interlude

README.md

@@ -20,6 +20,9 @@ WanGP supports the Wan (and derived models), Hunyuan Video and LTV Video models
 **Follow DeepBeepMeep on Twitter/X to get the Latest News**: https://x.com/deepbeepmeep
 
 ## 🔥 Latest Updates
+### July 27 2025: WanGP v7.3 : Interlude
+While waiting for Wan 2.2, you will appreciate the model selection hierarchy which is very useful to collect even more models. You will also appreciate that WanGP remembers which model you used last in each model family.
+
 ### July 26 2025: WanGP v7.2 : Ode to Vace
 I am really convinced that Vace can do everything the other models can do and in a better way especially as Vace can be combined with Multitalk.
 
@@ -27,7 +30,7 @@ Here are some new Vace improvements:
 - I have provided a default finetune named *Vace Cocktail*  which is a model created on the fly using the Wan text 2 video model and the Loras used to build FusioniX. The weight of the *Detail Enhancer* Lora has been reduced to improve identity preservation. Copy the model definition in *defaults/vace_14B_cocktail.json* in the *finetunes/* folder to change the Cocktail composition. Cocktail contains already some Loras acccelerators so no need to add on top a Lora Accvid, Causvid or Fusionix, ... . The whole point of Cocktail is to be able  to build you own FusioniX (which originally is a combination of 4 loras) but without the inconvenient of FusioniX.
 - Talking about identity preservation, it tends to go away when one generates a single Frame instead of a Video which is shame for our Vace photoshop. But there is a solution : I have added an Advanced Quality option, that tells WanGP to generate a little more than a frame (it will still keep only the first frame). It will be a little slower but you will be amazed how Vace Cocktail combined with this option will preserve identities (bye bye *Phantom*). 
 - As in practise I have observed one switches frequently between *Vace text2video* and *Vace text2image* I have put them in the same place they are now just one tab away, no need to reload the model. Likewise *Wan text2video* and *Wan tex2image* have been merged.
-- Color fixing when using Sliding Windows. A new postprocessing *Colot Correction* applied automatically by default (you can disable it in the *Advanced tab Sliding Window*) will try to match the colors of the new window with that of the previous window. It doesnt fix all the unwanted artifacts of the new window but at least this makes the transition smoother. Thanks to the multitalk team for the original code.
+- Color fixing when using Sliding Windows. A new postprocessing *Color Correction* applied automatically by default (you can disable it in the *Advanced tab Sliding Window*) will try to match the colors of the new window with that of the previous window. It doesnt fix all the unwanted artifacts of the new window but at least this makes the transition smoother. Thanks to the multitalk team for the original code.
 
 Also you will enjoy our new real time statistics (CPU / GPU usage, RAM / VRAM used, ... ). Many thanks to **Redtash1** for providing the framework for this new feature ! You need to go in the Config tab to enable real time stats.
 

defaults/hunyuan.json

@@ -1,7 +1,7 @@
 {
 	"model":
 	{
-		"name": "Hunyuan Video text2video 720p 13B",
+		"name": "Hunyuan Video Text2video 720p 13B",
 		"architecture" : "hunyuan",
 		"description": "Probably the best text 2 video model available.",
 		"URLs": [

defaults/hunyuan_i2v.json

@@ -1,7 +1,7 @@
 {
 	"model":
 	{
-		"name": "Hunyuan Video image2video 720p 13B",
+		"name": "Hunyuan Video Image2video 720p 13B",
 		"architecture" : "hunyuan_i2v",
 		"description": "A good looking image 2 video model, but not so good in prompt adherence.",
 		"URLs": [

defaults/hunyuan_t2v_accvideo.json

@@ -1,6 +1,6 @@
 {
   "model": {
-    "name": "Hunyuan AccVideo 720p 13B",
+    "name": "Hunyuan Video AccVideo 720p 13B",
     "architecture": "hunyuan",
     "description": " AccVideo is a novel efficient distillation method to accelerate video diffusion models with synthetic datset. Our method is 8.5x faster than HunyuanVideo.",
     "URLs": [

defaults/hunyuan_t2v_fast.json

@@ -1,6 +1,6 @@
 {
   "model": {
-    "name": "Hunyuan Fast Video 720p 13B",
+    "name": "Hunyuan Video FastHunyuan 720p 13B",
     "architecture": "hunyuan",
     "description": "Fast Hunyuan is an accelerated HunyuanVideo model. It can sample high quality videos with 6 diffusion steps.",
     "URLs": [

defaults/i2v.json

@@ -1,7 +1,7 @@
 {
 	"model":
 	{
-		"name": "Wan2.1 image2video 480p 14B",
+		"name": "Wan2.1 Image2video 480p 14B",
 		"architecture" : "i2v",
 		"description": "The standard Wan Image 2 Video specialized to generate 480p images. It also offers Start and End Image support (End Image is not supported in the original model but seems to work well)",
 		"URLs": [

defaults/i2v_720p.json

@@ -1,7 +1,7 @@
 {
 	"model":
 	{
-		"name": "Wan2.1 image2video 720p 14B",
+		"name": "Wan2.1 Image2video 720p 14B",
 		"architecture" : "i2v",
 		"description": "The standard Wan Image 2 Video specialized to generate 720p images. It also offers Start and End Image support (End Image is not supported in the original model but seems to work well).",
 		"URLs": [

defaults/i2v_fusionix.json

@@ -1,7 +1,7 @@
 {
 	"model":
 	{
-		"name": "Wan2.1 image2video 480p FusioniX 14B",
+		"name": "Wan2.1 Image2video 480p FusioniX 14B",
 		"architecture" : "i2v",
 		"description": "A powerful merged image-to-video model based on the original WAN 2.1 I2V model, enhanced using multiple open-source components and LoRAs to boost motion realism, temporal consistency, and expressive detail.",
 		"URLs": "i2v",

defaults/t2v.json

@@ -1,7 +1,7 @@
 {
 	"model":
 	{
-		"name": "Wan2.1 text2video 14B",
+		"name": "Wan2.1 Text2video 14B",
 		"architecture" : "t2v",
 		"description": "The original Wan Text 2 Video model. Most other models have been built on top of it",
 		"URLs": [

defaults/t2v_1.3B.json

@@ -1,7 +1,7 @@
 {
 	"model":
 	{
-		"name": "Wan2.1 text2video 1.3B",
+		"name": "Wan2.1 Text2video 1.3B",
 		"architecture" : "t2v_1.3B",
 		"description": "The light version of the original Wan Text 2 Video model. Most other models have been built on top of it",
 		"URLs": [

defaults/t2v_fusionix.json

@@ -1,7 +1,7 @@
 {
 	"model":
 	{
-		"name": "Wan2.1 text2video FusioniX 14B",
+		"name": "Wan2.1 Text2video FusioniX 14B",
 		"architecture" : "t2v",
 		"description": "A powerful merged text-to-video model based on the original WAN 2.1 T2V model, enhanced using multiple open-source components and LoRAs to boost motion realism, temporal consistency, and expressive detail.",
 		"URLs": [

defaults/t2v_sf.json

@@ -1,6 +1,6 @@
 {
     "model": {
-        "name": "Wan2.1 text2video Self-Forcing 14B",
+        "name": "Wan2.1 Text2video Self-Forcing 14B",
         "architecture": "t2v",
         "description": "This model is an advanced text-to-video generation model. This approach allows the model to generate videos with significantly fewer inference steps (4 or 8 steps) and without classifier-free guidance, substantially reducing video generation time while maintaining high quality outputs.",
         "URLs": [

preprocessing/matanyone/app.py

@@ -709,13 +709,10 @@ def export_image_mask(image_input, image_mask):
     return Image.fromarray(image_input), image_mask
 
 
-def export_to_current_video_engine(model_type, foreground_video_output, alpha_video_output):
+def export_to_current_video_engine( foreground_video_output, alpha_video_output):
     gr.Info("Original Video and Full Mask have been transferred")
     # return "MV#" + str(time.time()), foreground_video_output, alpha_video_output
-    if "custom_edit" in model_type and False:
+    return foreground_video_output, alpha_video_output
-        return gr.update(), alpha_video_output
-    else:
-        return foreground_video_output, alpha_video_output
 
 
 def teleport_to_video_tab(tab_state):
@@ -724,7 +721,7 @@ def teleport_to_video_tab(tab_state):
     return gr.Tabs(selected="video_gen")
 
 
-def display(tabs, tab_state, model_choice, vace_video_input, vace_image_input, vace_video_mask, vace_image_mask, vace_image_refs):
+def display(tabs, tab_state, vace_video_input, vace_image_input, vace_video_mask, vace_image_mask, vace_image_refs):
     # my_tab.select(fn=load_unload_models, inputs=[], outputs=[])
 
     media_url = "https://github.com/pq-yang/MatAnyone/releases/download/media/"
@@ -866,7 +863,7 @@ def display(tabs, tab_state, model_choice, vace_video_input, vace_image_input, v
                             with gr.Row(visible= True):
                                 export_to_current_video_engine_btn = gr.Button("Export to Control Video Input and Video Mask Input", visible= False)
                                     
-                export_to_current_video_engine_btn.click(  fn=export_to_current_video_engine, inputs= [model_choice, foreground_video_output, alpha_video_output], outputs= [vace_video_input, vace_video_mask]).then( #video_prompt_video_guide_trigger, 
+                export_to_current_video_engine_btn.click(  fn=export_to_current_video_engine, inputs= [foreground_video_output, alpha_video_output], outputs= [vace_video_input, vace_video_mask]).then( #video_prompt_video_guide_trigger, 
                     fn=teleport_to_video_tab, inputs= [tab_state], outputs= [tabs])
 
 

wgp.py

@@ -51,7 +51,7 @@ AUTOSAVE_FILENAME = "queue.zip"
 PROMPT_VARS_MAX = 10
 
 target_mmgp_version = "3.5.1"
-WanGP_version = "7.2"
+WanGP_version = "7.3"
 settings_version = 2.22
 max_source_video_frames = 3000
 prompt_enhancer_image_caption_model, prompt_enhancer_image_caption_processor, prompt_enhancer_llm_model, prompt_enhancer_llm_tokenizer = None, None, None, None
@@ -1691,6 +1691,8 @@ for path in  ["wan2.1_Vace_1.3B_preview_bf16.safetensors", "sky_reels2_diffusion
         print(f"Removing old version of model '{path}'. A new version of this model will be downloaded next time you use it.")
         os.remove( os.path.join("ckpts" , path))
 
+families_infos = {"wan":(0, "Wan2.1"), "ltxv":(1, "LTX Video"), "hunyuan":(2, "Hunyuan Video"), "flux":(3, "Flux 1"), "unknown": (100, "Unknown") }
+
 models_def = {}
 
 modules_files = {
@@ -2858,7 +2860,7 @@ def apply_changes(  state,
     if args.lock_config:
         return
     if gen_in_progress:
-        return "<DIV ALIGN=CENTER>Unable to change config when a generation is in progress</DIV>",*[gr.update()]*5
+        return "<DIV ALIGN=CENTER>Unable to change config when a generation is in progress</DIV>",*[gr.update()]*6
     global offloadobj, wan_model, server_config, loras, loras_names, default_loras_choices, default_loras_multis_str, default_lora_preset_prompt, default_lora_preset, loras_presets
     server_config = {
         "attention_mode" : attention_choice,  
@@ -2887,8 +2889,10 @@ def apply_changes(  state,
         "max_frames_multiplier" : max_frames_multiplier_choice,
         "display_stats" : display_stats_choice,
         "last_model_type" : state["model_type"],
+        "last_model_per_family":  state["last_model_per_family"],
         "last_advanced_choice": state["advanced"], 
         "last_resolution_choice": last_resolution_choice, 
+        "last_resolution_per_group":  state["last_resolution_per_group"],
     }
 
     if Path(server_config_filename).is_file():
@@ -2928,11 +2932,11 @@ def apply_changes(  state,
         model_choice = gr.Dropdown()
     else:
         reload_needed = True
-        model_choice = generate_dropdown_model_list(transformer_type)
+        model_family, model_choice = generate_dropdown_model_list(transformer_type)
 
     header = generate_header(state["model_type"], compile=compile, attention_mode= attention_mode)
     mmaudio_enabled = server_config["mmaudio_enabled"] > 0
-    return "<DIV ALIGN=CENTER>The new configuration has been succesfully applied</DIV>", header, model_choice, gr.Row(visible= server_config["enhancer_enabled"] == 1),  gr.Row(visible= mmaudio_enabled), gr.Column(visible= mmaudio_enabled)
+    return "<DIV ALIGN=CENTER>The new configuration has been succesfully applied</DIV>", header, model_family, model_choice, gr.Row(visible= server_config["enhancer_enabled"] == 1),  gr.Row(visible= mmaudio_enabled), gr.Column(visible= mmaudio_enabled)
 
 
 
@@ -3230,7 +3234,7 @@ def select_video(state, input_file_list, event_data: gr.EventData):
             values +=[video_creation_date]
             labels +=["Creation Date"]
         else: 
-            video_prompt =  configs.get("prompt", "")[:512]
+            video_prompt =  configs.get("prompt", "")[:1024]
             video_video_prompt_type = configs.get("video_prompt_type", "")
             video_image_prompt_type = configs.get("image_prompt_type", "")
             video_audio_prompt_type = configs.get("audio_prompt_type", "")
@@ -3246,6 +3250,7 @@ def select_video(state, input_file_list, event_data: gr.EventData):
                                  + [ v for s,v in map_video_prompt.items() if check(video_video_prompt_type,s)] \
                                  + [ v for s,v in map_audio_prompt.items() if all_letters(video_audio_prompt_type,s)] 
             video_model_type =  configs.get("model_type", "t2v")
+            model_family = get_model_family(video_model_type)
             video_other_prompts = ", ".join(video_other_prompts)
             video_resolution = configs.get("resolution", "") + f" (real: {width}x{height})"
             video_length = configs.get("video_length", 0)
@@ -3263,7 +3268,7 @@ def select_video(state, input_file_list, event_data: gr.EventData):
                 video_length_summary += f"{frames_count/fps:.1f}s, {round(fps)} fps)"
             video_guidance_scale = configs.get("guidance_scale", None)
             video_embedded_guidance_scale = configs.get("embedded_guidance_scale ", None)
-            if get_model_family(video_model_type) in ["hunyuan", "flux"]:
+            if model_family in ["hunyuan", "flux"]:
                 video_guidance_scale = video_embedded_guidance_scale
                 video_guidance_label = "Embedded Guidance Scale"
             else:
@@ -3293,6 +3298,10 @@ def select_video(state, input_file_list, event_data: gr.EventData):
             if len(video_outpainting) >0 and any_letters(video_image_prompt_type, "VFK"):
                 values += [video_outpainting]
                 labels += ["Outpainting"]
+            video_sample_solver = configs.get("sample_solver", "")
+            if model_family == "wan":
+                values += ["unipc" if len(video_sample_solver) ==0 else video_sample_solver]
+                labels += ["Sampler Solver"]                                        
             values += [video_resolution, video_length_summary, video_seed, video_guidance_scale, video_flow_shift, video_num_inference_steps]
             labels += [ "Resolution", video_length_label, "Seed", video_guidance_label, "Flow Shift", "Num Inference steps"]
             video_negative_prompt = configs.get("negative_prompt", "")
@@ -5572,7 +5581,7 @@ def apply_lset(state, wizard_prompt_activated, lset_name, loras_choices, loras_m
     lset_name = get_lset_name(state, lset_name)
     if len(lset_name) == 0:
         gr.Info("Please choose a Lora Preset or Setting File in the list or create one")
-        return wizard_prompt_activated, loras_choices, loras_mult_choices, prompt, gr.update(), gr.update(), gr.update()
+        return wizard_prompt_activated, loras_choices, loras_mult_choices, prompt, gr.update(), gr.update(), gr.update(), gr.update()
     else:
         current_model_type = state["model_type"]
         if lset_name.endswith(".lset"):
@@ -5592,7 +5601,7 @@ def apply_lset(state, wizard_prompt_activated, lset_name, loras_choices, loras_m
                 state["apply_success"] = 1
                 wizard_prompt_activated = "on"
 
-            return wizard_prompt_activated, loras_choices, loras_mult_choices, prompt, get_unique_id(), gr.update(), gr.update()
+            return wizard_prompt_activated, loras_choices, loras_mult_choices, prompt, get_unique_id(), gr.update(), gr.update(), gr.update()
         else:
             configs, _ = get_settings_from_file(state, os.path.join(get_lora_dir(current_model_type), lset_name), True, True, True)
             if configs == None:
@@ -5605,10 +5614,10 @@ def apply_lset(state, wizard_prompt_activated, lset_name, loras_choices, loras_m
 
             if model_type == current_model_type:
                 set_model_settings(state, current_model_type, configs)        
-                return *[gr.update()] * 4, gr.update(), gr.update(), get_unique_id()
+                return *[gr.update()] * 4, gr.update(), gr.update(), gr.update(), get_unique_id()
             else:
                 set_model_settings(state, model_type, configs)        
-                return *[gr.update()] * 4, gr.update(), generate_dropdown_model_list(model_type), gr.update()
+                return *[gr.update()] * 4, gr.update(), *generate_dropdown_model_list(model_type), gr.update()
 
 def extract_prompt_from_wizard(state, variables_names, prompt, wizard_prompt, allow_null_values, *args):
 
@@ -6044,9 +6053,9 @@ def use_video_settings(state, input_file_list, choice):
             else:
                 gr.Info(f"Settings Loaded from Video with prompt '{prompt[:100]}'")
             if models_compatible:
-                return gr.update(), str(time.time())
+                return gr.update(), gr.update(), str(time.time())
             else:
-                return generate_dropdown_model_list(model_type), gr.update()
+                return *generate_dropdown_model_list(model_type), gr.update()
     else:
         gr.Info(f"No Video is Selected")
 
@@ -6144,10 +6153,10 @@ def load_settings_from_file(state, file_path):
 
     if model_type == current_model_type:
         set_model_settings(state, current_model_type, configs)        
-        return gr.update(), str(time.time()), None
+        return gr.update(), gr.update(), str(time.time()), None
     else:
         set_model_settings(state, model_type, configs)        
-        return generate_dropdown_model_list(model_type), gr.update(), None
+        return *generate_dropdown_model_list(model_type), gr.update(), None
 
 def save_inputs(
             target,
@@ -6324,7 +6333,11 @@ def change_model(state, model_choice):
         return
     model_filename = get_model_filename(model_choice, transformer_quantization, transformer_dtype_policy)
     state["model_filename"] = model_filename
+    last_model_per_family = state["last_model_per_family"] 
+    last_model_per_family[get_model_family(model_choice)] = model_choice
+    server_config["last_model_per_family"] = last_model_per_family
     server_config["last_model_type"] = model_choice
+
     with open(server_config_filename, "w", encoding="utf-8") as writer:
         writer.write(json.dumps(server_config, indent=4))
 
@@ -6613,21 +6626,31 @@ def group_resolutions(resolutions, selected_resolution):
     available_groups.reverse()
     return available_groups, selected_group_resolutions, selected_group
 
-def change_resolution_group(selected_group):
+def change_resolution_group(state, selected_group):
     resolution_choices = get_resolution_choices(None)    
     group_resolution_choices = [ resolution for resolution in resolution_choices if categorize_resolution(resolution[1]) == selected_group ]
-    return gr.update(choices= group_resolution_choices, value= group_resolution_choices[0][1] ) 
+
+    last_resolution_per_group = state["last_resolution_per_group"]
+    last_resolution = last_resolution_per_group.get(selected_group, "")
+    if len(last_resolution) == 0 or not any( [last_resolution == resolution[1] for resolution in group_resolution_choices]):
+        last_resolution = group_resolution_choices[0][1]
+    return gr.update(choices= group_resolution_choices, value= last_resolution ) 
     
 
-def record_last_resolution(resolution):
+
+def record_last_resolution(state, resolution):
     server_config["last_resolution_choice"] = resolution
+    selected_group = categorize_resolution(resolution)
+    last_resolution_per_group = state["last_resolution_per_group"]
+    last_resolution_per_group[selected_group ] = resolution
+    server_config["last_resolution_per_group"] = last_resolution_per_group
     with open(server_config_filename, "w", encoding="utf-8") as writer:
         writer.write(json.dumps(server_config, indent=4))
 
 def get_max_frames(nb):
     return (nb - 1) * server_config.get("max_frames_multiplier",1) + 1
 
-def generate_video_tab(update_form = False, state_dict = None, ui_defaults = None, model_choice = None, header = None, main = None):
+def generate_video_tab(update_form = False, state_dict = None, ui_defaults = None, model_family = None, model_choice = None, header = None, main = None):
     global inputs_names #, advanced
 
     if update_form:
@@ -6643,6 +6666,8 @@ def generate_video_tab(update_form = False, state_dict = None, ui_defaults = Non
         state_dict["model_filename"] = model_filename
         state_dict["model_type"] = model_type
         state_dict["advanced"] = advanced_ui
+        state_dict["last_model_per_family"] = server_config.get("last_model_per_family", {})
+        state_dict["last_resolution_per_group"] = server_config.get("last_resolution_per_group", {})
         gen = dict()
         gen["queue"] = []
         state_dict["gen"] = gen
@@ -7562,8 +7587,8 @@ def generate_video_tab(update_form = False, state_dict = None, ui_defaults = Non
             target_settings = gr.Text(value = "settings", interactive= False, visible= False)
             last_choice = gr.Number(value =-1, interactive= False, visible= False)
 
-            resolution_group.input(fn=change_resolution_group, inputs=[resolution_group], outputs=[resolution])
+            resolution_group.input(fn=change_resolution_group, inputs=[state, resolution_group], outputs=[resolution])
-            resolution.change(fn=record_last_resolution, inputs=[resolution])
+            resolution.change(fn=record_last_resolution, inputs=[state, resolution])
 
             
             audio_prompt_type_remux.change(fn=refresh_audio_prompt_type_remux, inputs=[state, audio_prompt_type, audio_prompt_type_remux], outputs=[audio_prompt_type])
@@ -7635,7 +7660,7 @@ def generate_video_tab(update_form = False, state_dict = None, ui_defaults = Non
             ).then(fn=save_inputs,
                 inputs =[target_state] + gen_inputs,
                 outputs= None
-            ).then( fn=use_video_settings, inputs =[state, output, last_choice] , outputs= [model_choice, refresh_form_trigger])
+            ).then( fn=use_video_settings, inputs =[state, output, last_choice] , outputs= [model_family, model_choice, refresh_form_trigger])
 
             video_info_add_videos_btn.click(fn=add_videos_to_gallery, inputs =[state, output, last_choice, files_to_load], outputs = [output, files_to_load, video_info_tabs] )
             gr.on(triggers=[video_info_eject_video_btn.click, video_info_eject_video2_btn.click, video_info_eject_image_btn.click], fn=eject_video_from_gallery, inputs =[state, output, last_choice], outputs = [output, video_info, video_buttons_row] )
@@ -7657,7 +7682,7 @@ def generate_video_tab(update_form = False, state_dict = None, ui_defaults = Non
             confirm_delete_lset_btn.click(delete_lset, inputs=[state, lset_name], outputs=[lset_name, apply_lset_btn, refresh_lora_btn, delete_lset_btn, save_lset_btn,confirm_delete_lset_btn, cancel_lset_btn ])
             cancel_lset_btn.click(cancel_lset, inputs=[], outputs=[apply_lset_btn, refresh_lora_btn, delete_lset_btn, save_lset_btn, confirm_delete_lset_btn,confirm_save_lset_btn, cancel_lset_btn,save_lset_prompt_drop ])
             apply_lset_btn.click(fn=save_inputs, inputs =[target_state] + gen_inputs, outputs= None).then(fn=apply_lset, 
-                inputs=[state, wizard_prompt_activated_var, lset_name,loras_choices, loras_multipliers, prompt], outputs=[wizard_prompt_activated_var, loras_choices, loras_multipliers, prompt, fill_wizard_prompt_trigger, model_choice, refresh_form_trigger])
+                inputs=[state, wizard_prompt_activated_var, lset_name,loras_choices, loras_multipliers, prompt], outputs=[wizard_prompt_activated_var, loras_choices, loras_multipliers, prompt, fill_wizard_prompt_trigger, model_family, model_choice, refresh_form_trigger])
             refresh_lora_btn.click(refresh_lora_list, inputs=[state, lset_name,loras_choices], outputs=[lset_name, loras_choices])
             refresh_lora_btn2.click(refresh_lora_list, inputs=[state, lset_name,loras_choices], outputs=[lset_name, loras_choices])
 
@@ -7693,7 +7718,7 @@ def generate_video_tab(update_form = False, state_dict = None, ui_defaults = Non
             ).then(fn=save_inputs,
                 inputs =[target_state] + gen_inputs,
                 outputs= None
-            ).then(fn=load_settings_from_file, inputs =[state, settings_file] , outputs= [model_choice, refresh_form_trigger, settings_file])
+            ).then(fn=load_settings_from_file, inputs =[state, settings_file] , outputs= [model_family, model_choice, refresh_form_trigger, settings_file])
 
 
             fill_wizard_prompt_trigger.change(
@@ -7709,6 +7734,8 @@ def generate_video_tab(update_form = False, state_dict = None, ui_defaults = Non
                 outputs= [prompt]
             )                
 
+            model_family.input(fn=change_model_family, inputs=[state, model_family], outputs= [model_choice])
+
             model_choice.change(fn=validate_wizard_prompt,
                 inputs= [state, wizard_prompt_activated_var, wizard_variables_var,  prompt, wizard_prompt, *prompt_vars] ,
                 outputs= [prompt]
@@ -7896,13 +7923,13 @@ def generate_download_tab(lset_name,loras_choices, state):
     download_loras_btn.click(fn=download_loras, inputs=[], outputs=[download_status_row, download_status]).then(fn=refresh_lora_list, inputs=[state, lset_name,loras_choices], outputs=[lset_name, loras_choices])
 
     
-def generate_configuration_tab(state, blocks, header, model_choice, resolution, prompt_enhancer_row, mmaudio_tab, PP_MMAudio_col):
+def generate_configuration_tab(state, blocks, header, model_family, model_choice, resolution, prompt_enhancer_row, mmaudio_tab, PP_MMAudio_col):
     gr.Markdown("Please click Apply Changes at the bottom so that the changes are effective. Some choices below may be locked if the app has been launched by specifying a config preset.")
     with gr.Column():
         with gr.Tabs():
             # with gr.Row(visible=advanced_ui) as advanced_row:
             with gr.Tab("General"):
-                dropdown_choices = get_sorted_dropdown(displayed_model_types)
+                dropdown_families, dropdown_choices = get_sorted_dropdown(displayed_model_types, None)
 
                 transformer_types_choices = gr.Dropdown(
                     choices= dropdown_choices,
@@ -8185,7 +8212,7 @@ def generate_configuration_tab(state, blocks, header, model_choice, resolution,
                     display_stats_choice,
                     resolution,
                 ],
-                outputs= [msg , header, model_choice, prompt_enhancer_row, mmaudio_tab, PP_MMAudio_col]
+                outputs= [msg , header, model_family, model_choice, prompt_enhancer_row, mmaudio_tab, PP_MMAudio_col]
         )
 
 def generate_about_tab():
@@ -8232,30 +8259,63 @@ def generate_info_tab():
         with gr.Tab("Vace", id="vace"):
             gr.Markdown(vace)
 
+def compact_name(family_name, model_name):
+    if model_name.startswith(family_name):
+        return model_name[len(family_name):].strip()
+    return model_name
 
-def  get_sorted_dropdown(dropdown_types):
+def get_sorted_dropdown(dropdown_types, current_model_family):
-    families_order = {"wan":0, "ltxv":1, "hunyuan":2, "flux": 3, "unknown": 100 }
+    models_families = [get_model_family(type) for type in dropdown_types] 
-    dropdown_classes = [  families_order[get_model_family(type)] for type in dropdown_types]
+    families = {}
-    dropdown_names = [ get_model_name(type) for type in dropdown_types]
+    for family in models_families:
+        if family not in families: families[family] = 1
 
-    dropdown_choices = [model[1:] for model in sorted(zip(dropdown_classes, dropdown_names , dropdown_types), key=lambda c: (c[0], c[1]))]
+    families_orders = [  families_infos[family][0]  for family in families ]
-    return dropdown_choices
+    families_labels = [  families_infos[family][1]  for family in families ]
+    sorted_familes = [ info[1:] for info in sorted(zip(families_orders, families_labels, families), key=lambda c: c[0])]
+    if current_model_family is None:
+        dropdown_choices = [ (families_infos[family][0], get_model_name(model_type), model_type) for model_type, family in zip(dropdown_types, models_families)]
+    else:
+        dropdown_choices = [ (families_infos[family][0], compact_name(families_infos[family][1], get_model_name(model_type)), model_type) for model_type, family in zip( dropdown_types, models_families) if family == current_model_family]
+    dropdown_choices = sorted(dropdown_choices, key=lambda c: (c[0], c[1]))
+    dropdown_choices = [model[1:] for model in dropdown_choices] 
+    return sorted_familes, dropdown_choices
 
 def generate_dropdown_model_list(current_model_type):
     dropdown_types= transformer_types if len(transformer_types) > 0 else displayed_model_types 
     if current_model_type not in dropdown_types:
         dropdown_types.append(current_model_type)
-    dropdown_choices = get_sorted_dropdown(dropdown_types)
+    current_model_family = get_model_family(current_model_type)
+    sorted_familes, dropdown_choices = get_sorted_dropdown(dropdown_types, current_model_family)
 
-    return gr.Dropdown(
+    dropdown_families = gr.Dropdown(
+        choices= sorted_familes,
+        value= current_model_family,
+        show_label= False,
+        scale= 1,
+        elem_id="family_list",
+        min_width=50
+        )
+
+    return dropdown_families, gr.Dropdown(
         choices= dropdown_choices,
         value= current_model_type,
         show_label= False,
-        scale= 2,
+        scale= 4,
         elem_id="model_list",
-        elem_classes="model_list_class",
         )
 
+def change_model_family(state, current_model_family):
+    dropdown_types= transformer_types if len(transformer_types) > 0 else displayed_model_types 
+    current_family_name = families_infos[current_model_family][1]
+    models_families = [get_model_family(type) for type in dropdown_types] 
+    dropdown_choices = [ (compact_name(current_family_name,  get_model_name(model_type)), model_type) for model_type, family in zip(dropdown_types, models_families) if family == current_model_family ]
+    dropdown_choices = sorted(dropdown_choices, key=lambda c: c[0])
+    last_model_per_family = state.get("last_model_per_family", {})
+    model_type = last_model_per_family.get(current_model_family, "")
+    if len(model_type) == "" or model_type not in displayed_model_types:  model_type = dropdown_choices[0][1]
+    return gr.Dropdown(choices= dropdown_choices, value = model_type )
+
 def set_new_tab(tab_state, new_tab_no):
     global vmc_event_handler    
 
@@ -8399,13 +8459,21 @@ def create_ui():
             --layout-gap: 0px !important;
         }    
         .postprocess span {margin-top:4px;margin-bottom:4px} 
-        #model_list{
+        #model_list, #family_list{
         background-color:black;
         padding:1px}
 
-        #model_list input {
+        #model_list input, #family_list input {
         font-size:25px}
 
+        #family_list div div {
+        border-radius: 4px 0px 0px 4px;
+        }
+
+        #model_list div div {
+        border-radius: 0px 4px 4px 0px;
+        }
+
         .title-with-lines {
             display: flex;
             align-items: center;
@@ -8722,10 +8790,11 @@ def create_ui():
                 with gr.Row():
                     if args.lock_model:    
                         gr.Markdown("<div class='title-with-lines'><div class=line></div><h2>" + get_model_name(transformer_type) + "</h2><div class=line></div>")
-                        model_choice = gr.Dropdown(visible=False, value= transformer_type)
+                        model_family = gr.Dropdown(visible=False, value= "")
+                        model_choice = gr.Dropdown(visible=False, value= transformer_type, choices= [transformer_type])
                     else:
                         gr.Markdown("<div class='title-with-lines'><div class=line width=100%></div></div>")
-                        model_choice = generate_dropdown_model_list(transformer_type)
+                        model_family, model_choice = generate_dropdown_model_list(transformer_type)
                         gr.Markdown("<div class='title-with-lines'><div class=line width=100%></div></div>")
                 with gr.Row():
                     header = gr.Markdown(generate_header(transformer_type, compile, attention_mode), visible= True)
@@ -8735,16 +8804,16 @@ def create_ui():
                 with gr.Row():
                     (   state, loras_choices, lset_name, resolution,
                         video_guide, image_guide, video_mask, image_mask, image_refs, prompt_enhancer_row, mmaudio_tab, PP_MMAudio_col
-                    ) = generate_video_tab(model_choice=model_choice, header=header, main = main)
+                    ) = generate_video_tab(model_family=model_family, model_choice=model_choice, header=header, main = main)
             with gr.Tab("Guides", id="info") as info_tab:
                 generate_info_tab()
             with gr.Tab("Video Mask Creator", id="video_mask_creator") as video_mask_creator:
-                matanyone_app.display(main_tabs, tab_state, model_choice, video_guide, image_guide, video_mask, image_mask, image_refs)
+                matanyone_app.display(main_tabs, tab_state, video_guide, image_guide, video_mask, image_mask, image_refs)
             if not args.lock_config:
                 with gr.Tab("Downloads", id="downloads") as downloads_tab:
                     generate_download_tab(lset_name, loras_choices, state)
                 with gr.Tab("Configuration", id="configuration") as configuration_tab:
-                    generate_configuration_tab(state, main, header, model_choice, resolution, prompt_enhancer_row, mmaudio_tab, PP_MMAudio_col)
+                    generate_configuration_tab(state, main, header, model_family, model_choice, resolution, prompt_enhancer_row, mmaudio_tab, PP_MMAudio_col)
             with gr.Tab("About"):
                 generate_about_tab()
         if stats_app is not None: