SD3 Support.
comfyanonymous
9 months ago
parent
a5e6a632f9
commit
8c4a9befa7
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from comfy import sd1_clip
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from comfy import sdxl_clip
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from transformers import T5TokenizerFast
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import comfy.t5
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import torch
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import os
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import comfy.model_management
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class T5XXLModel(sd1_clip.SDClipModel):
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def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None):
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textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_config_xxl.json")
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super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 1, "pad": 0}, model_class=comfy.t5.T5)
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class T5XXLTokenizer(sd1_clip.SDTokenizer):
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def __init__(self, embedding_directory=None):
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tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer")
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super().__init__(tokenizer_path, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=77)
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class SDT5XXLTokenizer(sd1_clip.SD1Tokenizer):
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def __init__(self, embedding_directory=None):
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super().__init__(embedding_directory=embedding_directory, clip_name="t5xxl", tokenizer=T5XXLTokenizer)
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class SDT5XXLModel(sd1_clip.SD1ClipModel):
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def __init__(self, device="cpu", dtype=None, **kwargs):
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super().__init__(device=device, dtype=dtype, clip_name="t5xxl", clip_model=T5XXLModel, **kwargs)
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class SD3Tokenizer:
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def __init__(self, embedding_directory=None):
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self.clip_l = sd1_clip.SDTokenizer(embedding_directory=embedding_directory)
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self.clip_g = sdxl_clip.SDXLClipGTokenizer(embedding_directory=embedding_directory)
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self.t5xxl = T5XXLTokenizer(embedding_directory=embedding_directory)
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def tokenize_with_weights(self, text:str, return_word_ids=False):
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out = {}
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out["g"] = self.clip_g.tokenize_with_weights(text, return_word_ids)
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out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids)
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out["t5xxl"] = self.t5xxl.tokenize_with_weights(text, return_word_ids)
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return out
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def untokenize(self, token_weight_pair):
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return self.clip_g.untokenize(token_weight_pair)
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class SD3ClipModel(torch.nn.Module):
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def __init__(self, device="cpu", dtype=None):
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super().__init__()
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self.clip_l = sd1_clip.SDClipModel(layer="hidden", layer_idx=-2, device=device, dtype=dtype, layer_norm_hidden_state=False, return_projected_pooled=False)
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self.clip_g = sdxl_clip.SDXLClipG(device=device, dtype=dtype)
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self.t5xxl = T5XXLModel(device=device, dtype=dtype)
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def set_clip_options(self, options):
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self.clip_l.set_clip_options(options)
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self.clip_g.set_clip_options(options)
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self.t5xxl.set_clip_options(options)
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def reset_clip_options(self):
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self.clip_g.reset_clip_options()
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self.clip_l.reset_clip_options()
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self.t5xxl.reset_clip_options()
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def encode_token_weights(self, token_weight_pairs):
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token_weight_pairs_l = token_weight_pairs["l"]
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token_weight_pairs_g = token_weight_pairs["g"]
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token_weight_pars_t5 = token_weight_pairs["t5xxl"]
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lg_out = None
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if len(token_weight_pairs_g) > 0 or len(token_weight_pairs_l) > 0:
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l_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l)
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g_out, g_pooled = self.clip_g.encode_token_weights(token_weight_pairs_g)
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lg_out = torch.cat([l_out, g_out], dim=-1)
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lg_out = torch.nn.functional.pad(lg_out, (0, 4096 - lg_out.shape[-1]))
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out = lg_out
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pooled = torch.cat((l_pooled, g_pooled), dim=-1)
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else:
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pooled = torch.zeros((1, 1280 + 768), device=comfy.model_management.intermediate_device())
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t5_out, t5_pooled = self.t5xxl.encode_token_weights(token_weight_pars_t5)
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if lg_out is not None:
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out = torch.cat([lg_out, t5_out], dim=-2)
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else:
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out = t5_out
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return out, pooled
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def load_sd(self, sd):
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if "text_model.encoder.layers.30.mlp.fc1.weight" in sd:
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return self.clip_g.load_sd(sd)
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elif "text_model.encoder.layers.1.mlp.fc1.weight" in sd:
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return self.clip_l.load_sd(sd)
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else:
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return self.t5xxl.load_sd(sd)
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import torch
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import math
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from comfy.ldm.modules.attention import optimized_attention_for_device
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class T5LayerNorm(torch.nn.Module):
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def __init__(self, hidden_size, eps=1e-6, dtype=None, device=None, operations=None):
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super().__init__()
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self.weight = torch.nn.Parameter(torch.empty(hidden_size, dtype=dtype, device=device))
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self.variance_epsilon = eps
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def forward(self, x):
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variance = x.pow(2).mean(-1, keepdim=True)
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x = x * torch.rsqrt(variance + self.variance_epsilon)
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return self.weight.to(device=x.device, dtype=x.dtype) * x
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class T5DenseActDense(torch.nn.Module):
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def __init__(self, model_dim, ff_dim, dtype, device, operations):
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super().__init__()
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self.wi = operations.Linear(model_dim, ff_dim, bias=False, dtype=dtype, device=device)
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self.wo = operations.Linear(ff_dim, model_dim, bias=False, dtype=dtype, device=device)
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# self.dropout = nn.Dropout(config.dropout_rate)
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def forward(self, x):
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x = torch.nn.functional.relu(self.wi(x))
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# x = self.dropout(x)
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x = self.wo(x)
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return x
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class T5DenseGatedActDense(torch.nn.Module):
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def __init__(self, model_dim, ff_dim, dtype, device, operations):
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super().__init__()
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self.wi_0 = operations.Linear(model_dim, ff_dim, bias=False, dtype=dtype, device=device)
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self.wi_1 = operations.Linear(model_dim, ff_dim, bias=False, dtype=dtype, device=device)
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self.wo = operations.Linear(ff_dim, model_dim, bias=False, dtype=dtype, device=device)
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# self.dropout = nn.Dropout(config.dropout_rate)
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def forward(self, x):
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hidden_gelu = torch.nn.functional.gelu(self.wi_0(x), approximate="tanh")
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hidden_linear = self.wi_1(x)
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x = hidden_gelu * hidden_linear
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# x = self.dropout(x)
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x = self.wo(x)
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return x
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class T5LayerFF(torch.nn.Module):
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def __init__(self, model_dim, ff_dim, ff_activation, dtype, device, operations):
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super().__init__()
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if ff_activation == "gelu_pytorch_tanh":
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self.DenseReluDense = T5DenseGatedActDense(model_dim, ff_dim, dtype, device, operations)
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elif ff_activation == "relu":
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self.DenseReluDense = T5DenseActDense(model_dim, ff_dim, dtype, device, operations)
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self.layer_norm = T5LayerNorm(model_dim, dtype=dtype, device=device, operations=operations)
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# self.dropout = nn.Dropout(config.dropout_rate)
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def forward(self, x):
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forwarded_states = self.layer_norm(x)
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forwarded_states = self.DenseReluDense(forwarded_states)
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# x = x + self.dropout(forwarded_states)
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x += forwarded_states
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return x
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class T5Attention(torch.nn.Module):
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def __init__(self, model_dim, inner_dim, num_heads, relative_attention_bias, dtype, device, operations):
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super().__init__()
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# Mesh TensorFlow initialization to avoid scaling before softmax
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self.q = operations.Linear(model_dim, inner_dim, bias=False, dtype=dtype, device=device)
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self.k = operations.Linear(model_dim, inner_dim, bias=False, dtype=dtype, device=device)
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self.v = operations.Linear(model_dim, inner_dim, bias=False, dtype=dtype, device=device)
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self.o = operations.Linear(inner_dim, model_dim, bias=False, dtype=dtype, device=device)
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self.num_heads = num_heads
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self.relative_attention_bias = None
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if relative_attention_bias:
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self.relative_attention_num_buckets = 32
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self.relative_attention_max_distance = 128
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self.relative_attention_bias = torch.nn.Embedding(self.relative_attention_num_buckets, self.num_heads, device=device)
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@staticmethod
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def _relative_position_bucket(relative_position, bidirectional=True, num_buckets=32, max_distance=128):
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"""
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Adapted from Mesh Tensorflow:
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https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593
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Translate relative position to a bucket number for relative attention. The relative position is defined as
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memory_position - query_position, i.e. the distance in tokens from the attending position to the attended-to
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position. If bidirectional=False, then positive relative positions are invalid. We use smaller buckets for
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small absolute relative_position and larger buckets for larger absolute relative_positions. All relative
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positions >=max_distance map to the same bucket. All relative positions <=-max_distance map to the same bucket.
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This should allow for more graceful generalization to longer sequences than the model has been trained on
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Args:
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relative_position: an int32 Tensor
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bidirectional: a boolean - whether the attention is bidirectional
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num_buckets: an integer
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max_distance: an integer
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Returns:
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a Tensor with the same shape as relative_position, containing int32 values in the range [0, num_buckets)
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"""
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relative_buckets = 0
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if bidirectional:
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num_buckets //= 2
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relative_buckets += (relative_position > 0).to(torch.long) * num_buckets
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relative_position = torch.abs(relative_position)
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else:
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relative_position = -torch.min(relative_position, torch.zeros_like(relative_position))
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# now relative_position is in the range [0, inf)
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# half of the buckets are for exact increments in positions
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max_exact = num_buckets // 2
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is_small = relative_position < max_exact
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# The other half of the buckets are for logarithmically bigger bins in positions up to max_distance
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relative_position_if_large = max_exact + (
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torch.log(relative_position.float() / max_exact)
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/ math.log(max_distance / max_exact)
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* (num_buckets - max_exact)
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).to(torch.long)
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relative_position_if_large = torch.min(
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relative_position_if_large, torch.full_like(relative_position_if_large, num_buckets - 1)
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)
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relative_buckets += torch.where(is_small, relative_position, relative_position_if_large)
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return relative_buckets
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def compute_bias(self, query_length, key_length, device):
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"""Compute binned relative position bias"""
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context_position = torch.arange(query_length, dtype=torch.long, device=device)[:, None]
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memory_position = torch.arange(key_length, dtype=torch.long, device=device)[None, :]
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relative_position = memory_position - context_position # shape (query_length, key_length)
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relative_position_bucket = self._relative_position_bucket(
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relative_position, # shape (query_length, key_length)
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bidirectional=True,
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num_buckets=self.relative_attention_num_buckets,
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max_distance=self.relative_attention_max_distance,
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)
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values = self.relative_attention_bias(relative_position_bucket) # shape (query_length, key_length, num_heads)
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values = values.permute([2, 0, 1]).unsqueeze(0) # shape (1, num_heads, query_length, key_length)
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return values
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def forward(self, x, mask=None, past_bias=None, optimized_attention=None):
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q = self.q(x)
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k = self.k(x)
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v = self.v(x)
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if self.relative_attention_bias is not None:
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past_bias = self.compute_bias(x.shape[1], x.shape[1], x.device)
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if past_bias is not None:
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if mask is not None:
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mask = mask + past_bias
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else:
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mask = past_bias
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out = optimized_attention(q, k * ((k.shape[-1] / self.num_heads) ** 0.5), v, self.num_heads, mask)
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return self.o(out), past_bias
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class T5LayerSelfAttention(torch.nn.Module):
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def __init__(self, model_dim, inner_dim, ff_dim, num_heads, relative_attention_bias, dtype, device, operations):
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super().__init__()
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self.SelfAttention = T5Attention(model_dim, inner_dim, num_heads, relative_attention_bias, dtype, device, operations)
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self.layer_norm = T5LayerNorm(model_dim, dtype=dtype, device=device, operations=operations)
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# self.dropout = nn.Dropout(config.dropout_rate)
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def forward(self, x, mask=None, past_bias=None, optimized_attention=None):
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normed_hidden_states = self.layer_norm(x)
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output, past_bias = self.SelfAttention(self.layer_norm(x), mask=mask, past_bias=past_bias, optimized_attention=optimized_attention)
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# x = x + self.dropout(attention_output)
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x += output
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return x, past_bias
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class T5Block(torch.nn.Module):
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def __init__(self, model_dim, inner_dim, ff_dim, ff_activation, num_heads, relative_attention_bias, dtype, device, operations):
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super().__init__()
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self.layer = torch.nn.ModuleList()
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self.layer.append(T5LayerSelfAttention(model_dim, inner_dim, ff_dim, num_heads, relative_attention_bias, dtype, device, operations))
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self.layer.append(T5LayerFF(model_dim, ff_dim, ff_activation, dtype, device, operations))
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def forward(self, x, mask=None, past_bias=None, optimized_attention=None):
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x, past_bias = self.layer[0](x, mask, past_bias, optimized_attention)
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x = self.layer[-1](x)
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return x, past_bias
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class T5Stack(torch.nn.Module):
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def __init__(self, num_layers, model_dim, inner_dim, ff_dim, ff_activation, num_heads, dtype, device, operations):
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super().__init__()
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self.block = torch.nn.ModuleList(
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[T5Block(model_dim, inner_dim, ff_dim, ff_activation, num_heads, relative_attention_bias=(i == 0), dtype=dtype, device=device, operations=operations) for i in range(num_layers)]
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)
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self.final_layer_norm = T5LayerNorm(model_dim, dtype=dtype, device=device, operations=operations)
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# self.dropout = nn.Dropout(config.dropout_rate)
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def forward(self, x, attention_mask=None, intermediate_output=None, final_layer_norm_intermediate=True):
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mask = None
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if attention_mask is not None:
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mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])).expand(attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1])
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mask = mask.masked_fill(mask.to(torch.bool), float("-inf"))
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intermediate = None
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optimized_attention = optimized_attention_for_device(x.device, mask=attention_mask is not None, small_input=True)
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past_bias = None
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for i, l in enumerate(self.block):
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x, past_bias = l(x, mask, past_bias, optimized_attention)
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if i == intermediate_output:
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intermediate = x.clone()
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x = self.final_layer_norm(x)
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if intermediate is not None and final_layer_norm_intermediate:
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intermediate = self.final_layer_norm(intermediate)
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return x, intermediate
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class T5(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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self.num_layers = config_dict["num_layers"]
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model_dim = config_dict["d_model"]
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self.encoder = T5Stack(self.num_layers, model_dim, model_dim, config_dict["d_ff"], config_dict["dense_act_fn"], config_dict["num_heads"], dtype, device, operations)
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self.dtype = dtype
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self.shared = torch.nn.Embedding(config_dict["vocab_size"], model_dim, device=device)
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def get_input_embeddings(self):
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return self.shared
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def set_input_embeddings(self, embeddings):
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self.shared = embeddings
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def forward(self, input_ids, *args, **kwargs):
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x = self.shared(input_ids)
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return self.encoder(x, *args, **kwargs)
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{
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"d_ff": 3072,
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"d_kv": 64,
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"d_model": 768,
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"decoder_start_token_id": 0,
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"dropout_rate": 0.1,
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"eos_token_id": 1,
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"dense_act_fn": "relu",
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"initializer_factor": 1.0,
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"is_encoder_decoder": true,
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"layer_norm_epsilon": 1e-06,
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"model_type": "t5",
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"num_decoder_layers": 12,
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"num_heads": 12,
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"num_layers": 12,
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"output_past": true,
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"pad_token_id": 0,
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"relative_attention_num_buckets": 32,
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"tie_word_embeddings": false,
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"vocab_size": 32128
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}
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@ -0,0 +1,21 @@
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{
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"d_ff": 10240,
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"d_kv": 64,
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"d_model": 4096,
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"decoder_start_token_id": 0,
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"dropout_rate": 0.1,
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"eos_token_id": 1,
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"dense_act_fn": "gelu_pytorch_tanh",
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"initializer_factor": 1.0,
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"is_encoder_decoder": true,
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"layer_norm_epsilon": 1e-06,
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"model_type": "t5",
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"num_decoder_layers": 24,
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"num_heads": 64,
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"num_layers": 24,
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"output_past": true,
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"pad_token_id": 0,
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"relative_attention_num_buckets": 32,
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"tie_word_embeddings": false,
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"vocab_size": 32128
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}
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@ -0,0 +1,125 @@
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||||
{
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|
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File diff suppressed because one or more lines are too long
@ -0,0 +1,939 @@
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
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||||
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|
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||||
"32049": {
|
||||
"content": "<extra_id_50>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32050": {
|
||||
"content": "<extra_id_49>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32051": {
|
||||
"content": "<extra_id_48>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32052": {
|
||||
"content": "<extra_id_47>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32053": {
|
||||
"content": "<extra_id_46>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32054": {
|
||||
"content": "<extra_id_45>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32055": {
|
||||
"content": "<extra_id_44>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32056": {
|
||||
"content": "<extra_id_43>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32057": {
|
||||
"content": "<extra_id_42>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32058": {
|
||||
"content": "<extra_id_41>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32059": {
|
||||
"content": "<extra_id_40>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32060": {
|
||||
"content": "<extra_id_39>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32061": {
|
||||
"content": "<extra_id_38>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32062": {
|
||||
"content": "<extra_id_37>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32063": {
|
||||
"content": "<extra_id_36>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32064": {
|
||||
"content": "<extra_id_35>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32065": {
|
||||
"content": "<extra_id_34>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32066": {
|
||||
"content": "<extra_id_33>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32067": {
|
||||
"content": "<extra_id_32>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32068": {
|
||||
"content": "<extra_id_31>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32069": {
|
||||
"content": "<extra_id_30>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32070": {
|
||||
"content": "<extra_id_29>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32071": {
|
||||
"content": "<extra_id_28>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32072": {
|
||||
"content": "<extra_id_27>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32073": {
|
||||
"content": "<extra_id_26>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32074": {
|
||||
"content": "<extra_id_25>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32075": {
|
||||
"content": "<extra_id_24>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32076": {
|
||||
"content": "<extra_id_23>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32077": {
|
||||
"content": "<extra_id_22>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32078": {
|
||||
"content": "<extra_id_21>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32079": {
|
||||
"content": "<extra_id_20>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32080": {
|
||||
"content": "<extra_id_19>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32081": {
|
||||
"content": "<extra_id_18>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32082": {
|
||||
"content": "<extra_id_17>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32083": {
|
||||
"content": "<extra_id_16>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32084": {
|
||||
"content": "<extra_id_15>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32085": {
|
||||
"content": "<extra_id_14>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32086": {
|
||||
"content": "<extra_id_13>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32087": {
|
||||
"content": "<extra_id_12>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32088": {
|
||||
"content": "<extra_id_11>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32089": {
|
||||
"content": "<extra_id_10>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32090": {
|
||||
"content": "<extra_id_9>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32091": {
|
||||
"content": "<extra_id_8>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32092": {
|
||||
"content": "<extra_id_7>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32093": {
|
||||
"content": "<extra_id_6>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32094": {
|
||||
"content": "<extra_id_5>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32095": {
|
||||
"content": "<extra_id_4>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32096": {
|
||||
"content": "<extra_id_3>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32097": {
|
||||
"content": "<extra_id_2>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32098": {
|
||||
"content": "<extra_id_1>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32099": {
|
||||
"content": "<extra_id_0>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<extra_id_0>",
|
||||
"<extra_id_1>",
|
||||
"<extra_id_2>",
|
||||
"<extra_id_3>",
|
||||
"<extra_id_4>",
|
||||
"<extra_id_5>",
|
||||
"<extra_id_6>",
|
||||
"<extra_id_7>",
|
||||
"<extra_id_8>",
|
||||
"<extra_id_9>",
|
||||
"<extra_id_10>",
|
||||
"<extra_id_11>",
|
||||
"<extra_id_12>",
|
||||
"<extra_id_13>",
|
||||
"<extra_id_14>",
|
||||
"<extra_id_15>",
|
||||
"<extra_id_16>",
|
||||
"<extra_id_17>",
|
||||
"<extra_id_18>",
|
||||
"<extra_id_19>",
|
||||
"<extra_id_20>",
|
||||
"<extra_id_21>",
|
||||
"<extra_id_22>",
|
||||
"<extra_id_23>",
|
||||
"<extra_id_24>",
|
||||
"<extra_id_25>",
|
||||
"<extra_id_26>",
|
||||
"<extra_id_27>",
|
||||
"<extra_id_28>",
|
||||
"<extra_id_29>",
|
||||
"<extra_id_30>",
|
||||
"<extra_id_31>",
|
||||
"<extra_id_32>",
|
||||
"<extra_id_33>",
|
||||
"<extra_id_34>",
|
||||
"<extra_id_35>",
|
||||
"<extra_id_36>",
|
||||
"<extra_id_37>",
|
||||
"<extra_id_38>",
|
||||
"<extra_id_39>",
|
||||
"<extra_id_40>",
|
||||
"<extra_id_41>",
|
||||
"<extra_id_42>",
|
||||
"<extra_id_43>",
|
||||
"<extra_id_44>",
|
||||
"<extra_id_45>",
|
||||
"<extra_id_46>",
|
||||
"<extra_id_47>",
|
||||
"<extra_id_48>",
|
||||
"<extra_id_49>",
|
||||
"<extra_id_50>",
|
||||
"<extra_id_51>",
|
||||
"<extra_id_52>",
|
||||
"<extra_id_53>",
|
||||
"<extra_id_54>",
|
||||
"<extra_id_55>",
|
||||
"<extra_id_56>",
|
||||
"<extra_id_57>",
|
||||
"<extra_id_58>",
|
||||
"<extra_id_59>",
|
||||
"<extra_id_60>",
|
||||
"<extra_id_61>",
|
||||
"<extra_id_62>",
|
||||
"<extra_id_63>",
|
||||
"<extra_id_64>",
|
||||
"<extra_id_65>",
|
||||
"<extra_id_66>",
|
||||
"<extra_id_67>",
|
||||
"<extra_id_68>",
|
||||
"<extra_id_69>",
|
||||
"<extra_id_70>",
|
||||
"<extra_id_71>",
|
||||
"<extra_id_72>",
|
||||
"<extra_id_73>",
|
||||
"<extra_id_74>",
|
||||
"<extra_id_75>",
|
||||
"<extra_id_76>",
|
||||
"<extra_id_77>",
|
||||
"<extra_id_78>",
|
||||
"<extra_id_79>",
|
||||
"<extra_id_80>",
|
||||
"<extra_id_81>",
|
||||
"<extra_id_82>",
|
||||
"<extra_id_83>",
|
||||
"<extra_id_84>",
|
||||
"<extra_id_85>",
|
||||
"<extra_id_86>",
|
||||
"<extra_id_87>",
|
||||
"<extra_id_88>",
|
||||
"<extra_id_89>",
|
||||
"<extra_id_90>",
|
||||
"<extra_id_91>",
|
||||
"<extra_id_92>",
|
||||
"<extra_id_93>",
|
||||
"<extra_id_94>",
|
||||
"<extra_id_95>",
|
||||
"<extra_id_96>",
|
||||
"<extra_id_97>",
|
||||
"<extra_id_98>",
|
||||
"<extra_id_99>"
|
||||
],
|
||||
"clean_up_tokenization_spaces": true,
|
||||
"eos_token": "</s>",
|
||||
"extra_ids": 100,
|
||||
"legacy": false,
|
||||
"model_max_length": 512,
|
||||
"pad_token": "<pad>",
|
||||
"sp_model_kwargs": {},
|
||||
"tokenizer_class": "T5Tokenizer",
|
||||
"unk_token": "<unk>"
|
||||
}
|
||||
@ -0,0 +1,87 @@
|
||||
import folder_paths
|
||||
import comfy.sd
|
||||
import comfy.model_management
|
||||
import nodes
|
||||
import torch
|
||||
|
||||
class TripleCLIPLoader:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {"required": { "clip_name1": (folder_paths.get_filename_list("clip"), ), "clip_name2": (folder_paths.get_filename_list("clip"), ), "clip_name3": (folder_paths.get_filename_list("clip"), )
|
||||
}}
|
||||
RETURN_TYPES = ("CLIP",)
|
||||
FUNCTION = "load_clip"
|
||||
|
||||
CATEGORY = "advanced/loaders"
|
||||
|
||||
def load_clip(self, clip_name1, clip_name2, clip_name3):
|
||||
clip_path1 = folder_paths.get_full_path("clip", clip_name1)
|
||||
clip_path2 = folder_paths.get_full_path("clip", clip_name2)
|
||||
clip_path3 = folder_paths.get_full_path("clip", clip_name3)
|
||||
clip = comfy.sd.load_clip(ckpt_paths=[clip_path1, clip_path2, clip_path3], embedding_directory=folder_paths.get_folder_paths("embeddings"))
|
||||
return (clip,)
|
||||
|
||||
class EmptySD3LatentImage:
|
||||
def __init__(self):
|
||||
self.device = comfy.model_management.intermediate_device()
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {"required": { "width": ("INT", {"default": 512, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
|
||||
"height": ("INT", {"default": 512, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
|
||||
"batch_size": ("INT", {"default": 1, "min": 1, "max": 4096})}}
|
||||
RETURN_TYPES = ("LATENT",)
|
||||
FUNCTION = "generate"
|
||||
|
||||
CATEGORY = "latent/sd3"
|
||||
|
||||
def generate(self, width, height, batch_size=1):
|
||||
latent = torch.ones([batch_size, 16, height // 8, width // 8], device=self.device) * 0.0609
|
||||
return ({"samples":latent}, )
|
||||
|
||||
class CLIPTextEncodeSD3:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {"required": {
|
||||
"clip": ("CLIP", ),
|
||||
"clip_l": ("STRING", {"multiline": True, "dynamicPrompts": True}),
|
||||
"clip_g": ("STRING", {"multiline": True, "dynamicPrompts": True}),
|
||||
"t5xxl": ("STRING", {"multiline": True, "dynamicPrompts": True}),
|
||||
"empty_padding": (["none", "empty_prompt"], )
|
||||
}}
|
||||
RETURN_TYPES = ("CONDITIONING",)
|
||||
FUNCTION = "encode"
|
||||
|
||||
CATEGORY = "advanced/conditioning"
|
||||
|
||||
def encode(self, clip, clip_l, clip_g, t5xxl, empty_padding):
|
||||
no_padding = empty_padding == "none"
|
||||
|
||||
tokens = clip.tokenize(clip_g)
|
||||
if len(clip_g) == 0 and no_padding:
|
||||
tokens["g"] = []
|
||||
|
||||
if len(clip_l) == 0 and no_padding:
|
||||
tokens["l"] = []
|
||||
else:
|
||||
tokens["l"] = clip.tokenize(clip_l)["l"]
|
||||
|
||||
if len(t5xxl) == 0 and no_padding:
|
||||
tokens["t5xxl"] = []
|
||||
else:
|
||||
tokens["t5xxl"] = clip.tokenize(t5xxl)["t5xxl"]
|
||||
if len(tokens["l"]) != len(tokens["g"]):
|
||||
empty = clip.tokenize("")
|
||||
while len(tokens["l"]) < len(tokens["g"]):
|
||||
tokens["l"] += empty["l"]
|
||||
while len(tokens["l"]) > len(tokens["g"]):
|
||||
tokens["g"] += empty["g"]
|
||||
cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True)
|
||||
return ([[cond, {"pooled_output": pooled}]], )
|
||||
|
||||
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
"TripleCLIPLoader": TripleCLIPLoader,
|
||||
"EmptySD3LatentImage": EmptySD3LatentImage,
|
||||
"CLIPTextEncodeSD3": CLIPTextEncodeSD3,
|
||||
}
|
||||
Loading…
Reference in New Issue