mirror of
https://github.com/comfyanonymous/ComfyUI.git
synced 2025-01-11 02:15:17 +00:00
0075c6d096
This change allows supports for diffusion models where all the linears are scaled fp8 while the other weights are the original precision.
560 lines
31 KiB
Python
560 lines
31 KiB
Python
import comfy.supported_models
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import comfy.supported_models_base
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import comfy.utils
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import math
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import logging
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import torch
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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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time_stack_cross = '{}1.time_stack.0.attn2.to_q.weight'.format(prefix) in state_dict or '{}1.time_mix_blocks.0.attn2.to_q.weight'.format(prefix) in state_dict
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return last_transformer_depth, context_dim, use_linear_in_transformer, time_stack, time_stack_cross
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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 '{}joint_blocks.0.context_block.attn.qkv.weight'.format(key_prefix) in state_dict_keys: #mmdit model
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unet_config = {}
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unet_config["in_channels"] = state_dict['{}x_embedder.proj.weight'.format(key_prefix)].shape[1]
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patch_size = state_dict['{}x_embedder.proj.weight'.format(key_prefix)].shape[2]
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unet_config["patch_size"] = patch_size
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final_layer = '{}final_layer.linear.weight'.format(key_prefix)
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if final_layer in state_dict:
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unet_config["out_channels"] = state_dict[final_layer].shape[0] // (patch_size * patch_size)
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unet_config["depth"] = state_dict['{}x_embedder.proj.weight'.format(key_prefix)].shape[0] // 64
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unet_config["input_size"] = None
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y_key = '{}y_embedder.mlp.0.weight'.format(key_prefix)
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if y_key in state_dict_keys:
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unet_config["adm_in_channels"] = state_dict[y_key].shape[1]
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context_key = '{}context_embedder.weight'.format(key_prefix)
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if context_key in state_dict_keys:
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in_features = state_dict[context_key].shape[1]
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out_features = state_dict[context_key].shape[0]
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unet_config["context_embedder_config"] = {"target": "torch.nn.Linear", "params": {"in_features": in_features, "out_features": out_features}}
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num_patches_key = '{}pos_embed'.format(key_prefix)
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if num_patches_key in state_dict_keys:
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num_patches = state_dict[num_patches_key].shape[1]
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unet_config["num_patches"] = num_patches
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unet_config["pos_embed_max_size"] = round(math.sqrt(num_patches))
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rms_qk = '{}joint_blocks.0.context_block.attn.ln_q.weight'.format(key_prefix)
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if rms_qk in state_dict_keys:
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unet_config["qk_norm"] = "rms"
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unet_config["pos_embed_scaling_factor"] = None #unused for inference
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context_processor = '{}context_processor.layers.0.attn.qkv.weight'.format(key_prefix)
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if context_processor in state_dict_keys:
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unet_config["context_processor_layers"] = count_blocks(state_dict_keys, '{}context_processor.layers.'.format(key_prefix) + '{}.')
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return unet_config
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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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if '{}transformer.rotary_pos_emb.inv_freq'.format(key_prefix) in state_dict_keys: #stable audio dit
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unet_config = {}
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unet_config["audio_model"] = "dit1.0"
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return unet_config
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if '{}double_layers.0.attn.w1q.weight'.format(key_prefix) in state_dict_keys: #aura flow dit
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unet_config = {}
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unet_config["max_seq"] = state_dict['{}positional_encoding'.format(key_prefix)].shape[1]
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unet_config["cond_seq_dim"] = state_dict['{}cond_seq_linear.weight'.format(key_prefix)].shape[1]
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double_layers = count_blocks(state_dict_keys, '{}double_layers.'.format(key_prefix) + '{}.')
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single_layers = count_blocks(state_dict_keys, '{}single_layers.'.format(key_prefix) + '{}.')
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unet_config["n_double_layers"] = double_layers
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unet_config["n_layers"] = double_layers + single_layers
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return unet_config
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if '{}mlp_t5.0.weight'.format(key_prefix) in state_dict_keys: #Hunyuan DiT
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unet_config = {}
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unet_config["image_model"] = "hydit"
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unet_config["depth"] = count_blocks(state_dict_keys, '{}blocks.'.format(key_prefix) + '{}.')
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unet_config["hidden_size"] = state_dict['{}x_embedder.proj.weight'.format(key_prefix)].shape[0]
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if unet_config["hidden_size"] == 1408 and unet_config["depth"] == 40: #DiT-g/2
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unet_config["mlp_ratio"] = 4.3637
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if state_dict['{}extra_embedder.0.weight'.format(key_prefix)].shape[1] == 3968:
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unet_config["size_cond"] = True
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unet_config["use_style_cond"] = True
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unet_config["image_model"] = "hydit1"
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return unet_config
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if '{}double_blocks.0.img_attn.norm.key_norm.scale'.format(key_prefix) in state_dict_keys: #Flux
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dit_config = {}
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dit_config["image_model"] = "flux"
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dit_config["in_channels"] = 16
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dit_config["vec_in_dim"] = 768
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dit_config["context_in_dim"] = 4096
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dit_config["hidden_size"] = 3072
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dit_config["mlp_ratio"] = 4.0
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dit_config["num_heads"] = 24
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dit_config["depth"] = count_blocks(state_dict_keys, '{}double_blocks.'.format(key_prefix) + '{}.')
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dit_config["depth_single_blocks"] = count_blocks(state_dict_keys, '{}single_blocks.'.format(key_prefix) + '{}.')
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dit_config["axes_dim"] = [16, 56, 56]
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dit_config["theta"] = 10000
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dit_config["qkv_bias"] = True
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dit_config["guidance_embed"] = "{}guidance_in.in_layer.weight".format(key_prefix) in state_dict_keys
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return dit_config
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if '{}input_blocks.0.0.weight'.format(key_prefix) not in state_dict_keys:
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return None
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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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video_model_cross = 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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video_model_cross = out[4]
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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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unet_config["disable_temporal_crossattention"] = not video_model_cross
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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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if unet_config is None:
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return None
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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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model_config = comfy.supported_models_base.BASE(unet_config)
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scaled_fp8_weight = state_dict.get("{}scaled_fp8".format(unet_key_prefix), None)
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if scaled_fp8_weight is not None:
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model_config.scaled_fp8 = scaled_fp8_weight.dtype
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if model_config.scaled_fp8 == torch.float32:
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model_config.scaled_fp8 = torch.float8_e4m3fn
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return model_config
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def unet_prefix_from_state_dict(state_dict):
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candidates = ["model.diffusion_model.", #ldm/sgm models
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"model.model.", #audio models
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]
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counts = {k: 0 for k in candidates}
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for k in state_dict:
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for c in candidates:
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if k.startswith(c):
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counts[c] += 1
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break
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top = max(counts, key=counts.get)
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if counts[top] > 5:
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return top
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else:
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return "model." #aura flow and others
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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],
|
|
'use_temporal_attention': False, 'use_temporal_resblock': False}
|
|
|
|
SDXL_refiner = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
|
|
'num_classes': 'sequential', 'adm_in_channels': 2560, 'dtype': dtype, 'in_channels': 4, 'model_channels': 384,
|
|
'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [0, 0, 4, 4, 4, 4, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 4,
|
|
'use_linear_in_transformer': True, 'context_dim': 1280, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 4, 4, 4, 4, 4, 4, 0, 0, 0],
|
|
'use_temporal_attention': False, 'use_temporal_resblock': False}
|
|
|
|
SD21 = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
|
|
'adm_in_channels': None, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [2, 2, 2, 2],
|
|
'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1, 'use_linear_in_transformer': True,
|
|
'context_dim': 1024, 'num_head_channels': 64, 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],
|
|
'use_temporal_attention': False, 'use_temporal_resblock': False}
|
|
|
|
SD21_uncliph = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
|
|
'num_classes': 'sequential', 'adm_in_channels': 2048, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
|
|
'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1,
|
|
'use_linear_in_transformer': True, 'context_dim': 1024, 'num_head_channels': 64, 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],
|
|
'use_temporal_attention': False, 'use_temporal_resblock': False}
|
|
|
|
SD21_unclipl = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
|
|
'num_classes': 'sequential', 'adm_in_channels': 1536, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
|
|
'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1,
|
|
'use_linear_in_transformer': True, 'context_dim': 1024, 'num_head_channels': 64, 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],
|
|
'use_temporal_attention': False, 'use_temporal_resblock': False}
|
|
|
|
SD15 = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'adm_in_channels': None,
|
|
'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0],
|
|
'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1, 'use_linear_in_transformer': False, 'context_dim': 768, 'num_heads': 8,
|
|
'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],
|
|
'use_temporal_attention': False, 'use_temporal_resblock': False}
|
|
|
|
SDXL_mid_cnet = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
|
|
'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
|
|
'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 0, 0, 1, 1], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 1,
|
|
'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 0, 0, 0, 1, 1, 1],
|
|
'use_temporal_attention': False, 'use_temporal_resblock': False}
|
|
|
|
SDXL_small_cnet = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
|
|
'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
|
|
'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 0, 0, 0, 0], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 0,
|
|
'use_linear_in_transformer': True, 'num_head_channels': 64, 'context_dim': 1, 'transformer_depth_output': [0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
'use_temporal_attention': False, 'use_temporal_resblock': False}
|
|
|
|
SDXL_diffusers_inpaint = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
|
|
'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 9, 'model_channels': 320,
|
|
'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 10, 10], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 10,
|
|
'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 2, 2, 2, 10, 10, 10],
|
|
'use_temporal_attention': False, 'use_temporal_resblock': False}
|
|
|
|
SDXL_diffusers_ip2p = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
|
|
'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 8, 'model_channels': 320,
|
|
'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 10, 10], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 10,
|
|
'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 2, 2, 2, 10, 10, 10],
|
|
'use_temporal_attention': False, 'use_temporal_resblock': False}
|
|
|
|
SSD_1B = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
|
|
'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
|
|
'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 4, 4], 'transformer_depth_output': [0, 0, 0, 1, 1, 2, 10, 4, 4],
|
|
'channel_mult': [1, 2, 4], 'transformer_depth_middle': -1, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64,
|
|
'use_temporal_attention': False, 'use_temporal_resblock': False}
|
|
|
|
Segmind_Vega = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
|
|
'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
|
|
'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 1, 1, 2, 2], 'transformer_depth_output': [0, 0, 0, 1, 1, 1, 2, 2, 2],
|
|
'channel_mult': [1, 2, 4], 'transformer_depth_middle': -1, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64,
|
|
'use_temporal_attention': False, 'use_temporal_resblock': False}
|
|
|
|
KOALA_700M = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
|
|
'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
|
|
'num_res_blocks': [1, 1, 1], 'transformer_depth': [0, 2, 5], 'transformer_depth_output': [0, 0, 2, 2, 5, 5],
|
|
'channel_mult': [1, 2, 4], 'transformer_depth_middle': -2, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64,
|
|
'use_temporal_attention': False, 'use_temporal_resblock': False}
|
|
|
|
KOALA_1B = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
|
|
'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
|
|
'num_res_blocks': [1, 1, 1], 'transformer_depth': [0, 2, 6], 'transformer_depth_output': [0, 0, 2, 2, 6, 6],
|
|
'channel_mult': [1, 2, 4], 'transformer_depth_middle': 6, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64,
|
|
'use_temporal_attention': False, 'use_temporal_resblock': False}
|
|
|
|
SD09_XS = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
|
|
'adm_in_channels': None, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [1, 1, 1],
|
|
'transformer_depth': [1, 1, 1], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': -2, 'use_linear_in_transformer': True,
|
|
'context_dim': 1024, 'num_head_channels': 64, 'transformer_depth_output': [1, 1, 1, 1, 1, 1],
|
|
'use_temporal_attention': False, 'use_temporal_resblock': False, 'disable_self_attentions': [True, False, False]}
|
|
|
|
SD_XS = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
|
|
'adm_in_channels': None, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [1, 1, 1],
|
|
'transformer_depth': [0, 1, 1], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': -2, 'use_linear_in_transformer': False,
|
|
'context_dim': 768, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 1, 1, 1, 1],
|
|
'use_temporal_attention': False, 'use_temporal_resblock': False}
|
|
|
|
SD15_diffusers_inpaint = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'adm_in_channels': None,
|
|
'dtype': dtype, 'in_channels': 9, 'model_channels': 320, 'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0],
|
|
'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1, 'use_linear_in_transformer': False, 'context_dim': 768, 'num_heads': 8,
|
|
'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],
|
|
'use_temporal_attention': False, 'use_temporal_resblock': False}
|
|
|
|
|
|
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, SD15_diffusers_inpaint]
|
|
|
|
for unet_config in supported_models:
|
|
matches = True
|
|
for k in match:
|
|
if match[k] != unet_config[k]:
|
|
matches = False
|
|
break
|
|
if matches:
|
|
return convert_config(unet_config)
|
|
return None
|
|
|
|
def model_config_from_diffusers_unet(state_dict):
|
|
unet_config = unet_config_from_diffusers_unet(state_dict)
|
|
if unet_config is not None:
|
|
return model_config_from_unet_config(unet_config)
|
|
return None
|
|
|
|
def convert_diffusers_mmdit(state_dict, output_prefix=""):
|
|
out_sd = {}
|
|
|
|
if 'transformer_blocks.0.attn.norm_added_k.weight' in state_dict: #Flux
|
|
depth = count_blocks(state_dict, 'transformer_blocks.{}.')
|
|
depth_single_blocks = count_blocks(state_dict, 'single_transformer_blocks.{}.')
|
|
hidden_size = state_dict["x_embedder.bias"].shape[0]
|
|
sd_map = comfy.utils.flux_to_diffusers({"depth": depth, "depth_single_blocks": depth_single_blocks, "hidden_size": hidden_size}, output_prefix=output_prefix)
|
|
elif 'transformer_blocks.0.attn.add_q_proj.weight' in state_dict: #SD3
|
|
num_blocks = count_blocks(state_dict, 'transformer_blocks.{}.')
|
|
depth = state_dict["pos_embed.proj.weight"].shape[0] // 64
|
|
sd_map = comfy.utils.mmdit_to_diffusers({"depth": depth, "num_blocks": num_blocks}, output_prefix=output_prefix)
|
|
elif 'joint_transformer_blocks.0.attn.add_k_proj.weight' in state_dict: #AuraFlow
|
|
num_joint = count_blocks(state_dict, 'joint_transformer_blocks.{}.')
|
|
num_single = count_blocks(state_dict, 'single_transformer_blocks.{}.')
|
|
sd_map = comfy.utils.auraflow_to_diffusers({"n_double_layers": num_joint, "n_layers": num_joint + num_single}, output_prefix=output_prefix)
|
|
else:
|
|
return None
|
|
|
|
for k in sd_map:
|
|
weight = state_dict.get(k, None)
|
|
if weight is not None:
|
|
t = sd_map[k]
|
|
|
|
if not isinstance(t, str):
|
|
if len(t) > 2:
|
|
fun = t[2]
|
|
else:
|
|
fun = lambda a: a
|
|
offset = t[1]
|
|
if offset is not None:
|
|
old_weight = out_sd.get(t[0], None)
|
|
if old_weight is None:
|
|
old_weight = torch.empty_like(weight)
|
|
if old_weight.shape[offset[0]] < offset[1] + offset[2]:
|
|
exp = list(weight.shape)
|
|
exp[offset[0]] = offset[1] + offset[2]
|
|
new = torch.empty(exp, device=weight.device, dtype=weight.dtype)
|
|
new[:old_weight.shape[0]] = old_weight
|
|
old_weight = new
|
|
|
|
w = old_weight.narrow(offset[0], offset[1], offset[2])
|
|
else:
|
|
old_weight = weight
|
|
w = weight
|
|
w[:] = fun(weight)
|
|
t = t[0]
|
|
out_sd[t] = old_weight
|
|
else:
|
|
out_sd[t] = weight
|
|
state_dict.pop(k)
|
|
|
|
return out_sd
|