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import torch
from ldm_patched.modules import model_management
from ldm_patched.ldm.models.autoencoder import AutoencoderKL, AutoencodingEngine
import yaml
import ldm_patched.modules.utils
from . import clip_vision
from . import gligen
from . import diffusers_convert
from . import model_base
from . import model_detection
from . import sd1_clip
from . import sd2_clip
from . import sdxl_clip
import ldm_patched.modules.model_patcher
import ldm_patched.modules.lora
import ldm_patched.t2ia.adapter
import ldm_patched.modules.supported_models_base
import ldm_patched.taesd.taesd
def load_model_weights(model, sd):
m, u = model.load_state_dict(sd, strict=False)
m = set(m)
unexpected_keys = set(u)
k = list(sd.keys())
for x in k:
if x not in unexpected_keys:
w = sd.pop(x)
del w
if len(m) > 0:
print("extra", m)
return model
def load_clip_weights(model, sd):
k = list(sd.keys())
for x in k:
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()
sd = ldm_patched.modules.utils.transformers_convert(sd, "cond_stage_model.model.", "cond_stage_model.transformer.text_model.", 24)
return load_model_weights(model, sd)
def load_lora_for_models(model, clip, lora, strength_model, strength_clip):
key_map = {}
if model is not None:
key_map = ldm_patched.modules.lora.model_lora_keys_unet(model.model, key_map)
if clip is not None:
key_map = ldm_patched.modules.lora.model_lora_keys_clip(clip.cond_stage_model, key_map)
loaded = ldm_patched.modules.lora.load_lora(lora, key_map)
if model is not None:
new_modelpatcher = model.clone()
k = new_modelpatcher.add_patches(loaded, strength_model)
else:
k = ()
new_modelpatcher = None
if clip is not None:
new_clip = clip.clone()
k1 = new_clip.add_patches(loaded, strength_clip)
else:
k1 = ()
new_clip = None
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, target=None, embedding_directory=None, no_init=False):
if no_init:
return
params = target.params.copy()
clip = target.clip
tokenizer = target.tokenizer
load_device = model_management.text_encoder_device()
offload_device = model_management.text_encoder_offload_device()
params['device'] = offload_device
params['dtype'] = model_management.text_encoder_dtype(load_device)
self.cond_stage_model = clip(**(params))
self.tokenizer = tokenizer(embedding_directory=embedding_directory)
self.patcher = ldm_patched.modules.model_patcher.ModelPatcher(self.cond_stage_model, load_device=load_device, offload_device=offload_device)
self.layer_idx = None
def clone(self):
n = CLIP(no_init=True)
n.patcher = self.patcher.clone()
n.cond_stage_model = self.cond_stage_model
n.tokenizer = self.tokenizer
n.layer_idx = self.layer_idx
return n
def add_patches(self, patches, strength_patch=1.0, strength_model=1.0):
return self.patcher.add_patches(patches, strength_patch, strength_model)
def clip_layer(self, layer_idx):
self.layer_idx = layer_idx
def tokenize(self, text, return_word_ids=False):
return self.tokenizer.tokenize_with_weights(text, return_word_ids)
def encode_from_tokens(self, tokens, return_pooled=False):
if self.layer_idx is not None:
self.cond_stage_model.clip_layer(self.layer_idx)
else:
self.cond_stage_model.reset_clip_layer()
self.load_model()
cond, pooled = self.cond_stage_model.encode_token_weights(tokens)
if return_pooled:
return cond, pooled
return cond
def encode(self, text):
tokens = self.tokenize(text)
return self.encode_from_tokens(tokens)
def load_sd(self, sd):
return self.cond_stage_model.load_sd(sd)
def get_sd(self):
return self.cond_stage_model.state_dict()
def load_model(self):
model_management.load_model_gpu(self.patcher)
return self.patcher
def get_key_patches(self):
return self.patcher.get_key_patches()
class VAE:
def __init__(self, sd=None, device=None, config=None, dtype=None):
if 'decoder.up_blocks.0.resnets.0.norm1.weight' in sd.keys(): #diffusers format
sd = diffusers_convert.convert_vae_state_dict(sd)
self.memory_used_encode = lambda shape, dtype: (1767 * shape[2] * shape[3]) * model_management.dtype_size(dtype) #These are for AutoencoderKL and need tweaking (should be lower)
self.memory_used_decode = lambda shape, dtype: (2178 * shape[2] * shape[3] * 64) * model_management.dtype_size(dtype)
self.downscale_ratio = 8
self.latent_channels = 4
if config is None:
if "decoder.mid.block_1.mix_factor" in sd:
encoder_config = {'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}
decoder_config = encoder_config.copy()
decoder_config["video_kernel_size"] = [3, 1, 1]
decoder_config["alpha"] = 0.0
self.first_stage_model = AutoencodingEngine(regularizer_config={'target': "ldm_patched.ldm.models.autoencoder.DiagonalGaussianRegularizer"},
encoder_config={'target': "ldm_patched.ldm.modules.diffusionmodules.model.Encoder", 'params': encoder_config},
decoder_config={'target': "ldm_patched.ldm.modules.temporal_ae.VideoDecoder", 'params': decoder_config})
elif "taesd_decoder.1.weight" in sd:
self.first_stage_model = ldm_patched.taesd.taesd.TAESD()
else:
#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}
if 'encoder.down.2.downsample.conv.weight' not in sd: #Stable diffusion x4 upscaler VAE
ddconfig['ch_mult'] = [1, 2, 4]
self.downscale_ratio = 4
self.first_stage_model = AutoencoderKL(ddconfig=ddconfig, embed_dim=4)
else:
self.first_stage_model = AutoencoderKL(**(config['params']))
self.first_stage_model = self.first_stage_model.eval()
m, u = self.first_stage_model.load_state_dict(sd, strict=False)
if len(m) > 0:
print("Missing VAE keys", m)
if len(u) > 0:
print("Leftover VAE keys", u)
if device is None:
device = model_management.vae_device()
self.device = device
offload_device = model_management.vae_offload_device()
if dtype is None:
dtype = model_management.vae_dtype()
self.vae_dtype = dtype
self.first_stage_model.to(self.vae_dtype)
self.output_device = model_management.intermediate_device()
self.patcher = ldm_patched.modules.model_patcher.ModelPatcher(self.first_stage_model, load_device=self.device, offload_device=offload_device)
def decode_tiled_(self, samples, tile_x=64, tile_y=64, overlap = 16):
steps = samples.shape[0] * ldm_patched.modules.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x, tile_y, overlap)
steps += samples.shape[0] * ldm_patched.modules.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x // 2, tile_y * 2, overlap)
steps += samples.shape[0] * ldm_patched.modules.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x * 2, tile_y // 2, overlap)
pbar = ldm_patched.modules.utils.ProgressBar(steps)
decode_fn = lambda a: (self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)) + 1.0).float()
output = torch.clamp((
(ldm_patched.modules.utils.tiled_scale(samples, decode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = self.downscale_ratio, output_device=self.output_device, pbar = pbar) +
ldm_patched.modules.utils.tiled_scale(samples, decode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = self.downscale_ratio, output_device=self.output_device, pbar = pbar) +
ldm_patched.modules.utils.tiled_scale(samples, decode_fn, tile_x, tile_y, overlap, upscale_amount = self.downscale_ratio, output_device=self.output_device, pbar = pbar))
/ 3.0) / 2.0, min=0.0, max=1.0)
return output
def encode_tiled_(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64):
steps = pixel_samples.shape[0] * ldm_patched.modules.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x, tile_y, overlap)
steps += pixel_samples.shape[0] * ldm_patched.modules.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x // 2, tile_y * 2, overlap)
steps += pixel_samples.shape[0] * ldm_patched.modules.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x * 2, tile_y // 2, overlap)
pbar = ldm_patched.modules.utils.ProgressBar(steps)
encode_fn = lambda a: self.first_stage_model.encode((2. * a - 1.).to(self.vae_dtype).to(self.device)).float()
samples = ldm_patched.modules.utils.tiled_scale(pixel_samples, encode_fn, tile_x, tile_y, overlap, upscale_amount = (1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device, pbar=pbar)
samples += ldm_patched.modules.utils.tiled_scale(pixel_samples, encode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = (1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device, pbar=pbar)
samples += ldm_patched.modules.utils.tiled_scale(pixel_samples, encode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = (1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device, pbar=pbar)
samples /= 3.0
return samples
def decode(self, samples_in):
try:
memory_used = self.memory_used_decode(samples_in.shape, self.vae_dtype)
model_management.load_models_gpu([self.patcher], memory_required=memory_used)
free_memory = model_management.get_free_memory(self.device)
batch_number = int(free_memory / memory_used)
batch_number = max(1, batch_number)
pixel_samples = torch.empty((samples_in.shape[0], 3, round(samples_in.shape[2] * self.downscale_ratio), round(samples_in.shape[3] * self.downscale_ratio)), device=self.output_device)
for x in range(0, samples_in.shape[0], batch_number):
samples = samples_in[x:x+batch_number].to(self.vae_dtype).to(self.device)
pixel_samples[x:x+batch_number] = torch.clamp((self.first_stage_model.decode(samples).to(self.output_device).float() + 1.0) / 2.0, min=0.0, max=1.0)
except model_management.OOM_EXCEPTION as e:
print("Warning: Ran out of memory when regular VAE decoding, retrying with tiled VAE decoding.")
pixel_samples = self.decode_tiled_(samples_in)
pixel_samples = pixel_samples.to(self.output_device).movedim(1,-1)
return pixel_samples
def decode_tiled(self, samples, tile_x=64, tile_y=64, overlap = 16):
model_management.load_model_gpu(self.patcher)
output = self.decode_tiled_(samples, tile_x, tile_y, overlap)
return output.movedim(1,-1)
def encode(self, pixel_samples):
pixel_samples = pixel_samples.movedim(-1,1)
try:
memory_used = self.memory_used_encode(pixel_samples.shape, self.vae_dtype)
model_management.load_models_gpu([self.patcher], memory_required=memory_used)
free_memory = model_management.get_free_memory(self.device)
batch_number = int(free_memory / memory_used)
batch_number = max(1, batch_number)
samples = torch.empty((pixel_samples.shape[0], self.latent_channels, round(pixel_samples.shape[2] // self.downscale_ratio), round(pixel_samples.shape[3] // self.downscale_ratio)), device=self.output_device)
for x in range(0, pixel_samples.shape[0], batch_number):
pixels_in = (2. * pixel_samples[x:x+batch_number] - 1.).to(self.vae_dtype).to(self.device)
samples[x:x+batch_number] = self.first_stage_model.encode(pixels_in).to(self.output_device).float()
except model_management.OOM_EXCEPTION as e:
print("Warning: Ran out of memory when regular VAE encoding, retrying with tiled VAE encoding.")
samples = self.encode_tiled_(pixel_samples)
return samples
def encode_tiled(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64):
model_management.load_model_gpu(self.patcher)
pixel_samples = pixel_samples.movedim(-1,1)
samples = self.encode_tiled_(pixel_samples, tile_x=tile_x, tile_y=tile_y, overlap=overlap)
return samples
def get_sd(self):
return self.first_stage_model.state_dict()
class StyleModel:
def __init__(self, model, device="cpu"):
self.model = model
def get_cond(self, input):
return self.model(input.last_hidden_state)
def load_style_model(ckpt_path):
model_data = ldm_patched.modules.utils.load_torch_file(ckpt_path, safe_load=True)
keys = model_data.keys()
if "style_embedding" in keys:
model = ldm_patched.t2ia.adapter.StyleAdapter(width=1024, context_dim=768, num_head=8, n_layes=3, num_token=8)
else:
raise Exception("invalid style model {}".format(ckpt_path))
model.load_state_dict(model_data)
return StyleModel(model)
def load_clip(ckpt_paths, embedding_directory=None):
clip_data = []
for p in ckpt_paths:
clip_data.append(ldm_patched.modules.utils.load_torch_file(p, safe_load=True))
class EmptyClass:
pass
for i in range(len(clip_data)):
if "transformer.resblocks.0.ln_1.weight" in clip_data[i]:
clip_data[i] = ldm_patched.modules.utils.transformers_convert(clip_data[i], "", "text_model.", 32)
clip_target = EmptyClass()
clip_target.params = {}
if len(clip_data) == 1:
if "text_model.encoder.layers.30.mlp.fc1.weight" in clip_data[0]:
clip_target.clip = sdxl_clip.SDXLRefinerClipModel
clip_target.tokenizer = sdxl_clip.SDXLTokenizer
elif "text_model.encoder.layers.22.mlp.fc1.weight" in clip_data[0]:
clip_target.clip = sd2_clip.SD2ClipModel
clip_target.tokenizer = sd2_clip.SD2Tokenizer
else:
clip_target.clip = sd1_clip.SD1ClipModel
clip_target.tokenizer = sd1_clip.SD1Tokenizer
else:
clip_target.clip = sdxl_clip.SDXLClipModel
clip_target.tokenizer = sdxl_clip.SDXLTokenizer
clip = CLIP(clip_target, embedding_directory=embedding_directory)
for c in clip_data:
m, u = clip.load_sd(c)
if len(m) > 0:
print("clip missing:", m)
if len(u) > 0:
print("clip unexpected:", u)
return clip
def load_gligen(ckpt_path):
data = ldm_patched.modules.utils.load_torch_file(ckpt_path, safe_load=True)
model = gligen.load_gligen(data)
if model_management.should_use_fp16():
model = model.half()
return ldm_patched.modules.model_patcher.ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=model_management.unet_offload_device())
def load_checkpoint(config_path=None, ckpt_path=None, output_vae=True, output_clip=True, embedding_directory=None, state_dict=None, config=None):
#TODO: this function is a mess and should be removed eventually
if config is None:
with open(config_path, 'r') as stream:
config = yaml.safe_load(stream)
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']
fp16 = False
if "unet_config" in model_config_params:
if "params" in model_config_params["unet_config"]:
unet_config = model_config_params["unet_config"]["params"]
if "use_fp16" in unet_config:
fp16 = unet_config.pop("use_fp16")
if fp16:
unet_config["dtype"] = torch.float16
noise_aug_config = None
if "noise_aug_config" in model_config_params:
noise_aug_config = model_config_params["noise_aug_config"]
model_type = model_base.ModelType.EPS
if "parameterization" in model_config_params:
if model_config_params["parameterization"] == "v":
model_type = model_base.ModelType.V_PREDICTION
clip = None
vae = None
class WeightsLoader(torch.nn.Module):
pass
if state_dict is None:
state_dict = ldm_patched.modules.utils.load_torch_file(ckpt_path)
class EmptyClass:
pass
model_config = ldm_patched.modules.supported_models_base.BASE({})
from . import latent_formats
model_config.latent_format = latent_formats.SD15(scale_factor=scale_factor)
model_config.unet_config = model_detection.convert_config(unet_config)
if config['model']["target"].endswith("ImageEmbeddingConditionedLatentDiffusion"):
model = model_base.SD21UNCLIP(model_config, noise_aug_config["params"], model_type=model_type)
else:
model = model_base.BaseModel(model_config, model_type=model_type)
if config['model']["target"].endswith("LatentInpaintDiffusion"):
model.set_inpaint()
if fp16:
model = model.half()
offload_device = model_management.unet_offload_device()
model = model.to(offload_device)
model.load_model_weights(state_dict, "model.diffusion_model.")
if output_vae:
vae_sd = ldm_patched.modules.utils.state_dict_prefix_replace(state_dict, {"first_stage_model.": ""}, filter_keys=True)
vae = VAE(sd=vae_sd, config=vae_config)
if output_clip:
w = WeightsLoader()
clip_target = EmptyClass()
clip_target.params = clip_config.get("params", {})
if clip_config["target"].endswith("FrozenOpenCLIPEmbedder"):
clip_target.clip = sd2_clip.SD2ClipModel
clip_target.tokenizer = sd2_clip.SD2Tokenizer
clip = CLIP(clip_target, embedding_directory=embedding_directory)
w.cond_stage_model = clip.cond_stage_model.clip_h
elif clip_config["target"].endswith("FrozenCLIPEmbedder"):
clip_target.clip = sd1_clip.SD1ClipModel
clip_target.tokenizer = sd1_clip.SD1Tokenizer
clip = CLIP(clip_target, embedding_directory=embedding_directory)
w.cond_stage_model = clip.cond_stage_model.clip_l
load_clip_weights(w, state_dict)
return (ldm_patched.modules.model_patcher.ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=offload_device), clip, vae)
def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=False, embedding_directory=None, output_model=True):
sd = ldm_patched.modules.utils.load_torch_file(ckpt_path)
sd_keys = sd.keys()
clip = None
clipvision = None
vae = None
model = None
model_patcher = None
clip_target = None
parameters = ldm_patched.modules.utils.calculate_parameters(sd, "model.diffusion_model.")
unet_dtype = model_management.unet_dtype(model_params=parameters)
load_device = model_management.get_torch_device()
manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device)
class WeightsLoader(torch.nn.Module):
pass
model_config = model_detection.model_config_from_unet(sd, "model.diffusion_model.", unet_dtype)
model_config.set_manual_cast(manual_cast_dtype)
if model_config is None:
raise RuntimeError("ERROR: Could not detect model type of: {}".format(ckpt_path))
if model_config.clip_vision_prefix is not None:
if output_clipvision:
clipvision = clip_vision.load_clipvision_from_sd(sd, model_config.clip_vision_prefix, True)
if output_model:
inital_load_device = model_management.unet_inital_load_device(parameters, unet_dtype)
offload_device = model_management.unet_offload_device()
model = model_config.get_model(sd, "model.diffusion_model.", device=inital_load_device)
model.load_model_weights(sd, "model.diffusion_model.")
if output_vae:
vae_sd = ldm_patched.modules.utils.state_dict_prefix_replace(sd, {"first_stage_model.": ""}, filter_keys=True)
vae_sd = model_config.process_vae_state_dict(vae_sd)
vae = VAE(sd=vae_sd)
if output_clip:
w = WeightsLoader()
clip_target = model_config.clip_target()
if clip_target is not None:
clip = CLIP(clip_target, embedding_directory=embedding_directory)
w.cond_stage_model = clip.cond_stage_model
sd = model_config.process_clip_state_dict(sd)
load_model_weights(w, sd)
left_over = sd.keys()
if len(left_over) > 0:
print("left over keys:", left_over)
if output_model:
model_patcher = ldm_patched.modules.model_patcher.ModelPatcher(model, load_device=load_device, offload_device=model_management.unet_offload_device(), current_device=inital_load_device)
if inital_load_device != torch.device("cpu"):
print("loaded straight to GPU")
model_management.load_model_gpu(model_patcher)
return (model_patcher, clip, vae, clipvision)
def load_unet_state_dict(sd): #load unet in diffusers format
parameters = ldm_patched.modules.utils.calculate_parameters(sd)
unet_dtype = model_management.unet_dtype(model_params=parameters)
load_device = model_management.get_torch_device()
manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device)
if "input_blocks.0.0.weight" in sd: #ldm
model_config = model_detection.model_config_from_unet(sd, "", unet_dtype)
if model_config is None:
return None
new_sd = sd
else: #diffusers
model_config = model_detection.model_config_from_diffusers_unet(sd, unet_dtype)
if model_config is None:
return None
diffusers_keys = ldm_patched.modules.utils.unet_to_diffusers(model_config.unet_config)
new_sd = {}
for k in diffusers_keys:
if k in sd:
new_sd[diffusers_keys[k]] = sd.pop(k)
else:
print(diffusers_keys[k], k)
offload_device = model_management.unet_offload_device()
model_config.set_manual_cast(manual_cast_dtype)
model = model_config.get_model(new_sd, "")
model = model.to(offload_device)
model.load_model_weights(new_sd, "")
left_over = sd.keys()
if len(left_over) > 0:
print("left over keys in unet:", left_over)
return ldm_patched.modules.model_patcher.ModelPatcher(model, load_device=load_device, offload_device=offload_device)
def load_unet(unet_path):
sd = ldm_patched.modules.utils.load_torch_file(unet_path)
model = load_unet_state_dict(sd)
if model is None:
print("ERROR UNSUPPORTED UNET", unet_path)
raise RuntimeError("ERROR: Could not detect model type of: {}".format(unet_path))
return model
def save_checkpoint(output_path, model, clip=None, vae=None, clip_vision=None, metadata=None):
clip_sd = None
load_models = [model]
if clip is not None:
load_models.append(clip.load_model())
clip_sd = clip.get_sd()
model_management.load_models_gpu(load_models)
clip_vision_sd = clip_vision.get_sd() if clip_vision is not None else None
sd = model.model.state_dict_for_saving(clip_sd, vae.get_sd(), clip_vision_sd)
ldm_patched.modules.utils.save_torch_file(sd, output_path, metadata=metadata)