import os # Optional but fine os.environ["GRADIO_DISABLE_INTERACTIVE_MODE"] = "true" 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 torchao.quantization import quantize_ from torchao.quantization import Float8DynamicActivationFloat8WeightConfig, Int8WeightOnlyConfig import aoti # Optional: default SPACE_ID if not set in env (used only for docs / client-side URL building) SPACE_ID = os.environ.get("SPACE_ID", "presalesaiautomation-imagetovideo") # ========================================================= # MODEL CONFIGURATION # ========================================================= MODEL_ID = "Wan-AI/Wan2.2-I2V-A14B-Diffusers" HF_TOKEN = os.environ.get("HF_TOKEN") MAX_DIM = 832 MIN_DIM = 480 SQUARE_DIM = 640 MULTIPLE_OF = 16 MAX_SEED = np.iinfo(np.int32).max FIXED_FPS = 16 MIN_FRAMES_MODEL = 8 MAX_FRAMES_MODEL = 7720 MIN_DURATION = round(MIN_FRAMES_MODEL / FIXED_FPS, 1) MAX_DURATION = round(MAX_FRAMES_MODEL / FIXED_FPS, 1) # ========================================================= # LOAD PIPELINE # ========================================================= pipe = WanImageToVideoPipeline.from_pretrained( MODEL_ID, transformer=WanTransformer3DModel.from_pretrained( MODEL_ID, subfolder="transformer", torch_dtype=torch.bfloat16, device_map="cuda", token=HF_TOKEN, ), transformer_2=WanTransformer3DModel.from_pretrained( MODEL_ID, subfolder="transformer_2", torch_dtype=torch.bfloat16, device_map="cuda", token=HF_TOKEN, ), torch_dtype=torch.bfloat16, ).to("cuda") # ========================================================= # LOAD LORA ADAPTERS # ========================================================= pipe.load_lora_weights( "Kijai/WanVideo_comfy", weight_name="Lightx2v/lightx2v_I2V_14B_480p_cfg_step_distill_rank128_bf16.safetensors", adapter_name="lightx2v", ) pipe.load_lora_weights( "Kijai/WanVideo_comfy", weight_name="Lightx2v/lightx2v_I2V_14B_480p_cfg_step_distill_rank128_bf16.safetensors", adapter_name="lightx2v_2", load_into_transformer_2=True, ) pipe.set_adapters(["lightx2v", "lightx2v_2"], adapter_weights=[1.0, 1.0]) pipe.fuse_lora(adapter_names=["lightx2v"], lora_scale=3.0, components=["transformer"]) pipe.fuse_lora(adapter_names=["lightx2v_2"], lora_scale=1.0, components=["transformer_2"]) pipe.unload_lora_weights() # ========================================================= # QUANTIZATION & AOT OPTIMIZATION # ========================================================= quantize_(pipe.text_encoder, Int8WeightOnlyConfig()) quantize_(pipe.transformer, Float8DynamicActivationFloat8WeightConfig()) quantize_(pipe.transformer_2, Float8DynamicActivationFloat8WeightConfig()) aoti.aoti_blocks_load(pipe.transformer, "zerogpu-aoti/Wan2", variant="fp8da") aoti.aoti_blocks_load(pipe.transformer_2, "zerogpu-aoti/Wan2", variant="fp8da") # ========================================================= # DEFAULT PROMPTS # ========================================================= default_prompt_i2v = ( "Make this image come alive with dynamic, cinematic human motion. Create smooth, natural, " "lifelike animation with fluid transitions, expressive body movement, realistic physics, " "and elegant camera flow. Deliver a polished, high-quality motion style that feels immersive, " "artistic, and visually captivating." ) default_negative_prompt = ( "low quality, worst quality, motion artifacts, unstable motion, jitter, frame jitter, " "wobbling limbs, motion distortion, inconsistent movement, robotic movement, " "animation-like motion, awkward transitions, incorrect body mechanics, unnatural posing, " "off-balance poses, broken motion paths, frozen frames, duplicated frames, frame skipping, " "warped motion, stretching artifacts bad anatomy, incorrect proportions, deformed body, " "twisted torso, broken joints, dislocated limbs, distorted neck, unnatural spine curvature, " "malformed hands, extra fingers, missing fingers, fused fingers, distorted legs, extra limbs, " "collapsed feet, floating feet, foot sliding, foot jitter, backward walking, unnatural gait " "blurry details, long exposure blur, ghosting, shadow trails, smearing, washed-out colors, " "overexposure, underexposure, excessive contrast, blown highlights, poorly rendered clothing, " "fabric glitches, texture warping, clothing merging with body, incorrect cloth physics " "ugly background, cluttered scene, crowded background, random objects, unwanted text, " "subtitles, logos, graffiti, grain, noise, static artifacts, compression noise, jpeg artifacts, " "image-like stillness, painting-like look, cartoon texture, low-resolution textures" ) # ========================================================= # IMAGE RESIZING LOGIC # ========================================================= def resize_image(image: Image.Image) -> Image.Image: width, height = image.size if width == height: return image.resize((SQUARE_DIM, SQUARE_DIM), Image.LANCZOS) aspect_ratio = width / height MAX_ASPECT_RATIO = MAX_DIM / MIN_DIM MIN_ASPECT_RATIO = MIN_DIM / MAX_DIM image_to_resize = image if aspect_ratio > MAX_ASPECT_RATIO: crop_width = int(round(height * MAX_ASPECT_RATIO)) left = (width - crop_width) // 2 image_to_resize = image.crop((left, 0, left + crop_width, height)) elif aspect_ratio < MIN_ASPECT_RATIO: crop_height = int(round(width / MIN_ASPECT_RATIO)) top = (height - crop_height) // 2 image_to_resize = image.crop((0, top, width, top + crop_height)) if width > height: target_w = MAX_DIM target_h = int(round(target_w / aspect_ratio)) else: target_h = MAX_DIM target_w = int(round(target_h * aspect_ratio)) final_w = round(target_w / MULTIPLE_OF) * MULTIPLE_OF final_h = round(target_h / MULTIPLE_OF) * MULTIPLE_OF final_w = max(MIN_DIM, min(MAX_DIM, final_w)) final_h = max(MIN_DIM, min(MAX_DIM, final_h)) return image_to_resize.resize((final_w, final_h), Image.LANCZOS) # ========================================================= # UTILITY FUNCTIONS # ========================================================= def get_num_frames(duration_seconds: float): return 1 + int( np.clip(int(round(duration_seconds * FIXED_FPS)), MIN_FRAMES_MODEL, MAX_FRAMES_MODEL) ) def get_duration( input_image, prompt, steps, negative_prompt, duration_seconds, guidance_scale, guidance_scale_2, seed, randomize_seed, progress, ): BASE_FRAMES_HEIGHT_WIDTH = 81 * 832 * 624 BASE_STEP_DURATION = 15 width, height = resize_image(input_image).size frames = get_num_frames(duration_seconds) factor = frames * width * height / BASE_FRAMES_HEIGHT_WIDTH step_duration = BASE_STEP_DURATION * factor ** 1.5 return 10 + int(steps) * step_duration # ========================================================= # MAIN GENERATION FUNCTION (GPU TASK) # ========================================================= @spaces.GPU(duration=get_duration) def generate_video( input_image, prompt, steps=4, negative_prompt=default_negative_prompt, duration_seconds=MAX_DURATION, guidance_scale=1, guidance_scale_2=1, 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 = get_num_frames(duration_seconds) current_seed = random.randint(0, MAX_SEED) if randomize_seed else int(seed) resized_image = resize_image(input_image) output_frames_list = pipe( image=resized_image, prompt=prompt, negative_prompt=negative_prompt, height=resized_image.height, width=resized_image.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] 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 # ========================================================= # GRADIO UI + API EXPOSURE # ========================================================= with gr.Blocks() as demo: gr.Markdown("# 🚀 Dream Wan 2.2 Faster Pro (14B) — Ultra Fast I2V with Lightning LoRA") gr.Markdown("Optimized FP8 quantized pipeline with AoT blocks & 4-step fast inference ⚡") with gr.Row(): with gr.Column(): input_image_component = gr.Image(type="pil", label="Input Image") prompt_input = gr.Textbox(label="Prompt", value=default_prompt_i2v) duration_seconds_input = gr.Slider( minimum=0, maximum=MAX_DURATION, step=0.1, value=3.5, label="Duration (seconds)", info=f"Model range: {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 ) randomize_seed_checkbox = gr.Checkbox(label="Randomize seed", value=True) steps_slider = gr.Slider( minimum=1, maximum=30, step=1, value=6, label="Inference Steps" ) guidance_scale_input = gr.Slider( minimum=0.0, maximum=10.0, step=0.5, value=1, label="Guidance Scale (high noise)", ) guidance_scale_2_input = gr.Slider( minimum=0.0, maximum=10.0, step=0.5, value=1, label="Guidance Scale 2 (low noise)", ) generate_button = gr.Button("🎬 Generate Video", variant="primary") with gr.Column(): video_output = gr.Video(label="Generated Video", autoplay=True) seed_output = gr.Number(label="Seed") ui_inputs = [ input_image_component, prompt_input, steps_slider, negative_prompt_input, duration_seconds_input, guidance_scale_input, guidance_scale_2_input, seed_input, randomize_seed_checkbox, ] # 🔴 THIS exposes the HTTP API: /gradio_api/call/generate_video_sync generate_button.click( fn=generate_video, inputs=ui_inputs, outputs=[video_output, seed_output], api_name="generate_video_sync", ) # ============================================ # ZeroGPU / Spaces startup (same pattern as old code) # ============================================ demo.queue() # allow queued GPU jobs if __name__ == "__main__": demo.launch(server_name="0.0.0.0", server_port=7860)