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import marimo
__generated_with = "0.9.14"
app = marimo.App(width="medium")
@app.cell(hide_code=True)
def __():
import random
import marimo as mo
import numpy as np
import tqdm
import transformers
# Patch tqdm to work marimo notebooks
tqdm.auto.tqdm = tqdm.notebook.tqdm
import torch
from diffusers import DiffusionPipeline
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024
model_repo_id = (
"stabilityai/sdxl-turbo" # Replace to the model you would like to use
)
return (
DiffusionPipeline,
MAX_IMAGE_SIZE,
MAX_SEED,
mo,
model_repo_id,
np,
random,
torch,
tqdm,
transformers,
)
@app.cell
def __(mo, model_repo_id):
mo.md(f"""# HuggingFace Text-to-Image: **{model_repo_id}**""")
return
@app.cell(hide_code=True)
def __():
examples = [
"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
"An astronaut riding a green horse",
"A delicious ceviche cheesecake slice",
]
return (examples,)
@app.cell
def __(
DiffusionPipeline,
MAX_SEED,
mo,
model_repo_id,
random,
torch,
):
device = "cuda" if torch.cuda.is_available() else "cpu"
if torch.cuda.is_available():
torch_dtype = torch.float16
else:
torch_dtype = torch.float32
pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
pipe = pipe.to(device)
def infer(
prompt,
negative_prompt,
seed,
randomize_seed,
width,
height,
guidance_scale,
num_inference_steps,
):
if randomize_seed:
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)
image = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
width=width,
height=height,
generator=generator,
).images[0]
return image, seed
mo.output.clear() # Clear loading tdqm
return device, infer, pipe, torch_dtype
@app.cell
def __(mo):
get_prompt, set_prompt = mo.state("")
return get_prompt, set_prompt
@app.cell
def __(get_prompt, mo, set_prompt):
prompt = mo.ui.text_area(
placeholder="Enter your prompt",
label="Prompt",
full_width=True,
value=get_prompt(),
on_change=set_prompt,
)
return (prompt,)
@app.cell
def __(examples, mo, set_prompt):
def _on_click(example):
def handle(v):
set_prompt(example)
return handle
buttons = mo.ui.array(
[
mo.ui.button(label=example, on_click=_on_click(example))
for example in examples
]
)
example_options = mo.vstack(buttons)
return buttons, example_options
@app.cell
def __(MAX_IMAGE_SIZE, MAX_SEED, example_options, mo, prompt):
run_button = mo.ui.run_button(label="Run", kind="success", full_width=True)
negative_prompt = mo.ui.text_area(
placeholder="Enter a negative prompt", label="Negative prompt"
)
seed = mo.ui.slider(start=0, stop=MAX_SEED, value=0, label="Seed")
randomize_seed = mo.ui.checkbox(label="Randomize seed", value=True)
width = mo.ui.slider(
start=256, stop=MAX_IMAGE_SIZE, step=32, value=1024, label="Width"
)
height = mo.ui.slider(
start=256, stop=MAX_IMAGE_SIZE, step=32, value=1024, label="Height"
)
guidance_scale = mo.ui.slider(
start=0.0, stop=10.0, step=0.1, value=0.0, label="Guidance scale"
)
num_inference_steps = mo.ui.slider(
start=1, stop=50, step=1, value=2, label="Number of inference steps"
)
# Create advanced settings in an accordion
advanced_settings = mo.accordion(
{
"::lucide:list:: Examples": example_options,
"::lucide:settings:: Advanced Settings": mo.hstack(
[
mo.vstack([negative_prompt, seed, randomize_seed]),
mo.vstack(
[width, height, guidance_scale, num_inference_steps],
align="end",
),
]
).style(padding="10px"),
},
)
# Layout the main interface
mo.vstack([prompt, run_button, advanced_settings])
return (
advanced_settings,
guidance_scale,
height,
negative_prompt,
num_inference_steps,
randomize_seed,
run_button,
seed,
width,
)
@app.cell
def __(mo):
get_image, set_image = mo.state(None)
return get_image, set_image
@app.cell
def __(
guidance_scale,
height,
infer,
mo,
negative_prompt,
num_inference_steps,
prompt,
randomize_seed,
run_button,
seed,
set_image,
width,
):
mo.stop(not run_button.value)
_image, _seed = infer(
prompt.value,
negative_prompt.value,
seed.value,
randomize_seed.value,
width.value,
height.value,
guidance_scale.value,
num_inference_steps.value,
)
set_image(_image)
mo.output.clear() # Clear loading tdqm
return
@app.cell
def __(get_image):
get_image()
return
if __name__ == "__main__":
app.run()
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