qol
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@ -11,9 +11,6 @@ import ldm_patched.controlnet.cldm
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import ldm_patched.t2ia.adapter
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compute_controlnet_weighting = None
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def broadcast_image_to(tensor, target_batch_size, batched_number):
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current_batch_size = tensor.shape[0]
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#print(current_batch_size, target_batch_size)
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@ -32,6 +29,71 @@ def broadcast_image_to(tensor, target_batch_size, batched_number):
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else:
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return torch.cat([tensor] * batched_number, dim=0)
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def get_at(array, index, default=None):
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return array[index] if 0 <= index < len(array) else default
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def compute_controlnet_weighting(control, cnet):
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positive_advanced_weighting = getattr(cnet, 'positive_advanced_weighting', None)
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negative_advanced_weighting = getattr(cnet, 'negative_advanced_weighting', None)
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advanced_frame_weighting = getattr(cnet, 'advanced_frame_weighting', None)
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advanced_sigma_weighting = getattr(cnet, 'advanced_sigma_weighting', None)
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advanced_mask_weighting = getattr(cnet, 'advanced_mask_weighting', None)
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transformer_options = cnet.transformer_options
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if positive_advanced_weighting is None and negative_advanced_weighting is None \
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and advanced_frame_weighting is None and advanced_sigma_weighting is None \
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and advanced_mask_weighting is None:
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return control
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cond_or_uncond = transformer_options['cond_or_uncond']
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sigmas = transformer_options['sigmas']
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cond_mark = transformer_options['cond_mark']
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if advanced_frame_weighting is not None:
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advanced_frame_weighting = torch.Tensor(advanced_frame_weighting * len(cond_or_uncond)).to(sigmas)
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assert advanced_frame_weighting.shape[0] == cond_mark.shape[0], \
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'Frame weighting list length is different from batch size!'
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if advanced_sigma_weighting is not None:
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advanced_sigma_weighting = torch.cat([advanced_sigma_weighting(sigmas)] * len(cond_or_uncond))
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for k, v in control.items():
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for i in range(len(v)):
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control_signal = control[k][i]
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B, C, H, W = control_signal.shape
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positive_weight = 1.0
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negative_weight = 1.0
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sigma_weight = 1.0
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frame_weight = 1.0
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if positive_advanced_weighting is not None:
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positive_weight = get_at(positive_advanced_weighting.get(k, []), i, 1.0)
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if negative_advanced_weighting is not None:
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negative_weight = get_at(negative_advanced_weighting.get(k, []), i, 1.0)
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if advanced_sigma_weighting is not None:
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sigma_weight = advanced_sigma_weighting
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if advanced_frame_weighting is not None:
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frame_weight = advanced_frame_weighting
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final_weight = positive_weight * (1.0 - cond_mark) + negative_weight * cond_mark
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final_weight = final_weight * sigma_weight * frame_weight
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if isinstance(advanced_mask_weighting, torch.Tensor):
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control_signal = control_signal * torch.nn.functional.interpolate(advanced_mask_weighting, size=(H, W), mode='bilinear')
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control[k][i] = control_signal * final_weight[:, None, None, None]
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return control
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class ControlBase:
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def __init__(self, device=None):
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self.cond_hint_original = None
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@ -119,8 +181,7 @@ class ControlBase:
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out[key].append(x)
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if compute_controlnet_weighting is not None:
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out = compute_controlnet_weighting(out, self)
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out = compute_controlnet_weighting(out, self)
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if control_prev is not None:
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for x in ['input', 'middle', 'output']:
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@ -1,10 +1,6 @@
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import torch
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def get_at(array, index, default=None):
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return array[index] if 0 <= index < len(array) else default
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def apply_controlnet_advanced(
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unet,
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controlnet,
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@ -79,61 +75,3 @@ def apply_controlnet_advanced(
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m.add_patched_controlnet(cnet)
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return m
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def compute_controlnet_weighting(control, cnet):
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positive_advanced_weighting = cnet.positive_advanced_weighting
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negative_advanced_weighting = cnet.negative_advanced_weighting
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advanced_frame_weighting = cnet.advanced_frame_weighting
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advanced_sigma_weighting = cnet.advanced_sigma_weighting
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advanced_mask_weighting = cnet.advanced_mask_weighting
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transformer_options = cnet.transformer_options
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if positive_advanced_weighting is None and negative_advanced_weighting is None \
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and advanced_frame_weighting is None and advanced_sigma_weighting is None \
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and advanced_mask_weighting is None:
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return control
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cond_or_uncond = transformer_options['cond_or_uncond']
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sigmas = transformer_options['sigmas']
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cond_mark = transformer_options['cond_mark']
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if advanced_frame_weighting is not None:
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advanced_frame_weighting = torch.Tensor(advanced_frame_weighting * len(cond_or_uncond)).to(sigmas)
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assert advanced_frame_weighting.shape[0] == cond_mark.shape[0], \
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'Frame weighting list length is different from batch size!'
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if advanced_sigma_weighting is not None:
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advanced_sigma_weighting = torch.cat([advanced_sigma_weighting(sigmas)] * len(cond_or_uncond))
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for k, v in control.items():
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for i in range(len(v)):
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control_signal = control[k][i]
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B, C, H, W = control_signal.shape
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positive_weight = 1.0
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negative_weight = 1.0
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sigma_weight = 1.0
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frame_weight = 1.0
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if positive_advanced_weighting is not None:
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positive_weight = get_at(positive_advanced_weighting.get(k, []), i, 1.0)
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if negative_advanced_weighting is not None:
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negative_weight = get_at(negative_advanced_weighting.get(k, []), i, 1.0)
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if advanced_sigma_weighting is not None:
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sigma_weight = advanced_sigma_weighting
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if advanced_frame_weighting is not None:
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frame_weight = advanced_frame_weighting
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final_weight = positive_weight * (1.0 - cond_mark) + negative_weight * cond_mark
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final_weight = final_weight * sigma_weight * frame_weight
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if isinstance(advanced_mask_weighting, torch.Tensor):
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control_signal = control_signal * torch.nn.functional.interpolate(advanced_mask_weighting, size=(H, W), mode='bilinear')
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control[k][i] = control_signal * final_weight[:, None, None, None]
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return control
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@ -38,10 +38,6 @@ def build_loaded(module, loader_name):
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def patch_all_basics():
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import ldm_patched.modules.controlnet
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import modules_forge.controlnet
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ldm_patched.modules.controlnet.compute_controlnet_weighting = modules_forge.controlnet.compute_controlnet_weighting
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build_loaded(safetensors.torch, 'load_file')
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build_loaded(torch, 'load')
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return
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