Create app.py

app.py

@@ -0,0 +1,150 @@
+import gradio as gr
+import numpy as np
+
+import spaces
+import torch
+import spaces
+import random 
+
+from diffusers import AutoPipelineForText2Image
+from PIL import Image
+
+
+MAX_SEED = np.iinfo(np.int32).max
+MAX_IMAGE_SIZE = 2048
+
+pipe = AutoPipelineForText2Image.from_pretrained(
+    "ostris/Flex.2-preview",
+    custom_pipeline="pipeline.py",
+    torch_dtype=torch.bfloat16,
+).to("cuda")
+
+# def calculate_optimal_dimensions(image: Image.Image):
+#     # Extract the original dimensions
+#     original_width, original_height = image.size
+    
+#     # Set constants
+#     MIN_ASPECT_RATIO = 9 / 16
+#     MAX_ASPECT_RATIO = 16 / 9
+#     FIXED_DIMENSION = 1024
+
+#     # Calculate the aspect ratio of the original image
+#     original_aspect_ratio = original_width / original_height
+
+#     # Determine which dimension to fix
+#     if original_aspect_ratio > 1:  # Wider than tall
+#         width = FIXED_DIMENSION
+#         height = round(FIXED_DIMENSION / original_aspect_ratio)
+#     else:  # Taller than wide
+#         height = FIXED_DIMENSION
+#         width = round(FIXED_DIMENSION * original_aspect_ratio)
+
+#     # Ensure dimensions are multiples of 8
+#     width = (width // 8) * 8
+#     height = (height // 8) * 8
+
+#     # Enforce aspect ratio limits
+#     calculated_aspect_ratio = width / height
+#     if calculated_aspect_ratio > MAX_ASPECT_RATIO:
+#         width = (height * MAX_ASPECT_RATIO // 8) * 8
+#     elif calculated_aspect_ratio < MIN_ASPECT_RATIO:
+#         height = (width / MIN_ASPECT_RATIO // 8) * 8
+
+#     # Ensure width and height remain above the minimum dimensions
+#     width = max(width, 576) if width == FIXED_DIMENSION else width
+#     height = max(height, 576) if height == FIXED_DIMENSION else height
+
+#     return width, height
+
+@spaces.GPU
+def infer(edit_images, prompt, seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=3.5, num_inference_steps=28, progress=gr.Progress(track_tqdm=True)):
+    image = edit_images["background"]
+    width, height = calculate_optimal_dimensions(image)
+    mask = edit_images["layers"][0]
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    image = pipe(
+        prompt=prompt,
+        # image=image,
+        # mask_image=mask,
+        inpaint_image=image,
+        inpaint_mask=mask,
+        height=height,
+        width=width,
+        guidance_scale=guidance_scale,
+        num_inference_steps=num_inference_steps,
+        generator=torch.Generator("cpu").manual_seed(seed)
+    ).images[0]
+    return image, seed
+    
+examples = [
+    "a tiny astronaut hatching from an egg on the moon",
+    "a cat holding a sign that says hello world",
+    "an anime illustration of a wiener schnitzel",
+]
+
+css="""
+#col-container {
+    margin: 0 auto;
+    max-width: 1000px;
+}
+"""
+
+with gr.Blocks(css=css) as demo:
+    
+    with gr.Column(elem_id="col-container"):
+        gr.Markdown(f"""# Flex.2 Preview - inpaint
+        """)
+        with gr.Row():
+            with gr.Column():
+                edit_image = gr.ImageEditor(
+                    label='Upload and draw mask for inpainting',
+                    type='pil',
+                    sources=["upload", "webcam"],
+                    image_mode='RGB',
+                    layers=False,
+                    brush=gr.Brush(colors=["#FFFFFF"], color_mode="fixed"),
+                    height=600
+                )
+                prompt = gr.Text(
+                    label="Prompt",
+                    show_label=False,
+                    max_lines=1,
+                    placeholder="Enter your prompt",
+                    container=False,
+                )
+                run_button = gr.Button("Run")
+                
+            result = gr.Image(label="Result", show_label=False)
+        
+        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():
+                
+                height = gr.Slider(64, 2048, value=512, step=64, label="Height")
+                width = gr.Slider(64, 2048, value=512, step=64, label="Width")
+            
+            with gr.Row():
+
+                guidance_scale = gr.Slider(0.0, 20.0, value=3.5, step=0.1, label="Guidance Scale")
+  
+                num_inference_steps = gr.Slider(1, 100, value=50, step=1, label="Inference Steps")
+
+    gr.on(
+        triggers=[run_button.click, prompt.submit],
+        fn = infer,
+        inputs = [edit_image, prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
+        outputs = [result, seed]
+    )
+
+demo.launch()