1

app.py

@@ -2,6 +2,7 @@ import os
 import gc
 import torch
 import gradio as gr
+import spaces
 
 from diffusers import WanAnimatePipeline
 from diffusers.utils import load_image, load_video, export_to_video
@@ -16,6 +17,8 @@ MODEL_ID = "Wan-AI/Wan2.2-Animate-14B-Diffusers"
 OUTPUT_DIR = "/tmp/outputs"
 os.makedirs(OUTPUT_DIR, exist_ok=True)
 
+pipe = None
+
 
 # ============================================================
 # PRESETS
@@ -24,10 +27,10 @@ os.makedirs(OUTPUT_DIR, exist_ok=True)
 PRESETS = {
     "Realistic": {
         "prompt": (
-            "A highly realistic video of the reference character performing "
-            "the movements from the input motion video. Preserve identity, "
-            "facial appearance, hairstyle and clothing. Natural body motion, "
-            "realistic lighting and physics."
+            "A highly realistic video of the reference character "
+            "performing the movements from the input motion video. "
+            "Preserve identity, facial appearance, hairstyle and clothing. "
+            "Natural body motion, realistic lighting and physics."
         ),
         "steps": 20,
         "guidance": 1.0,
@@ -69,34 +72,34 @@ PRESETS = {
 
 
 # ============================================================
-# LOAD MODEL
+# MODEL LOADING
 # ============================================================
 
-print("Loading Wan2.2 Animate...")
+def get_pipeline():
 
-if not torch.cuda.is_available():
-    raise RuntimeError(
-        "CUDA GPU is required. "
-        "Use a GPU-enabled Hugging Face Space."
-    )
+    global pipe
 
-print("GPU:", torch.cuda.get_device_name(0))
+    if pipe is None:
 
-pipe = WanAnimatePipeline.from_pretrained(
-    MODEL_ID,
-    torch_dtype=torch.bfloat16,
-)
+        print("Loading Wan2.2 Animate...")
+
+        pipe = WanAnimatePipeline.from_pretrained(
+            MODEL_ID,
+            torch_dtype=torch.bfloat16,
+        )
+
+        pipe.enable_model_cpu_offload()
 
-# Important for limited VRAM
-pipe.enable_model_cpu_offload()
+        print("Wan2.2 Animate loaded.")
 
-print("Model loaded.")
+    return pipe
 
 
 # ============================================================
 # GENERATION
 # ============================================================
 
+@spaces.GPU
 def generate_video(
     prompt,
     reference_image,
@@ -109,54 +112,83 @@ def generate_video(
 ):
 
     if reference_image is None:
-        raise gr.Error("Please upload a reference image.")
+        raise gr.Error(
+            "Please upload a reference image."
+        )
 
     if motion_video is None:
-        raise gr.Error("Please upload a motion video.")
+        raise gr.Error(
+            "Please upload a motion video."
+        )
+
+    # --------------------------------------------------------
+    # LOAD MODEL AFTER ZERO GPU ALLOCATION
+    # --------------------------------------------------------
+
+    progress(
+        0.05,
+        desc="Loading Wan2.2 Animate..."
+    )
+
+    pipe = get_pipeline()
 
     # --------------------------------------------------------
     # PRESET
     # --------------------------------------------------------
 
-    preset_config = PRESETS[preset]
+    config = PRESETS[preset]
 
     if not prompt or not prompt.strip():
-        prompt = preset_config["prompt"]
+        prompt = config["prompt"]
 
     # --------------------------------------------------------
     # LOAD INPUTS
     # --------------------------------------------------------
 
-    progress(0.1, desc="Loading reference image...")
+    progress(
+        0.15,
+        desc="Loading reference image..."
+    )
 
-    image = load_image(reference_image)
+    image = load_image(
+        reference_image
+    )
 
-    progress(0.2, desc="Loading motion video...")
+    progress(
+        0.25,
+        desc="Loading motion video..."
+    )
 
-    motion = load_video(motion_video)
+    motion = load_video(
+        motion_video
+    )
 
     # --------------------------------------------------------
-    # CONDITIONING
+    # TEMPORARY CONDITIONING
     #
-    # For the basic workflow we use the motion video as
-    # both pose and face conditioning.
-    #
-    # For best results, replace this with Wan Animate's
-    # official preprocessing pipeline.
+    # NOTE:
+    # For the real Wan2.2 Animate workflow, the motion video
+    # should be processed into dedicated pose/face inputs.
+    # This is the basic prototype.
     # --------------------------------------------------------
 
     pose_video = motion
     face_video = motion
 
     # --------------------------------------------------------
-    # GENERATION
+    # GENERATE
     # --------------------------------------------------------
 
-    progress(0.3, desc="Generating video...")
+    progress(
+        0.35,
+        desc="Generating video..."
+    )
 
     generator = torch.Generator(
         device="cuda"
-    ).manual_seed(int(seed))
+    ).manual_seed(
+        int(seed)
+    )
 
     with torch.inference_mode():
 
@@ -168,20 +200,27 @@ def generate_video(
             mode="animate",
             segment_frame_length=77,
             prev_segment_conditioning_frames=1,
-            guidance_scale=float(guidance_scale),
-            num_inference_steps=int(inference_steps),
+            guidance_scale=float(
+                guidance_scale
+            ),
+            num_inference_steps=int(
+                inference_steps
+            ),
             generator=generator,
         ).frames[0]
 
-    progress(0.9, desc="Encoding output video...")
-
     # --------------------------------------------------------
-    # SAVE
+    # EXPORT
     # --------------------------------------------------------
 
+    progress(
+        0.9,
+        desc="Encoding video..."
+    )
+
     output_path = os.path.join(
         OUTPUT_DIR,
-        f"generated_{int(seed)}.mp4"
+        f"output_{int(seed)}.mp4"
     )
 
     export_to_video(
@@ -190,20 +229,18 @@ def generate_video(
         fps=30,
     )
 
-    # --------------------------------------------------------
-    # CLEANUP
-    # --------------------------------------------------------
+    progress(
+        1.0,
+        desc="Done"
+    )
 
     gc.collect()
-    torch.cuda.empty_cache()
-
-    progress(1.0, desc="Done")
 
     return output_path
 
 
 # ============================================================
-# PRESET HANDLER
+# PRESET UPDATE
 # ============================================================
 
 def update_preset(preset):
@@ -229,39 +266,35 @@ with gr.Blocks(
         """
         # 🎬 Wan2.2 Character Animation
 
-        Generate a video using:
+        **Reference Photo + Motion Video + Prompt → Video**
 
-        **Reference Photo + Motion Video + Prompt → Generated Video**
-
-        The reference image provides the character identity.
+        The reference photo provides the character identity.
         The motion video provides the movement.
         """
     )
 
     with gr.Row():
 
-        # ----------------------------------------------------
-        # LEFT
-        # ----------------------------------------------------
-
         with gr.Column():
 
             prompt = gr.Textbox(
                 label="Prompt",
+                lines=5,
                 placeholder=(
                     "Describe the generated video..."
                 ),
-                lines=5,
             )
 
             preset = gr.Dropdown(
-                choices=list(PRESETS.keys()),
+                choices=list(
+                    PRESETS.keys()
+                ),
                 value="Realistic",
                 label="Style Preset",
             )
 
             reference_image = gr.Image(
-                label="Reference Photo / Character",
+                label="Reference Photo",
                 type="filepath",
             )
 
@@ -270,10 +303,6 @@ with gr.Blocks(
                 sources=["upload"],
             )
 
-        # ----------------------------------------------------
-        # RIGHT
-        # ----------------------------------------------------
-
         with gr.Column():
 
             output_video = gr.Video(
@@ -282,12 +311,12 @@ with gr.Blocks(
             )
 
             generate_button = gr.Button(
-                "🎬 Generate Video",
+                "🎬 Generate",
                 variant="primary",
             )
 
     # ========================================================
-    # ADVANCED SETTINGS
+    # ADVANCED
     # ========================================================
 
     with gr.Accordion(
@@ -318,12 +347,12 @@ with gr.Blocks(
         )
 
     # ========================================================
-    # PRESET EVENT
+    # EVENTS
     # ========================================================
 
     preset.change(
         fn=update_preset,
-        inputs=[preset],
+        inputs=preset,
         outputs=[
             prompt,
             inference_steps,
@@ -331,10 +360,6 @@ with gr.Blocks(
         ],
     )
 
-    # ========================================================
-    # GENERATE EVENT
-    # ========================================================
-
     generate_button.click(
         fn=generate_video,
         inputs=[
@@ -354,11 +379,9 @@ with gr.Blocks(
 # LAUNCH
 # ============================================================
 
-if __name__ == "__main__":
-
-    demo.queue(
-        max_size=10,
-        default_concurrency_limit=1,
-    )
+demo.queue(
+    max_size=10,
+    default_concurrency_limit=1,
+)
 
-    demo.launch()
+demo.launch()

appold.py

@@ -0,0 +1,364 @@
+import os
+import gc
+import torch
+import gradio as gr
+
+from diffusers import WanAnimatePipeline
+from diffusers.utils import load_image, load_video, export_to_video
+
+
+# ============================================================
+# CONFIG
+# ============================================================
+
+MODEL_ID = "Wan-AI/Wan2.2-Animate-14B-Diffusers"
+
+OUTPUT_DIR = "/tmp/outputs"
+os.makedirs(OUTPUT_DIR, exist_ok=True)
+
+
+# ============================================================
+# PRESETS
+# ============================================================
+
+PRESETS = {
+    "Realistic": {
+        "prompt": (
+            "A highly realistic video of the reference character performing "
+            "the movements from the input motion video. Preserve identity, "
+            "facial appearance, hairstyle and clothing. Natural body motion, "
+            "realistic lighting and physics."
+        ),
+        "steps": 20,
+        "guidance": 1.0,
+    },
+
+    "Cinematic": {
+        "prompt": (
+            "A cinematic photorealistic video of the character performing "
+            "the exact movements and actions from the reference motion video. "
+            "Natural facial expressions, realistic skin, detailed clothing, "
+            "cinematic lighting, shallow depth of field, professional camera."
+        ),
+        "steps": 20,
+        "guidance": 1.0,
+    },
+
+    "Portrait": {
+        "prompt": (
+            "A photorealistic portrait video of the reference character. "
+            "Preserve the character's identity and facial features while "
+            "accurately following the body and facial motion from the input video. "
+            "Natural expression, realistic skin and cinematic portrait lighting."
+        ),
+        "steps": 20,
+        "guidance": 1.0,
+    },
+
+    "Anime": {
+        "prompt": (
+            "An anime-style cinematic video featuring the reference character, "
+            "accurately reproducing the movement and performance from the input "
+            "motion video. Consistent character identity, expressive animation, "
+            "detailed anime background."
+        ),
+        "steps": 20,
+        "guidance": 1.0,
+    },
+}
+
+
+# ============================================================
+# LOAD MODEL
+# ============================================================
+
+print("Loading Wan2.2 Animate...")
+
+if not torch.cuda.is_available():
+    raise RuntimeError(
+        "CUDA GPU is required. "
+        "Use a GPU-enabled Hugging Face Space."
+    )
+
+print("GPU:", torch.cuda.get_device_name(0))
+
+pipe = WanAnimatePipeline.from_pretrained(
+    MODEL_ID,
+    torch_dtype=torch.bfloat16,
+)
+
+# Important for limited VRAM
+pipe.enable_model_cpu_offload()
+
+print("Model loaded.")
+
+
+# ============================================================
+# GENERATION
+# ============================================================
+
+def generate_video(
+    prompt,
+    reference_image,
+    motion_video,
+    preset,
+    seed,
+    inference_steps,
+    guidance_scale,
+    progress=gr.Progress(),
+):
+
+    if reference_image is None:
+        raise gr.Error("Please upload a reference image.")
+
+    if motion_video is None:
+        raise gr.Error("Please upload a motion video.")
+
+    # --------------------------------------------------------
+    # PRESET
+    # --------------------------------------------------------
+
+    preset_config = PRESETS[preset]
+
+    if not prompt or not prompt.strip():
+        prompt = preset_config["prompt"]
+
+    # --------------------------------------------------------
+    # LOAD INPUTS
+    # --------------------------------------------------------
+
+    progress(0.1, desc="Loading reference image...")
+
+    image = load_image(reference_image)
+
+    progress(0.2, desc="Loading motion video...")
+
+    motion = load_video(motion_video)
+
+    # --------------------------------------------------------
+    # CONDITIONING
+    #
+    # For the basic workflow we use the motion video as
+    # both pose and face conditioning.
+    #
+    # For best results, replace this with Wan Animate's
+    # official preprocessing pipeline.
+    # --------------------------------------------------------
+
+    pose_video = motion
+    face_video = motion
+
+    # --------------------------------------------------------
+    # GENERATION
+    # --------------------------------------------------------
+
+    progress(0.3, desc="Generating video...")
+
+    generator = torch.Generator(
+        device="cuda"
+    ).manual_seed(int(seed))
+
+    with torch.inference_mode():
+
+        result = pipe(
+            image=image,
+            pose_video=pose_video,
+            face_video=face_video,
+            prompt=prompt,
+            mode="animate",
+            segment_frame_length=77,
+            prev_segment_conditioning_frames=1,
+            guidance_scale=float(guidance_scale),
+            num_inference_steps=int(inference_steps),
+            generator=generator,
+        ).frames[0]
+
+    progress(0.9, desc="Encoding output video...")
+
+    # --------------------------------------------------------
+    # SAVE
+    # --------------------------------------------------------
+
+    output_path = os.path.join(
+        OUTPUT_DIR,
+        f"generated_{int(seed)}.mp4"
+    )
+
+    export_to_video(
+        result,
+        output_path,
+        fps=30,
+    )
+
+    # --------------------------------------------------------
+    # CLEANUP
+    # --------------------------------------------------------
+
+    gc.collect()
+    torch.cuda.empty_cache()
+
+    progress(1.0, desc="Done")
+
+    return output_path
+
+
+# ============================================================
+# PRESET HANDLER
+# ============================================================
+
+def update_preset(preset):
+
+    config = PRESETS[preset]
+
+    return (
+        config["prompt"],
+        config["steps"],
+        config["guidance"],
+    )
+
+
+# ============================================================
+# GRADIO UI
+# ============================================================
+
+with gr.Blocks(
+    title="Wan2.2 Character Animation"
+) as demo:
+
+    gr.Markdown(
+        """
+        # 🎬 Wan2.2 Character Animation
+
+        Generate a video using:
+
+        **Reference Photo + Motion Video + Prompt → Generated Video**
+
+        The reference image provides the character identity.
+        The motion video provides the movement.
+        """
+    )
+
+    with gr.Row():
+
+        # ----------------------------------------------------
+        # LEFT
+        # ----------------------------------------------------
+
+        with gr.Column():
+
+            prompt = gr.Textbox(
+                label="Prompt",
+                placeholder=(
+                    "Describe the generated video..."
+                ),
+                lines=5,
+            )
+
+            preset = gr.Dropdown(
+                choices=list(PRESETS.keys()),
+                value="Realistic",
+                label="Style Preset",
+            )
+
+            reference_image = gr.Image(
+                label="Reference Photo / Character",
+                type="filepath",
+            )
+
+            motion_video = gr.Video(
+                label="Motion Video",
+                sources=["upload"],
+            )
+
+        # ----------------------------------------------------
+        # RIGHT
+        # ----------------------------------------------------
+
+        with gr.Column():
+
+            output_video = gr.Video(
+                label="Generated Video",
+                autoplay=True,
+            )
+
+            generate_button = gr.Button(
+                "🎬 Generate Video",
+                variant="primary",
+            )
+
+    # ========================================================
+    # ADVANCED SETTINGS
+    # ========================================================
+
+    with gr.Accordion(
+        "Advanced Settings",
+        open=False,
+    ):
+
+        seed = gr.Number(
+            label="Seed",
+            value=42,
+            precision=0,
+        )
+
+        inference_steps = gr.Slider(
+            minimum=5,
+            maximum=50,
+            value=20,
+            step=1,
+            label="Inference Steps",
+        )
+
+        guidance_scale = gr.Slider(
+            minimum=0.5,
+            maximum=5.0,
+            value=1.0,
+            step=0.1,
+            label="Guidance Scale",
+        )
+
+    # ========================================================
+    # PRESET EVENT
+    # ========================================================
+
+    preset.change(
+        fn=update_preset,
+        inputs=[preset],
+        outputs=[
+            prompt,
+            inference_steps,
+            guidance_scale,
+        ],
+    )
+
+    # ========================================================
+    # GENERATE EVENT
+    # ========================================================
+
+    generate_button.click(
+        fn=generate_video,
+        inputs=[
+            prompt,
+            reference_image,
+            motion_video,
+            preset,
+            seed,
+            inference_steps,
+            guidance_scale,
+        ],
+        outputs=output_video,
+    )
+
+
+# ============================================================
+# LAUNCH
+# ============================================================
+
+if __name__ == "__main__":
+
+    demo.queue(
+        max_size=10,
+        default_concurrency_limit=1,
+    )
+
+    demo.launch()