Update app.py

app.py

@@ -1,142 +1,353 @@
+from functools import partial
+
+import cv2
+import random
+from typing import Tuple, Optional
+
 import gradio as gr
 import numpy as np
-import random
-#import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
+import requests
+import spaces
 import torch
+from PIL import Image, ImageFilter
+from diffusers import FluxInpaintPipeline
+from gradio_client import Client, handle_file
 
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/sdxl-turbo" #Replace to the model you would like to use
+MARKDOWN = """
+# FLUX.1 Inpainting 🔥
 
-if torch.cuda.is_available():
-    torch_dtype = torch.float16
-else:
-    torch_dtype = torch.float32
-
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
+Shoutout to [Black Forest Labs](https://huggingface.co/black-forest-labs) team for 
+creating this amazing model, and a big thanks to [Gothos](https://github.com/Gothos) 
+for taking it to the next level by enabling inpainting with the FLUX.
+"""
 
 MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
-
-#@spaces.GPU #[uncomment to use ZeroGPU]
-def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, progress=gr.Progress(track_tqdm=True)):
-
-    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] 
-    
-    return image, seed
-
-examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
+IMAGE_SIZE = 1024
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+PIPE = FluxInpaintPipeline.from_pretrained(
+    "black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16).to(DEVICE)
+CLIENT = Client("SkalskiP/florence-sam-masking")
+
+
+EXAMPLES = [
+    [
+        {
+            "background": Image.open(requests.get("https://media.roboflow.com/spaces/doge-2-image.png", stream=True).raw),
+            "layers": [Image.open(requests.get("https://media.roboflow.com/spaces/doge-2-mask-2-removebg.png", stream=True).raw)],
+            "composite": Image.open(requests.get("https://media.roboflow.com/spaces/doge-2-composite-2.png", stream=True).raw),
+        },
+        "little lion",
+        "",
+        5,
+        5,
+        42,
+        False,
+        0.85,
+        20
+    ],
+    [
+        {
+            "background": Image.open(requests.get("https://media.roboflow.com/spaces/doge-5.jpeg", stream=True).raw),
+            "layers": None,
+            "composite": None
+        },
+        "big blue eyes",
+        "eyes",
+        10,
+        5,
+        42,
+        False,
+        0.9,
+        20
+    ]
 ]
 
-css="""
-#col-container {
-    margin: 0 auto;
-    max-width: 640px;
-}
-"""
 
-with gr.Blocks(css=css) as demo:
-    
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(f"""
-        # Text-to-Image Gradio Template
-        """)
-        
-        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)
-        
-        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,
-            )
-            
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-            
+def calculate_image_dimensions_for_flux(
+    original_resolution_wh: Tuple[int, int],
+    maximum_dimension: int = IMAGE_SIZE
+) -> Tuple[int, int]:
+    width, height = original_resolution_wh
+
+    if width > height:
+        scaling_factor = maximum_dimension / width
+    else:
+        scaling_factor = maximum_dimension / height
+
+    new_width = int(width * scaling_factor)
+    new_height = int(height * scaling_factor)
+
+    new_width = new_width - (new_width % 32)
+    new_height = new_height - (new_height % 32)
+
+    return new_width, new_height
+
+
+def is_mask_empty(image: Image.Image) -> bool:
+    gray_img = image.convert("L")
+    pixels = list(gray_img.getdata())
+    return all(pixel == 0 for pixel in pixels)
+
+
+def process_mask(
+    mask: Image.Image,
+    mask_inflation: Optional[int] = None,
+    mask_blur: Optional[int] = None
+) -> Image.Image:
+    """
+    Inflates and blurs the white regions of a mask.
+
+    Args:
+        mask (Image.Image): The input mask image.
+        mask_inflation (Optional[int]): The number of pixels to inflate the mask by.
+        mask_blur (Optional[int]): The radius of the Gaussian blur to apply.
+
+    Returns:
+        Image.Image: The processed mask with inflated and/or blurred regions.
+    """
+    if mask_inflation and mask_inflation > 0:
+        mask_array = np.array(mask)
+        kernel = np.ones((mask_inflation, mask_inflation), np.uint8)
+        mask_array = cv2.dilate(mask_array, kernel, iterations=1)
+        mask = Image.fromarray(mask_array)
+
+    if mask_blur and mask_blur > 0:
+        mask = mask.filter(ImageFilter.GaussianBlur(radius=mask_blur))
+
+    return mask
+
+
+def set_client_for_session(request: gr.Request):
+    try:
+        x_ip_token = request.headers['x-ip-token']
+        return Client("SkalskiP/florence-sam-masking", headers={"X-IP-Token": x_ip_token})
+    except:
+        return CLIENT
+
+
+@spaces.GPU(duration=50)
+def run_flux(
+    image: Image.Image,
+    mask: Image.Image,
+    prompt: str,
+    seed_slicer: int,
+    randomize_seed_checkbox: bool,
+    strength_slider: float,
+    num_inference_steps_slider: int,
+    resolution_wh: Tuple[int, int],
+) -> Image.Image:
+    print("Running FLUX...")
+    width, height = resolution_wh
+    if randomize_seed_checkbox:
+        seed_slicer = random.randint(0, MAX_SEED)
+    generator = torch.Generator().manual_seed(seed_slicer)
+    return PIPE(
+        prompt=prompt,
+        image=image,
+        mask_image=mask,
+        width=width,
+        height=height,
+        strength=strength_slider,
+        generator=generator,
+        num_inference_steps=num_inference_steps_slider
+    ).images[0]
+
+
+def process(
+    client,
+    input_image_editor: dict,
+    inpainting_prompt_text: str,
+    masking_prompt_text: str,
+    mask_inflation_slider: int,
+    mask_blur_slider: int,
+    seed_slicer: int,
+    randomize_seed_checkbox: bool,
+    strength_slider: float,
+    num_inference_steps_slider: int
+):
+    if not inpainting_prompt_text:
+        gr.Info("Please enter inpainting text prompt.")
+        return None, None
+
+    image_path = input_image_editor['background']
+    mask_path = input_image_editor['layers'][0]
+
+    image = Image.open(image_path)
+    mask = Image.open(mask_path)
+
+    if not image:
+        gr.Info("Please upload an image.")
+        return None, None
+
+    if is_mask_empty(mask) and not masking_prompt_text:
+        gr.Info("Please draw a mask or enter a masking prompt.")
+        return None, None
+
+    if not is_mask_empty(mask) and masking_prompt_text:
+        gr.Info("Both mask and masking prompt are provided. Please provide only one.")
+        return None, None
+
+    if is_mask_empty(mask):
+        print("Generating mask...")
+        mask = client.predict(
+            image_input=handle_file(image_path),
+            text_input=masking_prompt_text,
+            api_name="/process_image")
+        mask = Image.open(mask)
+        print("Mask generated.")
+
+    width, height = calculate_image_dimensions_for_flux(original_resolution_wh=image.size)
+    image = image.resize((width, height), Image.LANCZOS)
+    mask = mask.resize((width, height), Image.LANCZOS)
+    mask = process_mask(mask, mask_inflation=mask_inflation_slider, mask_blur=mask_blur_slider)
+    image = run_flux(
+        image=image,
+        mask=mask,
+        prompt=inpainting_prompt_text,
+        seed_slicer=seed_slicer,
+        randomize_seed_checkbox=randomize_seed_checkbox,
+        strength_slider=strength_slider,
+        num_inference_steps_slider=num_inference_steps_slider,
+        resolution_wh=(width, height)
+    )
+    return image, mask
+
+
+process_example = partial(process, client=CLIENT)
+
+
+with gr.Blocks() as demo:
+    client_component = gr.State()
+    gr.Markdown(MARKDOWN)
+    with gr.Row():
+        with gr.Column():
+            input_image_editor_component = gr.ImageEditor(
+                label='Image',
+                type='filepath',
+                sources=["upload", "webcam"],
+                image_mode='RGB',
+                layers=False,
+                brush=gr.Brush(colors=["#FFFFFF"], color_mode="fixed"))
+
             with gr.Row():
-                
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024, #Replace with defaults that work for your model
-                )
-                
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024, #Replace with defaults that work for your model
+                inpainting_prompt_text_component = gr.Text(
+                    label="Inpainting prompt",
+                    show_label=False,
+                    max_lines=1,
+                    placeholder="Enter text to generate inpainting",
+                    container=False,
                 )
-            
-            with gr.Row():
-                
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0, #Replace with defaults that work for your model
+                submit_button_component = gr.Button(
+                    value='Submit', variant='primary', scale=0)
+
+            with gr.Accordion("Advanced Settings", open=False):
+                masking_prompt_text_component = gr.Text(
+                    label="Masking prompt",
+                    show_label=False,
+                    max_lines=1,
+                    placeholder="Enter text to generate masking",
+                    container=False,
                 )
-                
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
+
+                with gr.Row():
+                    mask_inflation_slider_component = gr.Slider(
+                        label="Mask inflation",
+                        info="Adjusts the amount of mask edge expansion before "
+                             "inpainting.",
+                        minimum=0,
+                        maximum=20,
+                        step=1,
+                        value=5,
+                    )
+
+                    mask_blur_slider_component = gr.Slider(
+                        label="Mask blur",
+                        info="Controls the intensity of the Gaussian blur applied to "
+                             "the mask edges.",
+                        minimum=0,
+                        maximum=20,
+                        step=1,
+                        value=5,
+                    )
+
+                seed_slicer_component = gr.Slider(
+                    label="Seed",
+                    minimum=0,
+                    maximum=MAX_SEED,
                     step=1,
-                    value=2, #Replace with defaults that work for your model
+                    value=42,
                 )
-        
-        gr.Examples(
-            examples = examples,
-            inputs = [prompt]
-        )
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn = infer,
-        inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
-        outputs = [result, seed]
+
+                randomize_seed_checkbox_component = gr.Checkbox(
+                    label="Randomize seed", value=True)
+
+                with gr.Row():
+                    strength_slider_component = gr.Slider(
+                        label="Strength",
+                        info="Indicates extent to transform the reference `image`. "
+                             "Must be between 0 and 1. `image` is used as a starting "
+                             "point and more noise is added the higher the `strength`.",
+                        minimum=0,
+                        maximum=1,
+                        step=0.01,
+                        value=0.85,
+                    )
+
+                    num_inference_steps_slider_component = gr.Slider(
+                        label="Number of inference steps",
+                        info="The number of denoising steps. More denoising steps "
+                             "usually lead to a higher quality image at the",
+                        minimum=1,
+                        maximum=50,
+                        step=1,
+                        value=20,
+                    )
+        with gr.Column():
+            output_image_component = gr.Image(
+                type='pil', image_mode='RGB', label='Generated image', format="png")
+            with gr.Accordion("Debug", open=False):
+                output_mask_component = gr.Image(
+                    type='pil', image_mode='RGB', label='Input mask', format="png")
+    gr.Examples(
+        fn=process_example,
+        examples=EXAMPLES,
+        inputs=[
+            input_image_editor_component,
+            inpainting_prompt_text_component,
+            masking_prompt_text_component,
+            mask_inflation_slider_component,
+            mask_blur_slider_component,
+            seed_slicer_component,
+            randomize_seed_checkbox_component,
+            strength_slider_component,
+            num_inference_steps_slider_component
+        ],
+        outputs=[
+            output_image_component,
+            output_mask_component
+        ],
+        run_on_click=False
+    )
+
+    submit_button_component.click(
+        fn=process,
+        inputs=[
+            client_component,
+            input_image_editor_component,
+            inpainting_prompt_text_component,
+            masking_prompt_text_component,
+            mask_inflation_slider_component,
+            mask_blur_slider_component,
+            seed_slicer_component,
+            randomize_seed_checkbox_component,
+            strength_slider_component,
+            num_inference_steps_slider_component
+        ],
+        outputs=[
+            output_image_component,
+            output_mask_component
+        ]
     )
+    demo.load(set_client_for_session, None, client_component)
 
-demo.queue().launch()
+demo.launch(debug=False, show_error=True)