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change for ollama
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@ -3,6 +3,7 @@
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// I'll gradually clean and extend it
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// Note: Even when using identical normalized image inputs (see normalize_image_u8_to_f32()) we have a significant difference in resulting embeddings compared to pytorch
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#include "clip.h"
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#include "common.h"
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#include "log.h"
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#include "ggml.h"
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#include "ggml-alloc.h"
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@ -466,7 +466,7 @@ static bool bicubic_resize(const clip_image_u8 &img, clip_image_u8 &dst, int tar
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return true;
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}
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std::vector<std::vector<clip_image_u8 *>> slice_image(const clip_image_u8 * img, const int max_slice_nums=9, const int scale_resolution=448, const int patch_size=14, const bool never_split=false) {
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std::vector<std::vector<clip_image_u8 *>> slice_image(const clip_image_u8 * img, const int max_slice_nums, const int scale_resolution, const int patch_size, const bool never_split) {
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const std::pair<int, int> original_size={img->nx,img->ny};
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const int original_width = img->nx;
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const int original_height = img->ny;
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@ -34,6 +34,7 @@ MINICPMV_API bool llava_validate_embed_size(const struct llama_context * ctx_lla
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MINICPMV_API bool llava_image_embed_make_with_clip_img(struct clip_ctx * ctx_clip, int n_threads, const struct clip_image_u8 * img, float ** image_embd_out, int * n_img_pos_out);
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/** build an image embed from image file bytes */
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MINICPMV_API std::vector<std::vector<clip_image_u8 *>> slice_image(const clip_image_u8 * img, const int max_slice_nums=9, const int scale_resolution=448, const int patch_size=14, const bool never_split=false);
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MINICPMV_API std::vector<std::vector<struct llava_image_embed *>> llava_image_embed_make_with_bytes_slice(struct clip_ctx * ctx_clip, int n_threads, const unsigned char * image_bytes, int image_bytes_length);
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/** build an image embed from a path to an image filename */
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MINICPMV_API std::vector<std::vector<struct llava_image_embed *>> llava_image_embed_make_with_filename_slice(struct clip_ctx * ctx_clip, int n_threads, const char * image_path);
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