You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
119 lines
4.2 KiB
119 lines
4.2 KiB
1 year ago
|
|
||
|
|
from . import supported_models
|
||
|
|
|
||
|
|
def count_blocks(state_dict_keys, prefix_string):
|
||
|
|
count = 0
|
||
|
|
while True:
|
||
|
|
c = False
|
||
|
|
for k in state_dict_keys:
|
||
|
|
if k.startswith(prefix_string.format(count)):
|
||
|
|
c = True
|
||
|
|
break
|
||
|
|
if c == False:
|
||
|
|
break
|
||
|
|
count += 1
|
||
|
|
return count
|
||
|
|
|
||
|
|
def detect_unet_config(state_dict, key_prefix, use_fp16):
|
||
|
|
state_dict_keys = list(state_dict.keys())
|
||
|
|
|
||
|
|
unet_config = {
|
||
|
|
"use_checkpoint": False,
|
||
|
|
"image_size": 32,
|
||
|
|
"out_channels": 4,
|
||
|
|
"use_spatial_transformer": True,
|
||
|
|
"legacy": False
|
||
|
|
}
|
||
|
|
|
||
|
|
y_input = '{}label_emb.0.0.weight'.format(key_prefix)
|
||
|
|
if y_input in state_dict_keys:
|
||
|
|
unet_config["num_classes"] = "sequential"
|
||
|
|
unet_config["adm_in_channels"] = state_dict[y_input].shape[1]
|
||
|
|
else:
|
||
|
|
unet_config["adm_in_channels"] = None
|
||
|
|
|
||
|
|
unet_config["use_fp16"] = use_fp16
|
||
|
|
model_channels = state_dict['{}input_blocks.0.0.weight'.format(key_prefix)].shape[0]
|
||
|
|
in_channels = state_dict['{}input_blocks.0.0.weight'.format(key_prefix)].shape[1]
|
||
|
|
|
||
|
|
num_res_blocks = []
|
||
|
|
channel_mult = []
|
||
|
|
attention_resolutions = []
|
||
|
|
transformer_depth = []
|
||
|
|
context_dim = None
|
||
|
|
use_linear_in_transformer = False
|
||
|
|
|
||
|
|
|
||
|
|
current_res = 1
|
||
|
|
count = 0
|
||
|
|
|
||
|
|
last_res_blocks = 0
|
||
|
|
last_transformer_depth = 0
|
||
|
|
last_channel_mult = 0
|
||
|
|
|
||
|
|
while True:
|
||
|
|
prefix = '{}input_blocks.{}.'.format(key_prefix, count)
|
||
|
|
block_keys = sorted(list(filter(lambda a: a.startswith(prefix), state_dict_keys)))
|
||
|
|
if len(block_keys) == 0:
|
||
|
|
break
|
||
|
|
|
||
|
|
if "{}0.op.weight".format(prefix) in block_keys: #new layer
|
||
|
|
if last_transformer_depth > 0:
|
||
|
|
attention_resolutions.append(current_res)
|
||
|
|
transformer_depth.append(last_transformer_depth)
|
||
|
|
num_res_blocks.append(last_res_blocks)
|
||
|
|
channel_mult.append(last_channel_mult)
|
||
|
|
|
||
|
|
current_res *= 2
|
||
|
|
last_res_blocks = 0
|
||
|
|
last_transformer_depth = 0
|
||
|
|
last_channel_mult = 0
|
||
|
|
else:
|
||
|
|
res_block_prefix = "{}0.in_layers.0.weight".format(prefix)
|
||
|
|
if res_block_prefix in block_keys:
|
||
|
|
last_res_blocks += 1
|
||
|
|
last_channel_mult = state_dict["{}0.out_layers.3.weight".format(prefix)].shape[0] // model_channels
|
||
|
|
|
||
|
|
transformer_prefix = prefix + "1.transformer_blocks."
|
||
|
|
transformer_keys = sorted(list(filter(lambda a: a.startswith(transformer_prefix), state_dict_keys)))
|
||
|
|
if len(transformer_keys) > 0:
|
||
|
|
last_transformer_depth = count_blocks(state_dict_keys, transformer_prefix + '{}')
|
||
|
|
if context_dim is None:
|
||
|
|
context_dim = state_dict['{}0.attn2.to_k.weight'.format(transformer_prefix)].shape[1]
|
||
|
|
use_linear_in_transformer = len(state_dict['{}1.proj_in.weight'.format(prefix)].shape) == 2
|
||
|
|
|
||
|
|
count += 1
|
||
|
|
|
||
|
|
if last_transformer_depth > 0:
|
||
|
|
attention_resolutions.append(current_res)
|
||
|
|
transformer_depth.append(last_transformer_depth)
|
||
|
|
num_res_blocks.append(last_res_blocks)
|
||
|
|
channel_mult.append(last_channel_mult)
|
||
|
|
transformer_depth_middle = count_blocks(state_dict_keys, '{}middle_block.1.transformer_blocks.'.format(key_prefix) + '{}')
|
||
|
|
|
||
|
|
if len(set(num_res_blocks)) == 1:
|
||
|
|
num_res_blocks = num_res_blocks[0]
|
||
|
|
|
||
|
|
if len(set(transformer_depth)) == 1:
|
||
|
|
transformer_depth = transformer_depth[0]
|
||
|
|
|
||
|
|
unet_config["in_channels"] = in_channels
|
||
|
|
unet_config["model_channels"] = model_channels
|
||
|
|
unet_config["num_res_blocks"] = num_res_blocks
|
||
|
|
unet_config["attention_resolutions"] = attention_resolutions
|
||
|
|
unet_config["transformer_depth"] = transformer_depth
|
||
|
|
unet_config["channel_mult"] = channel_mult
|
||
|
|
unet_config["transformer_depth_middle"] = transformer_depth_middle
|
||
|
|
unet_config['use_linear_in_transformer'] = use_linear_in_transformer
|
||
|
|
unet_config["context_dim"] = context_dim
|
||
|
|
return unet_config
|
||
|
|
|
||
|
|
|
||
|
|
def model_config_from_unet(state_dict, unet_key_prefix, use_fp16):
|
||
|
|
unet_config = detect_unet_config(state_dict, unet_key_prefix, use_fp16)
|
||
|
|
for model_config in supported_models.models:
|
||
|
|
if model_config.matches(unet_config):
|
||
|
|
return model_config(unet_config)
|
||
|
|
|
||
|
|
return None
|