#https://github.com/huggingface/diffusers/tree/main/examples/dreambooth
#export MODEL_NAME="stabilityai/stable-diffusion-2-1-base"
#export INSTANCE_DIR="./data_example"
#export OUTPUT_DIR="./output_example"

#accelerate launch train_lora_dreambooth.py \
#  --pretrained_model_name_or_path=$MODEL_NAME  \
#  --instance_data_dir=$INSTANCE_DIR \
#  --output_dir=$OUTPUT_DIR \
#  --instance_prompt="style of sks" \
#  --resolution=512 \
#  --train_batch_size=1 \
#  --gradient_accumulation_steps=1 \
#  --learning_rate=1e-4 \
#  --lr_scheduler="constant" \
#  --lr_warmup_steps=0 \
#  --max_train_steps=30000

from diffusers import StableDiffusionPipeline
from lora_diffusion import monkeypatch_lora, tune_lora_scale
import torch
import os 
#os.system('python file.py')
import subprocess
# If your shell script has shebang,
# you can omit shell=True argument.
subprocess.run("./run_lora_db.sh", shell=True)

#####
model_id = "stabilityai/stable-diffusion-2-1-base"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")
prompt = "style of sks, baby lion"
torch.manual_seed(1)
#image = pipe(prompt, num_inference_steps=50, guidance_scale= 7).images[0] #no need
#image  # nice. diffusers are cool. #no need
finetuned_lora_weights = "./lora_weight.pt"

#####
#my fine tuned weights
def monkeypatching( alpha): #, prompt, pipe): finetuned_lora_weights
    monkeypatch_lora(pipe.unet, torch.load(finetuned_lora_weights)) #"./lora_weight.pt")) 
    tune_lora_scale(pipe.unet, alpha) #1.00)
    image = pipe(prompt, num_inference_steps=50, guidance_scale=7).images[0]
    image.save("./illust_lora.jpg") #"./contents/illust_lora.jpg")
    return image
 
with gr.Blocks() as demo:
    with gr.Row():
        in_images =  gr.Image(label="Upload images to fine-tune for LORA")
        #in_prompt = gr.Textbox(label="Enter a ")
        in_steps = gr.Number(label="Enter number of steps")
        in_alpha = gr.Slider(0.1,1.0, step=0.01, label="Set Alpha level - higher value has more chances to overfit")
        b1 = gr.Button(value="Create LORA model")
    with gr.Row():
        out_image = gr.Image(label="Image generated by LORA model")
    b1.click(fn = monkeypatching, inputs=in_alpha, outputs=out_image)

demo.launch(debug=True, show_error=True)