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| import gradio as gr | |
| from src.process import * | |
| THEME=gr.themes.Default(font=[gr.themes.GoogleFont("IBM Plex Mono"), "ui-monospace","monospace"], | |
| primary_hue="lime", | |
| secondary_hue="emerald", | |
| neutral_hue="stone", | |
| ) | |
| def create_demo(): | |
| with gr.Blocks(theme=THEME) as demo: | |
| gr.Markdown('# 🎭 theaTRON') | |
| gr.Markdown('<img src="file/data/image-only.png">') | |
| gr.Markdown('Type what you want to see and the app will create images with the faces in the input while preserving body pose. Should work for multiple humans too!') | |
| prompt = gr.Textbox(value="Astronauts, photograph inside the ISS International Space Station corridor", label="Prompt:") | |
| n_prompt = gr.Textbox(value="", label="Negative Prompt: Avoid these features in the image...") | |
| with gr.Group(): | |
| with gr.Box(): | |
| with gr.Column(): | |
| with gr.Row() as main_blocks: | |
| with gr.Column() as input_step: | |
| gr.Markdown('# Input') | |
| with gr.Tab("Upload Image"): | |
| image_upload = gr.Image(source='upload', | |
| height=500, | |
| type='pil',#numpy', | |
| tool=None, | |
| elem_classes="image_upload", | |
| label='Image Upload') | |
| with gr.Tab("Webcam"): | |
| image_cam = gr.Image(source='webcam', | |
| height=500, | |
| type='pil',#numpy', | |
| tool=None, | |
| elem_classes="image_upload", | |
| label='Webcam') | |
| send_button = gr.Button(label='Generate', value='Generate') | |
| with gr.Column() as output_step: | |
| gr.Markdown('# Output') | |
| output_image = gr.Gallery(label="Generated images", | |
| show_label=False, | |
| preview=True, | |
| elem_id="output_image", | |
| object_fit="contain", | |
| height="auto") | |
| with gr.Accordion('Settings', open=False): | |
| num_steps = gr.Slider(label='Steps', | |
| minimum=1, | |
| maximum=100, | |
| value=25, | |
| step=1) | |
| original_resolution=gr.Checkbox(value=False, | |
| label="Preserve Resolution", | |
| info="Prevent Downscaling to 512 pixels (default)") | |
| seed = gr.Slider(label='Seed', | |
| minimum=-1, | |
| maximum=2147483647, | |
| step=1, | |
| randomize=True) | |
| with gr.Accordion('How does it work?', open=False): | |
| gr.Markdown('This demo was created by Mikolaj Czerkawski [@mikonvergence](https://twitter.com/mikonvergence) based on several 🌱 open-source tools. It only puts together existing models, so in some sense, nothing new here!') | |
| gr.Markdown('## Pipeline Details') | |
| gr.Markdown('### Step 1: Face Detection') | |
| gr.Markdown('I use the model from kornia for face detection, since at the time of release, Segment Anything does not yet work with text prompts. Thank you, kornia team! https://kornia.readthedocs.io/en/latest/applications/face_detection.html') | |
| gr.Markdown('### Step 2: Segment Anything') | |
| gr.Markdown('I use Segment Anything from Meta (via HF transformers library) to segment the face based on the detection points. https://huggingface.co/docs/transformers/main/model_doc/sam') | |
| gr.Markdown('### Step 3: Mask Post-processing') | |
| gr.Markdown('The mask is blurred to achieve a smoother blend when recomposing the photo. Also, if a separate mask is used for the top of the head and the face, then the potential space between the two masks is morphologically filled.') | |
| gr.Markdown('### Step 4: ControlNetInpaint') | |
| gr.Markdown('Finally, the resulting mask is used with the ControlNetInpaint tool [ControlNetInpaint](https://github.com/mikonvergence/ControlNetInpaint) and the pose guide.') | |
| gr.Markdown('---') | |
| gr.Markdown('# Learn More About Diffusion 💡') | |
| gr.Markdown('Check out my open-source ⏩[DiffusionFastForward](https://github.com/mikonvergence/DiffusionFastForward) course. It contains example code, executable notebooks, videos, notes, and a few use cases for training from scratch!') | |
| send_button.click(fn=forward, inputs=[image_cam, image_upload, prompt, n_prompt, num_steps, seed, original_resolution], outputs=[output_image]) | |
| return demo | |
| if __name__ == "__main__": | |
| demo = create_demo() | |
| demo.launch(debug=True, | |
| share=True) |