upd

app.py

@@ -1,38 +1,27 @@
-#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"
-
-    
 from diffusers import StableDiffusionPipeline
 from lora_diffusion import monkeypatch_lora, tune_lora_scale
 import torch
 import os 
 import gradio as gr
-#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_NAME="stabilityai/stable-diffusion-2-1-base"
+INSTANCE_DIR="./data_example"
+OUTPUT_DIR="./output_example"
+
 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"
+#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"
+#finetuned_lora_weights = "./lora_weight.pt"
 
 #global var
 counter = 0
 
-#####
-#my fine tuned weights
-def monkeypatching(alpha): #, prompt, pipe): finetuned_lora_weights
+#Getting Lora fine-tuned weights
+def monkeypatching(alpha, in_prompt): #, prompt, pipe): finetuned_lora_weights
     global counter
     if counter == 0 :
         monkeypatch_lora(pipe.unet, torch.load("./output_example/lora_weight.pt")) #finetuned_lora_weights
@@ -40,6 +29,7 @@ def monkeypatching(alpha): #, prompt, pipe): finetuned_lora_weights
         counter +=1
     else :
         tune_lora_scale(pipe.unet, alpha) #1.00)
+    prompt = "style of sks, " + in_prompt #"baby lion"
     image = pipe(prompt, num_inference_steps=50, guidance_scale=7).images[0]
     image.save("./illust_lora.jpg") #"./contents/illust_lora.jpg")
     return image
@@ -73,9 +63,11 @@ with gr.Blocks() as demo:
         b1 = gr.Button(value="Train LORA model")
         b2 = gr.Button(value="Inference using LORA model")
     with gr.Row():
+        in_prompt = gr.Textbox(label="Enter a prompt for fine-tuned LORA model")
         out_image = gr.Image(label="Image generated by LORA model")
         out_file = gr.File(label="Lora trained model weights")
     b1.click(fn = accelerate_train_lora, inputs=in_steps, outputs=out_file)
-    b2.click(fn = monkeypatching, inputs=in_alpha, outputs=out_image)
+    b2.click(fn = monkeypatching, inputs=[in_alpha, in_prompt], outputs=out_image)
 
-demo.launch(debug=True, show_error=True)
+demo.queue(concurrency_count=3) 
+demo.launch(debug=True, show_error=True)