Create app.py

app.py

@@ -0,0 +1,155 @@
+from __future__ import annotations
+import os
+os.system("pip install -e git+https://github.com/CompVis/taming-transformers.git@master#egg=taming-transformers")
+os.system("pip install -e git+https://github.com/alvanli/RDM-Region-Aware-Diffusion-Model.git@main#egg=guided_diffusion")
+os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "False"
+
+import math
+import random
+
+import gradio as gr
+import torch
+from PIL import Image, ImageOps
+from run_edit import run_model
+from cool_models import make_models
+
+help_text = """"""
+
+
+def main():
+    segmodel, model, diffusion, ldm, bert, clip_model, model_params = make_models()
+
+    def load_sample():
+        SAMPLE_IMAGE = "./flower1.jpg"
+        input_image = Image.open(SAMPLE_IMAGE)
+        from_text = "a flower"
+        instruction = "a sunflower"
+        negative_prompt = ""
+        seed = 42
+        guidance_scale = 5.0
+        clip_guidance_scale = 150
+        cutn = 16
+        l2_sim_lambda = 10_000
+
+        edited_image_1 = run_model(
+            segmodel, model, diffusion, ldm, bert, clip_model, model_params,
+            from_text, instruction, negative_prompt, input_image.convert('RGB'), seed, guidance_scale, clip_guidance_scale, cutn, l2_sim_lambda
+        )
+        
+        return [
+            input_image, from_text, instruction, negative_prompt, seed, guidance_scale, 
+            clip_guidance_scale, cutn, l2_sim_lambda, edited_image_1
+        ]
+
+
+    def generate(
+        input_image: Image.Image,
+        from_text: str,
+        instruction: str,
+        negative_prompt: str,
+        randomize_seed: bool,
+        seed: int,
+        guidance_scale: float,
+        clip_guidance_scale: float,
+        cutn: int,
+        l2_sim_lambda: float
+    ):
+        seed = random.randint(0, 100000) if randomize_seed else seed
+
+        if instruction == "":
+            return [seed, input_image]
+
+        generator = torch.manual_seed(seed)
+
+        edited_image_1 = run_model(
+            segmodel, model, diffusion, ldm, bert, clip_model, model_params,
+            from_text, instruction, negative_prompt, input_image.convert('RGB'), seed, guidance_scale, clip_guidance_scale, cutn, l2_sim_lambda
+        )
+
+        return [seed, edited_image_1]
+
+    def reset():
+        return [
+            "Randomize Seed", 42, None, 5.0,
+            150, 16, 10000
+        ]
+
+    with gr.Blocks() as demo:
+        gr.Markdown("""
+        #### RDM: Region-Aware Diffusion for Zero-shot Text-driven Image Editing
+        Original Github Repo: https://github.com/haha-lisa/RDM-Region-Aware-Diffusion-Model  <br/>
+        Instructions: <br/>
+        - In the "From Text" field, specify the object you are trying to modify,
+        - In the "edit instruction" field, specify what you want that area to be turned into
+        """)
+        with gr.Row():
+            with gr.Column(scale=1, min_width=100):
+                generate_button = gr.Button("Generate")
+            with gr.Column(scale=1, min_width=100):
+                load_button = gr.Button("Load Example")
+            with gr.Column(scale=1, min_width=100):
+                reset_button = gr.Button("Reset")
+            with gr.Column(scale=3):
+                from_text = gr.Textbox(lines=1, label="From Text", interactive=True)
+                instruction = gr.Textbox(lines=1, label="Edit Instruction", interactive=True)
+                negative_prompt = gr.Textbox(lines=1, label="Negative Prompt", interactive=True)
+
+        with gr.Row():
+            input_image = gr.Image(label="Input Image", type="pil", interactive=True)
+            edited_image_1 = gr.Image(label=f"Edited Image", type="pil", interactive=False)
+            # edited_image_2 = gr.Image(label=f"Edited Image", type="pil", interactive=False)
+            input_image.style(height=512, width=512)
+            edited_image_1.style(height=512, width=512)
+            # edited_image_2.style(height=512, width=512)
+
+        with gr.Row():
+            # steps = gr.Number(value=50, precision=0, label="Steps", interactive=True)
+            seed = gr.Number(value=1371, precision=0, label="Seed", interactive=True)
+            guidance_scale = gr.Number(value=5.0, precision=1, label="Guidance Scale", interactive=True)
+            clip_guidance_scale = gr.Number(value=150, precision=1, label="Clip Guidance Scale", interactive=True)
+            cutn = gr.Number(value=16, precision=1, label="Number of Cuts", interactive=True)
+            l2_sim_lambda = gr.Number(value=10000, precision=1, label="L2 similarity to original image")
+
+            randomize_seed = gr.Radio(
+                ["Fix Seed", "Randomize Seed"],
+                value="Randomize Seed",
+                type="index",
+                show_label=False,
+                interactive=True,
+            )
+            # use_ddim = gr.Checkbox(label="Use 50-step DDIM?", value=True)
+            # use_ddpm = gr.Checkbox(label="Use 50-step DDPM?", value=True)
+        
+        gr.Markdown(help_text)
+
+        generate_button.click(
+            fn=generate,
+            inputs=[    
+                input_image, from_text, instruction, negative_prompt, randomize_seed, 
+                seed, guidance_scale, clip_guidance_scale, cutn, l2_sim_lambda
+            ],
+            outputs=[seed, edited_image_1],
+        )
+
+        load_button.click(
+            fn=load_sample,
+            inputs=[],
+            outputs=[input_image, from_text, instruction, negative_prompt, seed, guidance_scale, clip_guidance_scale, cutn, l2_sim_lambda, edited_image_1],
+        )
+
+
+        reset_button.click(
+            fn=reset,
+            inputs=[],
+            outputs=[
+                randomize_seed, seed, edited_image_1, guidance_scale,
+                clip_guidance_scale, cutn, l2_sim_lambda
+            ],
+        )
+
+    demo.queue(concurrency_count=1)
+    demo.launch(share=False, server_name="0.0.0.0")
+
+
+if __name__ == "__main__":
+    main()