import gradio as gr
import spaces
from clip_slider_pipeline import CLIPSliderFlux
from diffusers import FluxPipeline, AutoencoderTiny
import torch
import numpy as np
import cv2
from PIL import Image
from diffusers.utils import load_image
from diffusers.pipelines.flux.pipeline_flux_controlnet import FluxControlNetPipeline
from diffusers.models.controlnet_flux import FluxControlNetModel
from diffusers.utils import export_to_gif
def process_controlnet_img(image):
controlnet_img = np.array(image)
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
# load pipelines
taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=torch.bfloat16).to("cuda")
pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell",
vae=taef1,
torch_dtype=torch.bfloat16)
pipe.transformer.to(memory_format=torch.channels_last)
pipe.transformer = torch.compile(pipe.transformer, mode="max-autotune", fullgraph=True)
#pipe.enable_model_cpu_offload()
clip_slider = CLIPSliderFlux(pipe, device=torch.device("cuda"))
base_model = 'black-forest-labs/FLUX.1-schnell'
controlnet_model = 'InstantX/FLUX.1-dev-Controlnet-Canny-alpha'
# controlnet = FluxControlNetModel.from_pretrained(controlnet_model, torch_dtype=torch.bfloat16)
# pipe_controlnet = FluxControlNetPipeline.from_pretrained(base_model, controlnet=controlnet, torch_dtype=torch.bfloat16)
# t5_slider_controlnet = T5SliderFlux(sd_pipe=pipe_controlnet,device=torch.device("cuda"))
def convert_to_centered_scale(num):
if num <= 0:
raise ValueError("Input must be a positive integer")
if num % 2 == 0: # even
start = -(num // 2 - 1)
end = num // 2
else: # odd
start = -(num // 2)
end = num // 2
return tuple(range(start, end + 1))
@spaces.GPU(duration=200)
def generate(concept_1, concept_2, scale, prompt, seed=42, recalc_directions=True, iterations=200, steps=4, interm_steps=9, guidance_scale=3.5,
x_concept_1="", x_concept_2="",
avg_diff_x=None,
img2img_type = None, img = None,
controlnet_scale= None, ip_adapter_scale=None,
total_images=[],
progress=gr.Progress(track_tqdm=True)
):
slider_x = [concept_1, concept_2]
# 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)
#torch.manual_seed(seed)
if not sorted(slider_x) == sorted([x_concept_1, x_concept_2]) or recalc_directions:
#avg_diff = clip_slider.find_latent_direction(slider_x[0], slider_x[1], num_iterations=iterations).to(torch.float16)
avg_diff = clip_slider.find_latent_direction(slider_x[0], slider_x[1], num_iterations=iterations)
x_concept_1, x_concept_2 = slider_x[0], slider_x[1]
images = []
high_scale = scale
low_scale = -1 * scale
for i in range(interm_steps):
cur_scale = low_scale + (high_scale - low_scale) * i / (interm_steps - 1)
image = clip_slider.generate(prompt,
width=768,
height=768,
#guidance_scale=guidance_scale,
scale=cur_scale, seed=seed, num_inference_steps=steps, avg_diff=avg_diff)
images.append(image)
canvas = Image.new('RGB', (256*interm_steps, 256))
for i, im in enumerate(images):
canvas.paste(im.resize((256,256)), (256 * i, 0))
comma_concepts_x = f"{slider_x[1]}, {slider_x[0]}"
scale_total = convert_to_centered_scale(interm_steps)
scale_min = scale_total[0]
scale_max = scale_total[-1]
scale_middle = scale_total.index(0)
post_generation_slider_update = gr.update(label=comma_concepts_x, value=0, minimum=scale_min, maximum=scale_max, interactive=True)
avg_diff_x = avg_diff.cpu()
return gr.update(label=comma_concepts_x, interactive=True, value=scale), x_concept_1, x_concept_2, avg_diff_x, export_to_gif(images, "clip.gif", fps=5), canvas, images, images[scale_middle], post_generation_slider_update
@spaces.GPU
def update_scales(x,prompt,seed, steps, interm_steps, guidance_scale,
avg_diff_x,
img2img_type = None, img = None,
controlnet_scale= None, ip_adapter_scale=None, total_images=[], progress=gr.Progress(track_tqdm=True)):
print("Hola", x)
avg_diff = avg_diff_x.cuda()
# for spectrum generation
images = []
high_scale = x
low_scale = -1 * x
if img2img_type=="controlnet canny" and img is not None:
control_img = process_controlnet_img(img)
image = t5_slider_controlnet.generate(prompt, guidance_scale=guidance_scale, image=control_img, controlnet_conditioning_scale =controlnet_scale, scale=x, seed=seed, num_inference_steps=steps, avg_diff=avg_diff)
elif img2img_type=="ip adapter" and img is not None:
image = clip_slider.generate(prompt, guidance_scale=guidance_scale, ip_adapter_image=img, scale=x,seed=seed, num_inference_steps=steps, avg_diff=avg_diff)
else:
for i in range(interm_steps):
cur_scale = low_scale + (high_scale - low_scale) * i / (steps - 1)
image = clip_slider.generate(prompt,
width=768,
height=768,
#guidance_scale=guidance_scale,
scale=cur_scale, seed=seed, num_inference_steps=steps, avg_diff=avg_diff)
images.append(image)
canvas = Image.new('RGB', (256*interm_steps, 256))
for i, im in enumerate(images):
canvas.paste(im.resize((256,256)), (256 * i, 0))
scale_total = convert_to_centered_scale(interm_steps)
scale_min = scale_total[0]
scale_max = scale_total[-1]
scale_middle = scale_total.index(0)
post_generation_slider_update = gr.update(minimum=scale_min, maximum=scale_max, visible=True)
return export_to_gif(images, "clip.gif", fps=5), canvas, images, images[scale_middle], post_generation_slider_update
def update_pre_generated_images(slider_value, total_images):
number_images = len(total_images)
if(number_images > 0):
scale_tuple = convert_to_centered_scale(number_images)
return total_images[scale_tuple.index(slider_value)]
else:
return None
def reset_recalc_directions():
return True
css_old = '''
#group {
position: relative;
width: 600px; /* Increased width */
height: 600px; /* Increased height */
margin-bottom: 20px;
background-color: white;
}
#x {
position: absolute;
bottom: 20px; /* Moved further down */
left: 30px; /* Adjusted left margin */
width: 540px; /* Increased width to match the new container size */
}
#y {
position: absolute;
bottom: 200px; /* Increased bottom margin to ensure proper spacing from #x */
left: 20px; /* Adjusted left margin */
width: 540px; /* Increased width to match the new container size */
transform: rotate(-90deg);
transform-origin: left bottom;
}
#image_out {
position: absolute;
width: 80%; /* Adjust width as needed */
right: 10px;
top: 10px; /* Increased top margin to clear space occupied by #x */
}
'''
intro = """
Latent Navigation
Exploring CLIP text space with FLUX.1 schnell 🪐
code
|
"""
css='''
#strip, #gif{min-height: 50px}
'''
examples = [["winter", "summer", 1.25, "a dog in the park"]]
image_seq = gr.Image(label="Strip", elem_id="strip")
output_image = gr.Image(label="Gif", elem_id="gif")
post_generation_image = gr.Image(label="Generated Images")
post_generation_slider = gr.Slider(minimum=-2, maximum=2, value=0, step=1, interactive=False)
with gr.Blocks(css=css) as demo:
gr.HTML(intro)
x_concept_1 = gr.State("")
x_concept_2 = gr.State("")
total_images = gr.State([])
# y_concept_1 = gr.State("")
# y_concept_2 = gr.State("")
avg_diff_x = gr.State()
#avg_diff_y = gr.State()
recalc_directions = gr.State(False)
#with gr.Tab("text2image"):
with gr.Row():
with gr.Column():
with gr.Row():
concept_1 = gr.Textbox(label="1st concept to compare", placeholder="winter")
concept_2 = gr.Textbox(label="2nd concept to compare", placeholder="summer")
#slider_x = gr.Dropdown(label="Slider concept range", allow_custom_value=True, multiselect=True, max_choices=2)
#slider_y = gr.Dropdown(label="Slider Y concept range", allow_custom_value=True, multiselect=True, max_choices=2)
prompt = gr.Textbox(label="Prompt", placeholder="A dog in the park")
x = gr.Slider(minimum=0, value=1.25, step=0.1, maximum=2.5, label="Strength", info="maximum strength on each direction")
submit = gr.Button("Generate directions")
gr.Examples(
examples=examples,
inputs=[concept_1, concept_2, x, prompt],
fn=generate,
outputs=[x, x_concept_1, x_concept_2, avg_diff_x, output_image, image_seq, total_images, post_generation_image, post_generation_slider],
cache_examples="lazy"
)
with gr.Column():
with gr.Group(elem_id="group"):
post_generation_image.render()
post_generation_slider.render()
#y = gr.Slider(minimum=-10, value=0, maximum=10, elem_id="y", interactive=False)
with gr.Row():
with gr.Column(scale=4, min_width=50):
image_seq.render()
with gr.Column(scale=2, min_width=50):
output_image.render()
# with gr.Row():
# generate_butt = gr.Button("generate")
with gr.Accordion(label="advanced options", open=False):
iterations = gr.Slider(label = "num iterations", minimum=0, value=200, maximum=400)
steps = gr.Slider(label = "num inference steps", minimum=1, value=4, maximum=10)
interm_steps = gr.Slider(label = "num of intermediate images", minimum=1, value=5, maximum=65)
guidance_scale = gr.Slider(
label="Guidance scale",
minimum=0.1,
maximum=10.0,
step=0.1,
value=5,
)
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)
# img2img_type = gr.Radio(["controlnet canny", "ip adapter"], label="", info="", visible=False, value="controlnet canny")
# prompt_a = gr.Textbox(label="Prompt")
# submit_a = gr.Button("Submit")
# with gr.Column():
# 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.Row():
# generate_butt_a = gr.Button("generate")
# with gr.Accordion(label="advanced options", open=False):
# iterations_a = gr.Slider(label = "num iterations", minimum=0, value=200, maximum=300)
# steps_a = gr.Slider(label = "num inference steps", minimum=1, value=8, maximum=30)
# guidance_scale_a = gr.Slider(
# label="Guidance scale",
# minimum=0.1,
# maximum=10.0,
# step=0.1,
# value=5,
# )
# controlnet_conditioning_scale = gr.Slider(
# label="controlnet conditioning scale",
# minimum=0.5,
# maximum=5.0,
# step=0.1,
# value=0.7,
# )
# ip_adapter_scale = gr.Slider(
# label="ip adapter scale",
# minimum=0.5,
# maximum=5.0,
# step=0.1,
# value=0.8,
# visible=False
# )
# 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, guidance_scale, x_concept_1, x_concept_2, y_concept_1, y_concept_2, avg_diff_x, avg_diff_y],
# outputs=[x, y, x_concept_1, x_concept_2, y_concept_1, y_concept_2, avg_diff_x, avg_diff_y, output_image])
submit.click(fn=generate,
inputs=[concept_1, concept_2, x, prompt, seed, recalc_directions, iterations, steps, interm_steps, guidance_scale, x_concept_1, x_concept_2, avg_diff_x, total_images],
outputs=[x, x_concept_1, x_concept_2, avg_diff_x, output_image, image_seq, total_images, post_generation_image, post_generation_slider])
iterations.change(fn=reset_recalc_directions, outputs=[recalc_directions])
seed.change(fn=reset_recalc_directions, outputs=[recalc_directions])
#x.release(fn=update_scales, inputs=[x, prompt, seed, steps, interm_steps, guidance_scale, avg_diff_x, total_images], outputs=[output_image, image_seq, total_images, post_generation_image, post_generation_slider], trigger_mode='always_last')
# generate_butt_a.click(fn=update_scales, inputs=[x_a,y_a, prompt_a, seed_a, steps_a, guidance_scale_a, avg_diff_x, avg_diff_y, img2img_type, image, controlnet_conditioning_scale, ip_adapter_scale], outputs=[output_image_a])
# submit_a.click(fn=generate,
# inputs=[slider_x_a, slider_y_a, prompt_a, seed_a, iterations_a, steps_a, guidance_scale_a, x_concept_1, x_concept_2, y_concept_1, y_concept_2, avg_diff_x, avg_diff_y, img2img_type, image, controlnet_conditioning_scale, ip_adapter_scale],
# outputs=[x_a, y_a, x_concept_1, x_concept_2, y_concept_1, y_concept_2, avg_diff_x, avg_diff_y, output_image_a])
post_generation_slider.change(fn=update_pre_generated_images, inputs=[post_generation_slider, total_images], outputs=[post_generation_image], queue=False, show_progress="hidden", concurrency_limit=None)
if __name__ == "__main__":
demo.launch()