Create app.py

app.py

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+import os
+import torch
+import gradio as gr
+import numpy as np
+from PIL import Image
+from einops import rearrange
+import requests
+from diffusers.utils import load_image
+from diffusers import FluxControlNetPipeline, FluxControlNetModel
+from gradio_imageslider import ImageSlider
+
+# Pretrained model paths
+base_model = 'black-forest-labs/FLUX.1-dev'
+controlnet_model = 'InstantX/FLUX.1-dev-Controlnet-Union'
+
+# Load the ControlNet and pipeline models
+controlnet = FluxControlNetModel.from_pretrained(controlnet_model, torch_dtype=torch.bfloat16)
+pipe = FluxControlNetPipeline.from_pretrained(base_model, controlnet=controlnet, torch_dtype=torch.bfloat16)
+pipe.to("cuda")
+
+# Define control modes
+CONTROL_MODES = {
+    0: "Canny",
+    1: "Tile",
+    2: "Depth",
+    3: "Blur",
+    4: "Pose",
+    5: "Gray (Low)",
+    6: "LQ"
+}
+
+def preprocess_image(image, target_width, target_height):
+    image = image.resize((target_width, target_height), Image.LANCZOS)
+    return image
+
+def generate_image(prompt, control_image, control_mode, controlnet_conditioning_scale, num_steps, guidance, width, height, seed, random_seed):
+    if random_seed:
+        seed = np.random.randint(0, 10000)
+    
+    # Ensure width and height are multiples of 16
+    width = 16 * (width // 16)
+    height = 16 * (height // 16)
+    
+    # Set the seed for reproducibility
+    torch.manual_seed(seed)
+    
+    # Preprocess control image
+    control_image = preprocess_image(control_image, width, height)
+    
+    # Generate the image with the selected control mode and other parameters
+    with torch.no_grad():
+        image = pipe(
+            prompt,
+            control_image=control_image,
+            control_mode=control_mode,
+            width=width,
+            height=height,
+            controlnet_conditioning_scale=controlnet_conditioning_scale,
+            num_inference_steps=num_steps,
+            guidance_scale=guidance
+        ).images[0]
+    
+    return image
+
+# Define the Gradio interface
+interface = gr.Interface(
+    fn=generate_image,
+    inputs=[
+        gr.Textbox(label="Prompt"),
+        gr.Image(type="pil", label="Control Image"),
+        gr.Dropdown(choices=[f"{i}: {name}" for i, name in CONTROL_MODES.items()], type="index", label="Control Mode", default=0),
+        gr.Slider(minimum=0.1, maximum=1.0, step=0.1, value=0.5, label="ControlNet Conditioning Scale"),
+        gr.Slider(step=1, minimum=1, maximum=64, value=24, label="Num Steps"),
+        gr.Slider(minimum=0.1, maximum=10, value=3.5, label="Guidance"),
+        gr.Slider(minimum=128, maximum=1024, step=128, value=512, label="Width"),
+        gr.Slider(minimum=128, maximum=1024, step=128, value=512, label="Height"),
+        gr.Number(value=42, label="Seed"),
+        gr.Checkbox(label="Random Seed")
+    ],
+    outputs=ImageSlider(label="Generated Image"),
+    title="FLUX.1 Controlnet with Multiple Modes",
+    description="Generate images using ControlNet and a text prompt with adjustable control modes."
+)
+
+if __name__ == "__main__":
+    interface.launch()