sdxl_thumbsup.py

# -*- coding: utf-8 -*-
"""SDXL-Thumbsup.ipynb

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1T0tqXsscUsDxLSt6MIiqEmNVYEuCejGL

# Training DreamBooth LoRA with Stable Diffusion XL on Trump Thumbs Up Images:

## Linking Drive
"""

from google.colab import drive
drive.mount('/content/drive')

import warnings
warnings.filterwarnings("ignore")

"""## Installing & Login to Hugging Face

"""

!pip install huggingface-hub

!git config --global credential.helper store

!huggingface-cli login

"""##Cloning Hugging Face/diffusers - [Repo](https://github.com/huggingface/diffusers)

"""

import os
import subprocess


subprocess.run(["git", "clone", "https://github.com/huggingface/diffusers"])
os.chdir("diffusers")
subprocess.run(["pip", "install", "-e", "."])

"""## Installing Requirements - Dreambooth SDXL"""

os.chdir("examples/dreambooth")

!pip install -r requirements_sdxl.txt

"""## Write Basic Configuration for Accelerate"""

from accelerate.utils import write_basic_config
write_basic_config()

"""##  Load and Display Images from Drive"""

import os
import cv2
from matplotlib import pyplot as plt

dir = '/content/drive/MyDrive/SDXL/Images/thumbsup'
count = 0
max_images = 10

for img_name in os.listdir(dir):
    img_path = os.path.join(dir, img_name)

    if img_path.lower().endswith(('.png', '.jpg', '.jpeg')):
        img = cv2.imread(img_path)

        if img is None:
            print(f"Failed to load image: {img_path}")
            continue

        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

        plt.imshow(img)
        plt.axis('off')
        plt.show()

        count += 1
        if count >= max_images:
            break

"""## Installing Required Libraries"""

!pip install tensorrt bitsandbytes xformers wandb

pip install --upgrade diffusers accelerate

"""## Logging into Weights and Biases"""

!wandb login

"""## Train DreamBooth LoRA Model with Stable Diffusion XL"""

!accelerate launch train_dreambooth_lora_sdxl.py \
  --pretrained_model_name_or_path="stabilityai/stable-diffusion-xl-base-1.0" \
  --instance_data_dir="/content/drive/MyDrive/SDXL/Images/thumbsup" \
  --pretrained_vae_model_name_or_path="stabilityai/sdxl-vae" \
  --output_dir="/content/drive/MyDrive/SDXL/Output-Complex" \
  --mixed_precision="fp16" \
  --instance_prompt="a high-quality photo of Trump showing thumbs up" \
  --resolution=1024 \
  --train_batch_size=1 \
  --gradient_accumulation_steps=2 \
  --learning_rate=2e-4 \
  --lr_scheduler="constant_with_warmup" \
  --lr_warmup_steps=0 \
  --max_train_steps=500 \
  --validation_prompt="A high-quality photo of Trump showing thumbs up in a taco restaurant, detailed, sharp focus" \
  --validation_epochs=15 \
  --seed="42" \
  --push_to_hub \
  --gradient_checkpointing \
  --checkpointing_steps=100 \
  --use_8bit_adam \
  --prior_loss_weight=0.8 \
  --num_class_images=10 \
  --report_to="wandb"

# Commented out IPython magic to ensure Python compatibility.
# %cd ..

!pip uninstall diffusers
!pip install -e ./diffusers

"""## Load LoRA Weights and Generate Images

"""

from huggingface_hub.repocard import RepoCard
from diffusers import DiffusionPipeline
import torch

pipeline = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16)
pipeline = pipeline.to("cuda")
pipeline.load_lora_weights("/content/diffusers/examples/dreambooth/pytorch_lora_weights.safetensors")

image = pipeline("A high quality picture of Trump showing the thumbs up in Paris", num_inference_steps=50).images[0]

image_path = "/content/drive/MyDrive/SDXL/Output-Complex/Trump1.png"
image.save(image_path)

print("Image saved at:", image_path)

image = pipeline("A picture of Trump showing the thumbs up as a Anime character, detailed, sharp focus", num_inference_steps=50).images[0]

image_path = "/content/drive/MyDrive/SDXL/Output-Complex/Trump2.png"
image.save(image_path)

print("Image saved at:", image_path)

image = pipeline("A picture of Trump showing thumbsup in whitehouse", num_inference_steps=50).images[0]

image_path = "/content/drive/MyDrive/SDXL/Output-Complex/Trump3.png"
image.save(image_path)

print("Image saved at:", image_path)

image = pipeline("A high quality picture of Trump showing the thumbs up as The Statue of Liberty", num_inference_steps=50).images[0]

image_path = "/content/drive/MyDrive/SDXL/Output-Complex/Trump4.png"
image.save(image_path)

print("Image saved at:", image_path)

image = pipeline("A high quality picture of Trump showing thumbs up in a lake with a laptop", num_inference_steps=50).images[0]

image_path = "/content/drive/MyDrive/SDXL/Output-Complex/Trump5.png"
image.save(image_path)

print("Image saved at:", image_path)

"""## Push to Hugging Face Hub"""

from huggingface_hub import HfApi

api = HfApi()

username = "Paresh1879"
repo_name = "stable-diffusion-xl-thumbsup-extend"

api.upload_folder(repo_id=f"{username}/{repo_name}", folder_path="/content/drive/MyDrive/SDXL")