import gradio as gr import numpy as np import random import spaces # Uncomment if you're using ZeroGPU from diffusers import DiffusionPipeline import torch from tags import participant_tags, tribe_tags, role_tags, skin_tone_tags, body_type_tags, tattoo_tags, piercing_tags, expression_tags, eye_tags, hair_style_tags, position_tags, fetish_tags, location_tags, camera_tags, atmosphere_tags device = "cuda" if torch.cuda.is_available() else "cpu" model_repo_id = "John6666/wai-ani-nsfw-ponyxl-v8-sdxl" # Replace with your desired model if torch.cuda.is_available(): torch_dtype = torch.float16 else: torch_dtype = torch.float32 pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype) pipe.to(device) MAX_SEED = np.iinfo(np.int32).max MAX_IMAGE_SIZE = 1024 @spaces.GPU # Uncomment if using ZeroGPU def infer( prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, selected_participant_tags, selected_tribe_tags, selected_role_tags, selected_skin_tone_tags, selected_body_type_tags, selected_tattoo_tags, selected_piercing_tags, selected_expression_tags, selected_eye_tags, selected_hair_style_tags, selected_position_tags, selected_fetish_tags, selected_location_tags, selected_camera_tags, selected_atmosphere_tags, active_tab, progress=gr.Progress(track_tqdm=True) ): # Handle the active tab and generate the prompt accordingly if active_tab == "Prompt Input": final_prompt = f"score_9, score_8_up, score_7_up, source_anime, {prompt}" else: tag_list = ( [participant_tags[tag] for tag in selected_participant_tags] + [tribe_tags[tag] for tag in selected_tribe_tags] + [role_tags[tag] for tag in selected_role_tags] + [skin_tone_tags[tag] for tag in selected_skin_tone_tags] + [body_type_tags[tag] for tag in selected_body_type_tags] + [tattoo_tags[tag] for tag in selected_tattoo_tags] + [piercing_tags[tag] for tag in selected_piercing_tags] + [expression_tags[tag] for tag in selected_expression_tags] + [eye_tags[tag] for tag in selected_eye_tags] + [hair_style_tags[tag] for tag in selected_hair_style_tags] + [position_tags[tag] for tag in selected_position_tags] + [fetish_tags[tag] for tag in selected_fetish_tags] + [location_tags[tag] for tag in selected_location_tags] + [camera_tags[tag] for tag in selected_camera_tags] + [atmosphere_tags[tag] for tag in selected_atmosphere_tags] ) final_prompt = f"score_9, score_8_up, score_7_up, source_anime, {', '.join(tag_list)}" # Concatenate additional negative prompts additional_negatives = "worst quality, bad quality, jpeg artifacts, source_cartoon, 3d, (censor), monochrome, blurry, lowres, watermark" full_negative_prompt = f"{additional_negatives}, {negative_prompt}" if randomize_seed: seed = random.randint(0, MAX_SEED) generator = torch.Generator(device=device).manual_seed(seed) image = pipe( prompt=final_prompt, negative_prompt=full_negative_prompt, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps, width=width, height=height, generator=generator ).images[0] return image, seed, f"Prompt: {final_prompt}\nNegative Prompt: {full_negative_prompt}" css = """ #col-container { margin: 0 auto; max-width: 640px; } """ with gr.Blocks(css=css) as demo: with gr.Column(elem_id="col-container"): gr.Markdown("# Image Generator with Tags and Prompts") result = gr.Image(label="Result", show_label=False) prompt_info = gr.Textbox(label="Prompts Used", lines=3, interactive=False) active_tab = gr.State("Prompt Input") with gr.Tabs() as tabs: # Prompt Input Tab with gr.TabItem("Prompt Input"): prompt = gr.Textbox(label="Prompt", placeholder="Enter your custom prompt") tabs.select(lambda: "Prompt Input", inputs=None, outputs=active_tab) # Straight Tab with gr.TabItem("Straight"): selected_participant_tags = gr.CheckboxGroup(choices=list(participant_tags.keys()), label="Participant Tags") selected_tribe_tags = gr.CheckboxGroup(choices=list(tribe_tags.keys()), label="Tribe Tags") selected_role_tags = gr.CheckboxGroup(choices=list(role_tags.keys()), label="Role Tags") selected_skin_tone_tags = gr.CheckboxGroup(choices=list(skin_tone_tags.keys()), label="Skin Tone Tags") selected_body_type_tags = gr.CheckboxGroup(choices=list(body_type_tags.keys()), label="Body Type Tags") selected_tattoo_tags = gr.CheckboxGroup(choices=list(tattoo_tags.keys()), label="Tattoo Tags") selected_piercing_tags = gr.CheckboxGroup(choices=list(piercing_tags.keys()), label="Piercing Tags") selected_expression_tags = gr.CheckboxGroup(choices=list(expression_tags.keys()), label="Expression Tags") selected_eye_tags = gr.CheckboxGroup(choices=list(eye_tags.keys()), label="Eye Tags") selected_hair_style_tags = gr.CheckboxGroup(choices=list(hair_style_tags.keys()), label="Hair Style Tags") selected_position_tags = gr.CheckboxGroup(choices=list(position_tags.keys()), label="Position Tags") selected_fetish_tags = gr.CheckboxGroup(choices=list(fetish_tags.keys()), label="Fetish Tags") selected_location_tags = gr.CheckboxGroup(choices=list(location_tags.keys()), label="Location Tags") selected_camera_tags = gr.CheckboxGroup(choices=list(camera_tags.keys()), label="Camera Tags") selected_atmosphere_tags = gr.CheckboxGroup(choices=list(atmosphere_tags.keys()), label="Atmosphere Tags") tabs.select(lambda: "Straight", inputs=None, outputs=active_tab) # Gay Tab with gr.TabItem("Gay"): selected_participant_tags = gr.CheckboxGroup(choices=list(participant_tags.keys()), label="Participant Tags") selected_tribe_tags = gr.CheckboxGroup(choices=list(tribe_tags.keys()), label="Tribe Tags") selected_role_tags = gr.CheckboxGroup(choices=list(role_tags.keys()), label="Role Tags") selected_skin_tone_tags = gr.CheckboxGroup(choices=list(skin_tone_tags.keys()), label="Skin Tone Tags") selected_body_type_tags = gr.CheckboxGroup(choices=list(body_type_tags.keys()), label="Body Type Tags") selected_tattoo_tags = gr.CheckboxGroup(choices=list(tattoo_tags.keys()), label="Tattoo Tags") selected_piercing_tags = gr.CheckboxGroup(choices=list(piercing_tags.keys()), label="Piercing Tags") selected_expression_tags = gr.CheckboxGroup(choices=list(expression_tags.keys()), label="Expression Tags") selected_eye_tags = gr.CheckboxGroup(choices=list(eye_tags.keys()), label="Eye Tags") selected_hair_style_tags = gr.CheckboxGroup(choices=list(hair_style_tags.keys()), label="Hair Style Tags") selected_position_tags = gr.CheckboxGroup(choices=list(position_tags.keys()), label="Position Tags") selected_fetish_tags = gr.CheckboxGroup(choices=list(fetish_tags.keys()), label="Fetish Tags") selected_location_tags = gr.CheckboxGroup(choices=list(location_tags.keys()), label="Location Tags") selected_camera_tags = gr.CheckboxGroup(choices=list(camera_tags.keys()), label="Camera Tags") selected_atmosphere_tags = gr.CheckboxGroup(choices=list(atmosphere_tags.keys()), label="Atmosphere Tags") tabs.select(lambda: "Gay", inputs=None, outputs=active_tab) # Lesbian Tab with gr.TabItem("Lesbian"): selected_participant_tags = gr.CheckboxGroup(choices=list(participant_tags.keys()), label="Participant Tags") selected_tribe_tags = gr.CheckboxGroup(choices=list(tribe_tags.keys()), label="Tribe Tags") selected_role_tags = gr.CheckboxGroup(choices=list(role_tags.keys()), label="Role Tags") selected_skin_tone_tags = gr.CheckboxGroup(choices=list(skin_tone_tags.keys()), label="Skin Tone Tags") selected_body_type_tags = gr.CheckboxGroup(choices=list(body_type_tags.keys()), label="Body Type Tags") selected_tattoo_tags = gr.CheckboxGroup(choices=list(tattoo_tags.keys()), label="Tattoo Tags") selected_piercing_tags = gr.CheckboxGroup(choices=list(piercing_tags.keys()), label="Piercing Tags") selected_expression_tags = gr.CheckboxGroup(choices=list(expression_tags.keys()), label="Expression Tags") selected_eye_tags = gr.CheckboxGroup(choices=list(eye_tags.keys()), label="Eye Tags") selected_hair_style_tags = gr.CheckboxGroup(choices=list(hair_style_tags.keys()), label="Hair Style Tags") selected_position_tags = gr.CheckboxGroup(choices=list(position_tags.keys()), label="Position Tags") selected_fetish_tags = gr.CheckboxGroup(choices=list(fetish_tags.keys()), label="Fetish Tags") selected_location_tags = gr.CheckboxGroup(choices=list(location_tags.keys()), label="Location Tags") selected_camera_tags = gr.CheckboxGroup(choices=list(camera_tags.keys()), label="Camera Tags") selected_atmosphere_tags = gr.CheckboxGroup(choices=list(atmosphere_tags.keys()), label="Atmosphere Tags") tabs.select(lambda: "Lesbian", inputs=None, outputs=active_tab) # Advanced Settings with gr.Accordion("Advanced Settings", open=False): negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="Enter negative prompt") seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0) randomize_seed = gr.Checkbox(label="Randomize Seed", value=True) with gr.Row(): width = gr.Slider(label="Width", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=1024) height = gr.Slider(label="Height", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=1024) with gr.Row(): guidance_scale = gr.Slider(label="Guidance Scale", minimum=0.0, maximum=10.0, step=0.1, value=7) num_inference_steps = gr.Slider(label="Number of Inference Steps", minimum=1, maximum=50, step=1, value=35) run_button = gr.Button("Run") run_button.click( infer, inputs=[prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, selected_participant_tags, selected_tribe_tags, selected_role_tags, selected_skin_tone_tags, selected_body_type_tags, selected_tattoo_tags, selected_piercing_tags, selected_expression_tags, selected_eye_tags, selected_hair_style_tags, selected_position_tags, selected_fetish_tags, selected_location_tags, selected_camera_tags, selected_atmosphere_tags, active_tab], outputs=[result, seed, prompt_info] ) demo.queue().launch()