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import gradio as gr
import numpy as np
import random
# import spaces #[uncomment to use ZeroGPU]
from diffusers import DiffusionPipeline
import torch
from typing import Optional
device = "cuda" if torch.cuda.is_available() else "cpu"
model_repo_id_default = "CompVis/stable-diffusion-v1-4"
if torch.cuda.is_available():
torch_dtype = torch.float16
else:
torch_dtype = torch.float32
pipe = DiffusionPipeline.from_pretrained(model_repo_id_default, torch_dtype=torch_dtype)
pipe = pipe.to(device)
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024
# @spaces.GPU #[uncomment to use ZeroGPU]
def infer(
prompt: str,
negative_prompt: str,
width: int,
height: int,
num_inference_steps: int,
progress=gr.Progress(track_tqdm=True),
model_id: Optional[str] = 'CompVis/stable-diffusion-v1-4',
seed: Optional[int] = 42,
guidance_scale: Optional[float] = 7.0,
):
generator = torch.Generator().manual_seed(seed)
if model_id != model_repo_id_default:
pipe = DiffusionPipeline.from_pretrained(model_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]
return image, pipe.name_or_path
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(" # DEMO Text-to-Image")
with gr.Row():
model_id = gr.Textbox(
label="Model ID",
max_lines=1,
placeholder="Enter model id like 'CompVis/stable-diffusion-v1-4'",
value="CompVis/stable-diffusion-v1-4"
)
prompt = gr.Textbox(
label="Prompt",
max_lines=1,
placeholder="Enter your prompt",
)
negative_prompt = gr.Textbox(
label="Negative prompt",
max_lines=1,
placeholder="Enter a negative prompt",
)
with gr.Row():
seed = gr.Number(
label="Seed",
minimum=0,
maximum=MAX_SEED,
step=1,
value=42,
)
with gr.Row():
guidance_scale = gr.Slider(
label="Guidance scale",
minimum=0.0,
maximum=10.0,
step=0.1,
value=7.0,
)
with gr.Accordion("Optional Settings", open=False):
with gr.Row():
width = gr.Slider(
label="Width",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=1024,
)
with gr.Row():
height = gr.Slider(
label="Height",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=1024,
)
with gr.Row():
num_inference_steps = gr.Slider(
label="Number of inference steps",
minimum=1,
maximum=50,
step=1,
value=2,
)
run_button = gr.Button("Run", scale=1, variant="primary")
result = gr.Image(label="Result", show_label=False)
inferenced_model_name = gr.Label(show_label=True, value=model_id.value)
gr.on(
triggers=[run_button.click, prompt.submit],
fn=infer,
inputs=[
prompt,
negative_prompt,
seed,
width,
height,
guidance_scale,
num_inference_steps,
],
outputs=[
result,
inferenced_model_name,
],
)
if __name__ == "__main__":
demo.launch() |