import torch from diffusers import AutoencoderKLWan, WanImageToVideoPipeline, UniPCMultistepScheduler from diffusers.utils import export_to_video from transformers import CLIPVisionModel import gradio as gr import tempfile import spaces from huggingface_hub import hf_hub_download import numpy as np from PIL import Image import random MODEL_ID = "Wan-AI/Wan2.1-I2V-14B-480P-Diffusers" LORA_REPO_ID = "Kijai/WanVideo_comfy" LORA_FILENAME = "Wan21_CausVid_14B_T2V_lora_rank32.safetensors" image_encoder = CLIPVisionModel.from_pretrained(MODEL_ID, subfolder="image_encoder", torch_dtype=torch.float32) vae = AutoencoderKLWan.from_pretrained(MODEL_ID, subfolder="vae", torch_dtype=torch.float32) pipe = WanImageToVideoPipeline.from_pretrained( MODEL_ID, vae=vae, image_encoder=image_encoder, torch_dtype=torch.bfloat16 ) pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config, flow_shift=8.0) pipe.to("cuda") causvid_path = hf_hub_download(repo_id=LORA_REPO_ID, filename=LORA_FILENAME) pipe.load_lora_weights(causvid_path, adapter_name="causvid_lora") pipe.set_adapters(["causvid_lora"], adapter_weights=[0.95]) pipe.fuse_lora() MOD_VALUE = 32 DEFAULT_H_SLIDER_VALUE = 512 DEFAULT_W_SLIDER_VALUE = 896 NEW_FORMULA_MAX_AREA = 480.0 * 832.0 SLIDER_MIN_H, SLIDER_MAX_H = 128, 896 SLIDER_MIN_W, SLIDER_MAX_W = 128, 896 MAX_SEED = np.iinfo(np.int32).max FIXED_FPS = 24 MIN_FRAMES_MODEL = 8 MAX_FRAMES_MODEL = 81 default_prompt_i2v = "make this image come alive, cinematic motion, smooth animation" default_negative_prompt = "Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards, watermark, text, signature" def _calculate_new_dimensions_wan(pil_image, mod_val, calculation_max_area, min_slider_h, max_slider_h, min_slider_w, max_slider_w, default_h, default_w): orig_w, orig_h = pil_image.size if orig_w <= 0 or orig_h <= 0: return default_h, default_w aspect_ratio = orig_h / orig_w calc_h = round(np.sqrt(calculation_max_area * aspect_ratio)) calc_w = round(np.sqrt(calculation_max_area / aspect_ratio)) calc_h = max(mod_val, (calc_h // mod_val) * mod_val) calc_w = max(mod_val, (calc_w // mod_val) * mod_val) new_h = int(np.clip(calc_h, min_slider_h, (max_slider_h // mod_val) * mod_val)) new_w = int(np.clip(calc_w, min_slider_w, (max_slider_w // mod_val) * mod_val)) return new_h, new_w def handle_image_upload_for_dims_wan(uploaded_pil_image, current_h_val, current_w_val): if uploaded_pil_image is None: return gr.update(value=DEFAULT_H_SLIDER_VALUE), gr.update(value=DEFAULT_W_SLIDER_VALUE) try: new_h, new_w = _calculate_new_dimensions_wan( uploaded_pil_image, MOD_VALUE, NEW_FORMULA_MAX_AREA, SLIDER_MIN_H, SLIDER_MAX_H, SLIDER_MIN_W, SLIDER_MAX_W, DEFAULT_H_SLIDER_VALUE, DEFAULT_W_SLIDER_VALUE ) return gr.update(value=new_h), gr.update(value=new_w) except Exception as e: gr.Warning("Error attempting to calculate new dimensions") return gr.update(value=DEFAULT_H_SLIDER_VALUE), gr.update(value=DEFAULT_W_SLIDER_VALUE) def get_duration(input_image, prompt, height, width, negative_prompt, duration_seconds, guidance_scale, steps, seed, randomize_seed, progress): if steps > 4 and duration_seconds > 2: return 90 elif steps > 4 or duration_seconds > 2: return 75 else: return 60 @spaces.GPU(duration=get_duration) def generate_video(input_image, prompt, height, width, negative_prompt=default_negative_prompt, duration_seconds = 2, 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.") target_h = max(MOD_VALUE, (int(height) // MOD_VALUE) * MOD_VALUE) target_w = max(MOD_VALUE, (int(width) // MOD_VALUE) * MOD_VALUE) 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 = input_image.resize((target_w, target_h)) with torch.inference_mode(): output_frames_list = pipe( image=resized_image, prompt=prompt, negative_prompt=negative_prompt, height=target_h, width=target_w, 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("# Fast 4 steps Wan 2.1 I2V (14B) with CausVid LoRA") 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=round(MIN_FRAMES_MODEL/FIXED_FPS,1), maximum=round(MAX_FRAMES_MODEL/FIXED_FPS,1), step=0.1, value=2, 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) with gr.Row(): height_input = gr.Slider(minimum=SLIDER_MIN_H, maximum=SLIDER_MAX_H, step=MOD_VALUE, value=DEFAULT_H_SLIDER_VALUE, label=f"Output Height (multiple of {MOD_VALUE})") width_input = gr.Slider(minimum=SLIDER_MIN_W, maximum=SLIDER_MAX_W, step=MOD_VALUE, value=DEFAULT_W_SLIDER_VALUE, label=f"Output Width (multiple of {MOD_VALUE})") steps_slider = gr.Slider(minimum=1, maximum=30, step=1, value=4, 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) input_image_component.upload( fn=handle_image_upload_for_dims_wan, inputs=[input_image_component, height_input, width_input], outputs=[height_input, width_input] ) input_image_component.clear( fn=handle_image_upload_for_dims_wan, inputs=[input_image_component, height_input, width_input], outputs=[height_input, width_input] ) ui_inputs = [ input_image_component, prompt_input, height_input, width_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=[ ["peng.png", "a penguin playfully dancing in the snow, Antarctica", 896, 512], ["forg.jpg", "the frog jumps around", 448, 832], ], inputs=[input_image_component, prompt_input, height_input, width_input], outputs=[video_output, seed_input], fn=generate_video, cache_examples="lazy" ) if __name__ == "__main__": demo.queue().launch()