support loading multiple sd loras (up to 4 at once)

This commit is contained in:
Concedo 2026-02-19 13:57:58 +08:00
parent a089284d13
commit bf3f2e1ba8
3 changed files with 106 additions and 49 deletions

View file

@ -6,6 +6,7 @@ const int images_max = 8;
const int audio_max = 4;
const int logprobs_max = 10;
const int overridekv_max = 4;
const int lora_filenames_max = 4;
// match kobold's sampler list and order
enum samplers
@ -188,7 +189,7 @@ struct sd_load_model_inputs
const char * clip1_filename = nullptr;
const char * clip2_filename = nullptr;
const char * vae_filename = nullptr;
const char * lora_filename = nullptr;
const char * lora_filenames[lora_filenames_max] = {};
const float lora_multiplier = 1.0f;
const int lora_apply_mode = 0;
const char * photomaker_filename = nullptr;

View file

@ -59,6 +59,7 @@ default_vae_tile_threshold = 768
default_native_ctx = 16384
overridekv_max = 4
default_autofit_padding = 1024
lora_filenames_max = 4
# abuse prevention
stop_token_max = 256
@ -311,7 +312,7 @@ class sd_load_model_inputs(ctypes.Structure):
("clip1_filename", ctypes.c_char_p),
("clip2_filename", ctypes.c_char_p),
("vae_filename", ctypes.c_char_p),
("lora_filename", ctypes.c_char_p),
("lora_filenames", ctypes.c_char_p * lora_filenames_max),
("lora_multiplier", ctypes.c_float),
("lora_apply_mode", ctypes.c_int),
("photomaker_filename", ctypes.c_char_p),
@ -1931,7 +1932,7 @@ def sd_quant_option(value):
except Exception:
return 0
def sd_load_model(model_filename,vae_filename,lora_filename,t5xxl_filename,clip1_filename,clip2_filename,photomaker_filename,upscaler_filename):
def sd_load_model(model_filename,vae_filename,lora_filenames,t5xxl_filename,clip1_filename,clip2_filename,photomaker_filename,upscaler_filename):
global args
inputs = sd_load_model_inputs()
inputs.model_filename = model_filename.encode("UTF-8")
@ -1954,7 +1955,12 @@ def sd_load_model(model_filename,vae_filename,lora_filename,t5xxl_filename,clip1
inputs.taesd = True if args.sdvaeauto else False
inputs.tiled_vae_threshold = args.sdtiledvae
inputs.vae_filename = vae_filename.encode("UTF-8")
inputs.lora_filename = lora_filename.encode("UTF-8")
for n in range(lora_filenames_max):
if n >= len(lora_filenames):
inputs.lora_filenames[n] = "".encode("UTF-8")
else:
inputs.lora_filenames[n] = lora_filenames[n].encode("UTF-8")
inputs.lora_multiplier = args.sdloramult
inputs.t5xxl_filename = t5xxl_filename.encode("UTF-8")
inputs.clip1_filename = clip1_filename.encode("UTF-8")
@ -5173,7 +5179,7 @@ def RunServerMultiThreaded(addr, port, server_handler):
sys.exit(0)
# Based on https://github.com/mathgeniuszach/xdialog/blob/main/xdialog/zenity_dialogs.py - MIT license | - Expanded version by Henk717
def zenity(filetypes=None, initialdir="", initialfile="", **kwargs) -> Tuple[int, str]:
def zenity(filetypes=None, initialdir="", initialfile="", multiple=False, **kwargs) -> Tuple[int, object]:
global zenity_recent_dir, zenity_permitted
if not zenity_permitted:
@ -5238,6 +5244,10 @@ def zenity(filetypes=None, initialdir="", initialfile="", **kwargs) -> Tuple[int
initialpath = os.path.join(initialdir, initialfile)
args.append(f'--filename={initialpath}')
if multiple:
args.append("--multiple")
args.append("--separator=|")
clean_env = os.environ.copy()
clean_env.pop("LD_LIBRARY_PATH", None)
clean_env["PATH"] = "/usr/bin:/bin"
@ -5252,15 +5262,18 @@ def zenity(filetypes=None, initialdir="", initialfile="", **kwargs) -> Tuple[int
result = procres.stdout.decode('utf-8').strip()
if procres.returncode==0 and result:
directory = result
if not os.path.isdir(result):
directory = os.path.dirname(result)
if multiple:
result = tuple(result.split("|"))
directory = result[0]
if not os.path.isdir(directory):
directory = os.path.dirname(directory)
zenity_recent_dir = directory
return (procres.returncode, result)
# note: In this section we wrap around file dialogues to allow for zenity
def zentk_askopenfilename(**options):
try:
result = zenity(filetypes=options.get("filetypes"), initialdir=options.get("initialdir"), title=options.get("title"))[1]
result = zenity(filetypes=options.get("filetypes"), initialdir=options.get("initialdir"), multiple=False, title=options.get("title"))[1]
if result and not os.path.isfile(result):
print("A folder was selected while we need a file, ignoring selection.")
return ''
@ -5269,9 +5282,21 @@ def zentk_askopenfilename(**options):
result = askopenfilename(**options)
return result
def zentk_askopenfilenames(**options):
try:
result = zenity(filetypes=options.get("filetypes"), initialdir=options.get("initialdir"), multiple=True, title=options.get("title"))[1]
for itm in result:
if itm and not os.path.isfile(itm):
print("A folder was selected while we need a file, ignoring selection.")
return ''
except Exception:
from tkinter.filedialog import askopenfilenames
result = askopenfilenames(**options)
return result
def zentk_askdirectory(**options):
try:
result = zenity(initialdir=options.get("initialdir"), title=options.get("title"), directory=True)[1]
result = zenity(initialdir=options.get("initialdir"), multiple=False, title=options.get("title"), directory=True)[1]
except Exception:
from tkinter.filedialog import askdirectory
result = askdirectory(**options)
@ -5279,7 +5304,7 @@ def zentk_askdirectory(**options):
def zentk_asksaveasfilename(**options):
try:
result = zenity(filetypes=options.get("filetypes"), initialdir=options.get("initialdir"), initialfile=options.get("initialfile"), title=options.get("title"), save=True)[1]
result = zenity(filetypes=options.get("filetypes"), initialdir=options.get("initialdir"), initialfile=options.get("initialfile"), multiple=False, title=options.get("title"), save=True)[1]
except Exception:
from tkinter.filedialog import asksaveasfilename
result = asksaveasfilename(**options)
@ -5724,7 +5749,7 @@ def show_gui():
return entry, label
#file dialog types: 0=openfile,1=savefile,2=opendir
def makefileentry(parent, text, searchtext, var, row=0, width=200, filetypes=[], onchoosefile=None, singlerow=False, singlecol=True, dialog_type=0, tooltiptxt=""):
def makefileentry(parent, text, searchtext, var, row=0, width=200, filetypes=[], onchoosefile=None, singlerow=False, singlecol=True, dialog_type=0, tooltiptxt="", multiple=False):
label = makelabel(parent, text, row,0,tooltiptxt,columnspan=3)
def getfilename(var, text):
initialDir = os.path.dirname(var.get())
@ -5740,7 +5765,11 @@ def show_gui():
fnam = str(fnam).strip()
fnam = f"{fnam}.jsondb" if ".jsondb" not in fnam.lower() else fnam
else:
fnam = zentk_askopenfilename(title=text,filetypes=filetypes, initialdir=initialDir)
if multiple:
fnam = zentk_askopenfilenames(title=text,filetypes=filetypes, initialdir=initialDir)
fnam = "|".join(fnam)
else:
fnam = zentk_askopenfilename(title=text,filetypes=filetypes, initialdir=initialDir)
if fnam:
var.set(fnam)
if onchoosefile:
@ -6383,7 +6412,7 @@ def show_gui():
makelabelcombobox(images_tab, "Compress Weights: ", sd_quant_var, 8, width=(60), padx=(126), labelpadx=8, tooltiptxt="Quantizes the SD model weights to save memory.\nHigher levels save more memory, and cause more quality degradation.", values=sd_quant_choices)
sd_quant_var.trace_add("write", changed_gpulayers_estimate)
makefileentry(images_tab, "Image LoRA:", "Select SD lora file",sd_lora_var, 20, width=160, singlerow=True, filetypes=[("*.safetensors *.gguf", "*.safetensors *.gguf")],tooltiptxt="Select a .safetensors or .gguf SD LoRA model file to be loaded. Should be unquantized!")
makefileentry(images_tab, "Image LoRA:", "Select SD lora file",sd_lora_var, 20, width=160, singlerow=True, filetypes=[("*.safetensors *.gguf", "*.safetensors *.gguf")],tooltiptxt="Select a .safetensors or .gguf SD LoRA model file to be loaded. Should be unquantized!", multiple=True)
makelabelentry(images_tab, "Multiplier:" , sd_loramult_var, 20, 50,padx=(390),singleline=True,tooltip="What mutiplier value to apply the SD LoRA with.",labelpadx=(330))
makefileentry(images_tab, "T5-XXL File:", "Select T5-XXL model file (SD3, Flux, WAN)",sd_t5xxl_var, 24, width=280, singlerow=True, filetypes=[("*.safetensors *.gguf","*.safetensors *.gguf")],tooltiptxt="Select a .safetensors t5xxl file to be loaded.")
@ -6711,10 +6740,10 @@ def show_gui():
args.sdupscaler = sd_upscaler_var.get()
args.sdquant = sd_quant_option(sd_quant_var.get())
if sd_lora_var.get() != "":
args.sdlora = sd_lora_var.get()
args.sdlora = [item.strip() for item in sd_lora_var.get().split("|") if item]
args.sdloramult = float(sd_loramult_var.get())
else:
args.sdlora = ""
args.sdlora = None
if gen_defaults_var.get() != "":
args.gendefaults = gen_defaults_var.get()
@ -6959,8 +6988,13 @@ def show_gui():
sd_upscaler_var.set(dict["sdupscaler"] if ("sdupscaler" in dict and dict["sdupscaler"]) else "")
sd_vaeauto_var.set(1 if ("sdvaeauto" in dict and dict["sdvaeauto"]) else 0)
sd_tiled_vae_var.set(str(dict["sdtiledvae"]) if ("sdtiledvae" in dict and dict["sdtiledvae"]) else str(default_vae_tile_threshold))
sd_lora_var.set(dict["sdlora"] if ("sdlora" in dict and dict["sdlora"]) else "")
if "sdlora" in dict and dict["sdlora"]:
if isinstance((dict["sdlora"]), list):
sd_lora_var.set("|".join(dict["sdlora"]))
else:
sd_lora_var.set(dict["sdlora"] if ("sdlora" in dict and dict["sdlora"]) else "")
else:
sd_lora_var.set("")
sd_loramult_var.set(str(dict["sdloramult"]) if ("sdloramult" in dict and dict["sdloramult"]) else "1.0")
gen_defaults_var.set(dict["gendefaults"] if ("gendefaults" in dict and dict["gendefaults"]) else "")
gen_defaults_overwrite_var.set(1 if "gendefaultsoverwrite" in dict and dict["gendefaultsoverwrite"] else 0)
@ -7401,6 +7435,8 @@ def convert_invalid_args(args):
dict["gendefaults"] = dict["sdgendefaults"]
if "flashattention" in dict and "noflashattention" not in dict:
dict["noflashattention"] = not dict["flashattention"]
if "sdlora" in dict and isinstance(dict["sdlora"], str):
dict["sdlora"] = ([dict["sdlora"]] if dict["sdlora"] else None)
return args
def setuptunnel(global_memory, has_sd):
@ -8220,10 +8256,11 @@ def kcpp_main_process(launch_args, g_memory=None, gui_launcher=False):
dlfile = download_model_from_url(args.sdvae,[".gguf",".safetensors"],min_file_size=500000)
if dlfile:
args.sdvae = dlfile
if args.sdlora and args.sdlora!="":
dlfile = download_model_from_url(args.sdlora,[".gguf",".safetensors"],min_file_size=500000)
if dlfile:
args.sdlora = dlfile
if args.sdlora and len(args.sdlora)>0:
for i in range(0,len(args.sdlora)):
dlfile = download_model_from_url(args.sdlora[i],[".gguf",".safetensors"],min_file_size=500000)
if dlfile:
args.sdlora[i] = dlfile
if args.mmproj and args.mmproj!="":
dlfile = download_model_from_url(args.mmproj,[".gguf"],min_file_size=500000)
if dlfile:
@ -8499,18 +8536,20 @@ def kcpp_main_process(launch_args, g_memory=None, gui_launcher=False):
exitcounter = 999
exit_with_error(2,f"Cannot find image model file: {imgmodel}")
else:
imglora = ""
imgloras = []
imgvae = ""
imgt5xxl = ""
imgclip1 = ""
imgclip2 = ""
imgphotomaker = ""
imgupscaler = ""
if args.sdlora:
if os.path.exists(args.sdlora):
imglora = os.path.abspath(args.sdlora)
else:
print("Missing SD LORA model file...")
if args.sdlora and len(args.sdlora)>0:
for i in range (0,len(args.sdlora)):
curr = args.sdlora[i]
if os.path.exists(curr):
imgloras.append(os.path.abspath(curr))
else:
print(f"Missing SD LORA model file {curr}...")
if args.sdvae:
if os.path.exists(args.sdvae):
imgvae = os.path.abspath(args.sdvae)
@ -8547,7 +8586,7 @@ def kcpp_main_process(launch_args, g_memory=None, gui_launcher=False):
friendlysdmodelname = os.path.basename(imgmodel)
friendlysdmodelname = os.path.splitext(friendlysdmodelname)[0]
friendlysdmodelname = sanitize_string(friendlysdmodelname)
loadok = sd_load_model(imgmodel,imgvae,imglora,imgt5xxl,imgclip1,imgclip2,imgphotomaker,imgupscaler)
loadok = sd_load_model(imgmodel,imgvae,imgloras,imgt5xxl,imgclip1,imgclip2,imgphotomaker,imgupscaler)
print("Load Image Model OK: " + str(loadok))
if not loadok:
exitcounter = 999
@ -9008,8 +9047,8 @@ if __name__ == '__main__':
sdparsergroupvae.add_argument("--sdvaeauto", help="Uses a built-in tiny VAE via TAE SD, which is very fast, and fixed bad VAEs.", action='store_true')
sdparsergrouplora = sdparsergroup.add_mutually_exclusive_group()
sdparsergrouplora.add_argument("--sdquant", metavar=('[quantization level 0/1/2]'), help="If specified, loads the model quantized to save memory. 0=off, 1=q8, 2=q4", type=int, choices=[0,1,2], nargs="?", const=2, default=0)
sdparsergrouplora.add_argument("--sdlora", metavar=('[filename]'), help="Specify an image generation LORA safetensors model to be applied.", default="")
sdparsergroup.add_argument("--sdloramult", metavar=('[amount]'), help="Multiplier for the image LORA model to be applied.", type=float, default=1.0)
sdparsergrouplora.add_argument("--sdlora", metavar=('[filename]'), help="Specify image generation LoRAs safetensors models to be applied. Multiple LoRAs are accepted.", nargs='+')
sdparsergroup.add_argument("--sdloramult", metavar=('[amount]'), help="Multiplier for the image LoRA model to be applied.", type=float, default=1.0)
sdparsergroup.add_argument("--sdtiledvae", metavar=('[maxres]'), help="Adjust the automatic VAE tiling trigger for images above this size. 0 disables vae tiling.", type=int, default=default_vae_tile_threshold)
whisperparsergroup = parser.add_argument_group('Whisper Transcription Commands')
whisperparsergroup.add_argument("--whispermodel", metavar=('[filename]'), help="Specify a Whisper .bin model to enable Speech-To-Text transcription.", default="")

View file

@ -78,8 +78,8 @@ struct SDParams {
bool chroma_use_dit_mask = true;
std::string lora_path;
sd_lora_t lora_spec;
std::vector<std::string> lora_paths;
std::vector<sd_lora_t> lora_specs;
uint32_t lora_count;
};
@ -207,7 +207,15 @@ bool sdtype_load_model(const sd_load_model_inputs inputs) {
set_sd_quiet(sd_is_quiet);
executable_path = inputs.executable_path;
std::string taesdpath = "";
std::string lorafilename = inputs.lora_filename;
std::vector<std::string> lorafilenames;
for(int i=0;i<lora_filenames_max;++i)
{
std::string temp = inputs.lora_filenames[i];
if(temp!="")
{
lorafilenames.push_back(temp);
}
}
std::string vaefilename = inputs.vae_filename;
std::string t5xxl_filename = inputs.t5xxl_filename;
std::string clip1_filename = inputs.clip1_filename;
@ -223,13 +231,16 @@ bool sdtype_load_model(const sd_load_model_inputs inputs) {
int lora_apply_mode = std::max(0, std::min(2, inputs.lora_apply_mode));
if(lorafilename!="")
if(lorafilenames.size()>0)
{
const char* lora_apply_mode_name = lora_apply_mode == 1 ? "immediately"
: lora_apply_mode == 2 ? "at runtime"
: "auto";
printf("With LoRA: %s at %f power, apply mode: %s\n",
lorafilename.c_str(),inputs.lora_multiplier,lora_apply_mode_name);
for(int i=0;i<lorafilenames.size();++i)
{
const char* lora_apply_mode_name = lora_apply_mode == 1 ? "immediately"
: lora_apply_mode == 2 ? "at runtime"
: "auto";
printf("With LoRA: %s at %f power, apply mode: %s\n",
lorafilenames[i].c_str(),inputs.lora_multiplier,lora_apply_mode_name);
}
}
if(inputs.taesd)
{
@ -315,7 +326,7 @@ bool sdtype_load_model(const sd_load_model_inputs inputs) {
sd_params->clip_l_path = clip1_filename;
sd_params->clip_g_path = clip2_filename;
sd_params->stacked_id_embeddings_path = photomaker_filename;
sd_params->lora_path = lorafilename;
sd_params->lora_paths = lorafilenames;
//if t5 is set, and model is a gguf, load it as a diffusion model path
bool endswithgguf = (sd_params->model_path.rfind(".gguf") == sd_params->model_path.size() - 5);
if((sd_params->t5xxl_path!="" || sd_params->clip_l_path!="" || sd_params->clip_g_path!="") && endswithgguf)
@ -405,15 +416,21 @@ bool sdtype_load_model(const sd_load_model_inputs inputs) {
std::filesystem::path mpath(inputs.model_filename);
sdmodelfilename = mpath.filename().string();
sd_params->lora_spec = {};
sd_params->lora_spec.path = sd_params->lora_path.c_str();
sd_params->lora_spec.multiplier = inputs.lora_multiplier;
if(sd_params->lora_path!="" && sd_params->lora_spec.multiplier>0)
sd_params->lora_specs.clear();
sd_params->lora_specs.reserve(lora_filenames_max*2);
for(int i=0;i<sd_params->lora_paths.size();++i)
{
printf("\nApply LoRA...\n");
sd_params->lora_count = 1;
sd_ctx->sd->apply_loras(&sd_params->lora_spec, sd_params->lora_count);
sd_lora_t spec = {};
spec.path = sd_params->lora_paths[i].c_str();
spec.multiplier = inputs.lora_multiplier;
sd_params->lora_specs.push_back(spec);
}
if(sd_params->lora_specs.size()>0 && inputs.lora_multiplier>0)
{
printf("\nApply %d LoRAs...\n",sd_params->lora_specs.size());
sd_params->lora_count = sd_params->lora_specs.size();
sd_ctx->sd->apply_loras(sd_params->lora_specs.data(), sd_params->lora_count);
}
input_extraimage_buffers.reserve(max_extra_images);
@ -1011,7 +1028,7 @@ sd_generation_outputs sdtype_generate(const sd_generation_inputs inputs)
// needs to be "reapplied" because sdcpp tracks previously applied LoRAs
// and weights, and apply/unapply the differences at each gen
params.loras = &sd_params->lora_spec;
params.loras = sd_params->lora_specs.data();
params.lora_count = sd_params->lora_count;
params.ref_images = reference_imgs.data();