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README.md

@@ -20,21 +20,27 @@ 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 : 
-### September 25 2025: WanGP v8.73 - Here Are ~~Two~~Three New Contenders in the Vace Arena !
+### September 25 2025: WanGP v8.73 - ~~Here Are Two Three New Contenders in the Vace Arena !~~ The Never Ending Release 
 
-So in today's release you will find two Wannabe Vace that covers each only a subset of Vace features but offers some interesting advantages:
+So in ~~today's~~ this release you will find two Wannabe Vace that covers each only a subset of Vace features but offers some interesting advantages:
 - **Wan 2.2 Animate**: this model is specialized in *Body Motion* and *Facial Motion transfers*. It does that very well. You can use this model to either *Replace* a person in an in Video or *Animate* the person of your choice using an existing *Pose Video* (remember *Animate Anyone* ?). By default it will keep the original soundtrack. *Wan 2.2 Animate* seems to be under the hood a derived i2v model and should support the corresponding Loras Accelerators (for instance *FusioniX t2v*). Also as a WanGP exclusivity, you will find support for *Outpainting*.
 
 In order to use Wan 2.2 Animate you will need first to stop by the *Mat Anyone* embedded tool, to extract the *Video Mask* of the person from which you want to extract the motion.
 
+With version WanGP 8.74, there is an extra option that allows you to apply *Relighting* when Replacing a person. Also, you can now Animate a person without providing a Video Mask to target the source of the motion (with the risk it will be less precise) 
+
 - **Lucy Edit**: this one claims to be a *Nano Banana* for Videos. Give it a video and asks it to change it (it is specialized in clothes changing) and voila ! The nice thing about it is that is it based on the *Wan 2.2 5B* model and therefore is very fast especially if you the *FastWan* finetune that is also part of the package.
 
 Also because I wanted to spoil you:
 - **Qwen Edit Plus**: also known as the *Qwen Edit 25th September Update* which is specialized in combining multiple Objects / People. There is also a new support for *Pose transfer* & *Recolorisation*. All of this made easy to use in WanGP. You will find right now only the quantized version since HF crashes when uploading the unquantized version.
 
+- **T2V Video 2 Video Masking**: ever wanted to apply a Lora, a process (for instance Upsampling) or a Text Prompt on only a (moving) part of a Source Video. Look no further, I have added *Masked Video 2 Video* (which works also in image2image) in the *Text 2 Video* models. As usual you just need to use *Matanyone* to creatre the mask.
+
+
 *Update 8.71*: fixed Fast Lucy Edit that didnt contain the lora
 *Update 8.72*: shadow drop of Qwen Edit Plus
 *Update 8.73*: Qwen Preview & InfiniteTalk Start image 
+*Update 8.74*: Animate Relighting / Nomask mode , t2v Masked Video to Video  
 
 ### September 15 2025: WanGP v8.6 - Attack of the Clones
 

defaults/animate.json

@@ -2,12 +2,16 @@
     "model": {
         "name": "Wan2.2 Animate",
         "architecture": "animate",
-        "description": "Wan-Animate takes a video and a character image as input, and generates a video in either 'animation' or 'replacement' mode.",
+        "description": "Wan-Animate takes a video and a character image as input, and generates a video in either 'Animation' or 'Replacement' mode. Sliding Window of 81 frames at least are recommeded to obtain the best Style continuity.",
         "URLs": [
             "https://huggingface.co/DeepBeepMeep/Wan2.2/resolve/main/wan2.2_animate_14B_bf16.safetensors",
             "https://huggingface.co/DeepBeepMeep/Wan2.2/resolve/main/wan2.2_animate_14B_quanto_fp16_int8.safetensors",
             "https://huggingface.co/DeepBeepMeep/Wan2.2/resolve/main/wan2.2_animate_14B_quanto_bf16_int8.safetensors"
         ],
+		"preload_URLs" :
+		[
+			"https://huggingface.co/DeepBeepMeep/Wan2.2/resolve/main/wan2.2_animate_relighting_lora.safetensors"
+		],
         "group": "wan2_2"
     }
 }

models/flux/sampling.py

@@ -292,7 +292,7 @@ def denoise(
         callback(-1, None, True)
 
     original_image_latents = None if img_cond_seq is None else img_cond_seq.clone() 
-
+    original_timesteps = timesteps
     morph, first_step = False, 0
     if img_msk_latents is not None:
         randn = torch.randn_like(original_image_latents)
@@ -309,7 +309,7 @@ def denoise(
     updated_num_steps= len(timesteps) -1
     if callback != None:
         from shared.utils.loras_mutipliers import update_loras_slists
-        update_loras_slists(model, loras_slists, updated_num_steps)
+        update_loras_slists(model, loras_slists, len(original_timesteps))
         callback(-1, None, True, override_num_inference_steps = updated_num_steps)
     from mmgp import offload
     # this is ignored for schnell

models/qwen/pipeline_qwenimage.py

@@ -825,7 +825,7 @@ class QwenImagePipeline(): #DiffusionPipeline
         )
         num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
         self._num_timesteps = len(timesteps)
-
+        original_timesteps = timesteps
         # handle guidance
         if self.transformer.guidance_embeds:
             guidance = torch.full([1], guidance_scale, device=device, dtype=torch.float32)
@@ -860,7 +860,7 @@ class QwenImagePipeline(): #DiffusionPipeline
         updated_num_steps= len(timesteps)
         if callback != None:
             from shared.utils.loras_mutipliers import update_loras_slists
-            update_loras_slists(self.transformer, loras_slists, updated_num_steps)
+            update_loras_slists(self.transformer, loras_slists, len(original_timesteps))
             callback(-1, None, True, override_num_inference_steps = updated_num_steps)
 
 

models/wan/any2video.py

@@ -34,6 +34,7 @@ from shared.utils.vace_preprocessor import VaceVideoProcessor
 from shared.utils.basic_flowmatch import FlowMatchScheduler
 from shared.utils.utils import get_outpainting_frame_location, resize_lanczos, calculate_new_dimensions, convert_image_to_tensor, fit_image_into_canvas
 from .multitalk.multitalk_utils import MomentumBuffer, adaptive_projected_guidance, match_and_blend_colors, match_and_blend_colors_with_mask
+from shared.utils.audio_video import save_video
 from mmgp import safetensors2
 from shared.utils.audio_video import save_video
 
@@ -434,7 +435,7 @@ class WanAny2V:
         start_step_no = 0
         ref_images_count = 0
         trim_frames = 0
-        extended_overlapped_latents = clip_image_start = clip_image_end = None
+        extended_overlapped_latents = clip_image_start = clip_image_end = image_mask_latents = None
         no_noise_latents_injection = infinitetalk
         timestep_injection = False
         lat_frames = int((frame_num - 1) // self.vae_stride[0]) + 1
@@ -585,7 +586,7 @@ class WanAny2V:
             kwargs['cam_emb'] = cam_emb
 
         # Video 2 Video
-        if denoising_strength < 1. and input_frames != None:
+        if "G" in video_prompt_type and input_frames != None:
             height, width = input_frames.shape[-2:]
             source_latents = self.vae.encode([input_frames])[0].unsqueeze(0)
             injection_denoising_step = 0
@@ -616,6 +617,14 @@ class WanAny2V:
                     if hasattr(sample_scheduler, "sigmas"): sample_scheduler.sigmas= sample_scheduler.sigmas[injection_denoising_step:]
                     injection_denoising_step = 0
 
+            if input_masks is not None and not "U" in video_prompt_type:
+                image_mask_latents = torch.nn.functional.interpolate(input_masks, size= source_latents.shape[-2:], mode="nearest").unsqueeze(0)
+                if image_mask_latents.shape[2] !=1:
+                    image_mask_latents = torch.cat([ image_mask_latents[:,:, :1], torch.nn.functional.interpolate(image_mask_latents, size= (source_latents.shape[-3]-1, *source_latents.shape[-2:]), mode="nearest") ], dim=2)
+                image_mask_latents = torch.where(image_mask_latents>=0.5, 1., 0. )[:1].to(self.device)
+                # save_video(image_mask_latents.squeeze(0), "mama.mp4", value_range=(0,1) )
+                # image_mask_rebuilt = image_mask_latents.repeat_interleave(8, dim=-1).repeat_interleave(8, dim=-2).unsqueeze(0)
+
         # Phantom
         if phantom:
             lat_input_ref_images_neg = None
@@ -737,7 +746,7 @@ class WanAny2V:
         denoising_extra = ""
         from shared.utils.loras_mutipliers import update_loras_slists, get_model_switch_steps
 
-        phase_switch_step, phase_switch_step2, phases_description = get_model_switch_steps(timesteps, updated_num_steps, guide_phases, 0 if self.model2 is None else model_switch_phase, switch_threshold, switch2_threshold )
+        phase_switch_step, phase_switch_step2, phases_description = get_model_switch_steps(original_timesteps,guide_phases, 0 if self.model2 is None else model_switch_phase, switch_threshold, switch2_threshold )
         if len(phases_description) > 0:  set_header_text(phases_description)
         guidance_switch_done =  guidance_switch2_done = False
         if guide_phases > 1: denoising_extra = f"Phase 1/{guide_phases} High Noise" if self.model2 is not None else f"Phase 1/{guide_phases}"
@@ -748,8 +757,8 @@ class WanAny2V:
                 denoising_extra = f"Phase {phase_no}/{guide_phases} {'Low Noise' if trans == self.model2 else 'High Noise'}" if self.model2 is not None else f"Phase {phase_no}/{guide_phases}"
                 callback(step_no-1, denoising_extra = denoising_extra)
             return guide_scale, guidance_switch_done, trans, denoising_extra
-        update_loras_slists(self.model, loras_slists, updated_num_steps, phase_switch_step= phase_switch_step, phase_switch_step2= phase_switch_step2)
+        update_loras_slists(self.model, loras_slists, len(original_timesteps), phase_switch_step= phase_switch_step, phase_switch_step2= phase_switch_step2)
-        if self.model2 is not None: update_loras_slists(self.model2, loras_slists, updated_num_steps, phase_switch_step= phase_switch_step, phase_switch_step2= phase_switch_step2)
+        if self.model2 is not None: update_loras_slists(self.model2, loras_slists, len(original_timesteps), phase_switch_step= phase_switch_step, phase_switch_step2= phase_switch_step2)
         callback(-1, None, True, override_num_inference_steps = updated_num_steps, denoising_extra = denoising_extra)
 
         def clear():
@@ -762,6 +771,7 @@ class WanAny2V:
             scheduler_kwargs = {} if isinstance(sample_scheduler, FlowMatchScheduler) else {"generator": seed_g}
         # b, c, lat_f, lat_h, lat_w
         latents = torch.randn(batch_size, *target_shape, dtype=torch.float32, device=self.device, generator=seed_g)
+        if "G" in video_prompt_type: randn = latents
         if apg_switch != 0:  
             apg_momentum = -0.75
             apg_norm_threshold = 55
@@ -784,23 +794,21 @@ class WanAny2V:
                 timestep = torch.full((target_shape[-3],), t, dtype=torch.int64, device=latents.device)
                 timestep[:source_latents.shape[2]] = 0
                         
-            kwargs.update({"t": timestep, "current_step": start_step_no + i})  
+            kwargs.update({"t": timestep, "current_step_no": i, "real_step_no": start_step_no + i })  
             kwargs["slg_layers"] = slg_layers if int(slg_start * sampling_steps) <= i < int(slg_end * sampling_steps) else None
 
             if denoising_strength < 1 and i <= injection_denoising_step:
                 sigma = t / 1000
-                noise = torch.randn(batch_size, *target_shape, dtype=torch.float32, device=self.device, generator=seed_g)
                 if inject_from_start:
-                    new_latents = latents.clone()
+                    noisy_image = latents.clone()
-                    new_latents[:,:, :source_latents.shape[2] ] = noise[:, :, :source_latents.shape[2] ] * sigma + (1 - sigma) * source_latents
+                    noisy_image[:,:, :source_latents.shape[2] ] = randn[:, :, :source_latents.shape[2] ] * sigma + (1 - sigma) * source_latents
                     for latent_no, keep_latent in enumerate(latent_keep_frames):
                         if not keep_latent:
-                            new_latents[:, :, latent_no:latent_no+1 ] = latents[:, :, latent_no:latent_no+1]
+                            noisy_image[:, :, latent_no:latent_no+1 ] = latents[:, :, latent_no:latent_no+1]
-                    latents = new_latents
+                    latents = noisy_image
-                    new_latents = None
+                    noisy_image = None
                 else:
-                    latents = noise * sigma + (1 - sigma) * source_latents
+                    latents = randn * sigma + (1 - sigma) * source_latents
-                noise = None
 
             if extended_overlapped_latents != None:
                 if no_noise_latents_injection:
@@ -940,6 +948,13 @@ class WanAny2V:
                     latents,
                     **scheduler_kwargs)[0]
 
+
+            if image_mask_latents is not None:
+                sigma = 0 if i == len(timesteps)-1 else timesteps[i+1]/1000
+                noisy_image = randn * sigma + (1 - sigma) * source_latents
+                latents = noisy_image * (1-image_mask_latents) + image_mask_latents * latents  
+
+
             if callback is not None:
                 latents_preview = latents
                 if ref_images_before and ref_images_count > 0: latents_preview = latents_preview[:, :, ref_images_count: ] 
@@ -999,3 +1014,11 @@ class WanAny2V:
             setattr(target, "face_adapter_fuser_blocks", module )
         delattr(model, "face_adapter")
 
+    def get_loras_transformer(self, get_model_recursive_prop, base_model_type, model_type, video_prompt_type, model_mode, **kwargs):
+        if base_model_type == "animate":
+            if "1" in video_prompt_type:
+                preloadURLs = get_model_recursive_prop(model_type,  "preload_URLs")
+                if len(preloadURLs) > 0: 
+                    return [os.path.join("ckpts", os.path.basename(preloadURLs[0]))] , [1]
+        return [], []
+

models/wan/modules/model.py

@@ -1220,7 +1220,8 @@ class WanModel(ModelMixin, ConfigMixin):
         y=None,
         freqs = None,
         pipeline = None,
-        current_step = 0,
+        current_step_no = 0,
+        real_step_no = 0,
         x_id= 0,
         max_steps = 0, 
         slg_layers=None,
@@ -1310,9 +1311,9 @@ class WanModel(ModelMixin, ConfigMixin):
             del causal_mask
 
         offload.shared_state["embed_sizes"] = grid_sizes 
-        offload.shared_state["step_no"] = current_step 
+        offload.shared_state["step_no"] = real_step_no 
         offload.shared_state["max_steps"] = max_steps
-        if current_step == 0 and x_id == 0: clear_caches()
+        if current_step_no == 0 and x_id == 0: clear_caches()
         # arguments
 
         kwargs = dict(
@@ -1336,7 +1337,7 @@ class WanModel(ModelMixin, ConfigMixin):
         if standin_ref is not None:
             standin_cache_enabled = False
             kwargs["standin_phase"] = 2
-            if current_step == 0 or not standin_cache_enabled :
+            if current_step_no == 0 or not standin_cache_enabled :
                 standin_x = self.patch_embedding(standin_ref).to(modulation_dtype).flatten(2).transpose(1, 2)
                 standin_e = self.time_embedding( sinusoidal_embedding_1d(self.freq_dim, torch.zeros_like(t)).to(modulation_dtype) )
                 standin_e0 = self.time_projection(standin_e).unflatten(1, (6, self.dim)).to(e.dtype)
@@ -1401,7 +1402,7 @@ class WanModel(ModelMixin, ConfigMixin):
         skips_steps_cache = self.cache
         if skips_steps_cache != None: 
             if skips_steps_cache.cache_type == "mag":
-                if current_step <= skips_steps_cache.start_step:
+                if real_step_no <= skips_steps_cache.start_step:
                     should_calc = True
                 elif skips_steps_cache.one_for_all and x_id != 0: # not joint pass, not main pas, one for all
                     assert len(x_list) == 1
@@ -1410,7 +1411,7 @@ class WanModel(ModelMixin, ConfigMixin):
                     x_should_calc = []
                     for i in range(1 if skips_steps_cache.one_for_all else len(x_list)):
                         cur_x_id = i if joint_pass else x_id  
-                        cur_mag_ratio = skips_steps_cache.mag_ratios[current_step * 2 + cur_x_id] # conditional and unconditional in one list
+                        cur_mag_ratio = skips_steps_cache.mag_ratios[real_step_no * 2 + cur_x_id] # conditional and unconditional in one list
                         skips_steps_cache.accumulated_ratio[cur_x_id] *= cur_mag_ratio # magnitude ratio between current step and the cached step
                         skips_steps_cache.accumulated_steps[cur_x_id] += 1 # skip steps plus 1
                         cur_skip_err = np.abs(1-skips_steps_cache.accumulated_ratio[cur_x_id]) # skip error of current steps
@@ -1430,7 +1431,7 @@ class WanModel(ModelMixin, ConfigMixin):
                 if x_id != 0:
                     should_calc = skips_steps_cache.should_calc
                 else:
-                    if current_step <= skips_steps_cache.start_step or current_step == skips_steps_cache.num_steps-1:
+                    if real_step_no <= skips_steps_cache.start_step or real_step_no == skips_steps_cache.num_steps-1:
                         should_calc = True
                         skips_steps_cache.accumulated_rel_l1_distance = 0
                     else:

models/wan/wan_handler.py

@@ -124,12 +124,20 @@ class family_handler():
 
         if base_model_type in ["t2v"]: 
             extra_model_def["guide_custom_choices"] = {
-                "choices":[("Use Text Prompt Only", ""),("Video to Video guided by Text Prompt", "GUV")],
+                "choices":[("Use Text Prompt Only", ""),
+                           ("Video to Video guided by Text Prompt", "GUV"),
+                           ("Video to Video guided by Text Prompt and Restricted to the Area of the Video Mask", "GVA")],
                 "default": "",
-                "letters_filter": "GUV",
+                "letters_filter": "GUVA",
                 "label": "Video to Video"
             }
 
+            extra_model_def["mask_preprocessing"] = {
+                "selection":[ "", "A"],
+                "visible": False
+            }
+
+
         if base_model_type in ["infinitetalk"]: 
             extra_model_def["no_background_removal"] = True
             extra_model_def["all_image_refs_are_background_ref"] = True
@@ -160,14 +168,22 @@ class family_handler():
         if base_model_type in ["animate"]:
             extra_model_def["guide_custom_choices"] = {
             "choices":[
-                ("Animate Person in Reference Image using Motion of Person in Control Video", "PVBXAKI"),
+                ("Animate Person in Reference Image using Motion of Whole Control Video", "PVBKI"),
-                ("Replace Person in Control Video Person in Reference Image", "PVBAI"),
+                ("Animate Person in Reference Image using Motion of Targeted Person in Control Video", "PVBXAKI"),
+                ("Replace Person in Control Video by Person in Reference Image", "PVBAI"),
+                ("Replace Person in Control Video by Person in Reference Image and Apply Relighting Process", "PVBAI1"),
             ],
-            "default": "KI",
+            "default": "PVBKI",
-            "letters_filter": "PVBXAKI",
+            "letters_filter": "PVBXAKI1",
             "label": "Type of Process",
             "show_label" : False,
             }
+
+            extra_model_def["mask_preprocessing"] = {
+                "selection":[ "", "A", "XA"],
+                "visible": False
+            }
+
             extra_model_def["video_guide_outpainting"] = [0,1]
             extra_model_def["keep_frames_video_guide_not_supported"] = True
             extra_model_def["extract_guide_from_window_start"] = True
@@ -480,8 +496,8 @@ class family_handler():
                 "video_prompt_type": "UV", 
             })
         elif base_model_type in ["animate"]: 
-            ui_defaults.update({
+            ui_defaults.update({ 
-                "video_prompt_type": "PVBXAKI", 
+                "video_prompt_type": "PVBKI", 
                 "mask_expand": 20,
                 "audio_prompt_type": "R",
             })

shared/utils/loras_mutipliers.py

@@ -99,7 +99,7 @@ def parse_loras_multipliers(loras_multipliers, nb_loras, num_inference_steps, me
     
     return  loras_list_mult_choices_nums, slists_dict, ""
 
-def update_loras_slists(trans, slists_dict, num_inference_steps, phase_switch_step = None, phase_switch_step2 = None ):
+def update_loras_slists(trans, slists_dict, num_inference_steps, phase_switch_step = None, phase_switch_step2 = None):
     from mmgp import offload
     sz = len(slists_dict["phase1"])
     slists = [ expand_slist(slists_dict, i, num_inference_steps, phase_switch_step, phase_switch_step2 ) for i in range(sz)  ]
@@ -108,7 +108,8 @@ def update_loras_slists(trans, slists_dict, num_inference_steps, phase_switch_st
 
 
 
-def get_model_switch_steps(timesteps, total_num_steps, guide_phases, model_switch_phase, switch_threshold, switch2_threshold ):
+def get_model_switch_steps(timesteps, guide_phases, model_switch_phase, switch_threshold, switch2_threshold ):
+    total_num_steps = len(timesteps)
     model_switch_step = model_switch_step2 = None
     for i, t in enumerate(timesteps):
         if guide_phases >=2 and model_switch_step is None and t <= switch_threshold: model_switch_step = i

wgp.py

@@ -63,7 +63,7 @@ AUTOSAVE_FILENAME = "queue.zip"
 PROMPT_VARS_MAX = 10
 
 target_mmgp_version = "3.6.0"
-WanGP_version = "8.73"
+WanGP_version = "8.74"
 settings_version = 2.36
 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
@@ -3699,13 +3699,15 @@ def extract_faces_from_video_with_mask(input_video_path, input_mask_path, max_fr
     any_mask = input_mask_path != None
     video = get_resampled_video(input_video_path, start_frame, max_frames, target_fps)
     if len(video) == 0: return None
+    frame_height, frame_width, _ = video[0].shape
+    num_frames = len(video)
     if any_mask:
         mask_video = get_resampled_video(input_mask_path, start_frame, max_frames, target_fps)
-    frame_height, frame_width, _ = video[0].shape
+        num_frames = min(num_frames, len(mask_video))
-
-    num_frames = min(len(video), len(mask_video))
     if num_frames == 0: return None
-    video, mask_video = video[:num_frames], mask_video[:num_frames]
+    video = video[:num_frames]
+    if any_mask:
+        mask_video = mask_video[:num_frames]
 
     from preprocessing.face_preprocessor  import FaceProcessor 
     face_processor = FaceProcessor()
@@ -6970,39 +6972,49 @@ def refresh_video_prompt_type_alignment(state, video_prompt_type, video_prompt_t
 
 all_guide_processes ="PDESLCMUVB"
 
-def refresh_video_prompt_type_video_guide(state, video_prompt_type, video_prompt_type_video_guide,  image_mode, old_image_mask_guide_value, old_image_guide_value, old_image_mask_value ):
+def refresh_video_prompt_type_video_guide(state, filter_type, video_prompt_type, video_prompt_type_video_guide,  image_mode, old_image_mask_guide_value, old_image_guide_value, old_image_mask_value ):
-    old_video_prompt_type = video_prompt_type
-    video_prompt_type = del_in_sequence(video_prompt_type, all_guide_processes)
-    video_prompt_type = add_to_sequence(video_prompt_type, video_prompt_type_video_guide)
-    visible = "V" in video_prompt_type
     model_type = state["model_type"]
     model_def = get_model_def(model_type)
+    old_video_prompt_type = video_prompt_type
+    if filter_type == "alt":
+        guide_custom_choices = model_def.get("guide_custom_choices",{})
+        letter_filter = guide_custom_choices.get("letters_filter","")
+    else:
+        letter_filter = all_guide_processes
+    video_prompt_type = del_in_sequence(video_prompt_type, letter_filter)
+    video_prompt_type = add_to_sequence(video_prompt_type, video_prompt_type_video_guide)
+    visible = "V" in video_prompt_type
     any_outpainting= image_mode in model_def.get("video_guide_outpainting", [])
     mask_visible = visible and "A" in video_prompt_type and not "U" in video_prompt_type
     image_outputs =  image_mode > 0
     keep_frames_video_guide_visible = not image_outputs and visible and not model_def.get("keep_frames_video_guide_not_supported", False)
     image_mask_guide, image_guide, image_mask = switch_image_guide_editor(image_mode, old_video_prompt_type , video_prompt_type, old_image_mask_guide_value, old_image_guide_value, old_image_mask_value )
+    # mask_video_input_visible =  image_mode == 0 and mask_visible
     mask_preprocessing = model_def.get("mask_preprocessing", None)
     if mask_preprocessing  is not None:
         mask_selector_visible = mask_preprocessing.get("visible", True)
     else:
         mask_selector_visible = True
-    return video_prompt_type,  gr.update(visible = visible and not image_outputs), image_guide, gr.update(visible = keep_frames_video_guide_visible), gr.update(visible = visible and "G" in video_prompt_type), gr.update(visible= (visible or "F" in video_prompt_type or "K" in video_prompt_type) and any_outpainting), gr.update(visible= visible and mask_selector_visible and  not "U" in video_prompt_type ) ,  gr.update(visible= mask_visible and not image_outputs), image_mask, image_mask_guide, gr.update(visible= mask_visible) 
-
-
-def refresh_video_prompt_type_video_guide_alt(state, video_prompt_type, video_prompt_type_video_guide_alt, image_mode):
-    model_def = get_model_def(state["model_type"])
-    guide_custom_choices = model_def.get("guide_custom_choices",{})
-    video_prompt_type = del_in_sequence(video_prompt_type, guide_custom_choices.get("letters_filter",""))
-    video_prompt_type = add_to_sequence(video_prompt_type, video_prompt_type_video_guide_alt)
-    control_video_visible = "V" in video_prompt_type
     ref_images_visible = "I" in video_prompt_type
-    denoising_strength_visible = "G" in video_prompt_type
+    return video_prompt_type,  gr.update(visible = visible and not image_outputs), image_guide, gr.update(visible = keep_frames_video_guide_visible), gr.update(visible = visible and "G" in video_prompt_type), gr.update(visible= (visible or "F" in video_prompt_type or "K" in video_prompt_type) and any_outpainting), gr.update(visible= visible and mask_selector_visible and  not "U" in video_prompt_type ) ,  gr.update(visible= mask_visible and not image_outputs), image_mask, image_mask_guide, gr.update(visible= mask_visible),  gr.update(visible = ref_images_visible ) 
-    return video_prompt_type,  gr.update(visible = control_video_visible and image_mode ==0), gr.update(visible = control_video_visible and image_mode >=1), gr.update(visible = ref_images_visible ), gr.update(visible = denoising_strength_visible )
+
- 
+
-# def refresh_video_prompt_video_guide_trigger(state, video_prompt_type, video_prompt_type_video_guide):
+# def refresh_video_prompt_type_video_guide_alt(state, video_prompt_type, video_prompt_type_video_guide_alt, image_mode, old_image_mask_guide_value, old_image_guide_value, old_image_mask_value ):
-#     video_prompt_type_video_guide = video_prompt_type_video_guide.split("#")[0]
+#     old_video_prompt_type = video_prompt_type
-#     return refresh_video_prompt_type_video_guide(state, video_prompt_type, video_prompt_type_video_guide)
+#     model_def = get_model_def(state["model_type"])
+#     guide_custom_choices = model_def.get("guide_custom_choices",{})
+#     video_prompt_type = del_in_sequence(video_prompt_type, guide_custom_choices.get("letters_filter",""))
+#     video_prompt_type = add_to_sequence(video_prompt_type, video_prompt_type_video_guide_alt)
+#     image_outputs =  image_mode > 0
+#     control_video_visible = "V" in video_prompt_type
+#     ref_images_visible = "I" in video_prompt_type
+#     denoising_strength_visible = "G" in video_prompt_type
+#     mask_expand_visible =  control_video_visible and "A" in video_prompt_type and not "U" in video_prompt_type 
+#     mask_video_input_visible =  image_mode == 0 and mask_expand_visible
+#     image_mask_guide, image_guide, image_mask = switch_image_guide_editor(image_mode, old_video_prompt_type , video_prompt_type, old_image_mask_guide_value, old_image_guide_value, old_image_mask_value )
+#     keep_frames_video_guide_visible = not image_outputs and visible and not model_def.get("keep_frames_video_guide_not_supported", False)
+
+#     return video_prompt_type,  gr.update(visible = control_video_visible and image_mode ==0), gr.update(visible = control_video_visible and image_mode >=1), gr.update(visible = ref_images_visible ), gr.update(visible = denoising_strength_visible ), gr.update(visible = mask_video_input_visible ), gr.update(visible = mask_expand_visible), image_mask_guide, image_guide, image_mask, gr.update(visible = keep_frames_video_guide_visible)
 
 def refresh_preview(state):
     gen = get_gen_info(state)
@@ -8294,9 +8306,10 @@ def generate_video_tab(update_form = False, state_dict = None, ui_defaults = Non
             image_prompt_type_radio.change(fn=refresh_image_prompt_type_radio, inputs=[state, image_prompt_type, image_prompt_type_radio], outputs=[image_prompt_type, image_start_row, image_end_row, video_source, keep_frames_video_source, image_prompt_type_endcheckbox], show_progress="hidden" ) 
             image_prompt_type_endcheckbox.change(fn=refresh_image_prompt_type_endcheckbox, inputs=[state, image_prompt_type, image_prompt_type_radio, image_prompt_type_endcheckbox], outputs=[image_prompt_type, image_end_row] ) 
             video_prompt_type_image_refs.input(fn=refresh_video_prompt_type_image_refs, inputs = [state, video_prompt_type, video_prompt_type_image_refs,image_mode], outputs = [video_prompt_type, image_refs_row, remove_background_images_ref,  image_refs_relative_size, frames_positions,video_guide_outpainting_col], show_progress="hidden")
-            video_prompt_type_video_guide.input(fn=refresh_video_prompt_type_video_guide, inputs = [state, video_prompt_type, video_prompt_type_video_guide, image_mode, image_mask_guide, image_guide, image_mask], outputs = [video_prompt_type, video_guide, image_guide, keep_frames_video_guide, denoising_strength, video_guide_outpainting_col, video_prompt_type_video_mask, video_mask, image_mask, image_mask_guide, mask_expand], show_progress="hidden") 
+            video_prompt_type_video_guide.input(fn=refresh_video_prompt_type_video_guide,     inputs = [state, gr.State(""),   video_prompt_type, video_prompt_type_video_guide,     image_mode, image_mask_guide, image_guide, image_mask], outputs = [video_prompt_type, video_guide, image_guide, keep_frames_video_guide, denoising_strength, video_guide_outpainting_col, video_prompt_type_video_mask, video_mask, image_mask, image_mask_guide, mask_expand, image_refs_row], show_progress="hidden") 
-            video_prompt_type_video_guide_alt.input(fn=refresh_video_prompt_type_video_guide_alt, inputs = [state, video_prompt_type, video_prompt_type_video_guide_alt, image_mode], outputs = [video_prompt_type, video_guide, image_guide, image_refs_row, denoising_strength ], show_progress="hidden")
+            video_prompt_type_video_guide_alt.input(fn=refresh_video_prompt_type_video_guide, inputs = [state, gr.State("alt"),video_prompt_type, video_prompt_type_video_guide_alt, image_mode, image_mask_guide, image_guide, image_mask], outputs = [video_prompt_type, video_guide, image_guide, keep_frames_video_guide, denoising_strength, video_guide_outpainting_col, video_prompt_type_video_mask, video_mask, image_mask, image_mask_guide, mask_expand, image_refs_row], show_progress="hidden") 
-            video_prompt_type_video_mask.input(fn=refresh_video_prompt_type_video_mask, inputs = [state, video_prompt_type, video_prompt_type_video_mask, image_mode, image_mask_guide, image_guide, image_mask], outputs = [video_prompt_type, video_mask, image_mask_guide, image_guide, image_mask, mask_expand], show_progress="hidden")
+            # video_prompt_type_video_guide_alt.input(fn=refresh_video_prompt_type_video_guide_alt, inputs = [state, video_prompt_type, video_prompt_type_video_guide_alt, image_mode, image_mask_guide, image_guide, image_mask], outputs = [video_prompt_type, video_guide, image_guide, image_refs_row, denoising_strength, video_mask, mask_expand, image_mask_guide, image_guide, image_mask, keep_frames_video_guide ], show_progress="hidden")
+            video_prompt_type_video_mask.input(fn=refresh_video_prompt_type_video_mask, inputs = [state, video_prompt_type, video_prompt_type_video_mask, image_mode, image_mask_guide, image_guide, image_mask], outputs = [video_prompt_type, video_mask, image_mask_guide, image_guide, image_mask, mask_expand], show_progress="hidden") 
             video_prompt_type_alignment.input(fn=refresh_video_prompt_type_alignment, inputs = [state, video_prompt_type, video_prompt_type_alignment], outputs = [video_prompt_type])
             multi_prompts_gen_type.select(fn=refresh_prompt_labels, inputs=[multi_prompts_gen_type, image_mode], outputs=[prompt, wizard_prompt, image_end], show_progress="hidden")
             video_guide_outpainting_top.input(fn=update_video_guide_outpainting, inputs=[video_guide_outpainting, video_guide_outpainting_top, gr.State(0)], outputs = [video_guide_outpainting], trigger_mode="multiple" )