Spaces:
Sleeping
Sleeping
File size: 4,010 Bytes
75b503f 6c1f991 19c67d0 6c1f991 219ad6f 6c1f991 19c67d0 6c1f991 74c8944 bd07714 219ad6f 6c1f991 |
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 |
import spaces
import torch
from diffusers import FluxPipeline
import gradio as gr
import random
import numpy as np
import os
if torch.cuda.is_available():
device = "cuda"
print("Using GPU")
else:
device = "cpu"
print("Using CPU")
# login hf token
HF_TOKEN = os.getenv("HF_TOKEN")
MAX_SEED = np.iinfo(np.int32).max
CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES", "0") == "1"
# Initialize the pipeline and download the model
pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16)
pipe.to(device)
# Enable memory optimizations
pipe.enable_attention_slicing()
# Define the image generation function
@spaces.GPU(duration=180)
def generate_image(prompt, num_inference_steps, height, width, guidance_scale, seed, num_images_per_prompt, progress=gr.Progress(track_tqdm=True)):
if seed == 0:
seed = random.randint(1, MAX_SEED)
generator = torch.Generator().manual_seed(seed)
with torch.inference_mode():
output = pipe(
prompt=prompt,
num_inference_steps=num_inference_steps,
height=height,
width=width,
guidance_scale=guidance_scale,
generator=generator,
num_images_per_prompt=num_images_per_prompt
).images
return output
# Create the Gradio interface
examples = [
["Full-body, realistic photo of a network engineer in a data center, conducting an experiment"]
]
css = '''
.gradio-container{max-width: 100% !important}
h1{text-align:center}
'''
with gr.Blocks(css=css) as demo:
with gr.Row():
with gr.Column():
gr.HTML(
"""
<h1 style='text-align: center'>
FLUX.1-dev
</h1>
"""
)
gr.HTML(
"""
Made by csit.udru.ac.th for non-commercial license
"""
)
with gr.Group():
with gr.Row():
prompt = gr.Textbox(label="Prompt", show_label=False, info="", placeholder="Describe the image you want")
run_button = gr.Button("Generate", scale=0)
result = gr.Gallery(label="Generated AI Images", elem_id="gallery")
with gr.Accordion("Advanced options", open=False):
with gr.Row():
num_inference_steps = gr.Slider(label="Number of Inference Steps", info="The number of denoising steps of the image. More denoising steps usually lead to a higher quality image at the cost of slower inference", minimum=1, maximum=50, value=25, step=1)
guidance_scale = gr.Slider(label="Guidance Scale", info="Controls how much the image generation process follows the text prompt. Higher values make the image stick more closely to the input text.", minimum=0.0, maximum=7.0, value=3.5, step=0.1)
with gr.Row():
width = gr.Slider(label="Width", info="Width of the Image", minimum=256, maximum=1024, step=32, value=1024)
height = gr.Slider(label="Height", info="Height of the Image", minimum=256, maximum=1024, step=32, value=1024)
with gr.Row():
seed = gr.Slider(value=42, minimum=0, maximum=MAX_SEED, step=1, label="Seed", info="A starting point to initiate the generation process, put 0 for a random one")
num_images_per_prompt = gr.Slider(label="Images Per Prompt", info="Number of Images to generate with the settings",minimum=1, maximum=4, step=1, value=2)
gr.Examples(
examples=examples,
fn=generate_image,
inputs=[prompt, num_inference_steps, height, width, guidance_scale, seed, num_images_per_prompt],
outputs=[result],
cache_examples=CACHE_EXAMPLES
)
gr.on(
triggers=[
prompt.submit,
run_button.click,
],
fn=generate_image,
inputs=[prompt, num_inference_steps, height, width, guidance_scale, seed, num_images_per_prompt],
outputs=[result],
)
demo.queue().launch(share=False) |