458 lines
20 KiB
Python
458 lines
20 KiB
Python
from .tuneavideo.models.unet import UNet3DConditionModel
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from diffusers import DDIMScheduler
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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 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 to them. It splits
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attention layers, and takes into account additional replacements 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 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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# new_item = new_item.replace('norm.weight', 'group_norm.weight')
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# new_item = new_item.replace('norm.bias', 'group_norm.bias')
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# new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')
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# new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')
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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 convert_ldm_unet_checkpoint(checkpoint, config, path=None, extract_ema=False, controlnet=False):
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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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keys = list(checkpoint.keys())
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if controlnet:
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unet_key = "control_model."
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else:
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unet_key = "model.diffusion_model."
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# at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
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if sum(k.startswith("model_ema") for k in keys) > 100 and extract_ema:
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print(f"Checkpoint {path} has both EMA and non-EMA weights.")
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print(
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"In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA"
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" weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag."
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)
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for key in keys:
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if key.startswith("model.diffusion_model"):
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flat_ema_key = "model_ema." + "".join(key.split(".")[1:])
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unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(flat_ema_key)
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else:
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if sum(k.startswith("model_ema") for k in keys) > 100:
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print(
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"In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA"
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" weights (usually better for inference), please make sure to add the `--extract_ema` flag."
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)
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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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if config["class_embed_type"] is None:
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# No parameters to port
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...
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elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection":
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new_checkpoint["class_embedding.linear_1.weight"] = unet_state_dict["label_emb.0.0.weight"]
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new_checkpoint["class_embedding.linear_1.bias"] = unet_state_dict["label_emb.0.0.bias"]
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new_checkpoint["class_embedding.linear_2.weight"] = unet_state_dict["label_emb.0.2.weight"]
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new_checkpoint["class_embedding.linear_2.bias"] = unet_state_dict["label_emb.0.2.bias"]
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else:
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raise NotImplementedError(f"Not implemented `class_embed_type`: {config['class_embed_type']}")
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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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if not controlnet:
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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]
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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]
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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]
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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 = [
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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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]
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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(
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f"input_blocks.{i}.0.op.bias"
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)
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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(
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paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
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)
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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(
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paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
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)
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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(
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attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
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)
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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(
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paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
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)
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output_block_list = {k: sorted(v) for k, v in output_block_list.items()}
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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.weight"] = unet_state_dict[
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f"output_blocks.{i}.{index}.conv.weight"
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]
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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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# 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(
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paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
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)
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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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if controlnet:
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# conditioning embedding
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orig_index = 0
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new_checkpoint["controlnet_cond_embedding.conv_in.weight"] = unet_state_dict.pop(
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f"input_hint_block.{orig_index}.weight"
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)
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new_checkpoint["controlnet_cond_embedding.conv_in.bias"] = unet_state_dict.pop(
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f"input_hint_block.{orig_index}.bias"
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)
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orig_index += 2
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diffusers_index = 0
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while diffusers_index < 6:
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new_checkpoint[f"controlnet_cond_embedding.blocks.{diffusers_index}.weight"] = unet_state_dict.pop(
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f"input_hint_block.{orig_index}.weight"
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)
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new_checkpoint[f"controlnet_cond_embedding.blocks.{diffusers_index}.bias"] = unet_state_dict.pop(
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f"input_hint_block.{orig_index}.bias"
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)
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diffusers_index += 1
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orig_index += 2
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new_checkpoint["controlnet_cond_embedding.conv_out.weight"] = unet_state_dict.pop(
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f"input_hint_block.{orig_index}.weight"
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)
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new_checkpoint["controlnet_cond_embedding.conv_out.bias"] = unet_state_dict.pop(
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f"input_hint_block.{orig_index}.bias"
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)
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# down blocks
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for i in range(num_input_blocks):
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new_checkpoint[f"controlnet_down_blocks.{i}.weight"] = unet_state_dict.pop(f"zero_convs.{i}.0.weight")
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new_checkpoint[f"controlnet_down_blocks.{i}.bias"] = unet_state_dict.pop(f"zero_convs.{i}.0.bias")
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# mid block
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new_checkpoint["controlnet_mid_block.weight"] = unet_state_dict.pop("middle_block_out.0.weight")
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new_checkpoint["controlnet_mid_block.bias"] = unet_state_dict.pop("middle_block_out.0.bias")
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return new_checkpoint
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def create_unet_diffusers_config(original_config, image_size: int, controlnet=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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if controlnet:
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unet_params = original_config.model.params.control_stage_config.params
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else:
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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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vae_scale_factor = 8
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head_dim = unet_params.num_heads if "num_heads" in unet_params else None
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use_linear_projection = (
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unet_params.use_linear_in_transformer if "use_linear_in_transformer" in unet_params else False
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)
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if use_linear_projection:
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# stable diffusion 2-base-512 and 2-768
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if head_dim is None:
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head_dim = [5, 10, 20, 20]
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class_embed_type = None
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config = dict(
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sample_size=image_size // vae_scale_factor,
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in_channels=unet_params.in_channels,
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down_block_types=tuple(down_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,
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attention_head_dim=head_dim,
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use_linear_projection=use_linear_projection,
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class_embed_type=class_embed_type,
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)
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if not controlnet:
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config["out_channels"] = unet_params.out_channels
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config["up_block_types"] = tuple(up_block_types)
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return config
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def convert_unet_checkpoint(checkpoint, original_config, extract_ema=False):
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upcast_attention = False
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if "global_step" in checkpoint:
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global_step = checkpoint["global_step"]
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else:
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global_step = None
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key_name = "model.diffusion_model.input_blocks.2.1.transformer_blocks.0.attn2.to_k.weight"
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if key_name in checkpoint and checkpoint[key_name].shape[-1] == 1024:
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if global_step == 110000:
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# v2.1 needs to upcast attention
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upcast_attention = True
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try:
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parameterization = original_config.model.params.parameterization
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if parameterization == "v":
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image_size = 768
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else:
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image_size = 512
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except AttributeError:
|
|
image_size = 512
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|
unet_config = create_unet_diffusers_config(original_config, image_size=image_size)
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|
unet_config["upcast_attention"] = upcast_attention
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|
#unet = UNet2DConditionModel(**unet_config)
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|
converted_unet_checkpoint = convert_ldm_unet_checkpoint(checkpoint, unet_config, extract_ema=extract_ema)
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#unet.load_state_dict(converted_unet_checkpoint)
|
|
|
|
# convert to 3D
|
|
unet_config["_class_name"] = UNet3DConditionModel.__name__
|
|
unet_config["down_block_types"] = [
|
|
"CrossAttnDownBlock3D",
|
|
"CrossAttnDownBlock3D",
|
|
"CrossAttnDownBlock3D",
|
|
"DownBlock3D"
|
|
]
|
|
unet_config["up_block_types"] = [
|
|
"UpBlock3D",
|
|
"CrossAttnUpBlock3D",
|
|
"CrossAttnUpBlock3D",
|
|
"CrossAttnUpBlock3D"
|
|
]
|
|
|
|
unet = UNet3DConditionModel.from_config(unet_config)
|
|
for k, v in unet.state_dict().items():
|
|
if '_temp.' in k:
|
|
converted_unet_checkpoint.update({k: v})
|
|
unet.load_state_dict(converted_unet_checkpoint)
|
|
|
|
return unet
|
|
|
|
def convert_scheduler_checkpoint(model):
|
|
beta_end = model.model.linear_end
|
|
beta_start = model.model.linear_start
|
|
num_train_timesteps = model.model.num_timesteps
|
|
prediction_type = "v_prediction" if model.model.parameterization == "v" else "epsilon"
|
|
scheduler = DDIMScheduler(
|
|
beta_end=beta_end,
|
|
beta_schedule="scaled_linear",
|
|
beta_start=beta_start,
|
|
num_train_timesteps=num_train_timesteps,
|
|
steps_offset=1,
|
|
clip_sample=False,
|
|
set_alpha_to_one=False,
|
|
prediction_type=prediction_type,
|
|
)
|
|
return scheduler |