mirror of
https://github.com/Wan-Video/Wan2.1.git
synced 2025-11-04 06:15:17 +00:00
Added --batch_size parameter to generate multiple videos per run without reloading the model.
Fixed seed synchronization handling by pre-generating them before the batch.
This commit is contained in:
pftq
GitHub
parent
d7b01803de
commit
3d8f5f1a04
304
generate.py
304
generate.py
@ -187,6 +187,13 @@ def _parse_args():
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default=5.0,
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help="Classifier free guidance scale.")
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# 20250226 pftq: batch size to do more videos without reloading the model
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parser.add_argument(
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"--batch_size",
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type=int,
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default=1,
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help="Number of videos per run without reloading the model again.")
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args = parser.parse_args()
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_validate_args(args)
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@ -270,142 +277,173 @@ def generate(args):
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dist.broadcast_object_list(base_seed, src=0)
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args.base_seed = base_seed[0]
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if "t2v" in args.task or "t2i" in args.task:
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if args.prompt is None:
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args.prompt = EXAMPLE_PROMPT[args.task]["prompt"]
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logging.info(f"Input prompt: {args.prompt}")
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if args.use_prompt_extend:
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logging.info("Extending prompt ...")
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if rank == 0:
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prompt_output = prompt_expander(
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args.prompt,
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tar_lang=args.prompt_extend_target_lang,
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seed=args.base_seed)
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if prompt_output.status == False:
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logging.info(
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f"Extending prompt failed: {prompt_output.message}")
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logging.info("Falling back to original prompt.")
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input_prompt = args.prompt
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else:
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input_prompt = prompt_output.prompt
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input_prompt = [input_prompt]
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else:
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input_prompt = [None]
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if dist.is_initialized():
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dist.broadcast_object_list(input_prompt, src=0)
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args.prompt = input_prompt[0]
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logging.info(f"Extended prompt: {args.prompt}")
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logging.info("Creating WanT2V pipeline.")
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wan_t2v = wan.WanT2V(
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config=cfg,
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checkpoint_dir=args.ckpt_dir,
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device_id=device,
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rank=rank,
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t5_fsdp=args.t5_fsdp,
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dit_fsdp=args.dit_fsdp,
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use_usp=(args.ulysses_size > 1 or args.ring_size > 1),
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t5_cpu=args.t5_cpu,
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)
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logging.info(
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f"Generating {'image' if 't2i' in args.task else 'video'} ...")
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video = wan_t2v.generate(
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args.prompt,
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size=SIZE_CONFIGS[args.size],
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frame_num=args.frame_num,
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shift=args.sample_shift,
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sample_solver=args.sample_solver,
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sampling_steps=args.sample_steps,
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guide_scale=args.sample_guide_scale,
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seed=args.base_seed,
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offload_model=args.offload_model)
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else:
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if args.prompt is None:
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args.prompt = EXAMPLE_PROMPT[args.task]["prompt"]
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if args.image is None:
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args.image = EXAMPLE_PROMPT[args.task]["image"]
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logging.info(f"Input prompt: {args.prompt}")
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logging.info(f"Input image: {args.image}")
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img = Image.open(args.image).convert("RGB")
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if args.use_prompt_extend:
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logging.info("Extending prompt ...")
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if rank == 0:
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prompt_output = prompt_expander(
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args.prompt,
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tar_lang=args.prompt_extend_target_lang,
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image=img,
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seed=args.base_seed)
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if prompt_output.status == False:
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logging.info(
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f"Extending prompt failed: {prompt_output.message}")
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logging.info("Falling back to original prompt.")
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input_prompt = args.prompt
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else:
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input_prompt = prompt_output.prompt
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input_prompt = [input_prompt]
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else:
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input_prompt = [None]
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if dist.is_initialized():
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dist.broadcast_object_list(input_prompt, src=0)
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args.prompt = input_prompt[0]
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logging.info(f"Extended prompt: {args.prompt}")
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logging.info("Creating WanI2V pipeline.")
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wan_i2v = wan.WanI2V(
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config=cfg,
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checkpoint_dir=args.ckpt_dir,
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device_id=device,
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rank=rank,
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t5_fsdp=args.t5_fsdp,
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dit_fsdp=args.dit_fsdp,
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use_usp=(args.ulysses_size > 1 or args.ring_size > 1),
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t5_cpu=args.t5_cpu,
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)
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logging.info("Generating video ...")
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video = wan_i2v.generate(
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args.prompt,
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img,
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max_area=MAX_AREA_CONFIGS[args.size],
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frame_num=args.frame_num,
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shift=args.sample_shift,
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sample_solver=args.sample_solver,
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sampling_steps=args.sample_steps,
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guide_scale=args.sample_guide_scale,
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seed=args.base_seed,
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offload_model=args.offload_model)
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if rank == 0:
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if args.save_file is None:
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formatted_time = datetime.now().strftime("%Y%m%d_%H%M%S")
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formatted_prompt = args.prompt.replace(" ", "_").replace("/",
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"_")[:50]
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suffix = '.png' if "t2i" in args.task else '.mp4'
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args.save_file = f"{args.task}_{args.size}_{args.ulysses_size}_{args.ring_size}_{formatted_prompt}_{formatted_time}" + suffix
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if "t2i" in args.task:
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logging.info(f"Saving generated image to {args.save_file}")
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cache_image(
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tensor=video.squeeze(1)[None],
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save_file=args.save_file,
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nrow=1,
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normalize=True,
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value_range=(-1, 1))
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# 20250226 pftq: Do more videos in one batch without reloading the model
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wan_t2v = None
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wan_i2v = None
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# 20250226 pftq: Pre-generate and sync seeds for all videos
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seeds = []
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if dist.is_initialized():
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if rank == 0:
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seeds = [args.base_seed if args.base_seed >= 0 else random.randint(0, sys.maxsize)]
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seeds.extend(random.randint(0, sys.maxsize) for _ in range(args.batch_size - 1))
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else:
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logging.info(f"Saving generated video to {args.save_file}")
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cache_video(
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tensor=video[None],
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save_file=args.save_file,
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fps=cfg.sample_fps,
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nrow=1,
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normalize=True,
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value_range=(-1, 1))
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logging.info("Finished.")
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seeds = [None] * args.batch_size
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dist.broadcast_object_list(seeds, src=0)
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else:
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seeds = [args.base_seed if args.base_seed >= 0 else random.randint(0, sys.maxsize)]
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seeds.extend(random.randint(0, sys.maxsize) for _ in range(args.batch_size - 1))
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for batch_index in range(args.batch_size):
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if batch_index > 0:
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args.base_seed = seeds[batch_index]
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if args.batch_size > 1:
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logging.info("Batch size: "+str(args.batch_size)+" | Video #"+str(batch_index+1))
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if "t2v" in args.task or "t2i" in args.task:
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if args.prompt is None:
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args.prompt = EXAMPLE_PROMPT[args.task]["prompt"]
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logging.info(f"Input prompt: {args.prompt}")
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if args.use_prompt_extend and batch_index == 0: # 20250226 pftq: extend once
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logging.info("Extending prompt ...")
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if rank == 0:
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prompt_output = prompt_expander(
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args.prompt,
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tar_lang=args.prompt_extend_target_lang,
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seed=args.base_seed)
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if prompt_output.status == False:
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logging.info(
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f"Extending prompt failed: {prompt_output.message}")
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logging.info("Falling back to original prompt.")
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input_prompt = args.prompt
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else:
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input_prompt = prompt_output.prompt
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input_prompt = [input_prompt]
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else:
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input_prompt = [None]
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if dist.is_initialized():
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dist.broadcast_object_list(input_prompt, src=0)
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args.prompt = input_prompt[0]
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logging.info(f"Extended prompt: {args.prompt}")
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if batch_index == 0: # 20250226 pftq: only load the model once
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logging.info("Creating WanT2V pipeline.")
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wan_t2v = wan.WanT2V(
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config=cfg,
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checkpoint_dir=args.ckpt_dir,
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device_id=device,
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rank=rank,
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t5_fsdp=args.t5_fsdp,
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dit_fsdp=args.dit_fsdp,
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use_usp=(args.ulysses_size > 1 or args.ring_size > 1),
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t5_cpu=args.t5_cpu,
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)
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logging.info(
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f"Generating {'image' if 't2i' in args.task else 'video'} ...")
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video = wan_t2v.generate(
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args.prompt,
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size=SIZE_CONFIGS[args.size],
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frame_num=args.frame_num,
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shift=args.sample_shift,
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sample_solver=args.sample_solver,
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sampling_steps=args.sample_steps,
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guide_scale=args.sample_guide_scale,
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seed=args.base_seed,
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offload_model=args.offload_model)
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else:
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if args.prompt is None:
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args.prompt = EXAMPLE_PROMPT[args.task]["prompt"]
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if args.image is None:
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args.image = EXAMPLE_PROMPT[args.task]["image"]
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logging.info(f"Input prompt: {args.prompt}")
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logging.info(f"Input image: {args.image}")
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if batch_index == 0: # 20250226 pftq: load once
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img = Image.open(args.image).convert("RGB")
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if args.use_prompt_extend and batch_index == 0: # 20250226 pftq: extend once
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logging.info("Extending prompt ...")
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if rank == 0:
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prompt_output = prompt_expander(
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args.prompt,
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tar_lang=args.prompt_extend_target_lang,
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image=img,
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seed=args.base_seed)
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if prompt_output.status == False:
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logging.info(
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f"Extending prompt failed: {prompt_output.message}")
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logging.info("Falling back to original prompt.")
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input_prompt = args.prompt
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else:
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input_prompt = prompt_output.prompt
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input_prompt = [input_prompt]
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else:
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input_prompt = [None]
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if dist.is_initialized():
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dist.broadcast_object_list(input_prompt, src=0)
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args.prompt = input_prompt[0]
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logging.info(f"Extended prompt: {args.prompt}")
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if batch_index == 0: # 20250226 pftq: only load the model once
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logging.info("Creating WanI2V pipeline.")
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wan_i2v = wan.WanI2V(
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config=cfg,
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checkpoint_dir=args.ckpt_dir,
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device_id=device,
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rank=rank,
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t5_fsdp=args.t5_fsdp,
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dit_fsdp=args.dit_fsdp,
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use_usp=(args.ulysses_size > 1 or args.ring_size > 1),
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t5_cpu=args.t5_cpu,
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)
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logging.info("Generating video...")
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video = wan_i2v.generate(
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args.prompt,
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img,
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max_area=MAX_AREA_CONFIGS[args.size],
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frame_num=args.frame_num,
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shift=args.sample_shift,
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sample_solver=args.sample_solver,
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sampling_steps=args.sample_steps,
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guide_scale=args.sample_guide_scale,
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seed=args.base_seed,
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offload_model=args.offload_model)
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if rank == 0:
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if args.save_file is None:
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formatted_time = datetime.now().strftime("%Y%m%d_%H%M%S")
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formatted_prompt = args.prompt.replace(" ", "_").replace("/",
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"_")[:50]
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suffix = '.png' if "t2i" in args.task else '.mp4'
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args.save_file = f"{args.task}_{args.size}_{args.ulysses_size}_{args.ring_size}_{formatted_prompt}_{formatted_time}" + suffix
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# 20250226 pftq: if multiple videos per batch, change the filename index
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if args.batch_size > 1:
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if batch_index == 0: args.save_file = args.save_file.replace(".mp4", "__"+str(batch_index+1)+".mp4")
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else: args.save_file = args.save_file.replace("__"+str(batch_index)+".mp4", "__"+str(batch_index+1)+".mp4")
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if "t2i" in args.task:
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logging.info(f"Saving generated image to {args.save_file}")
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cache_image(
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tensor=video.squeeze(1)[None],
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save_file=args.save_file,
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nrow=1,
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normalize=True,
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value_range=(-1, 1))
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else:
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logging.info(f"Saving generated video to {args.save_file}")
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cache_video(
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tensor=video[None],
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save_file=args.save_file,
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fps=cfg.sample_fps,
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nrow=1,
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normalize=True,
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value_range=(-1, 1))
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logging.info("Finished.")
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if __name__ == "__main__":
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args = _parse_args()
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generate(args)
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