Spaces:
Running
on
Zero
Running
on
Zero
File size: 4,918 Bytes
703e263 |
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 |
from .svd_image_encoder import SVDImageEncoder
from transformers import CLIPImageProcessor
import torch
class IpAdapterXLCLIPImageEmbedder(SVDImageEncoder):
def __init__(self):
super().__init__(embed_dim=1664, encoder_intermediate_size=8192, projection_dim=1280, num_encoder_layers=48, num_heads=16, head_dim=104)
self.image_processor = CLIPImageProcessor()
def forward(self, image):
pixel_values = self.image_processor(images=image, return_tensors="pt").pixel_values
pixel_values = pixel_values.to(device=self.embeddings.class_embedding.device, dtype=self.embeddings.class_embedding.dtype)
return super().forward(pixel_values)
class IpAdapterImageProjModel(torch.nn.Module):
def __init__(self, cross_attention_dim=2048, clip_embeddings_dim=1280, clip_extra_context_tokens=4):
super().__init__()
self.cross_attention_dim = cross_attention_dim
self.clip_extra_context_tokens = clip_extra_context_tokens
self.proj = torch.nn.Linear(clip_embeddings_dim, self.clip_extra_context_tokens * cross_attention_dim)
self.norm = torch.nn.LayerNorm(cross_attention_dim)
def forward(self, image_embeds):
clip_extra_context_tokens = self.proj(image_embeds).reshape(-1, self.clip_extra_context_tokens, self.cross_attention_dim)
clip_extra_context_tokens = self.norm(clip_extra_context_tokens)
return clip_extra_context_tokens
class IpAdapterModule(torch.nn.Module):
def __init__(self, input_dim, output_dim):
super().__init__()
self.to_k_ip = torch.nn.Linear(input_dim, output_dim, bias=False)
self.to_v_ip = torch.nn.Linear(input_dim, output_dim, bias=False)
def forward(self, hidden_states):
ip_k = self.to_k_ip(hidden_states)
ip_v = self.to_v_ip(hidden_states)
return ip_k, ip_v
class SDXLIpAdapter(torch.nn.Module):
def __init__(self):
super().__init__()
shape_list = [(2048, 640)] * 4 + [(2048, 1280)] * 50 + [(2048, 640)] * 6 + [(2048, 1280)] * 10
self.ipadapter_modules = torch.nn.ModuleList([IpAdapterModule(*shape) for shape in shape_list])
self.image_proj = IpAdapterImageProjModel()
self.set_full_adapter()
def set_full_adapter(self):
map_list = sum([
[(7, i) for i in range(2)],
[(10, i) for i in range(2)],
[(15, i) for i in range(10)],
[(18, i) for i in range(10)],
[(25, i) for i in range(10)],
[(28, i) for i in range(10)],
[(31, i) for i in range(10)],
[(35, i) for i in range(2)],
[(38, i) for i in range(2)],
[(41, i) for i in range(2)],
[(21, i) for i in range(10)],
], [])
self.call_block_id = {i: j for j, i in enumerate(map_list)}
def set_less_adapter(self):
map_list = sum([
[(7, i) for i in range(2)],
[(10, i) for i in range(2)],
[(15, i) for i in range(10)],
[(18, i) for i in range(10)],
[(25, i) for i in range(10)],
[(28, i) for i in range(10)],
[(31, i) for i in range(10)],
[(35, i) for i in range(2)],
[(38, i) for i in range(2)],
[(41, i) for i in range(2)],
[(21, i) for i in range(10)],
], [])
self.call_block_id = {i: j for j, i in enumerate(map_list) if j>=34 and j<44}
def forward(self, hidden_states, scale=1.0):
hidden_states = self.image_proj(hidden_states)
hidden_states = hidden_states.view(1, -1, hidden_states.shape[-1])
ip_kv_dict = {}
for (block_id, transformer_id) in self.call_block_id:
ipadapter_id = self.call_block_id[(block_id, transformer_id)]
ip_k, ip_v = self.ipadapter_modules[ipadapter_id](hidden_states)
if block_id not in ip_kv_dict:
ip_kv_dict[block_id] = {}
ip_kv_dict[block_id][transformer_id] = {
"ip_k": ip_k,
"ip_v": ip_v,
"scale": scale
}
return ip_kv_dict
@staticmethod
def state_dict_converter():
return SDXLIpAdapterStateDictConverter()
class SDXLIpAdapterStateDictConverter:
def __init__(self):
pass
def from_diffusers(self, state_dict):
state_dict_ = {}
for name in state_dict["ip_adapter"]:
names = name.split(".")
layer_id = str(int(names[0]) // 2)
name_ = ".".join(["ipadapter_modules"] + [layer_id] + names[1:])
state_dict_[name_] = state_dict["ip_adapter"][name]
for name in state_dict["image_proj"]:
name_ = "image_proj." + name
state_dict_[name_] = state_dict["image_proj"][name]
return state_dict_
def from_civitai(self, state_dict):
return self.from_diffusers(state_dict)
|