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Running
on
Zero
Running
on
Zero
| import gradio as gr | |
| import spaces | |
| import torch | |
| from clip_slider_pipeline import CLIPSliderXL | |
| from diffusers import StableDiffusionXLPipeline, EulerDiscreteScheduler | |
| flash_pipe = StableDiffusionXLPipeline.from_pretrained("sd-community/sdxl-flash").to("cuda", torch.float16) | |
| flash_pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config) | |
| clip_slider = CLIPSliderXL(flash_pipe, device=torch.device("cuda")) | |
| def generate(slider_x, slider_y, prompt, x_concept_1, x_concept_2, y_concept_1, y_concept_2): | |
| # check if avg diff for directions need to be re-calculated | |
| if not sorted(slider_x) == sorted([x_concept_1, x_concept_2]): | |
| clip_slider.avg_diff = clip_slider.find_latent_direction(slider_x[0], slider_x[0]) | |
| x_concept_1, x_concept_2 = slider_x[0], slider_x[1] | |
| if not sorted(slider_y) == sorted([y_concept_1, y_concept_2]): | |
| clip_slider.avg_diff_2nd = clip_slider.find_latent_direction(slider_y[0], slider_y[0]) | |
| y_concept_1, y_concept_2 = slider_y[0], slider_y[1] | |
| comma_concepts_x = ', '.join(slider_x) | |
| comma_concepts_y = ', '.join(slider_y) | |
| image = clip_slider(prompt, scale=0, scale_2nd=0, num_inference_steps=8) | |
| return gr.update(label=comma_concepts_x, interactive=True),gr.update(label=comma_concepts_y, interactive=True), x_concept_1, x_concept_2, y_concept_1, y_concept_2, image | |
| def update_x(x,y,prompt): | |
| image = clip_slider(prompt, scale=x, scale_2nd=y, num_inference_steps=8) | |
| return image | |
| def update_y(x,y,prompt): | |
| image = clip_slider(prompt, scale=x, scale_2nd=y, num_inference_steps=8) | |
| return image | |
| css = ''' | |
| #group { | |
| position: relative; | |
| width: 420px; | |
| height: 420px; | |
| margin-bottom: 20px; | |
| background-color: white | |
| } | |
| #x { | |
| position: absolute; | |
| bottom: 0; | |
| left: 25px; | |
| width: 400px; | |
| } | |
| #y { | |
| position: absolute; | |
| bottom: 20px; | |
| left: 67px; | |
| width: 400px; | |
| transform: rotate(-90deg); | |
| transform-origin: left bottom; | |
| } | |
| #image_out{position:absolute; width: 80%; right: 10px; top: 40px} | |
| ''' | |
| with gr.Blocks(css=css) as demo: | |
| x_concept_1 = gr.State("") | |
| x_concept_2 = gr.State("") | |
| y_concept_1 = gr.State("") | |
| y_concept_2 = gr.State("") | |
| with gr.Row(): | |
| with gr.Column(): | |
| slider_x = gr.Dropdown(label="Slider X concept range", allow_custom_value=True, multiselect=True, max_choices=2) | |
| slider_y = gr.Dropdown(label="Slider X concept range", allow_custom_value=True, multiselect=True, max_choices=2) | |
| prompt = gr.Textbox(label="Prompt") | |
| submit = gr.Button("Submit") | |
| with gr.Group(elem_id="group"): | |
| x = gr.Slider(minimum=-10, value=0, maximum=10, elem_id="x", interactive=False) | |
| y = gr.Slider(minimum=-10, value=0, maximum=10, elem_id="y", interactive=False) | |
| output_image = gr.Image(elem_id="image_out") | |
| submit.click(fn=generate, | |
| inputs=[slider_x, slider_y, prompt, x_concept_1, x_concept_2, y_concept_1, y_concept_2], | |
| outputs=[x, y, x_concept_1, x_concept_2, y_concept_1, y_concept_2, output_image]) | |
| x.change(fn=update_x, inputs=[x,y, prompt], outputs=[output_image]) | |
| y.change(fn=update_y, inputs=[x,y, prompt], outputs=[output_image]) | |
| if __name__ == "__main__": | |
| demo.launch() |