Spaces:
Runtime error
Runtime error
File size: 10,442 Bytes
4450790 |
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 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 |
import torch
from comfy.ldm.modules.attention import optimized_attention_for_device
import comfy.ops
class CLIPAttention(torch.nn.Module):
def __init__(self, embed_dim, heads, dtype, device, operations):
super().__init__()
self.heads = heads
self.q_proj = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device)
self.k_proj = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device)
self.v_proj = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device)
self.out_proj = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device)
def forward(self, x, mask=None, optimized_attention=None):
q = self.q_proj(x)
k = self.k_proj(x)
v = self.v_proj(x)
out = optimized_attention(q, k, v, self.heads, mask)
return self.out_proj(out)
ACTIVATIONS = {"quick_gelu": lambda a: a * torch.sigmoid(1.702 * a),
"gelu": torch.nn.functional.gelu,
"gelu_pytorch_tanh": lambda a: torch.nn.functional.gelu(a, approximate="tanh"),
}
class CLIPMLP(torch.nn.Module):
def __init__(self, embed_dim, intermediate_size, activation, dtype, device, operations):
super().__init__()
self.fc1 = operations.Linear(embed_dim, intermediate_size, bias=True, dtype=dtype, device=device)
self.activation = ACTIVATIONS[activation]
self.fc2 = operations.Linear(intermediate_size, embed_dim, bias=True, dtype=dtype, device=device)
def forward(self, x):
x = self.fc1(x)
x = self.activation(x)
x = self.fc2(x)
return x
class CLIPLayer(torch.nn.Module):
def __init__(self, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations):
super().__init__()
self.layer_norm1 = operations.LayerNorm(embed_dim, dtype=dtype, device=device)
self.self_attn = CLIPAttention(embed_dim, heads, dtype, device, operations)
self.layer_norm2 = operations.LayerNorm(embed_dim, dtype=dtype, device=device)
self.mlp = CLIPMLP(embed_dim, intermediate_size, intermediate_activation, dtype, device, operations)
def forward(self, x, mask=None, optimized_attention=None):
x += self.self_attn(self.layer_norm1(x), mask, optimized_attention)
x += self.mlp(self.layer_norm2(x))
return x
class CLIPEncoder(torch.nn.Module):
def __init__(self, num_layers, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations):
super().__init__()
self.layers = torch.nn.ModuleList([CLIPLayer(embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations) for i in range(num_layers)])
def forward(self, x, mask=None, intermediate_output=None):
optimized_attention = optimized_attention_for_device(x.device, mask=mask is not None, small_input=True)
if intermediate_output is not None:
if intermediate_output < 0:
intermediate_output = len(self.layers) + intermediate_output
intermediate = None
for i, l in enumerate(self.layers):
x = l(x, mask, optimized_attention)
if i == intermediate_output:
intermediate = x.clone()
return x, intermediate
class CLIPEmbeddings(torch.nn.Module):
def __init__(self, embed_dim, vocab_size=49408, num_positions=77, dtype=None, device=None, operations=None):
super().__init__()
self.token_embedding = operations.Embedding(vocab_size, embed_dim, dtype=dtype, device=device)
self.position_embedding = operations.Embedding(num_positions, embed_dim, dtype=dtype, device=device)
def forward(self, input_tokens, dtype=torch.float32):
return self.token_embedding(input_tokens, out_dtype=dtype) + comfy.ops.cast_to(self.position_embedding.weight, dtype=dtype, device=input_tokens.device)
class CLIPTextModel_(torch.nn.Module):
def __init__(self, config_dict, dtype, device, operations):
num_layers = config_dict["num_hidden_layers"]
embed_dim = config_dict["hidden_size"]
heads = config_dict["num_attention_heads"]
intermediate_size = config_dict["intermediate_size"]
intermediate_activation = config_dict["hidden_act"]
num_positions = config_dict["max_position_embeddings"]
self.eos_token_id = config_dict["eos_token_id"]
super().__init__()
self.embeddings = CLIPEmbeddings(embed_dim, num_positions=num_positions, dtype=dtype, device=device, operations=operations)
self.encoder = CLIPEncoder(num_layers, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations)
self.final_layer_norm = operations.LayerNorm(embed_dim, dtype=dtype, device=device)
def forward(self, input_tokens, attention_mask=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=torch.float32):
x = self.embeddings(input_tokens, dtype=dtype)
mask = None
if attention_mask is not None:
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])
mask = mask.masked_fill(mask.to(torch.bool), float("-inf"))
causal_mask = torch.empty(x.shape[1], x.shape[1], dtype=x.dtype, device=x.device).fill_(float("-inf")).triu_(1)
if mask is not None:
mask += causal_mask
else:
mask = causal_mask
x, i = self.encoder(x, mask=mask, intermediate_output=intermediate_output)
x = self.final_layer_norm(x)
if i is not None and final_layer_norm_intermediate:
i = self.final_layer_norm(i)
pooled_output = x[torch.arange(x.shape[0], device=x.device), (torch.round(input_tokens).to(dtype=torch.int, device=x.device) == self.eos_token_id).int().argmax(dim=-1),]
return x, i, pooled_output
class CLIPTextModel(torch.nn.Module):
def __init__(self, config_dict, dtype, device, operations):
super().__init__()
self.num_layers = config_dict["num_hidden_layers"]
self.text_model = CLIPTextModel_(config_dict, dtype, device, operations)
embed_dim = config_dict["hidden_size"]
self.text_projection = operations.Linear(embed_dim, embed_dim, bias=False, dtype=dtype, device=device)
self.dtype = dtype
def get_input_embeddings(self):
return self.text_model.embeddings.token_embedding
def set_input_embeddings(self, embeddings):
self.text_model.embeddings.token_embedding = embeddings
def forward(self, *args, **kwargs):
x = self.text_model(*args, **kwargs)
out = self.text_projection(x[2])
return (x[0], x[1], out, x[2])
class CLIPVisionEmbeddings(torch.nn.Module):
def __init__(self, embed_dim, num_channels=3, patch_size=14, image_size=224, model_type="", dtype=None, device=None, operations=None):
super().__init__()
num_patches = (image_size // patch_size) ** 2
if model_type == "siglip_vision_model":
self.class_embedding = None
patch_bias = True
else:
num_patches = num_patches + 1
self.class_embedding = torch.nn.Parameter(torch.empty(embed_dim, dtype=dtype, device=device))
patch_bias = False
self.patch_embedding = operations.Conv2d(
in_channels=num_channels,
out_channels=embed_dim,
kernel_size=patch_size,
stride=patch_size,
bias=patch_bias,
dtype=dtype,
device=device
)
self.position_embedding = operations.Embedding(num_patches, embed_dim, dtype=dtype, device=device)
def forward(self, pixel_values):
embeds = self.patch_embedding(pixel_values).flatten(2).transpose(1, 2)
if self.class_embedding is not None:
embeds = torch.cat([comfy.ops.cast_to_input(self.class_embedding, embeds).expand(pixel_values.shape[0], 1, -1), embeds], dim=1)
return embeds + comfy.ops.cast_to_input(self.position_embedding.weight, embeds)
class CLIPVision(torch.nn.Module):
def __init__(self, config_dict, dtype, device, operations):
super().__init__()
num_layers = config_dict["num_hidden_layers"]
embed_dim = config_dict["hidden_size"]
heads = config_dict["num_attention_heads"]
intermediate_size = config_dict["intermediate_size"]
intermediate_activation = config_dict["hidden_act"]
model_type = config_dict["model_type"]
self.embeddings = CLIPVisionEmbeddings(embed_dim, config_dict["num_channels"], config_dict["patch_size"], config_dict["image_size"], model_type=model_type, dtype=dtype, device=device, operations=operations)
if model_type == "siglip_vision_model":
self.pre_layrnorm = lambda a: a
self.output_layernorm = True
else:
self.pre_layrnorm = operations.LayerNorm(embed_dim)
self.output_layernorm = False
self.encoder = CLIPEncoder(num_layers, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations)
self.post_layernorm = operations.LayerNorm(embed_dim)
def forward(self, pixel_values, attention_mask=None, intermediate_output=None):
x = self.embeddings(pixel_values)
x = self.pre_layrnorm(x)
#TODO: attention_mask?
x, i = self.encoder(x, mask=None, intermediate_output=intermediate_output)
if self.output_layernorm:
x = self.post_layernorm(x)
pooled_output = x
else:
pooled_output = self.post_layernorm(x[:, 0, :])
return x, i, pooled_output
class CLIPVisionModelProjection(torch.nn.Module):
def __init__(self, config_dict, dtype, device, operations):
super().__init__()
self.vision_model = CLIPVision(config_dict, dtype, device, operations)
if "projection_dim" in config_dict:
self.visual_projection = operations.Linear(config_dict["hidden_size"], config_dict["projection_dim"], bias=False)
else:
self.visual_projection = lambda a: a
def forward(self, *args, **kwargs):
x = self.vision_model(*args, **kwargs)
out = self.visual_projection(x[2])
return (x[0], x[1], out)
|