ali_flux_schnell_pipe.py

import torch
from diffusers import FluxPipeline
import os
import pandas as pd

device = "cuda:0"
pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16).to(device)
  
root_dir = "/DATA/DATA3/zhaoyu/T2I_model_SD35/diffusers/examples/dreambooth"

# #---------------------------------------------------------
# #----------------------------------------------------------------------- 
# prompt_file = os.path.join(root_dir,"prompt.txt")
# with open(prompt_file, 'r') as f:
#     prompts =  [line.strip() for line in f]

# image_root_dir =root_dir 
# save_dir= os.path.join(image_root_dir, "image_0_flux_schnell")
# os.makedirs(save_dir, exist_ok=True)
# cnt = 1
# prompts = prompts[cnt - 1:]  # 截取的时候是包含当前这一项的，也就是包含cnt-1这一项
# #----------------------------------------------------------------------- 
# for index,prompt in enumerate(prompts,start=cnt-1):

#     image_name = f"fintune_2epoch_{index:06d}.png"
#     save_path = os.path.join(save_dir, image_name)
#     image = pipe(
#         prompt,
#         guidance_scale=0.0,
#         num_inference_steps=4,
#         max_sequence_length=256,
#         generator=torch.Generator(device).manual_seed(42)
#     ).images[0]
#     image.save(save_path)

#---------------------------------------------------------
#----------------------------------------------------------------------- 
# 设定路径 
csv_file = os.path.join(root_dir,"data.csv")
df = pd.read_csv(csv_file)

prompts= df["prompt"]
prompts = prompts[:300]

image_root_dir =os.path.join(root_dir,"AGIQA-300") 

save_dir= os.path.join(image_root_dir, "image_0_flux_schnell")
os.makedirs(save_dir, exist_ok=True)
cnt = 1
prompts = prompts[cnt - 1:]  # 截取的时候是包含当前这一项的，也就是包含cnt-1这一项
#----------------------------------------------------------------------- 
for index,prompt in enumerate(prompts,start=cnt-1):

    image_name = f"fintune_2epoch_{index:06d}.png"
    save_path = os.path.join(save_dir, image_name)
    image = pipe(
        prompt,
        guidance_scale=0.0,
        num_inference_steps=4,
        max_sequence_length=256,
        generator=torch.Generator(device).manual_seed(42)
    ).images[0]
    image.save(save_path)

#---------------------------------------------------------
#----------------------------------------------------------------------- 

# 设定路径 
# 设定路径 
csv_file = os.path.join(root_dir,"dataset/AGIQA-1K/AIGC_MOS_Zscore.csv")
df = pd.read_csv(csv_file)

prompts= df["Prompt"]
prompts = prompts[:720]

image_root_dir =os.path.join(root_dir,"AGIQA-1K") 

save_dir= os.path.join(image_root_dir, "image_0_flux_schnell")
os.makedirs(save_dir, exist_ok=True)
cnt = 1
prompts = prompts[cnt - 1:]  # 截取的时候是包含当前这一项的，也就是包含cnt-1这一项
#----------------------------------------------------------------------- 
for index,prompt in enumerate(prompts,start=cnt-1):

    image_name = f"fintune_2epoch_{index:06d}.png"
    save_path = os.path.join(save_dir, image_name)
    image = pipe(
        prompt,
        guidance_scale=0.0,
        num_inference_steps=4,
        max_sequence_length=256,
        generator=torch.Generator(device).manual_seed(42)
    ).images[0]
    image.save(save_path)

#---------------------------------------------------------
#----------------------------------------------------------------------- 


# 设定路径 
prompt_file = os.path.join(root_dir,"dataset/genaibench/genai_prompts.txt")

with open(prompt_file, 'r') as f:
    prompts =  [line.strip() for line in f]



image_root_dir =os.path.join(root_dir,"genai_bench") 

save_dir= os.path.join(image_root_dir, "image_0_flux_schnell")
os.makedirs(save_dir, exist_ok=True)
cnt = 1
prompts = prompts[cnt - 1:]  # 截取的时候是包含当前这一项的，也就是包含cnt-1这一项
#----------------------------------------------------------------------- 
for index,prompt in enumerate(prompts,start=cnt-1):

    image_name = f"fintune_2epoch_{index:06d}.png"
    save_path = os.path.join(save_dir, image_name)
    image = pipe(
        prompt,
        guidance_scale=0.0,
        num_inference_steps=4,
        max_sequence_length=256,
        generator=torch.Generator(device).manual_seed(42)
    ).images[0]
    image.save(save_path)

#---------------------------------------------------------
#----------------------------------------------------------------------- 


# 设定路径 
prompt_file = os.path.join(root_dir,"dataset/TIFA/tifa_prompts.txt")

with open(prompt_file, 'r') as f:
    prompts =  [line.strip() for line in f]



image_root_dir =os.path.join(root_dir,"TIFA") 

save_dir= os.path.join(image_root_dir, "image_0_flux_schnell")
os.makedirs(save_dir, exist_ok=True)
cnt = 1
prompts = prompts[cnt - 1:]  # 截取的时候是包含当前这一项的，也就是包含cnt-1这一项
#----------------------------------------------------------------------- 
for index,prompt in enumerate(prompts,start=cnt-1):

    image_name = f"fintune_2epoch_{index:06d}.png"
    save_path = os.path.join(save_dir, image_name)
    image = pipe(
        prompt,
        guidance_scale=0.0,
        num_inference_steps=4,
        max_sequence_length=256,
        generator=torch.Generator(device).manual_seed(42)
    ).images[0]
    image.save(save_path)

#---------------------------------------------------------
#-----------------------------------------------------------------------