Spaces:
Running
on
L40S
Running
on
L40S
File size: 16,429 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 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 |
import torch
from einops import rearrange, repeat
from .flux_dit import RoPEEmbedding, TimestepEmbeddings, FluxJointTransformerBlock, FluxSingleTransformerBlock, RMSNorm
from .utils import hash_state_dict_keys, init_weights_on_device
class FluxControlNet(torch.nn.Module):
def __init__(self, disable_guidance_embedder=False, num_joint_blocks=5, num_single_blocks=10, num_mode=0, mode_dict={}, additional_input_dim=0):
super().__init__()
self.pos_embedder = RoPEEmbedding(3072, 10000, [16, 56, 56])
self.time_embedder = TimestepEmbeddings(256, 3072)
self.guidance_embedder = None if disable_guidance_embedder else TimestepEmbeddings(256, 3072)
self.pooled_text_embedder = torch.nn.Sequential(torch.nn.Linear(768, 3072), torch.nn.SiLU(), torch.nn.Linear(3072, 3072))
self.context_embedder = torch.nn.Linear(4096, 3072)
self.x_embedder = torch.nn.Linear(64, 3072)
self.blocks = torch.nn.ModuleList([FluxJointTransformerBlock(3072, 24) for _ in range(num_joint_blocks)])
self.single_blocks = torch.nn.ModuleList([FluxSingleTransformerBlock(3072, 24) for _ in range(num_single_blocks)])
self.controlnet_blocks = torch.nn.ModuleList([torch.nn.Linear(3072, 3072) for _ in range(num_joint_blocks)])
self.controlnet_single_blocks = torch.nn.ModuleList([torch.nn.Linear(3072, 3072) for _ in range(num_single_blocks)])
self.mode_dict = mode_dict
self.controlnet_mode_embedder = torch.nn.Embedding(num_mode, 3072) if len(mode_dict) > 0 else None
self.controlnet_x_embedder = torch.nn.Linear(64 + additional_input_dim, 3072)
def prepare_image_ids(self, latents):
batch_size, _, height, width = latents.shape
latent_image_ids = torch.zeros(height // 2, width // 2, 3)
latent_image_ids[..., 1] = latent_image_ids[..., 1] + torch.arange(height // 2)[:, None]
latent_image_ids[..., 2] = latent_image_ids[..., 2] + torch.arange(width // 2)[None, :]
latent_image_id_height, latent_image_id_width, latent_image_id_channels = latent_image_ids.shape
latent_image_ids = latent_image_ids[None, :].repeat(batch_size, 1, 1, 1)
latent_image_ids = latent_image_ids.reshape(
batch_size, latent_image_id_height * latent_image_id_width, latent_image_id_channels
)
latent_image_ids = latent_image_ids.to(device=latents.device, dtype=latents.dtype)
return latent_image_ids
def patchify(self, hidden_states):
hidden_states = rearrange(hidden_states, "B C (H P) (W Q) -> B (H W) (C P Q)", P=2, Q=2)
return hidden_states
def align_res_stack_to_original_blocks(self, res_stack, num_blocks, hidden_states):
if len(res_stack) == 0:
return [torch.zeros_like(hidden_states)] * num_blocks
interval = (num_blocks + len(res_stack) - 1) // len(res_stack)
aligned_res_stack = [res_stack[block_id // interval] for block_id in range(num_blocks)]
return aligned_res_stack
def forward(
self,
hidden_states,
controlnet_conditioning,
timestep, prompt_emb, pooled_prompt_emb, guidance, text_ids, image_ids=None,
processor_id=None,
tiled=False, tile_size=128, tile_stride=64,
**kwargs
):
if image_ids is None:
image_ids = self.prepare_image_ids(hidden_states)
conditioning = self.time_embedder(timestep, hidden_states.dtype) + self.pooled_text_embedder(pooled_prompt_emb)
if self.guidance_embedder is not None:
guidance = guidance * 1000
conditioning = conditioning + self.guidance_embedder(guidance, hidden_states.dtype)
prompt_emb = self.context_embedder(prompt_emb)
if self.controlnet_mode_embedder is not None: # Different from FluxDiT
processor_id = torch.tensor([self.mode_dict[processor_id]], dtype=torch.int)
processor_id = repeat(processor_id, "D -> B D", B=1).to(text_ids.device)
prompt_emb = torch.concat([self.controlnet_mode_embedder(processor_id), prompt_emb], dim=1)
text_ids = torch.cat([text_ids[:, :1], text_ids], dim=1)
image_rotary_emb = self.pos_embedder(torch.cat((text_ids, image_ids), dim=1))
hidden_states = self.patchify(hidden_states)
hidden_states = self.x_embedder(hidden_states)
controlnet_conditioning = self.patchify(controlnet_conditioning) # Different from FluxDiT
hidden_states = hidden_states + self.controlnet_x_embedder(controlnet_conditioning) # Different from FluxDiT
controlnet_res_stack = []
for block, controlnet_block in zip(self.blocks, self.controlnet_blocks):
hidden_states, prompt_emb = block(hidden_states, prompt_emb, conditioning, image_rotary_emb)
controlnet_res_stack.append(controlnet_block(hidden_states))
controlnet_single_res_stack = []
hidden_states = torch.cat([prompt_emb, hidden_states], dim=1)
for block, controlnet_block in zip(self.single_blocks, self.controlnet_single_blocks):
hidden_states, prompt_emb = block(hidden_states, prompt_emb, conditioning, image_rotary_emb)
controlnet_single_res_stack.append(controlnet_block(hidden_states[:, prompt_emb.shape[1]:]))
controlnet_res_stack = self.align_res_stack_to_original_blocks(controlnet_res_stack, 19, hidden_states[:, prompt_emb.shape[1]:])
controlnet_single_res_stack = self.align_res_stack_to_original_blocks(controlnet_single_res_stack, 38, hidden_states[:, prompt_emb.shape[1]:])
return controlnet_res_stack, controlnet_single_res_stack
@staticmethod
def state_dict_converter():
return FluxControlNetStateDictConverter()
def quantize(self):
def cast_to(weight, dtype=None, device=None, copy=False):
if device is None or weight.device == device:
if not copy:
if dtype is None or weight.dtype == dtype:
return weight
return weight.to(dtype=dtype, copy=copy)
r = torch.empty_like(weight, dtype=dtype, device=device)
r.copy_(weight)
return r
def cast_weight(s, input=None, dtype=None, device=None):
if input is not None:
if dtype is None:
dtype = input.dtype
if device is None:
device = input.device
weight = cast_to(s.weight, dtype, device)
return weight
def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None):
if input is not None:
if dtype is None:
dtype = input.dtype
if bias_dtype is None:
bias_dtype = dtype
if device is None:
device = input.device
bias = None
weight = cast_to(s.weight, dtype, device)
bias = cast_to(s.bias, bias_dtype, device)
return weight, bias
class quantized_layer:
class QLinear(torch.nn.Linear):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def forward(self,input,**kwargs):
weight,bias= cast_bias_weight(self,input)
return torch.nn.functional.linear(input,weight,bias)
class QRMSNorm(torch.nn.Module):
def __init__(self, module):
super().__init__()
self.module = module
def forward(self,hidden_states,**kwargs):
weight= cast_weight(self.module,hidden_states)
input_dtype = hidden_states.dtype
variance = hidden_states.to(torch.float32).square().mean(-1, keepdim=True)
hidden_states = hidden_states * torch.rsqrt(variance + self.module.eps)
hidden_states = hidden_states.to(input_dtype) * weight
return hidden_states
class QEmbedding(torch.nn.Embedding):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def forward(self,input,**kwargs):
weight= cast_weight(self,input)
return torch.nn.functional.embedding(
input, weight, self.padding_idx, self.max_norm,
self.norm_type, self.scale_grad_by_freq, self.sparse)
def replace_layer(model):
for name, module in model.named_children():
if isinstance(module,quantized_layer.QRMSNorm):
continue
if isinstance(module, torch.nn.Linear):
with init_weights_on_device():
new_layer = quantized_layer.QLinear(module.in_features,module.out_features)
new_layer.weight = module.weight
if module.bias is not None:
new_layer.bias = module.bias
setattr(model, name, new_layer)
elif isinstance(module, RMSNorm):
if hasattr(module,"quantized"):
continue
module.quantized= True
new_layer = quantized_layer.QRMSNorm(module)
setattr(model, name, new_layer)
elif isinstance(module,torch.nn.Embedding):
rows, cols = module.weight.shape
new_layer = quantized_layer.QEmbedding(
num_embeddings=rows,
embedding_dim=cols,
_weight=module.weight,
# _freeze=module.freeze,
padding_idx=module.padding_idx,
max_norm=module.max_norm,
norm_type=module.norm_type,
scale_grad_by_freq=module.scale_grad_by_freq,
sparse=module.sparse)
setattr(model, name, new_layer)
else:
replace_layer(module)
replace_layer(self)
class FluxControlNetStateDictConverter:
def __init__(self):
pass
def from_diffusers(self, state_dict):
hash_value = hash_state_dict_keys(state_dict)
global_rename_dict = {
"context_embedder": "context_embedder",
"x_embedder": "x_embedder",
"time_text_embed.timestep_embedder.linear_1": "time_embedder.timestep_embedder.0",
"time_text_embed.timestep_embedder.linear_2": "time_embedder.timestep_embedder.2",
"time_text_embed.guidance_embedder.linear_1": "guidance_embedder.timestep_embedder.0",
"time_text_embed.guidance_embedder.linear_2": "guidance_embedder.timestep_embedder.2",
"time_text_embed.text_embedder.linear_1": "pooled_text_embedder.0",
"time_text_embed.text_embedder.linear_2": "pooled_text_embedder.2",
"norm_out.linear": "final_norm_out.linear",
"proj_out": "final_proj_out",
}
rename_dict = {
"proj_out": "proj_out",
"norm1.linear": "norm1_a.linear",
"norm1_context.linear": "norm1_b.linear",
"attn.to_q": "attn.a_to_q",
"attn.to_k": "attn.a_to_k",
"attn.to_v": "attn.a_to_v",
"attn.to_out.0": "attn.a_to_out",
"attn.add_q_proj": "attn.b_to_q",
"attn.add_k_proj": "attn.b_to_k",
"attn.add_v_proj": "attn.b_to_v",
"attn.to_add_out": "attn.b_to_out",
"ff.net.0.proj": "ff_a.0",
"ff.net.2": "ff_a.2",
"ff_context.net.0.proj": "ff_b.0",
"ff_context.net.2": "ff_b.2",
"attn.norm_q": "attn.norm_q_a",
"attn.norm_k": "attn.norm_k_a",
"attn.norm_added_q": "attn.norm_q_b",
"attn.norm_added_k": "attn.norm_k_b",
}
rename_dict_single = {
"attn.to_q": "a_to_q",
"attn.to_k": "a_to_k",
"attn.to_v": "a_to_v",
"attn.norm_q": "norm_q_a",
"attn.norm_k": "norm_k_a",
"norm.linear": "norm.linear",
"proj_mlp": "proj_in_besides_attn",
"proj_out": "proj_out",
}
state_dict_ = {}
for name, param in state_dict.items():
if name.endswith(".weight") or name.endswith(".bias"):
suffix = ".weight" if name.endswith(".weight") else ".bias"
prefix = name[:-len(suffix)]
if prefix in global_rename_dict:
state_dict_[global_rename_dict[prefix] + suffix] = param
elif prefix.startswith("transformer_blocks."):
names = prefix.split(".")
names[0] = "blocks"
middle = ".".join(names[2:])
if middle in rename_dict:
name_ = ".".join(names[:2] + [rename_dict[middle]] + [suffix[1:]])
state_dict_[name_] = param
elif prefix.startswith("single_transformer_blocks."):
names = prefix.split(".")
names[0] = "single_blocks"
middle = ".".join(names[2:])
if middle in rename_dict_single:
name_ = ".".join(names[:2] + [rename_dict_single[middle]] + [suffix[1:]])
state_dict_[name_] = param
else:
state_dict_[name] = param
else:
state_dict_[name] = param
for name in list(state_dict_.keys()):
if ".proj_in_besides_attn." in name:
name_ = name.replace(".proj_in_besides_attn.", ".to_qkv_mlp.")
param = torch.concat([
state_dict_[name.replace(".proj_in_besides_attn.", f".a_to_q.")],
state_dict_[name.replace(".proj_in_besides_attn.", f".a_to_k.")],
state_dict_[name.replace(".proj_in_besides_attn.", f".a_to_v.")],
state_dict_[name],
], dim=0)
state_dict_[name_] = param
state_dict_.pop(name.replace(".proj_in_besides_attn.", f".a_to_q."))
state_dict_.pop(name.replace(".proj_in_besides_attn.", f".a_to_k."))
state_dict_.pop(name.replace(".proj_in_besides_attn.", f".a_to_v."))
state_dict_.pop(name)
for name in list(state_dict_.keys()):
for component in ["a", "b"]:
if f".{component}_to_q." in name:
name_ = name.replace(f".{component}_to_q.", f".{component}_to_qkv.")
param = torch.concat([
state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_q.")],
state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_k.")],
state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_v.")],
], dim=0)
state_dict_[name_] = param
state_dict_.pop(name.replace(f".{component}_to_q.", f".{component}_to_q."))
state_dict_.pop(name.replace(f".{component}_to_q.", f".{component}_to_k."))
state_dict_.pop(name.replace(f".{component}_to_q.", f".{component}_to_v."))
if hash_value == "78d18b9101345ff695f312e7e62538c0":
extra_kwargs = {"num_mode": 10, "mode_dict": {"canny": 0, "tile": 1, "depth": 2, "blur": 3, "pose": 4, "gray": 5, "lq": 6}}
elif hash_value == "b001c89139b5f053c715fe772362dd2a":
extra_kwargs = {"num_single_blocks": 0}
elif hash_value == "52357cb26250681367488a8954c271e8":
extra_kwargs = {"num_joint_blocks": 6, "num_single_blocks": 0, "additional_input_dim": 4}
elif hash_value == "0cfd1740758423a2a854d67c136d1e8c":
extra_kwargs = {"num_joint_blocks": 4, "num_single_blocks": 1}
else:
extra_kwargs = {}
return state_dict_, extra_kwargs
def from_civitai(self, state_dict):
return self.from_diffusers(state_dict)
|