Update app.py

app.py

@@ -1,50 +1,62 @@
 import gradio as gr
-import random
-
-# Load each model separately
-model1 = gr.load("models/pimpilikipilapi1/NSFW_master")
-model2 = gr.load("models/prashanth970/flux-lora-uncensored")
-model3 = gr.load("models/DiegoJR1973/NSFW-TrioHMH-Flux")
-
-def generate_images(text, seed, width, height, guidance_scale, num_inference_steps):
-    if seed is not None:
-        random.seed(seed)
-
-    # Generate images using each model with the text prompt only
-    result_image1 = model1(text)
-    result_image2 = model2(text)
-    result_image3 = model3(text)
-
-    # Print parameters for debugging
-    print(f"Text: {text}, Seed: {seed}, Width: {width}, Height: {height}, Guidance Scale: {guidance_scale}, Steps: {num_inference_steps}")
-    
-    return result_image1, result_image2, result_image3
-
-def randomize_parameters():
-    seed = random.randint(0, 999999)
-    width = random.randint(512, 2048)
-    height = random.randint(512, 2048)
-    guidance_scale = round(random.uniform(0.1, 20.0), 1)
-    num_inference_steps = random.randint(1, 40)
-    
-    return seed, width, height, guidance_scale, num_inference_steps
-
-interface = gr.Interface(
-    fn=generate_images,
-    inputs=[
-        gr.Textbox(label="Type here your imagination:", placeholder="Type or click an example..."),
-        gr.Slider(label="Seed", minimum=0, maximum=999999, step=1),
-        gr.Slider(label="Width", minimum=512, maximum=2048, step=64, value=1024),
-        gr.Slider(label="Height", minimum=512, maximum=2048, step=64, value=1024),
-        gr.Slider(label="Guidance Scale", minimum=0.1, maximum=20.0, step=0.1, value=3.0),
-        gr.Slider(label="Number of inference steps", minimum=1, maximum=40, step=1, value=28),
-    ],
-    outputs=[
-        gr.Image(label="Generated Image from NSFW_master"),
-        gr.Image(label="Generated Image from flux-lora-uncensored"),
-        gr.Image(label="Generated Image from NSFW-TrioHMH-Flux")
-    ],
-    description="Generate images with three different models based on your prompt. Note: the models run on the CPU, which may affect performance.",
-)
-
-interface.launch()
+import time
+
+
+model1 = gr.Interface.load("models/pimpilikipilapi1/NSFW_master", live=False, preprocess=True, postprocess=False)
+model2 = gr.Interface.load("models/prashanth970/flux-lora-uncensored", live=False, preprocess=True, postprocess=False)
+model3 = gr.Interface.load("models/DiegoJR1973/NSFW-TrioHMH-Flux", live=False, preprocess=True, postprocess=False)
+
+models = {
+    1: model1,
+    2: model2,
+    3: model3
+}
+
+def send_it_idx(idx):
+    def send_it_fn(prompt):
+        model = models.get(idx)
+        if model:
+            return model(prompt)
+        return None
+    return send_it_fn
+
+def clear_fn():
+    return None, None, None
+
+with gr.Blocks(title="SD Models") as my_interface:
+    with gr.Column(scale=12):
+        with gr.Row():
+            primary_prompt = gr.Textbox(label="Prompt", value="")
+            with gr.Row(scale=6):
+                run = gr.Button("Run", variant="primary")
+                clear_btn = gr.Button("Clear")
+
+        sd_outputs = {}
+        for idx, model in models.items():
+            with gr.Column(scale=3, min_width=200):
+                with gr.Box():
+                    sd_outputs[idx] = gr.Image(label=f"Model {idx}")
+
+        with gr.Row(visible=False):
+            start_box = gr.Number(interactive=False)
+            end_box = gr.Number(interactive=False)
+            tog_box = gr.Textbox(value=0, interactive=False)
+
+        def all_task_start():
+            t_stamp = time.time()
+            return gr.update(value=t_stamp), gr.update(value=t_stamp), gr.update(value=0)
+
+        primary_prompt.submit(all_task_start, None, [start_box, end_box, tog_box])
+        run.click(all_task_start, None, [start_box, end_box, tog_box])
+
+        for idx, model in models.items():
+            run.click(model, inputs=[primary_prompt], outputs=[sd_outputs[idx]])
+
+        clear_btn.click(
+            clear_fn,
+            None,
+            [primary_prompt, *list(sd_outputs.values())]
+        )
+
+my_interface.queue(concurrency_count=100, status_update_rate=1)
+my_interface.launch(inline=True, show_api=False)