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import gradio as gr
import spaces
import torch
from PIL import Image
from diffusers import DiffusionPipeline
MODEL_CHOICES = [
"stabilityai/stable-diffusion-3-medium-diffusers",
"stabilityai/stable-diffusion-xl-base-1.0",
"stabilityai/stable-diffusion-2-1",
"runwayml/stable-diffusion-v1-5",
]
# Global Variables
current_model_id = "stabilityai/stable-diffusion-3-medium-diffusers"
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
pipe = DiffusionPipeline.from_pretrained(
current_model_id,
torch_dtype=torch.float16,
).to(device)
@spaces.GPU()
@torch.inference_mode()
def inference(
model_id: str,
prompt: str,
negative_prompt: str = "",
progress=gr.Progress(track_tqdm=True),
) -> Image.Image:
global current_model_id, pipe
if model_id != current_model_id:
try:
pipe = DiffusionPipeline.from_pretrained(
model_id,
torch_dtype=torch.float16,
).to(device)
current_model_id = model_id
except Exception as e:
raise gr.Error(str(e))
image = pipe(
prompt,
negative_prompt=negative_prompt,
).images[0]
return image
if __name__ == "__main__":
with gr.Blocks() as demo:
gr.Markdown(f"# Stable Diffusion Demo")
with gr.Row():
with gr.Column():
inputs = [
gr.Dropdown(
label="Model ID",
choices=MODEL_CHOICES,
value="stabilityai/stable-diffusion-3-medium-diffusers",
),
gr.Text(label="Prompt", value=""),
gr.Text(label="Negative Prompt", value=""),
]
with gr.Accordion("Additional Settings (W.I.P)", open=False):
additional_inputs = [
gr.Text(
label="Model URL",
lines=2,
placeholder="e.g. ) https://civitai.com/api/download/models/177164?type=Model&format=SafeTensor&size=full&fp=fp16"
),
gr.Number(label="Num Inference Steps", value=None, minimum=1, maximum=1000, step=1)
]
with gr.Column():
outputs = [
gr.Image(label="Image", type="pil"),
]
gr.Examples(
examples=[
["stabilityai/stable-diffusion-3-medium-diffusers", "A cat holding a sign that says Hello world", ""]
],
inputs=inputs
)
btn = gr.Button("Generate")
btn.click(fn=inference, inputs=inputs, outputs=outputs)
demo.queue().launch()
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