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| import gradio as gr | |
| from diffusers import DiffusionPipeline | |
| import os | |
| import torch | |
| import shutil | |
| 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 | |
| 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=0, 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, 113, 355, 980, 323, 979", 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) | |
| # Add example inputs and cache them for quicker demos | |
| gr.Interface( | |
| generate_image, | |
| inputs=[seed, num_ar_steps, class_labels, cfg_scale, cfg_schedule], | |
| outputs=image_output, | |
| # examples=[ | |
| # [0, 64, "207, 360, 388, 113, 355, 980, 323, 979", 4, "constant"], | |
| # ], | |
| # cache_examples=True | |
| ) | |
| demo.launch() | |