| import copy |
| import os |
| import torch |
| from pathlib import Path |
| from modules import devices |
|
|
| from scripts.adapter import PlugableAdapter, Adapter, StyleAdapter, Adapter_light |
| from scripts.controlnet_lllite import PlugableControlLLLite |
| from scripts.cldm import PlugableControlModel |
| from scripts.controlmodel_ipadapter import PlugableIPAdapter |
| from scripts.logging import logger |
| from scripts.controlnet_diffusers import convert_from_diffuser_state_dict |
| from scripts.controlnet_lora import controlnet_lora_hijack, force_load_state_dict |
|
|
| controlnet_default_config = {'adm_in_channels': None, |
| 'in_channels': 4, |
| 'model_channels': 320, |
| 'num_res_blocks': 2, |
| 'attention_resolutions': [1, 2, 4], |
| 'transformer_depth': [1, 1, 1, 0], |
| 'channel_mult': [1, 2, 4, 4], |
| 'transformer_depth_middle': 1, |
| 'use_linear_in_transformer': False, |
| 'context_dim': 768, |
| "num_heads": 8, |
| "global_average_pooling": False} |
|
|
| controlnet_sdxl_config = {'num_classes': 'sequential', |
| 'adm_in_channels': 2816, |
| 'in_channels': 4, |
| 'model_channels': 320, |
| 'num_res_blocks': 2, |
| 'attention_resolutions': [2, 4], |
| 'transformer_depth': [0, 2, 10], |
| 'channel_mult': [1, 2, 4], |
| 'transformer_depth_middle': 10, |
| 'use_linear_in_transformer': True, |
| 'context_dim': 2048, |
| "num_head_channels": 64, |
| "global_average_pooling": False} |
|
|
| controlnet_sdxl_mid_config = {'num_classes': 'sequential', |
| 'adm_in_channels': 2816, |
| 'in_channels': 4, |
| 'model_channels': 320, |
| 'num_res_blocks': 2, |
| 'attention_resolutions': [4], |
| 'transformer_depth': [0, 0, 1], |
| 'channel_mult': [1, 2, 4], |
| 'transformer_depth_middle': 1, |
| 'use_linear_in_transformer': True, |
| 'context_dim': 2048, |
| "num_head_channels": 64, |
| "global_average_pooling": False} |
|
|
| controlnet_sdxl_small_config = {'num_classes': 'sequential', |
| 'adm_in_channels': 2816, |
| 'in_channels': 4, |
| 'model_channels': 320, |
| 'num_res_blocks': 2, |
| 'attention_resolutions': [], |
| 'transformer_depth': [0, 0, 0], |
| 'channel_mult': [1, 2, 4], |
| 'transformer_depth_middle': 0, |
| 'use_linear_in_transformer': True, |
| "num_head_channels": 64, |
| 'context_dim': 1, |
| "global_average_pooling": False} |
|
|
| t2i_adapter_config = { |
| 'channels': [320, 640, 1280, 1280], |
| 'nums_rb': 2, |
| 'ksize': 1, |
| 'sk': True, |
| 'cin': 192, |
| 'use_conv': False |
| } |
|
|
| t2i_adapter_light_config = { |
| 'channels': [320, 640, 1280, 1280], |
| 'nums_rb': 4, |
| 'cin': 192, |
| } |
|
|
| t2i_adapter_style_config = { |
| 'width': 1024, |
| 'context_dim': 768, |
| 'num_head': 8, |
| 'n_layes': 3, |
| 'num_token': 8, |
| } |
|
|
|
|
| |
| def state_dict_key_replace(state_dict, keys_to_replace): |
| for x in keys_to_replace: |
| if x in state_dict: |
| state_dict[keys_to_replace[x]] = state_dict.pop(x) |
| return state_dict |
|
|
|
|
| |
| def state_dict_prefix_replace(state_dict, replace_prefix): |
| for rp in replace_prefix: |
| replace = list(map(lambda a: (a, "{}{}".format(replace_prefix[rp], a[len(rp):])), filter(lambda a: a.startswith(rp), state_dict.keys()))) |
| for x in replace: |
| state_dict[x[1]] = state_dict.pop(x[0]) |
| return state_dict |
|
|
|
|
| def build_model_by_guess(state_dict, unet, model_path): |
| if "lora_controlnet" in state_dict: |
| del state_dict['lora_controlnet'] |
| config = copy.deepcopy(controlnet_sdxl_config) |
| logger.info('controlnet_sdxl_config (using lora)') |
| config['global_average_pooling'] = False |
| config['hint_channels'] = int(state_dict['input_hint_block.0.weight'].shape[1]) |
| config['use_fp16'] = devices.dtype_unet == torch.float16 |
| with controlnet_lora_hijack(): |
| network = PlugableControlModel(config, state_dict=None) |
| force_load_state_dict(network.control_model, state_dict) |
| network.is_control_lora = True |
| network.to(devices.dtype_unet) |
| return network |
|
|
| if "controlnet_cond_embedding.conv_in.weight" in state_dict: |
| state_dict = convert_from_diffuser_state_dict(state_dict) |
|
|
| if 'adapter.body.0.resnets.0.block1.weight' in state_dict: |
| 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)] = "body.{}.".format(i * 2) |
| prefix_replace["adapter."] = "" |
| state_dict = state_dict_prefix_replace(state_dict, prefix_replace) |
|
|
| model_has_shuffle_in_filename = 'shuffle' in Path(os.path.abspath(model_path)).stem.lower() |
| state_dict = {k.replace("control_model.", ""): v for k, v in state_dict.items()} |
| state_dict = {k.replace("adapter.", ""): v for k, v in state_dict.items()} |
|
|
| if 'input_hint_block.0.weight' in state_dict: |
| if 'label_emb.0.0.bias' not in state_dict: |
| config = copy.deepcopy(controlnet_default_config) |
| logger.info('controlnet_default_config') |
| config['global_average_pooling'] = model_has_shuffle_in_filename |
| config['hint_channels'] = int(state_dict['input_hint_block.0.weight'].shape[1]) |
| config['context_dim'] = int(state_dict['input_blocks.5.1.transformer_blocks.0.attn2.to_k.weight'].shape[1]) |
| for key in state_dict.keys(): |
| p = state_dict[key] |
| if 'proj_in.weight' in key or 'proj_out.weight' in key: |
| if len(p.shape) == 2: |
| p = p[..., None, None] |
| state_dict[key] = p |
| else: |
| has_full_layers = 'input_blocks.8.1.transformer_blocks.9.norm3.weight' in state_dict |
| has_mid_layers = 'input_blocks.8.1.transformer_blocks.0.norm3.weight' in state_dict |
| if has_full_layers: |
| config = copy.deepcopy(controlnet_sdxl_config) |
| logger.info('controlnet_sdxl_config') |
| elif has_mid_layers: |
| config = copy.deepcopy(controlnet_sdxl_mid_config) |
| logger.info('controlnet_sdxl_mid_config') |
| else: |
| config = copy.deepcopy(controlnet_sdxl_small_config) |
| logger.info('controlnet_sdxl_small_config') |
| config['global_average_pooling'] = False |
| config['hint_channels'] = int(state_dict['input_hint_block.0.weight'].shape[1]) |
|
|
| if 'difference' in state_dict and unet is not None: |
| unet_state_dict = unet.state_dict() |
| unet_state_dict_keys = unet_state_dict.keys() |
| final_state_dict = {} |
| for key in state_dict.keys(): |
| p = state_dict[key] |
| if key in unet_state_dict_keys: |
| p_new = p + unet_state_dict[key].clone().cpu() |
| else: |
| p_new = p |
| final_state_dict[key] = p_new |
| state_dict = final_state_dict |
|
|
| config['use_fp16'] = devices.dtype_unet == torch.float16 |
|
|
| network = PlugableControlModel(config, state_dict) |
| network.to(devices.dtype_unet) |
| return network |
|
|
| if 'conv_in.weight' in state_dict: |
| logger.info('t2i_adapter_config') |
| cin = int(state_dict['conv_in.weight'].shape[1]) |
| channel = int(state_dict['conv_in.weight'].shape[0]) |
| ksize = int(state_dict['body.0.block2.weight'].shape[2]) |
| down_opts = tuple(filter(lambda item: item.endswith("down_opt.op.weight"), state_dict)) |
| use_conv = len(down_opts) > 0 |
| is_sdxl = cin == 256 or cin == 768 |
| adapter = Adapter( |
| cin=cin, |
| channels=[channel, channel*2, channel*4, channel*4], |
| nums_rb=2, |
| ksize=ksize, |
| sk=True, |
| use_conv=use_conv, |
| is_sdxl=is_sdxl |
| ).cpu() |
| adapter.load_state_dict(state_dict, strict=False) |
| network = PlugableAdapter(adapter) |
| return network |
|
|
| if 'style_embedding' in state_dict: |
| config = copy.deepcopy(t2i_adapter_style_config) |
| logger.info('t2i_adapter_style_config') |
| adapter = StyleAdapter(**config).cpu() |
| adapter.load_state_dict(state_dict, strict=False) |
| network = PlugableAdapter(adapter) |
| return network |
|
|
| if 'body.0.in_conv.weight' in state_dict: |
| config = copy.deepcopy(t2i_adapter_light_config) |
| logger.info('t2i_adapter_light_config') |
| config['cin'] = int(state_dict['body.0.in_conv.weight'].shape[1]) |
| adapter = Adapter_light(**config).cpu() |
| adapter.load_state_dict(state_dict, strict=False) |
| network = PlugableAdapter(adapter) |
| return network |
|
|
| if 'ip_adapter' in state_dict: |
| plus = "latents" in state_dict["image_proj"] |
| if plus: |
| channel = int(state_dict['image_proj']['proj_in.weight'].shape[1]) |
| else: |
| channel = int(state_dict['image_proj']['proj.weight'].shape[1]) |
| network = PlugableIPAdapter(state_dict, channel, plus) |
| network.to('cpu') |
| return network |
|
|
| if any('lllite' in k for k in state_dict.keys()): |
| network = PlugableControlLLLite(state_dict) |
| network.to('cpu') |
| return network |
|
|
| raise '[ControlNet Error] Cannot recognize the ControlModel!' |
|
|
