import gradio as gr import torch from semdiffusers import SemanticEditPipeline device='cuda' pipe = SemanticEditPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", ).to(device) def infer(prompt,seed): gen = torch.Generator(device=device) gen.manual_seed(seed) out = pipe(prompt=prompt, generator=gen, num_images_per_prompt=1, guidance_scale=7) images = out.images[0] out_edit = pipe(prompt=prompt, generator=gen, num_images_per_prompt=1, guidance_scale=7, editing_prompt=['male person', # Concepts to apply 'female person'], reverse_editing_direction=[True, False], # Direction of guidance i.e. decrease the first and increase the second concept edit_warmup_steps=[10, 10], # Warmup period for each concept edit_guidance_scale=[4, 4], # Guidance scale for each concept edit_threshold=[0.95, 0.95], # Threshold for each concept. Threshold equals the percentile of the latent space that will be discarded. I.e. threshold=0.99 uses 1% of the latent dimensions edit_momentum_scale=0.3, # Momentum scale that will be added to the latent guidance edit_mom_beta=0.6, # Momentum beta edit_weights=[1,1] # Weights of the individual concepts against each other ) images_edited = out_edit.images[0] return [(images, 'Stable Diffusion'), (images_edited, 'Fair Diffusion')] 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; } #prompt-text-input, #negative-prompt-text-input{padding: .45rem 0.625rem} #component-16{border-top-width: 1px!important;margin-top: 1em} .image_duplication{position: absolute; width: 100px; left: 50px} """ block = gr.Blocks(css=css) examples = [ [ 'A photo of the face of a firefighter', 21 ] ] with block: gr.HTML( """

FairDiffusion Demo

FairDiffusion is the latest strategy to introduce fairness after the deployment of generative text-to-image models
This unofficial demo is based on the Github Implementation.

""" ) with gr.Group(): with gr.Box(): with gr.Row(elem_id="prompt-container").style(mobile_collapse=False, equal_height=True): with gr.Column(): 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(height="auto") with gr.Accordion("Advanced settings", open=False): # with gr.Group(elem_id="container-advanced-btns"): # #advanced_button = gr.Button("Advanced options", elem_id="advanced-btn") # 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") seed = gr.Slider( label="Seed", minimum=0, maximum=2147483647, step=1, randomize=True, ) ex = gr.Examples(examples=examples, fn=infer, inputs=[text, seed], outputs=[gallery], cache_examples=True) ex.dataset.headers = [""] text.submit(infer, inputs=[text,seed], outputs=[gallery]) btn.click(infer, inputs=[text,seed], outputs=[gallery]) block.queue().launch()