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import gradio as gr
import numpy as np
import random
from diffusers import DiffusionPipeline
import torch
device = "cuda" if torch.cuda.is_available() else "cpu"
if torch.cuda.is_available():
torch.cuda.max_memory_allocated(device=device)
pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
pipe.enable_xformers_memory_efficient_attention()
pipe = pipe.to(device)
else:
pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", use_safetensors=True)
pipe = pipe.to(device)
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024
def infer(prompt_part1, color, dress_type, front_design, back_design, prompt_part5, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
front_prompt = f"front view of {prompt_part1} {color} colored plain {dress_type} with {front_design} design, {prompt_part5}"
back_prompt = f"back view of {prompt_part1} {color} colored plain {dress_type} with {back_design} design, {prompt_part5}"
if randomize_seed:
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)
front_image = pipe(
prompt=front_prompt,
negative_prompt=negative_prompt,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
width=width,
height=height,
generator=generator
).images[0]
back_image = pipe(
prompt=back_prompt,
negative_prompt=negative_prompt,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
width=width,
height=height,
generator=generator
).images[0]
return front_image, back_image
examples = [
["red", "t-shirt", "yellow stripes", "polka dots"],
["blue", "hoodie", "minimalist", "abstract art"],
["red", "sweat shirt", "geometric design", "plain"],
]
css = """
#col-container {
margin: 0 auto;
max-width: 520px;
}
"""
if torch.cuda.is_available():
power_device = "GPU"
else:
power_device = "CPU"
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.Markdown(f"""
# Text-to-Image Gradio Template
Currently running on {power_device}.
""")
with gr.Row():
prompt_part1 = gr.Textbox(
value="a single",
label="Prompt Part 1",
show_label=False,
interactive=False,
container=False,
elem_id="prompt_part1",
visible=False,
)
prompt_part2 = gr.Textbox(
label="color",
show_label=False,
max_lines=1,
placeholder="color (e.g., color category)",
container=False,
)
prompt_part3 = gr.Textbox(
label="dress_type",
show_label=False,
max_lines=1,
placeholder="dress_type (e.g., t-shirt, sweatshirt, shirt, hoodie)",
container=False,
)
prompt_part4_front = gr.Textbox(
label="front design",
show_label=False,
max_lines=1,
placeholder="front design",
container=False,
)
prompt_part4_back = gr.Textbox(
label="back design",
show_label=False,
max_lines=1,
placeholder="back design",
container=False,
)
prompt_part5 = gr.Textbox(
value="hanging on the plain wall",
label="Prompt Part 5",
show_label=False,
interactive=False,
container=False,
elem_id="prompt_part5",
visible=False,
)
run_button = gr.Button("Run", scale=0)
front_result = gr.Image(label="Front View Result", show_label=False)
back_result = gr.Image(label="Back View Result", show_label=False)
with gr.Row():
edit_button = gr.Button("Edit Front Design")
edit_front_design = gr.Image(interactive=True, tool="editor")
with gr.Row():
edit_button_back = gr.Button("Edit Back Design")
edit_back_design = gr.Image(interactive=True, tool="editor")
with gr.Accordion("Advanced Settings", open=False):
negative_prompt = gr.Textbox(
label="Negative prompt",
max_lines=1,
placeholder="Enter a negative prompt",
visible=False,
)
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():
width = gr.Slider(
label="Width",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=512,
)
height = gr.Slider(
label="Height",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=512,
)
with gr.Row():
guidance_scale = gr.Slider(
label="Guidance scale",
minimum=0.0,
maximum=10.0,
step=0.1,
value=0.0,
)
num_inference_steps = gr.Slider(
label="Number of inference steps",
minimum=1,
maximum=12,
step=1,
value=2,
)
gr.Examples(
examples=examples,
inputs=[prompt_part2, prompt_part3, prompt_part4_front, prompt_part4_back]
)
run_button.click(
fn=infer,
inputs=[prompt_part1, prompt_part2, prompt_part3, prompt_part4_front, prompt_part4_back, prompt_part5, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
outputs=[front_result, back_result]
)
edit_button.click(
fn=lambda x: x,
inputs=[front_result],
outputs=[edit_front_design],
)
edit_button_back.click(
fn=lambda x: x,
inputs=[back_result],
outputs=[edit_back_design],
)
demo.queue().launch()
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