Create app_t2v.py

app_t2v.py

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+# PyTorch 2.8 (temporary hack)
+import os
+os.system('pip install --upgrade --pre --extra-index-url https://download.pytorch.org/whl/nightly/cu126 "torch<2.9" spaces')
+
+# Actual demo code
+import spaces
+import torch
+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
+
+from optimization import optimize_pipeline_
+
+
+MODEL_ID = "Wan-AI/Wan2.2-T2V-A14B-Diffusers"
+
+LANDSCAPE_WIDTH = 832
+LANDSCAPE_HEIGHT = 480
+MAX_SEED = np.iinfo(np.int32).max
+
+FIXED_FPS = 24
+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)
+
+
+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,
+).to('cuda')
+
+
+optimize_pipeline_(pipe,
+    image=Image.new('RGB', (LANDSCAPE_WIDTH, LANDSCAPE_HEIGHT)),
+    prompt='prompt',
+    height=LANDSCAPE_HEIGHT,
+    width=LANDSCAPE_WIDTH,
+    num_frames=MAX_FRAMES_MODEL,
+)
+
+
+default_prompt_i2v = "make this image come alive, cinematic motion, smooth animation"
+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"
+
+
+def resize_image(image: Image.Image) -> Image.Image:
+    if image.height > image.width:
+        transposed = image.transpose(Image.Transpose.ROTATE_90)
+        resized = resize_image_landscape(transposed)
+        return resized.transpose(Image.Transpose.ROTATE_270)
+    return resize_image_landscape(image)
+
+
+def resize_image_landscape(image: Image.Image) -> Image.Image:
+    target_aspect = LANDSCAPE_WIDTH / LANDSCAPE_HEIGHT
+    width, height = image.size
+    in_aspect = width / height
+    if in_aspect > target_aspect:
+        new_width = round(height * target_aspect)
+        left = (width - new_width) // 2
+        image = image.crop((left, 0, left + new_width, height))
+    else:
+        new_height = round(width / target_aspect)
+        top = (height - new_height) // 2
+        image = image.crop((0, top, width, top + new_height))
+    return image.resize((LANDSCAPE_WIDTH, LANDSCAPE_HEIGHT), Image.LANCZOS)
+
+def get_duration(
+    input_image,
+    prompt,
+    negative_prompt,
+    duration_seconds,
+    guidance_scale,
+    steps,
+    seed,
+    randomize_seed,
+    progress,
+):
+    return steps * 15
+
+@spaces.GPU(duration=get_duration)
+def generate_video(
+    input_image,
+    prompt,
+    negative_prompt=default_negative_prompt,
+    duration_seconds = MAX_DURATION,
+    guidance_scale = 1,
+    steps = 4,
+    seed = 42,
+    randomize_seed = False,
+    progress=gr.Progress(track_tqdm=True),
+):
+    
+    if input_image is None:
+        raise gr.Error("Please upload an input image.")
+    
+    num_frames = np.clip(int(round(duration_seconds * FIXED_FPS)), MIN_FRAMES_MODEL, MAX_FRAMES_MODEL)
+    current_seed = random.randint(0, MAX_SEED) if randomize_seed else int(seed)
+    resized_image = resize_image(input_image)
+
+    output_frames_list = pipe(
+        image=None,
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        height=resized_image.height,
+        width=resized_image.width,
+        num_frames=num_frames,
+        guidance_scale=float(guidance_scale),
+        num_inference_steps=int(steps),
+        generator=torch.Generator(device="cuda").manual_seed(current_seed),
+    ).frames[0]
+
+    with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as tmpfile:
+        video_path = tmpfile.name
+
+    export_to_video(output_frames_list, video_path, fps=FIXED_FPS)
+
+    return video_path, current_seed
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Wan2.2-T2V-A14B AND I2V Testing")
+    #gr.Markdown("[CausVid](https://github.com/tianweiy/CausVid) is a distilled version of Wan 2.1 to run faster in just 4-8 steps, [extracted as LoRA by Kijai](https://huggingface.co/Kijai/WanVideo_comfy/blob/main/Wan21_CausVid_14B_T2V_lora_rank32.safetensors) and is compatible with 🧨 diffusers")
+    with gr.Row():
+        with gr.Column():
+            input_image_component = gr.Image(type="pil", label="Input Image (auto-resized to target H/W)")
+            prompt_input = gr.Textbox(label="Prompt", value=default_prompt_i2v)
+            duration_seconds_input = gr.Slider(minimum=MIN_DURATION, maximum=MAX_DURATION, step=0.1, value=MAX_DURATION, label="Duration (seconds)", info=f"Clamped to model's {MIN_FRAMES_MODEL}-{MAX_FRAMES_MODEL} frames at {FIXED_FPS}fps.")
+            
+            with gr.Accordion("Advanced Settings", open=False):
+                negative_prompt_input = gr.Textbox(label="Negative Prompt", value=default_negative_prompt, lines=3)
+                seed_input = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=42, interactive=True)
+                randomize_seed_checkbox = gr.Checkbox(label="Randomize seed", value=True, interactive=True)
+                steps_slider = gr.Slider(minimum=1, maximum=40, step=1, value=35, label="Inference Steps") 
+                guidance_scale_input = gr.Slider(minimum=0.0, maximum=20.0, step=0.5, value=1.0, label="Guidance Scale", visible=False)
+
+            generate_button = gr.Button("Generate Video", variant="primary")
+        with gr.Column():
+            video_output = gr.Video(label="Generated Video", autoplay=True, interactive=False)
+    
+    ui_inputs = [
+        input_image_component, prompt_input,
+        negative_prompt_input, duration_seconds_input,
+        guidance_scale_input, steps_slider, seed_input, randomize_seed_checkbox
+    ]
+    generate_button.click(fn=generate_video, inputs=ui_inputs, outputs=[video_output, seed_input])
+
+    gr.Examples(
+        examples=[ 
+            [
+                "wan_i2v_input.JPG",
+                "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside.",
+            ],
+        ],
+        inputs=[input_image_component, prompt_input], outputs=[video_output, seed_input], fn=generate_video, cache_examples="lazy"
+    )
+
+if __name__ == "__main__":
+    demo.queue().launch(mcp_server=True)