Spaces:
Running
on
L40S
Running
on
L40S
File size: 3,697 Bytes
4bf9661 |
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 |
from .svd_image_encoder import SVDImageEncoder
from .sd3_dit import RMSNorm
from transformers import CLIPImageProcessor
import torch
class MLPProjModel(torch.nn.Module):
def __init__(self, cross_attention_dim=768, id_embeddings_dim=512, num_tokens=4):
super().__init__()
self.cross_attention_dim = cross_attention_dim
self.num_tokens = num_tokens
self.proj = torch.nn.Sequential(
torch.nn.Linear(id_embeddings_dim, id_embeddings_dim*2),
torch.nn.GELU(),
torch.nn.Linear(id_embeddings_dim*2, cross_attention_dim*num_tokens),
)
self.norm = torch.nn.LayerNorm(cross_attention_dim)
def forward(self, id_embeds):
x = self.proj(id_embeds)
x = x.reshape(-1, self.num_tokens, self.cross_attention_dim)
x = self.norm(x)
return x
class IpAdapterModule(torch.nn.Module):
def __init__(self, num_attention_heads, attention_head_dim, input_dim):
super().__init__()
self.num_heads = num_attention_heads
self.head_dim = attention_head_dim
output_dim = num_attention_heads * attention_head_dim
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)
self.norm_added_k = RMSNorm(attention_head_dim, eps=1e-5, elementwise_affine=False)
def forward(self, hidden_states):
batch_size = hidden_states.shape[0]
# ip_k
ip_k = self.to_k_ip(hidden_states)
ip_k = ip_k.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
ip_k = self.norm_added_k(ip_k)
# ip_v
ip_v = self.to_v_ip(hidden_states)
ip_v = ip_v.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
return ip_k, ip_v
class FluxIpAdapter(torch.nn.Module):
def __init__(self, num_attention_heads=24, attention_head_dim=128, cross_attention_dim=4096, num_tokens=128, num_blocks=57):
super().__init__()
self.ipadapter_modules = torch.nn.ModuleList([IpAdapterModule(num_attention_heads, attention_head_dim, cross_attention_dim) for _ in range(num_blocks)])
self.image_proj = MLPProjModel(cross_attention_dim=cross_attention_dim, id_embeddings_dim=1152, num_tokens=num_tokens)
self.set_adapter()
def set_adapter(self):
self.call_block_id = {i:i for i in range(len(self.ipadapter_modules))}
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 in self.call_block_id:
ipadapter_id = self.call_block_id[block_id]
ip_k, ip_v = self.ipadapter_modules[ipadapter_id](hidden_states)
ip_kv_dict[block_id] = {
"ip_k": ip_k,
"ip_v": ip_v,
"scale": scale
}
return ip_kv_dict
@staticmethod
def state_dict_converter():
return FluxIpAdapterStateDictConverter()
class FluxIpAdapterStateDictConverter:
def __init__(self):
pass
def from_diffusers(self, state_dict):
state_dict_ = {}
for name in state_dict["ip_adapter"]:
name_ = 'ipadapter_modules.' + name
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)
|