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#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)
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