Update app.py

app.py

@@ -1,42 +1,63 @@
 import gradio as gr
-from diffusers import DiffusionPipeline
+from random import randint
+from all_models import models
 
+def load_fn(models):
+    global models_load
+    models_load = {}
+    
+    for model in models:
+        if model not in models_load.keys():
+            try:
+                m = gr.load(f'models/{model}')
+            except Exception as error:
+                m = gr.Interface(lambda txt: None, ['text'], ['image'])
+            models_load.update({model: m})
 
 
-# Function to generate image based on input text
-def generate_image(basem, model, prompt):
-    # Load the pipeline
-    
-    pipeline = DiffusionPipeline.from_pretrained(basem)
-    pipeline.load_lora_weights(model)
+load_fn(models)
 
-    # Generate the image using the text prompt
-    image = pipeline(prompt).images[0]
-    return image
 
-# Create Gradio interface
-with gr.Blocks() as demo:
-    # Title
-    gr.Markdown("# Text-to-Image Generation WebUI")
+num_models = 6
+default_models = models[:num_models]
+
+
+def extend_choices(choices):
+    return choices + (num_models - len(choices)) * ['NA']
+
+
+def update_imgbox(choices):
+    choices_plus = extend_choices(choices)
+    return [gr.Image(None, label = m, visible = (m != 'NA')) for m in choices_plus]
 
-    with gr.Row():
-        base_model = gr.Textbox(label="Enter your base model here", placeholder="John6666/mala-anime-mix-nsfw-pony-xl-v3-sdxl")
-        main_model = gr.Textbox(label="Enter your main model here", placeholder="nevreal/vMurderDrones")
-    
-    
-    # Input for text prompt
-    with gr.Row():
-        prompt = gr.Textbox(label="Enter your prompt here", placeholder="Type your text prompt...")
     
-    # Output image display
-    with gr.Column(scale=4):
-        output_image = gr.Image(label="Generated Image")
+def gen_fn(model_str, prompt):
+    if model_str == 'NA':
+        return None
+    noise = str(randint(0, 99999999999))
+    return models_load[model_str](f'{prompt} {noise}')
 
-    # Button to trigger the image generation
-    generate_button = gr.Button("Generate Image")
 
-    # When the button is clicked, call the generate_image function
-    generate_button.click(fn=generate_image, inputs=[base_model, main_model, prompt], outputs=output_image)
+with gr.Blocks() as demo:
+    model_choice2 = gr.Dropdown(models, label = 'Choose model', value = models[0], filterable = False)
+    txt_input2 = gr.Textbox(label = 'Prompt text')
+        
+    max_images = 6
+    num_images = gr.Slider(1, max_images, value = max_images, step = 1, label = 'Number of images')
+        
+    gen_button2 = gr.Button('Generate')
+    stop_button2 = gr.Button('Stop', variant = 'secondary', interactive = False)
+    gen_button2.click(lambda s: gr.update(interactive = True), None, stop_button2)
+        
+    with gr.Row():
+        output2 = [gr.Image(label = '') for _ in range(max_images)]
+
+    for i, o in enumerate(output2):
+        img_i = gr.Number(i, visible = False)
+        num_images.change(lambda i, n: gr.update(visible = (i < n)), [img_i, num_images], o)
+        gen_event2 = gen_button2.click(lambda i, n, m, t: gen_fn(m, t) if (i < n) else None, [img_i, num_images, model_choice2, txt_input2], o)
+        stop_button2.click(lambda s: gr.update(interactive = False), None, stop_button2, cancels = [gen_event2])
 
-# Launch the interface
-demo.launch()
+    
+demo.queue(concurrency_count = 36)                        
+demo.launch()