Update app.py

app.py

@@ -1,146 +1,69 @@
 import gradio as gr
-import numpy as np
-import random
-from diffusers import DiffusionPipeline
-import torch
+import subprocess
+import os
+import shutil
 
-device = "cuda" if torch.cuda.is_available() else "cpu"
+# Function to install Rust and Cargo, clone and build avif-decode
+def setup_avif_decode():
+    # Install Rust and Cargo
+    if not os.path.exists(os.path.expanduser("~/.cargo/bin/cargo")):
+        subprocess.run("curl https://sh.rustup.rs -sSf | sh -s -- -y", shell=True, check=True)
+        os.environ['PATH'] += os.pathsep + os.path.expanduser("~/.cargo/bin")
 
-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)
+    # Clone avif-decode if it doesn't exist
+    if not os.path.exists("avif-decode"):
+        subprocess.run("git clone https://github.com/kornelski/avif-decode.git", shell=True, check=True)
 
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
+    # Build avif-decode
+    subprocess.run("cd avif-decode && cargo build --release", shell=True, check=True)
 
-def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
+# Call setup function to ensure everything is installed and built
+setup_avif_decode()
 
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-        
-    generator = torch.Generator().manual_seed(seed)
+# Define the function to convert AVIF to PNG
+def convert_avif_to_png(avif_file):
+    avif_path = avif_file.name
+    png_path = avif_path.rsplit('.', 1)[0] + '.png'
     
-    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] 
+    # Run the avif-decode command
+    result = subprocess.run(["avif-decode/target/release/avif_decode", "-f", avif_path, png_path], capture_output=True, text=True)
     
-    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",
-]
+    if result.returncode == 0:
+        return png_path
+    else:
+        return f"Error converting {avif_file.name}: {result.stderr}"
 
-css="""
+css = """
 #col-container {
     margin: 0 auto;
     max-width: 520px;
 }
 """
 
-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}.
+        gr.Markdown("""
+        # AVIF to PNG Converter
+        Upload your AVIF files and get them converted to PNG.
         """)
         
         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,
+            avif_file = gr.File(
+                label="Upload AVIF File",
+                file_types=[".avif"],
+                file_count="multiple"
             )
             
-            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,
-                )
-                
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=12,
-                    step=1,
-                    value=2,
-                )
+            run_button = gr.Button("Convert", scale=0)
         
-        gr.Examples(
-            examples = examples,
-            inputs = [prompt]
-        )
+        result = gr.Gallery(label="Result")
 
     run_button.click(
-        fn = infer,
-        inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
-        outputs = [result]
+        fn=convert_avif_to_png,
+        inputs=[avif_file],
+        outputs=[result]
     )
 
-demo.queue().launch()
+demo.queue().launch()