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import gradio as gr
import numpy as np
import random
import torch
import os
from diffusers import DiffusionPipeline
from huggingface_hub import login
# Retrieve the Hugging Face API token from the environment variable
api_token = os.getenv('HUGGINGFACE_API_TOKEN')
if not api_token:
raise ValueError("HUGGINGFACE_API_TOKEN environment variable not set.")
# Log in to Hugging Face Hub
login(token=api_token)
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024
def infer(
prompt,
negative_prompt,
seed,
randomize_seed,
width,
height,
guidance_scale,
num_inference_steps,
):
# Load the pipeline only when this function is called
pipe = DiffusionPipeline.from_pretrained(
"black-forest-labs/FLUX.1-dev", use_auth_token=api_token
)
pipe.load_lora_weights("EvanZhouDev/open-genmoji", use_auth_token=api_token)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
pipe = pipe.to(device)
# Handle seed randomization
if randomize_seed:
seed = random.randint(0, MAX_SEED)
generator = torch.manual_seed(seed)
# Generate the image using the pipeline
result = pipe(
prompt=prompt,
negative_prompt=negative_prompt if negative_prompt else None,
width=width,
height=height,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
generator=generator,
).images[0]
return result, seed
examples = [
"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
"An astronaut riding a green horse",
"A delicious ceviche cheesecake slice",
]
css = """
#col-container {
margin: 0 auto;
max-width: 640px;
}
"""
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.Markdown(" # Text-to-Image Gradio Template")
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, variant="primary")
result = gr.Image(label="Result", show_label=False)
with gr.Accordion("Advanced Settings", open=False):
negative_prompt = gr.Text(
label="Negative prompt",
max_lines=1,
placeholder="Enter a negative prompt",
visible=True,
)
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=7.5,
)
num_inference_steps = gr.Slider(
label="Number of inference steps",
minimum=1,
maximum=50,
step=1,
value=25,
)
gr.Examples(examples=examples, inputs=[prompt])
# Run inference when run_button is clicked
run_button.click(
infer,
inputs=[
prompt,
negative_prompt,
seed,
randomize_seed,
width,
height,
guidance_scale,
num_inference_steps,
],
outputs=[result, seed],
)
if __name__ == "__main__":
demo.launch()
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