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import gradio as gr
from diffusers import StableDiffusionPipeline, UNet2DConditionModel
import torch
import random
import numpy as np
MODEL="UCLA-AGI/SPIN-Diffusion-iter3"
def set_seed(seed=5775709):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
set_seed()
def get_pipeline(device='cuda'):
model_id = "runwayml/stable-diffusion-v1-5"
#pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16, safety_checker = None, requires_safety_checker = False)
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
# load finetuned model
unet_id = MODEL
unet = UNet2DConditionModel.from_pretrained(unet_id, subfolder="unet", torch_dtype=torch.float16)
pipe.unet = unet
pipe = pipe.to(device)
return pipe
def generate(prompt: str, num_images: int=5, guidance_scale=7.5):
pipe = get_pipeline()
generator = torch.Generator(pipe.device).manual_seed(5775709)
# Ensure num_images is an integer
num_images = int(num_images)
images = pipe(prompt, generator=generator, guidance_scale=guidance_scale, num_inference_steps=50, num_images_per_prompt=num_images).images
images = [x.resize((512, 512)) for x in images]
return images
with gr.Blocks() as demo:
gr.Markdown("# SPIN-Diffusion 1.0 Demo")
with gr.Row():
prompt_input = gr.Textbox(label="Enter your prompt", placeholder="Type something...", lines=2)
generate_btn = gr.Button("Generate images")
guidance_scale = gr.Slider(label="Guidance Scale", minimum=0, maximum=50, value=9, step=0.1)
num_images_input = gr.Number(label="Number of images", value=5, minimum=1, maximum=10, step=1)
gallery = gr.Gallery(label="Generated images", elem_id="gallery", columns=5, object_fit="contain")
# Define your example prompts
examples = [
["The Eiffel Tower at sunset"],
["A futuristic city skyline"],
["A cat wearing a wizard hat"],
["A futuristic city at sunset"],
["A landscape with mountains and lakes"],
["A portrait of a robot in Renaissance style"],
]
# Add the Examples component linked to the prompt_input
gr.Examples(examples=examples, inputs=prompt_input, fn=generate, outputs=gallery)
generate_btn.click(fn=generate, inputs=[prompt_input, num_images_input, guidance_scale], outputs=gallery)
if __name__ == "__main__":
demo.launch(share=True)
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