from contextlib import nullcontext import gradio as gr import torch from torch import autocast from diffusers import StableDiffusionPipeline from ray.serve.gradio_integrations import GradioServer device = "cuda" if torch.cuda.is_available() else "cpu" context = autocast if device == "cuda" else nullcontext dtype = torch.float16 if device == "cuda" else torch.float32 # Sometimes the nsfw checker is confused by the Naruto images, you can disable try: if device == "cuda": pipe = StableDiffusionPipeline.from_pretrained("lambdalabs/sd-naruto-diffusers", torch_dtype=dtype) else: pipe = StableDiffusionOnnxPipeline.from_pretrained( "lambdalabs/sd-naruto-diffusers", revision="onnx", provider="CPUExecutionProvider" ) # onnx model revision not available except: pipe = StableDiffusionPipeline.from_pretrained("lambdalabs/sd-naruto-diffusers", torch_dtype=dtype) pipe = pipe.to(device) # Sometimes the nsfw checker is confused by the Naruto images, you can disable # it at your own risk here disable_safety = True if disable_safety: def null_safety(images, **kwargs): return images, False pipe.safety_checker = null_safety def infer(prompt, n_samples, steps, scale): with context("cuda"): images = pipe(n_samples*[prompt], guidance_scale=scale, num_inference_steps=steps).images return images css = """ a { color: inherit; text-decoration: underline; } .gradio-container { font-family: 'IBM Plex Sans', sans-serif; } .gr-button { color: white; border-color: #9d66e5; background: #9d66e5; } input[type='range'] { accent-color: #9d66e5; } .dark input[type='range'] { accent-color: #dfdfdf; } .container { max-width: 730px; margin: auto; padding-top: 1.5rem; } #gallery { min-height: 22rem; margin-bottom: 15px; margin-left: auto; margin-right: auto; border-bottom-right-radius: .5rem !important; border-bottom-left-radius: .5rem !important; } #gallery>div>.h-full { min-height: 20rem; } .details:hover { text-decoration: underline; } .gr-button { white-space: nowrap; } .gr-button:focus { border-color: rgb(147 197 253 / var(--tw-border-opacity)); outline: none; box-shadow: var(--tw-ring-offset-shadow), var(--tw-ring-shadow), var(--tw-shadow, 0 0 #0000); --tw-border-opacity: 1; --tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color); --tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(3px var(--tw-ring-offset-width)) var(--tw-ring-color); --tw-ring-color: rgb(191 219 254 / var(--tw-ring-opacity)); --tw-ring-opacity: .5; } #advanced-options { margin-bottom: 20px; } .footer { margin-bottom: 45px; margin-top: 35px; 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 .logo{ filter: invert(1); } .dark .footer { border-color: #303030; } .dark .footer>p { background: #0b0f19; } .acknowledgments h4{ margin: 1.25em 0 .25em 0; font-weight: bold; font-size: 115%; } """ block = gr.Blocks(css=css) examples = [ [ 'Bill Gates with a hoodie', 2, 7.5, ], [ 'Jon Snow ninja portrait', 2, 7.5, ], [ 'Leo Messi in the style of Naruto', 2, 7.5 ], ] with block: gr.HTML( """ <div style="text-align: center; max-width: 650px; margin: 0 auto;"> <div> <img class="logo" src="https://lambdalabs.com/hubfs/logos/lambda-logo.svg" alt="Lambda Logo" style="margin: auto; max-width: 7rem;"> <h1 style="font-weight: 900; font-size: 3rem;"> Naruto text to image </h1> </div> <p style="margin-bottom: 10px; font-size: 94%"> Generate new Naruto anime character from a text description, <a href="https://lambdalabs.com/blog/how-to-fine-tune-stable-diffusion-how-we-made-the-text-to-pokemon-model-at-lambda/">created by Lambda Labs</a>. </p> </div> """ ) with gr.Group(): with gr.Box(): with gr.Row().style(mobile_collapse=False, equal_height=True): text = gr.Textbox( label="Enter your prompt", show_label=False, max_lines=1, placeholder="Enter your prompt", ).style( border=(True, False, True, True), rounded=(True, False, False, True), container=False, ) btn = gr.Button("Generate image").style( margin=False, rounded=(False, True, True, False), ) gallery = gr.Gallery( label="Generated images", show_label=False, elem_id="gallery" ).style(grid=[2], height="auto") with gr.Row(elem_id="advanced-options"): samples = gr.Slider(label="Images", minimum=1, maximum=4, value=2, step=1) steps = gr.Slider(label="Steps", minimum=5, maximum=50, value=45, step=5) scale = gr.Slider( label="Guidance Scale", minimum=0, maximum=50, value=7.5, step=0.1 ) ex = gr.Examples(examples=examples, fn=infer, inputs=[text, samples, scale], outputs=gallery, cache_examples=False) ex.dataset.headers = [""] text.submit(infer, inputs=[text, samples, steps, scale], outputs=gallery) btn.click(infer, inputs=[text, samples, steps, scale], outputs=gallery) gr.HTML( """ <div class="footer"> <p> Gradio Demo by 🤗 Hugging Face and Lambda Labs </p> </div> <div class="acknowledgments"> <p> Put in a text prompt and generate your own Naruto anime character! <p> Here are some <a href="https://huggingface.co/lambdalabs/sd-naruto-diffusers">examples</a> of generated images. <p>If you want to find out how we made this model read about it in <a href="https://lambdalabs.com/blog/how-to-fine-tune-stable-diffusion-how-we-made-the-text-to-pokemon-model-at-lambda/">this blog post</a>. <p>And if you want to train your own Stable Diffusion variants, see our <a href="https://github.com/LambdaLabsML/examples/tree/main/stable-diffusion-finetuning">Examples Repo</a>! <p>Trained by Eole Cervenka at <a href="https://lambdalabs.com/">Lambda Labs</a>.</p> </div> """ ) #block.launch() io = block app = GradioServer.options(num_replicas=2, ray_actor_options={"num_cpus": 6.0, "num_gpus" : 1.0}).bind(io)