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from pick_score import PickScorer
from aesthetic_score import AestheticScorer
from hpsv2_score import HPSv2Scorer
from imagereward_score import load_imagereward
from diffusers import AutoencoderKL, StableDiffusionPipeline, \
StableDiffusionXLPipeline, DDIMScheduler, \
UNet2DConditionModel
import torch
import os
import json
from tqdm import tqdm
from huggingface_hub import hf_hub_download
from argparse import ArgumentParser
def load_origin_sd_v1_5(scheduler, inference_dtype):
pipe = StableDiffusionPipeline.from_pretrained(
'stable-diffusion-v1-5/stable-diffusion-v1-5',
torch_dtype=inference_dtype,
scheduler=scheduler,
safety_checker=None,
)
guidance_scale = 7.5
return pipe, guidance_scale
def load_spo_sd_v1_5(scheduler, inference_dtype):
pipe = StableDiffusionPipeline.from_pretrained(
'SPO-Diffusion-Models/SPO-SD-v1-5_4k-p_10ep',
torch_dtype=inference_dtype,
scheduler=scheduler,
safety_checker=None,
)
guidance_scale = 5.0
return pipe, guidance_scale
def load_diffusion_dpo_sd_v1_5(scheduler, inference_dtype):
unet = UNet2DConditionModel.from_pretrained('mhdang/dpo-sd1.5-text2image-v1', subfolder="unet", torch_dtype=inference_dtype)
pipe = StableDiffusionPipeline.from_pretrained(
'stable-diffusion-v1-5/stable-diffusion-v1-5',
torch_dtype=inference_dtype,
scheduler=scheduler,
safety_checker=None,
unet=unet,
)
guidance_scale = 7.5
return pipe, guidance_scale
def load_lpo_sd_v1_5(scheduler, inference_dtype):
unet = UNet2DConditionModel.from_pretrained(
'casiatao/LPO',
subfolder="lpo_sd15_merge/unet",
torch_dtype=inference_dtype
)
pipe = StableDiffusionPipeline.from_pretrained(
'stable-diffusion-v1-5/stable-diffusion-v1-5',
torch_dtype=inference_dtype,
scheduler=scheduler,
safety_checker=None,
unet=unet
)
guidance_scale = 5.0
return pipe, guidance_scale
def load_origin_sdxl(scheduler, inference_dtype):
pipe = StableDiffusionXLPipeline.from_pretrained(
'stabilityai/stable-diffusion-xl-base-1.0',
torch_dtype=inference_dtype,
scheduler=scheduler,
)
vae = AutoencoderKL.from_pretrained(
'madebyollin/sdxl-vae-fp16-fix',
torch_dtype=torch.float16,
)
pipe.vae = vae
guidance_scale = 5.0
return pipe, guidance_scale
def load_spo_sdxl(scheduler, inference_dtype):
pipe = StableDiffusionXLPipeline.from_pretrained(
'SPO-Diffusion-Models/SPO-SDXL_4k-p_10ep',
torch_dtype=inference_dtype,
scheduler=scheduler,
)
vae = AutoencoderKL.from_pretrained(
'madebyollin/sdxl-vae-fp16-fix',
torch_dtype=torch.float16,
)
pipe.vae = vae
guidance_scale = 5.0
return pipe, guidance_scale
def load_diffusion_dpo_sdxl(scheduler, inference_dtype):
unet = UNet2DConditionModel.from_pretrained('mhdang/dpo-sdxl-text2image-v1', subfolder="unet", torch_dtype=inference_dtype)
pipe = StableDiffusionXLPipeline.from_pretrained(
'stabilityai/stable-diffusion-xl-base-1.0',
torch_dtype=inference_dtype,
scheduler=scheduler,
unet=unet,
)
vae = AutoencoderKL.from_pretrained(
'madebyollin/sdxl-vae-fp16-fix',
torch_dtype=torch.float16,
)
pipe.vae = vae
guidance_scale = 5.0
return pipe, guidance_scale
def load_lpo_sdxl(scheduler, inference_dtype):
unet = UNet2DConditionModel.from_pretrained(
'casiatao/LPO',
subfolder="lpo_sdxl_merge/unet",
torch_dtype=inference_dtype
)
vae = AutoencoderKL.from_pretrained(
'madebyollin/sdxl-vae-fp16-fix',
torch_dtype=torch.float16,
)
pipe = StableDiffusionXLPipeline.from_pretrained(
'stabilityai/stable-diffusion-xl-base-1.0',
torch_dtype=inference_dtype,
scheduler=scheduler,
unet=unet,
vae=vae
)
guidance_scale = 5.0
return pipe, guidance_scale
model_dict = {
'origin_sd15': load_origin_sd_v1_5,
'spo_sd15': load_spo_sd_v1_5,
'diffusion_dpo_sd15': load_diffusion_dpo_sd_v1_5,
'lpo_sd15': load_lpo_sd_v1_5,
'origin_sdxl': load_origin_sdxl,
'spo_sdxl': load_spo_sdxl,
'diffusion_dpo_sdxl': load_diffusion_dpo_sdxl,
'lpo_sdxl': load_lpo_sdxl,
}
if __name__ == "__main__":
# hyperparameter
parser = ArgumentParser()
parser.add_argument("--model_name", type=str, default="origin_sdxl")
parser.add_argument("--batch_size", type=int, default=1)
parser.add_argument("--num_image_per_prompt", type=int, default=4)
parser.add_argument("--sample_steps", type=int, default=20)
parser.add_argument("--seed", type=int, default=42)
parser.add_argument("--device", type=str, default="cuda")
args = parser.parse_args()
model_name = args.model_name
batch_size = args.batch_size
num_image_per_prompt = args.num_image_per_prompt
sample_steps = args.sample_steps
seed = args.seed
device = args.device
# load preference model
pickscorer = PickScorer(processor_name_or_path="laion/CLIP-ViT-H-14-laion2B-s32B-b79K", model_pretrained_name_or_path="yuvalkirstain/PickScore_v1", device=device)
aesthetic_scorer = AestheticScorer(torch.float32, "openai/clip-vit-large-patch14", "./sac+logos+ava1-l14-linearMSE.pth")
aesthetic_scorer = aesthetic_scorer.to(device)
hpsv2_scorer = HPSv2Scorer(
clip_pretrained_name_or_path=hf_hub_download(repo_id="laion/CLIP-ViT-H-14-laion2B-s32B-b79K", filename="open_clip_pytorch_model.bin"),
model_pretrained_name_or_path=hf_hub_download(repo_id="xswu/HPSv2", filename="HPS_v2_compressed.pt"),
device=device
)
hpsv21_scorer = HPSv2Scorer(
clip_pretrained_name_or_path=hf_hub_download(repo_id="laion/CLIP-ViT-H-14-laion2B-s32B-b79K", filename="open_clip_pytorch_model.bin"),
model_pretrained_name_or_path=hf_hub_download(repo_id="xswu/HPSv2", filename="HPS_v2.1_compressed.pt"),
device=device
)
imagereward_scorer = load_imagereward(
model_path=hf_hub_download(repo_id="THUDM/ImageReward", filename="ImageReward.pt"),
med_config=hf_hub_download(repo_id="THUDM/ImageReward", filename="med_config.json"),
device=device
)
load_model_func = model_dict[model_name]
# load diffusion model
inference_dtype = torch.float16
scheduler = DDIMScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
pipe, guidance_scale = load_model_func(scheduler, inference_dtype)
pipe.to(device)
pipe.set_progress_bar_config(disable=True)
# load dataset
val_dataset = load_dataset("pickapic-anonymous/pickapic_v1", split="validation_unique", streaming=True)
# calculate preference score
caption_list = []
for i, sample in enumerate(val_dataset):
caption_list.append(sample['caption'])
batch_num = len(caption_list) // batch_size if len(caption_list) % batch_size == 0 else len(caption_list) // batch_size + 1
batched_caption_list = [caption_list[i*batch_size:(i+1)*batch_size] for i in range(batch_num)]
pickscore_list = []
aesthetic_score_list = []
hpsv2score_list = []
hpsv21score_list = []
imagereward_list = []
for batch_prompt in tqdm(batched_caption_list):
generator=torch.Generator(device=device).manual_seed(seed)
images = pipe(
batch_prompt,
guidance_scale=guidance_scale,
num_inference_steps=sample_steps,
generator=generator,
output_type='pil',
num_images_per_prompt=num_image_per_prompt,
).images
for prompt, image in zip(batch_prompt, images):
pickscore = pickscorer(prompt, [image])[0]
pickscore_list.append(pickscore)
aesthetic_score = aesthetic_scorer(image)[0].item()
aesthetic_score_list.append(aesthetic_score)
hpsv2_score = hpsv2_scorer.score(image, prompt)[0]
hpsv2score_list.append(hpsv2_score)
hpsv21_score = hpsv21_scorer.score(image, prompt)[0]
hpsv21score_list.append(hpsv21_score)
imagereward_score = imagereward_scorer.score(prompt, image)
imagereward_list.append(imagereward_score)
res_save_dir = './eval_results/pick_a_pic_val_score'
os.makedirs(res_save_dir, exist_ok=True)
file_name = f"{model_name}_ddim_cfg{guidance_scale}_step{sample_steps}_seed{seed}_{num_image_per_prompt}image_batch{batch_size}.json"
with open(os.path.join(res_save_dir, file_name), 'w', encoding='utf-8') as f:
json.dump({
'pickscore': torch.mean(torch.tensor(pickscore_list)).item(),
'aestheticscore': torch.mean(torch.tensor(aesthetic_score_list)).item(),
'hpsv2score': torch.mean(torch.tensor(hpsv2score_list)).item(),
'hpsv21score': torch.mean(torch.tensor(hpsv21score_list)).item(),
'imagerewardscore': torch.mean(torch.tensor(imagereward_list)).item(),
}, f, indent=4)
print(f"Pickscore: {torch.mean(torch.tensor(pickscore_list))}")
print(f"Aestheticscore: {torch.mean(torch.tensor(aesthetic_score_list))}")
print(f"HPSv2score: {torch.mean(torch.tensor(hpsv2score_list))}")
print(f"HPSv21score: {torch.mean(torch.tensor(hpsv21score_list))}")
print(f"Imagerewardscore: {torch.mean(torch.tensor(imagereward_list))}")
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