Spaces:
Running
Running
File size: 4,363 Bytes
66a500b f5b81b8 66a500b f5b81b8 66a500b 43c1fd4 f5b81b8 a52d3e7 66a500b a52d3e7 66a500b a52d3e7 66a500b a52d3e7 66a500b a52d3e7 66a500b a52d3e7 66a500b 43c1fd4 80621e1 43c1fd4 80621e1 43c1fd4 80621e1 f5b81b8 66a500b 80621e1 f5b81b8 66a500b f5b81b8 66a500b f5b81b8 66a500b f5b81b8 80621e1 a52d3e7 f5b81b8 a52d3e7 66a500b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 |
import gradio as gr
import numpy as np
import random
import torch
from diffusers import DiffusionPipeline
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,
user_info,
):
if not user_info or not user_info.get("username"):
return "Please log in to generate images.", None
# Load the pipeline only when this function is called
pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev")
pipe.load_lora_weights("EvanZhouDev/open-genmoji")
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")
# Login Button inside the Blocks context
login_button = gr.LoginButton()
login_button.activate()
user_info = gr.State()
def update_user_info(profile):
return profile
login_button.click(
update_user_info, inputs=None, outputs=user_info
)
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,
user_info,
],
outputs=[result, seed],
)
if __name__ == "__main__":
demo.launch()
|