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import inspect
import os
import time
from typing import Any, Callable, Dict, List, Optional, Union, Tuple
import gc
import torch
import numpy as np
from glob import glob
from diffusers import StableDiffusionXLPipeline, UNet2DConditionModel
from diffusers.loaders import TextualInversionLoaderMixin
from diffusers.image_processor import VaeImageProcessor
from diffusers.models import AutoencoderKL
from diffusers.schedulers import (DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
KarrasDiffusionSchedulers)
from diffusers.models.embeddings import TimestepEmbedding, Timesteps
from diffusers.utils.torch_utils import randn_tensor
from diffusers.utils import logging
from PIL import Image
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPTextModelWithProjection
from .lyrasd_vae_model import LyraSdVaeModel
from .module.lyrasd_ip_adapter import LyraIPAdapter
from .lora_util import add_text_lora_layer, add_xltext_lora_layer, add_lora_to_opt_model, load_state_dict
from safetensors.torch import load_file
class LyraSDXLPipelineBase(TextualInversionLoaderMixin):
def __init__(self, device=torch.device("cuda"), dtype=torch.float16, num_channels_unet=4, num_channels_latents=4, vae_scale_factor=8, vae_scaling_factor=0.13025) -> None:
self.device = device
self.dtype = dtype
self.num_channels_unet = num_channels_unet
self.num_channels_latents = num_channels_latents
self.vae_scale_factor = vae_scale_factor
self.vae_scaling_factor = vae_scaling_factor
self.unet_cache = {}
self.unet_in_channels = 4
self.controlnet_cache = {}
self.controlnet_add_embedding = {}
self.loaded_lora = {}
self.loaded_lora_strength = {}
self.scheduler = None
self.init_pipe()
def init_pipe(self):
self.vae = LyraSdVaeModel(
scale_factor=self.vae_scale_factor, scaling_factor=self.vae_scaling_factor, is_upcast=True)
self.unet = torch.classes.lyrasd.XLUnet2dConditionalModelOp(
"fp16",
self.num_channels_unet,
self.num_channels_latents)
self.default_sample_size = 128
self.addition_time_embed_dim = 256
flip_sin_to_cos, freq_shift = True, 0
self.projection_class_embeddings_input_dim, self.time_embed_dim = 2816, 1280
self.add_time_proj = Timesteps(
self.addition_time_embed_dim, flip_sin_to_cos, freq_shift).to(self.dtype).to(self.device)
self.add_embedding = TimestepEmbedding(
self.projection_class_embeddings_input_dim, self.time_embed_dim).to(self.dtype).to(self.device)
self.image_processor = VaeImageProcessor(
vae_scale_factor=self.vae_scale_factor)
self.mask_processor = VaeImageProcessor(
vae_scale_factor=self.vae_scale_factor, do_normalize=False, do_binarize=True, do_convert_grayscale=True
)
self.control_image_processor = VaeImageProcessor(
vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True, do_normalize=False
)
self.feature_extractor = CLIPImageProcessor()
def reload_pipe(self, model_path):
self.tokenizer = CLIPTokenizer.from_pretrained(
model_path, subfolder="tokenizer")
self.text_encoder = CLIPTextModel.from_pretrained(
model_path, subfolder="text_encoder").to(self.dtype).to(self.device)
self.tokenizer_2 = CLIPTokenizer.from_pretrained(
model_path, subfolder="tokenizer_2")
self.text_encoder_2 = CLIPTextModelWithProjection.from_pretrained(
model_path, subfolder="text_encoder_2").to(self.dtype).to(self.device)
self.reload_unet_model_v2(model_path)
self.reload_vae_model_v2(model_path)
if not self.scheduler:
self.scheduler = EulerAncestralDiscreteScheduler.from_pretrained(
model_path, subfolder="scheduler")
def load_embedding_weight(self, model, weight_path, unet_file_format="fp16"):
bin_list = glob(weight_path)
sate_dicts = model.state_dict()
dtype = np.float32 if unet_file_format == "fp32" else np.float16
for bin_file in bin_list:
weight = torch.from_numpy(np.fromfile(bin_file, dtype=dtype)).to(
self.dtype).to(self.device)
key = '.'.join(os.path.basename(bin_file).split('.')[1:-1])
weight = weight.reshape(sate_dicts[key].shape)
sate_dicts.update({key: weight})
model.load_state_dict(sate_dicts)
@property
def _execution_device(self):
if not hasattr(self.unet, "_hf_hook"):
return self.device
for module in self.unet.modules():
if (
hasattr(module, "_hf_hook")
and hasattr(module._hf_hook, "execution_device")
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device)
return self.device
def reload_unet_model(self, unet_path, unet_file_format='fp32'):
if len(unet_path) > 0 and unet_path[-1] != "/":
unet_path = unet_path + "/"
self.unet.reload_unet_model(unet_path, unet_file_format)
self.load_embedding_weight(
self.add_embedding, f"{unet_path}add_embedding*", unet_file_format=unet_file_format)
def reload_vae_model(self, vae_path, vae_file_format='fp32'):
if len(vae_path) > 0 and vae_path[-1] != "/":
vae_path = vae_path + "/"
return self.vae.reload_vae_model(vae_path, vae_file_format)
def load_lora(self, lora_model_path, lora_name, lora_strength, lora_file_format='fp32'):
if len(lora_model_path) > 0 and lora_model_path[-1] != "/":
lora_model_path = lora_model_path + "/"
lora = add_xltext_lora_layer(
self.text_encoder, self.text_encoder_2, lora_model_path, lora_strength, lora_file_format)
self.loaded_lora[lora_name] = lora
self.unet.load_lora(lora_model_path, lora_name,
lora_strength, lora_file_format)
def unload_lora(self, lora_name, clean_cache=False):
for layer_data in self.loaded_lora[lora_name]:
layer = layer_data['layer']
added_weight = layer_data['added_weight']
layer.weight.data -= added_weight
self.unet.unload_lora(lora_name, clean_cache)
del self.loaded_lora[lora_name]
gc.collect()
torch.cuda.empty_cache()
def load_lora_v2(self, lora_model_path, lora_name, lora_strength):
if lora_name in self.loaded_lora:
state_dict = self.loaded_lora[lora_name]
else:
state_dict = load_state_dict(lora_model_path)
self.loaded_lora[lora_name] = state_dict
self.loaded_lora_strength[lora_name] = lora_strength
add_lora_to_opt_model(state_dict, self.unet, self.text_encoder,
self.text_encoder_2, lora_strength)
def unload_lora_v2(self, lora_name, clean_cache=False):
state_dict = self.loaded_lora[lora_name]
lora_strength = self.loaded_lora_strength[lora_name]
add_lora_to_opt_model(state_dict, self.unet, self.text_encoder,
self.text_encoder_2, -1.0 * lora_strength)
del self.loaded_lora_strength[lora_name]
if clean_cache:
del self.loaded_lora[lora_name]
gc.collect()
torch.cuda.empty_cache()
def clean_lora_cache(self):
self.unet.clean_lora_cache()
def get_loaded_lora(self):
return self.unet.get_loaded_lora()
def _get_aug_emb(self, time_ids, text_embeds, dtype):
time_embeds = self.add_time_proj(time_ids.flatten())
time_embeds = time_embeds.reshape((text_embeds.shape[0], -1))
add_embeds = torch.concat([text_embeds, time_embeds], dim=-1)
add_embeds = add_embeds.to(dtype)
aug_emb = self.add_embedding(add_embeds)
return aug_emb
def load_ip_adapter(self, dir_ip_adapter, ip_plus, image_encoder_path, num_ip_tokens, ip_projection_dim, dir_face_in=None, num_fp_tokens=1, fp_projection_dim=None, sdxl=True):
self.ip_adapter_helper = LyraIPAdapter(self, sdxl, "cuda", dir_ip_adapter, ip_plus, image_encoder_path,
num_ip_tokens, ip_projection_dim, dir_face_in, num_fp_tokens, fp_projection_dim)
def reload_unet_model_v2(self, model_path):
checkpoint_file = os.path.join(
model_path, "unet/diffusion_pytorch_model.bin")
if not os.path.exists(checkpoint_file):
checkpoint_file = os.path.join(
model_path, "unet/diffusion_pytorch_model.safetensors")
if checkpoint_file in self.unet_cache:
state_dict = self.unet_cache[checkpoint_file]
else:
if "safetensors" in checkpoint_file:
state_dict = load_file(checkpoint_file)
else:
state_dict = torch.load(checkpoint_file, map_location="cpu")
for key in state_dict:
if len(state_dict[key].shape) == 4:
# converted_unet_checkpoint[key] = converted_unet_checkpoint[key].to(torch.float16).to("cuda").permute(0,2,3,1).contiguous().cpu()
state_dict[key] = state_dict[key].to(
torch.float16).permute(0, 2, 3, 1).contiguous()
state_dict[key] = state_dict[key].to(torch.float16)
self.unet_cache[checkpoint_file] = state_dict
self.unet.reload_unet_model_from_cache(state_dict, "cpu")
self.load_embedding_weight_v2(self.add_embedding, state_dict)
def load_embedding_weight_v2(self, model, state_dict):
sub_state_dict = {}
for k in state_dict:
if k.startswith("add_embedding"):
v = state_dict[k]
sub_k = ".".join(k.split(".")[1:])
sub_state_dict[sub_k] = v
model.load_state_dict(sub_state_dict)
def reload_vae_model_v2(self, model_path):
self.vae.reload_vae_model_v2(model_path)
def load_controlnet_model_v2(self, model_name, controlnet_path):
checkpoint_file = os.path.join(
controlnet_path, "diffusion_pytorch_model.bin")
if not os.path.exists(checkpoint_file):
checkpoint_file = os.path.join(
controlnet_path, "diffusion_pytorch_model.safetensors")
if checkpoint_file in self.controlnet_cache:
state_dict = self.controlnet_cache[checkpoint_file]
else:
if "safetensors" in checkpoint_file:
state_dict = load_file(checkpoint_file)
else:
state_dict = torch.load(checkpoint_file, map_location="cpu")
for key in state_dict:
if len(state_dict[key].shape) == 4:
# converted_unet_checkpoint[key] = converted_unet_checkpoint[key].to(torch.float16).to("cuda").permute(0,2,3,1).contiguous().cpu()
state_dict[key] = state_dict[key].to(
torch.float16).permute(0, 2, 3, 1).contiguous()
state_dict[key] = state_dict[key].to(torch.float16)
self.controlnet_cache[checkpoint_file] = state_dict
self.unet.load_controlnet_model_from_state_dict(
model_name, state_dict, "cpu")
add_embedding = TimestepEmbedding(
self.projection_class_embeddings_input_dim, self.time_embed_dim).to(self.dtype).to(self.device)
self.load_embedding_weight_v2(add_embedding, state_dict)
self.controlnet_add_embedding[model_name] = add_embedding
def unload_controlnet_model(self, model_name):
self.unet.unload_controlnet_model(model_name, True)
del self.controlnet_add_embedding[model_name]
def get_loaded_controlnet(self):
return self.unet.get_loaded_controlnet()
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