283 lines
13 KiB
Python
283 lines
13 KiB
Python
import os
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import sys
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import torch
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current_file_path = os.path.abspath(__file__)
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project_roots = [os.path.dirname(current_file_path), os.path.dirname(os.path.dirname(current_file_path)), os.path.dirname(os.path.dirname(os.path.dirname(current_file_path)))]
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for project_root in project_roots:
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sys.path.insert(0, project_root) if project_root not in sys.path else None
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from videox_fun.dist import set_multi_gpus_devices, shard_model
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from videox_fun.models import (AutoencoderKLMiniMaxH3,
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AutoencoderKLMiniMaxH3Audio,
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MiniMaxH3Transformer3DModel, Qwen2TokenizerFast,
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Qwen3VLForConditionalGeneration,
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Qwen3VLProcessor)
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from videox_fun.pipeline import MiniMaxH3Pipeline
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from videox_fun.utils import (MiniMaxH3Scheduler, apply_gpu_memory_mode,
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convert_model_weight_to_float8, merge_lora,
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save_videos_with_audio_grid, unmerge_lora)
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# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, model_group_offload, sequential_cpu_offload].
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# model_full_load means that the entire model will be moved to the GPU.
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#
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# model_full_load_and_qfloat8 means that the entire model will be moved to the GPU,
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# and the transformer model has been quantized to float8, which can save more GPU memory.
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#
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# model_cpu_offload means that the entire model will be moved to the CPU after use, which can save some GPU memory.
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#
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# model_cpu_offload_and_qfloat8 indicates that the entire model will be moved to the CPU after use,
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# and the transformer model has been quantized to float8, which can save more GPU memory.
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#
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# model_group_offload transfers internal layer groups between CPU/CUDA,
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# balancing memory efficiency and speed between full-module and leaf-level offloading methods.
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#
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# sequential_cpu_offload means that each layer of the model will be moved to the CPU after use,
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GPU_memory_mode = "model_group_offload"
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# Multi GPUs config
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# Please ensure that the product of ulysses_degree and ring_degree equals the number of GPUs used.
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# For example, if you are using 8 GPUs, you can set ulysses_degree = 2 and ring_degree = 4.
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# If you are using 1 GPU, you can set ulysses_degree = 1 and ring_degree = 1.
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ulysses_degree = 1
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ring_degree = 1
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# Use FSDP to save more GPU memory in multi gpus. The Qwen3-VL conditioner is ~62 GB, so with fsdp_dit alone every
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# rank still replicates it; fsdp_text_encoder shards it too. Note it must wrap the inner `text_encoder.model`
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# (Qwen3VLModel): encode_prompt calls that submodule directly, so a wrap on the top-level module would never fire.
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fsdp_dit = False
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fsdp_text_encoder = True
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# Compile will give a speedup in fixed resolution and need a little GPU memory.
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# The compile_dit is not compatible with sequential_cpu_offload.
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compile_dit = False
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# model path
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model_name = "models/Diffusion_Transformer/MiniMax-H3"
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# Load pretrained model if need
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# A full finetune goes in `transformer_path`, either as the `transformer` folder a training checkpoint writes
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# (`output_dir_minimax_h3/checkpoint-N/transformer`, config.json included) or as a single safetensors file. A LoRA
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# goes in `lora_path`: handed to `transformer_path` it would match no key at all and load nothing. A PDD LoRA
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# (parallel decoder) goes in `pdd_lora_path` and cannot be combined with `lora_path`.
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transformer_path = None
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vae_path = None
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lora_path = None
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pdd_lora_path = None
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# Other params
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# MiniMax-H3 generates at a fixed 24 fps, only accepts multiples of 32 as height / width, and snaps video_length up
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# to the next 17 * n + 5 the video VAE can decode (the duration has to stay between 5 and 15 seconds).
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# Leave sample_size as None to use MiniMax-H3's own 16:9 canvas (768x1344).
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sample_size = [704, 1280]
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video_length = 124
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fps = 24
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# Use torch.float16 if GPU does not support torch.bfloat16
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# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
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weight_dtype = torch.bfloat16
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prompt = "A red fox trotting through a snowy pine forest, snow crunching underfoot"
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seed = 43
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# Number of denoising steps, i.e. of model evaluations: num_inference_steps = 40 runs 40 of them.
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num_inference_steps = 40
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# The released checkpoint is guidance-distilled: leave guidance_scale at 1 to run one forward pass per step
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# with no CFG. A value above 1 enables classifier-free guidance with a negative_prompt, running two passes.
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guidance_scale = 1
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# The exponential sigma shifts of the two schedules. None keeps the ones of the checkpoint (12.0 video, 3.0 audio).
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flow_shift = None
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audio_flow_shift = None
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lora_weight = 0.55
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save_path = "samples/minimax-h3-videos-t2v"
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device = set_multi_gpus_devices(ulysses_degree, ring_degree)
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# `model_name` may point either at a converted diffusers layout or at an *original* MiniMax-H3 partition (e.g.
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# `MiniMax-H3/FL2VA`); the original shards are converted on the fly while loading, no intermediate copy on disk.
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# Transformer
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transformer = MiniMaxH3Transformer3DModel.from_pretrained(
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model_name,
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subfolder="transformer",
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low_cpu_mem_usage=True,
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torch_dtype=weight_dtype,
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)
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if transformer_path is not None:
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print(f"From checkpoint: {transformer_path}")
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if os.path.isdir(transformer_path):
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# A training checkpoint's `transformer` folder carries its own config.json, so the loader restores the
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# mixed-precision contract of the checkpoint (`_keep_in_fp32_modules`) by itself.
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transformer = MiniMaxH3Transformer3DModel.from_pretrained(
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transformer_path,
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low_cpu_mem_usage=True,
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torch_dtype=weight_dtype,
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)
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else:
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if transformer_path.endswith("safetensors"):
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from safetensors.torch import load_file
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state_dict = load_file(transformer_path)
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else:
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state_dict = torch.load(transformer_path, map_location="cpu")
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state_dict = state_dict["state_dict"] if "state_dict" in state_dict else state_dict
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m, u = transformer.load_state_dict(state_dict, strict=False)
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print(f"missing keys: {len(m)}, unexpected keys: {len(u)}")
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# `strict=False` accepts a file whose keys belong to another model — a LoRA checkpoint, say — by loading
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# nothing at all and silently generating with the base weights, so an unexpected key is a hard error.
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assert len(u) == 0, (
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f"{transformer_path} holds {len(u)} key(s) the transformer does not have, e.g. {u[:3]}. A LoRA "
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"checkpoint belongs in `lora_path`, not `transformer_path`."
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)
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pdd_config = None
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if pdd_lora_path is not None:
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if lora_path is not None:
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raise ValueError("`lora_path` and `pdd_lora_path` cannot be used together.")
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from videox_fun.models.minimax_h3_pdd import (load_pdd_lora,
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pdd_num_inference_steps,
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pdd_step_callback)
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pdd_config = load_pdd_lora(transformer, pdd_lora_path)
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num_inference_steps = pdd_num_inference_steps(pdd_config, num_inference_steps, teacher_default=40)
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# Video VAE. The released weights are float32 and the decode runs under float16 autocast, so the VAE is not
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# downcast even when the rest of the pipeline is bfloat16 (this is also how the training scripts load it).
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vae = AutoencoderKLMiniMaxH3.from_pretrained(
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model_name,
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subfolder="vae",
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low_cpu_mem_usage=True,
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)
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if vae_path is not None:
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print(f"From checkpoint: {vae_path}")
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if vae_path.endswith("safetensors"):
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from safetensors.torch import load_file
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state_dict = load_file(vae_path)
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else:
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state_dict = torch.load(vae_path, map_location="cpu")
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state_dict = state_dict["state_dict"] if "state_dict" in state_dict else state_dict
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m, u = vae.load_state_dict(state_dict, strict=False)
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print(f"missing keys: {len(m)}, unexpected keys: {len(u)}")
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# Audio VAE, waveform in / waveform out: MiniMax-H3 has no separate vocoder. Float32 as released, like the video VAE.
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audio_vae = AutoencoderKLMiniMaxH3Audio.from_pretrained(
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model_name,
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subfolder="audio_vae",
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low_cpu_mem_usage=True,
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)
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# Get Tokenizer and Processor
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tokenizer = Qwen2TokenizerFast.from_pretrained(os.path.join(model_name, "tokenizer"))
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processor = Qwen3VLProcessor.from_pretrained(os.path.join(model_name, "processor"))
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# Get Text encoder. MiniMax-H3 reads the unnormalized hidden state after the 50th decoder layer of Qwen3-VL.
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text_encoder = Qwen3VLForConditionalGeneration.from_pretrained(
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os.path.join(model_name, "text_encoder"),
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low_cpu_mem_usage=True,
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torch_dtype=weight_dtype,
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)
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text_encoder = text_encoder.eval()
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# Get Schedulers. MiniMax-H3 steps the video and the audio latents down two schedules inside one transformer call.
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scheduler = MiniMaxH3Scheduler.from_pretrained(model_name, subfolder="scheduler")
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audio_scheduler = MiniMaxH3Scheduler.from_pretrained(model_name, subfolder="audio_scheduler")
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pipeline = MiniMaxH3Pipeline(
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vae=vae,
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audio_vae=audio_vae,
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text_encoder=text_encoder,
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tokenizer=tokenizer,
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processor=processor,
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transformer=transformer,
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scheduler=scheduler,
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audio_scheduler=audio_scheduler,
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)
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# The float32 modules of the mixed-precision checkpoint stay untouched by the float8 quantization.
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fp8_exclude_module_name = [
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"proj_in", "audio_proj_in", "context_embedder", "time_embedder", "time_proj",
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"token_refiner", "norm_out", "proj_out", "audio_proj_out",
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]
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use_qfloat8 = "qfloat8" in GPU_memory_mode
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if use_qfloat8:
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convert_model_weight_to_float8(transformer, exclude_module_name=fp8_exclude_module_name, device=device)
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if ulysses_degree > 1 or ring_degree > 1:
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from functools import partial
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transformer.enable_multi_gpus_inference()
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if fsdp_dit:
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fp32_modules = [m for m in transformer.modules()
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if any(p.dtype == torch.float32 for p in m.parameters(recurse=False))]
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shard_fn = partial(shard_model, device_id=device, param_dtype=None, cast_dtype=False,
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module_to_wrapper=list(transformer.transformer_blocks),
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ignored_modules=fp32_modules)
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pipeline.transformer = shard_fn(pipeline.transformer)
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print("Add FSDP DIT")
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if fsdp_text_encoder:
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shard_fn = partial(shard_model, device_id=device, param_dtype=weight_dtype,
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module_to_wrapper=list(text_encoder.model.language_model.layers))
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pipeline.text_encoder.model = shard_fn(pipeline.text_encoder.model)
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print("Add FSDP TEXT ENCODER")
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if compile_dit:
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for i in range(len(pipeline.transformer.transformer_blocks)):
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pipeline.transformer.transformer_blocks[i] = torch.compile(pipeline.transformer.transformer_blocks[i])
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print("Add Compile")
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# Quantize (when the mode carries an "_and_<quant>" suffix) and then place the pipeline.
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# The order lives inside the helper: quantization has to happen before the offload hooks
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# are installed.
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# The FP8 conversion above has already run (before the FSDP sharding, on purpose); only the dequant
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# wrapper and the memory placement are left, which is what the preconverted tag installs.
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apply_gpu_memory_mode(pipeline, GPU_memory_mode, device, weight_dtype,
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quant_tag="qfloat8_preconverted" if GPU_memory_mode.endswith("_and_qfloat8") else None,
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exclude_module_name=[])
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generator = torch.Generator(device=device).manual_seed(seed)
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if lora_path is not None:
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pipeline = merge_lora(pipeline, lora_path, lora_weight, device=device, dtype=weight_dtype)
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pdd_callback = None if pdd_config is None else pdd_step_callback(
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transformer, scheduler, audio_scheduler, pdd_config, num_inference_steps
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)
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with torch.no_grad():
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output = pipeline(
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prompt=prompt,
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height=None if sample_size is None else sample_size[0],
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width=None if sample_size is None else sample_size[1],
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num_frames=video_length,
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num_inference_steps=num_inference_steps,
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flow_shift=flow_shift,
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audio_flow_shift=audio_flow_shift,
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guidance_scale=guidance_scale,
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generator=generator,
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output_type="pt",
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callback_on_step_end=pdd_callback,
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)
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print(f"[{os.environ.get('RANK', '0')}] generation done, decoding", flush=True)
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if lora_path is not None:
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pipeline = unmerge_lora(pipeline, lora_path, lora_weight, device=device, dtype=weight_dtype)
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sample = output.videos
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audio = output.audio
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audio_sample_rate = output.sampling_rate
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def save_results():
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if not os.path.exists(save_path):
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os.makedirs(save_path, exist_ok=True)
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index = len([path for path in os.listdir(save_path)]) + 1
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prefix = str(index).zfill(8)
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video_path = os.path.join(save_path, prefix + ".mp4")
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save_videos_with_audio_grid(sample, audio, video_path, fps=fps, audio_sample_rate=audio_sample_rate)
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if ulysses_degree * ring_degree > 1:
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import torch.distributed as dist
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if dist.get_rank() == 0:
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save_results()
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# Keep every rank alive until the saving rank finishes; an early exit of one rank makes the elastic launcher
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# terminate the others.
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dist.barrier()
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else:
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save_results()
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