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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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from transformers import CLIPTextModel, CLIPTokenizer |
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from diffusers import AutoencoderKL, UNet2DConditionModel, PNDMScheduler |
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from diffusers import UniPCMultistepScheduler |
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from diffusers import StableDiffusionPipeline |
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from PIL import Image |
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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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guidance_scale = 8.5 |
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pipe = StableDiffusionPipeline.from_pretrained(PATH,local_files_only=False ).to("cpu") |
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guidance_scale = 8.5 |
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def generate_images(prompt, guidance_scale, n_samples, num_inference_steps): |
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seeds = [random.randint(1, 10000) for _ in range(n_samples)] |
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images = [] |
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for seed in tqdm(seeds): |
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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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images.append(image) |
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return images |
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def gr_generate_images(prompt: str, num_images: int, num_inference: int): |
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prompt = prompt + "sks style" |
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images = generate_images(prompt, tokenizer, text_encoder, unet, vae, scheduler, guidance_scale, num_images, num_inference) |
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return images |
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with gr.Blocks() 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 v0.45b |
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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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""" |
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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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num_images_slider = gr.Slider( |
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minimum=1, |
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maximum=10, |
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step=1, |
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value=1, |
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label="Number of Images", |
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) |
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num_inference_steps_slider = gr.Slider( |
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minimum=1, |
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maximum=25, |
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step=1, |
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value=20, |
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label="Number of Inference Steps", |
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) |
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btn = gr.Button("Generate image").style(full_width=False) |
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gallery = gr.Gallery( |
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label="Generated images", show_label=False, elem_id="gallery" |
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).style(columns=[5], rows=[1], object_fit="contain", height="250px", width="250px") |
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btn.click(gr_generate_images, [text, num_images_slider,num_inference_steps_slider], 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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demo.queue().launch(share=True) |
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