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import gradio as gr
import numpy as np
import random
import torch
from diffusers import DiffusionPipeline, EDMEulerScheduler
# 🖥️ Detect device
device = "cuda" if torch.cuda.is_available() else "cpu"
# 🎯 Model ID and config
model_repo_id = "stabilityai/sdxl-turbo"
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024
# 🔁 Load model with EDM + VPred scheduler
pipe = DiffusionPipeline.from_pretrained(
model_repo_id,
torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
variant="fp16" if torch.cuda.is_available() else None,
)
# 🔄 Replace scheduler with EDM + V-prediction
pipe.scheduler = EDMEulerScheduler.from_config(pipe.scheduler.config)
# 🧠 Enable optimizations if GPU
if device == "cuda":
try:
pipe.enable_xformers_memory_efficient_attention()
except Exception as e:
print("⚠️ xFormers not available, using attention slicing.")
pipe.enable_attention_slicing()
pipe.enable_model_cpu_offload()
pipe.enable_vae_tiling()
pipe = pipe.to(device)
# 🚀 Inference function
def infer(
prompt,
negative_prompt,
seed,
randomize_seed,
width,
height,
guidance_scale,
num_inference_steps,
progress=gr.Progress(track_tqdm=True),
):
if randomize_seed:
seed = random.randint(0, MAX_SEED)
generator = torch.Generator(device=device).manual_seed(seed)
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]
return image, seed
# 🧪 Prompt examples
examples = [
"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
"An astronaut riding a green horse",
"A delicious ceviche cheesecake slice",
]
# 🎨 UI CSS
css = """
#col-container {
margin: 0 auto;
max-width: 640px;
}
"""
# 🧱 Gradio Interface
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.Markdown(" # Text-to-Image Gradio (EDM + VPred)")
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=False,
)
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=1024,
)
height = gr.Slider(
label="Height",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=1024,
)
with gr.Row():
guidance_scale = gr.Slider(
label="Guidance scale",
minimum=0.0,
maximum=10.0,
step=0.1,
value=2.5, # Optimal for SDXL-Turbo
)
num_inference_steps = gr.Slider(
label="Number of inference steps",
minimum=1,
maximum=20,
step=1,
value=4, # Low default for EDM
)
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,
],
outputs=[result, seed],
)
if __name__ == "__main__":
demo.launch() |