852 lines
38 KiB
Python
852 lines
38 KiB
Python
"""
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This file is part of ComfyUI.
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Copyright (C) 2024 Comfy
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This program is free software: you can redistribute it and/or modify
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it under the terms of the GNU General Public License as published by
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the Free Software Foundation, either version 3 of the License, or
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(at your option) any later version.
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This program is distributed in the hope that it will be useful,
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but WITHOUT ANY WARRANTY; without even the implied warranty of
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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GNU General Public License for more details.
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You should have received a copy of the GNU General Public License
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along with this program. If not, see <https://www.gnu.org/licenses/>.
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"""
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import torch
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from enum import Enum
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import math
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import os
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import logging
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import comfy.utils
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import comfy.model_management
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import comfy.model_detection
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import comfy.model_patcher
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import comfy.ops
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import comfy.latent_formats
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import comfy.cldm.cldm
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import comfy.t2i_adapter.adapter
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import comfy.ldm.cascade.controlnet
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import comfy.cldm.mmdit
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import comfy.ldm.hydit.controlnet
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import comfy.ldm.flux.controlnet
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import comfy.cldm.dit_embedder
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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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if current_batch_size == 1:
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return tensor
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per_batch = target_batch_size // batched_number
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tensor = tensor[:per_batch]
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if per_batch > tensor.shape[0]:
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tensor = torch.cat([tensor] * (per_batch // tensor.shape[0]) + [tensor[:(per_batch % tensor.shape[0])]], dim=0)
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current_batch_size = tensor.shape[0]
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if current_batch_size == target_batch_size:
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return tensor
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else:
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return torch.cat([tensor] * batched_number, dim=0)
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class StrengthType(Enum):
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CONSTANT = 1
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LINEAR_UP = 2
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class ControlBase:
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def __init__(self):
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self.cond_hint_original = None
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self.cond_hint = None
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self.strength = 1.0
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self.timestep_percent_range = (0.0, 1.0)
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self.latent_format = None
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self.vae = None
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self.global_average_pooling = False
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self.timestep_range = None
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self.compression_ratio = 8
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self.upscale_algorithm = 'nearest-exact'
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self.extra_args = {}
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self.previous_controlnet = None
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self.extra_conds = []
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self.strength_type = StrengthType.CONSTANT
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self.concat_mask = False
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self.extra_concat_orig = []
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self.extra_concat = None
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self.preprocess_image = lambda a: a
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def set_cond_hint(self, cond_hint, strength=1.0, timestep_percent_range=(0.0, 1.0), vae=None, extra_concat=[]):
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self.cond_hint_original = cond_hint
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self.strength = strength
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self.timestep_percent_range = timestep_percent_range
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if self.latent_format is not None:
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if vae is None:
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logging.warning("WARNING: no VAE provided to the controlnet apply node when this controlnet requires one.")
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self.vae = vae
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self.extra_concat_orig = extra_concat.copy()
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if self.concat_mask and len(self.extra_concat_orig) == 0:
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self.extra_concat_orig.append(torch.tensor([[[[1.0]]]]))
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return self
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def pre_run(self, model, percent_to_timestep_function):
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self.timestep_range = (percent_to_timestep_function(self.timestep_percent_range[0]), percent_to_timestep_function(self.timestep_percent_range[1]))
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if self.previous_controlnet is not None:
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self.previous_controlnet.pre_run(model, percent_to_timestep_function)
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def set_previous_controlnet(self, controlnet):
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self.previous_controlnet = controlnet
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return self
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def cleanup(self):
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if self.previous_controlnet is not None:
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self.previous_controlnet.cleanup()
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self.cond_hint = None
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self.extra_concat = None
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self.timestep_range = None
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def get_models(self):
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out = []
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if self.previous_controlnet is not None:
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out += self.previous_controlnet.get_models()
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return out
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def copy_to(self, c):
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c.cond_hint_original = self.cond_hint_original
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c.strength = self.strength
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c.timestep_percent_range = self.timestep_percent_range
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c.global_average_pooling = self.global_average_pooling
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c.compression_ratio = self.compression_ratio
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c.upscale_algorithm = self.upscale_algorithm
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c.latent_format = self.latent_format
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c.extra_args = self.extra_args.copy()
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c.vae = self.vae
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c.extra_conds = self.extra_conds.copy()
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c.strength_type = self.strength_type
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c.concat_mask = self.concat_mask
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c.extra_concat_orig = self.extra_concat_orig.copy()
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c.preprocess_image = self.preprocess_image
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def inference_memory_requirements(self, dtype):
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if self.previous_controlnet is not None:
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return self.previous_controlnet.inference_memory_requirements(dtype)
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return 0
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def control_merge(self, control, control_prev, output_dtype):
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out = {'input':[], 'middle':[], 'output': []}
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for key in control:
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control_output = control[key]
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applied_to = set()
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for i in range(len(control_output)):
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x = control_output[i]
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if x is not None:
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if self.global_average_pooling:
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x = torch.mean(x, dim=(2, 3), keepdim=True).repeat(1, 1, x.shape[2], x.shape[3])
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if x not in applied_to: #memory saving strategy, allow shared tensors and only apply strength to shared tensors once
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applied_to.add(x)
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if self.strength_type == StrengthType.CONSTANT:
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x *= self.strength
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elif self.strength_type == StrengthType.LINEAR_UP:
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x *= (self.strength ** float(len(control_output) - i))
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if output_dtype is not None and x.dtype != output_dtype:
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x = x.to(output_dtype)
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out[key].append(x)
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if control_prev is not None:
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for x in ['input', 'middle', 'output']:
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o = out[x]
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for i in range(len(control_prev[x])):
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prev_val = control_prev[x][i]
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if i >= len(o):
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o.append(prev_val)
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elif prev_val is not None:
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if o[i] is None:
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o[i] = prev_val
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else:
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if o[i].shape[0] < prev_val.shape[0]:
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o[i] = prev_val + o[i]
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else:
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o[i] = prev_val + o[i] #TODO: change back to inplace add if shared tensors stop being an issue
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return out
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def set_extra_arg(self, argument, value=None):
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self.extra_args[argument] = value
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class ControlNet(ControlBase):
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def __init__(self, control_model=None, global_average_pooling=False, compression_ratio=8, latent_format=None, load_device=None, manual_cast_dtype=None, extra_conds=["y"], strength_type=StrengthType.CONSTANT, concat_mask=False, preprocess_image=lambda a: a):
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super().__init__()
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self.control_model = control_model
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self.load_device = load_device
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if control_model is not None:
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self.control_model_wrapped = comfy.model_patcher.ModelPatcher(self.control_model, load_device=load_device, offload_device=comfy.model_management.unet_offload_device())
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self.compression_ratio = compression_ratio
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self.global_average_pooling = global_average_pooling
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self.model_sampling_current = None
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self.manual_cast_dtype = manual_cast_dtype
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self.latent_format = latent_format
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self.extra_conds += extra_conds
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self.strength_type = strength_type
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self.concat_mask = concat_mask
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self.preprocess_image = preprocess_image
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def get_control(self, x_noisy, t, cond, batched_number):
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control_prev = None
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if self.previous_controlnet is not None:
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control_prev = self.previous_controlnet.get_control(x_noisy, t, cond, batched_number)
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if self.timestep_range is not None:
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if t[0] > self.timestep_range[0] or t[0] < self.timestep_range[1]:
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if control_prev is not None:
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return control_prev
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else:
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return None
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dtype = self.control_model.dtype
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if self.manual_cast_dtype is not None:
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dtype = self.manual_cast_dtype
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if self.cond_hint is None or x_noisy.shape[2] * self.compression_ratio != self.cond_hint.shape[2] or x_noisy.shape[3] * self.compression_ratio != self.cond_hint.shape[3]:
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if self.cond_hint is not None:
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del self.cond_hint
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self.cond_hint = None
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compression_ratio = self.compression_ratio
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if self.vae is not None:
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compression_ratio *= self.vae.downscale_ratio
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else:
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if self.latent_format is not None:
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raise ValueError("This Controlnet needs a VAE but none was provided, please use a ControlNetApply node with a VAE input and connect it.")
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self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, x_noisy.shape[3] * compression_ratio, x_noisy.shape[2] * compression_ratio, self.upscale_algorithm, "center")
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self.cond_hint = self.preprocess_image(self.cond_hint)
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if self.vae is not None:
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loaded_models = comfy.model_management.loaded_models(only_currently_used=True)
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self.cond_hint = self.vae.encode(self.cond_hint.movedim(1, -1))
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comfy.model_management.load_models_gpu(loaded_models)
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if self.latent_format is not None:
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self.cond_hint = self.latent_format.process_in(self.cond_hint)
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if len(self.extra_concat_orig) > 0:
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to_concat = []
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for c in self.extra_concat_orig:
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c = c.to(self.cond_hint.device)
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c = comfy.utils.common_upscale(c, self.cond_hint.shape[3], self.cond_hint.shape[2], self.upscale_algorithm, "center")
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to_concat.append(comfy.utils.repeat_to_batch_size(c, self.cond_hint.shape[0]))
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self.cond_hint = torch.cat([self.cond_hint] + to_concat, dim=1)
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self.cond_hint = self.cond_hint.to(device=x_noisy.device, dtype=dtype)
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if x_noisy.shape[0] != self.cond_hint.shape[0]:
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self.cond_hint = broadcast_image_to(self.cond_hint, x_noisy.shape[0], batched_number)
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context = cond.get('crossattn_controlnet', cond['c_crossattn'])
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extra = self.extra_args.copy()
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for c in self.extra_conds:
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temp = cond.get(c, None)
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if temp is not None:
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extra[c] = temp.to(dtype)
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timestep = self.model_sampling_current.timestep(t)
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x_noisy = self.model_sampling_current.calculate_input(t, x_noisy)
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control = self.control_model(x=x_noisy.to(dtype), hint=self.cond_hint, timesteps=timestep.to(dtype), context=context.to(dtype), **extra)
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return self.control_merge(control, control_prev, output_dtype=None)
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def copy(self):
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c = ControlNet(None, global_average_pooling=self.global_average_pooling, load_device=self.load_device, manual_cast_dtype=self.manual_cast_dtype)
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c.control_model = self.control_model
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c.control_model_wrapped = self.control_model_wrapped
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self.copy_to(c)
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return c
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def get_models(self):
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out = super().get_models()
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out.append(self.control_model_wrapped)
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return out
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def pre_run(self, model, percent_to_timestep_function):
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super().pre_run(model, percent_to_timestep_function)
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self.model_sampling_current = model.model_sampling
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def cleanup(self):
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self.model_sampling_current = None
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super().cleanup()
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class ControlLoraOps:
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class Linear(torch.nn.Module, comfy.ops.CastWeightBiasOp):
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def __init__(self, in_features: int, out_features: int, bias: bool = True,
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device=None, dtype=None) -> None:
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factory_kwargs = {'device': device, 'dtype': dtype}
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super().__init__()
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self.in_features = in_features
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self.out_features = out_features
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self.weight = None
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self.up = None
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self.down = None
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self.bias = None
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def forward(self, input):
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weight, bias = comfy.ops.cast_bias_weight(self, input)
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if self.up is not None:
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return torch.nn.functional.linear(input, weight + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), bias)
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else:
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return torch.nn.functional.linear(input, weight, bias)
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class Conv2d(torch.nn.Module, comfy.ops.CastWeightBiasOp):
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def __init__(
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self,
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in_channels,
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out_channels,
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kernel_size,
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stride=1,
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padding=0,
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dilation=1,
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groups=1,
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bias=True,
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padding_mode='zeros',
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device=None,
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dtype=None
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):
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super().__init__()
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self.in_channels = in_channels
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self.out_channels = out_channels
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self.kernel_size = kernel_size
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self.stride = stride
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self.padding = padding
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self.dilation = dilation
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self.transposed = False
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self.output_padding = 0
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self.groups = groups
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self.padding_mode = padding_mode
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self.weight = None
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self.bias = None
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self.up = None
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self.down = None
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def forward(self, input):
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weight, bias = comfy.ops.cast_bias_weight(self, input)
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if self.up is not None:
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return torch.nn.functional.conv2d(input, weight + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), bias, self.stride, self.padding, self.dilation, self.groups)
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else:
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return torch.nn.functional.conv2d(input, weight, bias, self.stride, self.padding, self.dilation, self.groups)
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class ControlLora(ControlNet):
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def __init__(self, control_weights, global_average_pooling=False, model_options={}): #TODO? model_options
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ControlBase.__init__(self)
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self.control_weights = control_weights
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self.global_average_pooling = global_average_pooling
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self.extra_conds += ["y"]
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def pre_run(self, model, percent_to_timestep_function):
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super().pre_run(model, percent_to_timestep_function)
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controlnet_config = model.model_config.unet_config.copy()
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controlnet_config.pop("out_channels")
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controlnet_config["hint_channels"] = self.control_weights["input_hint_block.0.weight"].shape[1]
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self.manual_cast_dtype = model.manual_cast_dtype
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dtype = model.get_dtype()
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if self.manual_cast_dtype is None:
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class control_lora_ops(ControlLoraOps, comfy.ops.disable_weight_init):
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pass
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else:
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class control_lora_ops(ControlLoraOps, comfy.ops.manual_cast):
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pass
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dtype = self.manual_cast_dtype
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controlnet_config["operations"] = control_lora_ops
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controlnet_config["dtype"] = dtype
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self.control_model = comfy.cldm.cldm.ControlNet(**controlnet_config)
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self.control_model.to(comfy.model_management.get_torch_device())
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diffusion_model = model.diffusion_model
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sd = diffusion_model.state_dict()
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cm = self.control_model.state_dict()
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for k in sd:
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weight = sd[k]
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try:
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comfy.utils.set_attr_param(self.control_model, k, weight)
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except:
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pass
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for k in self.control_weights:
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if k not in {"lora_controlnet"}:
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comfy.utils.set_attr_param(self.control_model, k, self.control_weights[k].to(dtype).to(comfy.model_management.get_torch_device()))
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def copy(self):
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c = ControlLora(self.control_weights, global_average_pooling=self.global_average_pooling)
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self.copy_to(c)
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return c
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def cleanup(self):
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del self.control_model
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self.control_model = None
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super().cleanup()
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def get_models(self):
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out = ControlBase.get_models(self)
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return out
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def inference_memory_requirements(self, dtype):
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return comfy.utils.calculate_parameters(self.control_weights) * comfy.model_management.dtype_size(dtype) + ControlBase.inference_memory_requirements(self, dtype)
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def controlnet_config(sd, model_options={}):
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model_config = comfy.model_detection.model_config_from_unet(sd, "", True)
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unet_dtype = model_options.get("dtype", None)
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if unet_dtype is None:
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weight_dtype = comfy.utils.weight_dtype(sd)
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supported_inference_dtypes = list(model_config.supported_inference_dtypes)
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if weight_dtype is not None:
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supported_inference_dtypes.append(weight_dtype)
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unet_dtype = comfy.model_management.unet_dtype(model_params=-1, supported_dtypes=supported_inference_dtypes)
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load_device = comfy.model_management.get_torch_device()
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manual_cast_dtype = comfy.model_management.unet_manual_cast(unet_dtype, load_device)
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operations = model_options.get("custom_operations", None)
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if operations is None:
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operations = comfy.ops.pick_operations(unet_dtype, manual_cast_dtype, disable_fast_fp8=True)
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offload_device = comfy.model_management.unet_offload_device()
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return model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device
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def controlnet_load_state_dict(control_model, sd):
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missing, unexpected = control_model.load_state_dict(sd, strict=False)
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if len(missing) > 0:
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logging.warning("missing controlnet keys: {}".format(missing))
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if len(unexpected) > 0:
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logging.debug("unexpected controlnet keys: {}".format(unexpected))
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return control_model
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def load_controlnet_mmdit(sd, model_options={}):
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new_sd = comfy.model_detection.convert_diffusers_mmdit(sd, "")
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model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device = controlnet_config(new_sd, model_options=model_options)
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num_blocks = comfy.model_detection.count_blocks(new_sd, 'joint_blocks.{}.')
|
|
for k in sd:
|
|
new_sd[k] = sd[k]
|
|
|
|
concat_mask = False
|
|
control_latent_channels = new_sd.get("pos_embed_input.proj.weight").shape[1]
|
|
if control_latent_channels == 17: #inpaint controlnet
|
|
concat_mask = True
|
|
|
|
control_model = comfy.cldm.mmdit.ControlNet(num_blocks=num_blocks, control_latent_channels=control_latent_channels, operations=operations, device=offload_device, dtype=unet_dtype, **model_config.unet_config)
|
|
control_model = controlnet_load_state_dict(control_model, new_sd)
|
|
|
|
latent_format = comfy.latent_formats.SD3()
|
|
latent_format.shift_factor = 0 #SD3 controlnet weirdness
|
|
control = ControlNet(control_model, compression_ratio=1, latent_format=latent_format, concat_mask=concat_mask, load_device=load_device, manual_cast_dtype=manual_cast_dtype)
|
|
return control
|
|
|
|
|
|
class ControlNetSD35(ControlNet):
|
|
def pre_run(self, model, percent_to_timestep_function):
|
|
if self.control_model.double_y_emb:
|
|
missing, unexpected = self.control_model.orig_y_embedder.load_state_dict(model.diffusion_model.y_embedder.state_dict(), strict=False)
|
|
else:
|
|
missing, unexpected = self.control_model.x_embedder.load_state_dict(model.diffusion_model.x_embedder.state_dict(), strict=False)
|
|
super().pre_run(model, percent_to_timestep_function)
|
|
|
|
def copy(self):
|
|
c = ControlNetSD35(None, global_average_pooling=self.global_average_pooling, load_device=self.load_device, manual_cast_dtype=self.manual_cast_dtype)
|
|
c.control_model = self.control_model
|
|
c.control_model_wrapped = self.control_model_wrapped
|
|
self.copy_to(c)
|
|
return c
|
|
|
|
def load_controlnet_sd35(sd, model_options={}):
|
|
control_type = -1
|
|
if "control_type" in sd:
|
|
control_type = round(sd.pop("control_type").item())
|
|
|
|
# blur_cnet = control_type == 0
|
|
canny_cnet = control_type == 1
|
|
depth_cnet = control_type == 2
|
|
|
|
print(control_type, canny_cnet, depth_cnet)
|
|
new_sd = {}
|
|
for k in comfy.utils.MMDIT_MAP_BASIC:
|
|
if k[1] in sd:
|
|
new_sd[k[0]] = sd.pop(k[1])
|
|
for k in sd:
|
|
new_sd[k] = sd[k]
|
|
sd = new_sd
|
|
|
|
y_emb_shape = sd["y_embedder.mlp.0.weight"].shape
|
|
depth = y_emb_shape[0] // 64
|
|
hidden_size = 64 * depth
|
|
num_heads = depth
|
|
head_dim = hidden_size // num_heads
|
|
num_blocks = comfy.model_detection.count_blocks(new_sd, 'transformer_blocks.{}.')
|
|
|
|
load_device = comfy.model_management.get_torch_device()
|
|
offload_device = comfy.model_management.unet_offload_device()
|
|
unet_dtype = comfy.model_management.unet_dtype(model_params=-1)
|
|
|
|
manual_cast_dtype = comfy.model_management.unet_manual_cast(unet_dtype, load_device)
|
|
|
|
operations = model_options.get("custom_operations", None)
|
|
if operations is None:
|
|
operations = comfy.ops.pick_operations(unet_dtype, manual_cast_dtype, disable_fast_fp8=True)
|
|
|
|
control_model = comfy.cldm.dit_embedder.ControlNetEmbedder(img_size=None,
|
|
patch_size=2,
|
|
in_chans=16,
|
|
num_layers=num_blocks,
|
|
main_model_double=depth,
|
|
double_y_emb=y_emb_shape[0] == y_emb_shape[1],
|
|
attention_head_dim=head_dim,
|
|
num_attention_heads=num_heads,
|
|
adm_in_channels=2048,
|
|
device=offload_device,
|
|
dtype=unet_dtype,
|
|
operations=operations)
|
|
|
|
control_model = controlnet_load_state_dict(control_model, sd)
|
|
|
|
latent_format = comfy.latent_formats.SD3()
|
|
preprocess_image = lambda a: a
|
|
if canny_cnet:
|
|
preprocess_image = lambda a: (a * 255 * 0.5 + 0.5)
|
|
elif depth_cnet:
|
|
preprocess_image = lambda a: 1.0 - a
|
|
|
|
control = ControlNetSD35(control_model, compression_ratio=1, latent_format=latent_format, load_device=load_device, manual_cast_dtype=manual_cast_dtype, preprocess_image=preprocess_image)
|
|
return control
|
|
|
|
|
|
|
|
def load_controlnet_hunyuandit(controlnet_data, model_options={}):
|
|
model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device = controlnet_config(controlnet_data, model_options=model_options)
|
|
|
|
control_model = comfy.ldm.hydit.controlnet.HunYuanControlNet(operations=operations, device=offload_device, dtype=unet_dtype)
|
|
control_model = controlnet_load_state_dict(control_model, controlnet_data)
|
|
|
|
latent_format = comfy.latent_formats.SDXL()
|
|
extra_conds = ['text_embedding_mask', 'encoder_hidden_states_t5', 'text_embedding_mask_t5', 'image_meta_size', 'style', 'cos_cis_img', 'sin_cis_img']
|
|
control = ControlNet(control_model, compression_ratio=1, latent_format=latent_format, load_device=load_device, manual_cast_dtype=manual_cast_dtype, extra_conds=extra_conds, strength_type=StrengthType.CONSTANT)
|
|
return control
|
|
|
|
def load_controlnet_flux_xlabs_mistoline(sd, mistoline=False, model_options={}):
|
|
model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device = controlnet_config(sd, model_options=model_options)
|
|
control_model = comfy.ldm.flux.controlnet.ControlNetFlux(mistoline=mistoline, operations=operations, device=offload_device, dtype=unet_dtype, **model_config.unet_config)
|
|
control_model = controlnet_load_state_dict(control_model, sd)
|
|
extra_conds = ['y', 'guidance']
|
|
control = ControlNet(control_model, load_device=load_device, manual_cast_dtype=manual_cast_dtype, extra_conds=extra_conds)
|
|
return control
|
|
|
|
def load_controlnet_flux_instantx(sd, model_options={}):
|
|
new_sd = comfy.model_detection.convert_diffusers_mmdit(sd, "")
|
|
model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device = controlnet_config(new_sd, model_options=model_options)
|
|
for k in sd:
|
|
new_sd[k] = sd[k]
|
|
|
|
num_union_modes = 0
|
|
union_cnet = "controlnet_mode_embedder.weight"
|
|
if union_cnet in new_sd:
|
|
num_union_modes = new_sd[union_cnet].shape[0]
|
|
|
|
control_latent_channels = new_sd.get("pos_embed_input.weight").shape[1] // 4
|
|
concat_mask = False
|
|
if control_latent_channels == 17:
|
|
concat_mask = True
|
|
|
|
control_model = comfy.ldm.flux.controlnet.ControlNetFlux(latent_input=True, num_union_modes=num_union_modes, control_latent_channels=control_latent_channels, operations=operations, device=offload_device, dtype=unet_dtype, **model_config.unet_config)
|
|
control_model = controlnet_load_state_dict(control_model, new_sd)
|
|
|
|
latent_format = comfy.latent_formats.Flux()
|
|
extra_conds = ['y', 'guidance']
|
|
control = ControlNet(control_model, compression_ratio=1, latent_format=latent_format, concat_mask=concat_mask, load_device=load_device, manual_cast_dtype=manual_cast_dtype, extra_conds=extra_conds)
|
|
return control
|
|
|
|
def convert_mistoline(sd):
|
|
return comfy.utils.state_dict_prefix_replace(sd, {"single_controlnet_blocks.": "controlnet_single_blocks."})
|
|
|
|
|
|
def load_controlnet_state_dict(state_dict, model=None, model_options={}):
|
|
controlnet_data = state_dict
|
|
if 'after_proj_list.18.bias' in controlnet_data.keys(): #Hunyuan DiT
|
|
return load_controlnet_hunyuandit(controlnet_data, model_options=model_options)
|
|
|
|
if "lora_controlnet" in controlnet_data:
|
|
return ControlLora(controlnet_data, model_options=model_options)
|
|
|
|
controlnet_config = None
|
|
supported_inference_dtypes = None
|
|
|
|
if "controlnet_cond_embedding.conv_in.weight" in controlnet_data: #diffusers format
|
|
controlnet_config = comfy.model_detection.unet_config_from_diffusers_unet(controlnet_data)
|
|
diffusers_keys = comfy.utils.unet_to_diffusers(controlnet_config)
|
|
diffusers_keys["controlnet_mid_block.weight"] = "middle_block_out.0.weight"
|
|
diffusers_keys["controlnet_mid_block.bias"] = "middle_block_out.0.bias"
|
|
|
|
count = 0
|
|
loop = True
|
|
while loop:
|
|
suffix = [".weight", ".bias"]
|
|
for s in suffix:
|
|
k_in = "controlnet_down_blocks.{}{}".format(count, s)
|
|
k_out = "zero_convs.{}.0{}".format(count, s)
|
|
if k_in not in controlnet_data:
|
|
loop = False
|
|
break
|
|
diffusers_keys[k_in] = k_out
|
|
count += 1
|
|
|
|
count = 0
|
|
loop = True
|
|
while loop:
|
|
suffix = [".weight", ".bias"]
|
|
for s in suffix:
|
|
if count == 0:
|
|
k_in = "controlnet_cond_embedding.conv_in{}".format(s)
|
|
else:
|
|
k_in = "controlnet_cond_embedding.blocks.{}{}".format(count - 1, s)
|
|
k_out = "input_hint_block.{}{}".format(count * 2, s)
|
|
if k_in not in controlnet_data:
|
|
k_in = "controlnet_cond_embedding.conv_out{}".format(s)
|
|
loop = False
|
|
diffusers_keys[k_in] = k_out
|
|
count += 1
|
|
|
|
new_sd = {}
|
|
for k in diffusers_keys:
|
|
if k in controlnet_data:
|
|
new_sd[diffusers_keys[k]] = controlnet_data.pop(k)
|
|
|
|
if "control_add_embedding.linear_1.bias" in controlnet_data: #Union Controlnet
|
|
controlnet_config["union_controlnet_num_control_type"] = controlnet_data["task_embedding"].shape[0]
|
|
for k in list(controlnet_data.keys()):
|
|
new_k = k.replace('.attn.in_proj_', '.attn.in_proj.')
|
|
new_sd[new_k] = controlnet_data.pop(k)
|
|
|
|
leftover_keys = controlnet_data.keys()
|
|
if len(leftover_keys) > 0:
|
|
logging.warning("leftover keys: {}".format(leftover_keys))
|
|
controlnet_data = new_sd
|
|
elif "controlnet_blocks.0.weight" in controlnet_data:
|
|
if "double_blocks.0.img_attn.norm.key_norm.scale" in controlnet_data:
|
|
return load_controlnet_flux_xlabs_mistoline(controlnet_data, model_options=model_options)
|
|
elif "pos_embed_input.proj.weight" in controlnet_data:
|
|
if "transformer_blocks.0.adaLN_modulation.1.bias" in controlnet_data:
|
|
return load_controlnet_sd35(controlnet_data, model_options=model_options) #Stability sd3.5 format
|
|
else:
|
|
return load_controlnet_mmdit(controlnet_data, model_options=model_options) #SD3 diffusers controlnet
|
|
elif "controlnet_x_embedder.weight" in controlnet_data:
|
|
return load_controlnet_flux_instantx(controlnet_data, model_options=model_options)
|
|
elif "controlnet_blocks.0.linear.weight" in controlnet_data: #mistoline flux
|
|
return load_controlnet_flux_xlabs_mistoline(convert_mistoline(controlnet_data), mistoline=True, model_options=model_options)
|
|
|
|
pth_key = 'control_model.zero_convs.0.0.weight'
|
|
pth = False
|
|
key = 'zero_convs.0.0.weight'
|
|
if pth_key in controlnet_data:
|
|
pth = True
|
|
key = pth_key
|
|
prefix = "control_model."
|
|
elif key in controlnet_data:
|
|
prefix = ""
|
|
else:
|
|
net = load_t2i_adapter(controlnet_data, model_options=model_options)
|
|
if net is None:
|
|
logging.error("error could not detect control model type.")
|
|
return net
|
|
|
|
if controlnet_config is None:
|
|
model_config = comfy.model_detection.model_config_from_unet(controlnet_data, prefix, True)
|
|
supported_inference_dtypes = list(model_config.supported_inference_dtypes)
|
|
controlnet_config = model_config.unet_config
|
|
|
|
unet_dtype = model_options.get("dtype", None)
|
|
if unet_dtype is None:
|
|
weight_dtype = comfy.utils.weight_dtype(controlnet_data)
|
|
|
|
if supported_inference_dtypes is None:
|
|
supported_inference_dtypes = [comfy.model_management.unet_dtype()]
|
|
|
|
if weight_dtype is not None:
|
|
supported_inference_dtypes.append(weight_dtype)
|
|
|
|
unet_dtype = comfy.model_management.unet_dtype(model_params=-1, supported_dtypes=supported_inference_dtypes)
|
|
|
|
load_device = comfy.model_management.get_torch_device()
|
|
|
|
manual_cast_dtype = comfy.model_management.unet_manual_cast(unet_dtype, load_device)
|
|
operations = model_options.get("custom_operations", None)
|
|
if operations is None:
|
|
operations = comfy.ops.pick_operations(unet_dtype, manual_cast_dtype)
|
|
|
|
controlnet_config["operations"] = operations
|
|
controlnet_config["dtype"] = unet_dtype
|
|
controlnet_config["device"] = comfy.model_management.unet_offload_device()
|
|
controlnet_config.pop("out_channels")
|
|
controlnet_config["hint_channels"] = controlnet_data["{}input_hint_block.0.weight".format(prefix)].shape[1]
|
|
control_model = comfy.cldm.cldm.ControlNet(**controlnet_config)
|
|
|
|
if pth:
|
|
if 'difference' in controlnet_data:
|
|
if model is not None:
|
|
comfy.model_management.load_models_gpu([model])
|
|
model_sd = model.model_state_dict()
|
|
for x in controlnet_data:
|
|
c_m = "control_model."
|
|
if x.startswith(c_m):
|
|
sd_key = "diffusion_model.{}".format(x[len(c_m):])
|
|
if sd_key in model_sd:
|
|
cd = controlnet_data[x]
|
|
cd += model_sd[sd_key].type(cd.dtype).to(cd.device)
|
|
else:
|
|
logging.warning("WARNING: Loaded a diff controlnet without a model. It will very likely not work.")
|
|
|
|
class WeightsLoader(torch.nn.Module):
|
|
pass
|
|
w = WeightsLoader()
|
|
w.control_model = control_model
|
|
missing, unexpected = w.load_state_dict(controlnet_data, strict=False)
|
|
else:
|
|
missing, unexpected = control_model.load_state_dict(controlnet_data, strict=False)
|
|
|
|
if len(missing) > 0:
|
|
logging.warning("missing controlnet keys: {}".format(missing))
|
|
|
|
if len(unexpected) > 0:
|
|
logging.debug("unexpected controlnet keys: {}".format(unexpected))
|
|
|
|
global_average_pooling = model_options.get("global_average_pooling", False)
|
|
control = ControlNet(control_model, global_average_pooling=global_average_pooling, load_device=load_device, manual_cast_dtype=manual_cast_dtype)
|
|
return control
|
|
|
|
def load_controlnet(ckpt_path, model=None, model_options={}):
|
|
if "global_average_pooling" not in model_options:
|
|
filename = os.path.splitext(ckpt_path)[0]
|
|
if filename.endswith("_shuffle") or filename.endswith("_shuffle_fp16"): #TODO: smarter way of enabling global_average_pooling
|
|
model_options["global_average_pooling"] = True
|
|
|
|
cnet = load_controlnet_state_dict(comfy.utils.load_torch_file(ckpt_path, safe_load=True), model=model, model_options=model_options)
|
|
if cnet is None:
|
|
logging.error("error checkpoint does not contain controlnet or t2i adapter data {}".format(ckpt_path))
|
|
return cnet
|
|
|
|
class T2IAdapter(ControlBase):
|
|
def __init__(self, t2i_model, channels_in, compression_ratio, upscale_algorithm, device=None):
|
|
super().__init__()
|
|
self.t2i_model = t2i_model
|
|
self.channels_in = channels_in
|
|
self.control_input = None
|
|
self.compression_ratio = compression_ratio
|
|
self.upscale_algorithm = upscale_algorithm
|
|
if device is None:
|
|
device = comfy.model_management.get_torch_device()
|
|
self.device = device
|
|
|
|
def scale_image_to(self, width, height):
|
|
unshuffle_amount = self.t2i_model.unshuffle_amount
|
|
width = math.ceil(width / unshuffle_amount) * unshuffle_amount
|
|
height = math.ceil(height / unshuffle_amount) * unshuffle_amount
|
|
return width, height
|
|
|
|
def get_control(self, x_noisy, t, cond, batched_number):
|
|
control_prev = None
|
|
if self.previous_controlnet is not None:
|
|
control_prev = self.previous_controlnet.get_control(x_noisy, t, cond, batched_number)
|
|
|
|
if self.timestep_range is not None:
|
|
if t[0] > self.timestep_range[0] or t[0] < self.timestep_range[1]:
|
|
if control_prev is not None:
|
|
return control_prev
|
|
else:
|
|
return None
|
|
|
|
if self.cond_hint is None or x_noisy.shape[2] * self.compression_ratio != self.cond_hint.shape[2] or x_noisy.shape[3] * self.compression_ratio != self.cond_hint.shape[3]:
|
|
if self.cond_hint is not None:
|
|
del self.cond_hint
|
|
self.control_input = None
|
|
self.cond_hint = None
|
|
width, height = self.scale_image_to(x_noisy.shape[3] * self.compression_ratio, x_noisy.shape[2] * self.compression_ratio)
|
|
self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, width, height, self.upscale_algorithm, "center").float().to(self.device)
|
|
if self.channels_in == 1 and self.cond_hint.shape[1] > 1:
|
|
self.cond_hint = torch.mean(self.cond_hint, 1, keepdim=True)
|
|
if x_noisy.shape[0] != self.cond_hint.shape[0]:
|
|
self.cond_hint = broadcast_image_to(self.cond_hint, x_noisy.shape[0], batched_number)
|
|
if self.control_input is None:
|
|
self.t2i_model.to(x_noisy.dtype)
|
|
self.t2i_model.to(self.device)
|
|
self.control_input = self.t2i_model(self.cond_hint.to(x_noisy.dtype))
|
|
self.t2i_model.cpu()
|
|
|
|
control_input = {}
|
|
for k in self.control_input:
|
|
control_input[k] = list(map(lambda a: None if a is None else a.clone(), self.control_input[k]))
|
|
|
|
return self.control_merge(control_input, control_prev, x_noisy.dtype)
|
|
|
|
def copy(self):
|
|
c = T2IAdapter(self.t2i_model, self.channels_in, self.compression_ratio, self.upscale_algorithm)
|
|
self.copy_to(c)
|
|
return c
|
|
|
|
def load_t2i_adapter(t2i_data, model_options={}): #TODO: model_options
|
|
compression_ratio = 8
|
|
upscale_algorithm = 'nearest-exact'
|
|
|
|
if 'adapter' in t2i_data:
|
|
t2i_data = t2i_data['adapter']
|
|
if 'adapter.body.0.resnets.0.block1.weight' in t2i_data: #diffusers format
|
|
prefix_replace = {}
|
|
for i in range(4):
|
|
for j in range(2):
|
|
prefix_replace["adapter.body.{}.resnets.{}.".format(i, j)] = "body.{}.".format(i * 2 + j)
|
|
prefix_replace["adapter.body.{}.".format(i, j)] = "body.{}.".format(i * 2)
|
|
prefix_replace["adapter."] = ""
|
|
t2i_data = comfy.utils.state_dict_prefix_replace(t2i_data, prefix_replace)
|
|
keys = t2i_data.keys()
|
|
|
|
if "body.0.in_conv.weight" in keys:
|
|
cin = t2i_data['body.0.in_conv.weight'].shape[1]
|
|
model_ad = comfy.t2i_adapter.adapter.Adapter_light(cin=cin, channels=[320, 640, 1280, 1280], nums_rb=4)
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elif 'conv_in.weight' in keys:
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cin = t2i_data['conv_in.weight'].shape[1]
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channel = t2i_data['conv_in.weight'].shape[0]
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ksize = t2i_data['body.0.block2.weight'].shape[2]
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use_conv = False
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down_opts = list(filter(lambda a: a.endswith("down_opt.op.weight"), keys))
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if len(down_opts) > 0:
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use_conv = True
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xl = False
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if cin == 256 or cin == 768:
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xl = True
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model_ad = comfy.t2i_adapter.adapter.Adapter(cin=cin, channels=[channel, channel*2, channel*4, channel*4][:4], nums_rb=2, ksize=ksize, sk=True, use_conv=use_conv, xl=xl)
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elif "backbone.0.0.weight" in keys:
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model_ad = comfy.ldm.cascade.controlnet.ControlNet(c_in=t2i_data['backbone.0.0.weight'].shape[1], proj_blocks=[0, 4, 8, 12, 51, 55, 59, 63])
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compression_ratio = 32
|
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upscale_algorithm = 'bilinear'
|
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elif "backbone.10.blocks.0.weight" in keys:
|
|
model_ad = comfy.ldm.cascade.controlnet.ControlNet(c_in=t2i_data['backbone.0.weight'].shape[1], bottleneck_mode="large", proj_blocks=[0, 4, 8, 12, 51, 55, 59, 63])
|
|
compression_ratio = 1
|
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upscale_algorithm = 'nearest-exact'
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else:
|
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return None
|
|
|
|
missing, unexpected = model_ad.load_state_dict(t2i_data)
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if len(missing) > 0:
|
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logging.warning("t2i missing {}".format(missing))
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|
|
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if len(unexpected) > 0:
|
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logging.debug("t2i unexpected {}".format(unexpected))
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|
|
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return T2IAdapter(model_ad, model_ad.input_channels, compression_ratio, upscale_algorithm)
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