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import os |
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import spaces |
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import gradio as gr |
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import torch |
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import yaml |
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import numpy as np |
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from munch import munchify |
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import torchvision.transforms as transforms |
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from torchvision.transforms import functional as F |
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from diffusers import ( |
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AutoPipelineForInpainting, |
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) |
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from generate_dataset import outpainting_generator_rectangle, merge_images_horizontally |
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from ddim_with_prob import DDIMSchedulerCustom |
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transform = transforms.Compose([ |
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transforms.ToPILImage(), |
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transforms.Resize((512, 512), interpolation=F.InterpolationMode.LANCZOS), |
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]) |
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@spaces.GPU(duration=120) |
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def pref_inpainting(image, |
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box_width_ratio, |
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mask_random_start, |
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steps, |
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): |
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with open("./configs/paintreward_train_configs.yaml") as file: |
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config_dict= yaml.safe_load(file) |
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config = munchify(config_dict) |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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print(f"Current Device is {device}") |
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pipe_ours = AutoPipelineForInpainting.from_pretrained( |
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'./model_ckpt', torch_dtype=torch.float16, variant='fp16') |
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pipe_ours.scheduler = DDIMSchedulerCustom.from_config(pipe_ours.scheduler.config) |
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pipe_runway = AutoPipelineForInpainting.from_pretrained("runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16, variant='fp16') |
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pipe_ours = pipe_ours.to(device) |
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pipe_runway = pipe_runway.to(device) |
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print('Loading pipeline') |
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color, mask = outpainting_generator_rectangle(image, box_width_ratio/100, mask_random_start) |
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mask = mask.convert('L') |
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color, mask = np.array(color).transpose(2, 0, 1), np.array(mask) |
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mask = mask[None, ...] |
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mask_ = np.zeros_like(mask) |
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mask_[mask < 125] = 0 |
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mask_[mask >= 125] = 1 |
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color = torch.from_numpy(color).to(device) |
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mask = torch.from_numpy(mask).to(device) |
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color, mask = transform(color), transform(mask) |
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res_ours = pipe_ours(prompt='', image=color, mask_image=mask, eta=config.eta).images[0] |
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print('Running inference ours') |
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res_runway = pipe_runway(prompt="", image=color, mask_image=mask).images[0] |
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print('Running inference runway') |
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res_ours = merge_images_horizontally(color, res_ours, logo_path='./logo/pref_logo.png') |
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res_runway = merge_images_horizontally(color, res_runway, logo_path='./logo/runway_logo.png') |
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return res_ours, res_runway |
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inputs = [ |
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gr.Image(type="pil", image_mode="RGBA", label='Input Image'), |
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gr.Slider(25, 45, value=35, step=1, label="box_width_ratio"), |
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gr.Slider(0, 256, value=125, step=1, label="mask_random_start"), |
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gr.Slider(30, 100, value=50, step=5, label="steps"), |
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] |
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outputs = [ |
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gr.Image(type="pil", image_mode="RGBA", label='PrefPaint', container=True, width="100%"), |
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gr.Image(type="pil", image_mode="RGBA", label='RunwayPaint', container=True, width="100%"), |
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] |
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files = os.listdir("./assets") |
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examples = [ |
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[f"./assets/{file_name}", 25, 125, 50] for file_name in files |
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] |
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with gr.Blocks() as demo: |
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iface = gr.Interface( |
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fn=pref_inpainting, |
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inputs=inputs, |
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outputs=outputs, |
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title="Inpainting with Human Preference (Only one GPU is available, you may need to queue.)", |
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description="Upload an image and start your inpainting (Currently, only outpainting masks are supported; other mask types will be available soon.).", |
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theme="default", |
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examples=examples, |
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) |
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demo.launch() |
