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383 lines
22 KiB
383 lines
22 KiB
import comfy.supported_models |
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import comfy.supported_models_base |
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import logging |
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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): |
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state_dict_keys = list(state_dict.keys()) |
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if '{}clf.1.weight'.format(key_prefix) in state_dict_keys: #stable cascade |
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unet_config = {} |
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text_mapper_name = '{}clip_txt_mapper.weight'.format(key_prefix) |
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if text_mapper_name in state_dict_keys: |
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unet_config['stable_cascade_stage'] = 'c' |
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w = state_dict[text_mapper_name] |
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if w.shape[0] == 1536: #stage c lite |
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unet_config['c_cond'] = 1536 |
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unet_config['c_hidden'] = [1536, 1536] |
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unet_config['nhead'] = [24, 24] |
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unet_config['blocks'] = [[4, 12], [12, 4]] |
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elif w.shape[0] == 2048: #stage c full |
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unet_config['c_cond'] = 2048 |
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elif '{}clip_mapper.weight'.format(key_prefix) in state_dict_keys: |
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unet_config['stable_cascade_stage'] = 'b' |
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w = state_dict['{}down_blocks.1.0.channelwise.0.weight'.format(key_prefix)] |
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if w.shape[-1] == 640: |
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unet_config['c_hidden'] = [320, 640, 1280, 1280] |
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unet_config['nhead'] = [-1, -1, 20, 20] |
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unet_config['blocks'] = [[2, 6, 28, 6], [6, 28, 6, 2]] |
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unet_config['block_repeat'] = [[1, 1, 1, 1], [3, 3, 2, 2]] |
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elif w.shape[-1] == 576: #stage b lite |
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unet_config['c_hidden'] = [320, 576, 1152, 1152] |
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unet_config['nhead'] = [-1, 9, 18, 18] |
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unet_config['blocks'] = [[2, 4, 14, 4], [4, 14, 4, 2]] |
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unet_config['block_repeat'] = [[1, 1, 1, 1], [2, 2, 2, 2]] |
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return unet_config |
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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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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_key = '{}out.2.weight'.format(key_prefix) |
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if out_key in state_dict: |
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out_channels = state_dict[out_key].shape[0] |
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else: |
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out_channels = 4 |
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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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elif "{}middle_block.0.in_layers.0.weight".format(key_prefix) in state_dict_keys: |
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transformer_depth_middle = -1 |
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else: |
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transformer_depth_middle = -2 |
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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, state_dict=None): |
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for model_config in comfy.supported_models.models: |
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if model_config.matches(unet_config, state_dict): |
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return model_config(unet_config) |
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logging.error("no match {}".format(unet_config)) |
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return None |
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def model_config_from_unet(state_dict, unet_key_prefix, use_base_if_no_match=False): |
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unet_config = detect_unet_config(state_dict, unet_key_prefix) |
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model_config = model_config_from_unet_config(unet_config, state_dict) |
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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=None): |
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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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res_blocks = count_blocks(state_dict, "down_blocks.{}.resnets.".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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for i in range(res_blocks): |
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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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SDXL_diffusers_ip2p = {'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': 8, '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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KOALA_700M = {'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': [1, 1, 1], 'transformer_depth': [0, 2, 5], 'transformer_depth_output': [0, 0, 2, 2, 5, 5], |
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'channel_mult': [1, 2, 4], 'transformer_depth_middle': -2, '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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KOALA_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': [1, 1, 1], 'transformer_depth': [0, 2, 6], 'transformer_depth_output': [0, 0, 2, 2, 6, 6], |
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'channel_mult': [1, 2, 4], 'transformer_depth_middle': 6, '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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|
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SD09_XS = {'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': [1, 1, 1], |
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'transformer_depth': [1, 1, 1], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': -2, '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], |
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'use_temporal_attention': False, 'use_temporal_resblock': False, 'disable_self_attentions': [True, False, False]} |
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|
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SD_XS = {'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': [1, 1, 1], |
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'transformer_depth': [0, 1, 1], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': -2, 'use_linear_in_transformer': False, |
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'context_dim': 768, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 1, 1, 1, 1], |
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'use_temporal_attention': False, 'use_temporal_resblock': False} |
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|
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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, KOALA_700M, KOALA_1B, SD09_XS, SD_XS, SDXL_diffusers_ip2p] |
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|
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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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|
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def model_config_from_diffusers_unet(state_dict): |
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unet_config = unet_config_from_diffusers_unet(state_dict) |
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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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