Update app.py

app.py

@@ -1,146 +1,144 @@
 import gradio as gr
+import pandas as pd
 import numpy as np
 import random
-from diffusers import DiffusionPipeline
 import torch
+from transformers import pipeline
+from diffusers import DiffusionPipeline
 
+# Initialize the device for running the diffusion model
 device = "cuda" if torch.cuda.is_available() else "cpu"
+MAX_SEED = np.iinfo(np.int32).max
+MAX_IMAGE_SIZE = 1024
 
+# Set up the diffusion pipeline
 if torch.cuda.is_available():
     torch.cuda.max_memory_allocated(device=device)
     pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
     pipe.enable_xformers_memory_efficient_attention()
-    pipe = pipe.to(device)
-else: 
+else:
     pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", use_safetensors=True)
-    pipe = pipe.to(device)
+pipe = pipe.to(device)
 
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
+class ImagePromptGenerator:
+    def __init__(self, model_name="gpt2"):
+        # Initialize the text generation pipeline
+        self.generator = pipeline("text-generation", model=model_name, use_auth_token=True)
 
-def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
+    def generate_short_prompts(self, theme, num_prompts=5):
+        # Generate short prompts based on the theme
+        prompts = self.generator(f"{theme} concept", max_length=50, num_return_sequences=num_prompts)
+        short_prompts = [prompt['generated_text'].strip() for prompt in prompts]
+        return short_prompts
+
+    def enhance_prompt(self, short_prompt):
+        # Enhance the short prompt into a more detailed long prompt
+        long_prompt = self.generator(f"Elaborate: {short_prompt}", max_length=100, num_return_sequences=1)
+        return long_prompt[0]['generated_text'].strip()
 
+    def generate_prompts_csv(self, theme):
+        # Generate short prompts and enhance them
+        short_prompts = self.generate_short_prompts(theme)
+        long_prompts = [self.enhance_prompt(sp) for sp in short_prompts]
+        # Create a DataFrame
+        df = pd.DataFrame({"short": short_prompts, "long": long_prompts})
+        return df.to_csv(index=False)
+
+def generate_and_save_prompts(theme):
+    generator = ImagePromptGenerator()
+    csv_content = generator.generate_prompts_csv(theme)
+    return csv_content
+
+def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
-        
     generator = torch.Generator().manual_seed(seed)
     
     image = pipe(
-        prompt = prompt, 
-        negative_prompt = negative_prompt,
-        guidance_scale = guidance_scale, 
-        num_inference_steps = num_inference_steps, 
-        width = width, 
-        height = height,
-        generator = generator
-    ).images[0] 
+        prompt=prompt, 
+        negative_prompt=negative_prompt,
+        guidance_scale=guidance_scale, 
+        num_inference_steps=num_inference_steps, 
+        width=width, 
+        height=height,
+        generator=generator
+    ).images[0]
     
     return image
 
-examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
-]
+def gradio_interface(theme):
+    # Generate image prompts based on theme
+    csv_content = generate_and_save_prompts(theme)
+    return gr.File(content=csv_content, file_name=f"{theme}_image_prompts.csv")
 
-css="""
+css = """
 #col-container {
     margin: 0 auto;
     max-width: 520px;
 }
 """
 
-if torch.cuda.is_available():
-    power_device = "GPU"
-else:
-    power_device = "CPU"
+# Determine the computational power available
+power_device = "GPU" if torch.cuda.is_available() else "CPU"
 
 with gr.Blocks(css=css) as demo:
-    
     with gr.Column(elem_id="col-container"):
         gr.Markdown(f"""
         # Text-to-Image Gradio Template
         Currently running on {power_device}.
         """)
-        
+
         with gr.Row():
-            
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
-            )
-            
-            run_button = gr.Button("Run", scale=0)
+            theme = gr.Textbox(label="Theme for Image Generation", placeholder="Enter a theme to generate prompts")
+            prompt = gr.Textbox(label="Prompt for Image Generation", placeholder="Enter your prompt here or select from generated prompts", show_label=False)
+            generate_prompts_button = gr.Button("Generate Prompts")
+
+        with gr.Row():
+            run_button = gr.Button("Run")
         
         result = gr.Image(label="Result", show_label=False)
 
         with gr.Accordion("Advanced Settings", open=False):
-            
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=False,
-            )
-            
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
-            )
-            
+            negative_prompt = gr.Textbox(label="Negative prompt", placeholder="Enter a negative prompt")
+            seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
             randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
             
             with gr.Row():
-                
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=512,
-                )
-                
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=512,
-                )
+                width = gr.Slider(label="Width", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=512)
+                height = gr.Slider(label="Height", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=512)
             
             with gr.Row():
-                
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0,
-                )
-                
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=12,
-                    step=1,
-                    value=2,
-                )
-        
-        gr.Examples(
-            examples = examples,
-            inputs = [prompt]
+                guidance_scale = gr.Slider(label="Guidance scale", minimum=0.0, maximum=10.0, step=0.1, value=7.5)
+                num_inference_steps = gr.Slider(label="Number of inference steps", minimum=1, maximum=250, step=1, value=50)
+
+        generate_prompts_button.click(
+            fn=gradio_interface,
+            inputs=[theme],
+            outputs=[gr.File(label="Download Generated Prompts CSV")]
+        )
+
+        run_button.click(
+            fn=infer,
+            inputs=[prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
+            outputs=[result]
         )
 
-    run_button.click(
-        fn = infer,
-        inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
-        outputs = [result]
-    )
+demo.launch()
+'''
+Explanation:
+
+    Class ImagePromptGenerator: This class now includes methods to generate short prompts, enhance them, and output a CSV.
+
+    generate_and_save_prompts Function: This function generates a CSV of prompts based on the theme.
+
+    infer Function: This function generates an image based on the provided parameters using the diffusion model.
+
+    Gradio Interface: The interface now includes:
+        A textbox to input the theme for generating prompts.
+        A button to generate prompts based on the theme.
+        The original image generation interface with advanced settings.
 
-demo.queue().launch()
+    Button Actions:
+        Generate Prompts Button: Generates a list of prompts as a downloadable CSV file.
+        Run Button: Generates an image based on the provided prompt and settings.
+'''