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import comfy.memory_management
import comfy.supported_models
import comfy.supported_models_base
import comfy.utils
import math
import logging
import torch
def count_blocks(state_dict_keys, prefix_string):
count = 0
while True:
c = False
for k in state_dict_keys:
if k.startswith(prefix_string.format(count)):
c = True
break
if c == False:
break
count += 1
return count
def any_suffix_in(keys, prefix, main, suffix_list=[]):
for x in suffix_list:
if "{}{}{}".format(prefix, main, x) in keys:
return True
return False
def calculate_transformer_depth(prefix, state_dict_keys, state_dict):
context_dim = None
use_linear_in_transformer = False
transformer_prefix = prefix + "1.transformer_blocks."
transformer_keys = sorted(list(filter(lambda a: a.startswith(transformer_prefix), state_dict_keys)))
if len(transformer_keys) > 0:
last_transformer_depth = count_blocks(state_dict_keys, transformer_prefix + '{}')
context_dim = state_dict['{}0.attn2.to_k.weight'.format(transformer_prefix)].shape[1]
use_linear_in_transformer = len(state_dict['{}1.proj_in.weight'.format(prefix)].shape) == 2
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
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
return last_transformer_depth, context_dim, use_linear_in_transformer, time_stack, time_stack_cross
return None
def detect_unet_config(state_dict, key_prefix, metadata=None):
state_dict_keys = list(state_dict.keys())
if '{}joint_blocks.0.context_block.attn.qkv.weight'.format(key_prefix) in state_dict_keys: #mmdit model
unet_config = {}
unet_config["in_channels"] = state_dict['{}x_embedder.proj.weight'.format(key_prefix)].shape[1]
patch_size = state_dict['{}x_embedder.proj.weight'.format(key_prefix)].shape[2]
unet_config["patch_size"] = patch_size
final_layer = '{}final_layer.linear.weight'.format(key_prefix)
if final_layer in state_dict:
unet_config["out_channels"] = state_dict[final_layer].shape[0] // (patch_size * patch_size)
unet_config["depth"] = state_dict['{}x_embedder.proj.weight'.format(key_prefix)].shape[0] // 64
unet_config["input_size"] = None
y_key = '{}y_embedder.mlp.0.weight'.format(key_prefix)
if y_key in state_dict_keys:
unet_config["adm_in_channels"] = state_dict[y_key].shape[1]
context_key = '{}context_embedder.weight'.format(key_prefix)
if context_key in state_dict_keys:
in_features = state_dict[context_key].shape[1]
out_features = state_dict[context_key].shape[0]
unet_config["context_embedder_config"] = {"target": "torch.nn.Linear", "params": {"in_features": in_features, "out_features": out_features}}
num_patches_key = '{}pos_embed'.format(key_prefix)
if num_patches_key in state_dict_keys:
num_patches = state_dict[num_patches_key].shape[1]
unet_config["num_patches"] = num_patches
unet_config["pos_embed_max_size"] = round(math.sqrt(num_patches))
rms_qk = '{}joint_blocks.0.context_block.attn.ln_q.weight'.format(key_prefix)
if rms_qk in state_dict_keys:
unet_config["qk_norm"] = "rms"
unet_config["pos_embed_scaling_factor"] = None #unused for inference
context_processor = '{}context_processor.layers.0.attn.qkv.weight'.format(key_prefix)
if context_processor in state_dict_keys:
unet_config["context_processor_layers"] = count_blocks(state_dict_keys, '{}context_processor.layers.'.format(key_prefix) + '{}.')
unet_config["x_block_self_attn_layers"] = []
for key in state_dict_keys:
if key.startswith('{}joint_blocks.'.format(key_prefix)) and key.endswith('.x_block.attn2.qkv.weight'):
layer = key[len('{}joint_blocks.'.format(key_prefix)):-len('.x_block.attn2.qkv.weight')]
unet_config["x_block_self_attn_layers"].append(int(layer))
return unet_config
if '{}clf.1.weight'.format(key_prefix) in state_dict_keys: #stable cascade
unet_config = {}
text_mapper_name = '{}clip_txt_mapper.weight'.format(key_prefix)
if text_mapper_name in state_dict_keys:
unet_config['stable_cascade_stage'] = 'c'
w = state_dict[text_mapper_name]
if w.shape[0] == 1536: #stage c lite
unet_config['c_cond'] = 1536
unet_config['c_hidden'] = [1536, 1536]
unet_config['nhead'] = [24, 24]
unet_config['blocks'] = [[4, 12], [12, 4]]
elif w.shape[0] == 2048: #stage c full
unet_config['c_cond'] = 2048
elif '{}clip_mapper.weight'.format(key_prefix) in state_dict_keys:
unet_config['stable_cascade_stage'] = 'b'
w = state_dict['{}down_blocks.1.0.channelwise.0.weight'.format(key_prefix)]
if w.shape[-1] == 640:
unet_config['c_hidden'] = [320, 640, 1280, 1280]
unet_config['nhead'] = [-1, -1, 20, 20]
unet_config['blocks'] = [[2, 6, 28, 6], [6, 28, 6, 2]]
unet_config['block_repeat'] = [[1, 1, 1, 1], [3, 3, 2, 2]]
elif w.shape[-1] == 576: #stage b lite
unet_config['c_hidden'] = [320, 576, 1152, 1152]
unet_config['nhead'] = [-1, 9, 18, 18]
unet_config['blocks'] = [[2, 4, 14, 4], [4, 14, 4, 2]]
unet_config['block_repeat'] = [[1, 1, 1, 1], [2, 2, 2, 2]]
return unet_config
if '{}transformer.rotary_pos_emb.inv_freq'.format(key_prefix) in state_dict_keys: #stable audio dit
unet_config = {}
unet_config["audio_model"] = "dit1.0"
return unet_config
if '{}double_layers.0.attn.w1q.weight'.format(key_prefix) in state_dict_keys: #aura flow dit
unet_config = {}
unet_config["max_seq"] = state_dict['{}positional_encoding'.format(key_prefix)].shape[1]
unet_config["cond_seq_dim"] = state_dict['{}cond_seq_linear.weight'.format(key_prefix)].shape[1]
double_layers = count_blocks(state_dict_keys, '{}double_layers.'.format(key_prefix) + '{}.')
single_layers = count_blocks(state_dict_keys, '{}single_layers.'.format(key_prefix) + '{}.')
unet_config["n_double_layers"] = double_layers
unet_config["n_layers"] = double_layers + single_layers
return unet_config
if '{}mlp_t5.0.weight'.format(key_prefix) in state_dict_keys: #Hunyuan DiT
unet_config = {}
unet_config["image_model"] = "hydit"
unet_config["depth"] = count_blocks(state_dict_keys, '{}blocks.'.format(key_prefix) + '{}.')
unet_config["hidden_size"] = state_dict['{}x_embedder.proj.weight'.format(key_prefix)].shape[0]
if unet_config["hidden_size"] == 1408 and unet_config["depth"] == 40: #DiT-g/2
unet_config["mlp_ratio"] = 4.3637
if state_dict['{}extra_embedder.0.weight'.format(key_prefix)].shape[1] == 3968:
unet_config["size_cond"] = True
unet_config["use_style_cond"] = True
unet_config["image_model"] = "hydit1"
return unet_config
if '{}txt_in.individual_token_refiner.blocks.0.norm1.weight'.format(key_prefix) in state_dict_keys: #Hunyuan Video
dit_config = {}
in_w = state_dict['{}img_in.proj.weight'.format(key_prefix)]
out_w = state_dict['{}final_layer.linear.weight'.format(key_prefix)]
dit_config["image_model"] = "hunyuan_video"
dit_config["in_channels"] = in_w.shape[1] #SkyReels img2video has 32 input channels
dit_config["patch_size"] = list(in_w.shape[2:])
dit_config["out_channels"] = out_w.shape[0] // math.prod(dit_config["patch_size"])
if any(s.startswith('{}vector_in.'.format(key_prefix)) for s in state_dict_keys):
dit_config["vec_in_dim"] = 768
else:
dit_config["vec_in_dim"] = None
if len(dit_config["patch_size"]) == 2:
dit_config["axes_dim"] = [64, 64]
else:
dit_config["axes_dim"] = [16, 56, 56]
if any(s.startswith('{}time_r_in.'.format(key_prefix)) for s in state_dict_keys):
dit_config["meanflow"] = True
else:
dit_config["meanflow"] = False
dit_config["context_in_dim"] = state_dict['{}txt_in.input_embedder.weight'.format(key_prefix)].shape[1]
dit_config["hidden_size"] = in_w.shape[0]
dit_config["mlp_ratio"] = 4.0
dit_config["num_heads"] = in_w.shape[0] // 128
dit_config["depth"] = count_blocks(state_dict_keys, '{}double_blocks.'.format(key_prefix) + '{}.')
dit_config["depth_single_blocks"] = count_blocks(state_dict_keys, '{}single_blocks.'.format(key_prefix) + '{}.')
dit_config["theta"] = 256
dit_config["qkv_bias"] = True
if '{}byt5_in.fc1.weight'.format(key_prefix) in state_dict:
dit_config["byt5"] = True
else:
dit_config["byt5"] = False
guidance_keys = list(filter(lambda a: a.startswith("{}guidance_in.".format(key_prefix)), state_dict_keys))
dit_config["guidance_embed"] = len(guidance_keys) > 0
# HunyuanVideo 1.5
if '{}cond_type_embedding.weight'.format(key_prefix) in state_dict_keys:
dit_config["use_cond_type_embedding"] = True
else:
dit_config["use_cond_type_embedding"] = False
if '{}vision_in.proj.0.weight'.format(key_prefix) in state_dict_keys:
dit_config["vision_in_dim"] = state_dict['{}vision_in.proj.0.weight'.format(key_prefix)].shape[0]
dit_config["meanflow_sum"] = True
else:
dit_config["vision_in_dim"] = None
dit_config["meanflow_sum"] = False
return dit_config
if any_suffix_in(state_dict_keys, key_prefix, 'double_blocks.0.img_attn.norm.key_norm.', ["weight", "scale"]) and ('{}img_in.weight'.format(key_prefix) in state_dict_keys or any_suffix_in(state_dict_keys, key_prefix, 'distilled_guidance_layer.norms.0.', ["weight", "scale"])): #Flux, Chroma or Chroma Radiance (has no img_in.weight)
dit_config = {}
if '{}double_stream_modulation_img.lin.weight'.format(key_prefix) in state_dict_keys:
dit_config["image_model"] = "flux2"
dit_config["axes_dim"] = [32, 32, 32, 32]
dit_config["num_heads"] = 48
dit_config["mlp_ratio"] = 3.0
dit_config["theta"] = 2000
dit_config["out_channels"] = 128
dit_config["global_modulation"] = True
dit_config["mlp_silu_act"] = True
dit_config["qkv_bias"] = False
dit_config["ops_bias"] = False
dit_config["default_ref_method"] = "index"
dit_config["ref_index_scale"] = 10.0
dit_config["txt_ids_dims"] = [3]
patch_size = 1
else:
dit_config["image_model"] = "flux"
dit_config["axes_dim"] = [16, 56, 56]
dit_config["num_heads"] = 24
dit_config["mlp_ratio"] = 4.0
dit_config["theta"] = 10000
dit_config["out_channels"] = 16
dit_config["qkv_bias"] = True
dit_config["txt_ids_dims"] = []
patch_size = 2
dit_config["in_channels"] = 16
dit_config["hidden_size"] = 3072
dit_config["context_in_dim"] = 4096
dit_config["patch_size"] = patch_size
in_key = "{}img_in.weight".format(key_prefix)
if in_key in state_dict_keys:
w = state_dict[in_key]
dit_config["in_channels"] = w.shape[1] // (patch_size * patch_size)
dit_config["hidden_size"] = w.shape[0]
txt_in_key = "{}txt_in.weight".format(key_prefix)
if txt_in_key in state_dict_keys:
w = state_dict[txt_in_key]
dit_config["context_in_dim"] = w.shape[1]
dit_config["hidden_size"] = w.shape[0]
vec_in_key = '{}vector_in.in_layer.weight'.format(key_prefix)
if vec_in_key in state_dict_keys:
dit_config["vec_in_dim"] = state_dict[vec_in_key].shape[1]
else:
dit_config["vec_in_dim"] = None
dit_config["num_heads"] = dit_config["hidden_size"] // sum(dit_config["axes_dim"])
dit_config["depth"] = count_blocks(state_dict_keys, '{}double_blocks.'.format(key_prefix) + '{}.')
dit_config["depth_single_blocks"] = count_blocks(state_dict_keys, '{}single_blocks.'.format(key_prefix) + '{}.')
if any_suffix_in(state_dict_keys, key_prefix, 'distilled_guidance_layer.0.norms.0.', ["weight", "scale"]) or any_suffix_in(state_dict_keys, key_prefix, 'distilled_guidance_layer.norms.0.', ["weight", "scale"]): #Chroma
dit_config["image_model"] = "chroma"
dit_config["in_channels"] = 64
dit_config["out_channels"] = 64
dit_config["in_dim"] = 64
dit_config["out_dim"] = 3072
dit_config["hidden_dim"] = 5120
dit_config["n_layers"] = 5
if any_suffix_in(state_dict_keys, key_prefix, 'nerf_blocks.0.norm.', ["weight", "scale"]): #Chroma Radiance
dit_config["image_model"] = "chroma_radiance"
dit_config["in_channels"] = 3
dit_config["out_channels"] = 3
dit_config["patch_size"] = state_dict.get('{}img_in_patch.weight'.format(key_prefix)).size(dim=-1)
dit_config["nerf_hidden_size"] = 64
dit_config["nerf_mlp_ratio"] = 4
dit_config["nerf_depth"] = 4
dit_config["nerf_max_freqs"] = 8
dit_config["nerf_tile_size"] = 512
dit_config["nerf_final_head_type"] = "conv" if any_suffix_in(state_dict_keys, key_prefix, 'nerf_final_layer_conv.norm.', ["weight", "scale"]) else "linear"
dit_config["nerf_embedder_dtype"] = torch.float32
if "{}__x0__".format(key_prefix) in state_dict_keys: # x0 pred
dit_config["use_x0"] = True
else:
dit_config["use_x0"] = False
else:
dit_config["guidance_embed"] = "{}guidance_in.in_layer.weight".format(key_prefix) in state_dict_keys
dit_config["yak_mlp"] = '{}double_blocks.0.img_mlp.gate_proj.weight'.format(key_prefix) in state_dict_keys
dit_config["txt_norm"] = any_suffix_in(state_dict_keys, key_prefix, 'txt_norm.', ["weight", "scale"])
if dit_config["yak_mlp"] and dit_config["txt_norm"]: # Ovis model
dit_config["txt_ids_dims"] = [1, 2]
if dit_config.get("context_in_dim") == 3584 and dit_config["vec_in_dim"] is None: # LongCat-Image
dit_config["txt_ids_dims"] = [1, 2]
return dit_config
if '{}t5_yproj.weight'.format(key_prefix) in state_dict_keys: #Genmo mochi preview
dit_config = {}
dit_config["image_model"] = "mochi_preview"
dit_config["depth"] = 48
dit_config["patch_size"] = 2
dit_config["num_heads"] = 24
dit_config["hidden_size_x"] = 3072
dit_config["hidden_size_y"] = 1536
dit_config["mlp_ratio_x"] = 4.0
dit_config["mlp_ratio_y"] = 4.0
dit_config["learn_sigma"] = False
dit_config["in_channels"] = 12
dit_config["qk_norm"] = True
dit_config["qkv_bias"] = False
dit_config["out_bias"] = True
dit_config["attn_drop"] = 0.0
dit_config["patch_embed_bias"] = True
dit_config["posenc_preserve_area"] = True
dit_config["timestep_mlp_bias"] = True
dit_config["attend_to_padding"] = False
dit_config["timestep_scale"] = 1000.0
dit_config["use_t5"] = True
dit_config["t5_feat_dim"] = 4096
dit_config["t5_token_length"] = 256
dit_config["rope_theta"] = 10000.0
return dit_config
if '{}adaln_single.emb.timestep_embedder.linear_1.bias'.format(key_prefix) in state_dict_keys and '{}pos_embed.proj.bias'.format(key_prefix) in state_dict_keys:
# PixArt diffusers
return None
if '{}adaln_single.emb.timestep_embedder.linear_1.bias'.format(key_prefix) in state_dict_keys: #Lightricks ltxv
dit_config = {}
dit_config["image_model"] = "ltxav" if f'{key_prefix}audio_adaln_single.linear.weight' in state_dict_keys else "ltxv"
dit_config["num_layers"] = count_blocks(state_dict_keys, '{}transformer_blocks.'.format(key_prefix) + '{}.')
shape = state_dict['{}transformer_blocks.0.attn2.to_k.weight'.format(key_prefix)].shape
dit_config["attention_head_dim"] = shape[0] // 32
dit_config["cross_attention_dim"] = shape[1]
if metadata is not None and "config" in metadata:
dit_config.update(json.loads(metadata["config"]).get("transformer", {}))
return dit_config
if '{}genre_embedder.weight'.format(key_prefix) in state_dict_keys: #ACE-Step model
dit_config = {}
dit_config["audio_model"] = "ace"
dit_config["attention_head_dim"] = 128
dit_config["in_channels"] = 8
dit_config["inner_dim"] = 2560
dit_config["max_height"] = 16
dit_config["max_position"] = 32768
dit_config["max_width"] = 32768
dit_config["mlp_ratio"] = 2.5
dit_config["num_attention_heads"] = 20
dit_config["num_layers"] = 24
dit_config["out_channels"] = 8
dit_config["patch_size"] = [16, 1]
dit_config["rope_theta"] = 1000000.0
dit_config["speaker_embedding_dim"] = 512
dit_config["text_embedding_dim"] = 768
dit_config["ssl_encoder_depths"] = [8, 8]
dit_config["ssl_latent_dims"] = [1024, 768]
dit_config["ssl_names"] = ["mert", "m-hubert"]
dit_config["lyric_encoder_vocab_size"] = 6693
dit_config["lyric_hidden_size"] = 1024
return dit_config
if '{}t_block.1.weight'.format(key_prefix) in state_dict_keys: # PixArt
patch_size = 2
dit_config = {}
dit_config["num_heads"] = 16
dit_config["patch_size"] = patch_size
dit_config["hidden_size"] = 1152
dit_config["in_channels"] = 4
dit_config["depth"] = count_blocks(state_dict_keys, '{}blocks.'.format(key_prefix) + '{}.')
y_key = "{}y_embedder.y_embedding".format(key_prefix)
if y_key in state_dict_keys:
dit_config["model_max_length"] = state_dict[y_key].shape[0]
pe_key = "{}pos_embed".format(key_prefix)
if pe_key in state_dict_keys:
dit_config["input_size"] = int(math.sqrt(state_dict[pe_key].shape[1])) * patch_size
dit_config["pe_interpolation"] = dit_config["input_size"] // (512//8) # guess
ar_key = "{}ar_embedder.mlp.0.weight".format(key_prefix)
if ar_key in state_dict_keys:
dit_config["image_model"] = "pixart_alpha"
dit_config["micro_condition"] = True
else:
dit_config["image_model"] = "pixart_sigma"
dit_config["micro_condition"] = False
return dit_config
if '{}blocks.block0.blocks.0.block.attn.to_q.0.weight'.format(key_prefix) in state_dict_keys: # Cosmos
dit_config = {}
dit_config["image_model"] = "cosmos"
dit_config["max_img_h"] = 240
dit_config["max_img_w"] = 240
dit_config["max_frames"] = 128
concat_padding_mask = True
dit_config["in_channels"] = (state_dict['{}x_embedder.proj.1.weight'.format(key_prefix)].shape[1] // 4) - int(concat_padding_mask)
dit_config["out_channels"] = 16
dit_config["patch_spatial"] = 2
dit_config["patch_temporal"] = 1
dit_config["model_channels"] = state_dict['{}blocks.block0.blocks.0.block.attn.to_q.0.weight'.format(key_prefix)].shape[0]
dit_config["block_config"] = "FA-CA-MLP"
dit_config["concat_padding_mask"] = concat_padding_mask
dit_config["pos_emb_cls"] = "rope3d"
dit_config["pos_emb_learnable"] = False
dit_config["pos_emb_interpolation"] = "crop"
dit_config["block_x_format"] = "THWBD"
dit_config["affline_emb_norm"] = True
dit_config["use_adaln_lora"] = True
dit_config["adaln_lora_dim"] = 256
if dit_config["model_channels"] == 4096:
# 7B
dit_config["num_blocks"] = 28
dit_config["num_heads"] = 32
dit_config["extra_per_block_abs_pos_emb"] = True
dit_config["rope_h_extrapolation_ratio"] = 1.0
dit_config["rope_w_extrapolation_ratio"] = 1.0
dit_config["rope_t_extrapolation_ratio"] = 2.0
dit_config["extra_per_block_abs_pos_emb_type"] = "learnable"
else: # 5120
# 14B
dit_config["num_blocks"] = 36
dit_config["num_heads"] = 40
dit_config["extra_per_block_abs_pos_emb"] = True
dit_config["rope_h_extrapolation_ratio"] = 2.0
dit_config["rope_w_extrapolation_ratio"] = 2.0
dit_config["rope_t_extrapolation_ratio"] = 2.0
dit_config["extra_h_extrapolation_ratio"] = 2.0
dit_config["extra_w_extrapolation_ratio"] = 2.0
dit_config["extra_t_extrapolation_ratio"] = 2.0
dit_config["extra_per_block_abs_pos_emb_type"] = "learnable"
return dit_config
if '{}cap_embedder.1.weight'.format(key_prefix) in state_dict_keys and '{}noise_refiner.0.attention.k_norm.weight'.format(key_prefix) in state_dict_keys: # Lumina 2
dit_config = {}
dit_config["image_model"] = "lumina2"
dit_config["patch_size"] = 2
dit_config["in_channels"] = 16
w = state_dict['{}cap_embedder.1.weight'.format(key_prefix)]
dit_config["dim"] = w.shape[0]
dit_config["cap_feat_dim"] = w.shape[1]
dit_config["n_layers"] = count_blocks(state_dict_keys, '{}layers.'.format(key_prefix) + '{}.')
dit_config["qk_norm"] = True
if dit_config["dim"] == 2304: # Original Lumina 2
dit_config["n_heads"] = 24
dit_config["n_kv_heads"] = 8
dit_config["axes_dims"] = [32, 32, 32]
dit_config["axes_lens"] = [300, 512, 512]
dit_config["rope_theta"] = 10000.0
dit_config["ffn_dim_multiplier"] = 4.0
ctd_weight = state_dict.get('{}clip_text_pooled_proj.0.weight'.format(key_prefix), None)
if ctd_weight is not None: # NewBie
dit_config["clip_text_dim"] = ctd_weight.shape[0]
# NewBie also sets axes_lens = [1024, 512, 512] but it's not used in ComfyUI
elif dit_config["dim"] == 3840: # Z image
dit_config["n_heads"] = 30
dit_config["n_kv_heads"] = 30
dit_config["axes_dims"] = [32, 48, 48]
dit_config["axes_lens"] = [1536, 512, 512]
dit_config["rope_theta"] = 256.0
dit_config["ffn_dim_multiplier"] = (8.0 / 3.0)
dit_config["z_image_modulation"] = True
dit_config["time_scale"] = 1000.0
try:
dit_config["allow_fp16"] = torch.std(state_dict['{}layers.{}.ffn_norm1.weight'.format(key_prefix, dit_config["n_layers"] - 2)], unbiased=False).item() < 0.42
except Exception:
pass
if '{}cap_pad_token'.format(key_prefix) in state_dict_keys:
dit_config["pad_tokens_multiple"] = 32
sig_weight = state_dict.get('{}siglip_embedder.0.weight'.format(key_prefix), None)
if sig_weight is not None:
dit_config["siglip_feat_dim"] = sig_weight.shape[0]
dec_cond_key = '{}dec_net.cond_embed.weight'.format(key_prefix)
if dec_cond_key in state_dict_keys: # pixel-space variant
dit_config["image_model"] = "zimage_pixel"
# patch_size and in_channels are derived from x_embedder:
# x_embedder: Linear(patch_size * patch_size * in_channels, dim)
# The decoder also receives the full flat patch, so decoder_in_channels = x_embedder input dim.
x_emb_in = state_dict['{}x_embedder.weight'.format(key_prefix)].shape[1]
dec_out = state_dict['{}dec_net.final_layer.linear.weight'.format(key_prefix)].shape[0]
# patch_size: infer from decoder final layer output matching x_embedder input
# in_channels: infer from dec_net input_embedder (in_features = dec_in_ch + max_freqs^2)
embedder_w = state_dict['{}dec_net.input_embedder.embedder.0.weight'.format(key_prefix)]
dec_in_ch = dec_out # decoder in == decoder out (same pixel space)
dit_config["patch_size"] = round((x_emb_in / 3) ** 0.5) # assume RGB (in_channels=3)
dit_config["in_channels"] = 3
dit_config["decoder_in_channels"] = dec_in_ch
dit_config["decoder_hidden_size"] = state_dict[dec_cond_key].shape[0]
dit_config["decoder_num_res_blocks"] = count_blocks(
state_dict_keys, '{}dec_net.res_blocks.'.format(key_prefix) + '{}.'
)
dit_config["decoder_max_freqs"] = int((embedder_w.shape[1] - dec_in_ch) ** 0.5)
if '{}__x0__'.format(key_prefix) in state_dict_keys:
dit_config["use_x0"] = True
return dit_config
if '{}head.modulation'.format(key_prefix) in state_dict_keys: # Wan 2.1
dit_config = {}
dit_config["image_model"] = "wan2.1"
dim = state_dict['{}head.modulation'.format(key_prefix)].shape[-1]
out_dim = state_dict['{}head.head.weight'.format(key_prefix)].shape[0] // 4
dit_config["dim"] = dim
dit_config["out_dim"] = out_dim
dit_config["num_heads"] = dim // 128
dit_config["ffn_dim"] = state_dict['{}blocks.0.ffn.0.weight'.format(key_prefix)].shape[0]
dit_config["num_layers"] = count_blocks(state_dict_keys, '{}blocks.'.format(key_prefix) + '{}.')
dit_config["patch_size"] = (1, 2, 2)
dit_config["freq_dim"] = 256
dit_config["window_size"] = (-1, -1)
dit_config["qk_norm"] = True
dit_config["cross_attn_norm"] = True
dit_config["eps"] = 1e-6
dit_config["in_dim"] = state_dict['{}patch_embedding.weight'.format(key_prefix)].shape[1]
if '{}vace_patch_embedding.weight'.format(key_prefix) in state_dict_keys:
dit_config["model_type"] = "vace"
dit_config["vace_in_dim"] = state_dict['{}vace_patch_embedding.weight'.format(key_prefix)].shape[1]
dit_config["vace_layers"] = count_blocks(state_dict_keys, '{}vace_blocks.'.format(key_prefix) + '{}.')
elif '{}control_adapter.conv.weight'.format(key_prefix) in state_dict_keys:
if '{}img_emb.proj.0.bias'.format(key_prefix) in state_dict_keys:
dit_config["model_type"] = "camera"
else:
dit_config["model_type"] = "camera_2.2"
elif '{}casual_audio_encoder.encoder.final_linear.weight'.format(key_prefix) in state_dict_keys:
dit_config["model_type"] = "s2v"
elif '{}audio_proj.audio_proj_glob_1.layer.bias'.format(key_prefix) in state_dict_keys:
dit_config["model_type"] = "humo"
elif '{}face_adapter.fuser_blocks.0.k_norm.weight'.format(key_prefix) in state_dict_keys:
dit_config["model_type"] = "animate"
elif '{}patch_embedding_pose.weight'.format(key_prefix) in state_dict_keys:
dit_config["model_type"] = "scail"
else:
if '{}img_emb.proj.0.bias'.format(key_prefix) in state_dict_keys:
dit_config["model_type"] = "i2v"
else:
dit_config["model_type"] = "t2v"
flf_weight = state_dict.get('{}img_emb.emb_pos'.format(key_prefix))
if flf_weight is not None:
dit_config["flf_pos_embed_token_number"] = flf_weight.shape[1]
ref_conv_weight = state_dict.get('{}ref_conv.weight'.format(key_prefix))
if ref_conv_weight is not None:
dit_config["in_dim_ref_conv"] = ref_conv_weight.shape[1]
if metadata is not None and "config" in metadata:
dit_config.update(json.loads(metadata["config"]).get("transformer", {}))
return dit_config
if '{}latent_in.weight'.format(key_prefix) in state_dict_keys: # Hunyuan 3D
in_shape = state_dict['{}latent_in.weight'.format(key_prefix)].shape
dit_config = {}
dit_config["image_model"] = "hunyuan3d2"
dit_config["in_channels"] = in_shape[1]
dit_config["context_in_dim"] = state_dict['{}cond_in.weight'.format(key_prefix)].shape[1]
dit_config["hidden_size"] = in_shape[0]
dit_config["mlp_ratio"] = 4.0
dit_config["num_heads"] = 16
dit_config["depth"] = count_blocks(state_dict_keys, '{}double_blocks.'.format(key_prefix) + '{}.')
dit_config["depth_single_blocks"] = count_blocks(state_dict_keys, '{}single_blocks.'.format(key_prefix) + '{}.')
dit_config["qkv_bias"] = True
dit_config["guidance_embed"] = "{}guidance_in.in_layer.weight".format(key_prefix) in state_dict_keys
return dit_config
if f"{key_prefix}t_embedder.mlp.2.weight" in state_dict_keys and f"{key_prefix}blocks.0.attn1.k_norm.weight" in state_dict_keys: # Hunyuan 3D 2.1
dit_config = {}
dit_config["image_model"] = "hunyuan3d2_1"
dit_config["in_channels"] = state_dict[f"{key_prefix}x_embedder.weight"].shape[1]
dit_config["context_dim"] = 1024
dit_config["hidden_size"] = state_dict[f"{key_prefix}x_embedder.weight"].shape[0]
dit_config["mlp_ratio"] = 4.0
dit_config["num_heads"] = 16
dit_config["depth"] = count_blocks(state_dict_keys, f"{key_prefix}blocks.{{}}")
dit_config["qkv_bias"] = False
dit_config["guidance_cond_proj_dim"] = None#f"{key_prefix}t_embedder.cond_proj.weight" in state_dict_keys
return dit_config
if '{}caption_projection.0.linear.weight'.format(key_prefix) in state_dict_keys: # HiDream
dit_config = {}
dit_config["image_model"] = "hidream"
dit_config["attention_head_dim"] = 128
dit_config["axes_dims_rope"] = [64, 32, 32]
dit_config["caption_channels"] = [4096, 4096]
dit_config["max_resolution"] = [128, 128]
dit_config["in_channels"] = 16
dit_config["llama_layers"] = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31]
dit_config["num_attention_heads"] = 20
dit_config["num_routed_experts"] = 4
dit_config["num_activated_experts"] = 2
dit_config["num_layers"] = 16
dit_config["num_single_layers"] = 32
dit_config["out_channels"] = 16
dit_config["patch_size"] = 2
dit_config["text_emb_dim"] = 2048
return dit_config
if '{}blocks.0.mlp.layer1.weight'.format(key_prefix) in state_dict_keys: # Cosmos predict2
dit_config = {}
dit_config["image_model"] = "cosmos_predict2"
if "{}llm_adapter.blocks.0.cross_attn.q_proj.weight".format(key_prefix) in state_dict_keys:
dit_config["image_model"] = "anima"
dit_config["max_img_h"] = 240
dit_config["max_img_w"] = 240
dit_config["max_frames"] = 128
concat_padding_mask = True
dit_config["in_channels"] = (state_dict['{}x_embedder.proj.1.weight'.format(key_prefix)].shape[1] // 4) - int(concat_padding_mask)
dit_config["out_channels"] = 16
dit_config["patch_spatial"] = 2
dit_config["patch_temporal"] = 1
dit_config["model_channels"] = state_dict['{}x_embedder.proj.1.weight'.format(key_prefix)].shape[0]
dit_config["concat_padding_mask"] = concat_padding_mask
dit_config["crossattn_emb_channels"] = 1024
dit_config["pos_emb_cls"] = "rope3d"
dit_config["pos_emb_learnable"] = True
dit_config["pos_emb_interpolation"] = "crop"
dit_config["min_fps"] = 1
dit_config["max_fps"] = 30
dit_config["use_adaln_lora"] = True
dit_config["adaln_lora_dim"] = 256
if dit_config["model_channels"] == 2048:
dit_config["num_blocks"] = 28
dit_config["num_heads"] = 16
elif dit_config["model_channels"] == 5120:
dit_config["num_blocks"] = 36
dit_config["num_heads"] = 40
if dit_config["in_channels"] == 16:
dit_config["extra_per_block_abs_pos_emb"] = False
dit_config["rope_h_extrapolation_ratio"] = 4.0
dit_config["rope_w_extrapolation_ratio"] = 4.0
dit_config["rope_t_extrapolation_ratio"] = 1.0
elif dit_config["in_channels"] == 17: # img to video
if dit_config["model_channels"] == 2048:
dit_config["extra_per_block_abs_pos_emb"] = False
dit_config["rope_h_extrapolation_ratio"] = 3.0
dit_config["rope_w_extrapolation_ratio"] = 3.0
dit_config["rope_t_extrapolation_ratio"] = 1.0
elif dit_config["model_channels"] == 5120:
dit_config["rope_h_extrapolation_ratio"] = 2.0
dit_config["rope_w_extrapolation_ratio"] = 2.0
dit_config["rope_t_extrapolation_ratio"] = 0.8333333333333334
dit_config["extra_h_extrapolation_ratio"] = 1.0
dit_config["extra_w_extrapolation_ratio"] = 1.0
dit_config["extra_t_extrapolation_ratio"] = 1.0
dit_config["rope_enable_fps_modulation"] = False
return dit_config
if '{}time_caption_embed.timestep_embedder.linear_1.bias'.format(key_prefix) in state_dict_keys: # Omnigen2
dit_config = {}
dit_config["image_model"] = "omnigen2"
dit_config["axes_dim_rope"] = [40, 40, 40]
dit_config["axes_lens"] = [1024, 1664, 1664]
dit_config["ffn_dim_multiplier"] = None
dit_config["hidden_size"] = 2520
dit_config["in_channels"] = 16
dit_config["multiple_of"] = 256
dit_config["norm_eps"] = 1e-05
dit_config["num_attention_heads"] = 21
dit_config["num_kv_heads"] = 7
dit_config["num_layers"] = 32
dit_config["num_refiner_layers"] = 2
dit_config["out_channels"] = None
dit_config["patch_size"] = 2
dit_config["text_feat_dim"] = 2048
dit_config["timestep_scale"] = 1000.0
return dit_config
if '{}txt_norm.weight'.format(key_prefix) in state_dict_keys: # Qwen Image
dit_config = {}
dit_config["image_model"] = "qwen_image"
dit_config["in_channels"] = state_dict['{}img_in.weight'.format(key_prefix)].shape[1]
dit_config["num_layers"] = count_blocks(state_dict_keys, '{}transformer_blocks.'.format(key_prefix) + '{}.')
if "{}__index_timestep_zero__".format(key_prefix) in state_dict_keys: # 2511
dit_config["default_ref_method"] = "index_timestep_zero"
if "{}time_text_embed.addition_t_embedding.weight".format(key_prefix) in state_dict_keys: # Layered
dit_config["use_additional_t_cond"] = True
dit_config["default_ref_method"] = "negative_index"
return dit_config
if '{}visual_transformer_blocks.0.cross_attention.key_norm.weight'.format(key_prefix) in state_dict_keys: # Kandinsky 5
dit_config = {}
model_dim = state_dict['{}visual_embeddings.in_layer.bias'.format(key_prefix)].shape[0]
dit_config["model_dim"] = model_dim
if model_dim in [4096, 2560]: # pro video and lite image
dit_config["axes_dims"] = (32, 48, 48)
if model_dim == 2560: # lite image
dit_config["rope_scale_factor"] = (1.0, 1.0, 1.0)
elif model_dim == 1792: # lite video
dit_config["axes_dims"] = (16, 24, 24)
dit_config["time_dim"] = state_dict['{}time_embeddings.in_layer.bias'.format(key_prefix)].shape[0]
dit_config["image_model"] = "kandinsky5"
dit_config["ff_dim"] = state_dict['{}visual_transformer_blocks.0.feed_forward.in_layer.weight'.format(key_prefix)].shape[0]
dit_config["visual_embed_dim"] = state_dict['{}visual_embeddings.in_layer.weight'.format(key_prefix)].shape[1]
dit_config["num_text_blocks"] = count_blocks(state_dict_keys, '{}text_transformer_blocks.'.format(key_prefix) + '{}.')
dit_config["num_visual_blocks"] = count_blocks(state_dict_keys, '{}visual_transformer_blocks.'.format(key_prefix) + '{}.')
return dit_config
if '{}encoder.lyric_encoder.layers.0.input_layernorm.weight'.format(key_prefix) in state_dict_keys:
dit_config = {}
dit_config["audio_model"] = "ace1.5"
return dit_config
if '{}encoder.pan_blocks.1.cv4.conv.weight'.format(key_prefix) in state_dict_keys: # RT-DETR_v4
dit_config = {}
dit_config["image_model"] = "RT_DETR_v4"
dit_config["enc_h"] = state_dict['{}encoder.pan_blocks.1.cv4.conv.weight'.format(key_prefix)].shape[0]
return dit_config
if '{}input_blocks.0.0.weight'.format(key_prefix) not in state_dict_keys:
return None
unet_config = {
"use_checkpoint": False,
"image_size": 32,
"use_spatial_transformer": True,
"legacy": False
}
y_input = '{}label_emb.0.0.weight'.format(key_prefix)
if y_input in state_dict_keys:
unet_config["num_classes"] = "sequential"
unet_config["adm_in_channels"] = state_dict[y_input].shape[1]
else:
unet_config["adm_in_channels"] = None
model_channels = state_dict['{}input_blocks.0.0.weight'.format(key_prefix)].shape[0]
in_channels = state_dict['{}input_blocks.0.0.weight'.format(key_prefix)].shape[1]
out_key = '{}out.2.weight'.format(key_prefix)
if out_key in state_dict:
out_channels = state_dict[out_key].shape[0]
else:
out_channels = 4
num_res_blocks = []
channel_mult = []
transformer_depth = []
transformer_depth_output = []
context_dim = None
use_linear_in_transformer = False
video_model = False
video_model_cross = False
current_res = 1
count = 0
last_res_blocks = 0
last_channel_mult = 0
input_block_count = count_blocks(state_dict_keys, '{}input_blocks'.format(key_prefix) + '.{}.')
for count in range(input_block_count):
prefix = '{}input_blocks.{}.'.format(key_prefix, count)
prefix_output = '{}output_blocks.{}.'.format(key_prefix, input_block_count - count - 1)
block_keys = sorted(list(filter(lambda a: a.startswith(prefix), state_dict_keys)))
if len(block_keys) == 0:
break
block_keys_output = sorted(list(filter(lambda a: a.startswith(prefix_output), state_dict_keys)))
if "{}0.op.weight".format(prefix) in block_keys: #new layer
num_res_blocks.append(last_res_blocks)
channel_mult.append(last_channel_mult)
current_res *= 2
last_res_blocks = 0
last_channel_mult = 0
out = calculate_transformer_depth(prefix_output, state_dict_keys, state_dict)
if out is not None:
transformer_depth_output.append(out[0])
else:
transformer_depth_output.append(0)
else:
res_block_prefix = "{}0.in_layers.0.weight".format(prefix)
if res_block_prefix in block_keys:
last_res_blocks += 1
last_channel_mult = state_dict["{}0.out_layers.3.weight".format(prefix)].shape[0] // model_channels
out = calculate_transformer_depth(prefix, state_dict_keys, state_dict)
if out is not None:
transformer_depth.append(out[0])
if context_dim is None:
context_dim = out[1]
use_linear_in_transformer = out[2]
video_model = out[3]
video_model_cross = out[4]
else:
transformer_depth.append(0)
res_block_prefix = "{}0.in_layers.0.weight".format(prefix_output)
if res_block_prefix in block_keys_output:
out = calculate_transformer_depth(prefix_output, state_dict_keys, state_dict)
if out is not None:
transformer_depth_output.append(out[0])
else:
transformer_depth_output.append(0)
num_res_blocks.append(last_res_blocks)
channel_mult.append(last_channel_mult)
if "{}middle_block.1.proj_in.weight".format(key_prefix) in state_dict_keys:
transformer_depth_middle = count_blocks(state_dict_keys, '{}middle_block.1.transformer_blocks.'.format(key_prefix) + '{}')
elif "{}middle_block.0.in_layers.0.weight".format(key_prefix) in state_dict_keys:
transformer_depth_middle = -1
else:
transformer_depth_middle = -2
unet_config["in_channels"] = in_channels
unet_config["out_channels"] = out_channels
unet_config["model_channels"] = model_channels
unet_config["num_res_blocks"] = num_res_blocks
unet_config["transformer_depth"] = transformer_depth
unet_config["transformer_depth_output"] = transformer_depth_output
unet_config["channel_mult"] = channel_mult
unet_config["transformer_depth_middle"] = transformer_depth_middle
unet_config['use_linear_in_transformer'] = use_linear_in_transformer
unet_config["context_dim"] = context_dim
if video_model:
unet_config["extra_ff_mix_layer"] = True
unet_config["use_spatial_context"] = True
unet_config["merge_strategy"] = "learned_with_images"
unet_config["merge_factor"] = 0.0
unet_config["video_kernel_size"] = [3, 1, 1]
unet_config["use_temporal_resblock"] = True
unet_config["use_temporal_attention"] = True
unet_config["disable_temporal_crossattention"] = not video_model_cross
else:
unet_config["use_temporal_resblock"] = False
unet_config["use_temporal_attention"] = False
heatmap_key = '{}heatmap_head.conv_layers.0.weight'.format(key_prefix)
if heatmap_key in state_dict_keys:
unet_config["heatmap_head"] = True
return unet_config
def model_config_from_unet_config(unet_config, state_dict=None):
for model_config in comfy.supported_models.models:
if model_config.matches(unet_config, state_dict):
return model_config(unet_config)
logging.error("no match {}".format(unet_config))
return None
def model_config_from_unet(state_dict, unet_key_prefix, use_base_if_no_match=False, metadata=None):
unet_config = detect_unet_config(state_dict, unet_key_prefix, metadata=metadata)
if unet_config is None:
return None
model_config = model_config_from_unet_config(unet_config, state_dict)
if model_config is None and use_base_if_no_match:
model_config = comfy.supported_models_base.BASE(unet_config)
# Detect per-layer quantization (mixed precision)
quant_config = comfy.utils.detect_layer_quantization(state_dict, unet_key_prefix)
if quant_config:
model_config.quant_config = quant_config
logging.info("Detected mixed precision quantization")
return model_config
def unet_prefix_from_state_dict(state_dict):
candidates = ["model.diffusion_model.", #ldm/sgm models
"model.model.", #audio models
"net.", #cosmos
]
counts = {k: 0 for k in candidates}
for k in state_dict:
for c in candidates:
if k.startswith(c):
counts[c] += 1
break
top = max(counts, key=counts.get)
if counts[top] > 5:
return top
else:
return "model." #aura flow and others
def convert_config(unet_config):
new_config = unet_config.copy()
num_res_blocks = new_config.get("num_res_blocks", None)
channel_mult = new_config.get("channel_mult", None)
if isinstance(num_res_blocks, int):
num_res_blocks = len(channel_mult) * [num_res_blocks]
if "attention_resolutions" in new_config:
attention_resolutions = new_config.pop("attention_resolutions")
transformer_depth = new_config.get("transformer_depth", None)
transformer_depth_middle = new_config.get("transformer_depth_middle", None)
if isinstance(transformer_depth, int):
transformer_depth = len(channel_mult) * [transformer_depth]
if transformer_depth_middle is None:
transformer_depth_middle = transformer_depth[-1]
t_in = []
t_out = []
s = 1
for i in range(len(num_res_blocks)):
res = num_res_blocks[i]
d = 0
if s in attention_resolutions:
d = transformer_depth[i]
t_in += [d] * res
t_out += [d] * (res + 1)
s *= 2
transformer_depth = t_in
new_config["transformer_depth"] = t_in
new_config["transformer_depth_output"] = t_out
new_config["transformer_depth_middle"] = transformer_depth_middle
new_config["num_res_blocks"] = num_res_blocks
return new_config
def unet_config_from_diffusers_unet(state_dict, dtype=None):
if "conv_in.weight" not in state_dict:
return None
match = {}
transformer_depth = []
attn_res = 1
down_blocks = count_blocks(state_dict, "down_blocks.{}")
for i in range(down_blocks):
attn_blocks = count_blocks(state_dict, "down_blocks.{}.attentions.".format(i) + '{}')
res_blocks = count_blocks(state_dict, "down_blocks.{}.resnets.".format(i) + '{}')
for ab in range(attn_blocks):
transformer_count = count_blocks(state_dict, "down_blocks.{}.attentions.{}.transformer_blocks.".format(i, ab) + '{}')
transformer_depth.append(transformer_count)
if transformer_count > 0:
match["context_dim"] = state_dict["down_blocks.{}.attentions.{}.transformer_blocks.0.attn2.to_k.weight".format(i, ab)].shape[1]
attn_res *= 2
if attn_blocks == 0:
for i in range(res_blocks):
transformer_depth.append(0)
match["transformer_depth"] = transformer_depth
match["model_channels"] = state_dict["conv_in.weight"].shape[0]
match["in_channels"] = state_dict["conv_in.weight"].shape[1]
match["adm_in_channels"] = None
if "class_embedding.linear_1.weight" in state_dict:
match["adm_in_channels"] = state_dict["class_embedding.linear_1.weight"].shape[1]
elif "add_embedding.linear_1.weight" in state_dict:
match["adm_in_channels"] = state_dict["add_embedding.linear_1.weight"].shape[1]
SDXL = {'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, 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_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}
LotusD = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'adm_in_channels': 4,
'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}
supported_models = [LotusD, 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 '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)
elif 'adaln_single.emb.timestep_embedder.linear_1.bias' in state_dict and 'pos_embed.proj.bias' in state_dict: # PixArt
num_blocks = count_blocks(state_dict, 'transformer_blocks.{}.')
sd_map = comfy.utils.pixart_to_diffusers({"depth": num_blocks}, output_prefix=output_prefix)
elif 'noise_refiner.0.attention.norm_k.weight' in state_dict:
n_layers = count_blocks(state_dict, 'layers.{}.')
dim = state_dict['noise_refiner.0.attention.to_k.weight'].shape[0]
sd_map = comfy.utils.z_image_to_diffusers({"n_layers": n_layers, "dim": dim}, output_prefix=output_prefix)
for k in state_dict: # For zeta chroma
if k not in sd_map:
sd_map[k] = k
elif 'x_embedder.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 and 'pos_embed.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)
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
if old_weight is out_sd.get(t[0], None) and comfy.memory_management.aimdo_enabled:
old_weight = old_weight.clone()
w = old_weight.narrow(offset[0], offset[1], offset[2])
else:
if comfy.memory_management.aimdo_enabled:
weight = weight.clone()
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
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