_height    = image_size;
     if (ctx->has_minicpmv_projector) {
-        image_size_width     = load_image_size.first;
-        image_size_height    = load_image_size.second;
+        image_size_width     = imgs->data[0].nx;;
+        image_size_height    = imgs->data[0].ny;
     }
     const int patch_size    = hparams.patch_size;
     const int num_patches   = ((image_size_width / patch_size) * (image_size_height / patch_size));
@@ -2144,8 +2149,8 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
             //    -> https://huggingface.co/Qwen/Qwen-VL/tree/main
             //    -> https://huggingface.co/Qwen/Qwen-VL/blob/0547ed36a86561e2e42fecec8fd0c4f6953e33c4/visual.py#L23
             struct ggml_tensor * pos_embed = ggml_graph_get_tensor(gf, "pos_embed");
-            int pos_w = image_size_width/patch_size;
-            int pos_h = image_size_height/patch_size;
+            int pos_w = load_image_size.first/patch_size;
+            int pos_h = load_image_size.second/patch_size;
             int embed_dim = 4096;
             auto pos_embed_t = get_2d_sincos_pos_embed(embed_dim, std::make_pair(pos_w, pos_h));
 
diff --git a/examples/llava/llava.cpp b/examples/llava/llava.cpp
index 93a5b0ea4f424..dc9a01a45fece 100644
--- a/examples/llava/llava.cpp
+++ b/examples/llava/llava.cpp
@@ -410,13 +410,10 @@ void llava_image_embed_free(struct llava_image_embed * embed) {
     free(embed);
 }
 
-static bool encode_image_with_clip_uhd(clip_ctx * ctx_clip, int n_threads, const clip_image_u8 * img, float * image_embd, int * n_img_pos) {
+static bool encode_image_with_clip_uhd(clip_ctx * ctx_clip, int n_threads, const clip_image_u8 * img, float * image_embd, int * n_img_pos, std::pair<int, int> load_image_size) {
     // std::vector<clip_image_f32*> img_res_v; 
     // format VectN x H x W x RGB (N x 448 x 448 x 3)
     clip_image_f32 * img_res_v = clip_image_f32_init();
-    std::pair<int, int> load_image_size;
-    load_image_size.first = img->nx;
-    load_image_size.second = img->ny;
     uhd_normalize_image_u8_to_f32(ctx_clip, img, img_res_v);
 
     const int64_t t_img_enc_start_us = ggml_time_us();
@@ -545,6 +542,34 @@ static bool bicubic_resize(const clip_image_u8 &img, clip_image_u8 &dst, int tar
     return true;
 }
 
+static clip_image_u8 * only_v2_5_reshape_by_patch(clip_image_u8 * image, int patch_size) {
+    int width = image->nx;
+    int height = image->ny;
+    int num_patches = (height / patch_size) * (width / patch_size);
+    clip_image_u8 * patch = clip_image_u8_init();
+    patch->nx = patch_size * num_patches;
+    patch->ny = patch_size;
+    patch->buf.resize(3 * patch->nx * patch->ny);
+
+    int patch_index = 0;
+
+    for (int i = 0; i < height; i += patch_size) {
+        for (int j = 0; j < width; j += patch_size) {
+            for (int pi = 0; pi < patch_size; ++pi) {
+                for (int pj = 0; pj < patch_size; ++pj) {
+                    int input_index = ((i + pi) * width + (j + pj)) * 3;
+                    int output_index = (pi * patch_size * num_patches + patch_index * patch_size + pj) * 3;
+                    patch->buf[output_index] = image->buf[input_index];
+                    patch->buf[output_index+1] = image->buf[input_index+1];
+                    patch->buf[output_index+2] = image->buf[input_index+2];
+                }
+            }
+            patch_index++;
+        }
+    }
+    return patch;
+}
+
 // inspired from LLaVA-UHD:
 //    -> https://arxiv.org/pdf/2403.11703
 //    -> https://github.com/thunlp/LLaVA-UHD
@@ -657,7 +682,11 @@ struct uhd_image_embed * llava_image_embed_make_with_bytes_uhd(struct clip_ctx *
         for (size_t j = 0; j < imgs[i].size(); ++j) {
             float* image_embed = NULL;
             int n_image_pos = 0;
-            bool image_embed_result = llava_image_embed_make_with_clip_img_uhd(ctx_clip, n_threads, imgs[i][j], &image_embed, &n_image_pos);
+            int patch_size=14;
+            std::pair<int, int> load_image_size;
+            load_image_size.first = imgs[i][j]->nx;
+            load_image_size.second = imgs[i][j]->ny;
+            bool image_embed_result = llava_image_embed_make_with_clip_img_uhd(ctx_clip, n_threads, only_v2_5_reshape_by_patch(imgs[i][j], patch_size), &image_embed, &n_image_pos, load_image_size);
             if (!image_embed_result) {
                 LOG_TEE("%s: coulnd't embed the image\n", __func__);
                 return NULL;
@@ -672,7 +701,7 @@ struct uhd_image_embed * llava_image_embed_make_with_bytes_uhd(struct clip_ctx *
     return results;
 }
 
-bool llava_image_embed_make_with_clip_img_uhd(clip_ctx * ctx_clip, int n_threads, const clip_image_u8 * img, float ** image_embd_out, int * n_img_pos_out) {
+bool llava_image_embed_make_with_clip_img_uhd(clip_ctx * ctx_clip, int n_threads, const clip_image_u8 * img, float ** image_embd_out, int * n_img_pos_out, std::pair<int, int> load_image_size) {
     float * image_embd = (float *)malloc(clip_embd_nbytes(ctx_clip)*6); // TODO: base on gridsize/llava model
     if (!image_embd) {
         LOG_TEE("Unable to allocate memory for image embeddings\n");
@@ -680,7 +709,7 @@ bool llava_image_embed_make_with_clip_img_uhd(clip_ctx * ctx_clip, int n_threads
     }
 
     int n_img_pos;
-    if (!encode_image_with_clip_uhd(ctx_clip, n_threads, img, image_embd, &n_img_pos)) {
+    if (!encode_image_with_clip_uhd(ctx_clip, n_threads, img, image_embd, &n_img_pos, load_image_size)) {
         LOG_TEE("%s: cannot encode image, aborting\n", __func__);
         free(image_embd);
         return false;
diff --git a/examples/llava/llava.h b/examples/llava/llava.h
index 5f29f02c55a66..abb53d3d62f96 100644
--- a/examples/llava/llava.h
+++ b/examples/llava/llava.h
@@ -47,7 +47,7 @@ LLAVA_API void llava_image_embed_free(struct llava_image_embed * embed);
 /** build an image embed from image file bytes */
 LLAVA_API struct uhd_image_embed * llava_image_embed_make_with_bytes_uhd(struct clip_ctx * ctx_clip, int n_threads, const clip_image_u8 * img);
 /** build an image embed from a path to an image filename */
-LLAVA_API bool llava_image_embed_make_with_clip_img_uhd(struct clip_ctx * ctx_clip, int n_threads, const struct clip_image_u8 * img, float ** image_embd_out, int * n_img_pos_out);
+LLAVA_API bool llava_image_embed_make_with_clip_img_uhd(struct clip_ctx * ctx_clip, int n_threads, const struct clip_image_u8 * img, float ** image_embd_out, int * n_img_pos_out, std::pair<int, int> load_image_size = {448, 448});
 LLAVA_API bool llava_image_embed_make_with_clip_img_ollama(struct clip_ctx * ctx_clip, int n_threads, const struct clip_image_u8 * img, float ** image_embd_out, int * n_img_pos_out);
 LLAVA_API struct uhd_image_embed *  llava_image_embed_make_with_filename_uhd(struct clip_ctx * ctx_clip, int n_threads, const char * image_path);
 LLAVA_API void llava_image_embed_free_uhd(struct uhd_image_embed *  embed);