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import fcbh.supported_models
import fcbh.supported_models_base
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 calculate_transformer_depth(prefix, state_dict_keys, state_dict):
context_dim = None
use_linear_in_transformer = False
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 + '{}')
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
return last_transformer_depth, context_dim, use_linear_in_transformer
return None
def detect_unet_config(state_dict, key_prefix, dtype):
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["dtype"] = dtype
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 = []
transformer_depth_output = []
context_dim = None
use_linear_in_transformer = False
current_res = 1
count = 0
last_res_blocks = 0
last_channel_mult = 0
input_block_count = count_blocks(state_dict_keys, '{}input_blocks'.format(key_prefix) + '.{}.')
for count in range(input_block_count):
prefix = '{}input_blocks.{}.'.format(key_prefix, count)
prefix_output = '{}output_blocks.{}.'.format(key_prefix, input_block_count - count - 1)
block_keys = sorted(list(filter(lambda a: a.startswith(prefix), state_dict_keys)))
if len(block_keys) == 0:
break
block_keys_output = sorted(list(filter(lambda a: a.startswith(prefix_output), state_dict_keys)))
if "{}0.op.weight".format(prefix) in block_keys: #new layer
num_res_blocks.append(last_res_blocks)
channel_mult.append(last_channel_mult)
current_res *= 2
last_res_blocks = 0
last_channel_mult = 0
out = calculate_transformer_depth(prefix_output, state_dict_keys, state_dict)
if out is not None:
transformer_depth_output.append(out[0])
else:
transformer_depth_output.append(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
out = calculate_transformer_depth(prefix, state_dict_keys, state_dict)
if out is not None:
transformer_depth.append(out[0])
if context_dim is None:
context_dim = out[1]
use_linear_in_transformer = out[2]
else:
transformer_depth.append(0)
res_block_prefix = "{}0.in_layers.0.weight".format(prefix_output)
if res_block_prefix in block_keys_output:
out = calculate_transformer_depth(prefix_output, state_dict_keys, state_dict)
if out is not None:
transformer_depth_output.append(out[0])
else:
transformer_depth_output.append(0)
num_res_blocks.append(last_res_blocks)
channel_mult.append(last_channel_mult)
if "{}middle_block.1.proj_in.weight".format(key_prefix) in state_dict_keys:
transformer_depth_middle = count_blocks(state_dict_keys, '{}middle_block.1.transformer_blocks.'.format(key_prefix) + '{}')
else:
transformer_depth_middle = -1
unet_config["in_channels"] = in_channels
unet_config["model_channels"] = model_channels
unet_config["num_res_blocks"] = num_res_blocks
unet_config["transformer_depth"] = transformer_depth
unet_config["transformer_depth_output"] = transformer_depth_output
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_config(unet_config):
for model_config in fcbh.supported_models.models:
if model_config.matches(unet_config):
return model_config(unet_config)
print("no match", unet_config)
return None
def model_config_from_unet(state_dict, unet_key_prefix, dtype, use_base_if_no_match=False):
unet_config = detect_unet_config(state_dict, unet_key_prefix, dtype)
model_config = model_config_from_unet_config(unet_config)
if model_config is None and use_base_if_no_match:
return fcbh.supported_models_base.BASE(unet_config)
else:
return model_config
def convert_config(unet_config):
new_config = unet_config.copy()
num_res_blocks = new_config.get("num_res_blocks", None)
channel_mult = new_config.get("channel_mult", None)
if isinstance(num_res_blocks, int):
num_res_blocks = len(channel_mult) * [num_res_blocks]
if "attention_resolutions" in new_config:
attention_resolutions = new_config.pop("attention_resolutions")
transformer_depth = new_config.get("transformer_depth", None)
transformer_depth_middle = new_config.get("transformer_depth_middle", None)
if isinstance(transformer_depth, int):
transformer_depth = len(channel_mult) * [transformer_depth]
if transformer_depth_middle is None:
transformer_depth_middle = transformer_depth[-1]
t_in = []
t_out = []
s = 1
for i in range(len(num_res_blocks)):
res = num_res_blocks[i]
d = 0
if s in attention_resolutions:
d = transformer_depth[i]
t_in += [d] * res
t_out += [d] * (res + 1)
s *= 2
transformer_depth = t_in
transformer_depth_output = t_out
new_config["transformer_depth"] = t_in
new_config["transformer_depth_output"] = t_out
new_config["transformer_depth_middle"] = transformer_depth_middle
new_config["num_res_blocks"] = num_res_blocks
return new_config
def unet_config_from_diffusers_unet(state_dict, dtype):
match = {}
transformer_depth = []
attn_res = 1
down_blocks = count_blocks(state_dict, "down_blocks.{}")
for i in range(down_blocks):
attn_blocks = count_blocks(state_dict, "down_blocks.{}.attentions.".format(i) + '{}')
for ab in range(attn_blocks):
transformer_count = count_blocks(state_dict, "down_blocks.{}.attentions.{}.transformer_blocks.".format(i, ab) + '{}')
transformer_depth.append(transformer_count)
if transformer_count > 0:
match["context_dim"] = state_dict["down_blocks.{}.attentions.{}.transformer_blocks.0.attn2.to_k.weight".format(i, ab)].shape[1]
attn_res *= 2
if attn_blocks == 0:
transformer_depth.append(0)
transformer_depth.append(0)
match["transformer_depth"] = transformer_depth
match["model_channels"] = state_dict["conv_in.weight"].shape[0]
match["in_channels"] = state_dict["conv_in.weight"].shape[1]
match["adm_in_channels"] = None
if "class_embedding.linear_1.weight" in state_dict:
match["adm_in_channels"] = state_dict["class_embedding.linear_1.weight"].shape[1]
elif "add_embedding.linear_1.weight" in state_dict:
match["adm_in_channels"] = state_dict["add_embedding.linear_1.weight"].shape[1]
SDXL = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 10, 10], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 10,
'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 2, 2, 2, 10, 10, 10]}
SDXL_refiner = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
'num_classes': 'sequential', 'adm_in_channels': 2560, 'dtype': dtype, 'in_channels': 4, 'model_channels': 384,
'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [0, 0, 4, 4, 4, 4, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 4,
'use_linear_in_transformer': True, 'context_dim': 1280, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 4, 4, 4, 4, 4, 4, 0, 0, 0]}
SD21 = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
'adm_in_channels': None, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [2, 2, 2, 2],
'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1, 'use_linear_in_transformer': True,
'context_dim': 1024, 'num_head_channels': 64, 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0]}
SD21_uncliph = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
'num_classes': 'sequential', 'adm_in_channels': 2048, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1,
'use_linear_in_transformer': True, 'context_dim': 1024, 'num_head_channels': 64, 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0]}
SD21_unclipl = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
'num_classes': 'sequential', 'adm_in_channels': 1536, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1,
'use_linear_in_transformer': True, 'context_dim': 1024, 'num_head_channels': 64, 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0]}
SD15 = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'adm_in_channels': None,
'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0],
'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1, 'use_linear_in_transformer': False, 'context_dim': 768, 'num_heads': 8,
'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0]}
SDXL_mid_cnet = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 0, 0, 1, 1], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 1,
'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 0, 0, 0, 1, 1, 1]}
SDXL_small_cnet = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 0, 0, 0, 0], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 0,
'use_linear_in_transformer': True, 'num_head_channels': 64, 'context_dim': 1, 'transformer_depth_output': [0, 0, 0, 0, 0, 0, 0, 0, 0]}
SDXL_diffusers_inpaint = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 9, 'model_channels': 320,
'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 10, 10], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 10,
'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 2, 2, 2, 10, 10, 10]}
SSD_1B = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 4, 4], 'transformer_depth_output': [0, 0, 0, 1, 1, 2, 10, 4, 4],
'channel_mult': [1, 2, 4], 'transformer_depth_middle': -1, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64}
supported_models = [SDXL, SDXL_refiner, SD21, SD15, SD21_uncliph, SD21_unclipl, SDXL_mid_cnet, SDXL_small_cnet, SDXL_diffusers_inpaint, SSD_1B]
for unet_config in supported_models:
matches = True
for k in match:
if match[k] != unet_config[k]:
matches = False
break
if matches:
return convert_config(unet_config)
return None
def model_config_from_diffusers_unet(state_dict, dtype):
unet_config = unet_config_from_diffusers_unet(state_dict, dtype)
if unet_config is not None:
return model_config_from_unet_config(unet_config)
return None
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