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import gradio as gr
import torch
import numpy as np
from diffusers import DiffusionPipeline
from transformers import pipeline
pipe = pipeline('text-generation', model='daspartho/prompt-extend')
def extend_prompt(prompt):
return pipe(prompt+',', num_return_sequences=1)[0]["generated_text"]
def text_it(inputs):
return extend_prompt(inputs)
def load_pipeline(use_cuda):
device = "cuda" if use_cuda and torch.cuda.is_available() else "cpu"
if device == "cuda":
torch.cuda.max_memory_allocated(device=device)
torch.cuda.empty_cache()
pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
pipe.enable_xformers_memory_efficient_attention()
pipe = pipe.to(device)
torch.cuda.empty_cache()
else:
pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", use_safetensors=True)
pipe = pipe.to(device)
return pipe
def genie(prompt="sexy woman", use_details=True,steps=2, seed=398231747038484200, use_cuda=False):
pipe = load_pipeline(use_cuda)
generator = np.random.seed(0) if seed == 0 else torch.manual_seed(seed)
if use_details:
extended_prompt = extend_prompt(prompt)
else:
extended_prompt=prompt
int_image = pipe(prompt=extended_prompt, generator=generator, num_inference_steps=steps, guidance_scale=0.0).images[0]
return int_image, extended_prompt
# Custom HTML for the interface
html_code = '''
<style>
body {
background-color: #F0F0F0;
}
</style>
<h1 style="color:black; text-align:center;">Stable Diffusion Turbo with GPT</h1>
'''
with gr.Blocks() as myface:
gr.HTML(html_code) # Add the custom HTML
with gr.Row():
input_text = gr.Textbox(label='Text prompt.', lines=1)
with gr.Row():
details_checkbox = gr.Checkbox(label="details", info="Generate Details?")
steps_slider = gr.Slider(1, maximum=5, value=2, step=1, label='Number of Iterations')
seed_slider = gr.Slider(minimum=0, step=1, maximum=999999999999999999, randomize=False, value=398231747038484200)
cuda_checkbox = gr.Checkbox(label="cuda", info="Do you have cuda?")
with gr.Row():
generate_button = gr.Button("Generate")
with gr.Row():
output_image1 = gr.Image()
output_image2 = gr.Image()
with gr.Row():
output_text1 = gr.Textbox(label="Generated Text", lines=2)
output_text2 = gr.Textbox(label="Generated Text", lines=2)
with gr.Row():
output_image3 = gr.Image()
output_image4 = gr.Image()
with gr.Row():
output_text3 = gr.Textbox(label="Generated Text", lines=2)
output_text4 = gr.Textbox(label="Generated Text", lines=2)
generate_button.click(genie, inputs=[input_text, details_checkbox, steps_slider, seed_slider, cuda_checkbox], outputs=[output_image1, output_text1], concurrency_limit=10)
generate_button.click(genie, inputs=[input_text, details_checkbox, steps_slider, seed_slider, cuda_checkbox], outputs=[output_image2, output_text2], concurrency_limit=10)
generate_button.click(genie, inputs=[input_text, details_checkbox, steps_slider, seed_slider, cuda_checkbox], outputs=[output_image3, output_text3], concurrency_limit=10)
generate_button.click(genie, inputs=[input_text, details_checkbox, steps_slider, seed_slider, cuda_checkbox], outputs=[output_image4, output_text4], concurrency_limit=10)
myface.launch(inline=True, show_api=False, max_threads=200)
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