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import torch | |
from diffusers import StableDiffusionXLPipeline, DPMSolverSinglestepScheduler | |
import gradio as gr | |
import random | |
import numpy as np | |
import spaces | |
import os | |
if torch.cuda.is_available(): | |
device = "cuda" | |
print("Using GPU") | |
else: | |
device = "cpu" | |
print("Using CPU") | |
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 sdxl flash model | |
pipe = StableDiffusionXLPipeline.from_pretrained("sd-community/sdxl-flash", torch_dtype=torch.float16) | |
pipe.to(device) | |
# Ensure sampler uses "trailing" timesteps. | |
pipe.scheduler = DPMSolverSinglestepScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing") | |
# Define the image generation function | |
def generate_image(prompt, negative_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, 2**32-1) | |
generator = torch.Generator().manual_seed(seed) | |
output = pipe( | |
prompt=prompt, | |
negative_prompt=negative_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 = [ | |
["A white car racing fast to the moon."], | |
["A woman in a red dress singing on top of a building."], | |
["An astrounat on mars in a futuristic cyborg suit."], | |
] | |
css = ''' | |
.gradio-container{max-width: 1000px !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'> | |
SDXL Flash | |
</h1> | |
""" | |
) | |
gr.HTML( | |
""" | |
Made by <a href='https://linktr.ee/Nick088' target='_blank'>Nick088</a> | |
<br> <a href="https://discord.gg/osai"> <img src="https://img.shields.io/discord/1198701940511617164?color=%23738ADB&label=Discord&style=for-the-badge" alt="Discord"> </a> | |
""" | |
) | |
with gr.Group(): | |
with gr.Column(): | |
prompt = gr.Textbox(label="Prompt", info="Describe the image you want", placeholder="A cat...") | |
run_button = gr.Button("Run") | |
result = gr.Gallery(label="Generated AI Images", elem_id="gallery") | |
with gr.Accordion("Advanced options", open=False): | |
with gr.Row(): | |
negative_prompt = gr.Textbox(label="Negative Prompt", info="Describe what you don't want in the image", value="deformed, distorted, disfigured, poorly drawn, bad anatomy, incorrect anatomy, extra limb, missing limb, floating limbs, mutated hands and fingers, disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation", placeholder="Ugly, bad anatomy...") | |
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=15, value=8, 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=6.0, value=3.5, step=0.1) | |
with gr.Row(): | |
width = gr.Slider(label="Width", info="Width of the Image", minimum=256, maximum=1344, step=32, value=1024) | |
height = gr.Slider(label="Height", info="Height of the Image", minimum=256, maximum=1344, 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, negative_prompt, num_inference_steps, height, width, guidance_scale, seed, num_images_per_prompt], | |
outputs=[result], | |
cache_examples=CACHE_EXAMPLES | |
) | |
run_button.click( | |
fn=generate_image, | |
inputs=[prompt, negative_prompt, num_inference_steps, height, width, guidance_scale, seed, num_images_per_prompt], | |
outputs=[result], | |
) | |
demo.queue().launch(share=False) |