Update app.py

app.py

@@ -1,42 +1,49 @@
 import gradio as gr
+import random
+import os
 
-
+# Load all models
 models = {
-    "Flux Lora": "models/prashanth970/flux-lora-uncensored",
-    "TrioHMH Flux": "models/DiegoJR1973/NSFW-TrioHMH-Flux",
-    "Master": "models/pimpilikipilapi1/NSFW_master"
+    "Face Projection": gr.load("models/Purz/face-projection"),
+    "Flux LoRA Uncensored": gr.load("models/prashanth970/flux-lora-uncensored"),
+    "NSFW TrioHMH Flux": gr.load("models/DiegoJR1973/NSFW-TrioHMH-Flux"),
+    "NSFW Master": gr.load("models/pimpilikipilapi1/NSFW_master")
 }
 
+def generate_image(text, seed, width, height, guidance_scale, num_inference_steps):
+    if seed is not None:
+        random.seed(seed)
 
-def generate_image(text, model_name):
-    model_path = models[model_name]
-    print(f"Fetching model from: {model_path}")
-    
-    try:
-        model = gr.load(model_path)
-        result_image = model(text)
-        if isinstance(result_image, str):
-            return gr.Image(value=result_image)
-        elif isinstance(result_image, bytes):
-            return gr.Image(value=result_image)
-        else:
-            return result_image
-
-    except Exception as e:
-        print(f"Error loading model: {e}")
-        return None
+    result_images = {}
+    for model_name, model in models.items():
+        result_images[model_name] = model(text)
 
+    print(f"Width: {width}, Height: {height}, Guidance Scale: {guidance_scale}, Inference Steps: {num_inference_steps}")
+    
+    return [result_images[model_name] for model_name in models]
+
+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_image,
     inputs=[
-        gr.Textbox(label="Type here your imagination:", placeholder="Type your description here..."),
-        gr.Dropdown(label="Select Model", choices=list(models.keys()), value="Flux Lora")
+        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"),
+    outputs=[gr.Image(label=model_name) for model_name in models],
     theme="NoCrypt/miku",
     description="Sorry for the inconvenience. The model is currently running on the CPU, which might affect performance. We appreciate your understanding.",
 )
 
-
-interface.launch()
+interface.launch()