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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 detect_unet_config(state_dict, key_prefix, use_fp16): |
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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["use_fp16"] = use_fp16 |
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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: |
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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 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, use_fp16, use_base_if_no_match=False): |
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unet_config = detect_unet_config(state_dict, unet_key_prefix, use_fp16) |
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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 unet_config_from_diffusers_unet(state_dict, use_fp16): |
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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, 'use_fp16': use_fp16, '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, 'use_fp16': use_fp16, '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, 'use_fp16': use_fp16, '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, 'use_fp16': use_fp16, '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, 'use_fp16': use_fp16, '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, 'use_fp16': use_fp16, '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, 'use_fp16': use_fp16, '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, 'use_fp16': use_fp16, '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, 'use_fp16': use_fp16, '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, use_fp16): |
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unet_config = unet_config_from_diffusers_unet(state_dict, use_fp16) |
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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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