316 lines
17 KiB
Python
316 lines
17 KiB
Python
import comfy.supported_models
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import comfy.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 calculate_transformer_depth(prefix, state_dict_keys, state_dict):
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context_dim = None
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use_linear_in_transformer = False
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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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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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time_stack = '{}1.time_stack.0.attn1.to_q.weight'.format(prefix) in state_dict or '{}1.time_mix_blocks.0.attn1.to_q.weight'.format(prefix) in state_dict
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return last_transformer_depth, context_dim, use_linear_in_transformer, time_stack
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return None
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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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"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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out_channels = state_dict['{}out.2.weight'.format(key_prefix)].shape[0]
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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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transformer_depth_output = []
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context_dim = None
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use_linear_in_transformer = False
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video_model = 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_channel_mult = 0
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input_block_count = count_blocks(state_dict_keys, '{}input_blocks'.format(key_prefix) + '.{}.')
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for count in range(input_block_count):
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prefix = '{}input_blocks.{}.'.format(key_prefix, count)
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prefix_output = '{}output_blocks.{}.'.format(key_prefix, input_block_count - count - 1)
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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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block_keys_output = sorted(list(filter(lambda a: a.startswith(prefix_output), state_dict_keys)))
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if "{}0.op.weight".format(prefix) in block_keys: #new layer
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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_channel_mult = 0
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out = calculate_transformer_depth(prefix_output, state_dict_keys, state_dict)
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if out is not None:
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transformer_depth_output.append(out[0])
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else:
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transformer_depth_output.append(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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out = calculate_transformer_depth(prefix, state_dict_keys, state_dict)
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if out is not None:
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transformer_depth.append(out[0])
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if context_dim is None:
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context_dim = out[1]
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use_linear_in_transformer = out[2]
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video_model = out[3]
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else:
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transformer_depth.append(0)
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res_block_prefix = "{}0.in_layers.0.weight".format(prefix_output)
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if res_block_prefix in block_keys_output:
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out = calculate_transformer_depth(prefix_output, state_dict_keys, state_dict)
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if out is not None:
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transformer_depth_output.append(out[0])
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else:
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transformer_depth_output.append(0)
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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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if "{}middle_block.1.proj_in.weight".format(key_prefix) in state_dict_keys:
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transformer_depth_middle = count_blocks(state_dict_keys, '{}middle_block.1.transformer_blocks.'.format(key_prefix) + '{}')
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else:
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transformer_depth_middle = -1
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unet_config["in_channels"] = in_channels
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unet_config["out_channels"] = out_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["transformer_depth"] = transformer_depth
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unet_config["transformer_depth_output"] = transformer_depth_output
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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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if video_model:
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unet_config["extra_ff_mix_layer"] = True
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unet_config["use_spatial_context"] = True
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unet_config["merge_strategy"] = "learned_with_images"
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unet_config["merge_factor"] = 0.0
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unet_config["video_kernel_size"] = [3, 1, 1]
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unet_config["use_temporal_resblock"] = True
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unet_config["use_temporal_attention"] = True
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else:
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unet_config["use_temporal_resblock"] = False
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unet_config["use_temporal_attention"] = False
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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 comfy.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 comfy.supported_models_base.BASE(unet_config)
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else:
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return model_config
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def convert_config(unet_config):
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new_config = unet_config.copy()
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num_res_blocks = new_config.get("num_res_blocks", None)
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channel_mult = new_config.get("channel_mult", None)
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if isinstance(num_res_blocks, int):
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num_res_blocks = len(channel_mult) * [num_res_blocks]
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if "attention_resolutions" in new_config:
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attention_resolutions = new_config.pop("attention_resolutions")
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transformer_depth = new_config.get("transformer_depth", None)
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transformer_depth_middle = new_config.get("transformer_depth_middle", None)
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if isinstance(transformer_depth, int):
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transformer_depth = len(channel_mult) * [transformer_depth]
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if transformer_depth_middle is None:
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transformer_depth_middle = transformer_depth[-1]
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t_in = []
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t_out = []
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s = 1
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for i in range(len(num_res_blocks)):
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res = num_res_blocks[i]
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d = 0
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if s in attention_resolutions:
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d = transformer_depth[i]
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t_in += [d] * res
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t_out += [d] * (res + 1)
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s *= 2
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transformer_depth = t_in
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transformer_depth_output = t_out
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new_config["transformer_depth"] = t_in
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new_config["transformer_depth_output"] = t_out
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new_config["transformer_depth_middle"] = transformer_depth_middle
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new_config["num_res_blocks"] = num_res_blocks
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return new_config
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def unet_config_from_diffusers_unet(state_dict, dtype):
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match = {}
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transformer_depth = []
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attn_res = 1
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down_blocks = count_blocks(state_dict, "down_blocks.{}")
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for i in range(down_blocks):
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attn_blocks = count_blocks(state_dict, "down_blocks.{}.attentions.".format(i) + '{}')
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for ab in range(attn_blocks):
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transformer_count = count_blocks(state_dict, "down_blocks.{}.attentions.{}.transformer_blocks.".format(i, ab) + '{}')
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transformer_depth.append(transformer_count)
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if transformer_count > 0:
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match["context_dim"] = state_dict["down_blocks.{}.attentions.{}.transformer_blocks.0.attn2.to_k.weight".format(i, ab)].shape[1]
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attn_res *= 2
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if attn_blocks == 0:
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transformer_depth.append(0)
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transformer_depth.append(0)
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match["transformer_depth"] = transformer_depth
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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, 2, 2], 'transformer_depth': [0, 0, 2, 2, 10, 10], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 10,
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'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 2, 2, 2, 10, 10, 10],
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'use_temporal_attention': False, 'use_temporal_resblock': False}
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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, 2, 2, 2], 'transformer_depth': [0, 0, 4, 4, 4, 4, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 4,
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'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],
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'use_temporal_attention': False, 'use_temporal_resblock': False}
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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, 2, 2, 2],
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'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,
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'context_dim': 1024, 'num_head_channels': 64, 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],
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'use_temporal_attention': False, 'use_temporal_resblock': False}
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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, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1,
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'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],
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'use_temporal_attention': False, 'use_temporal_resblock': False}
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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, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1,
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'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],
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'use_temporal_attention': False, 'use_temporal_resblock': False}
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SD15 = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'adm_in_channels': None,
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'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],
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'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1, 'use_linear_in_transformer': False, 'context_dim': 768, 'num_heads': 8,
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'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],
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'use_temporal_attention': False, 'use_temporal_resblock': False}
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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, 2, 2], 'transformer_depth': [0, 0, 0, 0, 1, 1], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 1,
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'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 0, 0, 0, 1, 1, 1],
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'use_temporal_attention': False, 'use_temporal_resblock': False}
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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, 2, 2], 'transformer_depth': [0, 0, 0, 0, 0, 0], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 0,
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'use_linear_in_transformer': True, 'num_head_channels': 64, 'context_dim': 1, 'transformer_depth_output': [0, 0, 0, 0, 0, 0, 0, 0, 0],
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'use_temporal_attention': False, 'use_temporal_resblock': False}
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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, 2, 2], 'transformer_depth': [0, 0, 2, 2, 10, 10], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 10,
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'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 2, 2, 2, 10, 10, 10],
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'use_temporal_attention': False, 'use_temporal_resblock': False}
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SSD_1B = {'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, 2, 2], 'transformer_depth': [0, 0, 2, 2, 4, 4], 'transformer_depth_output': [0, 0, 0, 1, 1, 2, 10, 4, 4],
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'channel_mult': [1, 2, 4], 'transformer_depth_middle': -1, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64,
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'use_temporal_attention': False, 'use_temporal_resblock': False}
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Segmind_Vega = {'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, 2, 2], 'transformer_depth': [0, 0, 1, 1, 2, 2], 'transformer_depth_output': [0, 0, 0, 1, 1, 1, 2, 2, 2],
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'channel_mult': [1, 2, 4], 'transformer_depth_middle': -1, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64,
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'use_temporal_attention': False, 'use_temporal_resblock': False}
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supported_models = [SDXL, SDXL_refiner, SD21, SD15, SD21_uncliph, SD21_unclipl, SDXL_mid_cnet, SDXL_small_cnet, SDXL_diffusers_inpaint, SSD_1B, Segmind_Vega]
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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 convert_config(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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