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PATH = 'harpomaxx/deeplili' #stable diffusion 1.5
from PIL import Image
import torch
import torch.distributed as dist
import torch.multiprocessing as mp
import argparse
#from diffusers import StableDiffusionPipeline, LMSDiscreteScheduler
from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler
from tqdm.auto import tqdm
import random
import gradio as gr
def generate_image(prompt, guidance_scale, num_inference_steps):
seed = random.randint(1, 10000)
torch.manual_seed(seed)
image = pipe(prompt, num_inference_steps=num_inference_steps,guidance_scale=guidance_scale).images[0]
return image
def gr_generate_images(prompt: str, num_inference = 20, guidance_scale = 8 ):
prompt = prompt + "sks style"
image = generate_image(prompt, guidance_scale, num_inference)
return image
with gr.Blocks(css="style.css") as demo:
examples = [
[
'A black and white cute character on top of a hill',
1,
30
],
[
'Bubbles and mountains in the sky',
1,
20
],
[
'A tree with multiple eyes and a small flower muted colors',
1,
20
],
[
"3d character on top of a hill",
1,
20
],
[
"a poster of a large forest with black and white characters",
1,
20
],
]
gr.Markdown(
"""
<img src="https://github.com/harpomaxx/DeepLili/raw/main/images/lilifiallo/660.png" width="150" height="150">
# #DeepLili v0.5b
## Enter your prompt and generate a work of art in the style of Lili's Toy Art paintings.
## (English, Spanish)
"""
)
with gr.Column(variant="panel"):
with gr.Row(variant="compact"):
text = gr.Textbox(
label="Enter your prompt",
show_label=False,
max_lines=2,
placeholder="a white and black drawing of a cute character on top of a house with a little animal"
).style(
container=False,
)
with gr.Row(variant="compact"):
btn = gr.Button("Generate image").style(full_width=False)
gallery = gr.Image(
label="Generated image") # Use the full view height
btn.click(gr_generate_images, [text], gallery)
gr.Examples(examples, inputs=[text])
gr.HTML(
"""
<h6><a href="https://harpomaxx.github.io/"> harpomaxx </a></h6>
"""
)
if __name__ == "__main__":
# Check if CUDA is available
if torch.cuda.is_available():
device = "cuda"
dtype = torch.float16 # Use float16 for GPU to save memory
else:
device = "cpu"
dtype = torch.float32 # CPU does not support float16, use float32 instead
dpm = DPMSolverMultistepScheduler.from_pretrained(PATH, subfolder="scheduler")
pipe = StableDiffusionPipeline.from_pretrained(PATH,torch_dtype=dtype, scheduler=dpm).to(device)
demo.queue(concurrency_count=2,
).launch() |