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import gradio as gr
from PIL import Image
import torch
from diffusers import DiffusionPipeline, AutoencoderTiny
import os
SAFETY_CHECKER = os.environ.get("SAFETY_CHECKER", None)
TORCH_COMPILE = os.environ.get("TORCH_COMPILE", None)
if SAFETY_CHECKER:
pipe = DiffusionPipeline.from_pretrained(
"SimianLuo/LCM_Dreamshaper_v7",
custom_pipeline="lcm_txt2img",
scheduler=None,
)
else:
pipe = DiffusionPipeline.from_pretrained(
"SimianLuo/LCM_Dreamshaper_v7",
custom_pipeline="lcm_txt2img",
scheduler=None,
safety_checker=None,
)
pipe.to(device="cuda", dtype=torch.float16)
pipe.vae = AutoencoderTiny.from_pretrained(
"madebyollin/taesd", device="cuda", torch_dtype=torch.float16
)
pipe.vae = pipe.vae.cuda()
pipe.unet.to(memory_format=torch.channels_last)
pipe.set_progress_bar_config(disable=True)
if TORCH_COMPILE:
pipe.text_encoder = torch.compile(pipe.text_encoder, mode="max-autotune")
pipe.tokenizer = torch.compile(pipe.tokenizer, mode="max-autotune")
pipe.unet = torch.compile(pipe.unet, mode="max-autotune")
pipe.vae = torch.compile(pipe.vae, mode="max-autotune")
def predict(prompt1, prompt2, merge_ratio, guidance, steps, sharpness, seed=1231231):
torch.manual_seed(seed)
img = pipe(
prompt1=prompt1,
prompt2=prompt2,
sv=merge_ratio,
sharpness=sharpness,
width=512,
height=512,
num_inference_steps=steps,
guidance_scale=guidance,
lcm_origin_steps=50,
output_type="pil",
return_dict=False,
)
return img
css="""
#container{
margin: 0 auto;
max-width: 80rem;
}
#intro{
max-width: 32rem;
text-align: center;
margin: 0 auto;
}
"""
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="container"):
gr.Markdown(
"""# SDZoom
Welcome to sdzoom, a testbed application designed for optimizing and experimenting with various
configurations to achieve the fastest Stable Diffusion (SD) pipelines.
RTSD leverages the expertise provided by Latent Consistency Models (LCM). For more information about LCM,
visit their website at [Latent Consistency Models](https://latent-consistency-models.github.io/).
""", elem_id="intro"
)
with gr.Row():
with gr.Column():
image = gr.Image(type="pil")
with gr.Column():
merge_ratio = gr.Slider(
value=50, minimum=1, maximum=100, step=1, label="Merge Ratio"
)
guidance = gr.Slider(
label="Guidance", minimum=1, maximum=50, value=10.0, step=0.01
)
steps = gr.Slider(label="Steps", value=4, minimum=2, maximum=20, step=1)
sharpness = gr.Slider(
value=1.0, minimum=0, maximum=1, step=0.001, label="Sharpness"
)
seed = gr.Slider(randomize=True, minimum=0, maximum=12013012031030, label="Seed")
prompt1 = gr.Textbox(label="Prompt 1")
prompt2 = gr.Textbox(label="Prompt 2")
generate_bt = gr.Button("Generate")
inputs = [prompt1, prompt2, merge_ratio, guidance, steps, sharpness, seed]
gr.Examples(
examples=[
["Elon Musk", "Mark Zuckerberg", 50, 10.0, 4, 1.0, 1231231],
["Elon Musk", "Bill Gates", 50, 10.0, 4, 1.0, 53453],
[
"Asian women, intricate jewlery in her hair, 8k",
"Tom Cruise, intricate jewlery in her hair, 8k",
50,
10.0,
4,
1.0,
542343,
],
],
fn=predict,
inputs=inputs,
outputs=image,
)
generate_bt.click(fn=predict, inputs=inputs, outputs=image, show_progress=False)
seed.change(fn=predict, inputs=inputs, outputs=image, show_progress=False)
merge_ratio.change(
fn=predict, inputs=inputs, outputs=image, show_progress=False
)
guidance.change(fn=predict, inputs=inputs, outputs=image, show_progress=False)
steps.change(fn=predict, inputs=inputs, outputs=image, show_progress=False)
sharpness.change(fn=predict, inputs=inputs, outputs=image, show_progress=False)
prompt1.change(fn=predict, inputs=inputs, outputs=image, show_progress=False)
prompt2.change(fn=predict, inputs=inputs, outputs=image, show_progress=False)
demo.queue()
if __name__ == "__main__":
demo.launch()
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