# 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"') from huggingface_hub import HfApi, upload_file import os import uuid import subprocess import tempfile import logging import shutil import os from huggingface_hub import HfApi, upload_file from datetime import datetime import uuid # 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 import gc from optimization import optimize_pipeline_ from huggingface_hub import hf_hub_download MODEL_ID = "Wan-AI/Wan2.2-I2V-A14B-Diffusers" HF_MODEL = os.environ.get("HF_UPLOAD_REPO", "rahul7star/wan22lora-text-img-video-analysis") from huggingface_hub import HfApi, upload_file import os import uuid import os import uuid import logging from datetime import datetime def upscale_and_upload_4k(input_video_path: str, input_image, summary_text: str) -> str: """ Upscale a video to 4K and upload it to Hugging Face Hub along with the input image and a text summary. Args: input_video_path (str): Path to the original video. input_image (PIL.Image.Image or path-like): Input image to upload alongside the video. summary_text (str): Text summary or prompt to upload alongside the video. Returns: str: Hugging Face folder path where the video, image, and summary were uploaded. """ logging.info(f"Upscaling video to 4K for upload: {input_video_path}") # --- Upscale video --- with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as tmp_upscaled: upscaled_path = tmp_upscaled.name cmd = [ "ffmpeg", "-i", input_video_path, "-vf", "scale=3840:2160:flags=lanczos", "-c:v", "libx264", "-crf", "18", "-preset", "slow", "-y", upscaled_path, ] try: subprocess.run(cmd, check=True, capture_output=True) logging.info(f"✅ Upscaled video created at: {upscaled_path}") except subprocess.CalledProcessError as e: logging.error(f"FFmpeg failed:\n{e.stderr.decode()}") raise # --- Create HF folder --- today_str = datetime.now().strftime("%Y-%m-%d") unique_subfolder = f"upload_{uuid.uuid4().hex[:8]}" hf_folder = f"{today_str}-WAN-I2V/{unique_subfolder}" # --- Upload video --- video_filename = os.path.basename(input_video_path) video_hf_path = f"{hf_folder}/{video_filename}" upload_file( path_or_fileobj=upscaled_path, path_in_repo=video_hf_path, repo_id=HF_MODEL, repo_type="model", token=os.environ.get("HUGGINGFACE_HUB_TOKEN"), ) logging.info(f"✅ Uploaded 4K video to HF: {video_hf_path}") # --- Upload input image --- with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp_img: if isinstance(input_image, str): import shutil shutil.copy(input_image, tmp_img.name) else: input_image.save(tmp_img.name, format="PNG") tmp_img_path = tmp_img.name image_hf_path = f"{hf_folder}/input_image.png" upload_file( path_or_fileobj=tmp_img_path, path_in_repo=image_hf_path, repo_id=HF_MODEL, repo_type="model", token=os.environ.get("HUGGINGFACE_HUB_TOKEN"), ) logging.info(f"✅ Uploaded input image to HF: {image_hf_path}") # --- Upload summary text --- summary_file = tempfile.NamedTemporaryFile(delete=False, suffix=".txt").name with open(summary_file, "w", encoding="utf-8") as f: f.write(summary_text) summary_hf_path = f"{hf_folder}/summary.txt" upload_file( path_or_fileobj=summary_file, path_in_repo=summary_hf_path, repo_id=HF_MODEL, repo_type="model", token=os.environ.get("HUGGINGFACE_HUB_TOKEN"), ) logging.info(f"✅ Uploaded summary to HF: {summary_hf_path}") # --- Cleanup temporary files --- os.remove(upscaled_path) os.remove(tmp_img_path) os.remove(summary_file) return hf_folder LORA_REPO_ID = "rahul7star/wan2.2Lora" LORA_SETS = { "NF": { "high_noise": {"file": "DR34ML4Y_I2V_14B_HIGH.safetensors", "adapter_name": "nf_high"}, "low_noise": {"file": "DR34ML4Y_I2V_14B_LOW.safetensors", "adapter_name": "nf_low"} }, "BP": { "high_noise": {"file": "Wan2.2_BP-v1-HighNoise-I2V_T2V.safetensors", "adapter_name": "bp_high"}, "low_noise": {"file": "Wan2.2_BP-v1-LowNoise-I2V_T2V.safetensors", "adapter_name": "bp_low"} }, "Py-v1": { "high_noise": {"file": "wan2.2_i2v_highnoise_pov_missionary_v1.0.safetensors", "adapter_name": "py_high"}, "low_noise": {"file": "wan2.2_i2v_lownoise_pov_missionary_v1.0.safetensors", "adapter_name": "py_low"} } } LANDSCAPE_WIDTH = 832 LANDSCAPE_HEIGHT = 576 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) 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, ) for name, lora_set in LORA_SETS.items(): print(f"---LoRA 集合: {name} ---") # 加载 High Noise high_noise_config = lora_set["high_noise"] print(f"High Noise: {high_noise_config['file']}...") pipe.load_lora_weights(LORA_REPO_ID, weight_name=high_noise_config['file'], adapter_name=high_noise_config['adapter_name']) print("High Noise LoRA 加载完成。") # 加载 Low Noise low_noise_config = lora_set["low_noise"] print(f" Low Noise: {low_noise_config['file']}...") pipe.load_lora_weights(LORA_REPO_ID, weight_name=low_noise_config['file'], adapter_name=low_noise_config['adapter_name']) print("Low Noise LoRA ") print("。") for i in range(3): gc.collect() torch.cuda.synchronize() torch.cuda.empty_cache() 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, steps, negative_prompt, duration_seconds, guidance_scale, guidance_scale_2, seed, randomize_seed, selected_loras, progress, ): return int(steps) * 15 @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, selected_loras = [], progress=gr.Progress(track_tqdm=True), ): if input_image is None: raise gr.Error("Please upload an input image.") print("potmpt is ") print(prompt) 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) num_inference_steps = int(steps) switch_step = num_inference_steps // 2 class LoraSwitcher: def __init__(self, selected_lora_names): self.switched = False self.high_noise_adapters = [] self.low_noise_adapters = [] if selected_lora_names: for name in selected_lora_names: if name in LORA_SETS: self.high_noise_adapters.append(LORA_SETS[name]["high_noise"]["adapter_name"]) self.low_noise_adapters.append(LORA_SETS[name]["low_noise"]["adapter_name"]) def __call__(self, pipe, step_index, timestep, callback_kwargs): # LoRA 状态 if step_index == 0: self.switched = False # LoRA，则激活 High Noise 版本 if self.high_noise_adapters: print(f"激活 High Noise LoRA: {self.high_noise_adapters}") pipe.set_adapters(self.high_noise_adapters, adapter_weights=[1.0] * len(self.high_noise_adapters)) # 🔥 同时 fuse_lora try: print(f"Fuse High Noise LoRA: {self.high_noise_adapters}") pipe.fuse_lora() except Exception as e: print(f"Fuse High Noise LoRA 失败: {e}") # LoRA，则通过将权重设为0来禁用任何可能残留的 LoRA elif pipe.get_active_adapters(): active_adapters = pipe.get_active_adapters() print(f"未选择 LoRA，通过设置权重为0来禁用残留的 LoRA: {active_adapters}") pipe.set_adapters(active_adapters, adapter_weights=[0.0] * len(active_adapters)) #Low Noise LoRA（仅当有 LoRA 被选择时） if self.low_noise_adapters and step_index >= switch_step and not self.switched: print(f"在第 {step_index} 步切换到 Low Noise LoRA: {self.low_noise_adapters}") pipe.set_adapters(self.low_noise_adapters, adapter_weights=[1.0] * len(self.low_noise_adapters)) try: print(f"Fuse Low Noise LoRA: {self.low_noise_adapters}") pipe.fuse_lora() except Exception as e: print(f"Fuse Low Noise LoRA 失败: {e}") self.switched = True return callback_kwargs lora_switcher_callback = LoraSwitcher(selected_loras) 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=num_inference_steps, generator=torch.Generator(device="cuda").manual_seed(current_seed), callback_on_step_end=lora_switcher_callback, ).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) #upscale_and_upload_4k(video_path, input_image, prompt) return video_path, current_seed with gr.Blocks() as demo: gr.Markdown("# Fast 4 steps Wan 2.2 I2V (14B) with Lightning LoRA") gr.Markdown("run Wan 2.2 in just 4-8 steps, with [Lightning LoRA](https://huggingface.co/Kijai/WanVideo_comfy/tree/main/Wan22-Lightning), fp8 quantization & AoT compilation - compatible with 🧨 diffusers and ZeroGPU⚡️") with gr.Row(): # ensures columns align in height with gr.Column(): input_image_component = gr.Image( type="pil", label="Input Image (auto-resized to target H/W)", interactive=True, elem_classes=["flex-image"] ) 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=3.5, label="Duration (seconds)", info=f"Clamped to model's {MIN_FRAMES_MODEL}-{MAX_FRAMES_MODEL} frames at {FIXED_FPS}fps." ) lora_selection_checkbox = gr.CheckboxGroup( choices=list(LORA_SETS.keys()), label="选择要应用的 LoRA (可多选)", info="选择一个或多个 LoRA 风格进行组合。" ) 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=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 stage") guidance_scale_2_input = gr.Slider(minimum=0.0, maximum=10.0, step=0.5, value=1, label="Guidance Scale 2 - low noise stage") generate_button = gr.Button("Generate Video", variant="primary") with gr.Column(): video_output = gr.Video(label="Generated Video", autoplay=True, interactive=False, elem_classes=["stretch-video"]) 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, lora_selection_checkbox ] generate_button.click(fn=generate_video, inputs=ui_inputs, outputs=[video_output, seed_input]) if __name__ == "__main__": demo.queue().launch()