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| import gradio as gr | |
| import spaces | |
| import torch | |
| from clip_slider_pipeline import CLIPSliderXL | |
| from diffusers import StableDiffusionXLPipeline, EulerDiscreteScheduler, AutoencoderKL | |
| import time | |
| import numpy as np | |
| vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16) | |
| pipe = StableDiffusionXLPipeline.from_pretrained("sd-community/sdxl-flash", vae=vae).to("cuda", torch.float16) | |
| pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config) | |
| clip_slider = CLIPSliderXL(pipe, device=torch.device("cuda")) | |
| pipe_adapter = StableDiffusionXLPipeline.from_pretrained("sd-community/sdxl-flash").to("cuda", torch.float16) | |
| pipe_adapter.scheduler = EulerDiscreteScheduler.from_config(pipe_adapter.scheduler.config) | |
| #pipe_adapter.load_ip_adapter("h94/IP-Adapter", subfolder="sdxl_models", weight_name="ip-adapter_sdxl.bin") | |
| # scale = 0.8 | |
| # pipe_adapter.set_ip_adapter_scale(scale) | |
| clip_slider_ip = CLIPSliderXL(sd_pipe=pipe_adapter, | |
| device=torch.device("cuda")) | |
| def generate(slider_x, slider_y, prompt, seed, iterations, steps, | |
| x_concept_1, x_concept_2, y_concept_1, y_concept_2, | |
| avg_diff_x_1, avg_diff_x_2, | |
| avg_diff_y_1, avg_diff_y_2): | |
| start_time = time.time() | |
| # check if avg diff for directions need to be re-calculated | |
| print("slider_x", slider_x) | |
| print("x_concept_1", x_concept_1, "x_concept_2", x_concept_2) | |
| if not sorted(slider_x) == sorted([x_concept_1, x_concept_2]): | |
| avg_diff = clip_slider.find_latent_direction(slider_x[0], slider_x[1], num_iterations=iterations) | |
| avg_diff[0].to(torch.float16) | |
| avg_diff[1].to(torch.float16) | |
| x_concept_1, x_concept_2 = slider_x[0], slider_x[1] | |
| print("avg_diff[0].dtype", avg_diff[0].dtype) | |
| if not sorted(slider_y) == sorted([y_concept_1, y_concept_2]): | |
| avg_diff_2nd = clip_slider.find_latent_direction(slider_y[0], slider_y[1], num_iterations=iterations) | |
| avg_diff_2nd[0].to(torch.float16) | |
| avg_diff_2nd[1].to(torch.float16) | |
| y_concept_1, y_concept_2 = slider_y[0], slider_y[1] | |
| end_time = time.time() | |
| print(f"direction time: {end_time - start_time:.2f} ms") | |
| start_time = time.time() | |
| image = clip_slider.generate(prompt, scale=0, scale_2nd=0, seed=seed, num_inference_steps=steps, avg_diff=avg_diff, avg_diff_2nd=avg_diff_2nd) | |
| end_time = time.time() | |
| print(f"generation time: {end_time - start_time:.2f} ms") | |
| comma_concepts_x = ', '.join(slider_x) | |
| comma_concepts_y = ', '.join(slider_y) | |
| avg_diff_x_1 = avg_diff[0].cpu() | |
| avg_diff_x_2 = avg_diff[1].cpu() | |
| avg_diff_y_1 = avg_diff_2nd[0].cpu() | |
| avg_diff_y_2 = avg_diff_2nd[1].cpu() | |
| 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, avg_diff_x_1, avg_diff_x_2, avg_diff_y_1, avg_diff_y_2, image | |
| def update_x(x,y,prompt, seed, steps, avg_diff_x_1, avg_diff_x_2, avg_diff_y_1, avg_diff_y_2): | |
| avg_diff = (avg_diff_x_1.cuda(), avg_diff_x_2.cuda()) | |
| avg_diff_2nd = (avg_diff_y_1.cuda(), avg_diff_y_2.cuda()) | |
| image = clip_slider.generate(prompt, scale=x, scale_2nd=y, seed=seed, num_inference_steps=steps, avg_diff=avg_diff,avg_diff_2nd=avg_diff_2nd) | |
| return image | |
| def update_y(x,y,prompt, seed, steps, avg_diff_x_1, avg_diff_x_2, avg_diff_y_1, avg_diff_y_2): | |
| avg_diff = (avg_diff_x_1.cuda(), avg_diff_x_2.cuda()) | |
| avg_diff_2nd = (avg_diff_y_1.cuda(), avg_diff_y_2.cuda()) | |
| image = clip_slider.generate(prompt, scale=x, scale_2nd=y, seed=seed, num_inference_steps=steps, avg_diff=avg_diff,avg_diff_2nd=avg_diff_2nd) | |
| 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("") | |
| avg_diff_x_1 = gr.State() | |
| avg_diff_x_2 = gr.State() | |
| avg_diff_y_1 = gr.State() | |
| avg_diff_y_2 = gr.State() | |
| with gr.Tab(""): | |
| 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") | |
| with gr.Accordion(label="advanced options", open=False): | |
| iterations = gr.Slider(label = "num iterations", minimum=0, value=100, maximum=300) | |
| steps = gr.Slider(label = "num inference steps", minimum=1, value=8, maximum=30) | |
| seed = gr.Slider(minimum=0, maximum=np.iinfo(np.int32).max, label="Seed", interactive=True, randomize=True) | |
| with gr.Tab(label="image2image"): | |
| with gr.Row(): | |
| with gr.Column(): | |
| image = gr.ImageEditor(type="pil", image_mode="L", crop_size=(512, 512)) | |
| slider_x_a = gr.Dropdown(label="Slider X concept range", allow_custom_value=True, multiselect=True, max_choices=2) | |
| slider_y_a = gr.Dropdown(label="Slider X concept range", allow_custom_value=True, multiselect=True, max_choices=2) | |
| prompt_a = gr.Textbox(label="Prompt") | |
| submit_a = gr.Button("Submit") | |
| with gr.Group(elem_id="group"): | |
| x_a = gr.Slider(minimum=-10, value=0, maximum=10, elem_id="x", interactive=False) | |
| y_a = gr.Slider(minimum=-10, value=0, maximum=10, elem_id="y", interactive=False) | |
| output_image_a = gr.Image(elem_id="image_out") | |
| with gr.Accordion(label="advanced options", open=False): | |
| iterations_a = gr.Slider(label = "num iterations", minimum=0, value=100, maximum=300) | |
| steps_a = gr.Slider(label = "num inference steps", minimum=1, value=8, maximum=30) | |
| seed_a = gr.Slider(minimum=0, maximum=np.iinfo(np.int32).max, label="Seed", interactive=True, randomize=True) | |
| submit.click(fn=generate, | |
| inputs=[slider_x, slider_y, prompt, seed, iterations, steps, x_concept_1, x_concept_2, y_concept_1, y_concept_2, avg_diff_x_1, avg_diff_x_2, avg_diff_y_1, avg_diff_y_2], | |
| outputs=[x, y, x_concept_1, x_concept_2, y_concept_1, y_concept_2, avg_diff_x_1, avg_diff_x_2, avg_diff_y_1, avg_diff_y_2, output_image]) | |
| x.change(fn=update_x, inputs=[x,y, prompt, seed, steps, avg_diff_x_1, avg_diff_x_2, avg_diff_y_1, avg_diff_y_2], outputs=[output_image]) | |
| y.change(fn=update_y, inputs=[x,y, prompt, seed, steps, avg_diff_x_1, avg_diff_x_2, avg_diff_y_1, avg_diff_y_2], outputs=[output_image]) | |
| submit_a.click(fn=generate, | |
| inputs=[slider_x_a, slider_y_a, prompt_a, seed_a, iterations_a, steps_a, x_concept_1, x_concept_2, y_concept_1, y_concept_2, avg_diff_x_1, avg_diff_x_2, avg_diff_y_1, avg_diff_y_2], | |
| outputs=[x_a, y_a, x_concept_1, x_concept_2, y_concept_1, y_concept_2, avg_diff_x_1, avg_diff_x_2, avg_diff_y_1, avg_diff_y_2, output_image_a]) | |
| x_a.change(fn=update_x, inputs=[x_a,y_a, prompt_a, seed_a, steps_a, avg_diff_x_1, avg_diff_x_2, avg_diff_y_1, avg_diff_y_2], outputs=[output_image_a]) | |
| y_a.change(fn=update_y, inputs=[x_a,y_a, prompt, seed_a, steps_a, avg_diff_x_1, avg_diff_x_2, avg_diff_y_1, avg_diff_y_2], outputs=[output_image_a]) | |
| if __name__ == "__main__": | |
| demo.launch() |