Create app.py

app.py

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+import gradio as gr
+from diffusers import DiffusionPipeline
+import spaces
+
+def find_cuda():
+    # Check if CUDA_HOME or CUDA_PATH environment variables are set
+    cuda_home = os.environ.get('CUDA_HOME') or os.environ.get('CUDA_PATH')
+
+    if cuda_home and os.path.exists(cuda_home):
+        return cuda_home
+
+    # Search for the nvcc executable in the system's PATH
+    nvcc_path = shutil.which('nvcc')
+
+    if nvcc_path:
+        # Remove the 'bin/nvcc' part to get the CUDA installation path
+        cuda_path = os.path.dirname(os.path.dirname(nvcc_path))
+        return cuda_path
+
+    return None
+
+cuda_path = find_cuda()
+
+if cuda_path:
+    print(f"CUDA installation found at: {cuda_path}")
+else:
+    print("CUDA installation not found")
+    
+# check if cuda is available
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# load the pipeline/model
+pipeline = DiffusionPipeline.from_pretrained("jadechoghari/mar", trust_remote_code=True, custom_pipeline="jadechoghari/mar")
+
+# function that generates images
+@spaces.GPU
+def generate_image(seed, num_ar_steps, class_labels, cfg_scale, cfg_schedule):
+    generated_image = pipeline(
+        model_type="mar_huge",  # using mar_huge
+        seed=seed, 
+        num_ar_steps=num_ar_steps, 
+        class_labels=[int(label.strip()) for label in class_labels.split(',')],
+        cfg_scale=cfg_scale, 
+        cfg_schedule=cfg_schedule,
+        output_dir="./images"
+    )
+    return generated_image
+
+
+with gr.Blocks() as demo:
+    gr.Markdown("""
+    # MAR Image Generation Demo 🚀
+    
+    Welcome to the demo for **MAR** (Masked Autoregressive Model), a novel approach to image generation that eliminates the need for vector quantization. MAR uses a diffusion process to generate images in a continuous-valued space, resulting in faster, more efficient, and higher-quality outputs.
+    
+    Simply adjust the parameters below to create your custom images in real-time.
+
+    Make sure to provide valid **ImageNet class labels** to see the translation of text to image. For a complete list of ImageNet classes, check out [this reference](https://deeplearning.cms.waikato.ac.nz/user-guide/class-maps/IMAGENET/). 
+
+    For more details, visit the [GitHub repository](https://github.com/LTH14/mar).
+    """)
+
+    seed = gr.Number(value=42, label="Seed")
+    num_ar_steps = gr.Slider(minimum=1, maximum=256, value=64, label="Number of AR Steps")
+    class_labels = gr.Textbox(value="207, 360, 388", label="Class Labels (comma-separated ImageNet labels)")
+    cfg_scale = gr.Slider(minimum=1, maximum=10, value=4, label="CFG Scale")
+    cfg_schedule = gr.Dropdown(choices=["constant", "linear"], label="CFG Schedule", value="constant")
+
+    image_output = gr.Image(label="Generated Image")
+
+    generate_button = gr.Button("Generate Image")
+
+    # we link the button to the function and display the output
+    generate_button.click(generate_image, inputs=[seed, num_ar_steps, class_labels, cfg_scale, cfg_schedule], outputs=image_output)
+
+demo.launch()