nodes/siraxe-ComfyUI-WanVideoWrapper_QQ
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JJ
2026-04-04 03:37:25 +01:00
parent c8ecedb6b0
commit 78f94d775e
1 changed files with 36 additions and 9 deletions
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nodes/video_nodes_extra.py
+36 -9
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@@ -917,7 +917,7 @@ class VideoPrepAB:
"required": {
"image_A": ("IMAGE",),
"image_B": ("IMAGE",),
"mid_frames_color": ("FLOAT", {"default": 50, "min": 0, "max": 100, "step": 1}),
"mid_frames_RGB": ("STRING", {"default": "0,191,0"}),
"end_A_frames": ("INT", {"default": 24, "min": 0, "max": 10000, "step": 1}),
"start_B_frames": ("INT", {"default": 24, "min": 0, "max": 10000, "step": 1}),
"total_frames": ("INT", {"default": 121, "min": 1, "max": 993, "step": 8}),
@@ -935,7 +935,7 @@ class VideoPrepAB:
FUNCTION = "video_prep_ab"
CATEGORY = "WanVideoWrapper_QQ/video"
def video_prep_ab(self, image_A, image_B, mid_frames_color, end_A_frames, start_B_frames, total_frames, video_fps, audio_blend_frames, audio_A=None, audio_B=None):
def video_prep_ab(self, image_A, image_B, mid_frames_RGB, end_A_frames, start_B_frames, total_frames, video_fps, audio_blend_frames, audio_A=None, audio_B=None):
device = image_A.device
# Get dimensions
@@ -982,14 +982,41 @@ class VideoPrepAB:
else:
frames_b = torch.zeros((0, target_h, target_w, target_c), device=device)
# Create mid frames with color based on mid_frames_color (0-100 -> 0.0-1.0)
color_value = mid_frames_color / 100.0
# Parse RGB color string (e.g., "0,191,0") and convert to 0.0-1.0 range
try:
r_str, g_str, b_str = mid_frames_RGB.split(",")
r_val = float(r_str.strip()) / 255.0
g_val = float(g_str.strip()) / 255.0
b_val = float(b_str.strip()) / 255.0
except (ValueError, AttributeError):
# Fallback to grayscale if parsing fails
try:
val = float(mid_frames_RGB) / 100.0
r_val = g_val = b_val = val
except:
r_val = g_val = b_val = 0.5
if mid_frames_count > 0:
mid_frames = torch.full(
(mid_frames_count, target_h, target_w, target_c),
color_value,
device=device
)
# Create colored fill for each channel
if target_c == 1:
# Grayscale: use average of RGB
color_value = (r_val + g_val + b_val) / 3.0
mid_frames = torch.full(
(mid_frames_count, target_h, target_w, target_c),
color_value,
device=device
)
elif target_c == 3:
# RGB: create separate channel values
mid_frames = torch.zeros((mid_frames_count, target_h, target_w, target_c), device=device)
mid_frames[..., 0] = r_val
mid_frames[..., 1] = g_val
mid_frames[..., 2] = b_val
else: # RGBA or more channels
mid_frames = torch.zeros((mid_frames_count, target_h, target_w, target_c), device=device)
mid_frames[..., 0] = r_val
mid_frames[..., 1] = g_val
mid_frames[..., 2] = b_val
else:
mid_frames = torch.zeros((0, target_h, target_w, target_c), device=device)