update examples

app.py

@@ -1,7 +1,7 @@
 from diffusers import StableDiffusionPipeline
 from lora_diffusion import monkeypatch_lora, tune_lora_scale
 import torch
-import os 
+import os, shutil
 import gradio as gr
 import subprocess
 
@@ -41,9 +41,10 @@ def monkeypatching(alpha, in_prompt, wt): #, prompt, pipe): finetuned_lora_weigh
     image.save("./illust_lora.jpg") #"./contents/illust_lora.jpg")
     return image
 
-def accelerate_train_lora(steps):
+def accelerate_train_lora(steps, images):
     print("*********** inside accelerate_train_lora ***********")
-    #subprocess.run(accelerate launch {"./train_lora_dreambooth.py"} \
+    for file in images:
+        shutil.copy( file, './data_example')
     #subprocess.Popen(f'accelerate launch {"./train_lora_dreambooth.py"} \
     os.system( f'accelerate launch {"./train_lora_dreambooth.py"} \
       --pretrained_model_name_or_path={MODEL_NAME}  \
@@ -59,7 +60,7 @@ def accelerate_train_lora(steps):
       --max_train_steps={int(steps)}') #,shell=True) #30000
     print("*********** completing accelerate_train_lora ***********")
     #lora_trained_weights = "./output_example/lora_weight.pt" 
-    return "./output_example/lora_weight.pt"
+    return f"{OUTPUT_DIR}/lora_weight.pt"
     
 with gr.Blocks() as demo:
     gr.Markdown("""<h1><center>LORA - Low-rank Adaptation for Fast Text-to-Image Diffusion Fine-tuning</center></h1>
@@ -67,7 +68,7 @@ with gr.Blocks() as demo:
     gr.HTML("<p>You can skip the queue by duplicating this space and upgrading to gpu in settings: <a style='display:inline-block' href='https://huggingface.co/spaces/ysharma/Low-rank-Adaptation?duplicate=true'><img src='https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14' alt='Duplicate Space'></a></p>")
     gr.Markdown("""<b>NEW!!</b> : I have fine-tuned the SD model for 15,000 steps using 100 PlaygroundAI images and LORA. You can load this trained model using the example component. Load the weight and start using the Space with the Inference button. Feel free to toggle the Alpha value.""")
     gr.Markdown(
-        """**Main Features**<br>- Fine-tune Stable diffusion models twice as faster than dreambooth method, by Low-rank Adaptation.<br>- Get insanely small end result, easy to share and download.<br>- Easy to use, compatible with diffusers.<br>- Sometimes even better performance than full fine-tuning<br><br>Please refer to the GitHub repo this Space is based on, here - <a href = "https://github.com/cloneofsimo/lora">LORA</a>. You can also refer to this tweet by AK to quote/retweet/like here on <a href="https://twitter.com/_akhaliq/status/1601120767009513472">Twitter</a>.This Gradio Space is an attempt to explore this novel LORA approach to fine-tune Stable diffusion models, using the power and flexibility of Gradio! The higher number of steps results in longer training time and better fine-tuned SD models.<br><br><b>To use this Space well:</b><br>- First, upload your set of images (4-5), then enter the number of fine-tuning steps, and then press the 'Train LORA model' button. This will produce your fine-tuned model weights.<br>- Enter a prompt, set the alpha value using the Slider (nearer to 1 implies overfitting to the uploaded images), and then press the 'Inference' button. This will produce an image by the newly fine-tuned model.<br><b>Bonus:</b>You can download your fine-tuned model weights from the Gradio file component. The smaller size of LORA models (around 3-4 MB files) is the main highlight of this 'Low-rank Adaptation' approach of fine-tuning.""")
+        """**Main Features**<br>- Fine-tune Stable diffusion models twice as faster as dreambooth method by Low-rank Adaptation.<br>- Get insanely small end results, easy to share and download.<br>- Easy to use, compatible with diffusers.<br>- Sometimes even better performance than full fine-tuning<br><br>Please refer to the GitHub repo this Space is based on, here - <a href = "https://github.com/cloneofsimo/lora">LORA</a>. You can also refer to this tweet by AK to quote/retweet/like here on <a href="https://twitter.com/_akhaliq/status/1601120767009513472">Twitter</a>.This Gradio Space is an attempt to explore this novel LORA approach to fine-tune Stable diffusion models, using the power and flexibility of Gradio! The higher number of steps results in longer training time and better fine-tuned SD models.<br><br><b>To use this Space well:</b><br>- First, upload your set of images (4-5), then enter the number of fine-tuning steps, and then press the 'Train LORA model' button. This will produce your fine-tuned model weights.<br>- Enter a prompt, set the alpha value using the Slider (nearer to 1 implies overfitting to the uploaded images), and then press the 'Inference' button. This will produce an image by the newly fine-tuned model.<br><b>Bonus:</b>You can download your fine-tuned model weights from the Gradio file component. The smaller size of LORA models (around 3-4 MB files) is the main highlight of this 'Low-rank Adaptation' approach of fine-tuning.""")
     
     with gr.Row():
         in_images =  gr.File(label="Upload images to fine-tune for LORA", file_count="multiple")
@@ -92,13 +93,13 @@ with gr.Blocks() as demo:
         fn=monkeypatching,
         cache_examples=True,)
     gr.Examples(
-        examples=[[4000]],
-        inputs=in_steps,
+        examples=[[4000, ['./simba1.jpg', './simba2.jpg', './simba3.jpg', './simba4.jpg']]],
+        inputs=[in_steps, in_images],
         outputs=out_file,
         fn=accelerate_train_lora,
         cache_examples=True,)    
         
-    b1.click(fn = accelerate_train_lora, inputs=in_steps , outputs=out_file) 
+    b1.click(fn = accelerate_train_lora, inputs=[in_steps, in_images] , outputs=out_file) 
     b2.click(fn = monkeypatching, inputs=[in_alpha, in_prompt, out_file], outputs=out_image)
 
 demo.queue(concurrency_count=3)