792 lines
33 KiB
Python
792 lines
33 KiB
Python
# coding=utf-8
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# Copyright 2022 The HuggingFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" Conversion script for the Versatile Stable Diffusion checkpoints. """
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import argparse
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from argparse import Namespace
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import torch
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from diffusers import (
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AutoencoderKL,
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DDIMScheduler,
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DPMSolverMultistepScheduler,
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EulerAncestralDiscreteScheduler,
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EulerDiscreteScheduler,
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LMSDiscreteScheduler,
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PNDMScheduler,
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UNet2DConditionModel,
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VersatileDiffusionPipeline,
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)
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from diffusers.pipelines.versatile_diffusion.modeling_text_unet import UNetFlatConditionModel
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from transformers import (
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CLIPFeatureExtractor,
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CLIPTextModelWithProjection,
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CLIPTokenizer,
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CLIPVisionModelWithProjection,
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)
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SCHEDULER_CONFIG = Namespace(
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**{
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"beta_linear_start": 0.00085,
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"beta_linear_end": 0.012,
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"timesteps": 1000,
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"scale_factor": 0.18215,
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}
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)
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IMAGE_UNET_CONFIG = Namespace(
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**{
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"input_channels": 4,
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"model_channels": 320,
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"output_channels": 4,
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"num_noattn_blocks": [2, 2, 2, 2],
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"channel_mult": [1, 2, 4, 4],
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"with_attn": [True, True, True, False],
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"num_heads": 8,
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"context_dim": 768,
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"use_checkpoint": True,
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}
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)
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TEXT_UNET_CONFIG = Namespace(
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**{
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"input_channels": 768,
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"model_channels": 320,
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"output_channels": 768,
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"num_noattn_blocks": [2, 2, 2, 2],
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"channel_mult": [1, 2, 4, 4],
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"second_dim": [4, 4, 4, 4],
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"with_attn": [True, True, True, False],
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"num_heads": 8,
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"context_dim": 768,
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"use_checkpoint": True,
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}
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)
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AUTOENCODER_CONFIG = Namespace(
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**{
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"double_z": True,
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"z_channels": 4,
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"resolution": 256,
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"in_channels": 3,
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"out_ch": 3,
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"ch": 128,
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"ch_mult": [1, 2, 4, 4],
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"num_res_blocks": 2,
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"attn_resolutions": [],
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"dropout": 0.0,
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}
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)
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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_vae_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
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new_item = new_item.replace("nin_shortcut", "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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# 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 renew_vae_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("q.weight", "query.weight")
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new_item = new_item.replace("q.bias", "query.bias")
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new_item = new_item.replace("k.weight", "key.weight")
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new_item = new_item.replace("k.bias", "key.bias")
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new_item = new_item.replace("v.weight", "value.weight")
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new_item = new_item.replace("v.bias", "value.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 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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elif path["old"] in old_checkpoint:
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checkpoint[new_path] = old_checkpoint[path["old"]]
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def conv_attn_to_linear(checkpoint):
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keys = list(checkpoint.keys())
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attn_keys = ["query.weight", "key.weight", "value.weight"]
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for key in keys:
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if ".".join(key.split(".")[-2:]) in attn_keys:
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if checkpoint[key].ndim > 2:
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checkpoint[key] = checkpoint[key][:, :, 0, 0]
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elif "proj_attn.weight" in key:
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if checkpoint[key].ndim > 2:
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checkpoint[key] = checkpoint[key][:, :, 0]
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def create_image_unet_diffusers_config(unet_params):
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"""
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Creates a config for the diffusers based on the config of the VD model.
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"""
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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 unet_params.with_attn[i] 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 unet_params.with_attn[-i - 1] else "UpBlock2D"
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up_block_types.append(block_type)
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resolution //= 2
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if not all(n == unet_params.num_noattn_blocks[0] for n in unet_params.num_noattn_blocks):
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raise ValueError("Not all num_res_blocks are equal, which is not supported in this script.")
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config = dict(
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sample_size=None,
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in_channels=unet_params.input_channels,
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out_channels=unet_params.output_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_noattn_blocks[0],
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cross_attention_dim=unet_params.context_dim,
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attention_head_dim=unet_params.num_heads,
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)
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return config
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def create_text_unet_diffusers_config(unet_params):
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"""
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Creates a config for the diffusers based on the config of the VD model.
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"""
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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 = "CrossAttnDownBlockFlat" if unet_params.with_attn[i] else "DownBlockFlat"
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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 = "CrossAttnUpBlockFlat" if unet_params.with_attn[-i - 1] else "UpBlockFlat"
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up_block_types.append(block_type)
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resolution //= 2
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if not all(n == unet_params.num_noattn_blocks[0] for n in unet_params.num_noattn_blocks):
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raise ValueError("Not all num_res_blocks are equal, which is not supported in this script.")
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config = dict(
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sample_size=None,
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in_channels=(unet_params.input_channels, 1, 1),
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out_channels=(unet_params.output_channels, 1, 1),
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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_noattn_blocks[0],
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cross_attention_dim=unet_params.context_dim,
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attention_head_dim=unet_params.num_heads,
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)
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return config
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def create_vae_diffusers_config(vae_params):
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"""
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Creates a config for the diffusers based on the config of the VD model.
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"""
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block_out_channels = [vae_params.ch * mult for mult in vae_params.ch_mult]
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down_block_types = ["DownEncoderBlock2D"] * len(block_out_channels)
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up_block_types = ["UpDecoderBlock2D"] * len(block_out_channels)
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config = dict(
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sample_size=vae_params.resolution,
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in_channels=vae_params.in_channels,
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out_channels=vae_params.out_ch,
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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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latent_channels=vae_params.z_channels,
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layers_per_block=vae_params.num_res_blocks,
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)
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return config
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def create_diffusers_scheduler(original_config):
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schedular = DDIMScheduler(
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num_train_timesteps=original_config.model.params.timesteps,
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beta_start=original_config.model.params.linear_start,
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beta_end=original_config.model.params.linear_end,
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beta_schedule="scaled_linear",
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)
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return schedular
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def convert_vd_unet_checkpoint(checkpoint, config, unet_key, extract_ema=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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# 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:
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print("Checkpoint has both EMA and non-EMA weights.")
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if extract_ema:
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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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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"] = checkpoint["model.diffusion_model.time_embed.0.weight"]
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new_checkpoint["time_embedding.linear_1.bias"] = checkpoint["model.diffusion_model.time_embed.0.bias"]
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new_checkpoint["time_embedding.linear_2.weight"] = checkpoint["model.diffusion_model.time_embed.2.weight"]
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new_checkpoint["time_embedding.linear_2.bias"] = checkpoint["model.diffusion_model.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]
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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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elif f"input_blocks.{i}.0.weight" in unet_state_dict:
|
|
# text_unet uses linear layers in place of downsamplers
|
|
shape = unet_state_dict[f"input_blocks.{i}.0.weight"].shape
|
|
if shape[0] != shape[1]:
|
|
continue
|
|
new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.weight"] = unet_state_dict.pop(
|
|
f"input_blocks.{i}.0.weight"
|
|
)
|
|
new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.bias"] = unet_state_dict.pop(
|
|
f"input_blocks.{i}.0.bias"
|
|
)
|
|
|
|
paths = renew_resnet_paths(resnets)
|
|
meta_path = {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"}
|
|
assign_to_checkpoint(
|
|
paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
|
|
)
|
|
|
|
if len(attentions):
|
|
paths = renew_attention_paths(attentions)
|
|
meta_path = {"old": f"input_blocks.{i}.1", "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}"}
|
|
assign_to_checkpoint(
|
|
paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
|
|
)
|
|
|
|
resnet_0 = middle_blocks[0]
|
|
attentions = middle_blocks[1]
|
|
resnet_1 = middle_blocks[2]
|
|
|
|
resnet_0_paths = renew_resnet_paths(resnet_0)
|
|
assign_to_checkpoint(resnet_0_paths, new_checkpoint, unet_state_dict, config=config)
|
|
|
|
resnet_1_paths = renew_resnet_paths(resnet_1)
|
|
assign_to_checkpoint(resnet_1_paths, new_checkpoint, unet_state_dict, config=config)
|
|
|
|
attentions_paths = renew_attention_paths(attentions)
|
|
meta_path = {"old": "middle_block.1", "new": "mid_block.attentions.0"}
|
|
assign_to_checkpoint(
|
|
attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
|
|
)
|
|
|
|
for i in range(num_output_blocks):
|
|
block_id = i // (config["layers_per_block"] + 1)
|
|
layer_in_block_id = i % (config["layers_per_block"] + 1)
|
|
output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]]
|
|
output_block_list = {}
|
|
|
|
for layer in output_block_layers:
|
|
layer_id, layer_name = layer.split(".")[0], shave_segments(layer, 1)
|
|
if layer_id in output_block_list:
|
|
output_block_list[layer_id].append(layer_name)
|
|
else:
|
|
output_block_list[layer_id] = [layer_name]
|
|
|
|
if len(output_block_list) > 1:
|
|
resnets = [key for key in output_blocks[i] if f"output_blocks.{i}.0" in key]
|
|
attentions = [key for key in output_blocks[i] if f"output_blocks.{i}.1" in key]
|
|
|
|
paths = renew_resnet_paths(resnets)
|
|
|
|
meta_path = {"old": f"output_blocks.{i}.0", "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}"}
|
|
assign_to_checkpoint(
|
|
paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
|
|
)
|
|
|
|
if ["conv.weight", "conv.bias"] in output_block_list.values():
|
|
index = list(output_block_list.values()).index(["conv.weight", "conv.bias"])
|
|
new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = unet_state_dict[
|
|
f"output_blocks.{i}.{index}.conv.weight"
|
|
]
|
|
new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = unet_state_dict[
|
|
f"output_blocks.{i}.{index}.conv.bias"
|
|
]
|
|
# Clear attentions as they have been attributed above.
|
|
if len(attentions) == 2:
|
|
attentions = []
|
|
elif f"output_blocks.{i}.1.weight" in unet_state_dict:
|
|
# text_unet uses linear layers in place of upsamplers
|
|
shape = unet_state_dict[f"output_blocks.{i}.1.weight"].shape
|
|
if shape[0] != shape[1]:
|
|
continue
|
|
new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.weight"] = unet_state_dict.pop(
|
|
f"output_blocks.{i}.1.weight"
|
|
)
|
|
new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.bias"] = unet_state_dict.pop(
|
|
f"output_blocks.{i}.1.bias"
|
|
)
|
|
# Clear attentions as they have been attributed above.
|
|
if len(attentions) == 2:
|
|
attentions = []
|
|
elif f"output_blocks.{i}.2.weight" in unet_state_dict:
|
|
# text_unet uses linear layers in place of upsamplers
|
|
shape = unet_state_dict[f"output_blocks.{i}.2.weight"].shape
|
|
if shape[0] != shape[1]:
|
|
continue
|
|
new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.weight"] = unet_state_dict.pop(
|
|
f"output_blocks.{i}.2.weight"
|
|
)
|
|
new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.bias"] = unet_state_dict.pop(
|
|
f"output_blocks.{i}.2.bias"
|
|
)
|
|
|
|
if len(attentions):
|
|
paths = renew_attention_paths(attentions)
|
|
meta_path = {
|
|
"old": f"output_blocks.{i}.1",
|
|
"new": f"up_blocks.{block_id}.attentions.{layer_in_block_id}",
|
|
}
|
|
assign_to_checkpoint(
|
|
paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
|
|
)
|
|
else:
|
|
resnet_0_paths = renew_resnet_paths(output_block_layers, n_shave_prefix_segments=1)
|
|
for path in resnet_0_paths:
|
|
old_path = ".".join(["output_blocks", str(i), path["old"]])
|
|
new_path = ".".join(["up_blocks", str(block_id), "resnets", str(layer_in_block_id), path["new"]])
|
|
|
|
new_checkpoint[new_path] = unet_state_dict[old_path]
|
|
|
|
return new_checkpoint
|
|
|
|
|
|
def convert_vd_vae_checkpoint(checkpoint, config):
|
|
# extract state dict for VAE
|
|
vae_state_dict = {}
|
|
keys = list(checkpoint.keys())
|
|
for key in keys:
|
|
vae_state_dict[key] = checkpoint.get(key)
|
|
|
|
new_checkpoint = {}
|
|
|
|
new_checkpoint["encoder.conv_in.weight"] = vae_state_dict["encoder.conv_in.weight"]
|
|
new_checkpoint["encoder.conv_in.bias"] = vae_state_dict["encoder.conv_in.bias"]
|
|
new_checkpoint["encoder.conv_out.weight"] = vae_state_dict["encoder.conv_out.weight"]
|
|
new_checkpoint["encoder.conv_out.bias"] = vae_state_dict["encoder.conv_out.bias"]
|
|
new_checkpoint["encoder.conv_norm_out.weight"] = vae_state_dict["encoder.norm_out.weight"]
|
|
new_checkpoint["encoder.conv_norm_out.bias"] = vae_state_dict["encoder.norm_out.bias"]
|
|
|
|
new_checkpoint["decoder.conv_in.weight"] = vae_state_dict["decoder.conv_in.weight"]
|
|
new_checkpoint["decoder.conv_in.bias"] = vae_state_dict["decoder.conv_in.bias"]
|
|
new_checkpoint["decoder.conv_out.weight"] = vae_state_dict["decoder.conv_out.weight"]
|
|
new_checkpoint["decoder.conv_out.bias"] = vae_state_dict["decoder.conv_out.bias"]
|
|
new_checkpoint["decoder.conv_norm_out.weight"] = vae_state_dict["decoder.norm_out.weight"]
|
|
new_checkpoint["decoder.conv_norm_out.bias"] = vae_state_dict["decoder.norm_out.bias"]
|
|
|
|
new_checkpoint["quant_conv.weight"] = vae_state_dict["quant_conv.weight"]
|
|
new_checkpoint["quant_conv.bias"] = vae_state_dict["quant_conv.bias"]
|
|
new_checkpoint["post_quant_conv.weight"] = vae_state_dict["post_quant_conv.weight"]
|
|
new_checkpoint["post_quant_conv.bias"] = vae_state_dict["post_quant_conv.bias"]
|
|
|
|
# Retrieves the keys for the encoder down blocks only
|
|
num_down_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "encoder.down" in layer})
|
|
down_blocks = {
|
|
layer_id: [key for key in vae_state_dict if f"down.{layer_id}" in key] for layer_id in range(num_down_blocks)
|
|
}
|
|
|
|
# Retrieves the keys for the decoder up blocks only
|
|
num_up_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "decoder.up" in layer})
|
|
up_blocks = {
|
|
layer_id: [key for key in vae_state_dict if f"up.{layer_id}" in key] for layer_id in range(num_up_blocks)
|
|
}
|
|
|
|
for i in range(num_down_blocks):
|
|
resnets = [key for key in down_blocks[i] if f"down.{i}" in key and f"down.{i}.downsample" not in key]
|
|
|
|
if f"encoder.down.{i}.downsample.conv.weight" in vae_state_dict:
|
|
new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.weight"] = vae_state_dict.pop(
|
|
f"encoder.down.{i}.downsample.conv.weight"
|
|
)
|
|
new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.bias"] = vae_state_dict.pop(
|
|
f"encoder.down.{i}.downsample.conv.bias"
|
|
)
|
|
|
|
paths = renew_vae_resnet_paths(resnets)
|
|
meta_path = {"old": f"down.{i}.block", "new": f"down_blocks.{i}.resnets"}
|
|
assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
|
|
|
|
mid_resnets = [key for key in vae_state_dict if "encoder.mid.block" in key]
|
|
num_mid_res_blocks = 2
|
|
for i in range(1, num_mid_res_blocks + 1):
|
|
resnets = [key for key in mid_resnets if f"encoder.mid.block_{i}" in key]
|
|
|
|
paths = renew_vae_resnet_paths(resnets)
|
|
meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"}
|
|
assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
|
|
|
|
mid_attentions = [key for key in vae_state_dict if "encoder.mid.attn" in key]
|
|
paths = renew_vae_attention_paths(mid_attentions)
|
|
meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
|
|
assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
|
|
conv_attn_to_linear(new_checkpoint)
|
|
|
|
for i in range(num_up_blocks):
|
|
block_id = num_up_blocks - 1 - i
|
|
resnets = [
|
|
key for key in up_blocks[block_id] if f"up.{block_id}" in key and f"up.{block_id}.upsample" not in key
|
|
]
|
|
|
|
if f"decoder.up.{block_id}.upsample.conv.weight" in vae_state_dict:
|
|
new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.weight"] = vae_state_dict[
|
|
f"decoder.up.{block_id}.upsample.conv.weight"
|
|
]
|
|
new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.bias"] = vae_state_dict[
|
|
f"decoder.up.{block_id}.upsample.conv.bias"
|
|
]
|
|
|
|
paths = renew_vae_resnet_paths(resnets)
|
|
meta_path = {"old": f"up.{block_id}.block", "new": f"up_blocks.{i}.resnets"}
|
|
assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
|
|
|
|
mid_resnets = [key for key in vae_state_dict if "decoder.mid.block" in key]
|
|
num_mid_res_blocks = 2
|
|
for i in range(1, num_mid_res_blocks + 1):
|
|
resnets = [key for key in mid_resnets if f"decoder.mid.block_{i}" in key]
|
|
|
|
paths = renew_vae_resnet_paths(resnets)
|
|
meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"}
|
|
assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
|
|
|
|
mid_attentions = [key for key in vae_state_dict if "decoder.mid.attn" in key]
|
|
paths = renew_vae_attention_paths(mid_attentions)
|
|
meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
|
|
assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
|
|
conv_attn_to_linear(new_checkpoint)
|
|
return new_checkpoint
|
|
|
|
|
|
if __name__ == "__main__":
|
|
parser = argparse.ArgumentParser()
|
|
|
|
parser.add_argument(
|
|
"--unet_checkpoint_path", default=None, type=str, required=False, help="Path to the checkpoint to convert."
|
|
)
|
|
parser.add_argument(
|
|
"--vae_checkpoint_path", default=None, type=str, required=False, help="Path to the checkpoint to convert."
|
|
)
|
|
parser.add_argument(
|
|
"--optimus_checkpoint_path", default=None, type=str, required=False, help="Path to the checkpoint to convert."
|
|
)
|
|
parser.add_argument(
|
|
"--scheduler_type",
|
|
default="pndm",
|
|
type=str,
|
|
help="Type of scheduler to use. Should be one of ['pndm', 'lms', 'ddim', 'euler', 'euler-ancestral', 'dpm']",
|
|
)
|
|
parser.add_argument(
|
|
"--extract_ema",
|
|
action="store_true",
|
|
help=(
|
|
"Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights"
|
|
" or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield"
|
|
" higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning."
|
|
),
|
|
)
|
|
parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
|
|
|
|
args = parser.parse_args()
|
|
|
|
scheduler_config = SCHEDULER_CONFIG
|
|
|
|
num_train_timesteps = scheduler_config.timesteps
|
|
beta_start = scheduler_config.beta_linear_start
|
|
beta_end = scheduler_config.beta_linear_end
|
|
if args.scheduler_type == "pndm":
|
|
scheduler = PNDMScheduler(
|
|
beta_end=beta_end,
|
|
beta_schedule="scaled_linear",
|
|
beta_start=beta_start,
|
|
num_train_timesteps=num_train_timesteps,
|
|
skip_prk_steps=True,
|
|
steps_offset=1,
|
|
)
|
|
elif args.scheduler_type == "lms":
|
|
scheduler = LMSDiscreteScheduler(beta_start=beta_start, beta_end=beta_end, beta_schedule="scaled_linear")
|
|
elif args.scheduler_type == "euler":
|
|
scheduler = EulerDiscreteScheduler(beta_start=beta_start, beta_end=beta_end, beta_schedule="scaled_linear")
|
|
elif args.scheduler_type == "euler-ancestral":
|
|
scheduler = EulerAncestralDiscreteScheduler(
|
|
beta_start=beta_start, beta_end=beta_end, beta_schedule="scaled_linear"
|
|
)
|
|
elif args.scheduler_type == "dpm":
|
|
scheduler = DPMSolverMultistepScheduler(
|
|
beta_start=beta_start, beta_end=beta_end, beta_schedule="scaled_linear"
|
|
)
|
|
elif args.scheduler_type == "ddim":
|
|
scheduler = DDIMScheduler(
|
|
beta_start=beta_start,
|
|
beta_end=beta_end,
|
|
beta_schedule="scaled_linear",
|
|
clip_sample=False,
|
|
set_alpha_to_one=False,
|
|
steps_offset=1,
|
|
)
|
|
else:
|
|
raise ValueError(f"Scheduler of type {args.scheduler_type} doesn't exist!")
|
|
|
|
# Convert the UNet2DConditionModel models.
|
|
if args.unet_checkpoint_path is not None:
|
|
# image UNet
|
|
image_unet_config = create_image_unet_diffusers_config(IMAGE_UNET_CONFIG)
|
|
checkpoint = torch.load(args.unet_checkpoint_path)
|
|
converted_image_unet_checkpoint = convert_vd_unet_checkpoint(
|
|
checkpoint, image_unet_config, unet_key="model.diffusion_model.unet_image.", extract_ema=args.extract_ema
|
|
)
|
|
image_unet = UNet2DConditionModel(**image_unet_config)
|
|
image_unet.load_state_dict(converted_image_unet_checkpoint)
|
|
|
|
# text UNet
|
|
text_unet_config = create_text_unet_diffusers_config(TEXT_UNET_CONFIG)
|
|
converted_text_unet_checkpoint = convert_vd_unet_checkpoint(
|
|
checkpoint, text_unet_config, unet_key="model.diffusion_model.unet_text.", extract_ema=args.extract_ema
|
|
)
|
|
text_unet = UNetFlatConditionModel(**text_unet_config)
|
|
text_unet.load_state_dict(converted_text_unet_checkpoint)
|
|
|
|
# Convert the VAE model.
|
|
if args.vae_checkpoint_path is not None:
|
|
vae_config = create_vae_diffusers_config(AUTOENCODER_CONFIG)
|
|
checkpoint = torch.load(args.vae_checkpoint_path)
|
|
converted_vae_checkpoint = convert_vd_vae_checkpoint(checkpoint, vae_config)
|
|
|
|
vae = AutoencoderKL(**vae_config)
|
|
vae.load_state_dict(converted_vae_checkpoint)
|
|
|
|
tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14")
|
|
image_feature_extractor = CLIPFeatureExtractor.from_pretrained("openai/clip-vit-large-patch14")
|
|
text_encoder = CLIPTextModelWithProjection.from_pretrained("openai/clip-vit-large-patch14")
|
|
image_encoder = CLIPVisionModelWithProjection.from_pretrained("openai/clip-vit-large-patch14")
|
|
|
|
pipe = VersatileDiffusionPipeline(
|
|
scheduler=scheduler,
|
|
tokenizer=tokenizer,
|
|
image_feature_extractor=image_feature_extractor,
|
|
text_encoder=text_encoder,
|
|
image_encoder=image_encoder,
|
|
image_unet=image_unet,
|
|
text_unet=text_unet,
|
|
vae=vae,
|
|
)
|
|
pipe.save_pretrained(args.dump_path)
|