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import os
import random
from collections import OrderedDict
from typing import List
import numpy as np
from PIL import Image
from diffusers import T2IAdapter
from torch.utils.data import DataLoader
from diffusers import StableDiffusionXLAdapterPipeline, StableDiffusionAdapterPipeline
from tqdm import tqdm
from toolkit.config_modules import ModelConfig, GenerateImageConfig, preprocess_dataset_raw_config, DatasetConfig
from toolkit.data_transfer_object.data_loader import FileItemDTO, DataLoaderBatchDTO
from toolkit.sampler import get_sampler
from toolkit.stable_diffusion_model import StableDiffusion
import gc
import torch
from jobs.process import BaseExtensionProcess
from toolkit.data_loader import get_dataloader_from_datasets
from toolkit.train_tools import get_torch_dtype
from controlnet_aux.midas import MidasDetector
from diffusers.utils import load_image
def flush():
torch.cuda.empty_cache()
gc.collect()
class GenerateConfig:
def __init__(self, **kwargs):
self.prompts: List[str]
self.sampler = kwargs.get('sampler', 'ddpm')
self.neg = kwargs.get('neg', '')
self.seed = kwargs.get('seed', -1)
self.walk_seed = kwargs.get('walk_seed', False)
self.t2i_adapter_path = kwargs.get('t2i_adapter_path', None)
self.guidance_scale = kwargs.get('guidance_scale', 7)
self.sample_steps = kwargs.get('sample_steps', 20)
self.prompt_2 = kwargs.get('prompt_2', None)
self.neg_2 = kwargs.get('neg_2', None)
self.prompts = kwargs.get('prompts', None)
self.guidance_rescale = kwargs.get('guidance_rescale', 0.0)
self.ext = kwargs.get('ext', 'png')
self.adapter_conditioning_scale = kwargs.get('adapter_conditioning_scale', 1.0)
if kwargs.get('shuffle', False):
# shuffle the prompts
random.shuffle(self.prompts)
class ReferenceGenerator(BaseExtensionProcess):
def __init__(self, process_id: int, job, config: OrderedDict):
super().__init__(process_id, job, config)
self.output_folder = self.get_conf('output_folder', required=True)
self.device = self.get_conf('device', 'cuda')
self.model_config = ModelConfig(**self.get_conf('model', required=True))
self.generate_config = GenerateConfig(**self.get_conf('generate', required=True))
self.is_latents_cached = True
raw_datasets = self.get_conf('datasets', None)
if raw_datasets is not None and len(raw_datasets) > 0:
raw_datasets = preprocess_dataset_raw_config(raw_datasets)
self.datasets = None
self.datasets_reg = None
self.dtype = self.get_conf('dtype', 'float16')
self.torch_dtype = get_torch_dtype(self.dtype)
self.params = []
if raw_datasets is not None and len(raw_datasets) > 0:
for raw_dataset in raw_datasets:
dataset = DatasetConfig(**raw_dataset)
is_caching = dataset.cache_latents or dataset.cache_latents_to_disk
if not is_caching:
self.is_latents_cached = False
if dataset.is_reg:
if self.datasets_reg is None:
self.datasets_reg = []
self.datasets_reg.append(dataset)
else:
if self.datasets is None:
self.datasets = []
self.datasets.append(dataset)
self.progress_bar = None
self.sd = StableDiffusion(
device=self.device,
model_config=self.model_config,
dtype=self.dtype,
)
print(f"Using device {self.device}")
self.data_loader: DataLoader = None
self.adapter: T2IAdapter = None
def run(self):
super().run()
print("Loading model...")
self.sd.load_model()
device = torch.device(self.device)
if self.generate_config.t2i_adapter_path is not None:
self.adapter = T2IAdapter.from_pretrained(
self.generate_config.t2i_adapter_path,
torch_dtype=self.torch_dtype,
varient="fp16"
).to(device)
midas_depth = MidasDetector.from_pretrained(
"valhalla/t2iadapter-aux-models", filename="dpt_large_384.pt", model_type="dpt_large"
).to(device)
if self.model_config.is_xl:
pipe = StableDiffusionXLAdapterPipeline(
vae=self.sd.vae,
unet=self.sd.unet,
text_encoder=self.sd.text_encoder[0],
text_encoder_2=self.sd.text_encoder[1],
tokenizer=self.sd.tokenizer[0],
tokenizer_2=self.sd.tokenizer[1],
scheduler=get_sampler(self.generate_config.sampler),
adapter=self.adapter,
).to(device, dtype=self.torch_dtype)
else:
pipe = StableDiffusionAdapterPipeline(
vae=self.sd.vae,
unet=self.sd.unet,
text_encoder=self.sd.text_encoder,
tokenizer=self.sd.tokenizer,
scheduler=get_sampler(self.generate_config.sampler),
safety_checker=None,
feature_extractor=None,
requires_safety_checker=False,
adapter=self.adapter,
).to(device, dtype=self.torch_dtype)
pipe.set_progress_bar_config(disable=True)
pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
# midas_depth = torch.compile(midas_depth, mode="reduce-overhead", fullgraph=True)
self.data_loader = get_dataloader_from_datasets(self.datasets, 1, self.sd)
num_batches = len(self.data_loader)
pbar = tqdm(total=num_batches, desc="Generating images")
seed = self.generate_config.seed
# load images from datasets, use tqdm
for i, batch in enumerate(self.data_loader):
batch: DataLoaderBatchDTO = batch
file_item: FileItemDTO = batch.file_items[0]
img_path = file_item.path
img_filename = os.path.basename(img_path)
img_filename_no_ext = os.path.splitext(img_filename)[0]
output_path = os.path.join(self.output_folder, img_filename)
output_caption_path = os.path.join(self.output_folder, img_filename_no_ext + '.txt')
output_depth_path = os.path.join(self.output_folder, img_filename_no_ext + '.depth.png')
caption = batch.get_caption_list()[0]
img: torch.Tensor = batch.tensor.clone()
# image comes in -1 to 1. convert to a PIL RGB image
img = (img + 1) / 2
img = img.clamp(0, 1)
img = img[0].permute(1, 2, 0).cpu().numpy()
img = (img * 255).astype(np.uint8)
image = Image.fromarray(img)
width, height = image.size
min_res = min(width, height)
if self.generate_config.walk_seed:
seed = seed + 1
if self.generate_config.seed == -1:
# random
seed = random.randint(0, 1000000)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
# generate depth map
image = midas_depth(
image,
detect_resolution=min_res, # do 512 ?
image_resolution=min_res
)
# image.save(output_depth_path)
gen_images = pipe(
prompt=caption,
negative_prompt=self.generate_config.neg,
image=image,
num_inference_steps=self.generate_config.sample_steps,
adapter_conditioning_scale=self.generate_config.adapter_conditioning_scale,
guidance_scale=self.generate_config.guidance_scale,
).images[0]
os.makedirs(os.path.dirname(output_path), exist_ok=True)
gen_images.save(output_path)
# save caption
with open(output_caption_path, 'w') as f:
f.write(caption)
pbar.update(1)
batch.cleanup()
pbar.close()
print("Done generating images")
# cleanup
del self.sd
gc.collect()
torch.cuda.empty_cache()
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