Update app.py

app.py

@@ -1,10 +1,10 @@
 import gradio as gr
 import numpy as np
 import random
-import torch
-import spaces
+import spaces  # [uncomment to use ZeroGPU]
 from diffusers import DiffusionPipeline
-import importlib
+import torch
+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
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
 model_repo_id = "John6666/wai-ani-nsfw-ponyxl-v8-sdxl"  # Replace with your desired model
@@ -15,227 +15,194 @@ else:
     torch_dtype = torch.float32
 
 pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe.to(device)
+pipe = pipe.to(device)
 
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1024
 
-# Function to load tags dynamically based on the selected tab
-def load_tags(active_tab):
-    try:
-        if active_tab == "Gay":
-            return importlib.import_module('tags_gay')  # dynamically import the tags_gay module
-        elif active_tab == "Straight":
-            return importlib.import_module('tags_straight')  # dynamically import the tags_straight module
-        elif active_tab == "Lesbian":
-            return importlib.import_module('tags_lesbian')  # dynamically import the tags_lesbian module
-        else:
-            raise ValueError(f"Unknown tab: {active_tab}")
-    except Exception as e:
-        print(f"Error loading tags for {active_tab}: {str(e)}")
-        raise
-
-@spaces.GPU
-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)
-):
-    # Dynamically load the correct tags module based on active tab
-    tags_module = load_tags(active_tab)
-
-    # Get the tag dictionaries from the loaded module
-    participant_tags = tags_module.participant_tags
-    tribe_tags = tags_module.tribe_tags
-    role_tags = tags_module.role_tags
-    skin_tone_tags = tags_module.skin_tone_tags
-    body_type_tags = tags_module.body_type_tags
-    tattoo_tags = tags_module.tattoo_tags
-    piercing_tags = tags_module.piercing_tags
-    expression_tags = tags_module.expression_tags
-    eye_tags = tags_module.eye_tags
-    hair_style_tags = tags_module.hair_style_tags
-    position_tags = tags_module.position_tags
-    fetish_tags = tags_module.fetish_tags
-    location_tags = tags_module.location_tags
-    camera_tags = tags_module.camera_tags
-    atmosphere_tags = tags_module.atmosphere_tags
-
-    # Build the tag list using selected tags from each group
-    tag_list = []
-
-    # Add selected participant tags
-    for tag in selected_participant_tags:
-        if tag in participant_tags:
-            tag_list.append(participant_tags[tag])
-
-    # Add selected tribe tags
-    for tag in selected_tribe_tags:
-        if tag in tribe_tags:
-            tag_list.append(tribe_tags[tag])
-
-    # Add selected role tags
-    for tag in selected_role_tags:
-        if tag in role_tags:
-            tag_list.append(role_tags[tag])
-
-    # Add selected skin tone tags
-    for tag in selected_skin_tone_tags:
-        if tag in skin_tone_tags:
-            tag_list.append(skin_tone_tags[tag])
-
-    # Add selected body type tags
-    for tag in selected_body_type_tags:
-        if tag in body_type_tags:
-            tag_list.append(body_type_tags[tag])
-
-    # Add selected tattoo tags
-    for tag in selected_tattoo_tags:
-        if tag in tattoo_tags:
-            tag_list.append(tattoo_tags[tag])
-
-    # Add selected piercing tags
-    for tag in selected_piercing_tags:
-        if tag in piercing_tags:
-            tag_list.append(piercing_tags[tag])
-
-    # Add selected expression tags
-    for tag in selected_expression_tags:
-        if tag in expression_tags:
-            tag_list.append(expression_tags[tag])
-
-    # Add selected eye tags
-    for tag in selected_eye_tags:
-        if tag in eye_tags:
-            tag_list.append(eye_tags[tag])
-
-    # Add selected hair style tags
-    for tag in selected_hair_style_tags:
-        if tag in hair_style_tags:
-            tag_list.append(hair_style_tags[tag])
-
-    # Add selected position tags
-    for tag in selected_position_tags:
-        if tag in position_tags:
-            tag_list.append(position_tags[tag])
-
-    # Add selected fetish tags
-    for tag in selected_fetish_tags:
-        if tag in fetish_tags:
-            tag_list.append(fetish_tags[tag])
-
-    # Add selected location tags
-    for tag in selected_location_tags:
-        if tag in location_tags:
-            tag_list.append(location_tags[tag])
-
-    # Add selected camera tags
-    for tag in selected_camera_tags:
-        if tag in camera_tags:
-            tag_list.append(camera_tags[tag])
-
-    # Add selected atmosphere tags
-    for tag in selected_atmosphere_tags:
-        if tag in atmosphere_tags:
-            tag_list.append(atmosphere_tags[tag])
-
-    # Construct final prompt
-    final_prompt = f"score_9, score_8_up, score_7_up, source_anime, {', '.join(tag_list)}"
-
-    # Negative prompt
+@spaces.GPU  # [uncomment to use ZeroGPU]
+def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps,
+          selected_participant_tags, selected_tribe_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)):
+
+    if active_tab == "Prompt Input":
+        # Use the user-provided prompt
+        final_prompt = f'score_9, score_8_up, score_7_up, source_anime, {prompt}'
+    else:
+        # Use tags from the "Tag Selection" tab
+        selected_tags = (
+            [participant_tags[tag] for tag in selected_participant_tags] +
+            [tribe_tags[tag] for tag in selected_tribe_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]
+        )
+        tags_text = ', '.join(selected_tags)
+        final_prompt = f'score_9, score_8_up, score_7_up, source_anime, {tags_text}'
+
+    # Concatenate user-provided negative prompt with additional restrictions
     additional_negatives = "worst quality, bad quality, jpeg artifacts, source_cartoon, 3d, (censor), monochrome, blurry, lowres, watermark"
     full_negative_prompt = f"{additional_negatives}, {negative_prompt}"
 
-    # Handle random seed if needed
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
-    generator = torch.Generator(device=device).manual_seed(seed)
-
-    # Generate the image
-    try:
-        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]
-    except Exception as e:
-        print(f"Error generating image: {str(e)}")
-        raise
-
-    return image, seed, f"Prompt: {final_prompt}\nNegative Prompt: {full_negative_prompt}"
-
-# Gradio UI setup
+
+    generator = torch.Generator().manual_seed(seed)
+
+    # Generate the image with the final prompts
+    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, and the used prompts
+    return image, seed, f"Prompt used: {final_prompt}\nNegative prompt used: {full_negative_prompt}"
+
+
+examples = [
+    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
+    "An astronaut riding a green horse",
+    "A delicious ceviche cheesecake slice",
+]
+
 css = """
 #col-container {
     margin: 0 auto;
     max-width: 640px;
 }
+#run-button {
+    width: 100%;
+}
 """
 
 with gr.Blocks(css=css) as demo:
+
     with gr.Column(elem_id="col-container"):
-        gr.Markdown("# Image Generator with Tags and Prompts")
+        gr.Markdown("""# Text-to-Image Gradio Template""")
 
+        # Display result image at the top
         result = gr.Image(label="Result", show_label=False)
+
+        # Add a textbox to display the prompts used for generation
         prompt_info = gr.Textbox(label="Prompts Used", lines=3, interactive=False)
+
+        # State to track active tab
         active_tab = gr.State("Prompt Input")
 
+        # Tabbed interface to select either Prompt or Tags
         with gr.Tabs() as tabs:
-            # Tab setup for different categories
-            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)
-
-            with gr.TabItem("Straight"):
-                tags_module = load_tags("Straight")
-                selected_participant_tags = gr.CheckboxGroup(choices=list(tags_module.participant_tags.keys()), label="Participant Tags")
-                selected_tribe_tags = gr.CheckboxGroup(choices=list(tags_module.tribe_tags.keys()), label="Tribe Tags")
-                selected_role_tags = gr.CheckboxGroup(choices=list(tags_module.role_tags.keys()), label="Role Tags")
-                selected_skin_tone_tags = gr.CheckboxGroup(choices=list(tags_module.skin_tone_tags.keys()), label="Skin Tone Tags")
-                selected_body_type_tags = gr.CheckboxGroup(choices=list(tags_module.body_type_tags.keys()), label="Body Type Tags")
-                selected_tattoo_tags = gr.CheckboxGroup(choices=list(tags_module.tattoo_tags.keys()), label="Tattoo Tags")
-                selected_piercing_tags = gr.CheckboxGroup(choices=list(tags_module.piercing_tags.keys()), label="Piercing Tags")
-                selected_expression_tags = gr.CheckboxGroup(choices=list(tags_module.expression_tags.keys()), label="Expression Tags")
-                selected_eye_tags = gr.CheckboxGroup(choices=list(tags_module.eye_tags.keys()), label="Eye Tags")
-                selected_hair_style_tags = gr.CheckboxGroup(choices=list(tags_module.hair_style_tags.keys()), label="Hair Style Tags")
-                selected_position_tags = gr.CheckboxGroup(choices=list(tags_module.position_tags.keys()), label="Position Tags")
-                selected_fetish_tags = gr.CheckboxGroup(choices=list(tags_module.fetish_tags.keys()), label="Fetish Tags")
-                selected_location_tags = gr.CheckboxGroup(choices=list(tags_module.location_tags.keys()), label="Location Tags")
-                selected_camera_tags = gr.CheckboxGroup(choices=list(tags_module.camera_tags.keys()), label="Camera Tags")
-                selected_atmosphere_tags = gr.CheckboxGroup(choices=list(tags_module.atmosphere_tags.keys()), label="Atmosphere Tags")
-                tabs.select(lambda: "Straight", inputs=None, outputs=active_tab)
-
-        # Advanced settings
+            with gr.TabItem("Prompt Input") as prompt_tab:
+                prompt = gr.Textbox(
+                    label="Prompt",
+                    show_label=False,
+                    max_lines=1,
+                    placeholder="Enter your prompt",
+                    container=False,
+                )
+                prompt_tab.select(lambda: "Prompt Input", inputs=None, outputs=active_tab)
+
+            with gr.TabItem("Tag Selection") as tag_tab:
+                # Tag selection checkboxes for each tag group
+                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_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")
+                tag_tab.select(lambda: "Tag Selection", inputs=None, outputs=active_tab)
+
+        # Full-width "Run" button
+        run_button = gr.Button("Run", scale=0, elem_id="run-button")
+
         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)
+            negative_prompt = gr.Textbox(
+                label="Negative prompt",
+                max_lines=1,
+                placeholder="Enter a negative prompt",
+                visible=True,
+            )
+
+            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)
+                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)
+                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,
+                )
+
+        gr.Examples(
+            examples=examples,
+            inputs=[prompt]
+        )
 
-        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],
+                    selected_participant_tags, selected_tribe_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()
+demo.queue().launch()