Update app.py

app.py

@@ -2,65 +2,20 @@ import torch
 from diffusers import StableDiffusionPipeline
 import gradio as gr
 
-# Load the model
 model_id = "SG161222/RealVisXL_V4.0"
 pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
 pipe.to("cpu")  # Use "cuda" if GPU is available
 
-# Define placeholder functions and variables
-DEFAULT_STYLE_NAME = "default"
-default_negative = ""
-NUM_IMAGES_PER_PROMPT = 1
-
-def check_text(prompt, negative_prompt):
-    # Implement your text check logic here
-    return False
-
-def apply_style(style, prompt, negative_prompt):
-    # Implement your style application logic here
-    return prompt, negative_prompt
-
-def randomize_seed_fn(seed, randomize_seed):
-    # Implement your seed randomization logic here
-    return seed
-
-def save_image(image):
-    # Implement your image saving logic here
+def generate_image(prompt):
+    image = pipe(prompt).images[0]
     return image
+def chatbot(prompt):
+    # Generate the image based on the user's input
+    image = generate_image(prompt)
 
-def generate_image(prompt, negative_prompt="", use_negative_prompt=False, style=DEFAULT_STYLE_NAME, seed=0, width=1024, height=1024, guidance_scale=3, randomize_seed=False, use_resolution_binning=True, progress=gr.Progress(track_tqdm=True)):
-    if check_text(prompt, negative_prompt):
-        raise ValueError("Prompt contains restricted words.")
-    
-    prompt, negative_prompt = apply_style(style, prompt, negative_prompt)
-    seed = int(randomize_seed_fn(seed, randomize_seed))
-    generator = torch.Generator().manual_seed(seed)
-
-    if not use_negative_prompt:
-        negative_prompt = ""
-    negative_prompt += default_negative    
 
-    options = {
-        "prompt": prompt,
-        "negative_prompt": negative_prompt,
-        "width": width,
-        "height": height,
-        "guidance_scale": guidance_scale,
-        "num_inference_steps": 25,
-        "generator": generator,
-        "num_images_per_prompt": NUM_IMAGES_PER_PROMPT,
-        "use_resolution_binning": use_resolution_binning,
-        "output_type": "pil",
-    }
-    
-    images = pipe(**options).images
 
-    image_paths = [save_image(img) for img in images]
-    return image_paths, seed
 
-def chatbot(prompt):
-    # Generate the image based on the user's input
-    image = generate_image(prompt)
     return image
 
 # Create the Gradio interface
@@ -73,4 +28,4 @@ interface = gr.Interface(
 )
 
 # Launch the interface
-interface.launch()
+interface.launch()