import gradio as gr import torch from PIL.ImageDraw import Draw from diffusers import StableDiffusionPipeline from PIL import Image, ImageOps # Load pipeline once model_id = 'Deci/DeciDiffusion-v1-0' device = "cuda" if torch.cuda.is_available() else "cpu" pipe = StableDiffusionPipeline.from_pretrained(model_id, custom_pipeline=model_id, torch_dtype=torch.float32) pipe.unet = pipe.unet.from_pretrained(model_id, subfolder='flexible_unet', torch_dtype=torch.float32) pipe = pipe.to(device) def read_content(file_path: str) -> str: """read the content of target file """ with open(file_path, 'r', encoding='utf-8') as f: content = f.read() return content def predict(_prompt: str, _steps: int = 30, _seed: int = 42, _guidance_scale: float = 7.5, _negative_prompt: str = ""): _negative_prompt = [_negative_prompt] if _negative_prompt else None output = pipe(prompt=[_prompt], negative_prompt=_negative_prompt, num_inference_steps=int(_steps), guidance_scale=_guidance_scale, generator=torch.Generator(device).manual_seed(_seed), ) output_image = output.images[0] # Add border beneath the image with Deci logo + prompt if len(_prompt) > 52: _prompt = _prompt[:52] + "..." original_image_height = output_image.size[1] output_image = ImageOps.expand(output_image, border=(0, 0, 0, 64), fill='white') deci_logo = Image.open('https://huggingface.co/spaces/Deci/DeciDiffusion-v1-0/resolve/main/deci_logo_white.png') output_image.paste(deci_logo, (0, original_image_height)) Draw(output_image).text((deci_logo.size[0], original_image_height), _prompt, (127, 127, 127)) return output_image css = ''' .gradio-container { max-width: 1100px !important; background-image: url(https://huggingface.co/spaces/Deci/Deci-DeciDiffusionClean/resolve/main/background-image.png); background-size: cover; background-position: center center; background-repeat: no-repeat; } .footer {margin-bottom: 45px;margin-top: 35px !important;text-align: center;border-bottom: 1px solid #e5e5e5} .footer>p {font-size: .8rem; display: inline-block; padding: 0 10px;transform: translateY(10px);background: white} .dark .footer {border-color: #303030} .dark .footer>p {background: #0b0f19} .acknowledgments h4{margin: 1.25em 0 .25em 0;font-weight: bold;font-size: 115%} @keyframes spin { from { transform: rotate(0deg); } to { transform: rotate(360deg); } } ''' demo = gr.Blocks(css=css, elem_id="total-container") with demo: gr.HTML(read_content("header.html")) with gr.Row(): with gr.Column(): with gr.Row(mobile_collapse=False, equal_height=True): prompt = gr.Textbox(placeholder="Your prompt", show_label=False, elem_id="prompt", autofocus=True, lines=3, ) with gr.Accordion(label="Advanced Settings", open=False): with gr.Row(mobile_collapse=False, equal_height=True): steps = gr.Slider(value=30, minimum=15, maximum=50, step=1, label="steps", interactive=True) seed = gr.Slider(value=42, minimum=1, maximum=100, step=1, label="seed", interactive=True) guidance_scale = gr.Slider(value=7.5, minimum=1, maximum=15, step=0.1, label='guidance_scale', interactive=True) with gr.Row(mobile_collapse=False, equal_height=True): negative_prompt = gr.Textbox(label="negative_prompt", placeholder="Your negative prompt", info="what you don't want to see in the image", lines=3) with gr.Row(): btn = gr.Button(value="Generate!", elem_id="run_button") with gr.Column(): image_out = gr.Image(label="Output", elem_id="output-img", height=400) btn.click(fn=predict, inputs=[prompt, steps, seed, guidance_scale, negative_prompt], outputs=[image_out], api_name='run') gr.HTML( """ """ ) demo.queue(max_size=50).launch()