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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"))
@spaces.GPU
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 = avg_diff[0].to(torch.float16)
avg_diff_1 = avg_diff[1].to(torch.float16)
x_concept_1, x_concept_2 = slider_x[0], slider_x[1]
print("avg_diff_0", 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 = avg_diff_2nd[0].to(torch.float16)
avg_diff_2nd_1 = 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_0,avg_diff_1), avg_diff_2nd=(avg_diff_2nd_0,avg_diff_2nd_1))
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
@spaces.GPU
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
@spaces.GPU
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()