121 lines
4.3 KiB
Python
121 lines
4.3 KiB
Python
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from . import supported_models
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def count_blocks(state_dict_keys, prefix_string):
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count = 0
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while True:
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c = False
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for k in state_dict_keys:
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if k.startswith(prefix_string.format(count)):
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c = True
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break
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if c == False:
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break
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count += 1
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return count
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def detect_unet_config(state_dict, key_prefix, use_fp16):
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state_dict_keys = list(state_dict.keys())
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num_res_blocks = 2
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unet_config = {
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"use_checkpoint": False,
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"image_size": 32,
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"out_channels": 4,
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"num_res_blocks": num_res_blocks,
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"use_spatial_transformer": True,
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"legacy": False
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}
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y_input = '{}label_emb.0.0.weight'.format(key_prefix)
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if y_input in state_dict_keys:
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unet_config["num_classes"] = "sequential"
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unet_config["adm_in_channels"] = state_dict[y_input].shape[1]
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else:
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unet_config["adm_in_channels"] = None
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unet_config["use_fp16"] = use_fp16
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model_channels = state_dict['{}input_blocks.0.0.weight'.format(key_prefix)].shape[0]
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in_channels = state_dict['{}input_blocks.0.0.weight'.format(key_prefix)].shape[1]
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num_res_blocks = []
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channel_mult = []
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attention_resolutions = []
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transformer_depth = []
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context_dim = None
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use_linear_in_transformer = False
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current_res = 1
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count = 0
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last_res_blocks = 0
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last_transformer_depth = 0
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last_channel_mult = 0
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while True:
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prefix = '{}input_blocks.{}.'.format(key_prefix, count)
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block_keys = sorted(list(filter(lambda a: a.startswith(prefix), state_dict_keys)))
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if len(block_keys) == 0:
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break
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if "{}0.op.weight".format(prefix) in block_keys: #new layer
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if last_transformer_depth > 0:
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attention_resolutions.append(current_res)
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transformer_depth.append(last_transformer_depth)
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num_res_blocks.append(last_res_blocks)
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channel_mult.append(last_channel_mult)
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current_res *= 2
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last_res_blocks = 0
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last_transformer_depth = 0
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last_channel_mult = 0
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else:
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res_block_prefix = "{}0.in_layers.0.weight".format(prefix)
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if res_block_prefix in block_keys:
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last_res_blocks += 1
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last_channel_mult = state_dict["{}0.out_layers.3.weight".format(prefix)].shape[0] // model_channels
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transformer_prefix = prefix + "1.transformer_blocks."
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transformer_keys = sorted(list(filter(lambda a: a.startswith(transformer_prefix), state_dict_keys)))
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if len(transformer_keys) > 0:
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last_transformer_depth = count_blocks(state_dict_keys, transformer_prefix + '{}')
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if context_dim is None:
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context_dim = state_dict['{}0.attn2.to_k.weight'.format(transformer_prefix)].shape[1]
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use_linear_in_transformer = len(state_dict['{}1.proj_in.weight'.format(prefix)].shape) == 2
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count += 1
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if last_transformer_depth > 0:
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attention_resolutions.append(current_res)
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transformer_depth.append(last_transformer_depth)
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num_res_blocks.append(last_res_blocks)
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channel_mult.append(last_channel_mult)
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transformer_depth_middle = count_blocks(state_dict_keys, '{}middle_block.1.transformer_blocks.'.format(key_prefix) + '{}')
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if len(set(num_res_blocks)) == 1:
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num_res_blocks = num_res_blocks[0]
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if len(set(transformer_depth)) == 1:
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transformer_depth = transformer_depth[0]
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unet_config["in_channels"] = in_channels
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unet_config["model_channels"] = model_channels
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unet_config["num_res_blocks"] = num_res_blocks
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unet_config["attention_resolutions"] = attention_resolutions
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unet_config["transformer_depth"] = transformer_depth
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unet_config["channel_mult"] = channel_mult
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unet_config["transformer_depth_middle"] = transformer_depth_middle
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unet_config['use_linear_in_transformer'] = use_linear_in_transformer
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unet_config["context_dim"] = context_dim
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return unet_config
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def model_config_from_unet(state_dict, unet_key_prefix, use_fp16):
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unet_config = detect_unet_config(state_dict, unet_key_prefix, use_fp16)
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for model_config in supported_models.models:
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if model_config.matches(unet_config):
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return model_config(unet_config)
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return None
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