Update most var and function names for FX_Source_Img to avoid potential conflicts
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mgfxer
+52
-52
@@ -30,94 +30,94 @@ class EdgeFXSourceImages:
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# Define the return names for the function outputs
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RETURN_NAMES = ("first_timeline", "second_timeline", "first_text_output", "second_text_output", "total_frames")
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# Name of the function to be executed
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FUNCTION = "generate_mask_definitions"
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FUNCTION = "generate_mask_definitions_v2"
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# Category of the node
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CATEGORY = "advanced"
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def generate_mask_definitions(self, image_stream, num_images, hold_length, transition_length, padding_frames, push, retract):
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def generate_mask_definitions_v2(self, image_stream, num_images, hold_length, transition_length, padding_frames, push, retract):
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# Initialize lists to hold the timelines and mask definitions
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first_timeline = []
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second_timeline = []
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first_text_output = ""
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second_text_output = ""
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primary_timeline = []
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secondary_timeline = []
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primary_text_output = ""
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secondary_text_output = ""
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# Calculate the total number of frames for each image and the total number of frames for the entire animation
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frame_distance = hold_length + transition_length
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total_frames = num_images * frame_distance + padding_frames
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frame_interval = hold_length + transition_length
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total_frame_count = num_images * frame_interval + padding_frames
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# Get the actual number of images provided
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num_images_provided = image_stream.shape[0]
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provided_image_count = image_stream.shape[0]
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# Adjust hold value for the first cycle
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adjusted_first_hold = hold_length - retract + push
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adjusted_initial_hold = hold_length - retract + push
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# Generate the first timeline with adjusted first hold value
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# Generate the primary timeline with adjusted initial hold value
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for i in range(num_images):
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index = i % num_images_provided
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current_hold = adjusted_first_hold if i == 0 else hold_length
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frame_distance = current_hold + transition_length
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repeated_images = image_stream[index:index+1].repeat(frame_distance, 1, 1, 1)
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first_timeline.append(repeated_images)
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index = i % provided_image_count
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current_hold_duration = adjusted_initial_hold if i == 0 else hold_length
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frame_interval = current_hold_duration + transition_length
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repeated_images = image_stream[index:index+1].repeat(frame_interval, 1, 1, 1)
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primary_timeline.append(repeated_images)
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# Concatenate the list of tensors into a single tensor
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first_timeline = torch.cat(first_timeline, dim=0)
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primary_timeline = torch.cat(primary_timeline, dim=0)
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# Adjust padding length
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adjusted_padding_length = padding_frames + retract - push
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adjusted_padding_duration = padding_frames + retract - push
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# Generate the second timeline
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# Generate the secondary timeline
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for i in range(1, num_images):
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index = i % num_images_provided
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repeated_images = image_stream[index:index+1].repeat(frame_distance, 1, 1, 1)
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second_timeline.append(repeated_images)
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repeated_images = image_stream[0:1].repeat(frame_distance, 1, 1, 1)
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second_timeline.append(repeated_images)
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index = i % provided_image_count
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repeated_images = image_stream[index:index+1].repeat(frame_interval, 1, 1, 1)
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secondary_timeline.append(repeated_images)
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repeated_images = image_stream[0:1].repeat(frame_interval, 1, 1, 1)
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secondary_timeline.append(repeated_images)
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# Concatenate the list of tensors into a single tensor
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second_timeline = torch.cat(second_timeline, dim=0)
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secondary_timeline = torch.cat(secondary_timeline, dim=0)
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# Initialize frame number and create mask text outputs
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frame = 0
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while frame < num_images * frame_distance:
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frame_end_hold = frame + hold_length - 1
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frame_start_transition = frame_end_hold + 1
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frame_end_transition = frame_start_transition + transition_length - 1
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frame_counter = 0
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while frame_counter < num_images * frame_interval:
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hold_end_frame = frame_counter + hold_length - 1
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transition_start_frame = hold_end_frame + 1
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transition_end_frame = transition_start_frame + transition_length - 1
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first_text_output += f"{frame}:(1.0),\n"
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first_text_output += f"{frame_end_hold}:(1.0),\n"
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first_text_output += f"{frame_end_transition}:(0.0),\n"
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primary_text_output += f"{frame_counter}:(1.0),\n"
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primary_text_output += f"{hold_end_frame}:(1.0),\n"
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primary_text_output += f"{transition_end_frame}:(0.0),\n"
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second_text_output += f"{frame}:(0.0),\n"
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second_text_output += f"{frame_end_hold}:(0.0),\n"
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second_text_output += f"{frame_end_transition}:(1.0),\n"
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secondary_text_output += f"{frame_counter}:(0.0),\n"
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secondary_text_output += f"{hold_end_frame}:(0.0),\n"
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secondary_text_output += f"{transition_end_frame}:(1.0),\n"
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frame = frame_end_transition + 1
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frame_counter = transition_end_frame + 1
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# Add padding frames if specified
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if adjusted_padding_length > 0:
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padding_start_frame = frame
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last_image_first_timeline = first_timeline[-1:]
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last_image_second_timeline = second_timeline[-1:]
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if adjusted_padding_duration > 0:
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padding_start_frame = frame_counter
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last_image_primary_timeline = primary_timeline[-1:]
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last_image_secondary_timeline = secondary_timeline[-1:]
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# Add padding frames to the timelines
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padding_images_first = last_image_first_timeline.repeat(adjusted_padding_length, 1, 1, 1)
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first_timeline = torch.cat((first_timeline, padding_images_first), dim=0)
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padding_images_primary = last_image_primary_timeline.repeat(adjusted_padding_duration, 1, 1, 1)
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primary_timeline = torch.cat((primary_timeline, padding_images_primary), dim=0)
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padding_images_second = last_image_second_timeline.repeat(adjusted_padding_length, 1, 1, 1)
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second_timeline = torch.cat((second_timeline, padding_images_second), dim=0)
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padding_images_secondary = last_image_secondary_timeline.repeat(adjusted_padding_duration, 1, 1, 1)
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secondary_timeline = torch.cat((secondary_timeline, padding_images_secondary), dim=0)
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# Append padding frames to the text outputs
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for i in range(adjusted_padding_length):
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frame = padding_start_frame + i
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first_text_output += f"{frame}:(0.0),\n"
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second_text_output += f"{frame}:(1.0),\n" # Set to 1.0 for the second timeline
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for i in range(adjusted_padding_duration):
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frame_counter = padding_start_frame + i
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primary_text_output += f"{frame_counter}:(0.0),\n"
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secondary_text_output += f"{frame_counter}:(1.0),\n" # Set to 1.0 for the secondary timeline
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# Ensure the text output ends correctly
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first_text_output = first_text_output.strip().rstrip(',')
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second_text_output = second_text_output.strip().rstrip(',')
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primary_text_output = primary_text_output.strip().rstrip(',')
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secondary_text_output = secondary_text_output.strip().rstrip(',')
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# Return the generated timelines, text outputs, and total frames
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return first_timeline, second_timeline, first_text_output, second_text_output, total_frames
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return primary_timeline, secondary_timeline, primary_text_output, secondary_text_output, total_frame_count
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# Mapping the class to its name for the node system
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NODE_CLASS_MAPPINGS = {
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