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PATH = 'harpomaxx/deeplili' |
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from PIL import Image |
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import torch |
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import torch.distributed as dist |
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import torch.multiprocessing as mp |
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import argparse |
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from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler |
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from tqdm.auto import tqdm |
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import random |
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import gradio as gr |
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def generate_image(prompt, guidance_scale, num_inference_steps): |
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seed = random.randint(1, 10000) |
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torch.manual_seed(seed) |
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image = pipe(prompt, num_inference_steps=num_inference_steps,guidance_scale=guidance_scale).images[0] |
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return image |
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def gr_generate_images(prompt: str, num_inference = 20, guidance_scale = 8 ): |
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prompt = prompt + "sks style" |
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image = generate_image(prompt, guidance_scale, num_inference) |
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return image |
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with gr.Blocks(css="style.css") as demo: |
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examples = [ |
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[ |
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'A black and white cute character on top of a hill', |
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1, |
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30 |
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], |
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[ |
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'Bubbles and mountains in the sky', |
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1, |
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20 |
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], |
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[ |
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'A tree with multiple eyes and a small flower muted colors', |
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1, |
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20 |
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], |
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[ |
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"3d character on top of a hill", |
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1, |
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20 |
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], |
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[ |
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"a poster of a large forest with black and white characters", |
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1, |
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20 |
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], |
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] |
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gr.Markdown( |
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""" |
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<img src="https://github.com/harpomaxx/DeepLili/raw/main/images/lilifiallo/660.png" width="150" height="150"> |
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# #DeepLili v0.5b |
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## Enter your prompt and generate a work of art in the style of Lili's Toy Art paintings. |
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## (English, Spanish) |
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""" |
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) |
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with gr.Column(variant="panel"): |
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with gr.Row(variant="compact"): |
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text = gr.Textbox( |
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label="Enter your prompt", |
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show_label=False, |
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max_lines=2, |
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placeholder="a white and black drawing of a cute character on top of a house with a little animal" |
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).style( |
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container=False, |
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) |
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with gr.Row(variant="compact"): |
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btn = gr.Button("Generate image").style(full_width=False) |
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gallery = gr.Image( |
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label="Generated image") |
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btn.click(gr_generate_images, [text], gallery) |
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gr.Examples(examples, inputs=[text]) |
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gr.HTML( |
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""" |
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<h6><a href="https://harpomaxx.github.io/"> harpomaxx </a></h6> |
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""" |
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) |
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if __name__ == "__main__": |
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if torch.cuda.is_available(): |
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device = "cuda" |
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dtype = torch.float16 |
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else: |
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device = "cpu" |
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dtype = torch.float32 |
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dpm = DPMSolverMultistepScheduler.from_pretrained(PATH, subfolder="scheduler") |
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pipe = StableDiffusionPipeline.from_pretrained(PATH,torch_dtype=dtype, scheduler=dpm).to(device) |
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demo.queue(concurrency_count=2, |
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).launch() |
