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import logging
import gradio as gr
import numpy as np
import random
# import spaces #[uncomment to use ZeroGPU]
from diffusers import DiffusionPipeline
import torch
device = "cuda" if torch.cuda.is_available() else "cpu"
MODEL_REPO_IDS = ["stable-diffusion-v1-5/stable-diffusion-v1-5",
"black-forest-labs/FLUX.1-dev",
"black-forest-labs/FLUX.1-schnell",
"stabilityai/sdxl-turbo",
"stabilityai/stable-diffusion-xl-base-1.0",]
DEFAULT_MODEL_REPO_ID = "stabilityai/sdxl-turbo" # Replace to the model you would like to use
if torch.cuda.is_available():
torch_dtype = torch.float16
else:
torch_dtype = torch.float32
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024
# @spaces.GPU #[uncomment to use ZeroGPU]
def infer(
prompt,
negative_prompt,
seed,
randomize_seed,
width,
height,
guidance_scale,
num_inference_steps,
model_repo_ids = [DEFAULT_MODEL_REPO_ID],
progress=gr.Progress(track_tqdm=True),
):
if randomize_seed:
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)
images = []
for model_repo_id in model_repo_ids:
try:
image = None
pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
pipe = pipe.to(device)
image = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
width=width,
height=height,
generator=generator,
).images[0]
images.append(image)
except Exception as e:
logging.error(f"Error generating image using model {model_repo_id}", exc_info=e)
return images, seed
examples = [
"Local Pizzeria perspective from the table with a pizza and a glass of wine in focus and the background is a bit blared. Style should be as if a customer took the picture using his phone.",
"A butcher in a jungle, cold color palette, muted colors, detailed, 4k",
"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=4,
placeholder="Enter your prompt",
container=False,
)
run_button = gr.Button("Run", scale=0, variant="primary")
images = gr.Gallery(label="Generated Images")
with gr.Accordion("Advanced Settings", open=False):
negative_prompt = gr.Text(
label="Negative prompt",
max_lines=2,
placeholder="people faces, text ",
visible=False,
)
model_repo_ids = gr.Dropdown(
choices=MODEL_REPO_IDS,
multiselect = True,
value = [MODEL_REPO_IDS[0]]
)
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, # Replace with defaults that work for your model
)
height = gr.Slider(
label="Height",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=512, # Replace with defaults that work for your model
)
with gr.Row():
guidance_scale = gr.Slider(
label="Guidance scale",
minimum=0.0,
maximum=10.0,
step=0.1,
value=0.0, # Replace with defaults that work for your model
)
num_inference_steps = gr.Slider(
label="Number of inference steps",
minimum=1,
maximum=50,
step=1,
value=2, # Replace with defaults that work for your model
)
gr.Examples(examples=examples, inputs=[prompt])
gr.on(
triggers=[run_button.click, prompt.submit],
fn=infer,
inputs=[
prompt,
negative_prompt,
seed,
randomize_seed,
width,
height,
guidance_scale,
num_inference_steps,
model_repo_ids
],
outputs=[images, seed],
)
if __name__ == "__main__":
demo.launch()
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