feat: allow LoRAs with negative multiplier (#83)

* Allow Loras with negative weight, too.

There are a couple of loras, which serve to adjust certain concepts in
both positive and negative directions (like exposure, detail level etc).

The current code rejects them if loaded with a negative weight, but I
suggest that this check can simply be dropped.

* ignore lora in the case of multiplier == 0.f

---------

Co-authored-by: Urs Ganse <urs@nerd2nerd.org>
Co-authored-by: leejet <leejet714@gmail.com>

.clang-format

@@ -0,0 +1,13 @@
+BasedOnStyle: Chromium
+UseTab: Never
+IndentWidth: 4
+TabWidth: 4
+AllowShortIfStatementsOnASingleLine: false
+IndentCaseLabels: false
+ColumnLimit: 0
+AccessModifierOffset: -4
+NamespaceIndentation: All
+FixNamespaceComments: false 
+AlignAfterOpenBracket: true
+AlignConsecutiveAssignments: true
+IndentCaseLabels: true

.gitignore

@@ -3,3 +3,4 @@ test/
 
 .cache/
 *.swp
+.vscode/

CMakeLists.txt

@@ -24,6 +24,7 @@ endif()
 # general
 #option(SD_BUILD_TESTS                "sd: build tests"    ${SD_STANDALONE})
 option(SD_BUILD_EXAMPLES             "sd: build examples" ${SD_STANDALONE})
+option(BUILD_SHARED_LIBS             "sd: build shared libs" OFF)
 #option(SD_BUILD_SERVER               "sd: build server example"                           ON)
 
 

README.md

@@ -18,13 +18,19 @@ Inference of [Stable Diffusion](https://github.com/CompVis/stable-diffusion) in
 - Original `txt2img` and `img2img` mode
 - Negative prompt
 - [stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui) style tokenizer (not all the features, only token weighting for now)
+- LoRA support, same as [stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#lora)
+- Latent Consistency Models support(LCM/LCM-LoRA)
 - Sampling method
     - `Euler A`
     - `Euler`
     - `Heun`
+    - `DPM2`
     - `DPM++ 2M`
     - [`DPM++ 2M v2`](https://github.com/AUTOMATIC1111/stable-diffusion-webui/discussions/8457)
+    - `DPM++ 2S a`
+    - [`LCM`](https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/13952)
 - Cross-platform reproducibility (`--rng cuda`, consistent with the `stable-diffusion-webui GPU RNG`)
+- Embedds generation parameters into png output as webui-compatible text string
 - Supported platforms
     - Linux
     - Mac OS
@@ -38,7 +44,6 @@ Inference of [Stable Diffusion](https://github.com/CompVis/stable-diffusion) in
 - [ ] Make inference faster
     - The current implementation of ggml_conv_2d is slow and has high memory usage
 - [ ] Continuing to reduce memory usage (quantizing the weights of ggml_conv_2d)
-- [ ] LoRA support
 - [ ] k-quants support
 
 ## Usage
@@ -69,7 +74,7 @@ git submodule update
     ```shell
     curl -L -O https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt
     # curl -L -O https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.safetensors
-    # curl -L -o https://huggingface.co/stabilityai/stable-diffusion-2-1/blob/main/v2-1_768-nonema-pruned.safetensors
+    # curl -L -O https://huggingface.co/stabilityai/stable-diffusion-2-1/blob/main/v2-1_768-nonema-pruned.safetensors
     ```
 
 - convert weights to ggml model format
@@ -77,6 +82,7 @@ git submodule update
     ```shell
     cd models
     pip install -r requirements.txt
+    # (optional) python convert_diffusers_to_original_stable_diffusion.py --model_path [path to diffusers weights] --checkpoint_path [path to weights]
     python convert.py [path to weights] --out_type [output precision]
     # For example, python convert.py sd-v1-4.ckpt --out_type f16
     ```
@@ -120,6 +126,7 @@ arguments:
   -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 model
+  --lora-model-dir [DIR]             lora model directory
   -i, --init-img [IMAGE]             path to the input image, required by img2img
   -o, --output OUTPUT                path to write result image to (default: .\output.png)
   -p, --prompt [PROMPT]              the prompt to render
@@ -129,11 +136,12 @@ arguments:
                                      1.0 corresponds to full destruction of information in init image
   -H, --height H                     image height, in pixel space (default: 512)
   -W, --width W                      image width, in pixel space (default: 512)
-  --sampling-method {euler, euler_a, heun, dpm++2m, dpm++2mv2}
+  --sampling-method {euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, lcm}
                                      sampling method (default: "euler_a")
   --steps  STEPS                     number of sample steps (default: 20)
   --rng {std_default, cuda}          RNG (default: cuda)
   -s SEED, --seed SEED               RNG seed (default: 42, use random seed for < 0)
+  --schedule {discrete, karras}      Denoiser sigma schedule (default: discrete)
   -v, --verbose                      print extra info
 ```
 
@@ -162,6 +170,45 @@ Using formats of different precisions will yield results of varying quality.
   <img src="./assets/img2img_output.png" width="256x">
 </p>
 
+#### with LoRA
+
+- convert lora weights to ggml model format
+
+    ```shell
+    cd models
+    python convert.py [path to weights] --lora
+    # For example, python convert.py marblesh.safetensors
+    ```
+
+- You can specify the directory where the lora weights are stored via `--lora-model-dir`. If not specified, the default is the current working directory.
+
+- LoRA is specified via prompt, just like [stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#lora).
+
+Here's a simple example:
+
+```
+./bin/sd -m ../models/v1-5-pruned-emaonly-ggml-model-f16.bin -p "a lovely cat<lora:marblesh:1>" --lora-model-dir ../models
+```
+
+`../models/marblesh-ggml-lora.bin` will be applied to the model
+
+#### LCM/LCM-LoRA
+
+- Download LCM-LoRA form https://huggingface.co/latent-consistency/lcm-lora-sdv1-5
+- Specify LCM-LoRA by adding `<lora:lcm-lora-sdv1-5:1>` to prompt
+- It's advisable to set `--cfg-scale` to `1.0` instead of the default `7.0`. For `--steps`, a range of `2-8` steps is recommended. For `--sampling-method`, `lcm`/`euler_a` is recommended.
+
+Here's a simple example:
+
+```
+./bin/sd -m ../models/v1-5-pruned-emaonly-ggml-model-f16.bin -p "a lovely cat<lora:lcm-lora-sdv1-5:1>" --steps 4 --lora-model-dir ../models -v --cfg-scale 1
+```
+
+| without LCM-LoRA (--cfg-scale 7)  | with LCM-LoRA (--cfg-scale 1)  |
+| ----  |----    |
+| ![](./assets/without_lcm.png) |![](./assets/with_lcm.png)  |
+
+
 ### Docker
 
 #### Building using Docker
@@ -185,6 +232,11 @@ docker run -v /path/to/models:/models -v /path/to/output/:/output sd [args...]
 |  **Disk**        | 2.7G | 2.0G | 1.7G | 1.6G | 1.6G | 1.5G | 1.5G |
 |  **Memory**(txt2img - 512 x 512) | ~2.8G | ~2.3G | ~2.1G | ~2.0G | ~2.0G | ~2.0G | ~2.0G |
 
+## Contributors
+
+Thank you to all the people who have already contributed to stable-diffusion.cpp!
+
+[![Contributors](https://contrib.rocks/image?repo=leejet/stable-diffusion.cpp)](https://github.com/leejet/stable-diffusion.cpp/graphs/contributors)
 
 ## References
 
@@ -193,3 +245,4 @@ docker run -v /path/to/models:/models -v /path/to/output/:/output sd [args...]
 - [stable-diffusion-stability-ai](https://github.com/Stability-AI/stablediffusion)
 - [stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui)
 - [k-diffusion](https://github.com/crowsonkb/k-diffusion)
+- [latent-consistency-model](https://github.com/luosiallen/latent-consistency-model)

examples/main.cpp

@@ -77,8 +77,12 @@ const char* sample_method_str[] = {
     "euler_a",
     "euler",
     "heun",
+    "dpm2",
+    "dpm++2s_a",
     "dpm++2m",
-    "dpm++2mv2"};
+    "dpm++2mv2",
+    "lcm",
+};
 
 // Names of the sigma schedule overrides, same order as Schedule in stable-diffusion.h
 const char* schedule_str[] = {
@@ -87,29 +91,31 @@ const char* schedule_str[] = {
     "karras"};
 
 struct Option {
-    int n_threads = -1;
+    int n_threads    = -1;
     std::string mode = TXT2IMG;
     std::string model_path;
+    std::string lora_model_dir;
     std::string output_path = "output.png";
     std::string init_img;
     std::string prompt;
     std::string negative_prompt;
-    float cfg_scale = 7.0f;
-    int w = 512;
-    int h = 512;
+    float cfg_scale            = 7.0f;
+    int w                      = 512;
+    int h                      = 512;
     SampleMethod sample_method = EULER_A;
-    Schedule schedule = DEFAULT;
-    int sample_steps = 20;
-    float strength = 0.75f;
-    RNGType rng_type = CUDA_RNG;
-    int64_t seed = 42;
-    bool verbose = false;
+    Schedule schedule          = DEFAULT;
+    int sample_steps           = 20;
+    float strength             = 0.75f;
+    RNGType rng_type           = CUDA_RNG;
+    int64_t seed               = 42;
+    bool verbose               = false;
 
     void print() {
         printf("Option: \n");
         printf("    n_threads:       %d\n", n_threads);
         printf("    mode:            %s\n", mode.c_str());
         printf("    model_path:      %s\n", model_path.c_str());
+        printf("    lora_model_dir:  %s\n", lora_model_dir.c_str());
         printf("    output_path:     %s\n", output_path.c_str());
         printf("    init_img:        %s\n", init_img.c_str());
         printf("    prompt:          %s\n", prompt.c_str());
@@ -135,6 +141,7 @@ void print_usage(int argc, const char* argv[]) {
     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("  -m, --model [MODEL]                path to model\n");
+    printf("  --lora-model-dir [DIR]             lora model directory\n");
     printf("  -i, --init-img [IMAGE]             path to the input image, required by img2img\n");
     printf("  -o, --output OUTPUT                path to write result image to (default: .\\output.png)\n");
     printf("  -p, --prompt [PROMPT]              the prompt to render\n");
@@ -144,7 +151,7 @@ void print_usage(int argc, const char* argv[]) {
     printf("                                     1.0 corresponds to full destruction of information in init image\n");
     printf("  -H, --height H                     image height, in pixel space (default: 512)\n");
     printf("  -W, --width W                      image width, in pixel space (default: 512)\n");
-    printf("  --sampling-method {euler, euler_a, heun, dpm++2m, dpm++2mv2}\n");
+    printf("  --sampling-method {euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, lcm}\n");
     printf("                                     sampling method (default: \"euler_a\")\n");
     printf("  --steps  STEPS                     number of sample steps (default: 20)\n");
     printf("  --rng {std_default, cuda}          RNG (default: cuda)\n");
@@ -178,6 +185,12 @@ void parse_args(int argc, const char* argv[], Option* opt) {
                 break;
             }
             opt->model_path = argv[i];
+        } else if (arg == "--lora-model-dir") {
+            if (++i >= argc) {
+                invalid_arg = true;
+                break;
+            }
+            opt->lora_model_dir = argv[i];
         } else if (arg == "-i" || arg == "--init-img") {
             if (++i >= argc) {
                 invalid_arg = true;
@@ -252,7 +265,7 @@ void parse_args(int argc, const char* argv[], Option* opt) {
                 break;
             }
             const char* schedule_selected = argv[i];
-            int schedule_found = -1;
+            int schedule_found            = -1;
             for (int d = 0; d < N_SCHEDULES; d++) {
                 if (!strcmp(schedule_selected, schedule_str[d])) {
                     schedule_found = d;
@@ -275,7 +288,7 @@ void parse_args(int argc, const char* argv[], Option* opt) {
                 break;
             }
             const char* sample_method_selected = argv[i];
-            int sample_method_found = -1;
+            int sample_method_found            = -1;
             for (int m = 0; m < N_SAMPLE_METHODS; m++) {
                 if (!strcmp(sample_method_selected, sample_method_str[m])) {
                     sample_method_found = m;
@@ -363,6 +376,18 @@ void parse_args(int argc, const char* argv[], Option* opt) {
     }
 }
 
+std::string basename(const std::string& path) {
+    size_t pos = path.find_last_of('/');
+    if (pos != std::string::npos) {
+        return path.substr(pos + 1);
+    }
+    pos = path.find_last_of('\\');
+    if (pos != std::string::npos) {
+        return path.substr(pos + 1);
+    }
+    return path;
+}
+
 int main(int argc, const char* argv[]) {
     Option opt;
     parse_args(argc, argv, &opt);
@@ -379,6 +404,7 @@ int main(int argc, const char* argv[]) {
         vae_decode_only = false;
 
         int c = 0;
+
         unsigned char* img_data = stbi_load(opt.init_img.c_str(), &opt.w, &opt.h, &c, 3);
         if (img_data == NULL) {
             fprintf(stderr, "load image from '%s' failed\n", opt.init_img.c_str());
@@ -402,7 +428,7 @@ int main(int argc, const char* argv[]) {
         init_img.assign(img_data, img_data + (opt.w * opt.h * c));
     }
 
-    StableDiffusion sd(opt.n_threads, vae_decode_only, true, opt.rng_type);
+    StableDiffusion sd(opt.n_threads, vae_decode_only, true, opt.lora_model_dir, opt.rng_type);
     if (!sd.load_from_file(opt.model_path, opt.schedule)) {
         return 1;
     }
@@ -435,7 +461,24 @@ int main(int argc, const char* argv[]) {
         return 1;
     }
 
-    stbi_write_png(opt.output_path.c_str(), opt.w, opt.h, 3, img.data(), 0);
+    std::string parameter_string = opt.prompt + "\n";
+    if (opt.negative_prompt.size() != 0) {
+        parameter_string += "Negative prompt: " + opt.negative_prompt + "\n";
+    }
+    parameter_string += "Steps: " + std::to_string(opt.sample_steps) + ", ";
+    parameter_string += "CFG scale: " + std::to_string(opt.cfg_scale) + ", ";
+    parameter_string += "Seed: " + std::to_string(opt.seed) + ", ";
+    parameter_string += "Size: " + std::to_string(opt.w) + "x" + std::to_string(opt.h) + ", ";
+    parameter_string += "Model: " + basename(opt.model_path) + ", ";
+    parameter_string += "RNG: " + std::string(rng_type_to_str[opt.rng_type]) + ", ";
+    parameter_string += "Sampler: " + std::string(sample_method_str[opt.sample_method]);
+    if (opt.schedule == KARRAS) {
+        parameter_string += " karras";
+    }
+    parameter_string += ", ";
+    parameter_string += "Version: stable-diffusion.cpp";
+
+    stbi_write_png(opt.output_path.c_str(), opt.w, opt.h, 3, img.data(), 0, parameter_string.c_str());
     printf("save result image to '%s'\n", opt.output_path.c_str());
 
     return 0;

examples/stb_image_write.h

@@ -173,7 +173,7 @@ STBIWDEF int stbi_write_force_png_filter;
 #endif
 
 #ifndef STBI_WRITE_NO_STDIO
-STBIWDEF int stbi_write_png(char const *filename, int w, int h, int comp, const void  *data, int stride_in_bytes);
+STBIWDEF int stbi_write_png(char const *filename, int w, int h, int comp, const void  *data, int stride_in_bytes, const char* parameters = NULL);
 STBIWDEF int stbi_write_bmp(char const *filename, int w, int h, int comp, const void  *data);
 STBIWDEF int stbi_write_tga(char const *filename, int w, int h, int comp, const void  *data);
 STBIWDEF int stbi_write_hdr(char const *filename, int w, int h, int comp, const float *data);
@@ -1125,9 +1125,10 @@ static void stbiw__encode_png_line(unsigned char *pixels, int stride_bytes, int
    }
 }
 
-STBIWDEF unsigned char *stbi_write_png_to_mem(const unsigned char *pixels, int stride_bytes, int x, int y, int n, int *out_len)
+STBIWDEF unsigned char *stbi_write_png_to_mem(const unsigned char *pixels, int stride_bytes, int x, int y, int n, int *out_len, const char* parameters)
 {
    int force_filter = stbi_write_force_png_filter;
+   int param_length = 0;
    int ctype[5] = { -1, 0, 4, 2, 6 };
    unsigned char sig[8] = { 137,80,78,71,13,10,26,10 };
    unsigned char *out,*o, *filt, *zlib;
@@ -1177,10 +1178,15 @@ STBIWDEF unsigned char *stbi_write_png_to_mem(const unsigned char *pixels, int s
    STBIW_FREE(filt);
    if (!zlib) return 0;
 
+   if(parameters != NULL) {
+      param_length = strlen(parameters);
+      param_length += strlen("parameters") + 1; // For the name and the null-byte
+   }
+
    // each tag requires 12 bytes of overhead
-   out = (unsigned char *) STBIW_MALLOC(8 + 12+13 + 12+zlen + 12);
+   out = (unsigned char *) STBIW_MALLOC(8 + 12+13 + 12+zlen + 12 + ((parameters)?(param_length+12):0));
    if (!out) return 0;
-   *out_len = 8 + 12+13 + 12+zlen + 12;
+   *out_len = 8 + 12+13 + 12+zlen + 12 + ((parameters)?(param_length+12):0);
 
    o=out;
    STBIW_MEMMOVE(o,sig,8); o+= 8;
@@ -1195,6 +1201,17 @@ STBIWDEF unsigned char *stbi_write_png_to_mem(const unsigned char *pixels, int s
    *o++ = 0;
    stbiw__wpcrc(&o,13);
 
+   if(parameters != NULL) {
+      stbiw__wp32(o, param_length);
+      stbiw__wptag(o, "tEXt");
+      STBIW_MEMMOVE(o, "parameters", strlen("parameters"));
+      o+=strlen("parameters");
+      *o++ = 0; // Null pyte separator
+      STBIW_MEMMOVE(o, parameters, strlen(parameters));
+      o+=strlen(parameters);
+      stbiw__wpcrc(&o, param_length);
+   }
+
    stbiw__wp32(o, zlen);
    stbiw__wptag(o, "IDAT");
    STBIW_MEMMOVE(o, zlib, zlen);
@@ -1212,11 +1229,11 @@ STBIWDEF unsigned char *stbi_write_png_to_mem(const unsigned char *pixels, int s
 }
 
 #ifndef STBI_WRITE_NO_STDIO
-STBIWDEF int stbi_write_png(char const *filename, int x, int y, int comp, const void *data, int stride_bytes)
+STBIWDEF int stbi_write_png(char const *filename, int x, int y, int comp, const void *data, int stride_bytes, const char* parameters)
 {
    FILE *f;
    int len;
-   unsigned char *png = stbi_write_png_to_mem((const unsigned char *) data, stride_bytes, x, y, comp, &len);
+   unsigned char *png = stbi_write_png_to_mem((const unsigned char *) data, stride_bytes, x, y, comp, &len, parameters);
    if (png == NULL) return 0;
 
    f = stbiw__fopen(filename, "wb");
@@ -1231,7 +1248,7 @@ STBIWDEF int stbi_write_png(char const *filename, int x, int y, int comp, const
 STBIWDEF int stbi_write_png_to_func(stbi_write_func *func, void *context, int x, int y, int comp, const void *data, int stride_bytes)
 {
    int len;
-   unsigned char *png = stbi_write_png_to_mem((const unsigned char *) data, stride_bytes, x, y, comp, &len);
+   unsigned char *png = stbi_write_png_to_mem((const unsigned char *) data, stride_bytes, x, y, comp, &len, NULL);
    if (png == NULL) return 0;
    func(context, png, len);
    STBIW_FREE(png);

models/.gitignore

@@ -1,4 +1,5 @@
 *.bin
 *.ckpt
 *.safetensor
+*.safetensors
 *.log

models/convert.py

@@ -4,6 +4,7 @@ import os
 
 import numpy as np
 import torch
+import re
 import safetensors.torch
 
 this_file_dir = os.path.dirname(__file__)
@@ -179,9 +180,9 @@ def preprocess(state_dict):
         state_dict["alphas_cumprod"] = alphas_cumprod
     
     new_state_dict = {}
-    for name in state_dict.keys():
+    for name, w in state_dict.items():
         # ignore unused tensors
-        if not isinstance(state_dict[name], torch.Tensor):
+        if not isinstance(w, torch.Tensor):
             continue
         skip = False
         for unused_tensor in unused_tensors:
@@ -190,13 +191,25 @@ def preprocess(state_dict):
                 break
         if skip:
             continue
-        
+
+        # # convert BF16 to FP16
+        if w.dtype == torch.bfloat16:
+            w = w.to(torch.float16)
+
         # convert open_clip to hf CLIPTextModel (for SD2.x)
         open_clip_to_hf_clip_model = {
             "cond_stage_model.model.ln_final.bias": "cond_stage_model.transformer.text_model.final_layer_norm.bias",
             "cond_stage_model.model.ln_final.weight": "cond_stage_model.transformer.text_model.final_layer_norm.weight",
             "cond_stage_model.model.positional_embedding": "cond_stage_model.transformer.text_model.embeddings.position_embedding.weight",
             "cond_stage_model.model.token_embedding.weight": "cond_stage_model.transformer.text_model.embeddings.token_embedding.weight",
+            "first_stage_model.decoder.mid.attn_1.to_k.bias": "first_stage_model.decoder.mid.attn_1.k.bias",
+            "first_stage_model.decoder.mid.attn_1.to_k.weight": "first_stage_model.decoder.mid.attn_1.k.weight",
+            "first_stage_model.decoder.mid.attn_1.to_out.0.bias": "first_stage_model.decoder.mid.attn_1.proj_out.bias",
+            "first_stage_model.decoder.mid.attn_1.to_out.0.weight": "first_stage_model.decoder.mid.attn_1.proj_out.weight",
+            "first_stage_model.decoder.mid.attn_1.to_q.bias": "first_stage_model.decoder.mid.attn_1.q.bias",
+            "first_stage_model.decoder.mid.attn_1.to_q.weight": "first_stage_model.decoder.mid.attn_1.q.weight",
+            "first_stage_model.decoder.mid.attn_1.to_v.bias": "first_stage_model.decoder.mid.attn_1.v.bias",
+            "first_stage_model.decoder.mid.attn_1.to_v.weight": "first_stage_model.decoder.mid.attn_1.v.weight",
         }
         open_clip_to_hk_clip_resblock = {
             "attn.out_proj.bias": "self_attn.out_proj.bias",
@@ -214,22 +227,19 @@ def preprocess(state_dict):
         hf_clip_resblock_prefix = "cond_stage_model.transformer.text_model.encoder.layers."
         if name in open_clip_to_hf_clip_model:
             new_name = open_clip_to_hf_clip_model[name]
-            new_state_dict[new_name] = state_dict[name]
             print(f"preprocess {name} => {new_name}")
-            continue
+            name = new_name
         if name.startswith(open_clip_resblock_prefix):
             remain = name[len(open_clip_resblock_prefix):]
             idx = remain.split(".")[0]
             suffix = remain[len(idx)+1:]
             if suffix == "attn.in_proj_weight":
-                w = state_dict[name]
                 w_q, w_k, w_v = w.chunk(3)
                 for new_suffix, new_w in zip(["self_attn.q_proj.weight", "self_attn.k_proj.weight", "self_attn.v_proj.weight"], [w_q, w_k, w_v]):
                     new_name = hf_clip_resblock_prefix + idx + "." + new_suffix
                     new_state_dict[new_name] = new_w
                     print(f"preprocess {name}{w.size()} => {new_name}{new_w.size()}")
             elif suffix == "attn.in_proj_bias":
-                w = state_dict[name]
                 w_q, w_k, w_v = w.chunk(3)
                 for new_suffix, new_w in zip(["self_attn.q_proj.bias", "self_attn.k_proj.bias", "self_attn.v_proj.bias"], [w_q, w_k, w_v]):
                     new_name = hf_clip_resblock_prefix + idx + "." + new_suffix
@@ -238,37 +248,130 @@ def preprocess(state_dict):
             else:
                 new_suffix = open_clip_to_hk_clip_resblock[suffix]
                 new_name = hf_clip_resblock_prefix + idx + "." + new_suffix
-                new_state_dict[new_name] = state_dict[name]
+                new_state_dict[new_name] = w
                 print(f"preprocess {name} => {new_name}")
             continue
         
         # convert unet transformer linear to conv2d 1x1
         if name.startswith("model.diffusion_model.") and (name.endswith("proj_in.weight") or name.endswith("proj_out.weight")):
-            w = state_dict[name]
-            if len(state_dict[name].shape) == 2:
+            if len(w.shape) == 2:
                 new_w = w.unsqueeze(2).unsqueeze(3)
                 new_state_dict[name] = new_w
                 print(f"preprocess {name} {w.size()} => {name} {new_w.size()}")
                 continue
 
-        new_state_dict[name] = state_dict[name]
+        # convert vae attn block linear to conv2d 1x1
+        if name.startswith("first_stage_model.") and "attn_1" in name:
+            if len(w.shape) == 2:
+                new_w = w.unsqueeze(2).unsqueeze(3)
+                new_state_dict[name] = new_w
+                print(f"preprocess {name} {w.size()} => {name} {new_w.size()}")
+                continue
+
+        new_state_dict[name] = w
     return new_state_dict
 
-def convert(model_path, out_type = None, out_file=None):
+re_digits = re.compile(r"\d+")
+re_x_proj = re.compile(r"(.*)_([qkv]_proj)$")
+re_compiled = {}
+
+suffix_conversion = {
+    "attentions": {},
+    "resnets": {
+        "conv1": "in_layers_2",
+        "conv2": "out_layers_3",
+        "norm1": "in_layers_0",
+        "norm2": "out_layers_0",
+        "time_emb_proj": "emb_layers_1",
+        "conv_shortcut": "skip_connection",
+    }
+}
+
+
+def convert_diffusers_name_to_compvis(key):
+    def match(match_list, regex_text):
+        regex = re_compiled.get(regex_text)
+        if regex is None:
+            regex = re.compile(regex_text)
+            re_compiled[regex_text] = regex
+
+        r = re.match(regex, key)
+        if not r:
+            return False
+
+        match_list.clear()
+        match_list.extend([int(x) if re.match(re_digits, x) else x for x in r.groups()])
+        return True
+
+    m = []
+
+    if match(m, r"lora_unet_conv_in(.*)"):
+        return f'model_diffusion_model_input_blocks_0_0{m[0]}'
+
+    if match(m, r"lora_unet_conv_out(.*)"):
+        return f'model_diffusion_model_out_2{m[0]}'
+
+    if match(m, r"lora_unet_time_embedding_linear_(\d+)(.*)"):
+        return f"model_diffusion_model_time_embed_{m[0] * 2 - 2}{m[1]}"
+
+    if match(m, r"lora_unet_down_blocks_(\d+)_(attentions|resnets)_(\d+)_(.+)"):
+        suffix = suffix_conversion.get(m[1], {}).get(m[3], m[3])
+        return f"model_diffusion_model_input_blocks_{1 + m[0] * 3 + m[2]}_{1 if m[1] == 'attentions' else 0}_{suffix}"
+
+    if match(m, r"lora_unet_mid_block_(attentions|resnets)_(\d+)_(.+)"):
+        suffix = suffix_conversion.get(m[0], {}).get(m[2], m[2])
+        return f"model_diffusion_model_middle_block_{1 if m[0] == 'attentions' else m[1] * 2}_{suffix}"
+
+    if match(m, r"lora_unet_up_blocks_(\d+)_(attentions|resnets)_(\d+)_(.+)"):
+        suffix = suffix_conversion.get(m[1], {}).get(m[3], m[3])
+        return f"model_diffusion_model_output_blocks_{m[0] * 3 + m[2]}_{1 if m[1] == 'attentions' else 0}_{suffix}"
+
+    if match(m, r"lora_unet_down_blocks_(\d+)_downsamplers_0_conv"):
+        return f"model_diffusion_model_input_blocks_{3 + m[0] * 3}_0_op"
+
+    if match(m, r"lora_unet_up_blocks_(\d+)_upsamplers_0_conv"):
+        return f"model_diffusion_model_output_blocks_{2 + m[0] * 3}_{2 if m[0]>0 else 1}_conv"
+
+    if match(m, r"lora_te_text_model_encoder_layers_(\d+)_(.+)"):
+        return f"cond_stage_model_transformer_text_model_encoder_layers_{m[0]}_{m[1]}"
+
+    return None
+
+def preprocess_lora(state_dict):
+    new_state_dict = {}
+    for name, w in state_dict.items():
+        if not isinstance(w, torch.Tensor):
+            continue
+        name_without_network_parts, network_part = name.split(".", 1)
+        new_name_without_network_parts = convert_diffusers_name_to_compvis(name_without_network_parts)
+        if new_name_without_network_parts == None:
+            raise Exception(f"unknown lora tensor: {name}")
+        new_name = new_name_without_network_parts + "." + network_part
+        print(f"preprocess {name} => {new_name}")
+        new_state_dict[new_name] = w
+    return new_state_dict
+
+def convert(model_path, out_type = None, out_file=None, lora=False):
     # load model
-    with open(os.path.join(vocab_dir, "vocab.json"), encoding="utf-8") as f:
-        clip_vocab = json.load(f)
-    
+    if not lora:
+        with open(os.path.join(vocab_dir, "vocab.json"), encoding="utf-8") as f:
+            clip_vocab = json.load(f)
+
     state_dict = load_model_from_file(model_path)
-    model_type = SD1
-    if "cond_stage_model.model.token_embedding.weight" in state_dict.keys():
+    model_type = SD1 # lora only for SD1 now
+    if not lora and "cond_stage_model.model.token_embedding.weight" in state_dict.keys():
         model_type = SD2
         print("Stable diffuison 2.x")
     else:
         print("Stable diffuison 1.x")
-    state_dict = preprocess(state_dict)
+    if lora:
+        state_dict = preprocess_lora(state_dict)
+    else:
+        state_dict = preprocess(state_dict)
 
     # output option
+    if lora:
+        out_type = "f16" # only f16 for now
     if out_type == None:
         weight = state_dict["model.diffusion_model.input_blocks.0.0.weight"].numpy()
         if weight.dtype == np.float32:
@@ -280,7 +383,10 @@ def convert(model_path, out_type = None, out_file=None):
         else:
             raise Exception("unsupported weight type %s" % weight.dtype)
     if out_file == None:
-        out_file = os.path.splitext(os.path.basename(model_path))[0] + f"-ggml-model-{out_type}.bin"
+        if lora:
+            out_file = os.path.splitext(os.path.basename(model_path))[0] + f"-ggml-lora.bin"
+        else:
+            out_file = os.path.splitext(os.path.basename(model_path))[0] + f"-ggml-model-{out_type}.bin"
         out_file = os.path.join(os.getcwd(), out_file)
     print(f"Saving GGML compatible file to {out_file}")
 
@@ -293,14 +399,15 @@ def convert(model_path, out_type = None, out_file=None):
         file.write(struct.pack("i", ftype))
 
         # vocab
-        byte_encoder = bytes_to_unicode()
-        byte_decoder = {v: k for k, v in byte_encoder.items()}
-        file.write(struct.pack("i", len(clip_vocab)))
-        for key in clip_vocab:
-            text = bytearray([byte_decoder[c] for c in key])
-            file.write(struct.pack("i", len(text)))
-            file.write(text)
-        
+        if not lora:
+            byte_encoder = bytes_to_unicode()
+            byte_decoder = {v: k for k, v in byte_encoder.items()}
+            file.write(struct.pack("i", len(clip_vocab)))
+            for key in clip_vocab:
+                text = bytearray([byte_decoder[c] for c in key])
+                file.write(struct.pack("i", len(text)))
+                file.write(text)
+
         # weights
         for name in state_dict.keys():
             if not isinstance(state_dict[name], torch.Tensor):
@@ -321,7 +428,7 @@ def convert(model_path, out_type = None, out_file=None):
             old_type = data.dtype
 
             ttype = "f32"
-            if n_dims == 4:
+            if n_dims == 4 and not lora:
                 data = data.astype(np.float16)
                 ttype = "f16"
             elif n_dims == 2 and name[-7:] == ".weight":
@@ -364,6 +471,7 @@ if __name__ == "__main__":
     parser = argparse.ArgumentParser(description="Convert Stable Diffuison model to GGML compatible file format")
     parser.add_argument("--out_type", choices=["f32", "f16", "q4_0", "q4_1", "q5_0", "q5_1", "q8_0"], help="output format (default: based on input)")
     parser.add_argument("--out_file", help="path to write to; default: based on input and current working directory")
+    parser.add_argument("--lora", action='store_true', default = False, help="convert lora weight; default: false")
     parser.add_argument("model_path", help="model file path (*.pth, *.pt, *.ckpt, *.safetensors)")
     args = parser.parse_args()
-    convert(args.model_path, args.out_type, args.out_file)
+    convert(args.model_path, args.out_type, args.out_file, args.lora)

models/convert_diffusers_to_original_stable_diffusion.py

@@ -0,0 +1,335 @@
+# Copy from https://github.com/huggingface/diffusers/blob/main/scripts/convert_diffusers_to_original_stable_diffusion.py
+# LICENSE: https://github.com/huggingface/diffusers/blob/main/LICENSE
+# Script for converting a HF Diffusers saved pipeline to a Stable Diffusion checkpoint.
+# *Only* converts the UNet, VAE, and Text Encoder.
+# Does not convert optimizer state or any other thing.
+
+import argparse
+import os.path as osp
+import re
+
+import torch
+from safetensors.torch import load_file, save_file
+
+
+# =================#
+# UNet Conversion #
+# =================#
+
+unet_conversion_map = [
+    # (stable-diffusion, HF Diffusers)
+    ("time_embed.0.weight", "time_embedding.linear_1.weight"),
+    ("time_embed.0.bias", "time_embedding.linear_1.bias"),
+    ("time_embed.2.weight", "time_embedding.linear_2.weight"),
+    ("time_embed.2.bias", "time_embedding.linear_2.bias"),
+    ("input_blocks.0.0.weight", "conv_in.weight"),
+    ("input_blocks.0.0.bias", "conv_in.bias"),
+    ("out.0.weight", "conv_norm_out.weight"),
+    ("out.0.bias", "conv_norm_out.bias"),
+    ("out.2.weight", "conv_out.weight"),
+    ("out.2.bias", "conv_out.bias"),
+]
+
+unet_conversion_map_resnet = [
+    # (stable-diffusion, HF Diffusers)
+    ("in_layers.0", "norm1"),
+    ("in_layers.2", "conv1"),
+    ("out_layers.0", "norm2"),
+    ("out_layers.3", "conv2"),
+    ("emb_layers.1", "time_emb_proj"),
+    ("skip_connection", "conv_shortcut"),
+]
+
+unet_conversion_map_layer = []
+# hardcoded number of downblocks and resnets/attentions...
+# would need smarter logic for other networks.
+for i in range(4):
+    # loop over downblocks/upblocks
+
+    for j in range(2):
+        # loop over resnets/attentions for downblocks
+        hf_down_res_prefix = f"down_blocks.{i}.resnets.{j}."
+        sd_down_res_prefix = f"input_blocks.{3*i + j + 1}.0."
+        unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))
+
+        if i < 3:
+            # no attention layers in down_blocks.3
+            hf_down_atn_prefix = f"down_blocks.{i}.attentions.{j}."
+            sd_down_atn_prefix = f"input_blocks.{3*i + j + 1}.1."
+            unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))
+
+    for j in range(3):
+        # loop over resnets/attentions for upblocks
+        hf_up_res_prefix = f"up_blocks.{i}.resnets.{j}."
+        sd_up_res_prefix = f"output_blocks.{3*i + j}.0."
+        unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))
+
+        if i > 0:
+            # no attention layers in up_blocks.0
+            hf_up_atn_prefix = f"up_blocks.{i}.attentions.{j}."
+            sd_up_atn_prefix = f"output_blocks.{3*i + j}.1."
+            unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))
+
+    if i < 3:
+        # no downsample in down_blocks.3
+        hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0.conv."
+        sd_downsample_prefix = f"input_blocks.{3*(i+1)}.0.op."
+        unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))
+
+        # no upsample in up_blocks.3
+        hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0."
+        sd_upsample_prefix = f"output_blocks.{3*i + 2}.{1 if i == 0 else 2}."
+        unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))
+
+hf_mid_atn_prefix = "mid_block.attentions.0."
+sd_mid_atn_prefix = "middle_block.1."
+unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))
+
+for j in range(2):
+    hf_mid_res_prefix = f"mid_block.resnets.{j}."
+    sd_mid_res_prefix = f"middle_block.{2*j}."
+    unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))
+
+
+def convert_unet_state_dict(unet_state_dict):
+    # buyer beware: this is a *brittle* function,
+    # and correct output requires that all of these pieces interact in
+    # the exact order in which I have arranged them.
+    mapping = {k: k for k in unet_state_dict.keys()}
+    for sd_name, hf_name in unet_conversion_map:
+        mapping[hf_name] = sd_name
+    for k, v in mapping.items():
+        if "resnets" in k:
+            for sd_part, hf_part in unet_conversion_map_resnet:
+                v = v.replace(hf_part, sd_part)
+            mapping[k] = v
+    for k, v in mapping.items():
+        for sd_part, hf_part in unet_conversion_map_layer:
+            v = v.replace(hf_part, sd_part)
+        mapping[k] = v
+    new_state_dict = {v: unet_state_dict[k] for k, v in mapping.items()}
+    return new_state_dict
+
+
+# ================#
+# VAE Conversion #
+# ================#
+
+vae_conversion_map = [
+    # (stable-diffusion, HF Diffusers)
+    ("nin_shortcut", "conv_shortcut"),
+    ("norm_out", "conv_norm_out"),
+    ("mid.attn_1.", "mid_block.attentions.0."),
+]
+
+for i in range(4):
+    # down_blocks have two resnets
+    for j in range(2):
+        hf_down_prefix = f"encoder.down_blocks.{i}.resnets.{j}."
+        sd_down_prefix = f"encoder.down.{i}.block.{j}."
+        vae_conversion_map.append((sd_down_prefix, hf_down_prefix))
+
+    if i < 3:
+        hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0."
+        sd_downsample_prefix = f"down.{i}.downsample."
+        vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))
+
+        hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0."
+        sd_upsample_prefix = f"up.{3-i}.upsample."
+        vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))
+
+    # up_blocks have three resnets
+    # also, up blocks in hf are numbered in reverse from sd
+    for j in range(3):
+        hf_up_prefix = f"decoder.up_blocks.{i}.resnets.{j}."
+        sd_up_prefix = f"decoder.up.{3-i}.block.{j}."
+        vae_conversion_map.append((sd_up_prefix, hf_up_prefix))
+
+# this part accounts for mid blocks in both the encoder and the decoder
+for i in range(2):
+    hf_mid_res_prefix = f"mid_block.resnets.{i}."
+    sd_mid_res_prefix = f"mid.block_{i+1}."
+    vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))
+
+
+vae_conversion_map_attn = [
+    # (stable-diffusion, HF Diffusers)
+    ("norm.", "group_norm."),
+    ("q.", "query."),
+    ("k.", "key."),
+    ("v.", "value."),
+    ("proj_out.", "proj_attn."),
+]
+
+
+def reshape_weight_for_sd(w):
+    # convert HF linear weights to SD conv2d weights
+    return w.reshape(*w.shape, 1, 1)
+
+
+def convert_vae_state_dict(vae_state_dict):
+    mapping = {k: k for k in vae_state_dict.keys()}
+    for k, v in mapping.items():
+        for sd_part, hf_part in vae_conversion_map:
+            v = v.replace(hf_part, sd_part)
+        mapping[k] = v
+    for k, v in mapping.items():
+        if "attentions" in k:
+            for sd_part, hf_part in vae_conversion_map_attn:
+                v = v.replace(hf_part, sd_part)
+            mapping[k] = v
+    new_state_dict = {v: vae_state_dict[k] for k, v in mapping.items()}
+    weights_to_convert = ["q", "k", "v", "proj_out"]
+    for k, v in new_state_dict.items():
+        for weight_name in weights_to_convert:
+            if f"mid.attn_1.{weight_name}.weight" in k:
+                print(f"Reshaping {k} for SD format")
+                new_state_dict[k] = reshape_weight_for_sd(v)
+    return new_state_dict
+
+
+# =========================#
+# Text Encoder Conversion #
+# =========================#
+
+
+textenc_conversion_lst = [
+    # (stable-diffusion, HF Diffusers)
+    ("resblocks.", "text_model.encoder.layers."),
+    ("ln_1", "layer_norm1"),
+    ("ln_2", "layer_norm2"),
+    (".c_fc.", ".fc1."),
+    (".c_proj.", ".fc2."),
+    (".attn", ".self_attn"),
+    ("ln_final.", "transformer.text_model.final_layer_norm."),
+    ("token_embedding.weight", "transformer.text_model.embeddings.token_embedding.weight"),
+    ("positional_embedding", "transformer.text_model.embeddings.position_embedding.weight"),
+]
+protected = {re.escape(x[1]): x[0] for x in textenc_conversion_lst}
+textenc_pattern = re.compile("|".join(protected.keys()))
+
+# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
+code2idx = {"q": 0, "k": 1, "v": 2}
+
+
+def convert_text_enc_state_dict_v20(text_enc_dict):
+    new_state_dict = {}
+    capture_qkv_weight = {}
+    capture_qkv_bias = {}
+    for k, v in text_enc_dict.items():
+        if (
+            k.endswith(".self_attn.q_proj.weight")
+            or k.endswith(".self_attn.k_proj.weight")
+            or k.endswith(".self_attn.v_proj.weight")
+        ):
+            k_pre = k[: -len(".q_proj.weight")]
+            k_code = k[-len("q_proj.weight")]
+            if k_pre not in capture_qkv_weight:
+                capture_qkv_weight[k_pre] = [None, None, None]
+            capture_qkv_weight[k_pre][code2idx[k_code]] = v
+            continue
+
+        if (
+            k.endswith(".self_attn.q_proj.bias")
+            or k.endswith(".self_attn.k_proj.bias")
+            or k.endswith(".self_attn.v_proj.bias")
+        ):
+            k_pre = k[: -len(".q_proj.bias")]
+            k_code = k[-len("q_proj.bias")]
+            if k_pre not in capture_qkv_bias:
+                capture_qkv_bias[k_pre] = [None, None, None]
+            capture_qkv_bias[k_pre][code2idx[k_code]] = v
+            continue
+
+        relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k)
+        new_state_dict[relabelled_key] = v
+
+    for k_pre, tensors in capture_qkv_weight.items():
+        if None in tensors:
+            raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing")
+        relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k_pre)
+        new_state_dict[relabelled_key + ".in_proj_weight"] = torch.cat(tensors)
+
+    for k_pre, tensors in capture_qkv_bias.items():
+        if None in tensors:
+            raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing")
+        relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k_pre)
+        new_state_dict[relabelled_key + ".in_proj_bias"] = torch.cat(tensors)
+
+    return new_state_dict
+
+
+def convert_text_enc_state_dict(text_enc_dict):
+    return text_enc_dict
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument("--model_path", default=None, type=str, required=True, help="Path to the model to convert.")
+    parser.add_argument("--checkpoint_path", default=None, type=str, required=True, help="Path to the output model.")
+    parser.add_argument("--half", action="store_true", help="Save weights in half precision.")
+    parser.add_argument(
+        "--use_safetensors", action="store_true", help="Save weights use safetensors, default is ckpt."
+    )
+
+    args = parser.parse_args()
+
+    assert args.model_path is not None, "Must provide a model path!"
+
+    assert args.checkpoint_path is not None, "Must provide a checkpoint path!"
+
+    # Path for safetensors
+    unet_path = osp.join(args.model_path, "unet", "diffusion_pytorch_model.safetensors")
+    vae_path = osp.join(args.model_path, "vae", "diffusion_pytorch_model.safetensors")
+    text_enc_path = osp.join(args.model_path, "text_encoder", "model.safetensors")
+
+    # Load models from safetensors if it exists, if it doesn't pytorch
+    if osp.exists(unet_path):
+        unet_state_dict = load_file(unet_path, device="cpu")
+    else:
+        unet_path = osp.join(args.model_path, "unet", "diffusion_pytorch_model.bin")
+        unet_state_dict = torch.load(unet_path, map_location="cpu")
+
+    if osp.exists(vae_path):
+        vae_state_dict = load_file(vae_path, device="cpu")
+    else:
+        vae_path = osp.join(args.model_path, "vae", "diffusion_pytorch_model.bin")
+        vae_state_dict = torch.load(vae_path, map_location="cpu")
+
+    if osp.exists(text_enc_path):
+        text_enc_dict = load_file(text_enc_path, device="cpu")
+    else:
+        text_enc_path = osp.join(args.model_path, "text_encoder", "pytorch_model.bin")
+        text_enc_dict = torch.load(text_enc_path, map_location="cpu")
+
+    # Convert the UNet model
+    unet_state_dict = convert_unet_state_dict(unet_state_dict)
+    unet_state_dict = {"model.diffusion_model." + k: v for k, v in unet_state_dict.items()}
+
+    # Convert the VAE model
+    vae_state_dict = convert_vae_state_dict(vae_state_dict)
+    vae_state_dict = {"first_stage_model." + k: v for k, v in vae_state_dict.items()}
+
+    # Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
+    is_v20_model = "text_model.encoder.layers.22.layer_norm2.bias" in text_enc_dict
+
+    if is_v20_model:
+        # Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
+        text_enc_dict = {"transformer." + k: v for k, v in text_enc_dict.items()}
+        text_enc_dict = convert_text_enc_state_dict_v20(text_enc_dict)
+        text_enc_dict = {"cond_stage_model.model." + k: v for k, v in text_enc_dict.items()}
+    else:
+        text_enc_dict = convert_text_enc_state_dict(text_enc_dict)
+        text_enc_dict = {"cond_stage_model.transformer." + k: v for k, v in text_enc_dict.items()}
+
+    # Put together new checkpoint
+    state_dict = {**unet_state_dict, **vae_state_dict, **text_enc_dict}
+    if args.half:
+        state_dict = {k: v.half() for k, v in state_dict.items()}
+
+    if args.use_safetensors:
+        save_file(state_dict, args.checkpoint_path)
+    else:
+        state_dict = {"state_dict": state_dict}
+        torch.save(state_dict, args.checkpoint_path)

rng.h

@@ -5,26 +5,26 @@
 #include <vector>
 
 class RNG {
-   public:
-    virtual void manual_seed(uint64_t seed) = 0;
+public:
+    virtual void manual_seed(uint64_t seed)      = 0;
     virtual std::vector<float> randn(uint32_t n) = 0;
 };
 
 class STDDefaultRNG : public RNG {
-   private:
+private:
     std::default_random_engine generator;
 
-   public:
+public:
     void manual_seed(uint64_t seed) {
-        generator.seed(seed);
+        generator.seed((unsigned int)seed);
     }
 
     std::vector<float> randn(uint32_t n) {
         std::vector<float> result;
-        float mean = 0.0;
+        float mean   = 0.0;
         float stddev = 1.0;
         std::normal_distribution<float> distribution(mean, stddev);
-        for (int i = 0; i < n; i++) {
+        for (uint32_t i = 0; i < n; i++) {
             float random_number = distribution(generator);
             result.push_back(random_number);
         }

rng_philox.h

@@ -9,15 +9,15 @@
 // RNG imitiating torch cuda randn on CPU.
 // Port from: https://github.com/AUTOMATIC1111/stable-diffusion-webui/blob/5ef669de080814067961f28357256e8fe27544f4/modules/rng_philox.py
 class PhiloxRNG : public RNG {
-   private:
+private:
     uint64_t seed;
     uint32_t offset;
 
-   private:
+private:
     std::vector<uint32_t> philox_m = {0xD2511F53, 0xCD9E8D57};
     std::vector<uint32_t> philox_w = {0x9E3779B9, 0xBB67AE85};
-    float two_pow32_inv = 2.3283064e-10;
-    float two_pow32_inv_2pi = 2.3283064e-10 * 6.2831855;
+    float two_pow32_inv            = 2.3283064e-10f;
+    float two_pow32_inv_2pi        = 2.3283064e-10f * 6.2831855f;
 
     std::vector<uint32_t> uint32(uint64_t x) {
         std::vector<uint32_t> result(2);
@@ -27,10 +27,10 @@ class PhiloxRNG : public RNG {
     }
 
     std::vector<std::vector<uint32_t>> uint32(const std::vector<uint64_t>& x) {
-        int N = x.size();
+        uint32_t N = (uint32_t)x.size();
         std::vector<std::vector<uint32_t>> result(2, std::vector<uint32_t>(N));
 
-        for (int i = 0; i < N; ++i) {
+        for (uint32_t i = 0; i < N; ++i) {
             result[0][i] = static_cast<uint32_t>(x[i] & 0xFFFFFFFF);
             result[1][i] = static_cast<uint32_t>(x[i] >> 32);
         }
@@ -41,7 +41,7 @@ class PhiloxRNG : public RNG {
     //  A single round of the Philox 4x32 random number generator.
     void philox4_round(std::vector<std::vector<uint32_t>>& counter,
                        const std::vector<std::vector<uint32_t>>& key) {
-        uint32_t N = counter[0].size();
+        uint32_t N = (uint32_t)counter[0].size();
         for (uint32_t i = 0; i < N; i++) {
             std::vector<uint32_t> v1 = uint32(static_cast<uint64_t>(counter[0][i]) * static_cast<uint64_t>(philox_m[0]));
             std::vector<uint32_t> v2 = uint32(static_cast<uint64_t>(counter[2][i]) * static_cast<uint64_t>(philox_m[1]));
@@ -63,7 +63,7 @@ class PhiloxRNG : public RNG {
     std::vector<std::vector<uint32_t>> philox4_32(std::vector<std::vector<uint32_t>>& counter,
                                                   std::vector<std::vector<uint32_t>>& key,
                                                   int rounds = 10) {
-        uint32_t N = counter[0].size();
+        uint32_t N = (uint32_t)counter[0].size();
         for (int i = 0; i < rounds - 1; ++i) {
             philox4_round(counter, key);
 
@@ -81,20 +81,20 @@ class PhiloxRNG : public RNG {
         float u = x * two_pow32_inv + two_pow32_inv / 2;
         float v = y * two_pow32_inv_2pi + two_pow32_inv_2pi / 2;
 
-        float s = sqrt(-2.0 * log(u));
+        float s = sqrt(-2.0f * log(u));
 
         float r1 = s * sin(v);
         return r1;
     }
 
-   public:
+public:
     PhiloxRNG(uint64_t seed = 0) {
-        this->seed = seed;
+        this->seed   = seed;
         this->offset = 0;
     }
 
     void manual_seed(uint64_t seed) {
-        this->seed = seed;
+        this->seed   = seed;
         this->offset = 0;
     }
 
@@ -115,8 +115,8 @@ class PhiloxRNG : public RNG {
         std::vector<std::vector<uint32_t>> g = philox4_32(counter, key_uint32);
 
         std::vector<float> result;
-        for (int i = 0; i < n; ++i) {
-            result.push_back(box_muller(g[0][i], g[1][i]));
+        for (uint32_t i = 0; i < n; ++i) {
+            result.push_back(box_muller((float)g[0][i], (float)g[1][i]));
         }
         return result;
     }

stable-diffusion.h

@@ -20,8 +20,11 @@ enum SampleMethod {
     EULER_A,
     EULER,
     HEUN,
+    DPM2,
+    DPMPP2S_A,
     DPMPP2M,
     DPMPP2Mv2,
+    LCM,
     N_SAMPLE_METHODS
 };
 
@@ -35,18 +38,19 @@ enum Schedule {
 class StableDiffusionGGML;
 
 class StableDiffusion {
-   private:
+private:
     std::shared_ptr<StableDiffusionGGML> sd;
 
-   public:
-    StableDiffusion(int n_threads = -1,
-                    bool vae_decode_only = false,
+public:
+    StableDiffusion(int n_threads                = -1,
+                    bool vae_decode_only         = false,
                     bool free_params_immediately = false,
-                    RNGType rng_type = STD_DEFAULT_RNG);
+                    std::string lora_model_dir   = "",
+                    RNGType rng_type             = STD_DEFAULT_RNG);
     bool load_from_file(const std::string& file_path, Schedule d = DEFAULT);
     std::vector<uint8_t> txt2img(
-        const std::string& prompt,
-        const std::string& negative_prompt,
+        std::string prompt,
+        std::string negative_prompt,
         float cfg_scale,
         int width,
         int height,
@@ -55,8 +59,8 @@ class StableDiffusion {
         int64_t seed);
     std::vector<uint8_t> img2img(
         const std::vector<uint8_t>& init_img,
-        const std::string& prompt,
-        const std::string& negative_prompt,
+        std::string prompt,
+        std::string negative_prompt,
         float cfg_scale,
         int width,
         int height,