""" LongCat Text-to-Video (T2V) Example Script This script demonstrates LongCat T2V inference using the FastVideo Python API. It runs both basic generation (50 steps) and distill+refine generation (16 steps distill + 50 steps refinement to 720p). Usage: python examples/inference/basic/basic_longcat_t2v.py """ import glob import os from fastvideo import VideoGenerator # Common prompts and settings matching the shell script examples PROMPT = ("In a realistic photography style, a white boy around seven or eight years old " "sits on a park bench, wearing a light blue T-shirt, denim shorts, and white sneakers. " "He holds an ice cream cone with vanilla and chocolate flavors, and beside him is a " "medium-sized golden Labrador. Smiling, the boy offers the ice cream to the dog, " "who eagerly licks it with its tongue. The sun is shining brightly, and the background " "features a green lawn and several tall trees, creating a warm and loving scene.") 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") SEED = 42 def basic_generation(): """ Run basic LongCat T2V generation (50 steps at 480p). This uses the full 50-step denoising process for highest quality. """ print("=" * 60) print("LongCat T2V: Basic Generation (50 steps, 480p)") print("=" * 60) generator = VideoGenerator.from_config({ "model_path": "FastVideo/LongCat-Video-T2V-Diffusers", "engine": { "num_gpus": 1, "use_fsdp_inference": False, # set to True if GPU is out of memory "offload": { "dit": False, "vae": True, "text_encoder": True, "pin_cpu_memory": False, }, }, "pipeline": {"model": {"longcat": {"enable_bsa": False}}}, }) output_path = "outputs_video/longcat_t2v_basic" generator.generate({ "prompt": PROMPT, "negative_prompt": NEGATIVE_PROMPT, "sampling": { "height": 480, "width": 832, "num_frames": 93, "num_inference_steps": 50, "fps": 15, "guidance_scale": 4.0, "seed": SEED, }, "output": {"output_path": output_path, "save_video": True}, }) print(f"\nBasic generation complete! Video saved to: {output_path}") generator.shutdown() def distill_refine_generation(): """ Run LongCat T2V with distill+refine pipeline (16 steps + refinement to 720p). This uses the distilled LoRA for fast 480p generation (16 steps), then refines to 720p using the refinement LoRA with BSA enabled. """ print("\n" + "=" * 60) print("LongCat T2V: Distill + Refine Pipeline") print("=" * 60) # Stage 1: Distilled generation (16 steps at 480p) print("\n[Stage 1] Distilled generation (16 steps, 480p)") print("-" * 40) generator = VideoGenerator.from_config({ "model_path": "FastVideo/LongCat-Video-T2V-Diffusers", "engine": { "num_gpus": 1, "use_fsdp_inference": True, "offload": { "dit": False, "vae": True, "text_encoder": True, "pin_cpu_memory": False, }, }, "pipeline": { "model": {"longcat": {"enable_bsa": False}}, "components": { "lora_path": "FastVideo/LongCat-Video-T2V-Distilled-LoRA", "lora_nickname": "distilled", }, }, }) distill_output_path = "outputs_video/longcat_t2v_distill" generator.generate({ "prompt": PROMPT, "negative_prompt": NEGATIVE_PROMPT, "sampling": { "height": 480, "width": 832, "num_frames": 93, "num_inference_steps": 16, "fps": 15, "guidance_scale": 1.0, "seed": SEED, }, "output": {"output_path": distill_output_path, "save_video": True}, }) print(f"Distilled generation complete! Video saved to: {distill_output_path}") generator.shutdown() # Stage 2: Refinement (480p -> 720p) print("\n[Stage 2] Refinement (480p -> 720p with BSA)") print("-" * 40) # Find the actual saved video file from stage 1 video_files = glob.glob(os.path.join(distill_output_path, "*.mp4")) if not video_files: raise FileNotFoundError(f"No video file found in {distill_output_path}") # Use the most recently created video file distill_video_path = max(video_files, key=os.path.getmtime) print(f"Using stage 1 video: {distill_video_path}") # Create a new generator with refinement LoRA and BSA enabled refine_generator = VideoGenerator.from_config({ "model_path": "FastVideo/LongCat-Video-T2V-Diffusers", "engine": { "num_gpus": 1, "use_fsdp_inference": True, "offload": { "dit": True, "vae": True, "text_encoder": True, "pin_cpu_memory": False, }, }, "pipeline": { "model": { "longcat": { "enable_bsa": True, "bsa_sparsity": 0.875, "bsa_chunk_q": [4, 4, 8], "bsa_chunk_k": [4, 4, 8], }, }, "components": { "lora_path": "FastVideo/LongCat-Video-T2V-Refinement-LoRA", "lora_nickname": "refinement", }, }, }) refine_output_path = "outputs_video/longcat_t2v_refine_720p" refine_generator.generate({ "prompt": PROMPT, "negative_prompt": NEGATIVE_PROMPT, "inputs": {"refine_from": distill_video_path}, "sampling": { "height": 720, "width": 1280, "num_inference_steps": 50, "fps": 30, "guidance_scale": 1.0, "seed": SEED, }, "output": {"output_path": refine_output_path, "save_video": True}, "extensions": { "t_thresh": 0.5, "spatial_refine_only": False, "num_cond_frames": 0, }, }) print(f"Refinement complete! Video saved to: {refine_output_path}") refine_generator.shutdown() def main(): """Run both basic and distill+refine generation pipelines.""" print("\n" + "=" * 60) print("LongCat Text-to-Video Example") print("=" * 60 + "\n") # Run basic generation basic_generation() # Run distill+refine pipeline distill_refine_generation() print("\n" + "=" * 60) print("All generations complete!") print("=" * 60) if __name__ == "__main__": main()