Spaces:
Running
on
Zero
Running
on
Zero
File size: 2,024 Bytes
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import json
import random
import uuid
import gradio as gr
import spaces
import torch
from diffusers import DiffusionPipeline
from transformers import AutoModelForCausalLM, AutoTokenizer
device = torch.device("cuda:0")
diffusion_pipe = DiffusionPipeline.from_pretrained(
"playgroundai/playground-v2.5-1024px-aesthetic",
torch_dtype=torch.float16,
use_safetensors=True,
add_watermarker=False,
variant="fp16"
).to(device)
def save_image(img):
unique_name = str(uuid.uuid4()) + ".png"
img.save(unique_name)
return unique_name
@spaces.GPU(enable_queue=True)
def generate(
prompt: str,
progress=gr.Progress(track_tqdm=True),
):
seed = random.randint(0, 2147483647)
generator = torch.Generator().manual_seed(seed)
images = diffusion_pipe(
prompt=[prompt],
negative_prompt=None,
width=1024,
height=1024,
guidance_scale=3,
num_inference_steps=25,
generator=generator,
num_images_per_prompt=1,
use_resolution_binning=True,
output_type="pil",
).images
image_paths = [save_image(img) for img in images]
return image_paths
css = '''
.gradio-container{max-width: 560px !important}
h1{text-align:center}
'''
with gr.Blocks(css=css) as demo:
gr.Markdown("# Playground v2.5")
with gr.Group():
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)
result = gr.Gallery(label="Result", columns=2, rows=1, show_label=False)
gr.on(
triggers=[
prompt.submit,
run_button.click,
],
fn=generate,
inputs=[
prompt,
],
outputs=[result],
api_name="run",
)
if __name__ == "__main__":
demo.queue(max_size=20).launch()
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