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Running
on
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Running
on
Zero
Update app.py
Browse files
app.py
CHANGED
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@@ -7,7 +7,9 @@ import torch
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from tags import participant_tags, tribe_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
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device = "cuda" if torch.cuda.is_available() else "cpu"
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if torch.cuda.is_available():
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torch_dtype = torch.float16
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@@ -20,6 +22,14 @@ pipe = pipe.to(device)
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MAX_SEED = np.iinfo(np.int32).max
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MAX_IMAGE_SIZE = 1024
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@spaces.GPU # [uncomment to use ZeroGPU]
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def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps,
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selected_participant_tags, selected_tribe_tags, selected_skin_tone_tags, selected_body_type_tags,
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@@ -28,10 +38,8 @@ def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance
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selected_camera_tags, selected_atmosphere_tags, active_tab, progress=gr.Progress(track_tqdm=True)):
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if active_tab == "Prompt Input":
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# Use the user-provided prompt
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final_prompt = f'score_9, score_8_up, score_7_up, source_anime, {prompt}'
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else:
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# Use tags from the "Tag Selection" tab
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selected_tags = (
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[participant_tags[tag] for tag in selected_participant_tags] +
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[tribe_tags[tag] for tag in selected_tribe_tags] +
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@@ -51,7 +59,6 @@ def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance
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tags_text = ', '.join(selected_tags)
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final_prompt = f'score_9, score_8_up, score_7_up, source_anime, {tags_text}'
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# Concatenate user-provided negative prompt with additional restrictions
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additional_negatives = "worst quality, bad quality, jpeg artifacts, source_cartoon, 3d, (censor), monochrome, blurry, lowres, watermark"
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full_negative_prompt = f"{additional_negatives}, {negative_prompt}"
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@@ -71,10 +78,9 @@ def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance
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generator=generator
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).images[0]
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# Return image, seed, and the used prompts
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return image, seed, f"Prompt used: {final_prompt}\nNegative prompt used: {full_negative_prompt}"
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css = """
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#col-container {
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margin: 0 auto;
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@@ -123,45 +129,25 @@ css = """
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margin-bottom: 20px;
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}
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margin-bottom: 20px;
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display: flex;
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}
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flex: 1;
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}
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"""
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with gr.Blocks(css=css) as demo:
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with gr.Row():
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with gr.Column(elem_id="left-column"):
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gr.Markdown("""# Rainbow Media X""")
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# Add buttons for external links above the prompt using Markdown
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with gr.Row(elem_id="external-links"):
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gr.Markdown(
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"""
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<a href="https://example.com/space1" target="_blank">
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<button class="gradio-button" style="width: 100%;">Go to Space 1</button>
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</a>
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<a href="https://example.com/space2" target="_blank">
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<button class="gradio-button" style="width: 100%;">Go to Space 2</button>
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</a>
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<a href="https://example.com/space3" target="_blank">
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<button class="gradio-button" style="width: 100%;">Go to Space 3</button>
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</a>
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"""
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)
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# Display result image at the top
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result = gr.Image(label="Result", show_label=False, elem_id="result")
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# Add a textbox to display the prompts used for generation
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prompt_info = gr.Textbox(label="Prompts Used", lines=3, interactive=False, elem_id="prompt-info")
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# Advanced Settings and Run Button
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@@ -217,15 +203,10 @@ with gr.Blocks(css=css) as demo:
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value=35,
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)
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# Full-width "Run" button
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run_button = gr.Button("Run", elem_id="run-button")
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with gr.Column(elem_id="right-column"):
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# Removed the Prompt / Tag Input title here
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# State to track active tab
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active_tab = gr.State("Prompt Input")
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# Tabbed interface to select either Prompt or Tags
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with gr.Tabs() as tabs:
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with gr.TabItem("Prompt Input") as prompt_tab:
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prompt = gr.Textbox(
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prompt_tab.select(lambda: "Prompt Input", inputs=None, outputs=active_tab)
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with gr.TabItem("Tag Selection") as tag_tab:
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# Tag selection checkboxes for each tag group
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selected_participant_tags = gr.CheckboxGroup(choices=list(participant_tags.keys()), label="Participant Tags")
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selected_tribe_tags = gr.CheckboxGroup(choices=list(tribe_tags.keys()), label="Tribe Tags")
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selected_skin_tone_tags = gr.CheckboxGroup(choices=list(skin_tone_tags.keys()), label="Skin Tone Tags")
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@@ -256,6 +236,16 @@ with gr.Blocks(css=css) as demo:
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selected_atmosphere_tags = gr.CheckboxGroup(choices=list(atmosphere_tags.keys()), label="Atmosphere Tags")
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tag_tab.select(lambda: "Tag Selection", inputs=None, outputs=active_tab)
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run_button.click(
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infer,
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inputs=[prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps,
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from tags import participant_tags, tribe_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
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device = "cuda" if torch.cuda.is_available() else "cpu"
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# Default model version
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model_repo_id = "John6666/wai-ani-nsfw-ponyxl-v8-sdxl" # Default model V8
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if torch.cuda.is_available():
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torch_dtype = torch.float16
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MAX_SEED = np.iinfo(np.int32).max
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MAX_IMAGE_SIZE = 1024
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def update_model_version(version):
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"""Update the model version dynamically based on the selected version."""
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global model_repo_id, pipe
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model_repo_id = f"John6666/wai-ani-nsfw-ponyxl-{version}-sdxl"
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pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
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pipe = pipe.to(device)
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print(f"Model switched to {model_repo_id}")
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@spaces.GPU # [uncomment to use ZeroGPU]
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def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps,
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selected_participant_tags, selected_tribe_tags, selected_skin_tone_tags, selected_body_type_tags,
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selected_camera_tags, selected_atmosphere_tags, active_tab, progress=gr.Progress(track_tqdm=True)):
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if active_tab == "Prompt Input":
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final_prompt = f'score_9, score_8_up, score_7_up, source_anime, {prompt}'
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else:
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selected_tags = (
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[participant_tags[tag] for tag in selected_participant_tags] +
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[tribe_tags[tag] for tag in selected_tribe_tags] +
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tags_text = ', '.join(selected_tags)
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final_prompt = f'score_9, score_8_up, score_7_up, source_anime, {tags_text}'
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additional_negatives = "worst quality, bad quality, jpeg artifacts, source_cartoon, 3d, (censor), monochrome, blurry, lowres, watermark"
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full_negative_prompt = f"{additional_negatives}, {negative_prompt}"
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generator=generator
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).images[0]
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return image, seed, f"Prompt used: {final_prompt}\nNegative prompt used: {full_negative_prompt}"
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# CSS for button styling and horizontal layout
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css = """
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#col-container {
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margin: 0 auto;
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margin-bottom: 20px;
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}
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.button-group {
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display: flex;
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justify-content: space-between;
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}
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.button-group .gradio-button {
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flex: 1;
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margin: 0 10px;
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text-align: center;
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}
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"""
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# Gradio interface setup
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with gr.Blocks(css=css) as demo:
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with gr.Row():
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with gr.Column(elem_id="left-column"):
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gr.Markdown("""# Rainbow Media X""")
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result = gr.Image(label="Result", show_label=False, elem_id="result")
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prompt_info = gr.Textbox(label="Prompts Used", lines=3, interactive=False, elem_id="prompt-info")
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# Advanced Settings and Run Button
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value=35,
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)
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run_button = gr.Button("Run", elem_id="run-button")
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with gr.Column(elem_id="right-column"):
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active_tab = gr.State("Prompt Input")
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with gr.Tabs() as tabs:
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with gr.TabItem("Prompt Input") as prompt_tab:
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prompt = gr.Textbox(
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prompt_tab.select(lambda: "Prompt Input", inputs=None, outputs=active_tab)
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with gr.TabItem("Tag Selection") as tag_tab:
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selected_participant_tags = gr.CheckboxGroup(choices=list(participant_tags.keys()), label="Participant Tags")
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selected_tribe_tags = gr.CheckboxGroup(choices=list(tribe_tags.keys()), label="Tribe Tags")
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selected_skin_tone_tags = gr.CheckboxGroup(choices=list(skin_tone_tags.keys()), label="Skin Tone Tags")
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selected_atmosphere_tags = gr.CheckboxGroup(choices=list(atmosphere_tags.keys()), label="Atmosphere Tags")
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tag_tab.select(lambda: "Tag Selection", inputs=None, outputs=active_tab)
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# Add buttons for selecting model versions
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with gr.Row(elem_id="button-group"):
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link_button_v7 = gr.Button("V7 Model", elem_id="link-v7", variant="primary")
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link_button_v8 = gr.Button("V8 Model", elem_id="link-v8", variant="primary")
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link_button_v11 = gr.Button("V11 Model", elem_id="link-v11", variant="primary")
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link_button_v7.click(update_model_version, inputs=["V7"], outputs=[])
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link_button_v8.click(update_model_version, inputs=["V8"], outputs=[])
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link_button_v11.click(update_model_version, inputs=["V11"], outputs=[])
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run_button.click(
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infer,
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inputs=[prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps,
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