Update app.py

app.py

@@ -1,165 +1,40 @@
 import gradio as gr
-import numpy as np
-from PIL import Image
-import torch
-from diffusers import ControlNetModel, UniPCMultistepScheduler
-from hico_pipeline import StableDiffusionControlNetMultiLayoutPipeline
 
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-# Initialize model
-controlnet = ControlNetModel.from_pretrained("qihoo360/HiCo_T2I", torch_dtype=torch.float16)
-pipe = StableDiffusionControlNetMultiLayoutPipeline.from_pretrained(
-    "krnl/realisticVisionV51_v51VAE", controlnet=[controlnet], torch_dtype=torch.float16
-)
-pipe = pipe.to(device)
-pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
-
-MAX_SEED = np.iinfo(np.int32).max
-
-# Store objects
+# Arrays to be cleared
 object_classes_list = []
 object_bboxes_list = []
 
-# Function to add or update the prompt in the list
-def submit_prompt(prompt):
-    if object_classes_list:
-        object_classes_list[0] = prompt  # Overwrite the first element if it exists
-    else:
-        object_classes_list.insert(0, prompt)  # Add to the beginning if the list is empty
-
-    if not object_bboxes_list:
-        object_bboxes_list.insert(0, "0,0,512,512")  # Add the default bounding box if the list is empty
-
-    combined_list = [[cls, bbox] for cls, bbox in zip(object_classes_list, object_bboxes_list)]
-    return combined_list, gr.update(interactive=False)  # Make the prompt input non-editable
-
-# Function to add a new object with validation
-def add_object(object_class, bbox):
-    try:
-        # Split and convert bbox string into integers
-        x1, y1, x2, y2 = map(int, bbox.split(","))
-        
-        # Validate the coordinates
-        if x2 < x1 or y2 < y1:
-            return "Error: x2 cannot be less than x1 and y2 cannot be less than y1.", []
-        if x1 < 0 or y1 < 0 or x2 > 512 or y2 > 512:
-            return "Error: Coordinates must be between 0 and 512.", []
-
-        # If validation passes, add to the lists
-        object_classes_list.append(object_class)
-        object_bboxes_list.append(bbox)
-        combined_list = [[cls, bbox] for cls, bbox in zip(object_classes_list, object_bboxes_list)]
-        return combined_list
-
-    except ValueError:
-        return "Error: Invalid input format. Use x1,y1,x2,y2.", []
-
-# Function to generate images based on added objects
-def generate_image(prompt, guidance_scale, num_inference_steps, randomize_seed, seed):
-    img_width, img_height = 512, 512
-    r_image = np.zeros((img_height, img_width, 3), dtype=np.uint8)
-    list_cond_image = []
-
-    for bbox in object_bboxes_list:
-        x1, y1, x2, y2 = map(int, bbox.split(","))
-        cond_image = np.zeros_like(r_image, dtype=np.uint8)
-        cond_image[y1:y2, x1:x2] = 255
-        list_cond_image.append(Image.fromarray(cond_image).convert('RGB'))
-
-    if randomize_seed or seed is None:
-        seed = np.random.randint(0, MAX_SEED)
-
-    generator = torch.manual_seed(seed)
-
-    image = pipe(
-        prompt=prompt,
-        layo_prompt=object_classes_list,
-        guess_mode=False,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        image=list_cond_image,
-        fuse_type="avg",
-        width=512,
-        height=512
-    ).images[0]
-
-    return image, seed
+# Function to clear all arrays
+def clear_arrays():
+    object_classes_list.clear()
+    object_bboxes_list.clear()
+    return [], gr.update(value="", interactive=True)  # Clear result and reset prompt input
 
-# Gradio UI
 with gr.Blocks() as demo:
-    gr.Markdown("# Text-to-Image Generator with Object Addition")
-
-    # Put prompt and submit button in the same row
     with gr.Group():
         with gr.Row():
-            # Replace gr.Textbox with gr.Text for a single-line input field
+            # Prompt input and submit button
             prompt = gr.Text(
-                label="Prompt",  # Label for the input field
-                show_label=False,  # Hide the label
-                max_lines=1,  # Single-line input
-                placeholder="Enter your prompt here",  # Placeholder text
-                container=False  # Remove the container background
+                label="Prompt",
+                show_label=False,
+                max_lines=1,
+                placeholder="Enter your prompt here",
+                container=False
             )
-            # Replace the button with the simplified "Run" button
-            submit_button = gr.Button("Submit Prompt", scale=0)  # Add scale for button size
-
-    # Always visible DataFrame
-    objects_display = gr.Dataframe(
-        headers=["Object Class", "Bounding Box"],
-        value=[]
-    )
-
-    with gr.Row():
-        object_class_input = gr.Textbox(label="Object Class", placeholder="Enter object class (e.g., Object_1)")
-        bbox_input = gr.Textbox(label="Bounding Box (x1,y1,x2,y2)", placeholder="Enter bounding box coordinates")
+            submit_button = gr.Button("Submit Prompt", scale=0)
 
-    add_button = gr.Button("Add Object")
+        # Gallery to display results (for demonstration purposes)
+        result = gr.Gallery(label="Result", columns=3, show_label=False)
 
-    # Advanced settings in a collapsible accordion
-    with gr.Accordion("Advanced Settings", open=False):
-        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():
-            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=50,
-                step=1,
-                value=50
-            )
-
-    generate_button = gr.Button("Generate Image")
-    result = gr.Image(label="Generated Image")
-
-    # Submit the prompt and update the display
-    submit_button.click(
-        fn=submit_prompt,
-        inputs=prompt,
-        outputs=[objects_display, prompt]  # Update both the display and prompt input
-    )
-
-    # Add object and update display
-    add_button.click(
-        fn=add_object,
-        inputs=[object_class_input, bbox_input],
-        outputs=[objects_display]
-    )
+        # Refresh button to clear arrays
+        refresh_button = gr.Button("Refresh")
 
-    # Generate image based on added objects
-    generate_button.click(
-        fn=generate_image,
-        inputs=[prompt, guidance_scale, num_inference_steps, randomize_seed, seed],
-        outputs=[result, seed]
+    # Add functionality to the refresh button
+    refresh_button.click(
+        fn=clear_arrays,  # Function to clear arrays
+        inputs=None,
+        outputs=[result, prompt]  # Clear the result and reset the prompt input
     )
 
-if __name__ == "__main__":
-    demo.launch()
+# Launch the Gradio app
+demo.launch()