import gradio as gr from model import models from multit2i import (load_models, infer_fn, infer_rand_fn, save_gallery, change_model, warm_model, get_model_info_md, loaded_models, get_positive_prefix, get_positive_suffix, get_negative_prefix, get_negative_suffix, get_recom_prompt_type, set_recom_prompt_preset, get_tag_type) max_images = 8 MAX_SEED = 2**32-1 load_models(models) css = """ .model_info { text-align: center; } .output { width=112px; height=112px; !important; } .gallery { width=100%; min_height=768px; !important; } """ with gr.Blocks(theme="NoCrypt/miku@>=1.2.2", fill_width=True, css=css) as demo: with gr.Column(): with gr.Group(): model_name = gr.Dropdown(label="Select Model", choices=list(loaded_models.keys()), value=list(loaded_models.keys())[0], allow_custom_value=True) model_info = gr.Markdown(value=get_model_info_md(list(loaded_models.keys())[0]), elem_classes="model_info") with gr.Group(): clear_prompt = gr.Button(value="Clear Prompt 🗑️", size="sm", scale=1) prompt = gr.Text(label="Prompt", lines=2, max_lines=8, placeholder="1girl, solo, ...", show_copy_button=True) neg_prompt = gr.Text(label="Negative Prompt", lines=1, max_lines=8, placeholder="") with gr.Accordion("Advanced options", open=False): with gr.Row(): width = gr.Slider(label="Width", info="If 0, the default value is used.", maximum=1216, step=32, value=0) height = gr.Slider(label="Height", info="If 0, the default value is used.", maximum=1216, step=32, value=0) with gr.Row(): steps = gr.Slider(label="Number of inference steps", info="If 0, the default value is used.", maximum=100, step=1, value=0) cfg = gr.Slider(label="Guidance scale", info="If 0, the default value is used.", maximum=30.0, step=0.1, value=0) seed = gr.Slider(label="Seed", info="Randomize Seed if -1.", minimum=-1, maximum=MAX_SEED, step=1, value=-1) with gr.Accordion("Recommended Prompt", open=False): recom_prompt_preset = gr.Radio(label="Set Presets", choices=get_recom_prompt_type(), value="Common") with gr.Row(): positive_prefix = gr.CheckboxGroup(label="Use Positive Prefix", choices=get_positive_prefix(), value=[]) positive_suffix = gr.CheckboxGroup(label="Use Positive Suffix", choices=get_positive_suffix(), value=["Common"]) negative_prefix = gr.CheckboxGroup(label="Use Negative Prefix", choices=get_negative_prefix(), value=[]) negative_suffix = gr.CheckboxGroup(label="Use Negative Suffix", choices=get_negative_suffix(), value=["Common"]) image_num = gr.Slider(label="Number of images", minimum=1, maximum=max_images, value=1, step=1, interactive=True, scale=1) with gr.Row(): run_button = gr.Button("Generate Image", scale=6) random_button = gr.Button("Random Model 🎲", scale=3) stop_button = gr.Button('Stop', interactive=False, scale=1) with gr.Column(): with gr.Group(): with gr.Row(): output = [gr.Image(label='', elem_classes="output", type="filepath", format="png", show_download_button=True, show_share_button=False, show_label=False, interactive=False, min_width=80, visible=True) for _ in range(max_images)] with gr.Group(): results = gr.Gallery(label="Gallery", elem_classes="gallery", interactive=False, show_download_button=True, show_share_button=False, container=True, format="png", object_fit="cover", columns=2, rows=2) image_files = gr.Files(label="Download", interactive=False) clear_results = gr.Button("Clear Gallery / Download 🗑️") with gr.Column(): examples = gr.Examples( examples = [ ["souryuu asuka langley, 1girl, neon genesis evangelion, plugsuit, pilot suit, red bodysuit, sitting, crossing legs, black eye patch, cat hat, throne, symmetrical, looking down, from bottom, looking at viewer, outdoors"], ["sailor moon, magical girl transformation, sparkles and ribbons, soft pastel colors, crescent moon motif, starry night sky background, shoujo manga style"], ["kafuu chino, 1girl, solo"], ["1girl"], ["beautiful sunset"], ], inputs=[prompt], ) gr.Markdown( f"""This demo was created in reference to the following demos.
[Nymbo/Flood](https://huggingface.co/spaces/Nymbo/Flood), [Yntec/ToyWorldXL](https://huggingface.co/spaces/Yntec/ToyWorldXL), [Yntec/Diffusion80XX](https://huggingface.co/spaces/Yntec/Diffusion80XX). """ ) gr.DuplicateButton(value="Duplicate Space") gr.Markdown(f"Just a few edits to *model.py* are all it takes to complete your own collection.") gr.on(triggers=[run_button.click, prompt.submit, random_button.click], fn=lambda: gr.update(interactive=True), inputs=None, outputs=stop_button, show_api=False) model_name.change(change_model, [model_name], [model_info], queue=False, show_api=False)\ .success(warm_model, [model_name], None, queue=True, show_api=False) for i, o in enumerate(output): img_i = gr.Number(i, visible=False) image_num.change(lambda i, n: gr.update(visible = (i < n)), [img_i, image_num], o, show_api=False) gen_event = gr.on(triggers=[run_button.click, prompt.submit], fn=lambda i, n, m, t1, t2, n1, n2, n3, n4, n5, l1, l2, l3, l4: infer_fn(m, t1, t2, n1, n2, n3, n4, n5, l1, l2, l3, l4) if (i < n) else None, inputs=[img_i, image_num, model_name, prompt, neg_prompt, height, width, steps, cfg, seed, positive_prefix, positive_suffix, negative_prefix, negative_suffix], outputs=[o], queue=True, show_api=False) gen_event2 = gr.on(triggers=[random_button.click], fn=lambda i, n, m, t1, t2, n1, n2, n3, n4, n5, l1, l2, l3, l4: infer_rand_fn(m, t1, t2, n1, n2, n3, n4, n5, l1, l2, l3, l4) if (i < n) else None, inputs=[img_i, image_num, model_name, prompt, neg_prompt, height, width, steps, cfg, seed, positive_prefix, positive_suffix, negative_prefix, negative_suffix], outputs=[o], queue=True, show_api=False) o.change(save_gallery, [o, results], [results, image_files], show_api=False) stop_button.click(lambda: gr.update(interactive=False), None, stop_button, cancels=[gen_event, gen_event2], show_api=False) clear_prompt.click(lambda: None, None, [prompt], queue=False, show_api=False) clear_results.click(lambda: (None, None), None, [results, image_files], queue=False, show_api=False) recom_prompt_preset.change(set_recom_prompt_preset, [recom_prompt_preset], [positive_prefix, positive_suffix, negative_prefix, negative_suffix], queue=False, show_api=False) demo.queue(default_concurrency_limit=200, max_size=200) demo.launch(max_threads=400)