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Merge pull request #102 from space-nuko/precalculate-hashes
Precalculate .safetensors model hashes after training
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@@ -12,6 +12,7 @@ import math
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import os
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import random
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import hashlib
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from io import BytesIO
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from tqdm import tqdm
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import torch
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@@ -25,6 +26,7 @@ from PIL import Image
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import cv2
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from einops import rearrange
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from torch import einsum
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import safetensors.torch
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import library.model_util as model_util
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@@ -790,6 +792,49 @@ def calculate_sha256(filename):
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return hash_sha256.hexdigest()
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def precalculate_safetensors_hashes(tensors, metadata):
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"""Precalculate the model hashes needed by sd-webui-additional-networks to
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save time on indexing the model later."""
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# Because writing user metadata to the file can change the result of
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# sd_models.model_hash(), only retain the training metadata for purposes of
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# calculating the hash, as they are meant to be immutable
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metadata = {k: v for k, v in metadata.items() if k.startswith("ss_")}
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bytes = safetensors.torch.save(tensors, metadata)
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b = BytesIO(bytes)
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model_hash = addnet_hash_safetensors(b)
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legacy_hash = addnet_hash_legacy(b)
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return model_hash, legacy_hash
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def addnet_hash_legacy(b):
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"""Old model hash used by sd-webui-additional-networks for .safetensors format files"""
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m = hashlib.sha256()
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b.seek(0x100000)
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m.update(b.read(0x10000))
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return m.hexdigest()[0:8]
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def addnet_hash_safetensors(b):
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"""New model hash used by sd-webui-additional-networks for .safetensors format files"""
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hash_sha256 = hashlib.sha256()
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blksize = 1024 * 1024
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b.seek(0)
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header = b.read(8)
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n = int.from_bytes(header, "little")
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offset = n + 8
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b.seek(offset)
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for chunk in iter(lambda: b.read(blksize), b""):
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hash_sha256.update(chunk)
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return hash_sha256.hexdigest()
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# flash attention forwards and backwards
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# https://arxiv.org/abs/2205.14135
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@@ -7,6 +7,8 @@ import math
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import os
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import torch
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from library import train_util
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class LoRAModule(torch.nn.Module):
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"""
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@@ -221,6 +223,14 @@ class LoRANetwork(torch.nn.Module):
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if os.path.splitext(file)[1] == '.safetensors':
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from safetensors.torch import save_file
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# Precalculate model hashes to save time on indexing
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if metadata is None:
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metadata = {}
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model_hash, legacy_hash = train_util.precalculate_safetensors_hashes(state_dict, metadata)
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metadata["sshs_model_hash"] = model_hash
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metadata["sshs_legacy_hash"] = legacy_hash
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save_file(state_dict, file, metadata)
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else:
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torch.save(state_dict, file)
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