Merge pull request #2690 from b-fission/autodetect-modeltype

Auto-detect model type for safetensors files

kohya_gui/common_gui.py

@@ -5,6 +5,7 @@
 from easygui import msgbox, ynbox
 from typing import Optional
 from .custom_logging import setup_logging
+from .sd_modeltype import SDModelType
 
 import os
 import re
@@ -1014,6 +1015,13 @@ def set_pretrained_model_name_or_path_input(
     v_parameterization = gr.Checkbox(visible=True)
     sdxl = gr.Checkbox(visible=True)
 
+    # Auto-detect model type if safetensors file path is given
+    if pretrained_model_name_or_path.lower().endswith(".safetensors"):
+        detect = SDModelType(pretrained_model_name_or_path)
+        v2 = gr.Checkbox(value=detect.Is_SD2(), visible=True)
+        sdxl = gr.Checkbox(value=detect.Is_SDXL(), visible=True)
+        #TODO: v_parameterization
+
     # If a refresh method is provided, use it to update the choices for the Dropdown widget
     if refresh_method is not None:
         args = dict(

kohya_gui/extract_lora_gui.py

@@ -12,6 +12,7 @@
 )
 
 from .custom_logging import setup_logging
+from .sd_modeltype import SDModelType
 
 # Set up logging
 log = setup_logging()
@@ -337,6 +338,19 @@ def change_sdxl(sdxl):
                 outputs=[load_tuned_model_to, load_original_model_to],
             )
 
+            #secondary event on model_tuned for auto-detection of v2/SDXL
+            def change_modeltype_model_tuned(path):
+                detect = SDModelType(path)
+                v2 = gr.Checkbox(value=detect.Is_SD2())
+                sdxl = gr.Checkbox(value=detect.Is_SDXL())
+                return v2, sdxl
+
+            model_tuned.change(
+                change_modeltype_model_tuned,
+                inputs=model_tuned,
+                outputs=[v2, sdxl]
+            )
+
         extract_button = gr.Button("Extract LoRA model")
 
         extract_button.click(

kohya_gui/merge_lora_gui.py

@@ -16,6 +16,7 @@
     create_refresh_button, setup_environment
 )
 from .custom_logging import setup_logging
+from .sd_modeltype import SDModelType
 
 # Set up logging
 log = setup_logging()
@@ -145,6 +146,13 @@ def list_save_to(path):
                     show_progress=False,
                 )
 
+                #secondary event on sd_model for auto-detection of SDXL
+                sd_model.change(
+                    lambda path: gr.Checkbox(value=SDModelType(path).Is_SDXL()),
+                    inputs=sd_model,
+                    outputs=sdxl_model
+                )
+
             with gr.Group(), gr.Row():
                 lora_a_model = gr.Dropdown(
                     label='LoRA model "A" (path to the LoRA A model)',

kohya_gui/sd_modeltype.py

@@ -0,0 +1,47 @@
+from os.path import isfile
+from safetensors import safe_open
+import enum
+
+# methodology is based on https://github.com/AUTOMATIC1111/stable-diffusion-webui/blob/82a973c04367123ae98bd9abdf80d9eda9b910e2/modules/sd_models.py#L379-L403
+
+class ModelType(enum.Enum):
+    UNKNOWN = 0
+    SD1 = 1
+    SD2 = 2
+    SDXL = 3
+    SD3 = 4
+
+class SDModelType:
+    def __init__(self, safetensors_path):
+        self.model_type = ModelType.UNKNOWN
+
+        if not isfile(safetensors_path):
+            return
+
+        try:
+            st = safe_open(filename=safetensors_path, framework="numpy", device="cpu")
+            def hasKeyPrefix(pfx):
+                return any(k.startswith(pfx) for k in st.keys())
+
+            if "model.diffusion_model.x_embedder.proj.weight" in st.keys():
+                self.model_type = ModelType.SD3
+            elif hasKeyPrefix("conditioner."):
+                self.model_type = ModelType.SDXL
+            elif hasKeyPrefix("cond_stage_model.model."):
+                self.model_type = ModelType.SD2
+            elif hasKeyPrefix("model."):
+                self.model_type = ModelType.SD1
+        except:
+            pass
+
+    def Is_SD1(self):
+        return self.model_type == ModelType.SD1
+
+    def Is_SD2(self):
+        return self.model_type == ModelType.SD2
+
+    def Is_SDXL(self):
+        return self.model_type == ModelType.SDXL
+
+    def Is_SD3(self):
+        return self.model_type == ModelType.SD3