import gradio as gr import cv2 import torch from imwatermark import WatermarkEncoder import numpy as np from PIL import Image import re from datasets import load_dataset from diffusers import DiffusionPipeline, EulerDiscreteScheduler from share_btn import community_icon_html, loading_icon_html, share_js REPO_ID = "stabilityai/stable-diffusion-2" device = "cuda" if torch.cuda.is_available() else "cpu" wm = "SDV2" wm_encoder = WatermarkEncoder() wm_encoder.set_watermark('bytes', wm.encode('utf-8')) def put_watermark(img, wm_encoder=None): if wm_encoder is not None: img = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR) img = wm_encoder.encode(img, 'dwtDct') img = Image.fromarray(img[:, :, ::-1]) return img repo_id = "stabilityai/stable-diffusion-2" scheduler = EulerDiscreteScheduler.from_pretrained(repo_id, subfolder="scheduler", prediction_type="v_prediction") pipe = DiffusionPipeline.from_pretrained(repo_id, torch_dtype=torch.float16, revision="fp16", scheduler=scheduler) pipe = pipe.to(device) pipe.enable_xformers_memory_efficient_attention() #If you have duplicated this Space or is running locally, you can remove this snippet if "HUGGING_FACE_HUB_TOKEN" in os.environ: word_list_dataset = load_dataset("stabilityai/word-list", data_files="list.txt", use_auth_token=True) word_list = word_list_dataset["train"]['text'] def infer(prompt, samples, steps, scale, seed): #If you have duplicated this Space or is running locally, you can remove this snippet if "HUGGING_FACE_HUB_TOKEN" in os.environ: for filter in word_list: if re.search(rf"\b{filter}\b", prompt): raise gr.Error("Unsafe content found. Please try again with different prompts.") generator = torch.Generator(device=device).manual_seed(seed) images = pipe(prompt, width=768, height=768, num_inference_steps=steps, guidance_scale=scale, num_images_per_prompt=samples, generator=generator).images images_watermarked = [] for image in images: image = put_watermark(image, wm_encoder) images_watermarked.append(image) return images_watermarked css = """ .gradio-container { font-family: 'IBM Plex Sans', sans-serif; } .gr-button { color: white; border-color: black; background: black; } input[type='range'] { accent-color: black; } .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-btn { font-size: .7rem !important; line-height: 19px; margin-top: 12px; margin-bottom: 12px; padding: 2px 8px; border-radius: 14px !important; } #advanced-options { display: none; 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 .footer { border-color: #303030; } .dark .footer>p { background: #0b0f19; } .acknowledgments h4{ margin: 1.25em 0 .25em 0; font-weight: bold; font-size: 115%; } .animate-spin { animation: spin 1s linear infinite; } @keyframes spin { from { transform: rotate(0deg); } to { transform: rotate(360deg); } } #share-btn-container { display: flex; padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; width: 13rem; margin-top: 10px; margin-left: auto; } #share-btn { all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.25rem !important; padding-bottom: 0.25rem !important;right:0; } #share-btn * { all: unset; } #share-btn-container div:nth-child(-n+2){ width: auto !important; min-height: 0px !important; } #share-btn-container .wrap { display: none !important; } .gr-form{ flex: 1 1 50%; border-top-right-radius: 0; border-bottom-right-radius: 0; } #prompt-container{ gap: 0; } #component-9{margin-top: -19px} .image_duplication{position: absolute; width: 100px; left: 50px} """ block = gr.Blocks(css=css) examples = [ [ 'A high tech solarpunk utopia in the Amazon rainforest', 4, 25, 9, 1024, ], [ 'A pikachu fine dining with a view to the Eiffel Tower', 4, 25, 9, 1024, ], [ 'A mecha robot in a favela in expressionist style', 4, 25, 9, 1024, ], [ 'an insect robot preparing a delicious meal', 4, 25, 9, 1024, ], [ "A small cabin on top of a snowy mountain in the style of Disney, artstation", 4, 25, 9, 1024, ], ] with block: gr.HTML( """

Stable Diffusion 2 Demo

Stable Diffusion 2 is the latest text-to-image model from StabilityAI. Access Stable Diffusion 1 Space here
For faster generation and API access you can try DreamStudio Beta.

""" ) with gr.Group(): with gr.Box(): with gr.Row(elem_id="prompt-container").style(mobile_collapse=False, equal_height=True): text = gr.Textbox( label="Enter your prompt", show_label=False, max_lines=1, placeholder="Enter your prompt", elem_id="prompt-text-input", ).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), full_width=False, ) gallery = gr.Gallery( label="Generated images", show_label=False, elem_id="gallery" ).style(grid=[2], height="auto") with gr.Accordion("Custom options", open=False): samples = gr.Slider(label="Images", minimum=1, maximum=4, value=4, step=1) steps = gr.Slider(label="Steps", minimum=1, maximum=50, value=25, step=1) scale = gr.Slider( label="Guidance Scale", minimum=0, maximum=50, value=9, step=0.1 ) seed = gr.Slider( label="Seed", minimum=0, maximum=2147483647, step=1, randomize=True, ) with gr.Group(): with gr.Group(elem_id="share-btn-container"): community_icon = gr.HTML(community_icon_html) loading_icon = gr.HTML(loading_icon_html) share_button = gr.Button("Share to community", elem_id="share-btn") ex = gr.Examples(examples=examples, fn=infer, inputs=[text, samples, steps, scale, seed], outputs=[gallery], cache_examples=False) ex.dataset.headers = [""] text.submit(infer, inputs=[text, samples, steps, scale, seed], outputs=[gallery]) btn.click(infer, inputs=[text, samples, steps, scale, seed], outputs=[gallery]) share_button.click( None, [], [], _js=share_js, ) gr.HTML( """

LICENSE

The model is licensed with a CreativeML OpenRAIL++ license. The authors claim no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in this license. The license forbids you from sharing any content that violates any laws, produce any harm to a person, disseminate any personal information that would be meant for harm, spread misinformation and target vulnerable groups. For the full list of restrictions please read the license

Biases and content acknowledgment

Despite how impressive being able to turn text into image is, beware to the fact that this model may output content that reinforces or exacerbates societal biases, as well as realistic faces, pornography and violence. The model was trained on the LAION-5B dataset, which scraped non-curated image-text-pairs from the internet (the exception being the removal of illegal content) and is meant for research purposes. You can read more in the model card

""" ) block.queue(concurrency_count=1, max_size=50).launch(max_threads=150)