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import json
import random
import gradio as gr
import numpy as np
import spaces
import torch
from diffusers import DiffusionPipeline, LCMScheduler
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
model_id = "stabilityai/stable-diffusion-xl-base-1.0"
pipe = DiffusionPipeline.from_pretrained(model_id, variant="fp16")
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
pipe.load_lora_weights("jasperai/flash-sdxl", adapter_name="lora")
pipe.load_lora_weights("JacobLinCool/sdxl-lora-gdsc-1", adapter_name="gdsc")
pipe.set_adapters(["lora", "gdsc"], adapter_weights=[1.0, 1.0])
pipe.to(device=DEVICE, dtype=torch.float16)
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024
@spaces.GPU
def infer(
pre_prompt,
prompt,
seed,
randomize_seed,
num_inference_steps,
negative_prompt,
guidance_scale,
progress=gr.Progress(track_tqdm=True),
):
if randomize_seed:
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)
if pre_prompt != "":
prompt = f"{pre_prompt} {prompt}"
image = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
generator=generator,
).images[0]
return image
css = """
h1 {
text-align: center;
display:block;
}
p {
text-align: justify;
display:block;
}
"""
if torch.cuda.is_available():
power_device = "GPU"
else:
power_device = "CPU"
with gr.Blocks(css=css) as demo:
with gr.Row():
with gr.Column():
with gr.Row():
prompt = gr.Text(
label="Prompt",
show_label=False,
max_lines=1,
placeholder="Enter your prompt",
container=False,
scale=5,
)
run_button = gr.Button("Run", scale=1)
result = gr.Image(label="Result", show_label=False)
with gr.Accordion("Advanced Settings", open=False):
pre_prompt = gr.Text(
label="Pre-Prompt",
show_label=True,
max_lines=1,
placeholder="Pre Prompt from the LoRA config",
container=True,
scale=5,
)
seed = gr.Slider(
label="Seed",
minimum=0,
maximum=MAX_SEED,
step=1,
value=0,
)
randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
with gr.Row():
num_inference_steps = gr.Slider(
label="Number of inference steps",
minimum=4,
maximum=8,
step=1,
value=4,
)
with gr.Row():
guidance_scale = gr.Slider(
label="Guidance Scale",
minimum=1,
maximum=6,
step=0.5,
value=1,
)
negative_prompt = gr.Text(
label="Negative Prompt",
show_label=False,
max_lines=1,
placeholder="Enter a negative Prompt",
container=False,
)
run_button.click(
fn=infer,
inputs=[
pre_prompt,
prompt,
seed,
randomize_seed,
num_inference_steps,
negative_prompt,
guidance_scale,
],
outputs=[result],
)
demo.queue().launch() |