Spaces:
Running
on
Zero
Running
on
Zero
File size: 3,603 Bytes
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import gradio as gr
import numpy as np
import random
import spaces
import torch
from diffusers import DiffusionPipeline
dtype = torch.bfloat16
device = "cuda" if torch.cuda.is_available() else "cpu"
pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=dtype).to(device)
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 2048
@spaces.GPU()
def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, num_inference_steps=4, progress=gr.Progress(track_tqdm=True)):
if randomize_seed:
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)
image = pipe(
prompt = prompt,
width = width,
height = height,
num_inference_steps = num_inference_steps,
generator = generator,
guidance_scale=0.0
).images[0]
return image, seed
examples = [
"Semirealistic futuristic looking woman with purple and orange hair in the style of tom whalen, flat colors, purple orange and white broad strips paint in the background, outrun paint splotches",
"realistic photo of a caterpillar doing sick skateboard tricks. Low angle action shot. In a dedicated skate-park under an elevated highway.",
"masterpieces, close-portrait, female, makeup, pale skin, blonde hair, braided hairstyle, mysterious, red black symbols face, glossy blue eyes,best quality,masterpiece",
]
css="""
#col-container {
margin: 0 auto;
max-width: 520px;
}
"""
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
with gr.Row():
prompt = gr.Text(
label="Prompt",
show_label=False,
max_lines=1,
placeholder="Enter your prompt",
container=False,
)
run_button = gr.Button("Run", scale=0)
result = gr.Image(label="Result", show_label=False)
with gr.Accordion("Advanced Settings", open=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=1024,
)
height = gr.Slider(
label="Height",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=1024,
)
with gr.Row():
num_inference_steps = gr.Slider(
label="Number of inference steps",
minimum=1,
maximum=50,
step=1,
value=4,
)
gr.Examples(
examples = examples,
fn = infer,
inputs = [prompt],
outputs = [result, seed],
cache_examples="lazy"
)
gr.on(
triggers=[run_button.click, prompt.submit],
fn = infer,
inputs = [prompt, seed, randomize_seed, width, height, num_inference_steps],
outputs = [result, seed]
)
demo.launch() |