455 lines
24 KiB
Python
455 lines
24 KiB
Python
'''
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Borrowed and modified from sd-scripts, publicly available at
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https://github.com/kohya-ss/sd-scripts/blob/main/library/model_util.py
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'''
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from diffusers import UNet2DConditionModel
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# Model paras of stable diffusion in diffUsers
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NUM_TRAIN_TIMESTEPS = 1000
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BETA_START = 0.00085
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BETA_END = 0.0120
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UNET_PARAMS_MODEL_CHANNELS = 320
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UNET_PARAMS_CHANNEL_MULT = [1, 2, 4, 4]
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UNET_PARAMS_ATTENTION_RESOLUTIONS = [4, 2, 1]
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UNET_PARAMS_IMAGE_SIZE = 64 # fixed from old invalid value `32`
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UNET_PARAMS_IN_CHANNELS = 4
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UNET_PARAMS_OUT_CHANNELS = 4
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UNET_PARAMS_NUM_RES_BLOCKS = 2
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UNET_PARAMS_CONTEXT_DIM = 768
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UNET_PARAMS_NUM_HEADS = 8
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# UNET_PARAMS_USE_LINEAR_PROJECTION = False
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VAE_PARAMS_Z_CHANNELS = 4
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VAE_PARAMS_RESOLUTION = 256
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VAE_PARAMS_IN_CHANNELS = 3
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VAE_PARAMS_OUT_CH = 3
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VAE_PARAMS_CH = 128
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VAE_PARAMS_CH_MULT = [1, 2, 4, 4]
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VAE_PARAMS_NUM_RES_BLOCKS = 2
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# V2
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V2_UNET_PARAMS_ATTENTION_HEAD_DIM = [5, 10, 20, 20]
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V2_UNET_PARAMS_CONTEXT_DIM = 1024
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# V2_UNET_PARAMS_USE_LINEAR_PROJECTION = True
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def shave_segments(path, n_shave_prefix_segments=1):
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"""
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Removes segments. Positive values shave the first segments, negative shave the last segments.
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"""
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if n_shave_prefix_segments >= 0:
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return ".".join(path.split(".")[n_shave_prefix_segments:])
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else:
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return ".".join(path.split(".")[:n_shave_prefix_segments])
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def renew_resnet_paths(old_list, n_shave_prefix_segments=0):
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"""
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Updates paths inside resnets to the new naming scheme (local renaming)
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"""
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mapping = []
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for old_item in old_list:
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new_item = old_item.replace("in_layers.0", "norm1")
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new_item = new_item.replace("in_layers.2", "conv1")
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new_item = new_item.replace("out_layers.0", "norm2")
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new_item = new_item.replace("out_layers.3", "conv2")
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new_item = new_item.replace("emb_layers.1", "time_emb_proj")
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new_item = new_item.replace("skip_connection", "conv_shortcut")
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new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
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mapping.append({"old": old_item, "new": new_item})
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return mapping
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def renew_attention_paths(old_list, n_shave_prefix_segments=0):
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"""
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Updates paths inside attentions to the new naming scheme (local renaming)
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"""
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mapping = []
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for old_item in old_list:
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new_item = old_item
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mapping.append({"old": old_item, "new": new_item})
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return mapping
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def assign_to_checkpoint(
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paths, checkpoint, old_checkpoint, attention_paths_to_split=None, additional_replacements=None, config=None
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):
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"""
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This does the final conversion step: take locally converted weights and apply a global renaming
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to them. It splits attention layers, and takes into account additional replacements
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that may arise.
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Assigns the weights to the new checkpoint.
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"""
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assert isinstance(paths, list), "Paths should be a list of dicts containing 'old' and 'new' keys."
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# Splits the attention layers into three variables.
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if attention_paths_to_split is not None:
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for path, path_map in attention_paths_to_split.items():
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old_tensor = old_checkpoint[path]
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channels = old_tensor.shape[0] // 3
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target_shape = (-1, channels) if len(old_tensor.shape) == 3 else (-1)
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num_heads = old_tensor.shape[0] // config["num_head_channels"] // 3
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old_tensor = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:])
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query, key, value = old_tensor.split(channels // num_heads, dim=1)
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checkpoint[path_map["query"]] = query.reshape(target_shape)
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checkpoint[path_map["key"]] = key.reshape(target_shape)
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checkpoint[path_map["value"]] = value.reshape(target_shape)
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for path in paths:
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new_path = path["new"]
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# These have already been assigned
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if attention_paths_to_split is not None and new_path in attention_paths_to_split:
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continue
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# Global renaming happens here
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new_path = new_path.replace("middle_block.0", "mid_block.resnets.0")
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new_path = new_path.replace("middle_block.1", "mid_block.attentions.0")
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new_path = new_path.replace("middle_block.2", "mid_block.resnets.1")
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if additional_replacements is not None:
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for replacement in additional_replacements:
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new_path = new_path.replace(replacement["old"], replacement["new"])
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# proj_attn.weight has to be converted from conv 1D to linear
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if "proj_attn.weight" in new_path:
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checkpoint[new_path] = old_checkpoint[path["old"]][:, :, 0]
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else:
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checkpoint[new_path] = old_checkpoint[path["old"]]
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def linear_transformer_to_conv(checkpoint):
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keys = list(checkpoint.keys())
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tf_keys = ["proj_in.weight", "proj_out.weight"]
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for key in keys:
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if ".".join(key.split(".")[-2:]) in tf_keys:
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if checkpoint[key].ndim == 2:
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checkpoint[key] = checkpoint[key].unsqueeze(2).unsqueeze(2)
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def conv_transformer_to_linear(checkpoint):
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keys = list(checkpoint.keys())
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tf_keys = ["proj_in.weight", "proj_out.weight"]
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for key in keys:
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if ".".join(key.split(".")[-2:]) in tf_keys:
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if checkpoint[key].ndim > 2:
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checkpoint[key] = checkpoint[key][:, :, 0, 0]
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def create_unet_diffusers_config(v2, use_linear_projection_in_v2=False):
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"""
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Creates a config for the diffusers based on the config of the LDM model.
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"""
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# unet_params = original_config.model.params.unet_config.params
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block_out_channels = [UNET_PARAMS_MODEL_CHANNELS * mult for mult in UNET_PARAMS_CHANNEL_MULT]
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down_block_types = []
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resolution = 1
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for i in range(len(block_out_channels)):
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block_type = "CrossAttnDownBlock2D" if resolution in UNET_PARAMS_ATTENTION_RESOLUTIONS else "DownBlock2D"
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down_block_types.append(block_type)
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if i != len(block_out_channels) - 1:
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resolution *= 2
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up_block_types = []
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for i in range(len(block_out_channels)):
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block_type = "CrossAttnUpBlock2D" if resolution in UNET_PARAMS_ATTENTION_RESOLUTIONS else "UpBlock2D"
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up_block_types.append(block_type)
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resolution //= 2
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config = dict(
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sample_size=UNET_PARAMS_IMAGE_SIZE,
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in_channels=UNET_PARAMS_IN_CHANNELS,
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out_channels=UNET_PARAMS_OUT_CHANNELS,
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down_block_types=tuple(down_block_types),
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up_block_types=tuple(up_block_types),
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block_out_channels=tuple(block_out_channels),
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layers_per_block=UNET_PARAMS_NUM_RES_BLOCKS,
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cross_attention_dim=UNET_PARAMS_CONTEXT_DIM if not v2 else V2_UNET_PARAMS_CONTEXT_DIM,
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attention_head_dim=UNET_PARAMS_NUM_HEADS if not v2 else V2_UNET_PARAMS_ATTENTION_HEAD_DIM,
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# use_linear_projection=UNET_PARAMS_USE_LINEAR_PROJECTION if not v2 else V2_UNET_PARAMS_USE_LINEAR_PROJECTION,
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)
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if v2 and use_linear_projection_in_v2:
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config["use_linear_projection"] = True
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return config
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def convert_ldm_unet_checkpoint(v2, checkpoint, config):
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"""
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Takes a state dict and a config, and returns a converted checkpoint.
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"""
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# extract state_dict for UNet
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unet_state_dict = {}
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unet_key = "model.diffusion_model."
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keys = list(checkpoint.keys())
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for key in keys:
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if key.startswith(unet_key):
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unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(key)
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new_checkpoint = {}
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new_checkpoint["time_embedding.linear_1.weight"] = unet_state_dict["time_embed.0.weight"]
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new_checkpoint["time_embedding.linear_1.bias"] = unet_state_dict["time_embed.0.bias"]
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new_checkpoint["time_embedding.linear_2.weight"] = unet_state_dict["time_embed.2.weight"]
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new_checkpoint["time_embedding.linear_2.bias"] = unet_state_dict["time_embed.2.bias"]
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new_checkpoint["conv_in.weight"] = unet_state_dict["input_blocks.0.0.weight"]
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new_checkpoint["conv_in.bias"] = unet_state_dict["input_blocks.0.0.bias"]
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new_checkpoint["conv_norm_out.weight"] = unet_state_dict["out.0.weight"]
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new_checkpoint["conv_norm_out.bias"] = unet_state_dict["out.0.bias"]
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new_checkpoint["conv_out.weight"] = unet_state_dict["out.2.weight"]
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new_checkpoint["conv_out.bias"] = unet_state_dict["out.2.bias"]
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# Retrieves the keys for the input blocks only
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num_input_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "input_blocks" in layer})
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input_blocks = {
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layer_id: [key for key in unet_state_dict if f"input_blocks.{layer_id}." in key] for layer_id in range(num_input_blocks)
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}
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# Retrieves the keys for the middle blocks only
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num_middle_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "middle_block" in layer})
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middle_blocks = {
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layer_id: [key for key in unet_state_dict if f"middle_block.{layer_id}." in key] for layer_id in range(num_middle_blocks)
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}
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# Retrieves the keys for the output blocks only
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num_output_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "output_blocks" in layer})
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output_blocks = {
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layer_id: [key for key in unet_state_dict if f"output_blocks.{layer_id}." in key] for layer_id in range(num_output_blocks)
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}
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for i in range(1, num_input_blocks):
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block_id = (i - 1) // (config["layers_per_block"] + 1)
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layer_in_block_id = (i - 1) % (config["layers_per_block"] + 1)
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resnets = [key for key in input_blocks[i] if f"input_blocks.{i}.0" in key and f"input_blocks.{i}.0.op" not in key]
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attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key]
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if f"input_blocks.{i}.0.op.weight" in unet_state_dict:
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new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.weight"] = unet_state_dict.pop(
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f"input_blocks.{i}.0.op.weight"
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)
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new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.bias"] = unet_state_dict.pop(f"input_blocks.{i}.0.op.bias")
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paths = renew_resnet_paths(resnets)
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meta_path = {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"}
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assign_to_checkpoint(paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config)
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if len(attentions):
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paths = renew_attention_paths(attentions)
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meta_path = {"old": f"input_blocks.{i}.1", "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}"}
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assign_to_checkpoint(paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config)
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resnet_0 = middle_blocks[0]
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attentions = middle_blocks[1]
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resnet_1 = middle_blocks[2]
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resnet_0_paths = renew_resnet_paths(resnet_0)
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assign_to_checkpoint(resnet_0_paths, new_checkpoint, unet_state_dict, config=config)
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resnet_1_paths = renew_resnet_paths(resnet_1)
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assign_to_checkpoint(resnet_1_paths, new_checkpoint, unet_state_dict, config=config)
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attentions_paths = renew_attention_paths(attentions)
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meta_path = {"old": "middle_block.1", "new": "mid_block.attentions.0"}
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assign_to_checkpoint(attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config)
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for i in range(num_output_blocks):
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block_id = i // (config["layers_per_block"] + 1)
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layer_in_block_id = i % (config["layers_per_block"] + 1)
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output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]]
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output_block_list = {}
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for layer in output_block_layers:
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layer_id, layer_name = layer.split(".")[0], shave_segments(layer, 1)
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if layer_id in output_block_list:
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output_block_list[layer_id].append(layer_name)
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else:
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output_block_list[layer_id] = [layer_name]
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if len(output_block_list) > 1:
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resnets = [key for key in output_blocks[i] if f"output_blocks.{i}.0" in key]
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attentions = [key for key in output_blocks[i] if f"output_blocks.{i}.1" in key]
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resnet_0_paths = renew_resnet_paths(resnets)
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paths = renew_resnet_paths(resnets)
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meta_path = {"old": f"output_blocks.{i}.0", "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}"}
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assign_to_checkpoint(paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config)
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# オリジナル：
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# if ["conv.weight", "conv.bias"] in output_block_list.values():
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# index = list(output_block_list.values()).index(["conv.weight", "conv.bias"])
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# biasとweightの順番に依存しないようにする：もっといいやり方がありそうだが
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for l in output_block_list.values():
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l.sort()
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if ["conv.bias", "conv.weight"] in output_block_list.values():
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index = list(output_block_list.values()).index(["conv.bias", "conv.weight"])
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new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = unet_state_dict[
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f"output_blocks.{i}.{index}.conv.bias"
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]
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new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = unet_state_dict[
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f"output_blocks.{i}.{index}.conv.weight"
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]
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# Clear attentions as they have been attributed above.
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if len(attentions) == 2:
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attentions = []
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if len(attentions):
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paths = renew_attention_paths(attentions)
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meta_path = {
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"old": f"output_blocks.{i}.1",
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"new": f"up_blocks.{block_id}.attentions.{layer_in_block_id}",
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}
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assign_to_checkpoint(paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config)
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else:
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resnet_0_paths = renew_resnet_paths(output_block_layers, n_shave_prefix_segments=1)
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for path in resnet_0_paths:
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old_path = ".".join(["output_blocks", str(i), path["old"]])
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new_path = ".".join(["up_blocks", str(block_id), "resnets", str(layer_in_block_id), path["new"]])
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new_checkpoint[new_path] = unet_state_dict[old_path]
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# SDのv2では1*1のconv2dがlinearに変わっている
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# 誤って Diffusers 側を conv2d のままにしてしまったので、変換必要
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if v2 and not config.get('use_linear_projection', False):
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linear_transformer_to_conv(new_checkpoint)
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del unet_state_dict
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return new_checkpoint
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def convert_unet_state_dict_to_sd(unet_state_dict, v2=False):
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unet_conversion_map = [
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# (stable-diffusion, HF Diffusers)
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("time_embed.0.weight", "time_embedding.linear_1.weight"),
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("time_embed.0.bias", "time_embedding.linear_1.bias"),
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("time_embed.2.weight", "time_embedding.linear_2.weight"),
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("time_embed.2.bias", "time_embedding.linear_2.bias"),
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("input_blocks.0.0.weight", "conv_in.weight"),
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("input_blocks.0.0.bias", "conv_in.bias"),
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("out.0.weight", "conv_norm_out.weight"),
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("out.0.bias", "conv_norm_out.bias"),
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("out.2.weight", "conv_out.weight"),
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("out.2.bias", "conv_out.bias"),
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]
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unet_conversion_map_resnet = [
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# (stable-diffusion, HF Diffusers)
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("in_layers.0", "norm1"),
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("in_layers.2", "conv1"),
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("out_layers.0", "norm2"),
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("out_layers.3", "conv2"),
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("emb_layers.1", "time_emb_proj"),
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("skip_connection", "conv_shortcut"),
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]
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unet_conversion_map_layer = []
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for i in range(4):
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# loop over downblocks/upblocks
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for j in range(2):
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# loop over resnets/attentions for downblocks
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hf_down_res_prefix = f"down_blocks.{i}.resnets.{j}."
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sd_down_res_prefix = f"input_blocks.{3*i + j + 1}.0."
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unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))
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if i < 3:
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# no attention layers in down_blocks.3
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hf_down_atn_prefix = f"down_blocks.{i}.attentions.{j}."
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sd_down_atn_prefix = f"input_blocks.{3*i + j + 1}.1."
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unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))
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for j in range(3):
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# loop over resnets/attentions for upblocks
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hf_up_res_prefix = f"up_blocks.{i}.resnets.{j}."
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sd_up_res_prefix = f"output_blocks.{3*i + j}.0."
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unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))
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if i > 0:
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# no attention layers in up_blocks.0
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hf_up_atn_prefix = f"up_blocks.{i}.attentions.{j}."
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sd_up_atn_prefix = f"output_blocks.{3*i + j}.1."
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unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))
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if i < 3:
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# no downsample in down_blocks.3
|
|
hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0.conv."
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|
sd_downsample_prefix = f"input_blocks.{3*(i+1)}.0.op."
|
|
unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))
|
|
|
|
# no upsample in up_blocks.3
|
|
hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0."
|
|
sd_upsample_prefix = f"output_blocks.{3*i + 2}.{1 if i == 0 else 2}."
|
|
unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))
|
|
|
|
hf_mid_atn_prefix = "mid_block.attentions.0."
|
|
sd_mid_atn_prefix = "middle_block.1."
|
|
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))
|
|
|
|
for j in range(2):
|
|
hf_mid_res_prefix = f"mid_block.resnets.{j}."
|
|
sd_mid_res_prefix = f"middle_block.{2*j}."
|
|
unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))
|
|
|
|
# buyer beware: this is a *brittle* function,
|
|
# and correct output requires that all of these pieces interact in
|
|
# the exact order in which I have arranged them.
|
|
mapping = {k: k for k in unet_state_dict.keys()}
|
|
for sd_name, hf_name in unet_conversion_map:
|
|
mapping[hf_name] = sd_name
|
|
for k, v in mapping.items():
|
|
if "resnets" in k:
|
|
for sd_part, hf_part in unet_conversion_map_resnet:
|
|
v = v.replace(hf_part, sd_part)
|
|
mapping[k] = v
|
|
for k, v in mapping.items():
|
|
for sd_part, hf_part in unet_conversion_map_layer:
|
|
v = v.replace(hf_part, sd_part)
|
|
mapping[k] = v
|
|
new_state_dict = {v: unet_state_dict[k] for k, v in mapping.items()}
|
|
|
|
if v2:
|
|
conv_transformer_to_linear(new_state_dict)
|
|
|
|
return new_state_dict
|
|
|
|
|
|
def get_diffusers_unet(unet=None, state_dict=None, v2=False):
|
|
unet_config = create_unet_diffusers_config(v2, use_linear_projection_in_v2=False)
|
|
if unet is None:
|
|
unet = UNet2DConditionModel(**unet_config).to("cpu")
|
|
if state_dict:
|
|
converted_unet_checkpoint = convert_ldm_unet_checkpoint(v2, state_dict, unet_config)
|
|
try:
|
|
info = unet.load_state_dict(converted_unet_checkpoint)
|
|
except Exception as e:
|
|
for k in unexpected_keys:
|
|
converted_unet_checkpoint.pop(k)
|
|
info = unet.load_state_dict(converted_unet_checkpoint)
|
|
del converted_unet_checkpoint
|
|
print("loading diffusers u-net:", info)
|
|
return unet
|
|
|
|
unexpected_keys = ["up_blocks.1.attentions.0.transformer_blocks.0.attn1x.to_q.weight", "up_blocks.1.attentions.0.transformer_blocks.0.attn1x.to_k.weight", "up_blocks.1.attentions.0.transformer_blocks.0.attn1x.to_v.weight", "up_blocks.1.attentions.0.transformer_blocks.0.attn1x.to_out.0.weight", "up_blocks.1.attentions.0.transformer_blocks.0.attn1x.to_out.0.bias", "up_blocks.1.attentions.0.transformer_blocks.0.attn2x.to_q.weight", "up_blocks.1.attentions.0.transformer_blocks.0.attn2x.to_k.weight", "up_blocks.1.attentions.0.transformer_blocks.0.attn2x.to_v.weight", "up_blocks.1.attentions.0.transformer_blocks.0.attn2x.to_out.0.weight", "up_blocks.1.attentions.0.transformer_blocks.0.attn2x.to_out.0.bias", "up_blocks.1.attentions.1.transformer_blocks.0.attn1x.to_q.weight", "up_blocks.1.attentions.1.transformer_blocks.0.attn1x.to_k.weight", "up_blocks.1.attentions.1.transformer_blocks.0.attn1x.to_v.weight", "up_blocks.1.attentions.1.transformer_blocks.0.attn1x.to_out.0.weight", "up_blocks.1.attentions.1.transformer_blocks.0.attn1x.to_out.0.bias", "up_blocks.1.attentions.1.transformer_blocks.0.attn2x.to_q.weight", "up_blocks.1.attentions.1.transformer_blocks.0.attn2x.to_k.weight", "up_blocks.1.attentions.1.transformer_blocks.0.attn2x.to_v.weight", "up_blocks.1.attentions.1.transformer_blocks.0.attn2x.to_out.0.weight", "up_blocks.1.attentions.1.transformer_blocks.0.attn2x.to_out.0.bias", "up_blocks.1.attentions.2.transformer_blocks.0.attn1x.to_q.weight", "up_blocks.1.attentions.2.transformer_blocks.0.attn1x.to_k.weight", "up_blocks.1.attentions.2.transformer_blocks.0.attn1x.to_v.weight", "up_blocks.1.attentions.2.transformer_blocks.0.attn1x.to_out.0.weight", "up_blocks.1.attentions.2.transformer_blocks.0.attn1x.to_out.0.bias", "up_blocks.1.attentions.2.transformer_blocks.0.attn2x.to_q.weight", "up_blocks.1.attentions.2.transformer_blocks.0.attn2x.to_k.weight", "up_blocks.1.attentions.2.transformer_blocks.0.attn2x.to_v.weight", "up_blocks.1.attentions.2.transformer_blocks.0.attn2x.to_out.0.weight", "up_blocks.1.attentions.2.transformer_blocks.0.attn2x.to_out.0.bias", "up_blocks.2.attentions.0.transformer_blocks.0.attn1x.to_q.weight", "up_blocks.2.attentions.0.transformer_blocks.0.attn1x.to_k.weight", "up_blocks.2.attentions.0.transformer_blocks.0.attn1x.to_v.weight", "up_blocks.2.attentions.0.transformer_blocks.0.attn1x.to_out.0.weight", "up_blocks.2.attentions.0.transformer_blocks.0.attn1x.to_out.0.bias", "up_blocks.2.attentions.0.transformer_blocks.0.attn2x.to_q.weight", "up_blocks.2.attentions.0.transformer_blocks.0.attn2x.to_k.weight", "up_blocks.2.attentions.0.transformer_blocks.0.attn2x.to_v.weight", "up_blocks.2.attentions.0.transformer_blocks.0.attn2x.to_out.0.weight", "up_blocks.2.attentions.0.transformer_blocks.0.attn2x.to_out.0.bias", "up_blocks.2.attentions.1.transformer_blocks.0.attn1x.to_q.weight", "up_blocks.2.attentions.1.transformer_blocks.0.attn1x.to_k.weight", "up_blocks.2.attentions.1.transformer_blocks.0.attn1x.to_v.weight", "up_blocks.2.attentions.1.transformer_blocks.0.attn1x.to_out.0.weight", "up_blocks.2.attentions.1.transformer_blocks.0.attn1x.to_out.0.bias", "up_blocks.2.attentions.1.transformer_blocks.0.attn2x.to_q.weight", "up_blocks.2.attentions.1.transformer_blocks.0.attn2x.to_k.weight", "up_blocks.2.attentions.1.transformer_blocks.0.attn2x.to_v.weight", "up_blocks.2.attentions.1.transformer_blocks.0.attn2x.to_out.0.weight", "up_blocks.2.attentions.1.transformer_blocks.0.attn2x.to_out.0.bias", "up_blocks.2.attentions.2.transformer_blocks.0.attn1x.to_q.weight", "up_blocks.2.attentions.2.transformer_blocks.0.attn1x.to_k.weight", "up_blocks.2.attentions.2.transformer_blocks.0.attn1x.to_v.weight", "up_blocks.2.attentions.2.transformer_blocks.0.attn1x.to_out.0.weight", "up_blocks.2.attentions.2.transformer_blocks.0.attn1x.to_out.0.bias", "up_blocks.2.attentions.2.transformer_blocks.0.attn2x.to_q.weight", "up_blocks.2.attentions.2.transformer_blocks.0.attn2x.to_k.weight", "up_blocks.2.attentions.2.transformer_blocks.0.attn2x.to_v.weight", "up_blocks.2.attentions.2.transformer_blocks.0.attn2x.to_out.0.weight", "up_blocks.2.attentions.2.transformer_blocks.0.attn2x.to_out.0.bias", "mid_block.attentions.0.transformer_blocks.0.attn1x.to_q.weight", "mid_block.attentions.0.transformer_blocks.0.attn1x.to_k.weight", "mid_block.attentions.0.transformer_blocks.0.attn1x.to_v.weight", "mid_block.attentions.0.transformer_blocks.0.attn1x.to_out.0.weight", "mid_block.attentions.0.transformer_blocks.0.attn1x.to_out.0.bias", "mid_block.attentions.0.transformer_blocks.0.attn2x.to_q.weight", "mid_block.attentions.0.transformer_blocks.0.attn2x.to_k.weight", "mid_block.attentions.0.transformer_blocks.0.attn2x.to_v.weight", "mid_block.attentions.0.transformer_blocks.0.attn2x.to_out.0.weight", "mid_block.attentions.0.transformer_blocks.0.attn2x.to_out.0.bias"] |