Update app.py

app.py

@@ -1,40 +1,173 @@
 import gradio as gr
+import numpy as np
+from PIL import Image
+import torch
+from diffusers import ControlNetModel, UniPCMultistepScheduler
+from hico_pipeline import StableDiffusionControlNetMultiLayoutPipeline
 
-# Arrays to be cleared
+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
 object_classes_list = []
 object_bboxes_list = []
 
-# Function to clear all arrays
+# 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:
+        x1, y1, x2, y2 = map(int, bbox.split(","))
+        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.", []
+        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 and reset the UI
 def clear_arrays():
     object_classes_list.clear()
     object_bboxes_list.clear()
-    return [], gr.update(value="", interactive=True)  # Clear result and reset prompt input
+    return [], gr.update(value="", interactive=True)  # Clear the objects and reset the prompt
+
+# Gradio UI with custom CSS for orange buttons
+css = """
+button {
+    background-color: orange !important;
+    color: white !important;
+    border: none !important;
+    font-weight: bold;
+}
+"""
+
+with gr.Blocks(css=css) as demo:
+    gr.Markdown("# Text-to-Image Generator with Object Addition")
+
+    # Put prompt and submit button in the same row and adjust sizes
+    with gr.Row():
+        prompt = gr.Textbox(label="Prompt", placeholder="Enter your prompt here").style(width=500)
+        submit_button = gr.Button("Submit Prompt").style(width=100)
+
+    # 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")
+
+    add_button = gr.Button("Add Object")
+    refresh_button = gr.Button("Refresh")  # New Refresh button
+
+    # 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.Blocks() as demo:
-    with gr.Group():
         with gr.Row():
-            # Prompt input and submit button
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt here",
-                container=False
+            guidance_scale = gr.Slider(
+                label="Guidance scale",
+                minimum=0.0,
+                maximum=10.0,
+                step=0.1,
+                value=7.5
             )
-            submit_button = gr.Button("Submit Prompt", scale=0)
+            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")
 
-        # Gallery to display results (for demonstration purposes)
-        result = gr.Gallery(label="Result", columns=3, show_label=False)
+    # Submit the prompt and update the display
+    submit_button.click(
+        fn=submit_prompt,
+        inputs=prompt,
+        outputs=[objects_display, prompt]
+    )
 
-        # Refresh button to clear arrays
-        refresh_button = gr.Button("Refresh")
+    # Add object and update display
+    add_button.click(
+        fn=add_object,
+        inputs=[object_class_input, bbox_input],
+        outputs=[objects_display]
+    )
 
-    # Add functionality to the refresh button
+    # Refresh button to clear arrays and reset inputs
     refresh_button.click(
-        fn=clear_arrays,  # Function to clear arrays
+        fn=clear_arrays,
         inputs=None,
-        outputs=[result, prompt]  # Clear the result and reset the prompt input
+        outputs=[objects_display, prompt]
+    )
+
+    # 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]
     )
 
-# Launch the Gradio app
-demo.launch()
+if __name__ == "__main__":
+    demo.launch()