import torch from diffusers import AutoencoderKLWan, WanPipeline, WanImageToVideoPipeline, UniPCMultistepScheduler from diffusers.utils import export_to_video import gradio as gr import tempfile import spaces import numpy as np from PIL import Image import random MODEL_ID = "FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers" vae = AutoencoderKLWan.from_pretrained(MODEL_ID, subfolder="vae", torch_dtype=torch.float32) # Initialize pipelines text_to_video_pipe = WanPipeline.from_pretrained(MODEL_ID, vae=vae, torch_dtype=torch.bfloat16) image_to_video_pipe = WanImageToVideoPipeline.from_pretrained(MODEL_ID, vae=vae, torch_dtype=torch.bfloat16) for pipe in [text_to_video_pipe, image_to_video_pipe]: pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config, flow_shift=8.0) pipe.to("cuda") # Constants MOD_VALUE = 32 DEFAULT_H_SLIDER_VALUE = 896 DEFAULT_W_SLIDER_VALUE = 896 NEW_FORMULA_MAX_AREA = 720 * 1024 SLIDER_MIN_H, SLIDER_MAX_H = 256, 1024 SLIDER_MIN_W, SLIDER_MAX_W = 256, 1024 MAX_SEED = np.iinfo(np.int32).max FIXED_FPS = 24 MIN_FRAMES_MODEL = 25 MAX_FRAMES_MODEL = 193 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 > 4: return 90 elif steps > 4 or duration_seconds > 4: 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=0, steps=4, seed=44, randomize_seed=False, progress=gr.Progress(track_tqdm=True)): 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) if input_image is not None: resized_image = input_image.resize((target_w, target_h)) with torch.inference_mode(): output_frames_list = image_to_video_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] else: with torch.inference_mode(): output_frames_list = text_to_video_pipe( 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 Wan 2.2 TI2V 5B Demo") gr.Markdown("""This Demo is using [FastWan2.2-TI2V-5B](https://huggingface.co/FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers) which is fine-tuned with Sparse-distill method which allows wan to generate high quality videos in 3-5 steps.""") with gr.Row(): with gr.Column(): input_image_component = gr.Image(type="pil", label="Input Image (optional, 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=8, step=1, value=4, label="Inference Steps") guidance_scale_input = gr.Slider(minimum=0.0, maximum=5.0, step=0.01, value=0.0, label="Guidance Scale") 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=[ [None, "A person eating spaghetti", 1024, 720], ["cat.png", "The cat removes the glasses from its eyes.", 1088, 800], [None, "a penguin playfully dancing in the snow, Antarctica", 1024, 720], ["peng.png", "a penguin running towards camera joyfully, Antarctica", 896, 512], ], 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()