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54 lines
1.9 KiB
Python
54 lines
1.9 KiB
Python
import torch
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import numpy as np
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from .processors import Processor_id
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class ControlNetConfigUnit:
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def __init__(self, processor_id: Processor_id, model_path, scale=1.0):
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self.processor_id = processor_id
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self.model_path = model_path
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self.scale = scale
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class ControlNetUnit:
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def __init__(self, processor, model, scale=1.0):
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self.processor = processor
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self.model = model
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self.scale = scale
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class MultiControlNetManager:
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def __init__(self, controlnet_units=[]):
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self.processors = [unit.processor for unit in controlnet_units]
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self.models = [unit.model for unit in controlnet_units]
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self.scales = [unit.scale for unit in controlnet_units]
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def process_image(self, image, processor_id=None):
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if processor_id is None:
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processed_image = [processor(image) for processor in self.processors]
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else:
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processed_image = [self.processors[processor_id](image)]
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processed_image = torch.concat([
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torch.Tensor(np.array(image_, dtype=np.float32) / 255).permute(2, 0, 1).unsqueeze(0)
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for image_ in processed_image
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], dim=0)
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return processed_image
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def __call__(
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self,
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sample, timestep, encoder_hidden_states, conditionings,
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tiled=False, tile_size=64, tile_stride=32
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):
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res_stack = None
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for conditioning, model, scale in zip(conditionings, self.models, self.scales):
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res_stack_ = model(
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sample, timestep, encoder_hidden_states, conditioning,
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tiled=tiled, tile_size=tile_size, tile_stride=tile_stride
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)
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res_stack_ = [res * scale for res in res_stack_]
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if res_stack is None:
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res_stack = res_stack_
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else:
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res_stack = [i + j for i, j in zip(res_stack, res_stack_)]
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return res_stack
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