211 lines
10 KiB
Python
211 lines
10 KiB
Python
import fcbh.supported_models
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import fcbh.supported_models_base
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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, dtype):
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state_dict_keys = list(state_dict.keys())
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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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"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["dtype"] = dtype
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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_config(unet_config):
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for model_config in fcbh.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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print("no match", unet_config)
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return None
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def model_config_from_unet(state_dict, unet_key_prefix, dtype, use_base_if_no_match=False):
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unet_config = detect_unet_config(state_dict, unet_key_prefix, dtype)
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model_config = model_config_from_unet_config(unet_config)
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if model_config is None and use_base_if_no_match:
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return fcbh.supported_models_base.BASE(unet_config)
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else:
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return model_config
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def unet_config_from_diffusers_unet(state_dict, dtype):
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match = {}
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attention_resolutions = []
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attn_res = 1
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for i in range(5):
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k = "down_blocks.{}.attentions.1.transformer_blocks.0.attn2.to_k.weight".format(i)
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if k in state_dict:
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match["context_dim"] = state_dict[k].shape[1]
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attention_resolutions.append(attn_res)
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attn_res *= 2
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match["attention_resolutions"] = attention_resolutions
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match["model_channels"] = state_dict["conv_in.weight"].shape[0]
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match["in_channels"] = state_dict["conv_in.weight"].shape[1]
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match["adm_in_channels"] = None
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if "class_embedding.linear_1.weight" in state_dict:
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match["adm_in_channels"] = state_dict["class_embedding.linear_1.weight"].shape[1]
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elif "add_embedding.linear_1.weight" in state_dict:
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match["adm_in_channels"] = state_dict["add_embedding.linear_1.weight"].shape[1]
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SDXL = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
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'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
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'num_res_blocks': 2, 'attention_resolutions': [2, 4], 'transformer_depth': [0, 2, 10], 'channel_mult': [1, 2, 4],
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'transformer_depth_middle': 10, 'use_linear_in_transformer': True, 'context_dim': 2048, "num_head_channels": 64}
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SDXL_refiner = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
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'num_classes': 'sequential', 'adm_in_channels': 2560, 'dtype': dtype, 'in_channels': 4, 'model_channels': 384,
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'num_res_blocks': 2, 'attention_resolutions': [2, 4], 'transformer_depth': [0, 4, 4, 0], 'channel_mult': [1, 2, 4, 4],
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'transformer_depth_middle': 4, 'use_linear_in_transformer': True, 'context_dim': 1280, "num_head_channels": 64}
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SD21 = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
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'adm_in_channels': None, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': 2,
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'attention_resolutions': [1, 2, 4], 'transformer_depth': [1, 1, 1, 0], 'channel_mult': [1, 2, 4, 4],
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'transformer_depth_middle': 1, 'use_linear_in_transformer': True, 'context_dim': 1024, "num_head_channels": 64}
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SD21_uncliph = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
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'num_classes': 'sequential', 'adm_in_channels': 2048, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
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'num_res_blocks': 2, 'attention_resolutions': [1, 2, 4], 'transformer_depth': [1, 1, 1, 0], 'channel_mult': [1, 2, 4, 4],
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'transformer_depth_middle': 1, 'use_linear_in_transformer': True, 'context_dim': 1024, "num_head_channels": 64}
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SD21_unclipl = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
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'num_classes': 'sequential', 'adm_in_channels': 1536, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
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'num_res_blocks': 2, 'attention_resolutions': [1, 2, 4], 'transformer_depth': [1, 1, 1, 0], 'channel_mult': [1, 2, 4, 4],
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'transformer_depth_middle': 1, 'use_linear_in_transformer': True, 'context_dim': 1024}
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SD15 = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
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'adm_in_channels': None, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': 2,
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'attention_resolutions': [1, 2, 4], 'transformer_depth': [1, 1, 1, 0], 'channel_mult': [1, 2, 4, 4],
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'transformer_depth_middle': 1, 'use_linear_in_transformer': False, 'context_dim': 768, "num_heads": 8}
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SDXL_mid_cnet = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
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'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
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'num_res_blocks': 2, 'attention_resolutions': [4], 'transformer_depth': [0, 0, 1], 'channel_mult': [1, 2, 4],
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'transformer_depth_middle': 1, 'use_linear_in_transformer': True, 'context_dim': 2048, "num_head_channels": 64}
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SDXL_small_cnet = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
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'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
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'num_res_blocks': 2, 'attention_resolutions': [], 'transformer_depth': [0, 0, 0], 'channel_mult': [1, 2, 4],
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'transformer_depth_middle': 0, 'use_linear_in_transformer': True, "num_head_channels": 64, 'context_dim': 1}
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SDXL_diffusers_inpaint = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
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'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 9, 'model_channels': 320,
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'num_res_blocks': 2, 'attention_resolutions': [2, 4], 'transformer_depth': [0, 2, 10], 'channel_mult': [1, 2, 4],
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'transformer_depth_middle': 10, 'use_linear_in_transformer': True, 'context_dim': 2048, "num_head_channels": 64}
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supported_models = [SDXL, SDXL_refiner, SD21, SD15, SD21_uncliph, SD21_unclipl, SDXL_mid_cnet, SDXL_small_cnet, SDXL_diffusers_inpaint]
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for unet_config in supported_models:
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matches = True
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for k in match:
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if match[k] != unet_config[k]:
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matches = False
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break
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if matches:
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return unet_config
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return None
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def model_config_from_diffusers_unet(state_dict, dtype):
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unet_config = unet_config_from_diffusers_unet(state_dict, dtype)
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if unet_config is not None:
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return model_config_from_unet_config(unet_config)
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return None
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