467 lines
18 KiB
Python
467 lines
18 KiB
Python
import torch
|
|
import contextlib
|
|
|
|
import sd1_clip
|
|
import sd2_clip
|
|
import model_management
|
|
from ldm.util import instantiate_from_config
|
|
from ldm.models.autoencoder import AutoencoderKL
|
|
from omegaconf import OmegaConf
|
|
from .cldm import cldm
|
|
|
|
from . import utils
|
|
|
|
def load_torch_file(ckpt):
|
|
if ckpt.lower().endswith(".safetensors"):
|
|
import safetensors.torch
|
|
sd = safetensors.torch.load_file(ckpt, device="cpu")
|
|
else:
|
|
pl_sd = torch.load(ckpt, map_location="cpu")
|
|
if "global_step" in pl_sd:
|
|
print(f"Global Step: {pl_sd['global_step']}")
|
|
if "state_dict" in pl_sd:
|
|
sd = pl_sd["state_dict"]
|
|
else:
|
|
sd = pl_sd
|
|
return sd
|
|
|
|
def load_model_from_config(config, ckpt, verbose=False, load_state_dict_to=[]):
|
|
print(f"Loading model from {ckpt}")
|
|
|
|
sd = load_torch_file(ckpt)
|
|
model = instantiate_from_config(config.model)
|
|
|
|
m, u = model.load_state_dict(sd, strict=False)
|
|
|
|
k = list(sd.keys())
|
|
for x in k:
|
|
# print(x)
|
|
if x.startswith("cond_stage_model.transformer.") and not x.startswith("cond_stage_model.transformer.text_model."):
|
|
y = x.replace("cond_stage_model.transformer.", "cond_stage_model.transformer.text_model.")
|
|
sd[y] = sd.pop(x)
|
|
|
|
if 'cond_stage_model.transformer.text_model.embeddings.position_ids' in sd:
|
|
ids = sd['cond_stage_model.transformer.text_model.embeddings.position_ids']
|
|
if ids.dtype == torch.float32:
|
|
sd['cond_stage_model.transformer.text_model.embeddings.position_ids'] = ids.round()
|
|
|
|
keys_to_replace = {
|
|
"cond_stage_model.model.positional_embedding": "cond_stage_model.transformer.text_model.embeddings.position_embedding.weight",
|
|
"cond_stage_model.model.token_embedding.weight": "cond_stage_model.transformer.text_model.embeddings.token_embedding.weight",
|
|
"cond_stage_model.model.ln_final.weight": "cond_stage_model.transformer.text_model.final_layer_norm.weight",
|
|
"cond_stage_model.model.ln_final.bias": "cond_stage_model.transformer.text_model.final_layer_norm.bias",
|
|
}
|
|
|
|
for x in keys_to_replace:
|
|
if x in sd:
|
|
sd[keys_to_replace[x]] = sd.pop(x)
|
|
|
|
resblock_to_replace = {
|
|
"ln_1": "layer_norm1",
|
|
"ln_2": "layer_norm2",
|
|
"mlp.c_fc": "mlp.fc1",
|
|
"mlp.c_proj": "mlp.fc2",
|
|
"attn.out_proj": "self_attn.out_proj",
|
|
}
|
|
|
|
for resblock in range(24):
|
|
for x in resblock_to_replace:
|
|
for y in ["weight", "bias"]:
|
|
k = "cond_stage_model.model.transformer.resblocks.{}.{}.{}".format(resblock, x, y)
|
|
k_to = "cond_stage_model.transformer.text_model.encoder.layers.{}.{}.{}".format(resblock, resblock_to_replace[x], y)
|
|
if k in sd:
|
|
sd[k_to] = sd.pop(k)
|
|
|
|
for y in ["weight", "bias"]:
|
|
k_from = "cond_stage_model.model.transformer.resblocks.{}.attn.in_proj_{}".format(resblock, y)
|
|
if k_from in sd:
|
|
weights = sd.pop(k_from)
|
|
for x in range(3):
|
|
p = ["self_attn.q_proj", "self_attn.k_proj", "self_attn.v_proj"]
|
|
k_to = "cond_stage_model.transformer.text_model.encoder.layers.{}.{}.{}".format(resblock, p[x], y)
|
|
sd[k_to] = weights[1024*x:1024*(x + 1)]
|
|
|
|
for x in load_state_dict_to:
|
|
x.load_state_dict(sd, strict=False)
|
|
|
|
if len(m) > 0 and verbose:
|
|
print("missing keys:")
|
|
print(m)
|
|
if len(u) > 0 and verbose:
|
|
print("unexpected keys:")
|
|
print(u)
|
|
|
|
model.eval()
|
|
return model
|
|
|
|
LORA_CLIP_MAP = {
|
|
"mlp.fc1": "mlp_fc1",
|
|
"mlp.fc2": "mlp_fc2",
|
|
"self_attn.k_proj": "self_attn_k_proj",
|
|
"self_attn.q_proj": "self_attn_q_proj",
|
|
"self_attn.v_proj": "self_attn_v_proj",
|
|
"self_attn.out_proj": "self_attn_out_proj",
|
|
}
|
|
|
|
LORA_UNET_MAP = {
|
|
"proj_in": "proj_in",
|
|
"proj_out": "proj_out",
|
|
"transformer_blocks.0.attn1.to_q": "transformer_blocks_0_attn1_to_q",
|
|
"transformer_blocks.0.attn1.to_k": "transformer_blocks_0_attn1_to_k",
|
|
"transformer_blocks.0.attn1.to_v": "transformer_blocks_0_attn1_to_v",
|
|
"transformer_blocks.0.attn1.to_out.0": "transformer_blocks_0_attn1_to_out_0",
|
|
"transformer_blocks.0.attn2.to_q": "transformer_blocks_0_attn2_to_q",
|
|
"transformer_blocks.0.attn2.to_k": "transformer_blocks_0_attn2_to_k",
|
|
"transformer_blocks.0.attn2.to_v": "transformer_blocks_0_attn2_to_v",
|
|
"transformer_blocks.0.attn2.to_out.0": "transformer_blocks_0_attn2_to_out_0",
|
|
"transformer_blocks.0.ff.net.0.proj": "transformer_blocks_0_ff_net_0_proj",
|
|
"transformer_blocks.0.ff.net.2": "transformer_blocks_0_ff_net_2",
|
|
}
|
|
|
|
|
|
def load_lora(path, to_load):
|
|
lora = load_torch_file(path)
|
|
patch_dict = {}
|
|
loaded_keys = set()
|
|
for x in to_load:
|
|
A_name = "{}.lora_up.weight".format(x)
|
|
B_name = "{}.lora_down.weight".format(x)
|
|
alpha_name = "{}.alpha".format(x)
|
|
if A_name in lora.keys():
|
|
alpha = None
|
|
if alpha_name in lora.keys():
|
|
alpha = lora[alpha_name].item()
|
|
loaded_keys.add(alpha_name)
|
|
patch_dict[to_load[x]] = (lora[A_name], lora[B_name], alpha)
|
|
loaded_keys.add(A_name)
|
|
loaded_keys.add(B_name)
|
|
for x in lora.keys():
|
|
if x not in loaded_keys:
|
|
print("lora key not loaded", x)
|
|
return patch_dict
|
|
|
|
def model_lora_keys(model, key_map={}):
|
|
sdk = model.state_dict().keys()
|
|
|
|
counter = 0
|
|
for b in range(12):
|
|
tk = "model.diffusion_model.input_blocks.{}.1".format(b)
|
|
up_counter = 0
|
|
for c in LORA_UNET_MAP:
|
|
k = "{}.{}.weight".format(tk, c)
|
|
if k in sdk:
|
|
lora_key = "lora_unet_down_blocks_{}_attentions_{}_{}".format(counter // 2, counter % 2, LORA_UNET_MAP[c])
|
|
key_map[lora_key] = k
|
|
up_counter += 1
|
|
if up_counter >= 4:
|
|
counter += 1
|
|
for c in LORA_UNET_MAP:
|
|
k = "model.diffusion_model.middle_block.1.{}.weight".format(c)
|
|
if k in sdk:
|
|
lora_key = "lora_unet_mid_block_attentions_0_{}".format(LORA_UNET_MAP[c])
|
|
key_map[lora_key] = k
|
|
counter = 3
|
|
for b in range(12):
|
|
tk = "model.diffusion_model.output_blocks.{}.1".format(b)
|
|
up_counter = 0
|
|
for c in LORA_UNET_MAP:
|
|
k = "{}.{}.weight".format(tk, c)
|
|
if k in sdk:
|
|
lora_key = "lora_unet_up_blocks_{}_attentions_{}_{}".format(counter // 3, counter % 3, LORA_UNET_MAP[c])
|
|
key_map[lora_key] = k
|
|
up_counter += 1
|
|
if up_counter >= 4:
|
|
counter += 1
|
|
counter = 0
|
|
text_model_lora_key = "lora_te_text_model_encoder_layers_{}_{}"
|
|
for b in range(24):
|
|
for c in LORA_CLIP_MAP:
|
|
k = "transformer.text_model.encoder.layers.{}.{}.weight".format(b, c)
|
|
if k in sdk:
|
|
lora_key = text_model_lora_key.format(b, LORA_CLIP_MAP[c])
|
|
key_map[lora_key] = k
|
|
|
|
return key_map
|
|
|
|
class ModelPatcher:
|
|
def __init__(self, model):
|
|
self.model = model
|
|
self.patches = []
|
|
self.backup = {}
|
|
|
|
def clone(self):
|
|
n = ModelPatcher(self.model)
|
|
n.patches = self.patches[:]
|
|
return n
|
|
|
|
def add_patches(self, patches, strength=1.0):
|
|
p = {}
|
|
model_sd = self.model.state_dict()
|
|
for k in patches:
|
|
if k in model_sd:
|
|
p[k] = patches[k]
|
|
self.patches += [(strength, p)]
|
|
return p.keys()
|
|
|
|
def patch_model(self):
|
|
model_sd = self.model.state_dict()
|
|
for p in self.patches:
|
|
for k in p[1]:
|
|
v = p[1][k]
|
|
key = k
|
|
if key not in model_sd:
|
|
print("could not patch. key doesn't exist in model:", k)
|
|
continue
|
|
|
|
weight = model_sd[key]
|
|
if key not in self.backup:
|
|
self.backup[key] = weight.clone()
|
|
|
|
alpha = p[0]
|
|
mat1 = v[0]
|
|
mat2 = v[1]
|
|
if v[2] is not None:
|
|
alpha *= v[2] / mat2.shape[0]
|
|
weight += (alpha * torch.mm(mat1.flatten(start_dim=1).float(), mat2.flatten(start_dim=1).float())).reshape(weight.shape).type(weight.dtype).to(weight.device)
|
|
return self.model
|
|
def unpatch_model(self):
|
|
model_sd = self.model.state_dict()
|
|
for k in self.backup:
|
|
model_sd[k][:] = self.backup[k]
|
|
self.backup = {}
|
|
|
|
def load_lora_for_models(model, clip, lora_path, strength_model, strength_clip):
|
|
key_map = model_lora_keys(model.model)
|
|
key_map = model_lora_keys(clip.cond_stage_model, key_map)
|
|
loaded = load_lora(lora_path, key_map)
|
|
new_modelpatcher = model.clone()
|
|
k = new_modelpatcher.add_patches(loaded, strength_model)
|
|
new_clip = clip.clone()
|
|
k1 = new_clip.add_patches(loaded, strength_clip)
|
|
k = set(k)
|
|
k1 = set(k1)
|
|
for x in loaded:
|
|
if (x not in k) and (x not in k1):
|
|
print("NOT LOADED", x)
|
|
|
|
return (new_modelpatcher, new_clip)
|
|
|
|
|
|
class CLIP:
|
|
def __init__(self, config={}, embedding_directory=None, no_init=False):
|
|
if no_init:
|
|
return
|
|
self.target_clip = config["target"]
|
|
if "params" in config:
|
|
params = config["params"]
|
|
else:
|
|
params = {}
|
|
|
|
if self.target_clip == "ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder":
|
|
clip = sd2_clip.SD2ClipModel
|
|
tokenizer = sd2_clip.SD2Tokenizer
|
|
elif self.target_clip == "ldm.modules.encoders.modules.FrozenCLIPEmbedder":
|
|
clip = sd1_clip.SD1ClipModel
|
|
tokenizer = sd1_clip.SD1Tokenizer
|
|
|
|
self.cond_stage_model = clip(**(params))
|
|
self.tokenizer = tokenizer(embedding_directory=embedding_directory)
|
|
self.patcher = ModelPatcher(self.cond_stage_model)
|
|
|
|
def clone(self):
|
|
n = CLIP(no_init=True)
|
|
n.target_clip = self.target_clip
|
|
n.patcher = self.patcher.clone()
|
|
n.cond_stage_model = self.cond_stage_model
|
|
n.tokenizer = self.tokenizer
|
|
return n
|
|
|
|
def load_from_state_dict(self, sd):
|
|
self.cond_stage_model.transformer.load_state_dict(sd, strict=False)
|
|
|
|
def add_patches(self, patches, strength=1.0):
|
|
return self.patcher.add_patches(patches, strength)
|
|
|
|
def clip_layer(self, layer_idx):
|
|
return self.cond_stage_model.clip_layer(layer_idx)
|
|
|
|
def encode(self, text):
|
|
tokens = self.tokenizer.tokenize_with_weights(text)
|
|
try:
|
|
self.patcher.patch_model()
|
|
cond = self.cond_stage_model.encode_token_weights(tokens)
|
|
self.patcher.unpatch_model()
|
|
except Exception as e:
|
|
self.patcher.unpatch_model()
|
|
raise e
|
|
return cond
|
|
|
|
class VAE:
|
|
def __init__(self, ckpt_path=None, scale_factor=0.18215, device="cuda", config=None):
|
|
if config is None:
|
|
#default SD1.x/SD2.x VAE parameters
|
|
ddconfig = {'double_z': True, 'z_channels': 4, 'resolution': 256, 'in_channels': 3, 'out_ch': 3, 'ch': 128, 'ch_mult': [1, 2, 4, 4], 'num_res_blocks': 2, 'attn_resolutions': [], 'dropout': 0.0}
|
|
self.first_stage_model = AutoencoderKL(ddconfig, {'target': 'torch.nn.Identity'}, 4, monitor="val/rec_loss", ckpt_path=ckpt_path)
|
|
else:
|
|
self.first_stage_model = AutoencoderKL(**(config['params']), ckpt_path=ckpt_path)
|
|
self.first_stage_model = self.first_stage_model.eval()
|
|
self.scale_factor = scale_factor
|
|
self.device = device
|
|
|
|
def decode(self, samples):
|
|
model_management.unload_model()
|
|
self.first_stage_model = self.first_stage_model.to(self.device)
|
|
samples = samples.to(self.device)
|
|
pixel_samples = self.first_stage_model.decode(1. / self.scale_factor * samples)
|
|
pixel_samples = torch.clamp((pixel_samples + 1.0) / 2.0, min=0.0, max=1.0)
|
|
self.first_stage_model = self.first_stage_model.cpu()
|
|
pixel_samples = pixel_samples.cpu().movedim(1,-1)
|
|
return pixel_samples
|
|
|
|
def encode(self, pixel_samples):
|
|
model_management.unload_model()
|
|
self.first_stage_model = self.first_stage_model.to(self.device)
|
|
pixel_samples = pixel_samples.movedim(-1,1).to(self.device)
|
|
samples = self.first_stage_model.encode(2. * pixel_samples - 1.).sample() * self.scale_factor
|
|
self.first_stage_model = self.first_stage_model.cpu()
|
|
samples = samples.cpu()
|
|
return samples
|
|
|
|
class ControlNet:
|
|
def __init__(self, control_model, device="cuda"):
|
|
self.control_model = control_model
|
|
self.cond_hint_original = None
|
|
self.cond_hint = None
|
|
self.strength = 1.0
|
|
self.device = device
|
|
|
|
def get_control(self, x_noisy, t, cond_txt):
|
|
output_dtype = x_noisy.dtype
|
|
if self.cond_hint is None or x_noisy.shape[2] * 8 != self.cond_hint.shape[2] or x_noisy.shape[3] * 8 != self.cond_hint.shape[3]:
|
|
if self.cond_hint is not None:
|
|
del self.cond_hint
|
|
self.cond_hint = None
|
|
self.cond_hint = utils.common_upscale(self.cond_hint_original, x_noisy.shape[3] * 8, x_noisy.shape[2] * 8, 'nearest-exact', "center").to(self.control_model.dtype).to(self.device)
|
|
|
|
if self.control_model.dtype == torch.float16:
|
|
precision_scope = torch.autocast
|
|
else:
|
|
precision_scope = contextlib.nullcontext
|
|
|
|
with precision_scope(self.device):
|
|
self.control_model = model_management.load_if_low_vram(self.control_model)
|
|
control = self.control_model(x=x_noisy, hint=self.cond_hint, timesteps=t, context=cond_txt)
|
|
self.control_model = model_management.unload_if_low_vram(self.control_model)
|
|
out = []
|
|
autocast_enabled = torch.is_autocast_enabled()
|
|
for x in control:
|
|
x *= self.strength
|
|
if x.dtype != output_dtype and not autocast_enabled:
|
|
x = x.to(output_dtype)
|
|
out.append(x)
|
|
return out
|
|
|
|
def set_cond_hint(self, cond_hint, strength=1.0):
|
|
self.cond_hint_original = cond_hint
|
|
self.strength = strength
|
|
return self
|
|
|
|
def cleanup(self):
|
|
if self.cond_hint is not None:
|
|
del self.cond_hint
|
|
self.cond_hint = None
|
|
|
|
def copy(self):
|
|
c = ControlNet(self.control_model)
|
|
c.cond_hint_original = self.cond_hint_original
|
|
c.strength = self.strength
|
|
return c
|
|
|
|
def load_controlnet(ckpt_path):
|
|
controlnet_data = load_torch_file(ckpt_path)
|
|
pth_key = 'control_model.input_blocks.1.1.transformer_blocks.0.attn2.to_k.weight'
|
|
pth = False
|
|
sd2 = False
|
|
key = 'input_blocks.1.1.transformer_blocks.0.attn2.to_k.weight'
|
|
if pth_key in controlnet_data:
|
|
pth = True
|
|
key = pth_key
|
|
elif key in controlnet_data:
|
|
pass
|
|
else:
|
|
print("error checkpoint does not contain controlnet data", ckpt_path)
|
|
return None
|
|
|
|
context_dim = controlnet_data[key].shape[1]
|
|
|
|
use_fp16 = False
|
|
if controlnet_data[key].dtype == torch.float16:
|
|
use_fp16 = True
|
|
|
|
control_model = cldm.ControlNet(image_size=32,
|
|
in_channels=4,
|
|
hint_channels=3,
|
|
model_channels=320,
|
|
attention_resolutions=[ 4, 2, 1 ],
|
|
num_res_blocks=2,
|
|
channel_mult=[ 1, 2, 4, 4 ],
|
|
num_heads=8,
|
|
use_spatial_transformer=True,
|
|
transformer_depth=1,
|
|
context_dim=context_dim,
|
|
use_checkpoint=True,
|
|
legacy=False,
|
|
use_fp16=use_fp16)
|
|
|
|
if pth:
|
|
class WeightsLoader(torch.nn.Module):
|
|
pass
|
|
w = WeightsLoader()
|
|
w.control_model = control_model
|
|
w.load_state_dict(controlnet_data, strict=False)
|
|
else:
|
|
control_model.load_state_dict(controlnet_data, strict=False)
|
|
|
|
control = ControlNet(control_model)
|
|
return control
|
|
|
|
|
|
def load_clip(ckpt_path, embedding_directory=None):
|
|
clip_data = load_torch_file(ckpt_path)
|
|
config = {}
|
|
if "text_model.encoder.layers.22.mlp.fc1.weight" in clip_data:
|
|
config['target'] = 'ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder'
|
|
else:
|
|
config['target'] = 'ldm.modules.encoders.modules.FrozenCLIPEmbedder'
|
|
clip = CLIP(config=config, embedding_directory=embedding_directory)
|
|
clip.load_from_state_dict(clip_data)
|
|
return clip
|
|
|
|
def load_checkpoint(config_path, ckpt_path, output_vae=True, output_clip=True, embedding_directory=None):
|
|
config = OmegaConf.load(config_path)
|
|
model_config_params = config['model']['params']
|
|
clip_config = model_config_params['cond_stage_config']
|
|
scale_factor = model_config_params['scale_factor']
|
|
vae_config = model_config_params['first_stage_config']
|
|
|
|
clip = None
|
|
vae = None
|
|
|
|
class WeightsLoader(torch.nn.Module):
|
|
pass
|
|
|
|
w = WeightsLoader()
|
|
load_state_dict_to = []
|
|
if output_vae:
|
|
vae = VAE(scale_factor=scale_factor, config=vae_config)
|
|
w.first_stage_model = vae.first_stage_model
|
|
load_state_dict_to = [w]
|
|
|
|
if output_clip:
|
|
clip = CLIP(config=clip_config, embedding_directory=embedding_directory)
|
|
w.cond_stage_model = clip.cond_stage_model
|
|
load_state_dict_to = [w]
|
|
|
|
model = load_model_from_config(config, ckpt_path, verbose=False, load_state_dict_to=load_state_dict_to)
|
|
return (ModelPatcher(model), clip, vae)
|