FlashVSR: Add strength setting

FlashVSR/flashvsr_nodes.py

@@ -12,6 +12,7 @@ class WanVideoAddFlashVSRInput:
         return {"required": {
                     "embeds": ("WANVIDIMAGE_EMBEDS",),
                     "images": ("IMAGE", {"tooltip": "Low-res video frames to enhance"}),
+                    "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 2.0, "step": 0.01, "tooltip": "Strength to apply the FlashVSR latent"}),
                 }
         }
 
@@ -20,9 +21,10 @@ class WanVideoAddFlashVSRInput:
     FUNCTION = "add"
     CATEGORY = "WanVideoWrapper"
 
-    def add(self, embeds, images):
+    def add(self, embeds, images, strength):
         updated = dict(embeds)
         updated["flashvsr_LQ_images"] = images
+        updated["flashvsr_strength"] = strength
         return (updated,)
 
 

nodes_sampler.py

@@ -832,12 +832,14 @@ class WanVideoSampler:
         # FlashVSR
         flashvsr_LQ_latent = LQ_images = None
         flashvsr_LQ_images = image_embeds.get("flashvsr_LQ_images", None)
+        flashvsr_strength = image_embeds.get("flashvsr_strength", 1.0)
         if flashvsr_LQ_images is not None:
             LQ_images = flashvsr_LQ_images.unsqueeze(0).movedim(-1, 1).to(device, dtype) * 2 - 1
             if context_options is None:
                 flashvsr_LQ_latent = transformer.LQ_proj_in(LQ_images)
                 log.info(f"flashvsr_LQ_latent: {flashvsr_LQ_latent[0].shape}")
-                noise = noise[:, :-1]
+                if noise.shape[1] != 1:
+                    noise = noise[:, :-1]
                 seq_len = math.ceil((noise.shape[2] * noise.shape[3]) / 4 * noise.shape[1])
 
         latent = noise
@@ -1346,6 +1348,7 @@ class WanVideoSampler:
                     "seq_len_ovi": seq_len_ovi, # Audio latent model sequence length for Ovi
                     "ovi_negative_text_embeds": ovi_negative_text_embeds, # Audio latent model negative text embeds for Ovi
                     "flashvsr_LQ_latent": flashvsr_LQ_latent, # FlashVSR LQ latent for upsampling
+                    "flashvsr_strength": flashvsr_strength, # FlashVSR strength
                 }
 
                 batch_size = 1
@@ -1950,7 +1953,6 @@ class WanVideoSampler:
                                 end = c[-1] * 4 + 1 + 4
                                 center_indices = torch.arange(start, end, 1)
                                 center_indices = torch.clamp(center_indices, min=0, max=LQ_images.shape[2] - 1)
-                                print("FlashVSR LQ image indices:", center_indices)
                                 partial_flashvsr_LQ_images = LQ_images[:, :, center_indices].to(device, dtype)
                                 partial_flashvsr_LQ_latent = transformer.LQ_proj_in(partial_flashvsr_LQ_images)
 

wanvideo/modules/model.py

@@ -2153,7 +2153,7 @@ class WanModel(torch.nn.Module):
         wananim_pose_strength=1.0, wananim_face_strength=1.0,
         lynx_embeds=None,
         x_ovi=None, seq_len_ovi=None, ovi_negative_text_embeds=None,
-        flashvsr_LQ_latent=None,
+        flashvsr_LQ_latent=None, flashvsr_strength=1.0,
     ):
         r"""
         Forward pass through the diffusion model
@@ -2810,7 +2810,7 @@ class WanModel(torch.nn.Module):
                     lynx_ref_feature = None
                 # FlashVSR
                 if flashvsr_LQ_latent is not None and b < len(flashvsr_LQ_latent):
-                    x += flashvsr_LQ_latent[b].to(x)
+                    x += flashvsr_LQ_latent[b].to(x) * flashvsr_strength
                 # Prefetch blocks if enabled
                 if self.prefetch_blocks > 0:
                     for prefetch_offset in range(1, self.prefetch_blocks + 1):