316 lines
15 KiB
Python
316 lines
15 KiB
Python
import os
|
|
import sys
|
|
|
|
import torch
|
|
|
|
current_file_path = os.path.abspath(__file__)
|
|
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)))]
|
|
for project_root in project_roots:
|
|
sys.path.insert(0, project_root) if project_root not in sys.path else None
|
|
|
|
from videox_fun.dist import set_multi_gpus_devices, shard_model
|
|
from videox_fun.models import (AutoencoderKLMiniMaxH3,
|
|
AutoencoderKLMiniMaxH3Audio,
|
|
MiniMaxH3Transformer3DModel, Qwen2TokenizerFast,
|
|
Qwen3VLForConditionalGeneration,
|
|
Qwen3VLProcessor)
|
|
from videox_fun.pipeline import (MiniMaxH3AudioReference,
|
|
MiniMaxH3ImageReference, MiniMaxH3Pipeline,
|
|
MiniMaxH3VideoReference)
|
|
from videox_fun.utils import (MiniMaxH3Scheduler, apply_gpu_memory_mode,
|
|
convert_model_weight_to_float8, merge_lora,
|
|
save_videos_with_audio_grid, unmerge_lora)
|
|
|
|
# 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].
|
|
# model_full_load means that the entire model will be moved to the GPU.
|
|
#
|
|
# model_full_load_and_qfloat8 means that the entire model will be moved to the GPU,
|
|
# and the transformer model has been quantized to float8, which can save more GPU memory.
|
|
#
|
|
# model_cpu_offload means that the entire model will be moved to the CPU after use, which can save some GPU memory.
|
|
#
|
|
# model_cpu_offload_and_qfloat8 indicates that the entire model will be moved to the CPU after use,
|
|
# and the transformer model has been quantized to float8, which can save more GPU memory.
|
|
#
|
|
# model_group_offload transfers internal layer groups between CPU/CUDA,
|
|
# balancing memory efficiency and speed between full-module and leaf-level offloading methods.
|
|
#
|
|
# sequential_cpu_offload means that each layer of the model will be moved to the CPU after use,
|
|
# resulting in slower speeds but saving a large amount of GPU memory.
|
|
GPU_memory_mode = "model_group_offload"
|
|
# Multi GPUs config
|
|
# Please ensure that the product of ulysses_degree and ring_degree equals the number of GPUs used.
|
|
# For example, if you are using 8 GPUs, you can set ulysses_degree = 2 and ring_degree = 4.
|
|
# If you are using 1 GPU, you can set ulysses_degree = 1 and ring_degree = 1.
|
|
ulysses_degree = 1
|
|
ring_degree = 1
|
|
# Use FSDP to save more GPU memory in multi gpus. The Qwen3-VL conditioner is ~62 GB, so with fsdp_dit alone every
|
|
# rank still replicates it; fsdp_text_encoder shards it too. Note it must wrap the inner `text_encoder.model`
|
|
# (Qwen3VLModel): encode_prompt calls that submodule directly, so a wrap on the top-level module would never fire.
|
|
fsdp_dit = False
|
|
fsdp_text_encoder = True
|
|
# Compile will give a speedup in fixed resolution and need a little GPU memory.
|
|
# The compile_dit is not compatible with sequential_cpu_offload.
|
|
compile_dit = False
|
|
|
|
# model path
|
|
model_name = "models/Diffusion_Transformer/MiniMax-H3"
|
|
|
|
# Load pretrained model if need
|
|
# The `ref2va` weights ship in their own subfolder, same architecture as the base transformer. A full finetune goes
|
|
# in `transformer_path`, either as the `transformer` folder a training checkpoint writes (config.json included) or
|
|
# as a single safetensors file, overriding the `transformer_ref` subfolder. A LoRA goes in `lora_path`: handed to
|
|
# `transformer_path` it would match no key at all and load nothing. A PDD LoRA (parallel decoder) goes in
|
|
# `pdd_lora_path` and cannot be combined with `lora_path`; use a checkpoint trained with `--train_mode=ref2va`.
|
|
transformer_subfolder = "transformer_ref"
|
|
transformer_path = None
|
|
vae_path = None
|
|
lora_path = None
|
|
pdd_lora_path = None
|
|
|
|
# Other params
|
|
# MiniMax-H3 generates at a fixed 24 fps, only accepts multiples of 32 as height / width, and snaps video_length up
|
|
# to the next 17 * n + 5 the video VAE can decode (the duration has to stay between 5 and 15 seconds). References
|
|
# never bind the generated geometry: leaving height / width unset resolves MiniMax-H3's own 16:9 canvas.
|
|
sample_size = [1280, 704]
|
|
video_length = 124
|
|
fps = 24
|
|
|
|
# The references to condition on, **in the order the model should read them**: the order labels them in the prompt
|
|
# presentation and lays them out on the shared rotary clock. One entry per reference, `image=path`, `video=path` or
|
|
# `audio=path`; a video's own soundtrack is conditioned on with it. Budgets of the released checkpoint: at most 9
|
|
# images, 3 videos, 3 audios and 12 references in total, and an audio reference cannot stand alone.
|
|
references = [
|
|
"video=asset/ref2va_video.mp4",
|
|
"audio=asset/ref2va_audio.wav",
|
|
]
|
|
|
|
# Use torch.float16 if GPU does not support torch.bfloat16
|
|
# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
|
|
weight_dtype = torch.bfloat16
|
|
prompt = "参考视频中的角色与场景，生成一段动作连贯、镜头流畅的续写视频，环境音与画面同步。"
|
|
seed = 43
|
|
# Number of denoising steps, i.e. of model evaluations: num_inference_steps = 50 runs 50 of them.
|
|
num_inference_steps = 50
|
|
# The released `ref2va` checkpoint is guidance-distilled with no unconditional branch, so `references` runs one
|
|
# forward pass per step and needs guidance_scale of 1 — the pipeline raises on anything above.
|
|
guidance_scale = 1.0
|
|
# The exponential sigma shifts of the two schedules. None keeps the ones of the checkpoint (12.0 video, 3.0 audio).
|
|
flow_shift = None
|
|
audio_flow_shift = None
|
|
lora_weight = 0.55
|
|
save_path = "samples/minimax-h3-videos-ref2va"
|
|
|
|
device = set_multi_gpus_devices(ulysses_degree, ring_degree)
|
|
|
|
# `model_name` may point either at a converted diffusers layout or at an *original* MiniMax-H3 partition; the
|
|
# original shards are converted on the fly while loading, no intermediate copy on disk. The transformer comes from
|
|
# the `transformer_ref` subfolder — the released `ref2va` weights, same architecture as the base model.
|
|
transformer = MiniMaxH3Transformer3DModel.from_pretrained(
|
|
model_name,
|
|
subfolder=transformer_subfolder,
|
|
low_cpu_mem_usage=True,
|
|
torch_dtype=weight_dtype,
|
|
)
|
|
|
|
if transformer_path is not None:
|
|
print(f"From checkpoint: {transformer_path}")
|
|
if os.path.isdir(transformer_path):
|
|
# A training checkpoint's `transformer` folder carries its own config.json, so the loader restores the
|
|
# mixed-precision contract of the checkpoint (`_keep_in_fp32_modules`) by itself.
|
|
transformer = MiniMaxH3Transformer3DModel.from_pretrained(
|
|
transformer_path,
|
|
low_cpu_mem_usage=True,
|
|
torch_dtype=weight_dtype,
|
|
)
|
|
else:
|
|
if transformer_path.endswith("safetensors"):
|
|
from safetensors.torch import load_file
|
|
state_dict = load_file(transformer_path)
|
|
else:
|
|
state_dict = torch.load(transformer_path, map_location="cpu")
|
|
state_dict = state_dict["state_dict"] if "state_dict" in state_dict else state_dict
|
|
|
|
m, u = transformer.load_state_dict(state_dict, strict=False)
|
|
print(f"missing keys: {len(m)}, unexpected keys: {len(u)}")
|
|
# `strict=False` accepts a file whose keys belong to another model — a LoRA checkpoint, say — by loading
|
|
# nothing at all and silently generating with the base weights, so an unexpected key is a hard error.
|
|
assert len(u) == 0, (
|
|
f"{transformer_path} holds {len(u)} key(s) the transformer does not have, e.g. {u[:3]}. A LoRA "
|
|
"checkpoint belongs in `lora_path`, not `transformer_path`."
|
|
)
|
|
|
|
pdd_config = None
|
|
if pdd_lora_path is not None:
|
|
if lora_path is not None:
|
|
raise ValueError("`lora_path` and `pdd_lora_path` cannot be used together.")
|
|
from videox_fun.models.minimax_h3_pdd import (load_pdd_lora,
|
|
pdd_num_inference_steps,
|
|
pdd_step_callback)
|
|
pdd_config = load_pdd_lora(transformer, pdd_lora_path)
|
|
num_inference_steps = pdd_num_inference_steps(pdd_config, num_inference_steps, teacher_default=50)
|
|
|
|
# Video VAE. The released weights are float32 and the decode runs under float16 autocast, so the VAE is not
|
|
# downcast even when the rest of the pipeline is bfloat16 (this is also how the training scripts load it).
|
|
vae = AutoencoderKLMiniMaxH3.from_pretrained(
|
|
model_name,
|
|
subfolder="vae",
|
|
low_cpu_mem_usage=True,
|
|
)
|
|
|
|
if vae_path is not None:
|
|
print(f"From checkpoint: {vae_path}")
|
|
if vae_path.endswith("safetensors"):
|
|
from safetensors.torch import load_file
|
|
state_dict = load_file(vae_path)
|
|
else:
|
|
state_dict = torch.load(vae_path, map_location="cpu")
|
|
state_dict = state_dict["state_dict"] if "state_dict" in state_dict else state_dict
|
|
|
|
m, u = vae.load_state_dict(state_dict, strict=False)
|
|
print(f"missing keys: {len(m)}, unexpected keys: {len(u)}")
|
|
|
|
# Audio VAE, waveform in / waveform out: MiniMax-H3 has no separate vocoder. Float32 as released, like the video VAE.
|
|
audio_vae = AutoencoderKLMiniMaxH3Audio.from_pretrained(
|
|
model_name,
|
|
subfolder="audio_vae",
|
|
low_cpu_mem_usage=True,
|
|
)
|
|
|
|
# Get Tokenizer and Processor
|
|
tokenizer = Qwen2TokenizerFast.from_pretrained(os.path.join(model_name, "tokenizer"))
|
|
processor = Qwen3VLProcessor.from_pretrained(os.path.join(model_name, "processor"))
|
|
|
|
# Get Text encoder. MiniMax-H3 reads the unnormalized hidden state after the 50th decoder layer of Qwen3-VL.
|
|
text_encoder = Qwen3VLForConditionalGeneration.from_pretrained(
|
|
os.path.join(model_name, "text_encoder"),
|
|
low_cpu_mem_usage=True,
|
|
torch_dtype=weight_dtype,
|
|
)
|
|
text_encoder = text_encoder.eval()
|
|
|
|
# Get Schedulers. MiniMax-H3 steps the video and the audio latents down two schedules inside one transformer call.
|
|
scheduler = MiniMaxH3Scheduler.from_pretrained(model_name, subfolder="scheduler")
|
|
audio_scheduler = MiniMaxH3Scheduler.from_pretrained(model_name, subfolder="audio_scheduler")
|
|
|
|
pipeline = MiniMaxH3Pipeline(
|
|
vae=vae,
|
|
audio_vae=audio_vae,
|
|
text_encoder=text_encoder,
|
|
tokenizer=tokenizer,
|
|
processor=processor,
|
|
transformer=transformer,
|
|
scheduler=scheduler,
|
|
audio_scheduler=audio_scheduler,
|
|
)
|
|
|
|
# The float32 modules of the mixed-precision checkpoint stay untouched by the float8 quantization.
|
|
fp8_exclude_module_name = [
|
|
"proj_in", "audio_proj_in", "context_embedder", "time_embedder", "time_proj",
|
|
"token_refiner", "norm_out", "proj_out", "audio_proj_out",
|
|
]
|
|
use_qfloat8 = "qfloat8" in GPU_memory_mode
|
|
if use_qfloat8:
|
|
convert_model_weight_to_float8(transformer, exclude_module_name=fp8_exclude_module_name, device=device)
|
|
|
|
if ulysses_degree > 1 or ring_degree > 1:
|
|
from functools import partial
|
|
transformer.enable_multi_gpus_inference()
|
|
if fsdp_dit:
|
|
fp32_modules = [m for m in transformer.modules()
|
|
if any(p.dtype == torch.float32 for p in m.parameters(recurse=False))]
|
|
shard_fn = partial(shard_model, device_id=device, param_dtype=None, cast_dtype=False,
|
|
module_to_wrapper=list(transformer.transformer_blocks),
|
|
ignored_modules=fp32_modules)
|
|
pipeline.transformer = shard_fn(pipeline.transformer)
|
|
print("Add FSDP DIT")
|
|
if fsdp_text_encoder:
|
|
shard_fn = partial(shard_model, device_id=device, param_dtype=weight_dtype,
|
|
module_to_wrapper=list(text_encoder.model.language_model.layers))
|
|
pipeline.text_encoder.model = shard_fn(pipeline.text_encoder.model)
|
|
print("Add FSDP TEXT ENCODER")
|
|
|
|
if compile_dit:
|
|
for i in range(len(pipeline.transformer.transformer_blocks)):
|
|
pipeline.transformer.transformer_blocks[i] = torch.compile(pipeline.transformer.transformer_blocks[i])
|
|
print("Add Compile")
|
|
|
|
# Quantize (when the mode carries an "_and_<quant>" suffix) and then place the pipeline.
|
|
# The order lives inside the helper: quantization has to happen before the offload hooks
|
|
# are installed.
|
|
# The FP8 conversion above has already run (before the FSDP sharding, on purpose); only the dequant
|
|
# wrapper and the memory placement are left, which is what the preconverted tag installs.
|
|
apply_gpu_memory_mode(pipeline, GPU_memory_mode, device, weight_dtype,
|
|
quant_tag="qfloat8_preconverted" if GPU_memory_mode.endswith("_and_qfloat8") else None,
|
|
exclude_module_name=[])
|
|
|
|
generator = torch.Generator(device=device).manual_seed(seed)
|
|
|
|
if lora_path is not None:
|
|
pipeline = merge_lora(pipeline, lora_path, lora_weight, device=device, dtype=weight_dtype)
|
|
|
|
|
|
def parse_reference(entry: str):
|
|
kind, _, media = entry.partition("=")
|
|
kind, media = kind.strip().lower(), media.strip()
|
|
if not media:
|
|
raise ValueError(f"A reference entry must be `image=path`, `video=path` or `audio=path`, got {entry!r}.")
|
|
if kind == "image":
|
|
return MiniMaxH3ImageReference.from_file(media)
|
|
if kind == "video":
|
|
return MiniMaxH3VideoReference.from_file(media)
|
|
if kind == "audio":
|
|
return MiniMaxH3AudioReference.from_file(media)
|
|
raise ValueError(f"A reference entry must start with `image=`, `video=` or `audio=`, got {entry!r}.")
|
|
|
|
# Decode every reference at the rate its container carries, which the pipeline's setup resamples onto MiniMax-H3's
|
|
# own 24 fps and the audio VAE's sample rate.
|
|
parsed_references = [parse_reference(entry) for entry in references]
|
|
|
|
pdd_callback = None if pdd_config is None else pdd_step_callback(
|
|
transformer, scheduler, audio_scheduler, pdd_config, num_inference_steps
|
|
)
|
|
|
|
with torch.no_grad():
|
|
output = pipeline(
|
|
prompt=prompt,
|
|
references=parsed_references,
|
|
height=None if sample_size is None else sample_size[0],
|
|
width=None if sample_size is None else sample_size[1],
|
|
num_frames=video_length,
|
|
num_inference_steps=num_inference_steps,
|
|
flow_shift=flow_shift,
|
|
audio_flow_shift=audio_flow_shift,
|
|
guidance_scale=guidance_scale,
|
|
generator=generator,
|
|
output_type="pt",
|
|
callback_on_step_end=pdd_callback,
|
|
)
|
|
print(f"[{os.environ.get('RANK', '0')}] generation done, decoding", flush=True)
|
|
|
|
if lora_path is not None:
|
|
pipeline = unmerge_lora(pipeline, lora_path, lora_weight, device=device, dtype=weight_dtype)
|
|
|
|
sample = output.videos
|
|
audio = output.audio
|
|
audio_sample_rate = output.sampling_rate
|
|
|
|
def save_results():
|
|
if not os.path.exists(save_path):
|
|
os.makedirs(save_path, exist_ok=True)
|
|
|
|
index = len([path for path in os.listdir(save_path)]) + 1
|
|
prefix = str(index).zfill(8)
|
|
video_path = os.path.join(save_path, prefix + ".mp4")
|
|
save_videos_with_audio_grid(sample, audio, video_path, fps=fps, audio_sample_rate=audio_sample_rate)
|
|
|
|
if ulysses_degree * ring_degree > 1:
|
|
import torch.distributed as dist
|
|
if dist.get_rank() == 0:
|
|
save_results()
|
|
# Keep every rank alive until the saving rank finishes; an early exit of one rank makes the elastic launcher
|
|
# terminate the others.
|
|
dist.barrier()
|
|
else:
|
|
save_results()
|