Update app.py

app.py

@@ -1,146 +1,232 @@
-import gradio as gr
-import numpy as np
+from typing import Tuple
+
+import requests
 import random
-from diffusers import DiffusionPipeline
+import numpy as np
+import gradio as gr
+import spaces
 import torch
+from PIL import Image
+from diffusers import FluxInpaintPipeline
 
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-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: 
-    pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", use_safetensors=True)
-    pipe = pipe.to(device)
+"""
+A big shoutout to [Black Forest Labs](https://huggingface.co/black-forest-labs) team for making their models available.
+Also a big thanks to [Gothos](https://github.com/Gothos) for enabling inpainting with the FLUX.
+"""
 
 MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
-
-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] 
-    
+IMAGE_SIZE = 1024
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+
+
+def remove_background(image: Image.Image, threshold: int = 50) -> Image.Image:
+    image = image.convert("RGBA")
+    data = image.getdata()
+    new_data = []
+    for item in data:
+        avg = sum(item[:3]) / 3
+        if avg < threshold:
+            new_data.append((0, 0, 0, 0))
+        else:
+            new_data.append(item)
+
+    image.putdata(new_data)
     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",
+
+EXAMPLES = [
+    [
+        {
+            "background": Image.open(requests.get("https://media.roboflow.com/spaces/doge-2-image.png", stream=True).raw),
+            "layers": [remove_background(Image.open(requests.get("https://media.roboflow.com/spaces/doge-2-mask-2.png", stream=True).raw))],
+            "composite": Image.open(requests.get("https://media.roboflow.com/spaces/doge-2-composite-2.png", stream=True).raw),
+        },
+        "little lion",
+        42,
+        False,
+        0.85,
+        30
+    ],
+    [
+        {
+            "background": Image.open(requests.get("https://media.roboflow.com/spaces/doge-2-image.png", stream=True).raw),
+            "layers": [remove_background(Image.open(requests.get("https://media.roboflow.com/spaces/doge-2-mask-3.png", stream=True).raw))],
+            "composite": Image.open(requests.get("https://media.roboflow.com/spaces/doge-2-composite-3.png", stream=True).raw),
+        },
+        "tribal tattoos",
+        42,
+        False,
+        0.85,
+        30
+    ]
 ]
 
-css="""
-#col-container {
-    margin: 0 auto;
-    max-width: 520px;
-}
-"""
+pipe = FluxInpaintPipeline.from_pretrained(
+    "black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16).to(DEVICE)
+
+
+def resize_image_dimensions(
+    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
+
+
+@spaces.GPU(duration=100)
+def process(
+    input_image_editor: dict,
+    input_text: str,
+    seed_slicer: int,
+    randomize_seed_checkbox: bool,
+    strength_slider: float,
+    num_inference_steps_slider: int,
+    progress=gr.Progress(track_tqdm=True)
+):
+    if not input_text:
+        gr.Info("Please enter a text prompt.")
+        return None, None
+
+    image = input_image_editor['background']
+    mask = input_image_editor['layers'][0]
+
+    if not image:
+        gr.Info("Please upload an image.")
+        return None, None
+
+    if not mask:
+        gr.Info("Please draw a mask on the image.")
+        return None, None
+
+    width, height = resize_image_dimensions(original_resolution_wh=image.size)
+    resized_image = image.resize((width, height), Image.LANCZOS)
+    resized_mask = mask.resize((width, height), Image.LANCZOS)
+
+    if randomize_seed_checkbox:
+        seed_slicer = random.randint(0, MAX_SEED)
+    generator = torch.Generator().manual_seed(seed_slicer)
+    result = pipe(
+        prompt=input_text,
+        image=resized_image,
+        mask_image=resized_mask,
+        width=width,
+        height=height,
+        strength=strength_slider,
+        generator=generator,
+        num_inference_steps=num_inference_steps_slider
+    ).images[0]
+    print('INFERENCE DONE')
+    return result, resized_mask
+
+
+with gr.Blocks() as demo:
+    gr.Markdown(MARKDOWN)
+    with gr.Row():
+        with gr.Column():
+            input_image_editor_component = gr.ImageEditor(
+                label='Image',
+                type='pil',
+                sources=["upload", "webcam"],
+                image_mode='RGB',
+                layers=False,
+                brush=gr.Brush(colors=["#FFFFFF"], color_mode="fixed"))
 
-if torch.cuda.is_available():
-    power_device = "GPU"
-else:
-    power_device = "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)
-        
-        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)
-            
-            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,
-                )
-            
             with gr.Row():
-                
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0,
+                input_text_component = gr.Text(
+                    label="Prompt",
+                    show_label=False,
+                    max_lines=1,
+                    placeholder="Enter your prompt",
+                    container=False,
                 )
-                
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=12,
+                submit_button_component = gr.Button(
+                    value='Submit', variant='primary', scale=0)
+
+            with gr.Accordion("Advanced Settings", open=False):
+                seed_slicer_component = gr.Slider(
+                    label="Seed",
+                    minimum=0,
+                    maximum=MAX_SEED,
                     step=1,
-                    value=2,
+                    value=42,
                 )
-        
+
+                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")
+    with gr.Row():
         gr.Examples(
-            examples = examples,
-            inputs = [prompt]
+            fn=process,
+            examples=EXAMPLES,
+            inputs=[
+                input_image_editor_component,
+                input_text_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=True,
+            cache_examples=True
         )
 
-    run_button.click(
-        fn = infer,
-        inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
-        outputs = [result]
+    submit_button_component.click(
+        fn=process,
+        inputs=[
+            input_image_editor_component,
+            input_text_component,
+            seed_slicer_component,
+            randomize_seed_checkbox_component,
+            strength_slider_component,
+            num_inference_steps_slider_component
+        ],
+        outputs=[
+            output_image_component,
+            output_mask_component
+        ]
     )
 
-demo.queue().launch()
+demo.launch(debug=False, show_error=True)