feat: change image dimensions requirement for DiT models (#742)

* Instruct-p2p support

* support 2 conditionings cfg

* Do not re-encode the exact same image twice

* fixes for 2-cfg

* Fix pix2pix latent inputs + improve inpainting a bit + fix naming

* prepare for other pix2pix-like models

* Support sdxl ip2p

* fix reference image embeddings

* Support 2-cond cfg properly in cli

* fix typo in help

* Support masks for ip2p models

* unify code style

* delete unused code

* use edit mode

* add img_cond

* format code

---------

Co-authored-by: leejet <leejet714@gmail.com>

Dockerfile.musa

@@ -1,6 +1,7 @@
-ARG MUSA_VERSION=rc3.1.1
+ARG MUSA_VERSION=rc4.2.0
+ARG UBUNTU_VERSION=22.04
 
-FROM mthreads/musa:${MUSA_VERSION}-devel-ubuntu22.04 as build
+FROM mthreads/musa:${MUSA_VERSION}-devel-ubuntu${UBUNTU_VERSION}-amd64 as build
 
 RUN apt-get update && apt-get install -y ccache cmake git
 
@@ -15,7 +16,7 @@ RUN mkdir build && cd build && \
         -DSD_MUSA=ON -DCMAKE_BUILD_TYPE=Release && \
     cmake --build . --config Release
 
-FROM mthreads/musa:${MUSA_VERSION}-runtime-ubuntu22.04 as runtime
+FROM mthreads/musa:${MUSA_VERSION}-runtime-ubuntu${UBUNTU_VERSION}-amd64 as runtime
 
 COPY --from=build /sd.cpp/build/bin/sd /sd
 

README.md

@@ -50,7 +50,7 @@ Inference of Stable Diffusion and Flux in pure C/C++
     - Linux
     - Mac OS
     - Windows
-    - Android (via Termux)
+    - Android (via Termux, [Local Diffusion](https://github.com/rmatif/Local-Diffusion))
 
 ### TODO
 
@@ -282,14 +282,14 @@ usage: ./bin/sd [arguments]
 
 arguments:
   -h, --help                         show this help message and exit
-  -M, --mode [MODEL]                 run mode (txt2img or img2img or convert, default: txt2img)
+  -M, --mode [MODE]                  run mode, one of: [img_gen, 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
   -m, --model [MODEL]                path to full model
   --diffusion-model                  path to the standalone diffusion model
   --clip_l                           path to the clip-l text encoder
   --clip_g                           path to the clip-g text encoder
-  --t5xxl                            path to the the t5xxl text encoder
+  --t5xxl                            path to the t5xxl text encoder
   --vae [VAE]                        path to vae
   --taesd [TAESD_PATH]               path to taesd. Using Tiny AutoEncoder for fast decoding (low quality)
   --control-net [CONTROL_PATH]       path to control net model
@@ -301,16 +301,18 @@ arguments:
   --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
+  --tensor-type-rules [EXPRESSION]   weight type per tensor pattern (example: "^vae\.=f16,model\.=q8_0")
   --lora-model-dir [DIR]             lora model directory
   -i, --init-img [IMAGE]             path to the input image, required by img2img
   --mask [MASK]                      path to the mask image, required by img2img with mask
   --control-image [IMAGE]            path to image condition, control net
-  -r, --ref_image [PATH]             reference image for Flux Kontext models (can be used multiple times)
+  -r, --ref-image [PATH]             reference image for Flux Kontext models (can be used multiple times)
   -o, --output OUTPUT                path to write result image to (default: ./output.png)
   -p, --prompt [PROMPT]              the prompt to render
   -n, --negative-prompt PROMPT       the negative prompt (default: "")
   --cfg-scale SCALE                  unconditional guidance scale: (default: 7.0)
-  --guidance SCALE                   guidance scale for img2img (default: 3.5)
+  --img-cfg-scale SCALE              image guidance scale for inpaint or instruct-pix2pix models: (default: same as --cfg-scale)
+  --guidance SCALE                   distilled guidance scale for models with guidance input (default: 3.5)
   --slg-scale SCALE                  skip layer guidance (SLG) scale, only for DiT models: (default: 0)
                                      0 means disabled, a value of 2.5 is nice for sd3.5 medium
   --eta SCALE                        eta in DDIM, only for DDIM and TCD: (default: 0)
@@ -319,7 +321,7 @@ arguments:
   --skip-layer-end END               SLG disabling point: (default: 0.2)
                                      SLG will be enabled at step int([STEPS]*[START]) and disabled at int([STEPS]*[END])
   --strength STRENGTH                strength for noising/unnoising (default: 0.75)
-  --style-ratio STYLE-RATIO          strength for keeping input identity (default: 20%)
+  --style-ratio STYLE-RATIO          strength for keeping input identity (default: 20)
   --control-strength STRENGTH        strength to apply Control Net (default: 0.9)
                                      1.0 corresponds to full destruction of information in init image
   -H, --height H                     image height, in pixel space (default: 512)
@@ -371,7 +373,7 @@ Using formats of different precisions will yield results of varying quality.
 
 
 ```
-./bin/sd --mode img2img -m ../models/sd-v1-4.ckpt -p "cat with blue eyes" -i ./output.png -o ./img2img_output.png --strength 0.4
+./bin/sd -m ../models/sd-v1-4.ckpt -p "cat with blue eyes" -i ./output.png -o ./img2img_output.png --strength 0.4
 ```
 
 <p align="center">
@@ -392,10 +394,12 @@ Using formats of different precisions will yield results of varying quality.
 
 These projects wrap `stable-diffusion.cpp` for easier use in other languages/frameworks.
 
-* Golang: [seasonjs/stable-diffusion](https://github.com/seasonjs/stable-diffusion)
+* Golang (non-cgo): [seasonjs/stable-diffusion](https://github.com/seasonjs/stable-diffusion)
+* Golang (cgo): [Binozo/GoStableDiffusion](https://github.com/Binozo/GoStableDiffusion)
 * C#: [DarthAffe/StableDiffusion.NET](https://github.com/DarthAffe/StableDiffusion.NET)
 * Python: [william-murray1204/stable-diffusion-cpp-python](https://github.com/william-murray1204/stable-diffusion-cpp-python)
 * Rust: [newfla/diffusion-rs](https://github.com/newfla/diffusion-rs)
+* Flutter/Dart: [rmatif/Local-Diffusion](https://github.com/rmatif/Local-Diffusion)
 
 ## UIs
 
@@ -404,6 +408,9 @@ These projects use `stable-diffusion.cpp` as a backend for their image generatio
 - [Jellybox](https://jellybox.com)
 - [Stable Diffusion GUI](https://github.com/fszontagh/sd.cpp.gui.wx)
 - [Stable Diffusion CLI-GUI](https://github.com/piallai/stable-diffusion.cpp)
+- [Local Diffusion](https://github.com/rmatif/Local-Diffusion)
+- [sd.cpp-webui](https://github.com/daniandtheweb/sd.cpp-webui)
+- [LocalAI](https://github.com/mudler/LocalAI)
 
 ## Contributors
 

conditioner.hpp

@@ -459,8 +459,8 @@ struct FrozenCLIPEmbedderWithCustomWords : public Conditioner {
                 if (sd_version_is_sdxl(version)) {
                     text_model2->compute(n_threads,
                                          input_ids2,
-                                         0,
-                                         NULL,
+                                         num_custom_embeddings,
+                                         token_embed_custom.data(),
                                          max_token_idx,
                                          false,
                                          &chunk_hidden_states2, work_ctx);
@@ -470,8 +470,8 @@ struct FrozenCLIPEmbedderWithCustomWords : public Conditioner {
                     if (chunk_idx == 0) {
                         text_model2->compute(n_threads,
                                              input_ids2,
-                                             0,
-                                             NULL,
+                                             num_custom_embeddings,
+                                             token_embed_custom.data(),
                                              max_token_idx,
                                              true,
                                              &pooled,

denoiser.hpp

@@ -181,7 +181,7 @@ struct AYSSchedule : SigmaSchedule {
             LOG_INFO("AYS using SVD noise levels");
             inputs = noise_levels[2];
         } else {
-            LOG_ERROR("Version not compatable with AYS scheduler");
+            LOG_ERROR("Version not compatible with AYS scheduler");
             return results;
         }
 

docs/kontext.md

@@ -27,7 +27,7 @@ You can download the preconverted gguf weights from [FLUX.1-Kontext-dev-GGUF](ht
 For example:
 
 ```
- .\bin\Release\sd.exe -M edit -r .\flux1-dev-q8_0.png --diffusion-model  ..\models\flux1-kontext-dev-q8_0.gguf --vae ..\models\ae.sft --clip_l ..\models\clip_l.safetensors --t5xxl ..\models\t5xxl_fp16.safetensors -p "change 'flux.cpp' to 'kontext.cpp'" --cfg-scale 1.0 --sampling-method euler -v
+ .\bin\Release\sd.exe -r .\flux1-dev-q8_0.png --diffusion-model  ..\models\flux1-kontext-dev-q8_0.gguf --vae ..\models\ae.sft --clip_l ..\models\clip_l.safetensors --t5xxl ..\models\t5xxl_fp16.safetensors -p "change 'flux.cpp' to 'kontext.cpp'" --cfg-scale 1.0 --sampling-method euler -v
 ```
 
 

ggml_extend.hpp

@@ -112,7 +112,7 @@ __STATIC_INLINE__ struct ggml_tensor* ggml_merge_lora(ggml_context* ctx, struct
 // [ne03,ne02,ne01,ne00] x [ne13,ne12,ne11,ne10] => [ne03*ne13,ne02*ne12,ne01*ne11,ne00*ne10]
 __STATIC_INLINE__ struct ggml_tensor* ggml_kronecker(ggml_context* ctx, struct ggml_tensor* a, struct ggml_tensor* b) {
     return ggml_mul(ctx,
-                    ggml_upscale_ext(ctx,
+                    ggml_interpolate(ctx,
                                      a,
                                      a->ne[0] * b->ne[0],
                                      a->ne[1] * b->ne[1],
@@ -840,18 +840,34 @@ __STATIC_INLINE__ struct ggml_tensor* ggml_nn_attention_ext(struct ggml_context*
 
     float scale = (1.0f / sqrt((float)d_head));
 
-    // if (flash_attn) {
-    //     LOG_DEBUG("attention_ext L_q:%d L_k:%d n_head:%d C:%d d_head:%d N:%d", L_q, L_k, n_head, C, d_head, N);
-    // }
+    int kv_pad = 0;
+    //if (flash_attn) {
+    //    LOG_DEBUG("attention_ext L_q:%d L_k:%d n_head:%d C:%d d_head:%d N:%d", L_q, L_k, n_head, C, d_head, N);
+    //}
     //  is there anything oddly shaped?? ping Green-Sky if you can trip this assert
     GGML_ASSERT(((L_k % 256 == 0) && L_q == L_k) || !(L_k % 256 == 0));
 
     bool can_use_flash_attn = true;
+    can_use_flash_attn = can_use_flash_attn && (
+        d_head == 64 ||
+        d_head == 80 ||
+        d_head == 96 ||
+        d_head == 112 ||
+        d_head == 128 ||
+        d_head == 256
+    );
+#if 0
     can_use_flash_attn      = can_use_flash_attn && L_k % 256 == 0;
-    can_use_flash_attn      = can_use_flash_attn && d_head % 64 == 0;  // double check
-
-    // cuda max d_head seems to be 256, cpu does seem to work with 512
-    can_use_flash_attn = can_use_flash_attn && d_head <= 256;  // double check
+#else
+    if (can_use_flash_attn && L_k % 256 != 0) {
+        // TODO(Green-Sky): might be worth just padding by default
+        if (L_k == 77 || L_k == 4208 || L_k == 3952) {
+            kv_pad = GGML_PAD(L_k, 256) - L_k;
+        } else {
+            can_use_flash_attn = false;
+        }
+    }
+#endif
 
     if (mask != nullptr) {
         // TODO(Green-Sky): figure out if we can bend t5 to work too
@@ -864,11 +880,18 @@ __STATIC_INLINE__ struct ggml_tensor* ggml_nn_attention_ext(struct ggml_context*
     ggml_tensor* kqv = nullptr;
     // GGML_ASSERT((flash_attn && can_use_flash_attn) || !flash_attn);
     if (can_use_flash_attn && flash_attn) {
-        // LOG_DEBUG("using flash attention");
+        //LOG_DEBUG(" uses flash attention");
+        if (kv_pad != 0) {
+            //LOG_DEBUG(" padding k and v dim1 by %d", kv_pad);
+            k = ggml_pad(ctx, k, 0, kv_pad, 0, 0);
+        }
         k = ggml_cast(ctx, k, GGML_TYPE_F16);
 
         v = ggml_cont(ctx, ggml_permute(ctx, v, 0, 2, 1, 3));  // [N, n_head, L_k, d_head]
         v = ggml_reshape_3d(ctx, v, d_head, L_k, n_head * N);  // [N * n_head, L_k, d_head]
+        if (kv_pad != 0) {
+            v = ggml_pad(ctx, v, 0, kv_pad, 0, 0);
+        }
         v = ggml_cast(ctx, v, GGML_TYPE_F16);
 
         if (mask != nullptr) {

lora.hpp

@@ -3,7 +3,7 @@
 
 #include "ggml_extend.hpp"
 
-#define LORA_GRAPH_SIZE 15360
+#define LORA_GRAPH_BASE_SIZE 10240
 
 struct LoraModel : public GGMLRunner {
     enum lora_t {
@@ -238,7 +238,8 @@ struct LoraModel : public GGMLRunner {
     }
 
     struct ggml_cgraph* build_lora_graph(std::map<std::string, struct ggml_tensor*> model_tensors, SDVersion version) {
-        struct ggml_cgraph* gf = ggml_new_graph_custom(compute_ctx, LORA_GRAPH_SIZE, false);
+        size_t lora_graph_size = LORA_GRAPH_BASE_SIZE + lora_tensors.size() * 10;
+        struct ggml_cgraph* gf = ggml_new_graph_custom(compute_ctx, lora_graph_size, false);
 
         zero_index = ggml_new_tensor_1d(compute_ctx, GGML_TYPE_I32, 1);
         set_backend_tensor_data(zero_index, zero_index_vec.data());

model.cpp

@@ -100,6 +100,7 @@ const char* unused_tensors[] = {
     "model_ema.diffusion_model",
     "embedding_manager",
     "denoiser.sigmas",
+    "text_encoders.t5xxl.transformer.encoder.embed_tokens.weight",  // only used during training
 };
 
 bool is_unused_tensor(std::string name) {
@@ -181,6 +182,64 @@ std::unordered_map<std::string, std::string> pmid_v2_name_map = {
 std::string convert_open_clip_to_hf_clip(const std::string& name) {
     std::string new_name = name;
     std::string prefix;
+    if (contains(new_name, ".enc.")) {
+        // llama.cpp naming convention for T5
+        size_t pos = new_name.find(".enc.");
+        if (pos != std::string::npos) {
+            new_name.replace(pos, 5, ".encoder.");
+        }
+        pos = new_name.find("blk.");
+        if (pos != std::string::npos) {
+            new_name.replace(pos, 4, "block.");
+        }
+        pos = new_name.find("output_norm.");
+        if (pos != std::string::npos) {
+            new_name.replace(pos, 12, "final_layer_norm.");
+        }
+        pos = new_name.find("attn_k.");
+        if (pos != std::string::npos) {
+            new_name.replace(pos, 7, "layer.0.SelfAttention.k.");
+        }
+        pos = new_name.find("attn_v.");
+        if (pos != std::string::npos) {
+            new_name.replace(pos, 7, "layer.0.SelfAttention.v.");
+        }
+        pos = new_name.find("attn_o.");
+        if (pos != std::string::npos) {
+            new_name.replace(pos, 7, "layer.0.SelfAttention.o.");
+        }
+        pos = new_name.find("attn_q.");
+        if (pos != std::string::npos) {
+            new_name.replace(pos, 7, "layer.0.SelfAttention.q.");
+        }
+        pos = new_name.find("attn_norm.");
+        if (pos != std::string::npos) {
+            new_name.replace(pos, 10, "layer.0.layer_norm.");
+        }
+        pos = new_name.find("ffn_norm.");
+        if (pos != std::string::npos) {
+            new_name.replace(pos, 9, "layer.1.layer_norm.");
+        }
+        pos = new_name.find("ffn_up.");
+        if (pos != std::string::npos) {
+            new_name.replace(pos, 7, "layer.1.DenseReluDense.wi_1.");
+        }
+        pos = new_name.find("ffn_down.");
+        if (pos != std::string::npos) {
+            new_name.replace(pos, 9, "layer.1.DenseReluDense.wo.");
+        }
+        pos = new_name.find("ffn_gate.");
+        if (pos != std::string::npos) {
+            new_name.replace(pos, 9, "layer.1.DenseReluDense.wi_0.");
+        }
+        pos = new_name.find("attn_rel_b.");
+        if (pos != std::string::npos) {
+            new_name.replace(pos, 11, "layer.0.SelfAttention.relative_attention_bias.");
+        }
+    } else if (name == "text_encoders.t5xxl.transformer.token_embd.weight") {
+        new_name = "text_encoders.t5xxl.transformer.shared.weight";
+    }
+
     if (starts_with(new_name, "conditioner.embedders.0.open_clip.")) {
         prefix   = "cond_stage_model.";
         new_name = new_name.substr(strlen("conditioner.embedders.0.open_clip."));
@@ -279,6 +338,10 @@ std::unordered_map<std::string, std::unordered_map<std::string, std::string>> su
             {"to_v", "v"},
             {"to_out_0", "proj_out"},
             {"group_norm", "norm"},
+            {"key", "k"},
+            {"query", "q"},
+            {"value", "v"},
+            {"proj_attn", "proj_out"},
         },
     },
     {
@@ -303,6 +366,10 @@ std::unordered_map<std::string, std::unordered_map<std::string, std::string>> su
             {"to_v", "v"},
             {"to_out.0", "proj_out"},
             {"group_norm", "norm"},
+            {"key", "k"},
+            {"query", "q"},
+            {"value", "v"},
+            {"proj_attn", "proj_out"},
         },
     },
     {
@@ -374,6 +441,10 @@ std::string convert_diffusers_name_to_compvis(std::string key, char seq) {
         return format("model%cdiffusion_model%ctime_embed%c", seq, seq, seq) + std::to_string(std::stoi(m[0]) * 2 - 2) + m[1];
     }
 
+    if (match(m, std::regex(format("unet%cadd_embedding%clinear_(\\d+)(.*)", seq, seq)), key)) {
+        return format("model%cdiffusion_model%clabel_emb%c0%c", seq, seq, seq, seq) + std::to_string(std::stoi(m[0]) * 2 - 2) + m[1];
+    }
+
     if (match(m, std::regex(format("unet%cdown_blocks%c(\\d+)%c(attentions|resnets)%c(\\d+)%c(.+)", seq, seq, seq, seq, seq)), key)) {
         std::string suffix = get_converted_suffix(m[1], m[3]);
         // LOG_DEBUG("%s %s %s %s", m[0].c_str(), m[1].c_str(), m[2].c_str(), m[3].c_str());
@@ -411,6 +482,19 @@ std::string convert_diffusers_name_to_compvis(std::string key, char seq) {
         return format("cond_stage_model%ctransformer%ctext_model", seq, seq) + m[0];
     }
 
+    // clip-g
+    if (match(m, std::regex(format("te%c1%ctext_model%cencoder%clayers%c(\\d+)%c(.+)", seq, seq, seq, seq, seq, seq)), key)) {
+        return format("cond_stage_model%c1%ctransformer%ctext_model%cencoder%clayers%c", seq, seq, seq, seq, seq, seq) + m[0] + seq + m[1];
+    }
+
+    if (match(m, std::regex(format("te%c1%ctext_model(.*)", seq, seq)), key)) {
+        return format("cond_stage_model%c1%ctransformer%ctext_model", seq, seq, seq) + m[0];
+    }
+
+    if (match(m, std::regex(format("te%c1%ctext_projection", seq, seq)), key)) {
+        return format("cond_stage_model%c1%ctransformer%ctext_model%ctext_projection", seq, seq, seq, seq);
+    }
+
     // vae
     if (match(m, std::regex(format("vae%c(.*)%cconv_norm_out(.*)", seq, seq)), key)) {
         return format("first_stage_model%c%s%cnorm_out%s", seq, m[0].c_str(), seq, m[1].c_str());
@@ -547,6 +631,8 @@ std::string convert_tensor_name(std::string name) {
             std::string new_key = convert_diffusers_name_to_compvis(name_without_network_parts, '.');
             if (new_key.empty()) {
                 new_name = name;
+            } else if (new_key == "cond_stage_model.1.transformer.text_model.text_projection") {
+                new_name = new_key;
             } else {
                 new_name = new_key + "." + network_part;
             }
@@ -729,6 +815,7 @@ void f8_e4m3_to_f16_vec(uint8_t* src, uint16_t* dst, int64_t n) {
         dst[i] = f8_e4m3_to_f16(src[i]);
     }
 }
+
 void f8_e5m2_to_f16_vec(uint8_t* src, uint16_t* dst, int64_t n) {
     // support inplace op
     for (int64_t i = n - 1; i >= 0; i--) {
@@ -736,6 +823,20 @@ void f8_e5m2_to_f16_vec(uint8_t* src, uint16_t* dst, int64_t n) {
     }
 }
 
+void f64_to_f32_vec(double* src, float* dst, int64_t n) {
+    // support inplace op
+    for (int64_t i = 0; i < n; i++) {
+        dst[i] = (float)src[i];
+    }
+}
+
+void i64_to_i32_vec(int64_t* src, int32_t* dst, int64_t n) {
+    // support inplace op
+    for (int64_t i = 0; i < n; i++) {
+        dst[i] = (int32_t)src[i];
+    }
+}
+
 void convert_tensor(void* src,
                     ggml_type src_type,
                     void* dst,
@@ -970,10 +1071,14 @@ ggml_type str_to_ggml_type(const std::string& dtype) {
         ttype = GGML_TYPE_F32;
     } else if (dtype == "F32") {
         ttype = GGML_TYPE_F32;
+    } else if (dtype == "F64") {
+        ttype = GGML_TYPE_F32;
     } else if (dtype == "F8_E4M3") {
         ttype = GGML_TYPE_F16;
     } else if (dtype == "F8_E5M2") {
         ttype = GGML_TYPE_F16;
+    } else if (dtype == "I64") {
+        ttype = GGML_TYPE_I32;
     }
     return ttype;
 }
@@ -986,6 +1091,7 @@ bool ModelLoader::init_from_safetensors_file(const std::string& file_path, const
     std::ifstream file(file_path, std::ios::binary);
     if (!file.is_open()) {
         LOG_ERROR("failed to open '%s'", file_path.c_str());
+        file_paths_.pop_back();
         return false;
     }
 
@@ -997,6 +1103,7 @@ bool ModelLoader::init_from_safetensors_file(const std::string& file_path, const
     // read header size
     if (file_size_ <= ST_HEADER_SIZE_LEN) {
         LOG_ERROR("invalid safetensor file '%s'", file_path.c_str());
+        file_paths_.pop_back();
         return false;
     }
 
@@ -1010,6 +1117,7 @@ bool ModelLoader::init_from_safetensors_file(const std::string& file_path, const
     size_t header_size_ = read_u64(header_size_buf);
     if (header_size_ >= file_size_) {
         LOG_ERROR("invalid safetensor file '%s'", file_path.c_str());
+        file_paths_.pop_back();
         return false;
     }
 
@@ -1020,6 +1128,7 @@ bool ModelLoader::init_from_safetensors_file(const std::string& file_path, const
     file.read(header_buf.data(), header_size_);
     if (!file) {
         LOG_ERROR("read safetensors header failed: '%s'", file_path.c_str());
+        file_paths_.pop_back();
         return false;
     }
 
@@ -1091,6 +1200,14 @@ bool ModelLoader::init_from_safetensors_file(const std::string& file_path, const
             tensor_storage.is_f8_e5m2 = true;
             // f8 -> f16
             GGML_ASSERT(tensor_storage.nbytes() == tensor_data_size * 2);
+        } else if (dtype == "F64") {
+            tensor_storage.is_f64 = true;
+            // f64 -> f32
+            GGML_ASSERT(tensor_storage.nbytes() * 2 == tensor_data_size);
+        } else if (dtype == "I64") {
+            tensor_storage.is_i64 = true;
+            // i64 -> i32
+            GGML_ASSERT(tensor_storage.nbytes() * 2 == tensor_data_size);
         } else {
             GGML_ASSERT(tensor_storage.nbytes() == tensor_data_size);
         }
@@ -1107,18 +1224,45 @@ bool ModelLoader::init_from_safetensors_file(const std::string& file_path, const
 /*================================================= DiffusersModelLoader ==================================================*/
 
 bool ModelLoader::init_from_diffusers_file(const std::string& file_path, const std::string& prefix) {
-    std::string unet_path = path_join(file_path, "unet/diffusion_pytorch_model.safetensors");
-    std::string vae_path  = path_join(file_path, "vae/diffusion_pytorch_model.safetensors");
-    std::string clip_path = path_join(file_path, "text_encoder/model.safetensors");
+    std::string unet_path   = path_join(file_path, "unet/diffusion_pytorch_model.safetensors");
+    std::string vae_path    = path_join(file_path, "vae/diffusion_pytorch_model.safetensors");
+    std::string clip_path   = path_join(file_path, "text_encoder/model.safetensors");
+    std::string clip_g_path = path_join(file_path, "text_encoder_2/model.safetensors");
 
     if (!init_from_safetensors_file(unet_path, "unet.")) {
         return false;
     }
+    for (auto ts : tensor_storages) {
+        if (ts.name.find("add_embedding") != std::string::npos || ts.name.find("label_emb") != std::string::npos) {
+            // probably SDXL
+            LOG_DEBUG("Fixing name for SDXL output blocks.2.2");
+            for (auto& tensor_storage : tensor_storages) {
+                int len  = 34;
+                auto pos = tensor_storage.name.find("unet.up_blocks.0.upsamplers.0.conv");
+                if (pos == std::string::npos) {
+                    len = 44;
+                    pos = tensor_storage.name.find("model.diffusion_model.output_blocks.2.1.conv");
+                }
+                if (pos != std::string::npos) {
+                    tensor_storage.name = "model.diffusion_model.output_blocks.2.2.conv" + tensor_storage.name.substr(len);
+                    LOG_DEBUG("NEW NAME: %s", tensor_storage.name.c_str());
+                    add_preprocess_tensor_storage_types(tensor_storages_types, tensor_storage.name, tensor_storage.type);
+                }
+            }
+            break;
+        }
+    }
+
     if (!init_from_safetensors_file(vae_path, "vae.")) {
-        return false;
+        LOG_WARN("Couldn't find working VAE in %s", file_path.c_str());
+        // return false;
     }
     if (!init_from_safetensors_file(clip_path, "te.")) {
-        return false;
+        LOG_WARN("Couldn't find working text encoder in %s", file_path.c_str());
+        // return false;
+    }
+    if (!init_from_safetensors_file(clip_g_path, "te.1.")) {
+        LOG_DEBUG("Couldn't find working second text encoder in %s", file_path.c_str());
     }
     return true;
 }
@@ -1481,6 +1625,15 @@ bool ModelLoader::init_from_ckpt_file(const std::string& file_path, const std::s
     return true;
 }
 
+bool ModelLoader::model_is_unet() {
+    for (auto& tensor_storage : tensor_storages) {
+        if (tensor_storage.name.find("model.diffusion_model.input_blocks.") != std::string::npos) {
+            return true;
+        }
+    }
+    return false;
+}
+
 SDVersion ModelLoader::get_sd_version() {
     TensorStorage token_embedding_weight, input_block_weight;
     bool input_block_checked = false;
@@ -1503,7 +1656,7 @@ SDVersion ModelLoader::get_sd_version() {
             if (tensor_storage.name.find("model.diffusion_model.joint_blocks.") != std::string::npos) {
                 return VERSION_SD3;
             }
-            if (tensor_storage.name.find("model.diffusion_model.input_blocks.") != std::string::npos) {
+            if (tensor_storage.name.find("model.diffusion_model.input_blocks.") != std::string::npos || tensor_storage.name.find("unet.down_blocks.") != std::string::npos) {
                 is_unet = true;
                 if (has_multiple_encoders) {
                     is_xl = true;
@@ -1512,7 +1665,7 @@ SDVersion ModelLoader::get_sd_version() {
                     }
                 }
             }
-            if (tensor_storage.name.find("conditioner.embedders.1") != std::string::npos || tensor_storage.name.find("cond_stage_model.1") != std::string::npos) {
+            if (tensor_storage.name.find("conditioner.embedders.1") != std::string::npos || tensor_storage.name.find("cond_stage_model.1") != std::string::npos || tensor_storage.name.find("te.1") != std::string::npos) {
                 has_multiple_encoders = true;
                 if (is_unet) {
                     is_xl = true;
@@ -1534,7 +1687,7 @@ SDVersion ModelLoader::get_sd_version() {
             token_embedding_weight = tensor_storage;
             // break;
         }
-        if (tensor_storage.name == "model.diffusion_model.input_blocks.0.0.weight" || tensor_storage.name == "model.diffusion_model.img_in.weight") {
+        if (tensor_storage.name == "model.diffusion_model.input_blocks.0.0.weight" || tensor_storage.name == "model.diffusion_model.img_in.weight" || tensor_storage.name == "unet.conv_in.weight") {
             input_block_weight  = tensor_storage;
             input_block_checked = true;
             if (found_family) {
@@ -1543,10 +1696,14 @@ SDVersion ModelLoader::get_sd_version() {
         }
     }
     bool is_inpaint = input_block_weight.ne[2] == 9;
+    bool is_ip2p    = input_block_weight.ne[2] == 8;
     if (is_xl) {
         if (is_inpaint) {
             return VERSION_SDXL_INPAINT;
         }
+        if (is_ip2p) {
+            return VERSION_SDXL_PIX2PIX;
+        }
         return VERSION_SDXL;
     }
 
@@ -1562,6 +1719,9 @@ SDVersion ModelLoader::get_sd_version() {
         if (is_inpaint) {
             return VERSION_SD1_INPAINT;
         }
+        if (is_ip2p) {
+            return VERSION_SD1_PIX2PIX;
+        }
         return VERSION_SD1;
     } else if (token_embedding_weight.ne[0] == 1024) {
         if (is_inpaint) {
@@ -1619,7 +1779,7 @@ ggml_type ModelLoader::get_diffusion_model_wtype() {
             continue;
         }
 
-        if (tensor_storage.name.find("model.diffusion_model.") == std::string::npos) {
+        if (tensor_storage.name.find("model.diffusion_model.") == std::string::npos && tensor_storage.name.find("unet.") == std::string::npos) {
             continue;
         }
 
@@ -1783,6 +1943,7 @@ bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb, ggml_backend
         };
         int tensor_count = 0;
         int64_t t1       = ggml_time_ms();
+        bool partial     = false;
         for (auto& tensor_storage : processed_tensor_storages) {
             if (tensor_storage.file_index != file_index) {
                 ++tensor_count;
@@ -1807,7 +1968,12 @@ bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb, ggml_backend
                 // for the CPU and Metal backend, we can copy directly into the tensor
                 if (tensor_storage.type == dst_tensor->type) {
                     GGML_ASSERT(ggml_nbytes(dst_tensor) == tensor_storage.nbytes());
-                    read_data(tensor_storage, (char*)dst_tensor->data, nbytes_to_read);
+                    if (tensor_storage.is_f64 || tensor_storage.is_i64) {
+                        read_buffer.resize(tensor_storage.nbytes_to_read());
+                        read_data(tensor_storage, (char*)read_buffer.data(), nbytes_to_read);
+                    } else {
+                        read_data(tensor_storage, (char*)dst_tensor->data, nbytes_to_read);
+                    }
 
                     if (tensor_storage.is_bf16) {
                         // inplace op
@@ -1818,9 +1984,13 @@ bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb, ggml_backend
                     } else if (tensor_storage.is_f8_e5m2) {
                         // inplace op
                         f8_e5m2_to_f16_vec((uint8_t*)dst_tensor->data, (uint16_t*)dst_tensor->data, tensor_storage.nelements());
+                    } else if (tensor_storage.is_f64) {
+                        f64_to_f32_vec((double*)read_buffer.data(), (float*)dst_tensor->data, tensor_storage.nelements());
+                    } else if (tensor_storage.is_i64) {
+                        i64_to_i32_vec((int64_t*)read_buffer.data(), (int32_t*)dst_tensor->data, tensor_storage.nelements());
                     }
                 } else {
-                    read_buffer.resize(tensor_storage.nbytes());
+                    read_buffer.resize(std::max(tensor_storage.nbytes(), tensor_storage.nbytes_to_read()));
                     read_data(tensor_storage, (char*)read_buffer.data(), nbytes_to_read);
 
                     if (tensor_storage.is_bf16) {
@@ -1832,13 +2002,19 @@ bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb, ggml_backend
                     } else if (tensor_storage.is_f8_e5m2) {
                         // inplace op
                         f8_e5m2_to_f16_vec((uint8_t*)read_buffer.data(), (uint16_t*)read_buffer.data(), tensor_storage.nelements());
+                    } else if (tensor_storage.is_f64) {
+                        // inplace op
+                        f64_to_f32_vec((double*)read_buffer.data(), (float*)read_buffer.data(), tensor_storage.nelements());
+                    } else if (tensor_storage.is_i64) {
+                        // inplace op
+                        i64_to_i32_vec((int64_t*)read_buffer.data(), (int32_t*)read_buffer.data(), tensor_storage.nelements());
                     }
 
                     convert_tensor((void*)read_buffer.data(), tensor_storage.type, dst_tensor->data,
                                    dst_tensor->type, (int)tensor_storage.nelements() / (int)tensor_storage.ne[0], (int)tensor_storage.ne[0]);
                 }
             } else {
-                read_buffer.resize(tensor_storage.nbytes());
+                read_buffer.resize(std::max(tensor_storage.nbytes(), tensor_storage.nbytes_to_read()));
                 read_data(tensor_storage, (char*)read_buffer.data(), nbytes_to_read);
 
                 if (tensor_storage.is_bf16) {
@@ -1850,6 +2026,12 @@ bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb, ggml_backend
                 } else if (tensor_storage.is_f8_e5m2) {
                     // inplace op
                     f8_e5m2_to_f16_vec((uint8_t*)read_buffer.data(), (uint16_t*)read_buffer.data(), tensor_storage.nelements());
+                } else if (tensor_storage.is_f64) {
+                    // inplace op
+                    f64_to_f32_vec((double*)read_buffer.data(), (float*)read_buffer.data(), tensor_storage.nelements());
+                } else if (tensor_storage.is_i64) {
+                    // inplace op
+                    i64_to_i32_vec((int64_t*)read_buffer.data(), (int32_t*)read_buffer.data(), tensor_storage.nelements());
                 }
 
                 if (tensor_storage.type == dst_tensor->type) {
@@ -1864,15 +2046,21 @@ bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb, ggml_backend
                     ggml_backend_tensor_set(dst_tensor, convert_buffer.data(), 0, ggml_nbytes(dst_tensor));
                 }
             }
-            int64_t t2 = ggml_time_ms();
-            pretty_progress(++tensor_count, processed_tensor_storages.size(), (t2 - t1) / 1000.0f);
-            t1 = t2;
+            size_t tensor_max = processed_tensor_storages.size();
+            int64_t t2        = ggml_time_ms();
+            pretty_progress(++tensor_count, tensor_max, (t2 - t1) / 1000.0f);
+            t1      = t2;
+            partial = tensor_count != tensor_max;
         }
 
         if (zip != NULL) {
             zip_close(zip);
         }
 
+        if (partial) {
+            printf("\n");
+        }
+
         if (!success) {
             break;
         }
@@ -1950,6 +2138,41 @@ bool ModelLoader::load_tensors(std::map<std::string, struct ggml_tensor*>& tenso
     return true;
 }
 
+std::vector<std::pair<std::string, ggml_type>> parse_tensor_type_rules(const std::string& tensor_type_rules) {
+    std::vector<std::pair<std::string, ggml_type>> result;
+    for (const auto& item : splitString(tensor_type_rules, ',')) {
+        if (item.size() == 0)
+            continue;
+        std::string::size_type pos = item.find('=');
+        if (pos == std::string::npos) {
+            LOG_WARN("ignoring invalid quant override \"%s\"", item.c_str());
+            continue;
+        }
+        std::string tensor_pattern = item.substr(0, pos);
+        std::string type_name      = item.substr(pos + 1);
+
+        ggml_type tensor_type = GGML_TYPE_COUNT;
+
+        if (type_name == "f32") {
+            tensor_type = GGML_TYPE_F32;
+        } else {
+            for (size_t i = 0; i < SD_TYPE_COUNT; i++) {
+                auto trait = ggml_get_type_traits((ggml_type)i);
+                if (trait->to_float && trait->type_size && type_name == trait->type_name) {
+                    tensor_type = (ggml_type)i;
+                }
+            }
+        }
+
+        if (tensor_type != GGML_TYPE_COUNT) {
+            result.emplace_back(tensor_pattern, tensor_type);
+        } else {
+            LOG_WARN("ignoring invalid quant override \"%s\"", item.c_str());
+        }
+    }
+    return result;
+}
+
 bool ModelLoader::tensor_should_be_converted(const TensorStorage& tensor_storage, ggml_type type) {
     const std::string& name = tensor_storage.name;
     if (type != GGML_TYPE_COUNT) {
@@ -1981,7 +2204,7 @@ bool ModelLoader::tensor_should_be_converted(const TensorStorage& tensor_storage
     return false;
 }
 
-bool ModelLoader::save_to_gguf_file(const std::string& file_path, ggml_type type) {
+bool ModelLoader::save_to_gguf_file(const std::string& file_path, ggml_type type, const std::string& tensor_type_rules_str) {
     auto backend    = ggml_backend_cpu_init();
     size_t mem_size = 1 * 1024 * 1024;  // for padding
     mem_size += tensor_storages.size() * ggml_tensor_overhead();
@@ -1991,12 +2214,23 @@ bool ModelLoader::save_to_gguf_file(const std::string& file_path, ggml_type type
 
     gguf_context* gguf_ctx = gguf_init_empty();
 
+    auto tensor_type_rules = parse_tensor_type_rules(tensor_type_rules_str);
+
     auto on_new_tensor_cb = [&](const TensorStorage& tensor_storage, ggml_tensor** dst_tensor) -> bool {
         const std::string& name = tensor_storage.name;
+        ggml_type tensor_type   = tensor_storage.type;
+        ggml_type dst_type      = type;
 
-        ggml_type tensor_type = tensor_storage.type;
-        if (tensor_should_be_converted(tensor_storage, type)) {
-            tensor_type = type;
+        for (const auto& tensor_type_rule : tensor_type_rules) {
+            std::regex pattern(tensor_type_rule.first);
+            if (std::regex_search(name, pattern)) {
+                dst_type = tensor_type_rule.second;
+                break;
+            }
+        }
+
+        if (tensor_should_be_converted(tensor_storage, dst_type)) {
+            tensor_type = dst_type;
         }
 
         ggml_tensor* tensor = ggml_new_tensor(ggml_ctx, tensor_type, tensor_storage.n_dims, tensor_storage.ne);
@@ -2055,7 +2289,7 @@ int64_t ModelLoader::get_params_mem_size(ggml_backend_t backend, ggml_type type)
     return mem_size;
 }
 
-bool convert(const char* input_path, const char* vae_path, const char* output_path, sd_type_t output_type) {
+bool convert(const char* input_path, const char* vae_path, const char* output_path, sd_type_t output_type, const char* tensor_type_rules) {
     ModelLoader model_loader;
 
     if (!model_loader.init_from_file(input_path)) {
@@ -2069,6 +2303,6 @@ bool convert(const char* input_path, const char* vae_path, const char* output_pa
             return false;
         }
     }
-    bool success = model_loader.save_to_gguf_file(output_path, (ggml_type)output_type);
+    bool success = model_loader.save_to_gguf_file(output_path, (ggml_type)output_type, tensor_type_rules);
     return success;
 }

model.h

@@ -21,10 +21,12 @@
 enum SDVersion {
     VERSION_SD1,
     VERSION_SD1_INPAINT,
+    VERSION_SD1_PIX2PIX,
     VERSION_SD2,
     VERSION_SD2_INPAINT,
     VERSION_SDXL,
     VERSION_SDXL_INPAINT,
+    VERSION_SDXL_PIX2PIX,
     VERSION_SVD,
     VERSION_SD3,
     VERSION_FLUX,
@@ -47,7 +49,7 @@ static inline bool sd_version_is_sd3(SDVersion version) {
 }
 
 static inline bool sd_version_is_sd1(SDVersion version) {
-    if (version == VERSION_SD1 || version == VERSION_SD1_INPAINT) {
+    if (version == VERSION_SD1 || version == VERSION_SD1_INPAINT || version == VERSION_SD1_PIX2PIX) {
         return true;
     }
     return false;
@@ -61,7 +63,7 @@ static inline bool sd_version_is_sd2(SDVersion version) {
 }
 
 static inline bool sd_version_is_sdxl(SDVersion version) {
-    if (version == VERSION_SDXL || version == VERSION_SDXL_INPAINT) {
+    if (version == VERSION_SDXL || version == VERSION_SDXL_INPAINT || version == VERSION_SDXL_PIX2PIX) {
         return true;
     }
     return false;
@@ -81,6 +83,14 @@ static inline bool sd_version_is_dit(SDVersion version) {
     return false;
 }
 
+static inline bool sd_version_is_unet_edit(SDVersion version) {
+    return version == VERSION_SD1_PIX2PIX || version == VERSION_SDXL_PIX2PIX;
+}
+
+static bool sd_version_is_inpaint_or_unet_edit(SDVersion version) {
+    return sd_version_is_unet_edit(version) || sd_version_is_inpaint(version);
+}
+
 enum PMVersion {
     PM_VERSION_1,
     PM_VERSION_2,
@@ -92,6 +102,8 @@ struct TensorStorage {
     bool is_bf16            = false;
     bool is_f8_e4m3         = false;
     bool is_f8_e5m2         = false;
+    bool is_f64             = false;
+    bool is_i64             = false;
     int64_t ne[SD_MAX_DIMS] = {1, 1, 1, 1, 1};
     int n_dims              = 0;
 
@@ -123,6 +135,8 @@ struct TensorStorage {
     int64_t nbytes_to_read() const {
         if (is_bf16 || is_f8_e4m3 || is_f8_e5m2) {
             return nbytes() / 2;
+        } else if (is_f64 || is_i64) {
+            return nbytes() * 2;
         } else {
             return nbytes();
         }
@@ -173,6 +187,10 @@ struct TensorStorage {
             type_name = "f8_e4m3";
         } else if (is_f8_e5m2) {
             type_name = "f8_e5m2";
+        } else if (is_f64) {
+            type_name = "f64";
+        } else if (is_i64) {
+            type_name = "i64";
         }
         ss << name << " | " << type_name << " | ";
         ss << n_dims << " [";
@@ -210,6 +228,7 @@ public:
     std::map<std::string, enum ggml_type> tensor_storages_types;
 
     bool init_from_file(const std::string& file_path, const std::string& prefix = "");
+    bool model_is_unet();
     SDVersion get_sd_version();
     ggml_type get_sd_wtype();
     ggml_type get_conditioner_wtype();
@@ -221,7 +240,7 @@ public:
                       ggml_backend_t backend,
                       std::set<std::string> ignore_tensors = {});
 
-    bool save_to_gguf_file(const std::string& file_path, ggml_type type);
+    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);
     ~ModelLoader() = default;

stable-diffusion.h

@@ -30,7 +30,8 @@ extern "C" {
 
 enum rng_type_t {
     STD_DEFAULT_RNG,
-    CUDA_RNG
+    CUDA_RNG,
+    RNG_TYPE_COUNT
 };
 
 enum sample_method_t {
@@ -46,7 +47,7 @@ enum sample_method_t {
     LCM,
     DDIM_TRAILING,
     TCD,
-    N_SAMPLE_METHODS
+    SAMPLE_METHOD_COUNT
 };
 
 enum schedule_t {
@@ -56,7 +57,7 @@ enum schedule_t {
     EXPONENTIAL,
     AYS,
     GITS,
-    N_SCHEDULES
+    SCHEDULE_COUNT
 };
 
 // same as enum ggml_type
@@ -103,8 +104,6 @@ enum sd_type_t {
     SD_TYPE_COUNT = 39,
 };
 
-SD_API const char* sd_type_name(enum sd_type_t type);
-
 enum sd_log_level_t {
     SD_LOG_DEBUG,
     SD_LOG_INFO,
@@ -112,6 +111,98 @@ enum sd_log_level_t {
     SD_LOG_ERROR
 };
 
+typedef struct {
+    const char* model_path;
+    const char* clip_l_path;
+    const char* clip_g_path;
+    const char* t5xxl_path;
+    const char* diffusion_model_path;
+    const char* vae_path;
+    const char* taesd_path;
+    const char* control_net_path;
+    const char* lora_model_dir;
+    const char* embedding_dir;
+    const char* stacked_id_embed_dir;
+    bool vae_decode_only;
+    bool vae_tiling;
+    bool free_params_immediately;
+    int n_threads;
+    enum sd_type_t wtype;
+    enum rng_type_t rng_type;
+    enum schedule_t schedule;
+    bool keep_clip_on_cpu;
+    bool keep_control_net_on_cpu;
+    bool keep_vae_on_cpu;
+    bool diffusion_flash_attn;
+    bool chroma_use_dit_mask;
+    bool chroma_use_t5_mask;
+    int chroma_t5_mask_pad;
+} sd_ctx_params_t;
+
+typedef struct {
+    uint32_t width;
+    uint32_t height;
+    uint32_t channel;
+    uint8_t* data;
+} sd_image_t;
+
+typedef struct {
+    int* layers;
+    size_t layer_count;
+    float layer_start;
+    float layer_end;
+    float scale;
+} sd_slg_params_t;
+
+typedef struct {
+    float txt_cfg;
+    float img_cfg;
+    float min_cfg;
+    float distilled_guidance;
+    sd_slg_params_t slg;
+} sd_guidance_params_t;
+
+typedef struct {
+    const char* prompt;
+    const char* negative_prompt;
+    int clip_skip;
+    sd_guidance_params_t guidance;
+    sd_image_t init_image;
+    sd_image_t* ref_images;
+    int ref_images_count;
+    sd_image_t mask_image;
+    int width;
+    int height;
+    enum sample_method_t sample_method;
+    int sample_steps;
+    float eta;
+    float strength;
+    int64_t seed;
+    int batch_count;
+    const sd_image_t* control_cond;
+    float control_strength;
+    float style_strength;
+    bool normalize_input;
+    const char* input_id_images_path;
+} sd_img_gen_params_t;
+
+typedef struct {
+    sd_image_t init_image;
+    int width;
+    int height;
+    sd_guidance_params_t guidance;
+    enum sample_method_t sample_method;
+    int sample_steps;
+    float strength;
+    int64_t seed;
+    int video_frames;
+    int motion_bucket_id;
+    int fps;
+    float augmentation_level;
+} sd_vid_gen_params_t;
+
+typedef struct sd_ctx_t sd_ctx_t;
+
 typedef void (*sd_log_cb_t)(enum sd_log_level_t level, const char* text, void* data);
 typedef void (*sd_progress_cb_t)(int step, int steps, float time, void* data);
 
@@ -120,134 +211,27 @@ SD_API void sd_set_progress_callback(sd_progress_cb_t cb, void* data);
 SD_API int32_t get_num_physical_cores();
 SD_API const char* sd_get_system_info();
 
-typedef struct {
-    uint32_t width;
-    uint32_t height;
-    uint32_t channel;
-    uint8_t* data;
-} sd_image_t;
+SD_API const char* sd_type_name(enum sd_type_t type);
+SD_API enum sd_type_t str_to_sd_type(const char* str);
+SD_API const char* sd_rng_type_name(enum rng_type_t rng_type);
+SD_API enum rng_type_t str_to_rng_type(const char* str);
+SD_API const char* sd_sample_method_name(enum sample_method_t sample_method);
+SD_API enum sample_method_t str_to_sample_method(const char* str);
+SD_API const char* sd_schedule_name(enum schedule_t schedule);
+SD_API enum schedule_t str_to_schedule(const char* str);
 
-typedef struct sd_ctx_t sd_ctx_t;
-
-SD_API sd_ctx_t* new_sd_ctx(const char* model_path,
-                            const char* clip_l_path,
-                            const char* clip_g_path,
-                            const char* t5xxl_path,
-                            const char* diffusion_model_path,
-                            const char* vae_path,
-                            const char* taesd_path,
-                            const char* control_net_path_c_str,
-                            const char* lora_model_dir,
-                            const char* embed_dir_c_str,
-                            const char* stacked_id_embed_dir_c_str,
-                            bool vae_decode_only,
-                            bool vae_tiling,
-                            bool free_params_immediately,
-                            int n_threads,
-                            enum sd_type_t wtype,
-                            enum rng_type_t rng_type,
-                            enum schedule_t s,
-                            bool keep_clip_on_cpu,
-                            bool keep_control_net_cpu,
-                            bool keep_vae_on_cpu,
-                            bool diffusion_flash_attn,
-                            bool chroma_use_dit_mask,
-                            bool chroma_use_t5_mask,
-                            int chroma_t5_mask_pad);
+SD_API void sd_ctx_params_init(sd_ctx_params_t* sd_ctx_params);
+SD_API char* sd_ctx_params_to_str(const sd_ctx_params_t* sd_ctx_params);
 
+SD_API sd_ctx_t* new_sd_ctx(const sd_ctx_params_t* sd_ctx_params);
 SD_API void free_sd_ctx(sd_ctx_t* sd_ctx);
 
-SD_API sd_image_t* txt2img(sd_ctx_t* sd_ctx,
-                           const char* prompt,
-                           const char* negative_prompt,
-                           int clip_skip,
-                           float cfg_scale,
-                           float guidance,
-                           float eta,
-                           int width,
-                           int height,
-                           enum sample_method_t sample_method,
-                           int sample_steps,
-                           int64_t seed,
-                           int batch_count,
-                           const sd_image_t* control_cond,
-                           float control_strength,
-                           float style_strength,
-                           bool normalize_input,
-                           const char* input_id_images_path,
-                           int* skip_layers,
-                           size_t skip_layers_count,
-                           float slg_scale,
-                           float skip_layer_start,
-                           float skip_layer_end);
+SD_API void sd_img_gen_params_init(sd_img_gen_params_t* sd_img_gen_params);
+SD_API char* sd_img_gen_params_to_str(const sd_img_gen_params_t* sd_img_gen_params);
+SD_API sd_image_t* generate_image(sd_ctx_t* sd_ctx, const sd_img_gen_params_t* sd_img_gen_params);
 
-SD_API sd_image_t* img2img(sd_ctx_t* sd_ctx,
-                           sd_image_t init_image,
-                           sd_image_t mask_image,
-                           const char* prompt,
-                           const char* negative_prompt,
-                           int clip_skip,
-                           float cfg_scale,
-                           float guidance,
-                           float eta,
-                           int width,
-                           int height,
-                           enum sample_method_t sample_method,
-                           int sample_steps,
-                           float strength,
-                           int64_t seed,
-                           int batch_count,
-                           const sd_image_t* control_cond,
-                           float control_strength,
-                           float style_strength,
-                           bool normalize_input,
-                           const char* input_id_images_path,
-                           int* skip_layers,
-                           size_t skip_layers_count,
-                           float slg_scale,
-                           float skip_layer_start,
-                           float skip_layer_end);
-
-SD_API sd_image_t* img2vid(sd_ctx_t* sd_ctx,
-                           sd_image_t init_image,
-                           int width,
-                           int height,
-                           int video_frames,
-                           int motion_bucket_id,
-                           int fps,
-                           float augmentation_level,
-                           float min_cfg,
-                           float cfg_scale,
-                           enum sample_method_t sample_method,
-                           int sample_steps,
-                           float strength,
-                           int64_t seed);
-
-SD_API sd_image_t* edit(sd_ctx_t* sd_ctx,
-                        sd_image_t* ref_images,
-                        int ref_images_count,
-                        const char* prompt,
-                        const char* negative_prompt,
-                        int clip_skip,
-                        float cfg_scale,
-                        float guidance,
-                        float eta,
-                        int width,
-                        int height,
-                        enum sample_method_t sample_method,
-                        int sample_steps,
-                        float strength,
-                        int64_t seed,
-                        int batch_count,
-                        const sd_image_t* control_cond,
-                        float control_strength,
-                        float style_strength,
-                        bool normalize_input,
-                        int* skip_layers,
-                        size_t skip_layers_count,
-                        float slg_scale,
-                        float skip_layer_start,
-                        float skip_layer_end);
+SD_API void sd_vid_gen_params_init(sd_vid_gen_params_t* sd_vid_gen_params);
+SD_API sd_image_t* generate_video(sd_ctx_t* sd_ctx, const sd_vid_gen_params_t* sd_vid_gen_params);  // broken
 
 typedef struct upscaler_ctx_t upscaler_ctx_t;
 
@@ -257,7 +241,11 @@ SD_API void free_upscaler_ctx(upscaler_ctx_t* upscaler_ctx);
 
 SD_API sd_image_t upscale(upscaler_ctx_t* upscaler_ctx, sd_image_t input_image, uint32_t upscale_factor);
 
-SD_API bool convert(const char* input_path, const char* vae_path, const char* output_path, enum sd_type_t output_type);
+SD_API bool convert(const char* input_path,
+                    const char* vae_path,
+                    const char* output_path,
+                    enum sd_type_t output_type,
+                    const char* tensor_type_rules);
 
 SD_API uint8_t* preprocess_canny(uint8_t* img,
                                  int width,

unet.hpp

@@ -207,6 +207,8 @@ public:
         }
         if (sd_version_is_inpaint(version)) {
             in_channels = 9;
+        } else if (sd_version_is_unet_edit(version)) {
+            in_channels = 8;
         }
 
         // dims is always 2

util.cpp

@@ -441,10 +441,6 @@ const char* sd_get_system_info() {
     return buffer;
 }
 
-const char* sd_type_name(enum sd_type_t type) {
-    return ggml_type_name((ggml_type)type);
-}
-
 sd_image_f32_t sd_image_t_to_sd_image_f32_t(sd_image_t image) {
     sd_image_f32_t converted_image;
     converted_image.width   = image.width;

util.h

@@ -7,6 +7,9 @@
 
 #include "stable-diffusion.h"
 
+#define SAFE_STR(s) ((s) ? (s) : "")
+#define BOOL_STR(b) ((b) ? "true" : "false")
+
 bool ends_with(const std::string& str, const std::string& ending);
 bool starts_with(const std::string& str, const std::string& start);
 bool contains(const std::string& str, const std::string& substr);