run_on_click = True,

app.py

@@ -1,335 +1,336 @@
-import os
-# PyTorch 2.8 (temporary hack)
-os.system('pip install --upgrade --pre --extra-index-url https://download.pytorch.org/whl/nightly/cu126 "torch<2.9" spaces')
-
-# --- 1. Model Download and Setup (Diffusers Backend) ---
-import spaces
-import torch
-from diffusers import FlowMatchEulerDiscreteScheduler
-from diffusers.pipelines.wan.pipeline_wan_i2v import WanImageToVideoPipeline
-from diffusers.models.transformers.transformer_wan import WanTransformer3DModel
-from diffusers.utils.export_utils import export_to_video
-import gradio as gr
-import tempfile
-import numpy as np
-from PIL import Image
-import random
-import gc
-from gradio_client import Client, handle_file # Import for API call
-
-# Import the optimization function from the separate file
-from optimization import optimize_pipeline_
-
-# --- Constants and Model Loading ---
-MODEL_ID = "Wan-AI/Wan2.2-I2V-A14B-Diffusers"
-
-# --- NEW: Flexible Dimension Constants ---
-MAX_DIMENSION = 832
-MIN_DIMENSION = 480
-DIMENSION_MULTIPLE = 16
-SQUARE_SIZE = 480
-
-MAX_SEED = np.iinfo(np.int32).max
-
-FIXED_FPS = 16
-MIN_FRAMES_MODEL = 8
-MAX_FRAMES_MODEL = 81
-
-MIN_DURATION = round(MIN_FRAMES_MODEL/FIXED_FPS, 1)
-MAX_DURATION = round(MAX_FRAMES_MODEL/FIXED_FPS, 1)
-
-default_negative_prompt = "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走,过曝，"
-
-print("Loading models into memory. This may take a few minutes...")
-
-pipe = WanImageToVideoPipeline.from_pretrained(
-    MODEL_ID,
-    transformer=WanTransformer3DModel.from_pretrained('cbensimon/Wan2.2-I2V-A14B-bf16-Diffusers',
-        subfolder='transformer',
-        torch_dtype=torch.bfloat16,
-        device_map='cuda',
-    ),
-    transformer_2=WanTransformer3DModel.from_pretrained('cbensimon/Wan2.2-I2V-A14B-bf16-Diffusers',
-        subfolder='transformer_2',
-        torch_dtype=torch.bfloat16,
-        device_map='cuda',
-    ),
-    torch_dtype=torch.bfloat16,
-)
-pipe.scheduler = FlowMatchEulerDiscreteScheduler.from_config(pipe.scheduler.config, shift=8.0)
-pipe.to('cuda')
-
-
-
-print("Optimizing pipeline...")
-for i in range(3):
-    gc.collect()
-    torch.cuda.synchronize()
-    torch.cuda.empty_cache()
-
-optimize_pipeline_(pipe,
-    image=Image.new('RGB', (MAX_DIMENSION, MIN_DIMENSION)), 
-    prompt='prompt',
-    height=MIN_DIMENSION,
-    width=MAX_DIMENSION,
-    num_frames=MAX_FRAMES_MODEL,
-)
-print("All models loaded and optimized. Gradio app is ready.")
-
-
-# --- 2. Image Processing and Application Logic ---
-def generate_end_frame(start_img, gen_prompt, progress=gr.Progress(track_tqdm=True)):
-    """Calls an external Gradio API to generate an image."""
-    if start_img is None:
-        raise gr.Error("Please provide a Start Frame first.")
-
-    hf_token = os.getenv("HF_TOKEN")
-    if not hf_token:
-        raise gr.Error("HF_TOKEN not found in environment variables. Please set it in your Space secrets.")
-
-    with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmpfile:
-        start_img.save(tmpfile.name)
-        tmp_path = tmpfile.name
-
-    progress(0.1, desc="Connecting to image generation API...")
-    client = Client("multimodalart/nano-banana-private")
-    
-    progress(0.5, desc=f"Generating with prompt: '{gen_prompt}'...")
-    try:
-        result = client.predict(
-            prompt=gen_prompt, 
-            images=[
-                {"image": handle_file(tmp_path)}
-            ],
-            manual_token=hf_token,
-            api_name="/unified_image_generator"
-        )
-    finally:
-        os.remove(tmp_path)
-
-    progress(1.0, desc="Done!")
-    print(result)
-    return result
-
-def switch_to_upload_tab():
-    """Returns a gr.Tabs update to switch to the first tab."""
-    return gr.Tabs(selected="upload_tab")
-
-
-def process_image_for_video(image: Image.Image) -> Image.Image:
-    """
-    Resizes an image based on the following rules for video generation:
-    1. The longest side will be scaled down to MAX_DIMENSION if it's larger.
-    2. The shortest side will be scaled up to MIN_DIMENSION if it's smaller.
-    3. The final dimensions will be rounded to the nearest multiple of DIMENSION_MULTIPLE.
-    4. Square images are resized to a fixed SQUARE_SIZE.
-    The aspect ratio is preserved as closely as possible.
-    """
-    width, height = image.size
-
-    # Rule 4: Handle square images
-    if width == height:
-        return image.resize((SQUARE_SIZE, SQUARE_SIZE), Image.Resampling.LANCZOS)
-
-    # Determine target dimensions while preserving aspect ratio
-    aspect_ratio = width / height
-    new_width, new_height = width, height
-
-    # Rule 1: Scale down if too large
-    if new_width > MAX_DIMENSION or new_height > MAX_DIMENSION:
-        if aspect_ratio > 1:  # Landscape
-            scale = MAX_DIMENSION / new_width
-        else:  # Portrait
-            scale = MAX_DIMENSION / new_height
-        new_width *= scale
-        new_height *= scale
-
-    # Rule 2: Scale up if too small
-    if new_width < MIN_DIMENSION or new_height < MIN_DIMENSION:
-        if aspect_ratio > 1:  # Landscape
-            scale = MIN_DIMENSION / new_height
-        else:  # Portrait
-            scale = MIN_DIMENSION / new_width
-        new_width *= scale
-        new_height *= scale
-
-    # Rule 3: Round to the nearest multiple of DIMENSION_MULTIPLE
-    final_width = int(round(new_width / DIMENSION_MULTIPLE) * DIMENSION_MULTIPLE)
-    final_height = int(round(new_height / DIMENSION_MULTIPLE) * DIMENSION_MULTIPLE)
-    
-    # Ensure final dimensions are at least the minimum
-    final_width = max(final_width, MIN_DIMENSION if aspect_ratio < 1 else SQUARE_SIZE)
-    final_height = max(final_height, MIN_DIMENSION if aspect_ratio > 1 else SQUARE_SIZE)
-
-
-    return image.resize((final_width, final_height), Image.Resampling.LANCZOS)
-
-def resize_and_crop_to_match(target_image, reference_image):
-    """Resizes and center-crops the target image to match the reference image's dimensions."""
-    ref_width, ref_height = reference_image.size
-    target_width, target_height = target_image.size
-    scale = max(ref_width / target_width, ref_height / target_height)
-    new_width, new_height = int(target_width * scale), int(target_height * scale)
-    resized = target_image.resize((new_width, new_height), Image.Resampling.LANCZOS)
-    left, top = (new_width - ref_width) // 2, (new_height - ref_height) // 2
-    return resized.crop((left, top, left + ref_width, top + ref_height))
-
-@spaces.GPU(duration=120)
-def generate_video(
-    start_image_pil,
-    end_image_pil,
-    prompt,
-    negative_prompt=default_negative_prompt,
-    duration_seconds=2.1,
-    steps=8,
-    guidance_scale=1,
-    guidance_scale_2=1,
-    seed=42,
-    randomize_seed=False,
-    progress=gr.Progress(track_tqdm=True)
-):
-    """
-    Generates a video by interpolating between a start and end image, guided by a text prompt,
-    using the diffusers Wan2.2 pipeline.
-    """
-    if start_image_pil is None or end_image_pil is None:
-        raise gr.Error("Please upload both a start and an end image.")
-
-    progress(0.1, desc="Preprocessing images...")
-
-    # Step 1: Process the start image to get our target dimensions based on the new rules.
-    processed_start_image = process_image_for_video(start_image_pil)
-    
-    # Step 2: Make the end image match the *exact* dimensions of the processed start image.
-    processed_end_image = resize_and_crop_to_match(end_image_pil, processed_start_image)
-    
-    target_height, target_width = processed_start_image.height, processed_start_image.width
-
-    # Handle seed and frame count
-    current_seed = random.randint(0, MAX_SEED) if randomize_seed else int(seed)
-    num_frames = np.clip(int(round(duration_seconds * FIXED_FPS)), MIN_FRAMES_MODEL, MAX_FRAMES_MODEL)
-
-    progress(0.2, desc=f"Generating {num_frames} frames at {target_width}x{target_height} (seed: {current_seed})...")
-
-    output_frames_list = pipe(
-        image=processed_start_image,
-        last_image=processed_end_image,
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        height=target_height,
-        width=target_width,
-        num_frames=num_frames,
-        guidance_scale=float(guidance_scale),
-        guidance_scale_2=float(guidance_scale_2),
-        num_inference_steps=int(steps),
-        generator=torch.Generator(device="cuda").manual_seed(current_seed),
-    ).frames[0]
-
-    progress(0.9, desc="Encoding and saving video...")
-
-    with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as tmpfile:
-        video_path = tmpfile.name
-
-    export_to_video(output_frames_list, video_path, fps=FIXED_FPS)
-
-    progress(1.0, desc="Done!")
-    return video_path, current_seed
-
-
-# --- 3. Gradio User Interface ---
-
-css = '''
-.fillable{max-width: 1100px !important}
-.dark .progress-text {color: white}
-#general_items{margin-top: 2em}
-#group_all{overflow:visible}
-#group_all .styler{overflow:visible}
-#group_tabs .tabitem{padding: 0}
-.tab-wrapper{margin-top: -33px;z-index: 999;position: absolute;width: 100%;background-color: var(--block-background-fill);padding: 0;}
-#component-9-button{width: 50%;justify-content: center}
-#component-11-button{width: 50%;justify-content: center}
-#or_item{text-align: center; padding-top: 1em; padding-bottom: 1em; font-size: 1.1em;margin-left: .5em;margin-right: .5em;width: calc(100% - 1em)}
-#fivesec{margin-top: 5em;margin-left: .5em;margin-right: .5em;width: calc(100% - 1em)}
-'''
-with gr.Blocks(theme=gr.themes.Citrus(), css=css) as app:
-    gr.Markdown("# Wan 2.2 First/Last Frame Video Fast")
-    gr.Markdown("Based on the [Wan 2.2 First/Last Frame workflow](https://www.reddit.com/r/StableDiffusion/comments/1me4306/psa_wan_22_does_first_frame_last_frame_out_of_the/), applied to 🧨 Diffusers + [lightx2v/Wan2.2-Lightning](https://huggingface.co/lightx2v/Wan2.2-Lightning) 8-step LoRA")
-
-    with gr.Row(elem_id="general_items"):
-        with gr.Column():
-            with gr.Group(elem_id="group_all"):
-                with gr.Row():
-                    start_image = gr.Image(type="pil", label="Start Frame", sources=["upload", "clipboard"])
-                    # Capture the Tabs component in a variable and assign IDs to tabs
-                    with gr.Tabs(elem_id="group_tabs") as tabs:
-                        with gr.TabItem("Upload", id="upload_tab"):
-                            end_image = gr.Image(type="pil", label="End Frame", sources=["upload", "clipboard"])
-                        with gr.TabItem("Generate", id="generate_tab"):
-                            generate_5seconds = gr.Button("Generate scene 5 seconds in the future", elem_id="fivesec")
-                            gr.Markdown("Generate a custom end-frame with an edit model like [Nano Banana](https://huggingface.co/spaces/multimodalart/nano-banana) or [Qwen Image Edit](https://huggingface.co/spaces/multimodalart/Qwen-Image-Edit-Fast)", elem_id="or_item")
-                prompt = gr.Textbox(label="Prompt", info="Describe the transition between the two images")
-
-                with gr.Accordion("Advanced Settings", open=False):
-                    duration_seconds_input = gr.Slider(minimum=MIN_DURATION, maximum=MAX_DURATION, step=0.1, value=2.1, label="Video Duration (seconds)", info=f"Clamped to model's {MIN_FRAMES_MODEL}-{MAX_FRAMES_MODEL} frames at {FIXED_FPS}fps.")
-                    negative_prompt_input = gr.Textbox(label="Negative Prompt", value=default_negative_prompt, lines=3)
-                    steps_slider = gr.Slider(minimum=1, maximum=30, step=1, value=8, label="Inference Steps")
-                    guidance_scale_input = gr.Slider(minimum=0.0, maximum=10.0, step=0.5, value=1.0, label="Guidance Scale - high noise")
-                    guidance_scale_2_input = gr.Slider(minimum=0.0, maximum=10.0, step=0.5, value=1.0, label="Guidance Scale - low noise")
-                    with gr.Row():
-                        seed_input = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=42)
-                        randomize_seed_checkbox = gr.Checkbox(label="Randomize seed", value=True)
-
-                generate_button = gr.Button("Generate Video", variant="primary")
-
-        with gr.Column():
-            output_video = gr.Video(label="Generated Video", autoplay=True)
-
-    # Main video generation button
-    ui_inputs = [
-        start_image,
-        end_image,
-        prompt,
-        negative_prompt_input,
-        duration_seconds_input,
-        steps_slider,
-        guidance_scale_input,
-        guidance_scale_2_input,
-        seed_input,
-        randomize_seed_checkbox
-    ]
-    ui_outputs = [output_video, seed_input]
-
-    generate_button.click(
-        fn=generate_video,
-        inputs=ui_inputs,
-        outputs=ui_outputs
-    )
-
-    generate_5seconds.click(
-        fn=switch_to_upload_tab,
-        inputs=None,
-        outputs=[tabs]
-    ).then( 
-        fn=lambda img: generate_end_frame(img, "this image is a still frame from a movie. generate a new frame with what happens on this scene 5 seconds in the future"),
-        inputs=[start_image],
-        outputs=[end_image]
-    ).success(
-        fn=generate_video,
-        inputs=ui_inputs,
-        outputs=ui_outputs
-    )
-
-    gr.Examples(
-        examples=[
-            ["poli_tower.png", "tower_takes_off.png", "the man turns around"],
-            ["ugly_sonic.jpeg", "squatting_sonic.png", "the character dodges the missiles"],
-            ["capyabara_zoomed.png", "capyabara.webp", "a dramatic dolly zoom"],
-        ],
-        inputs=[start_image, end_image, prompt],
-        outputs=ui_outputs,
-        fn=generate_video,
-        cache_examples="lazy",
-    )
-
-if __name__ == "__main__":
+import os
+# PyTorch 2.8 (temporary hack)
+os.system('pip install --upgrade --pre --extra-index-url https://download.pytorch.org/whl/nightly/cu126 "torch<2.9" spaces')
+
+# --- 1. Model Download and Setup (Diffusers Backend) ---
+import spaces
+import torch
+from diffusers import FlowMatchEulerDiscreteScheduler
+from diffusers.pipelines.wan.pipeline_wan_i2v import WanImageToVideoPipeline
+from diffusers.models.transformers.transformer_wan import WanTransformer3DModel
+from diffusers.utils.export_utils import export_to_video
+import gradio as gr
+import tempfile
+import numpy as np
+from PIL import Image
+import random
+import gc
+from gradio_client import Client, handle_file # Import for API call
+
+# Import the optimization function from the separate file
+from optimization import optimize_pipeline_
+
+# --- Constants and Model Loading ---
+MODEL_ID = "Wan-AI/Wan2.2-I2V-A14B-Diffusers"
+
+# --- NEW: Flexible Dimension Constants ---
+MAX_DIMENSION = 832
+MIN_DIMENSION = 480
+DIMENSION_MULTIPLE = 16
+SQUARE_SIZE = 480
+
+MAX_SEED = np.iinfo(np.int32).max
+
+FIXED_FPS = 16
+MIN_FRAMES_MODEL = 8
+MAX_FRAMES_MODEL = 81
+
+MIN_DURATION = round(MIN_FRAMES_MODEL/FIXED_FPS, 1)
+MAX_DURATION = round(MAX_FRAMES_MODEL/FIXED_FPS, 1)
+
+default_negative_prompt = "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走,过曝，"
+
+print("Loading models into memory. This may take a few minutes...")
+
+pipe = WanImageToVideoPipeline.from_pretrained(
+    MODEL_ID,
+    transformer=WanTransformer3DModel.from_pretrained('cbensimon/Wan2.2-I2V-A14B-bf16-Diffusers',
+        subfolder='transformer',
+        torch_dtype=torch.bfloat16,
+        device_map='cuda',
+    ),
+    transformer_2=WanTransformer3DModel.from_pretrained('cbensimon/Wan2.2-I2V-A14B-bf16-Diffusers',
+        subfolder='transformer_2',
+        torch_dtype=torch.bfloat16,
+        device_map='cuda',
+    ),
+    torch_dtype=torch.bfloat16,
+)
+pipe.scheduler = FlowMatchEulerDiscreteScheduler.from_config(pipe.scheduler.config, shift=8.0)
+pipe.to('cuda')
+
+
+
+print("Optimizing pipeline...")
+for i in range(3):
+    gc.collect()
+    torch.cuda.synchronize()
+    torch.cuda.empty_cache()
+
+optimize_pipeline_(pipe,
+    image=Image.new('RGB', (MAX_DIMENSION, MIN_DIMENSION)), 
+    prompt='prompt',
+    height=MIN_DIMENSION,
+    width=MAX_DIMENSION,
+    num_frames=MAX_FRAMES_MODEL,
+)
+print("All models loaded and optimized. Gradio app is ready.")
+
+
+# --- 2. Image Processing and Application Logic ---
+def generate_end_frame(start_img, gen_prompt, progress=gr.Progress(track_tqdm=True)):
+    """Calls an external Gradio API to generate an image."""
+    if start_img is None:
+        raise gr.Error("Please provide a Start Frame first.")
+
+    hf_token = os.getenv("HF_TOKEN")
+    if not hf_token:
+        raise gr.Error("HF_TOKEN not found in environment variables. Please set it in your Space secrets.")
+
+    with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmpfile:
+        start_img.save(tmpfile.name)
+        tmp_path = tmpfile.name
+
+    progress(0.1, desc="Connecting to image generation API...")
+    client = Client("multimodalart/nano-banana-private")
+    
+    progress(0.5, desc=f"Generating with prompt: '{gen_prompt}'...")
+    try:
+        result = client.predict(
+            prompt=gen_prompt, 
+            images=[
+                {"image": handle_file(tmp_path)}
+            ],
+            manual_token=hf_token,
+            api_name="/unified_image_generator"
+        )
+    finally:
+        os.remove(tmp_path)
+
+    progress(1.0, desc="Done!")
+    print(result)
+    return result
+
+def switch_to_upload_tab():
+    """Returns a gr.Tabs update to switch to the first tab."""
+    return gr.Tabs(selected="upload_tab")
+
+
+def process_image_for_video(image: Image.Image) -> Image.Image:
+    """
+    Resizes an image based on the following rules for video generation:
+    1. The longest side will be scaled down to MAX_DIMENSION if it's larger.
+    2. The shortest side will be scaled up to MIN_DIMENSION if it's smaller.
+    3. The final dimensions will be rounded to the nearest multiple of DIMENSION_MULTIPLE.
+    4. Square images are resized to a fixed SQUARE_SIZE.
+    The aspect ratio is preserved as closely as possible.
+    """
+    width, height = image.size
+
+    # Rule 4: Handle square images
+    if width == height:
+        return image.resize((SQUARE_SIZE, SQUARE_SIZE), Image.Resampling.LANCZOS)
+
+    # Determine target dimensions while preserving aspect ratio
+    aspect_ratio = width / height
+    new_width, new_height = width, height
+
+    # Rule 1: Scale down if too large
+    if new_width > MAX_DIMENSION or new_height > MAX_DIMENSION:
+        if aspect_ratio > 1:  # Landscape
+            scale = MAX_DIMENSION / new_width
+        else:  # Portrait
+            scale = MAX_DIMENSION / new_height
+        new_width *= scale
+        new_height *= scale
+
+    # Rule 2: Scale up if too small
+    if new_width < MIN_DIMENSION or new_height < MIN_DIMENSION:
+        if aspect_ratio > 1:  # Landscape
+            scale = MIN_DIMENSION / new_height
+        else:  # Portrait
+            scale = MIN_DIMENSION / new_width
+        new_width *= scale
+        new_height *= scale
+
+    # Rule 3: Round to the nearest multiple of DIMENSION_MULTIPLE
+    final_width = int(round(new_width / DIMENSION_MULTIPLE) * DIMENSION_MULTIPLE)
+    final_height = int(round(new_height / DIMENSION_MULTIPLE) * DIMENSION_MULTIPLE)
+    
+    # Ensure final dimensions are at least the minimum
+    final_width = max(final_width, MIN_DIMENSION if aspect_ratio < 1 else SQUARE_SIZE)
+    final_height = max(final_height, MIN_DIMENSION if aspect_ratio > 1 else SQUARE_SIZE)
+
+
+    return image.resize((final_width, final_height), Image.Resampling.LANCZOS)
+
+def resize_and_crop_to_match(target_image, reference_image):
+    """Resizes and center-crops the target image to match the reference image's dimensions."""
+    ref_width, ref_height = reference_image.size
+    target_width, target_height = target_image.size
+    scale = max(ref_width / target_width, ref_height / target_height)
+    new_width, new_height = int(target_width * scale), int(target_height * scale)
+    resized = target_image.resize((new_width, new_height), Image.Resampling.LANCZOS)
+    left, top = (new_width - ref_width) // 2, (new_height - ref_height) // 2
+    return resized.crop((left, top, left + ref_width, top + ref_height))
+
+@spaces.GPU(duration=120)
+def generate_video(
+    start_image_pil,
+    end_image_pil,
+    prompt,
+    negative_prompt=default_negative_prompt,
+    duration_seconds=2.1,
+    steps=8,
+    guidance_scale=1,
+    guidance_scale_2=1,
+    seed=42,
+    randomize_seed=False,
+    progress=gr.Progress(track_tqdm=True)
+):
+    """
+    Generates a video by interpolating between a start and end image, guided by a text prompt,
+    using the diffusers Wan2.2 pipeline.
+    """
+    if start_image_pil is None or end_image_pil is None:
+        raise gr.Error("Please upload both a start and an end image.")
+
+    progress(0.1, desc="Preprocessing images...")
+
+    # Step 1: Process the start image to get our target dimensions based on the new rules.
+    processed_start_image = process_image_for_video(start_image_pil)
+    
+    # Step 2: Make the end image match the *exact* dimensions of the processed start image.
+    processed_end_image = resize_and_crop_to_match(end_image_pil, processed_start_image)
+    
+    target_height, target_width = processed_start_image.height, processed_start_image.width
+
+    # Handle seed and frame count
+    current_seed = random.randint(0, MAX_SEED) if randomize_seed else int(seed)
+    num_frames = np.clip(int(round(duration_seconds * FIXED_FPS)), MIN_FRAMES_MODEL, MAX_FRAMES_MODEL)
+
+    progress(0.2, desc=f"Generating {num_frames} frames at {target_width}x{target_height} (seed: {current_seed})...")
+
+    output_frames_list = pipe(
+        image=processed_start_image,
+        last_image=processed_end_image,
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        height=target_height,
+        width=target_width,
+        num_frames=num_frames,
+        guidance_scale=float(guidance_scale),
+        guidance_scale_2=float(guidance_scale_2),
+        num_inference_steps=int(steps),
+        generator=torch.Generator(device="cuda").manual_seed(current_seed),
+    ).frames[0]
+
+    progress(0.9, desc="Encoding and saving video...")
+
+    with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as tmpfile:
+        video_path = tmpfile.name
+
+    export_to_video(output_frames_list, video_path, fps=FIXED_FPS)
+
+    progress(1.0, desc="Done!")
+    return video_path, current_seed
+
+
+# --- 3. Gradio User Interface ---
+
+css = '''
+.fillable{max-width: 1100px !important}
+.dark .progress-text {color: white}
+#general_items{margin-top: 2em}
+#group_all{overflow:visible}
+#group_all .styler{overflow:visible}
+#group_tabs .tabitem{padding: 0}
+.tab-wrapper{margin-top: -33px;z-index: 999;position: absolute;width: 100%;background-color: var(--block-background-fill);padding: 0;}
+#component-9-button{width: 50%;justify-content: center}
+#component-11-button{width: 50%;justify-content: center}
+#or_item{text-align: center; padding-top: 1em; padding-bottom: 1em; font-size: 1.1em;margin-left: .5em;margin-right: .5em;width: calc(100% - 1em)}
+#fivesec{margin-top: 5em;margin-left: .5em;margin-right: .5em;width: calc(100% - 1em)}
+'''
+with gr.Blocks(theme=gr.themes.Citrus(), css=css) as app:
+    gr.Markdown("# Wan 2.2 First/Last Frame Video Fast")
+    gr.Markdown("Based on the [Wan 2.2 First/Last Frame workflow](https://www.reddit.com/r/StableDiffusion/comments/1me4306/psa_wan_22_does_first_frame_last_frame_out_of_the/), applied to 🧨 Diffusers + [lightx2v/Wan2.2-Lightning](https://huggingface.co/lightx2v/Wan2.2-Lightning) 8-step LoRA")
+
+    with gr.Row(elem_id="general_items"):
+        with gr.Column():
+            with gr.Group(elem_id="group_all"):
+                with gr.Row():
+                    start_image = gr.Image(type="pil", label="Start Frame", sources=["upload", "clipboard"])
+                    # Capture the Tabs component in a variable and assign IDs to tabs
+                    with gr.Tabs(elem_id="group_tabs") as tabs:
+                        with gr.TabItem("Upload", id="upload_tab"):
+                            end_image = gr.Image(type="pil", label="End Frame", sources=["upload", "clipboard"])
+                        with gr.TabItem("Generate", id="generate_tab"):
+                            generate_5seconds = gr.Button("Generate scene 5 seconds in the future", elem_id="fivesec")
+                            gr.Markdown("Generate a custom end-frame with an edit model like [Nano Banana](https://huggingface.co/spaces/multimodalart/nano-banana) or [Qwen Image Edit](https://huggingface.co/spaces/multimodalart/Qwen-Image-Edit-Fast)", elem_id="or_item")
+                prompt = gr.Textbox(label="Prompt", info="Describe the transition between the two images")
+
+                with gr.Accordion("Advanced Settings", open=False):
+                    duration_seconds_input = gr.Slider(minimum=MIN_DURATION, maximum=MAX_DURATION, step=0.1, value=2.1, label="Video Duration (seconds)", info=f"Clamped to model's {MIN_FRAMES_MODEL}-{MAX_FRAMES_MODEL} frames at {FIXED_FPS}fps.")
+                    negative_prompt_input = gr.Textbox(label="Negative Prompt", value=default_negative_prompt, lines=3)
+                    steps_slider = gr.Slider(minimum=1, maximum=30, step=1, value=8, label="Inference Steps")
+                    guidance_scale_input = gr.Slider(minimum=0.0, maximum=10.0, step=0.5, value=1.0, label="Guidance Scale - high noise")
+                    guidance_scale_2_input = gr.Slider(minimum=0.0, maximum=10.0, step=0.5, value=1.0, label="Guidance Scale - low noise")
+                    with gr.Row():
+                        seed_input = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=42)
+                        randomize_seed_checkbox = gr.Checkbox(label="Randomize seed", value=True)
+
+                generate_button = gr.Button("Generate Video", variant="primary")
+
+        with gr.Column():
+            output_video = gr.Video(label="Generated Video", autoplay=True)
+
+    # Main video generation button
+    ui_inputs = [
+        start_image,
+        end_image,
+        prompt,
+        negative_prompt_input,
+        duration_seconds_input,
+        steps_slider,
+        guidance_scale_input,
+        guidance_scale_2_input,
+        seed_input,
+        randomize_seed_checkbox
+    ]
+    ui_outputs = [output_video, seed_input]
+
+    generate_button.click(
+        fn=generate_video,
+        inputs=ui_inputs,
+        outputs=ui_outputs
+    )
+
+    generate_5seconds.click(
+        fn=switch_to_upload_tab,
+        inputs=None,
+        outputs=[tabs]
+    ).then( 
+        fn=lambda img: generate_end_frame(img, "this image is a still frame from a movie. generate a new frame with what happens on this scene 5 seconds in the future"),
+        inputs=[start_image],
+        outputs=[end_image]
+    ).success(
+        fn=generate_video,
+        inputs=ui_inputs,
+        outputs=ui_outputs
+    )
+
+    gr.Examples(
+        examples = [
+            ["poli_tower.png", "tower_takes_off.png", "the man turns around"],
+            ["ugly_sonic.jpeg", "squatting_sonic.png", "the character dodges the missiles"],
+            ["capyabara_zoomed.png", "capyabara.webp", "a dramatic dolly zoom"],
+        ],
+        inputs = [start_image, end_image, prompt],
+        outputs = ui_outputs,
+        fn = generate_video,
+        run_on_click = True,
+        cache_examples = True,
+    )
+
+if __name__ == "__main__":
     app.launch(share=True)