424 lines
17 KiB
Python
424 lines
17 KiB
Python
import torch
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import math
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import struct
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import comfy.checkpoint_pickle
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import safetensors.torch
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def load_torch_file(ckpt, safe_load=False, device=None):
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if device is None:
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device = torch.device("cpu")
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if ckpt.lower().endswith(".safetensors"):
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sd = safetensors.torch.load_file(ckpt, device=device.type)
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else:
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if safe_load:
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if not 'weights_only' in torch.load.__code__.co_varnames:
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print("Warning torch.load doesn't support weights_only on this pytorch version, loading unsafely.")
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safe_load = False
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if safe_load:
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pl_sd = torch.load(ckpt, map_location=device, weights_only=True)
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else:
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pl_sd = torch.load(ckpt, map_location=device, pickle_module=comfy.checkpoint_pickle)
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if "global_step" in pl_sd:
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print(f"Global Step: {pl_sd['global_step']}")
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if "state_dict" in pl_sd:
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sd = pl_sd["state_dict"]
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else:
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sd = pl_sd
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return sd
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def save_torch_file(sd, ckpt, metadata=None):
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if metadata is not None:
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safetensors.torch.save_file(sd, ckpt, metadata=metadata)
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else:
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safetensors.torch.save_file(sd, ckpt)
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def calculate_parameters(sd, prefix=""):
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params = 0
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for k in sd.keys():
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if k.startswith(prefix):
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params += sd[k].nelement()
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return params
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def state_dict_key_replace(state_dict, keys_to_replace):
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for x in keys_to_replace:
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if x in state_dict:
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state_dict[keys_to_replace[x]] = state_dict.pop(x)
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return state_dict
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def state_dict_prefix_replace(state_dict, replace_prefix):
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for rp in replace_prefix:
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replace = list(map(lambda a: (a, "{}{}".format(replace_prefix[rp], a[len(rp):])), filter(lambda a: a.startswith(rp), state_dict.keys())))
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for x in replace:
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state_dict[x[1]] = state_dict.pop(x[0])
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return state_dict
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def transformers_convert(sd, prefix_from, prefix_to, number):
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keys_to_replace = {
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"{}positional_embedding": "{}embeddings.position_embedding.weight",
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"{}token_embedding.weight": "{}embeddings.token_embedding.weight",
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"{}ln_final.weight": "{}final_layer_norm.weight",
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"{}ln_final.bias": "{}final_layer_norm.bias",
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}
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for k in keys_to_replace:
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x = k.format(prefix_from)
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if x in sd:
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sd[keys_to_replace[k].format(prefix_to)] = sd.pop(x)
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resblock_to_replace = {
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"ln_1": "layer_norm1",
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"ln_2": "layer_norm2",
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"mlp.c_fc": "mlp.fc1",
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"mlp.c_proj": "mlp.fc2",
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"attn.out_proj": "self_attn.out_proj",
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}
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for resblock in range(number):
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for x in resblock_to_replace:
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for y in ["weight", "bias"]:
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k = "{}transformer.resblocks.{}.{}.{}".format(prefix_from, resblock, x, y)
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k_to = "{}encoder.layers.{}.{}.{}".format(prefix_to, resblock, resblock_to_replace[x], y)
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if k in sd:
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sd[k_to] = sd.pop(k)
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for y in ["weight", "bias"]:
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k_from = "{}transformer.resblocks.{}.attn.in_proj_{}".format(prefix_from, resblock, y)
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if k_from in sd:
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weights = sd.pop(k_from)
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shape_from = weights.shape[0] // 3
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for x in range(3):
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p = ["self_attn.q_proj", "self_attn.k_proj", "self_attn.v_proj"]
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k_to = "{}encoder.layers.{}.{}.{}".format(prefix_to, resblock, p[x], y)
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sd[k_to] = weights[shape_from*x:shape_from*(x + 1)]
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return sd
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UNET_MAP_ATTENTIONS = {
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"proj_in.weight",
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"proj_in.bias",
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"proj_out.weight",
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"proj_out.bias",
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"norm.weight",
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"norm.bias",
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}
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TRANSFORMER_BLOCKS = {
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"norm1.weight",
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"norm1.bias",
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"norm2.weight",
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"norm2.bias",
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"norm3.weight",
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"norm3.bias",
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"attn1.to_q.weight",
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"attn1.to_k.weight",
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"attn1.to_v.weight",
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"attn1.to_out.0.weight",
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"attn1.to_out.0.bias",
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"attn2.to_q.weight",
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"attn2.to_k.weight",
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"attn2.to_v.weight",
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"attn2.to_out.0.weight",
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"attn2.to_out.0.bias",
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"ff.net.0.proj.weight",
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"ff.net.0.proj.bias",
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"ff.net.2.weight",
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"ff.net.2.bias",
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}
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UNET_MAP_RESNET = {
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"in_layers.2.weight": "conv1.weight",
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"in_layers.2.bias": "conv1.bias",
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"emb_layers.1.weight": "time_emb_proj.weight",
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"emb_layers.1.bias": "time_emb_proj.bias",
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"out_layers.3.weight": "conv2.weight",
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"out_layers.3.bias": "conv2.bias",
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"skip_connection.weight": "conv_shortcut.weight",
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"skip_connection.bias": "conv_shortcut.bias",
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"in_layers.0.weight": "norm1.weight",
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"in_layers.0.bias": "norm1.bias",
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"out_layers.0.weight": "norm2.weight",
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"out_layers.0.bias": "norm2.bias",
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}
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UNET_MAP_BASIC = {
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("label_emb.0.0.weight", "class_embedding.linear_1.weight"),
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("label_emb.0.0.bias", "class_embedding.linear_1.bias"),
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("label_emb.0.2.weight", "class_embedding.linear_2.weight"),
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("label_emb.0.2.bias", "class_embedding.linear_2.bias"),
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("label_emb.0.0.weight", "add_embedding.linear_1.weight"),
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("label_emb.0.0.bias", "add_embedding.linear_1.bias"),
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("label_emb.0.2.weight", "add_embedding.linear_2.weight"),
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("label_emb.0.2.bias", "add_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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("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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}
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def unet_to_diffusers(unet_config):
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num_res_blocks = unet_config["num_res_blocks"]
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attention_resolutions = unet_config["attention_resolutions"]
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channel_mult = unet_config["channel_mult"]
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transformer_depth = unet_config["transformer_depth"]
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num_blocks = len(channel_mult)
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if isinstance(num_res_blocks, int):
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num_res_blocks = [num_res_blocks] * num_blocks
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if isinstance(transformer_depth, int):
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transformer_depth = [transformer_depth] * num_blocks
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transformers_per_layer = []
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res = 1
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for i in range(num_blocks):
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transformers = 0
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if res in attention_resolutions:
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transformers = transformer_depth[i]
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transformers_per_layer.append(transformers)
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res *= 2
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transformers_mid = unet_config.get("transformer_depth_middle", transformer_depth[-1])
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diffusers_unet_map = {}
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for x in range(num_blocks):
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n = 1 + (num_res_blocks[x] + 1) * x
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for i in range(num_res_blocks[x]):
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for b in UNET_MAP_RESNET:
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diffusers_unet_map["down_blocks.{}.resnets.{}.{}".format(x, i, UNET_MAP_RESNET[b])] = "input_blocks.{}.0.{}".format(n, b)
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if transformers_per_layer[x] > 0:
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for b in UNET_MAP_ATTENTIONS:
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diffusers_unet_map["down_blocks.{}.attentions.{}.{}".format(x, i, b)] = "input_blocks.{}.1.{}".format(n, b)
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for t in range(transformers_per_layer[x]):
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for b in TRANSFORMER_BLOCKS:
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diffusers_unet_map["down_blocks.{}.attentions.{}.transformer_blocks.{}.{}".format(x, i, t, b)] = "input_blocks.{}.1.transformer_blocks.{}.{}".format(n, t, b)
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n += 1
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for k in ["weight", "bias"]:
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diffusers_unet_map["down_blocks.{}.downsamplers.0.conv.{}".format(x, k)] = "input_blocks.{}.0.op.{}".format(n, k)
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i = 0
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for b in UNET_MAP_ATTENTIONS:
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diffusers_unet_map["mid_block.attentions.{}.{}".format(i, b)] = "middle_block.1.{}".format(b)
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for t in range(transformers_mid):
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for b in TRANSFORMER_BLOCKS:
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diffusers_unet_map["mid_block.attentions.{}.transformer_blocks.{}.{}".format(i, t, b)] = "middle_block.1.transformer_blocks.{}.{}".format(t, b)
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for i, n in enumerate([0, 2]):
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for b in UNET_MAP_RESNET:
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diffusers_unet_map["mid_block.resnets.{}.{}".format(i, UNET_MAP_RESNET[b])] = "middle_block.{}.{}".format(n, b)
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num_res_blocks = list(reversed(num_res_blocks))
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transformers_per_layer = list(reversed(transformers_per_layer))
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for x in range(num_blocks):
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n = (num_res_blocks[x] + 1) * x
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l = num_res_blocks[x] + 1
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for i in range(l):
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c = 0
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for b in UNET_MAP_RESNET:
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diffusers_unet_map["up_blocks.{}.resnets.{}.{}".format(x, i, UNET_MAP_RESNET[b])] = "output_blocks.{}.0.{}".format(n, b)
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c += 1
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if transformers_per_layer[x] > 0:
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c += 1
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for b in UNET_MAP_ATTENTIONS:
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diffusers_unet_map["up_blocks.{}.attentions.{}.{}".format(x, i, b)] = "output_blocks.{}.1.{}".format(n, b)
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for t in range(transformers_per_layer[x]):
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for b in TRANSFORMER_BLOCKS:
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diffusers_unet_map["up_blocks.{}.attentions.{}.transformer_blocks.{}.{}".format(x, i, t, b)] = "output_blocks.{}.1.transformer_blocks.{}.{}".format(n, t, b)
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if i == l - 1:
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for k in ["weight", "bias"]:
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diffusers_unet_map["up_blocks.{}.upsamplers.0.conv.{}".format(x, k)] = "output_blocks.{}.{}.conv.{}".format(n, c, k)
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n += 1
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for k in UNET_MAP_BASIC:
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diffusers_unet_map[k[1]] = k[0]
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return diffusers_unet_map
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def repeat_to_batch_size(tensor, batch_size):
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if tensor.shape[0] > batch_size:
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return tensor[:batch_size]
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elif tensor.shape[0] < batch_size:
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return tensor.repeat([math.ceil(batch_size / tensor.shape[0])] + [1] * (len(tensor.shape) - 1))[:batch_size]
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return tensor
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def convert_sd_to(state_dict, dtype):
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keys = list(state_dict.keys())
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for k in keys:
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state_dict[k] = state_dict[k].to(dtype)
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return state_dict
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def safetensors_header(safetensors_path, max_size=100*1024*1024):
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with open(safetensors_path, "rb") as f:
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header = f.read(8)
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length_of_header = struct.unpack('<Q', header)[0]
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if length_of_header > max_size:
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return None
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return f.read(length_of_header)
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def set_attr(obj, attr, value):
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attrs = attr.split(".")
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for name in attrs[:-1]:
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obj = getattr(obj, name)
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prev = getattr(obj, attrs[-1])
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setattr(obj, attrs[-1], torch.nn.Parameter(value))
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del prev
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def get_attr(obj, attr):
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attrs = attr.split(".")
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for name in attrs:
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obj = getattr(obj, name)
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return obj
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def bislerp(samples, width, height):
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def slerp(b1, b2, r):
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'''slerps batches b1, b2 according to ratio r, batches should be flat e.g. NxC'''
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c = b1.shape[-1]
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#norms
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b1_norms = torch.norm(b1, dim=-1, keepdim=True)
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b2_norms = torch.norm(b2, dim=-1, keepdim=True)
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#normalize
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b1_normalized = b1 / b1_norms
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b2_normalized = b2 / b2_norms
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#zero when norms are zero
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b1_normalized[b1_norms.expand(-1,c) == 0.0] = 0.0
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b2_normalized[b2_norms.expand(-1,c) == 0.0] = 0.0
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#slerp
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dot = (b1_normalized*b2_normalized).sum(1)
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omega = torch.acos(dot)
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so = torch.sin(omega)
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#technically not mathematically correct, but more pleasing?
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res = (torch.sin((1.0-r.squeeze(1))*omega)/so).unsqueeze(1)*b1_normalized + (torch.sin(r.squeeze(1)*omega)/so).unsqueeze(1) * b2_normalized
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res *= (b1_norms * (1.0-r) + b2_norms * r).expand(-1,c)
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#edge cases for same or polar opposites
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res[dot > 1 - 1e-5] = b1[dot > 1 - 1e-5]
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res[dot < 1e-5 - 1] = (b1 * (1.0-r) + b2 * r)[dot < 1e-5 - 1]
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return res
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def generate_bilinear_data(length_old, length_new):
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coords_1 = torch.arange(length_old).reshape((1,1,1,-1)).to(torch.float32)
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coords_1 = torch.nn.functional.interpolate(coords_1, size=(1, length_new), mode="bilinear")
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ratios = coords_1 - coords_1.floor()
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coords_1 = coords_1.to(torch.int64)
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coords_2 = torch.arange(length_old).reshape((1,1,1,-1)).to(torch.float32) + 1
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coords_2[:,:,:,-1] -= 1
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coords_2 = torch.nn.functional.interpolate(coords_2, size=(1, length_new), mode="bilinear")
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coords_2 = coords_2.to(torch.int64)
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return ratios, coords_1, coords_2
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n,c,h,w = samples.shape
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h_new, w_new = (height, width)
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#linear w
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ratios, coords_1, coords_2 = generate_bilinear_data(w, w_new)
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coords_1 = coords_1.expand((n, c, h, -1))
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coords_2 = coords_2.expand((n, c, h, -1))
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ratios = ratios.expand((n, 1, h, -1))
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pass_1 = samples.gather(-1,coords_1).movedim(1, -1).reshape((-1,c))
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pass_2 = samples.gather(-1,coords_2).movedim(1, -1).reshape((-1,c))
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ratios = ratios.movedim(1, -1).reshape((-1,1))
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result = slerp(pass_1, pass_2, ratios)
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result = result.reshape(n, h, w_new, c).movedim(-1, 1)
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#linear h
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ratios, coords_1, coords_2 = generate_bilinear_data(h, h_new)
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coords_1 = coords_1.reshape((1,1,-1,1)).expand((n, c, -1, w_new))
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coords_2 = coords_2.reshape((1,1,-1,1)).expand((n, c, -1, w_new))
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ratios = ratios.reshape((1,1,-1,1)).expand((n, 1, -1, w_new))
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pass_1 = result.gather(-2,coords_1).movedim(1, -1).reshape((-1,c))
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pass_2 = result.gather(-2,coords_2).movedim(1, -1).reshape((-1,c))
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ratios = ratios.movedim(1, -1).reshape((-1,1))
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result = slerp(pass_1, pass_2, ratios)
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result = result.reshape(n, h_new, w_new, c).movedim(-1, 1)
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return result
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def common_upscale(samples, width, height, upscale_method, crop):
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if crop == "center":
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old_width = samples.shape[3]
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old_height = samples.shape[2]
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old_aspect = old_width / old_height
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new_aspect = width / height
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x = 0
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y = 0
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if old_aspect > new_aspect:
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x = round((old_width - old_width * (new_aspect / old_aspect)) / 2)
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elif old_aspect < new_aspect:
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y = round((old_height - old_height * (old_aspect / new_aspect)) / 2)
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s = samples[:,:,y:old_height-y,x:old_width-x]
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else:
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s = samples
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if upscale_method == "bislerp":
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return bislerp(s, width, height)
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else:
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return torch.nn.functional.interpolate(s, size=(height, width), mode=upscale_method)
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def get_tiled_scale_steps(width, height, tile_x, tile_y, overlap):
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return math.ceil((height / (tile_y - overlap))) * math.ceil((width / (tile_x - overlap)))
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@torch.inference_mode()
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def tiled_scale(samples, function, tile_x=64, tile_y=64, overlap = 8, upscale_amount = 4, out_channels = 3, pbar = None):
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output = torch.empty((samples.shape[0], out_channels, round(samples.shape[2] * upscale_amount), round(samples.shape[3] * upscale_amount)), device="cpu")
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for b in range(samples.shape[0]):
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s = samples[b:b+1]
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out = torch.zeros((s.shape[0], out_channels, round(s.shape[2] * upscale_amount), round(s.shape[3] * upscale_amount)), device="cpu")
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out_div = torch.zeros((s.shape[0], out_channels, round(s.shape[2] * upscale_amount), round(s.shape[3] * upscale_amount)), device="cpu")
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for y in range(0, s.shape[2], tile_y - overlap):
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for x in range(0, s.shape[3], tile_x - overlap):
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s_in = s[:,:,y:y+tile_y,x:x+tile_x]
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ps = function(s_in).cpu()
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mask = torch.ones_like(ps)
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feather = round(overlap * upscale_amount)
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for t in range(feather):
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mask[:,:,t:1+t,:] *= ((1.0/feather) * (t + 1))
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|
mask[:,:,mask.shape[2] -1 -t: mask.shape[2]-t,:] *= ((1.0/feather) * (t + 1))
|
|
mask[:,:,:,t:1+t] *= ((1.0/feather) * (t + 1))
|
|
mask[:,:,:,mask.shape[3]- 1 - t: mask.shape[3]- t] *= ((1.0/feather) * (t + 1))
|
|
out[:,:,round(y*upscale_amount):round((y+tile_y)*upscale_amount),round(x*upscale_amount):round((x+tile_x)*upscale_amount)] += ps * mask
|
|
out_div[:,:,round(y*upscale_amount):round((y+tile_y)*upscale_amount),round(x*upscale_amount):round((x+tile_x)*upscale_amount)] += mask
|
|
if pbar is not None:
|
|
pbar.update(1)
|
|
|
|
output[b:b+1] = out/out_div
|
|
return output
|
|
|
|
|
|
PROGRESS_BAR_HOOK = None
|
|
def set_progress_bar_global_hook(function):
|
|
global PROGRESS_BAR_HOOK
|
|
PROGRESS_BAR_HOOK = function
|
|
|
|
class ProgressBar:
|
|
def __init__(self, total):
|
|
global PROGRESS_BAR_HOOK
|
|
self.total = total
|
|
self.current = 0
|
|
self.hook = PROGRESS_BAR_HOOK
|
|
|
|
def update_absolute(self, value, total=None, preview=None):
|
|
if total is not None:
|
|
self.total = total
|
|
if value > self.total:
|
|
value = self.total
|
|
self.current = value
|
|
if self.hook is not None:
|
|
self.hook(self.current, self.total, preview)
|
|
|
|
def update(self, value):
|
|
self.update_absolute(self.current + value)
|