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import gradio as gr
from PIL import Image
import torch
from diffusers import StableDiffusionPipeline
from free_lunch_utils import register_free_upblock2d, register_free_crossattn_upblock2d
# if sd_options == 'SD1.5':
# model = "runwayml/stable-diffusion-v1-5"
# elif sd_options == 'SD2.1':
# model = "stabilityai/stable-diffusion-2-1"
# else:
# model = "CompVis/stable-diffusion-v1-4"
torch.manual_seed(42)
model_id = "CompVis/stable-diffusion-v1-4"
# pip_sd = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
# pip_sd = pip_sd.to("cuda")
# pip_freeu = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
# pip_freeu = pip_freeu.to("cuda")
# # -------- freeu block registration
# register_free_upblock2d(pip_freeu, b1=1.2, b2=1.4, s1=0.9, s2=0.2)
# register_free_crossattn_upblock2d(pip_freeu, b1=1.2, b2=1.4, s1=0.9, s2=0.2)
# # -------- freeu block registration
model_id = "CompVis/stable-diffusion-v1-4"
pip_1_4 = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pip_1_4 = pip_1_4.to("cuda")
model_id = "runwayml/stable-diffusion-v1-5"
pip_1_5 = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pip_1_5 = pip_1_5.to("cuda")
model_id = "stabilityai/stable-diffusion-2-1"
pip_2_1 = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pip_2_1 = pip_2_1.to("cuda")
def infer(prompt, sd_options, seed, b1, b2, s1, s2):
if sd_options == 'SD1.5':
pip = pip_1_5
elif sd_options == 'SD2.1':
pip = pip_2_1
else:
pip = pip_1_4
register_free_upblock2d(pip, b1=1.0, b2=1.0, s1=1.0, s2=1.0)
register_free_crossattn_upblock2d(pip, b1=1.0, b2=1.0, s1=1.0, s2=1.0)
torch.manual_seed(seed)
print("Generating SD:")
sd_image = pip(prompt, num_inference_steps=25).images[0]
register_free_upblock2d(pip, b1=b1, b2=b2, s1=s1, s2=s1)
register_free_crossattn_upblock2d(pip, b1=b1, b2=b2, s1=s1, s2=s1)
torch.manual_seed(seed)
print("Generating FreeU:")
freeu_image = pip(prompt, num_inference_steps=25).images[0]
# First SD, then freeu
images = [sd_image, freeu_image]
return images
examples = [
[
"A small cabin on top of a snowy mountain in the style of Disney, artstation",
],
[
"a monkey doing yoga on the beach",
],
[
"half human half cat, a human cat hybrid",
],
[
"a hedgehog using a calculator",
],
[
"kanye west | diffuse lighting | fantasy | intricate elegant highly detailed lifelike photorealistic digital painting | artstation",
],
[
"astronaut pig",
],
[
"two people shouting at each other",
],
[
"A linked in profile picture of Elon Musk",
],
[
"A man looking out of a rainy window",
],
[
"close up, iron man, eating breakfast in a cabin, symmetrical balance, hyper-realistic --ar 16:9 --style raw"
],
[
'A high tech solarpunk utopia in the Amazon rainforest',
],
[
'A pikachu fine dining with a view to the Eiffel Tower',
],
[
'A mecha robot in a favela in expressionist style',
],
[
'an insect robot preparing a delicious meal',
],
]
css = """
h1 {
text-align: center;
}
#component-0 {
max-width: 730px;
margin: auto;
}
"""
block = gr.Blocks(css='style.css')
options = ['SD1.4', 'SD1.5', 'SD2.1']
with block:
gr.Markdown("SD vs. FreeU.")
with gr.Group():
with gr.Row():
sd_options = gr.Dropdown(["SD1.4", "SD1.5", "SD2.1"], label="SD options")
# if sd_options == 'SD1.5':
# sd = 1.5
# elif sd_options == 'SD2.1':
# sd = 2.1
# else:
# sd = 1.4
# pip = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
# pip = pip.to("cuda")
with gr.Row():
with gr.Column():
text = gr.Textbox(
label="Enter your prompt",
show_label=False,
max_lines=1,
placeholder="Enter your prompt",
container=False,
)
btn = gr.Button("Generate image", scale=0)
seed = gr.Slider(label='seed',
minimum=0,
maximum=1000,
step=1,
value=42)
with gr.Group():
with gr.Row():
with gr.Accordion('FreeU Parameters: b', open=True):
b1 = gr.Slider(label='b1: backbone factor of the first stage block of decoder',
minimum=1,
maximum=1.6,
step=0.1,
value=1)
b2 = gr.Slider(label='b2: backbone factor of the second stage block of decoder',
minimum=1,
maximum=1.6,
step=0.1,
value=1)
with gr.Accordion('FreeU Parameters: s', open=True):
s1 = gr.Slider(label='s1: skip factor of the first stage block of decoder',
minimum=0,
maximum=1,
step=0.1,
value=1)
s2 = gr.Slider(label='s2: skip factor of the second stage block of decoder',
minimum=0,
maximum=1,
step=0.1,
value=1)
with gr.Row():
with gr.Group():
# btn = gr.Button("Generate image", scale=0)
with gr.Row():
with gr.Column() as c1:
image_1 = gr.Image(interactive=False)
image_1_label = gr.Markdown("SD")
with gr.Group():
# btn = gr.Button("Generate image", scale=0)
with gr.Row():
with gr.Column() as c2:
image_2 = gr.Image(interactive=False)
image_2_label = gr.Markdown("FreeU")
ex = gr.Examples(examples=examples, fn=infer, inputs=[text, sd_options, seed, b1, b2, s1, s2], outputs=[image_1, image_2], cache_examples=False)
ex.dataset.headers = [""]
text.submit(infer, inputs=[text, sd_options, seed, b1, b2, s1, s2], outputs=[image_1, image_2])
btn.click(infer, inputs=[text, sd_options, seed, b1, b2, s1, s2], outputs=[image_1, image_2])
block.launch()
# block.queue(default_enabled=False).launch(share=False)
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