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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
from contextlib import contextmanager
def hash_state_dict_keys(state_dict, with_shape=True):
keys_str = convert_state_dict_keys_to_single_str(state_dict, with_shape=with_shape)
keys_str = keys_str.encode(encoding="UTF-8")
return hashlib.md5(keys_str).hexdigest()
@contextmanager
def init_weights_on_device(device = torch.device("meta"), include_buffers :bool = False):
old_register_parameter = torch.nn.Module.register_parameter
if include_buffers:
old_register_buffer = torch.nn.Module.register_buffer
def register_empty_parameter(module, name, param):
old_register_parameter(module, name, param)
if param is not None:
param_cls = type(module._parameters[name])
kwargs = module._parameters[name].__dict__
kwargs["requires_grad"] = param.requires_grad
module._parameters[name] = param_cls(module._parameters[name].to(device), **kwargs)
def register_empty_buffer(module, name, buffer, persistent=True):
old_register_buffer(module, name, buffer, persistent=persistent)
if buffer is not None:
module._buffers[name] = module._buffers[name].to(device)
def patch_tensor_constructor(fn):
def wrapper(*args, **kwargs):
kwargs["device"] = device
return fn(*args, **kwargs)
return wrapper
if include_buffers:
tensor_constructors_to_patch = {
torch_function_name: getattr(torch, torch_function_name)
for torch_function_name in ["empty", "zeros", "ones", "full"]
}
else:
tensor_constructors_to_patch = {}
try:
torch.nn.Module.register_parameter = register_empty_parameter
if include_buffers:
torch.nn.Module.register_buffer = register_empty_buffer
for torch_function_name in tensor_constructors_to_patch.keys():
setattr(torch, torch_function_name, patch_tensor_constructor(getattr(torch, torch_function_name)))
yield
finally:
torch.nn.Module.register_parameter = old_register_parameter
if include_buffers:
torch.nn.Module.register_buffer = old_register_buffer
for torch_function_name, old_torch_function in tensor_constructors_to_patch.items():
setattr(torch, torch_function_name, old_torch_function)
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}
elif hash_value == "7f9583eb8ba86642abb9a21a4b2c9e16":
extra_kwargs = {"num_joint_blocks": 4, "num_single_blocks": 10}
elif hash_value == "43ad5aaa27dd4ee01b832ed16773fa52":
extra_kwargs = {"num_joint_blocks": 6, "num_single_blocks": 0}
else:
extra_kwargs = {}
return state_dict_, extra_kwargs
def from_civitai(self, state_dict):
return self.from_diffusers(state_dict)
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