Update app.py

app.py

@@ -1,203 +1,6 @@
-import gradio
-import subprocess
-from PIL import Image
-import torch, torch.backends.cudnn, torch.backends.cuda
-from min_dalle import MinDalle
-from emoji import demojize
-import string
+import gradio as gr
 
-def filename_from_text(text: str) -> str:
-    text = demojize(text, delimiters=['', ''])
-    text = text.lower().encode('ascii', errors='ignore').decode()
-    allowed_chars = string.ascii_lowercase + ' '
-    text = ''.join(i for i in text.lower() if i in allowed_chars)
-    text = text[:64]
-    text = '-'.join(text.strip().split())
-    if len(text) == 0: text = 'blank'
-    return text
-
-def log_gpu_memory():
-    print(subprocess.check_output('nvidia-smi').decode('utf-8'))
-
-log_gpu_memory()
-
-model = MinDalle(
-    is_mega=True, 
-    is_reusable=True,
-    device='cuda',
-    dtype=torch.float32
-)
-
-log_gpu_memory()
-
-def run_model(
-    text: str,
-    grid_size: int,
-    is_seamless: bool,
-    save_as_png: bool,
-    temperature: float,
-    supercondition: str,
-    top_k: str
-) -> str:
-    torch.set_grad_enabled(False)
-    torch.backends.cudnn.enabled = True
-    torch.backends.cudnn.deterministic = False
-    torch.backends.cudnn.benchmark = True
-    torch.backends.cuda.matmul.allow_tf32 = True
-    torch.backends.cuda.matmul.allow_fp16_reduced_precision_reduction = True
-
-    print('text:', text)
-    print('grid_size:', grid_size) 
-    print('is_seamless:', is_seamless)
-    print('temperature:', temperature)
-    print('supercondition:', supercondition)
-    print('top_k:', top_k)
-    
-    try:
-        temperature = float(temperature)
-        assert(temperature > 1e-6)
-    except:
-        raise Exception('Temperature must be a positive nonzero number')
-    try:
-        grid_size = int(grid_size)
-        assert(grid_size <= 5)
-        assert(grid_size >= 1)
-    except:
-        raise Exception('Grid size must be between 1 and 5')
-    try:
-        top_k = int(top_k)
-        assert(top_k <= 16384)
-        assert(top_k >= 1)
-    except:
-        raise Exception('Top k must be between 1 and 16384')
-
-    with torch.no_grad():
-        image = model.generate_image(
-            text = text,
-            seed = -1,
-            grid_size = grid_size,
-            is_seamless = bool(is_seamless),
-            temperature = temperature,
-            supercondition_factor = float(supercondition),
-            top_k = top_k,
-            is_verbose = True
-        )
-
-    log_gpu_memory()
-
-    ext = 'png' if bool(save_as_png) else 'jpg'
-    filename = filename_from_text(text)
-    image_path = '{}.{}'.format(filename, ext)
-    image.save(image_path)
-
-    return image_path
-
-demo = gradio.Blocks(analytics_enabled=True)
-
-with demo:
-    with gradio.Row():
-        with gradio.Column():
-            input_text = gradio.Textbox(
-                label='Input Text', 
-                value='Moai statue giving a TED Talk',
-                lines=3
-            )
-            run_button = gradio.Button(value='Generate Image').style(full_width=True)
-            output_image = gradio.Image(
-                value='examples/moai-statue.jpg',
-                label='Output Image',
-                type='file',
-                interactive=False
-            )
-
-        with gradio.Column():
-            gradio.Markdown('## Settings')
-            with gradio.Row():
-                grid_size = gradio.Slider(
-                    label='Grid Size',
-                    value=5,
-                    minimum=1, 
-                    maximum=5,
-                    step=1
-                )
-                save_as_png = gradio.Checkbox(
-                    label='Output PNG',
-                    value=False
-                )
-                is_seamless = gradio.Checkbox(
-                    label='Seamless',
-                    value=False
-                )
-            gradio.Markdown('#### Advanced')
-            with gradio.Row():
-                temperature = gradio.Number(
-                    label='Temperature',
-                    value=1
-                )
-                top_k = gradio.Dropdown(
-                    label='Top-k',
-                    choices=[str(2 ** i) for i in range(15)],
-                    value='128'
-                )
-                supercondition = gradio.Dropdown(
-                    label='Super Condition',
-                    choices=[str(2 ** i) for i in range(2, 7)],
-                    value='16'
-                )
-
-            gradio.Markdown(
-                """
-                ####
-                - **Input Text**: For long prompts, only the first 64 text tokens will be used to generate the image.
-                - **Grid Size**: Size of the image grid. 3x3 takes about 15 seconds.
-                - **Seamless**: Tile images in image token space instead of pixel space.
-                - **Temperature**: High temperature increases the probability of sampling low scoring image tokens.
-                - **Top-k**: Each image token is sampled from the top-k scoring tokens.
-                - **Super Condition**: Higher values can result in better agreement with the text.
-                """
-            )
-
-    gradio.Examples(
-        examples=[
-            ['Rusty Iron Man suit found abandoned in the woods being reclaimed by nature', 3, 'examples/rusty-iron-man.jpg'],
-            ['Moai statue giving a TED Talk', 5, 'examples/moai-statue.jpg'],
-            ['Court sketch of Godzilla on trial', 5, 'examples/godzilla-trial.jpg'],
-            ['lofi nuclear war to relax and study to', 5, 'examples/lofi-nuclear-war.jpg'],
-            ['Karl Marx slimed at Kids Choice Awards', 4, 'examples/marx-slimed.jpg'],
-            ['Scientists trying to rhyme orange with banana', 4, 'examples/scientists-rhyme.jpg'],
-            ['Jesus turning water into wine on Americas Got Talent', 5, 'examples/jesus-talent.jpg'],
-            ['Elmo in a street riot throwing a Molotov cocktail, hyperrealistic', 5, 'examples/elmo-riot.jpg'],
-            ['Trail cam footage of gollum eating watermelon', 4, 'examples/gollum.jpg'],
-            ['Funeral at Whole Foods', 4, 'examples/funeral-whole-foods.jpg'],
-            ['Singularity, hyperrealism', 5, 'examples/singularity.jpg'],
-            ['Astronaut riding a horse hyperrealistic', 5, 'examples/astronaut-horse.jpg'],
-            ['An astronaut walking on Mars next to a Starship rocket, realistic', 5, 'examples/astronaut-mars.jpg'],
-            ['Nuclear explosion broccoli', 4, 'examples/nuclear-broccoli.jpg'],
-            ['Dali painting of WALL·E', 5, 'examples/dali-walle.jpg'],
-            ['Cleopatra checking her iPhone', 4, 'examples/cleopatra-iphone.jpg'],
-        ],
-        inputs=[
-            input_text,
-            grid_size,
-            output_image
-        ],
-        examples_per_page=20
-    )
-
-    run_button.click(
-        fn=run_model, 
-        inputs=[
-            input_text,
-            grid_size,
-            is_seamless,
-            save_as_png,
-            temperature,
-            supercondition,
-            top_k
-        ], 
-        outputs=[
-            output_image
-        ]
-    )
+with gr.Blocks() as demo:
+    gr.Gallery(["examples/dali-walle.jpg"])
 
 demo.launch()