mirror of
https://github.com/comfyanonymous/ComfyUI.git
synced 2025-12-19 19:13:02 +08:00
Merge branch 'master' into dr-support-pip-cm
This commit is contained in:
Dr.Lt.Data
commit
6b20418ad1
@ -23,8 +23,6 @@ class MusicDCAE(torch.nn.Module):
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else:
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self.source_sample_rate = source_sample_rate
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# self.resampler = torchaudio.transforms.Resample(source_sample_rate, 44100)
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self.transform = transforms.Compose([
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transforms.Normalize(0.5, 0.5),
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])
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@ -37,10 +35,6 @@ class MusicDCAE(torch.nn.Module):
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self.scale_factor = 0.1786
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self.shift_factor = -1.9091
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def load_audio(self, audio_path):
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audio, sr = torchaudio.load(audio_path)
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return audio, sr
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def forward_mel(self, audios):
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mels = []
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for i in range(len(audios)):
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@ -73,10 +67,8 @@ class MusicDCAE(torch.nn.Module):
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latent = self.dcae.encoder(mel.unsqueeze(0))
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latents.append(latent)
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latents = torch.cat(latents, dim=0)
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# latent_lengths = (audio_lengths / sr * 44100 / 512 / self.time_dimention_multiple).long()
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latents = (latents - self.shift_factor) * self.scale_factor
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return latents
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# return latents, latent_lengths
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@torch.no_grad()
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def decode(self, latents, audio_lengths=None, sr=None):
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@ -91,9 +83,7 @@ class MusicDCAE(torch.nn.Module):
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wav = self.vocoder.decode(mels[0]).squeeze(1)
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if sr is not None:
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# resampler = torchaudio.transforms.Resample(44100, sr).to(latents.device).to(latents.dtype)
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wav = torchaudio.functional.resample(wav, 44100, sr)
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# wav = resampler(wav)
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else:
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sr = 44100
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pred_wavs.append(wav)
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@ -101,7 +91,6 @@ class MusicDCAE(torch.nn.Module):
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if audio_lengths is not None:
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pred_wavs = [wav[:, :length].cpu() for wav, length in zip(pred_wavs, audio_lengths)]
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return torch.stack(pred_wavs)
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# return sr, pred_wavs
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def forward(self, audios, audio_lengths=None, sr=None):
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latents, latent_lengths = self.encode(audios=audios, audio_lengths=audio_lengths, sr=sr)
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@ -365,8 +365,8 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
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dit_config["patch_size"] = 2
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dit_config["in_channels"] = 16
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dit_config["dim"] = 2304
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dit_config["cap_feat_dim"] = 2304
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dit_config["n_layers"] = 26
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dit_config["cap_feat_dim"] = state_dict['{}cap_embedder.1.weight'.format(key_prefix)].shape[1]
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dit_config["n_layers"] = count_blocks(state_dict_keys, '{}layers.'.format(key_prefix) + '{}.')
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dit_config["n_heads"] = 24
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dit_config["n_kv_heads"] = 8
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dit_config["qk_norm"] = True
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@ -890,6 +890,7 @@ class TEModel(Enum):
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QWEN25_3B = 10
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QWEN25_7B = 11
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BYT5_SMALL_GLYPH = 12
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GEMMA_3_4B = 13
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def detect_te_model(sd):
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if "text_model.encoder.layers.30.mlp.fc1.weight" in sd:
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@ -912,6 +913,8 @@ def detect_te_model(sd):
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return TEModel.BYT5_SMALL_GLYPH
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return TEModel.T5_BASE
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if 'model.layers.0.post_feedforward_layernorm.weight' in sd:
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if 'model.layers.0.self_attn.q_norm.weight' in sd:
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return TEModel.GEMMA_3_4B
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return TEModel.GEMMA_2_2B
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if 'model.layers.0.self_attn.k_proj.bias' in sd:
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weight = sd['model.layers.0.self_attn.k_proj.bias']
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@ -1016,6 +1019,10 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
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clip_target.clip = comfy.text_encoders.lumina2.te(**llama_detect(clip_data))
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clip_target.tokenizer = comfy.text_encoders.lumina2.LuminaTokenizer
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tokenizer_data["spiece_model"] = clip_data[0].get("spiece_model", None)
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elif te_model == TEModel.GEMMA_3_4B:
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clip_target.clip = comfy.text_encoders.lumina2.te(**llama_detect(clip_data), model_type="gemma3_4b")
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clip_target.tokenizer = comfy.text_encoders.lumina2.NTokenizer
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tokenizer_data["spiece_model"] = clip_data[0].get("spiece_model", None)
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elif te_model == TEModel.LLAMA3_8:
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clip_target.clip = comfy.text_encoders.hidream.hidream_clip(**llama_detect(clip_data),
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clip_l=False, clip_g=False, t5=False, llama=True, dtype_t5=None, t5xxl_scaled_fp8=None)
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@ -3,6 +3,7 @@ import torch.nn as nn
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from dataclasses import dataclass
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from typing import Optional, Any
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import math
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import logging
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from comfy.ldm.modules.attention import optimized_attention_for_device
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import comfy.model_management
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@ -28,6 +29,9 @@ class Llama2Config:
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mlp_activation = "silu"
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qkv_bias = False
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rope_dims = None
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q_norm = None
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k_norm = None
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rope_scale = None
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@dataclass
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class Qwen25_3BConfig:
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@ -46,6 +50,9 @@ class Qwen25_3BConfig:
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mlp_activation = "silu"
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qkv_bias = True
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rope_dims = None
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q_norm = None
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k_norm = None
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rope_scale = None
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@dataclass
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class Qwen25_7BVLI_Config:
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@ -64,6 +71,9 @@ class Qwen25_7BVLI_Config:
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mlp_activation = "silu"
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qkv_bias = True
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rope_dims = [16, 24, 24]
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q_norm = None
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k_norm = None
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rope_scale = None
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@dataclass
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class Gemma2_2B_Config:
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@ -82,6 +92,32 @@ class Gemma2_2B_Config:
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mlp_activation = "gelu_pytorch_tanh"
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qkv_bias = False
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rope_dims = None
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q_norm = None
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k_norm = None
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sliding_attention = None
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rope_scale = None
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@dataclass
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class Gemma3_4B_Config:
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vocab_size: int = 262208
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hidden_size: int = 2560
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intermediate_size: int = 10240
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num_hidden_layers: int = 34
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num_attention_heads: int = 8
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num_key_value_heads: int = 4
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max_position_embeddings: int = 131072
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rms_norm_eps: float = 1e-6
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rope_theta = [10000.0, 1000000.0]
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transformer_type: str = "gemma3"
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head_dim = 256
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rms_norm_add = True
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mlp_activation = "gelu_pytorch_tanh"
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qkv_bias = False
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rope_dims = None
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q_norm = "gemma3"
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k_norm = "gemma3"
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sliding_attention = [False, False, False, False, False, 1024]
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rope_scale = [1.0, 8.0]
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class RMSNorm(nn.Module):
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def __init__(self, dim: int, eps: float = 1e-5, add=False, device=None, dtype=None):
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@ -106,25 +142,40 @@ def rotate_half(x):
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return torch.cat((-x2, x1), dim=-1)
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def precompute_freqs_cis(head_dim, position_ids, theta, rope_dims=None, device=None):
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theta_numerator = torch.arange(0, head_dim, 2, device=device).float()
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inv_freq = 1.0 / (theta ** (theta_numerator / head_dim))
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def precompute_freqs_cis(head_dim, position_ids, theta, rope_scale=None, rope_dims=None, device=None):
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if not isinstance(theta, list):
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theta = [theta]
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inv_freq_expanded = inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
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position_ids_expanded = position_ids[:, None, :].float()
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freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
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emb = torch.cat((freqs, freqs), dim=-1)
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cos = emb.cos()
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sin = emb.sin()
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if rope_dims is not None and position_ids.shape[0] > 1:
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mrope_section = rope_dims * 2
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cos = torch.cat([m[i % 3] for i, m in enumerate(cos.split(mrope_section, dim=-1))], dim=-1).unsqueeze(0)
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sin = torch.cat([m[i % 3] for i, m in enumerate(sin.split(mrope_section, dim=-1))], dim=-1).unsqueeze(0)
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else:
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cos = cos.unsqueeze(1)
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sin = sin.unsqueeze(1)
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out = []
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for index, t in enumerate(theta):
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theta_numerator = torch.arange(0, head_dim, 2, device=device).float()
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inv_freq = 1.0 / (t ** (theta_numerator / head_dim))
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return (cos, sin)
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if rope_scale is not None:
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if isinstance(rope_scale, list):
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inv_freq /= rope_scale[index]
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else:
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inv_freq /= rope_scale
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inv_freq_expanded = inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
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position_ids_expanded = position_ids[:, None, :].float()
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freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
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emb = torch.cat((freqs, freqs), dim=-1)
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cos = emb.cos()
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sin = emb.sin()
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if rope_dims is not None and position_ids.shape[0] > 1:
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mrope_section = rope_dims * 2
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cos = torch.cat([m[i % 3] for i, m in enumerate(cos.split(mrope_section, dim=-1))], dim=-1).unsqueeze(0)
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sin = torch.cat([m[i % 3] for i, m in enumerate(sin.split(mrope_section, dim=-1))], dim=-1).unsqueeze(0)
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else:
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cos = cos.unsqueeze(1)
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sin = sin.unsqueeze(1)
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out.append((cos, sin))
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if len(out) == 1:
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return out[0]
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return out
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def apply_rope(xq, xk, freqs_cis):
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@ -152,6 +203,14 @@ class Attention(nn.Module):
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self.v_proj = ops.Linear(config.hidden_size, self.num_kv_heads * self.head_dim, bias=config.qkv_bias, device=device, dtype=dtype)
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self.o_proj = ops.Linear(self.inner_size, config.hidden_size, bias=False, device=device, dtype=dtype)
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self.q_norm = None
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self.k_norm = None
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if config.q_norm == "gemma3":
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self.q_norm = RMSNorm(self.head_dim, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype)
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if config.k_norm == "gemma3":
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self.k_norm = RMSNorm(self.head_dim, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype)
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def forward(
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self,
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hidden_states: torch.Tensor,
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@ -168,6 +227,11 @@ class Attention(nn.Module):
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xk = xk.view(batch_size, seq_length, self.num_kv_heads, self.head_dim).transpose(1, 2)
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xv = xv.view(batch_size, seq_length, self.num_kv_heads, self.head_dim).transpose(1, 2)
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if self.q_norm is not None:
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xq = self.q_norm(xq)
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if self.k_norm is not None:
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xk = self.k_norm(xk)
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xq, xk = apply_rope(xq, xk, freqs_cis=freqs_cis)
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xk = xk.repeat_interleave(self.num_heads // self.num_kv_heads, dim=1)
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@ -192,7 +256,7 @@ class MLP(nn.Module):
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return self.down_proj(self.activation(self.gate_proj(x)) * self.up_proj(x))
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class TransformerBlock(nn.Module):
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def __init__(self, config: Llama2Config, device=None, dtype=None, ops: Any = None):
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def __init__(self, config: Llama2Config, index, device=None, dtype=None, ops: Any = None):
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super().__init__()
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self.self_attn = Attention(config, device=device, dtype=dtype, ops=ops)
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self.mlp = MLP(config, device=device, dtype=dtype, ops=ops)
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@ -226,7 +290,7 @@ class TransformerBlock(nn.Module):
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return x
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class TransformerBlockGemma2(nn.Module):
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def __init__(self, config: Llama2Config, device=None, dtype=None, ops: Any = None):
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def __init__(self, config: Llama2Config, index, device=None, dtype=None, ops: Any = None):
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super().__init__()
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self.self_attn = Attention(config, device=device, dtype=dtype, ops=ops)
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self.mlp = MLP(config, device=device, dtype=dtype, ops=ops)
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@ -235,6 +299,13 @@ class TransformerBlockGemma2(nn.Module):
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self.pre_feedforward_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype)
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self.post_feedforward_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype)
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if config.sliding_attention is not None: # TODO: implement. (Not that necessary since models are trained on less than 1024 tokens)
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self.sliding_attention = config.sliding_attention[index % len(config.sliding_attention)]
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else:
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self.sliding_attention = False
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self.transformer_type = config.transformer_type
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def forward(
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self,
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x: torch.Tensor,
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@ -242,6 +313,14 @@ class TransformerBlockGemma2(nn.Module):
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freqs_cis: Optional[torch.Tensor] = None,
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optimized_attention=None,
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):
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if self.transformer_type == 'gemma3':
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if self.sliding_attention:
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if x.shape[1] > self.sliding_attention:
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logging.warning("Warning: sliding attention not implemented, results may be incorrect")
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freqs_cis = freqs_cis[1]
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else:
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freqs_cis = freqs_cis[0]
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# Self Attention
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residual = x
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x = self.input_layernorm(x)
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@ -276,7 +355,7 @@ class Llama2_(nn.Module):
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device=device,
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dtype=dtype
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)
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if self.config.transformer_type == "gemma2":
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if self.config.transformer_type == "gemma2" or self.config.transformer_type == "gemma3":
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transformer = TransformerBlockGemma2
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self.normalize_in = True
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else:
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@ -284,8 +363,8 @@ class Llama2_(nn.Module):
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self.normalize_in = False
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self.layers = nn.ModuleList([
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transformer(config, device=device, dtype=dtype, ops=ops)
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for _ in range(config.num_hidden_layers)
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transformer(config, index=i, device=device, dtype=dtype, ops=ops)
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for i in range(config.num_hidden_layers)
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])
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self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype)
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# self.lm_head = ops.Linear(config.hidden_size, config.vocab_size, bias=False, device=device, dtype=dtype)
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@ -305,6 +384,7 @@ class Llama2_(nn.Module):
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freqs_cis = precompute_freqs_cis(self.config.head_dim,
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position_ids,
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self.config.rope_theta,
|
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self.config.rope_scale,
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self.config.rope_dims,
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device=x.device)
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@ -433,3 +513,12 @@ class Gemma2_2B(BaseLlama, torch.nn.Module):
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self.model = Llama2_(config, device=device, dtype=dtype, ops=operations)
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self.dtype = dtype
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class Gemma3_4B(BaseLlama, torch.nn.Module):
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def __init__(self, config_dict, dtype, device, operations):
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super().__init__()
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config = Gemma3_4B_Config(**config_dict)
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self.num_layers = config.num_hidden_layers
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self.model = Llama2_(config, device=device, dtype=dtype, ops=operations)
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self.dtype = dtype
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@ -11,23 +11,41 @@ class Gemma2BTokenizer(sd1_clip.SDTokenizer):
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def state_dict(self):
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return {"spiece_model": self.tokenizer.serialize_model()}
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class Gemma3_4BTokenizer(sd1_clip.SDTokenizer):
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def __init__(self, embedding_directory=None, tokenizer_data={}):
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tokenizer = tokenizer_data.get("spiece_model", None)
|
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super().__init__(tokenizer, pad_with_end=False, embedding_size=2560, embedding_key='gemma3_4b', tokenizer_class=SPieceTokenizer, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, tokenizer_args={"add_bos": True, "add_eos": False}, tokenizer_data=tokenizer_data)
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|
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def state_dict(self):
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return {"spiece_model": self.tokenizer.serialize_model()}
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class LuminaTokenizer(sd1_clip.SD1Tokenizer):
|
||||
def __init__(self, embedding_directory=None, tokenizer_data={}):
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super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, name="gemma2_2b", tokenizer=Gemma2BTokenizer)
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class NTokenizer(sd1_clip.SD1Tokenizer):
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def __init__(self, embedding_directory=None, tokenizer_data={}):
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super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, name="gemma3_4b", tokenizer=Gemma3_4BTokenizer)
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class Gemma2_2BModel(sd1_clip.SDClipModel):
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def __init__(self, device="cpu", layer="hidden", layer_idx=-2, dtype=None, attention_mask=True, model_options={}):
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||||
super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"start": 2, "pad": 0}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Gemma2_2B, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)
|
||||
|
||||
class Gemma3_4BModel(sd1_clip.SDClipModel):
|
||||
def __init__(self, device="cpu", layer="hidden", layer_idx=-2, dtype=None, attention_mask=True, model_options={}):
|
||||
super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"start": 2, "pad": 0}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Gemma3_4B, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)
|
||||
|
||||
class LuminaModel(sd1_clip.SD1ClipModel):
|
||||
def __init__(self, device="cpu", dtype=None, model_options={}):
|
||||
super().__init__(device=device, dtype=dtype, name="gemma2_2b", clip_model=Gemma2_2BModel, model_options=model_options)
|
||||
def __init__(self, device="cpu", dtype=None, model_options={}, name="gemma2_2b", clip_model=Gemma2_2BModel):
|
||||
super().__init__(device=device, dtype=dtype, name=name, clip_model=clip_model, model_options=model_options)
|
||||
|
||||
|
||||
def te(dtype_llama=None, llama_scaled_fp8=None):
|
||||
def te(dtype_llama=None, llama_scaled_fp8=None, model_type="gemma2_2b"):
|
||||
if model_type == "gemma2_2b":
|
||||
model = Gemma2_2BModel
|
||||
elif model_type == "gemma3_4b":
|
||||
model = Gemma3_4BModel
|
||||
|
||||
class LuminaTEModel_(LuminaModel):
|
||||
def __init__(self, device="cpu", dtype=None, model_options={}):
|
||||
if llama_scaled_fp8 is not None and "scaled_fp8" not in model_options:
|
||||
@ -35,5 +53,5 @@ def te(dtype_llama=None, llama_scaled_fp8=None):
|
||||
model_options["scaled_fp8"] = llama_scaled_fp8
|
||||
if dtype_llama is not None:
|
||||
dtype = dtype_llama
|
||||
super().__init__(device=device, dtype=dtype, model_options=model_options)
|
||||
super().__init__(device=device, dtype=dtype, name=model_type, model_options=model_options, clip_model=model)
|
||||
return LuminaTEModel_
|
||||
|
||||
@ -152,7 +152,7 @@ def validate_aspect_ratio(
|
||||
raise TypeError(
|
||||
f"Aspect ratio cannot reduce to any less than {minimum_ratio_str} ({minimum_ratio}), but was {aspect_ratio} ({calculated_ratio})."
|
||||
)
|
||||
elif calculated_ratio > maximum_ratio:
|
||||
if calculated_ratio > maximum_ratio:
|
||||
raise TypeError(
|
||||
f"Aspect ratio cannot reduce to any greater than {maximum_ratio_str} ({maximum_ratio}), but was {aspect_ratio} ({calculated_ratio})."
|
||||
)
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
@ -473,7 +473,7 @@ class MoonvalleyImg2VideoNode(comfy_io.ComfyNode):
|
||||
height=width_height["height"],
|
||||
use_negative_prompts=True,
|
||||
)
|
||||
"""Upload image to comfy backend to have a URL available for further processing"""
|
||||
|
||||
# Get MIME type from tensor - assuming PNG format for image tensors
|
||||
mime_type = "image/png"
|
||||
|
||||
@ -591,7 +591,6 @@ class MoonvalleyVideo2VideoNode(comfy_io.ComfyNode):
|
||||
validated_video = validate_video_to_video_input(video)
|
||||
video_url = await upload_video_to_comfyapi(validated_video, auth_kwargs=auth)
|
||||
|
||||
"""Validate prompts and inference input"""
|
||||
validate_prompts(prompt, negative_prompt)
|
||||
|
||||
# Only include motion_intensity for Motion Transfer
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
@ -146,7 +146,7 @@ class PixverseTextToVideoNode(comfy_io.ComfyNode):
|
||||
comfy_io.String.Input(
|
||||
"negative_prompt",
|
||||
default="",
|
||||
force_input=True,
|
||||
multiline=True,
|
||||
tooltip="An optional text description of undesired elements on an image.",
|
||||
optional=True,
|
||||
),
|
||||
@ -284,7 +284,7 @@ class PixverseImageToVideoNode(comfy_io.ComfyNode):
|
||||
comfy_io.String.Input(
|
||||
"negative_prompt",
|
||||
default="",
|
||||
force_input=True,
|
||||
multiline=True,
|
||||
tooltip="An optional text description of undesired elements on an image.",
|
||||
optional=True,
|
||||
),
|
||||
@ -425,7 +425,7 @@ class PixverseTransitionVideoNode(comfy_io.ComfyNode):
|
||||
comfy_io.String.Input(
|
||||
"negative_prompt",
|
||||
default="",
|
||||
force_input=True,
|
||||
multiline=True,
|
||||
tooltip="An optional text description of undesired elements on an image.",
|
||||
optional=True,
|
||||
),
|
||||
|
||||
@ -107,7 +107,7 @@ def recraft_multipart_parser(data, parent_key=None, formatter: callable=None, co
|
||||
# if list already exists exists, just extend list with data
|
||||
for check_list in lists_to_check:
|
||||
for conv_tuple in check_list:
|
||||
if conv_tuple[0] == parent_key and type(conv_tuple[1]) is list:
|
||||
if conv_tuple[0] == parent_key and isinstance(conv_tuple[1], list):
|
||||
conv_tuple[1].append(formatter(data))
|
||||
return True
|
||||
return False
|
||||
@ -119,7 +119,7 @@ def recraft_multipart_parser(data, parent_key=None, formatter: callable=None, co
|
||||
if formatter is None:
|
||||
formatter = lambda v: v # Multipart representation of value
|
||||
|
||||
if type(data) is not dict:
|
||||
if not isinstance(data, dict):
|
||||
# if list already exists exists, just extend list with data
|
||||
added = handle_converted_lists(data, parent_key, converted_to_check)
|
||||
if added:
|
||||
@ -136,9 +136,9 @@ def recraft_multipart_parser(data, parent_key=None, formatter: callable=None, co
|
||||
|
||||
for key, value in data.items():
|
||||
current_key = key if parent_key is None else f"{parent_key}[{key}]"
|
||||
if type(value) is dict:
|
||||
if isinstance(value, dict):
|
||||
converted.extend(recraft_multipart_parser(value, current_key, formatter, next_check).items())
|
||||
elif type(value) is list:
|
||||
elif isinstance(value, list):
|
||||
for ind, list_value in enumerate(value):
|
||||
iter_key = f"{current_key}[]"
|
||||
converted.extend(recraft_multipart_parser(list_value, iter_key, formatter, next_check, is_list=True).items())
|
||||
|
||||
@ -360,7 +360,7 @@ class RecordAudio:
|
||||
def load(self, audio):
|
||||
audio_path = folder_paths.get_annotated_filepath(audio)
|
||||
|
||||
waveform, sample_rate = torchaudio.load(audio_path)
|
||||
waveform, sample_rate = load(audio_path)
|
||||
audio = {"waveform": waveform.unsqueeze(0), "sample_rate": sample_rate}
|
||||
return (audio, )
|
||||
|
||||
|
||||
@ -1,3 +1,3 @@
|
||||
# This file is automatically generated by the build process when version is
|
||||
# updated in pyproject.toml.
|
||||
__version__ = "0.3.62"
|
||||
__version__ = "0.3.63"
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
[project]
|
||||
name = "ComfyUI"
|
||||
version = "0.3.62"
|
||||
version = "0.3.63"
|
||||
readme = "README.md"
|
||||
license = { file = "LICENSE" }
|
||||
requires-python = ">=3.9"
|
||||
@ -57,18 +57,14 @@ messages_control.disable = [
|
||||
"redefined-builtin",
|
||||
"unnecessary-lambda",
|
||||
"dangerous-default-value",
|
||||
"invalid-overridden-method",
|
||||
# next warnings should be fixed in future
|
||||
"bad-classmethod-argument", # Class method should have 'cls' as first argument
|
||||
"wrong-import-order", # Standard imports should be placed before third party imports
|
||||
"logging-fstring-interpolation", # Use lazy % formatting in logging functions
|
||||
"ungrouped-imports",
|
||||
"unnecessary-pass",
|
||||
"unidiomatic-typecheck",
|
||||
"unnecessary-lambda-assignment",
|
||||
"no-else-return",
|
||||
"no-else-raise",
|
||||
"invalid-overridden-method",
|
||||
"unused-variable",
|
||||
"pointless-string-statement",
|
||||
"redefined-outer-name",
|
||||
]
|
||||
|
||||
@ -1,5 +1,5 @@
|
||||
comfyui-frontend-package==1.27.7
|
||||
comfyui-workflow-templates==0.1.91
|
||||
comfyui-workflow-templates==0.1.93
|
||||
comfyui-embedded-docs==0.2.6
|
||||
comfyui_manager==4.0.2
|
||||
torch
|
||||
@ -26,6 +26,5 @@ av>=14.2.0
|
||||
#non essential dependencies:
|
||||
kornia>=0.7.1
|
||||
spandrel
|
||||
soundfile
|
||||
pydantic~=2.0
|
||||
pydantic-settings~=2.0
|
||||
|
||||
Loading…
Reference in New Issue
Block a user