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import sys
import numpy as np
import torch
from diffusers import FlowMatchEulerDiscreteScheduler
from PIL import Image
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.models import (AutoencoderKLLTX2Audio, AutoencoderKLLTX2Video,
Gemma3ForConditionalGeneration,
GemmaTokenizerFast, LTX2TextConnectors,
LTX2VideoTransformer3DModel, LTX2Vocoder)
from videox_fun.pipeline import LTX2I2VPipeline
from videox_fun.utils import (register_auto_device_hook,
safe_enable_group_offload)
from videox_fun.dist import set_multi_gpus_devices, shard_model
from videox_fun.utils.fm_solvers import FlowDPMSolverMultistepScheduler
from videox_fun.utils.fm_solvers_unipc import FlowUniPCMultistepScheduler
from videox_fun.utils.fp8_optimization import (convert_model_weight_to_float8,
convert_weight_dtype_wrapper,
replace_parameters_by_name)
from videox_fun.utils.lora_utils import merge_lora, unmerge_lora
from videox_fun.utils.utils import (filter_kwargs, get_image_to_video_latent,
save_videos_grid,
save_videos_with_audio_grid)
# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, 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 = "sequential_cpu_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.
fsdp_dit = False
fsdp_text_encoder = False
# 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/LTX-2"
# Choose the sampler in "Flow", "Flow_Unipc", "Flow_DPM++"
sampler_name = "Flow"
# Load pretrained model if need
transformer_path = None
vae_path = None
lora_path = None
# Other params
sample_size = [480, 832]
video_length = 121
fps = 24
# Use torch.float16 if GPU does not support torch.bfloat16
# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
weight_dtype = torch.bfloat16
# If you want to generate from text, please set the validation_image_start = None and validation_image_end = None
validation_image_start = "asset/1.png"
# prompts
prompt = "A brown dog barks on a sofa, sitting on a light-colored couch in a cozy room. Behind the dog, there is a framed painting on a shelf, surrounded by pink flowers. "
negative_prompt = "worst quality, inconsistent motion, blurry, jittery, distorted, static, low quality, artifacts"
guidance_scale = 6.0
seed = 43
num_inference_steps = 50
lora_weight = 0.55
save_path = "samples/ltx2-videos-i2v"
# Audio sample rate will be read from vocoder config
audio_sample_rate = 24000
device = set_multi_gpus_devices(ulysses_degree, ring_degree)
# Transformer
transformer = LTX2VideoTransformer3DModel.from_pretrained(
model_name,
subfolder="transformer",
low_cpu_mem_usage=True,
torch_dtype=weight_dtype,
)
if transformer_path is not None:
print(f"From checkpoint: {transformer_path}")
if transformer_path.endswith("safetensors"):
from safetensors.torch import load_file, safe_open
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)}")
# Video VAE
vae = AutoencoderKLLTX2Video.from_pretrained(
model_name,
subfolder="vae",
torch_dtype=weight_dtype,
)
if vae_path is not None:
print(f"From checkpoint: {vae_path}")
if vae_path.endswith("safetensors"):
from safetensors.torch import load_file, safe_open
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
audio_vae = AutoencoderKLLTX2Audio.from_pretrained(
model_name,
subfolder="audio_vae",
torch_dtype=weight_dtype,
)
# Get Tokenizer
tokenizer = GemmaTokenizerFast.from_pretrained(
model_name,
subfolder="tokenizer",
)
# Get Text encoder
text_encoder = Gemma3ForConditionalGeneration.from_pretrained(
model_name,
subfolder="text_encoder",
low_cpu_mem_usage=True,
torch_dtype=weight_dtype,
)
text_encoder = text_encoder.eval()
# Connectors
connectors = LTX2TextConnectors.from_pretrained(
model_name,
subfolder="connectors",
torch_dtype=weight_dtype,
)
# Vocoder
vocoder = LTX2Vocoder.from_pretrained(
model_name,
subfolder="vocoder",
torch_dtype=weight_dtype,
)
# Get Scheduler
Chosen_Scheduler = {
"Flow": FlowMatchEulerDiscreteScheduler,
"Flow_Unipc": FlowUniPCMultistepScheduler,
"Flow_DPM++": FlowDPMSolverMultistepScheduler,
}[sampler_name]
scheduler = Chosen_Scheduler.from_pretrained(
model_name,
subfolder="scheduler"
)
pipeline = LTX2I2VPipeline(
scheduler=scheduler,
vae=vae,
audio_vae=audio_vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
connectors=connectors,
transformer=transformer,
vocoder=vocoder,
)
if ulysses_degree > 1 or ring_degree > 1:
from functools import partial
transformer.enable_multi_gpus_inference()
if fsdp_dit:
shard_fn = partial(shard_model, device_id=device, param_dtype=weight_dtype,
module_to_wrapper=list(transformer.transformer_blocks))
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=text_encoder.language_model.layers)
text_encoder = shard_fn(text_encoder)
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")
if GPU_memory_mode == "sequential_cpu_offload":
pipeline.enable_sequential_cpu_offload(device=device)
elif GPU_memory_mode == "model_group_offload":
register_auto_device_hook(pipeline.transformer)
safe_enable_group_offload(pipeline, onload_device=device, offload_device="cpu", offload_type="leaf_level", use_stream=True)
elif GPU_memory_mode == "model_cpu_offload_and_qfloat8":
convert_model_weight_to_float8(transformer, exclude_module_name=["scale_shift_table", "audio_scale_shift_table", "video_a2v_cross_attn_scale_shift_table", "audio_a2v_cross_attn_scale_shift_table", ""], device=device)
convert_weight_dtype_wrapper(transformer, weight_dtype)
pipeline.enable_model_cpu_offload(device=device)
elif GPU_memory_mode == "model_cpu_offload":
pipeline.enable_model_cpu_offload(device=device)
elif GPU_memory_mode == "model_full_load_and_qfloat8":
convert_model_weight_to_float8(transformer, exclude_module_name=["scale_shift_table", "audio_scale_shift_table", "video_a2v_cross_attn_scale_shift_table", "audio_a2v_cross_attn_scale_shift_table", ""], device=device)
convert_weight_dtype_wrapper(transformer, weight_dtype)
pipeline.to(device=device)
else:
pipeline.to(device=device)
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)
with torch.no_grad():
output = pipeline(
image=Image.open(validation_image_start),
prompt=prompt,
negative_prompt=negative_prompt,
height=sample_size[0],
width=sample_size[1],
num_frames=video_length,
frame_rate=fps,
num_inference_steps=num_inference_steps,
guidance_scale=guidance_scale,
generator=generator,
output_type="pt",
)
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
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)
if video_length == 1:
video_path = os.path.join(save_path, prefix + ".png")
image = sample[0, :, 0]
image = image.transpose(0, 1).transpose(1, 2)
image = (image * 255).numpy().astype(np.uint8)
image = Image.fromarray(image)
image.save(video_path)
else:
video_path = os.path.join(save_path, prefix + ".mp4")
sr = getattr(pipeline.vocoder.config, "output_sampling_rate", audio_sample_rate)
save_videos_with_audio_grid(sample, audio, video_path, fps=fps, audio_sample_rate=sr)
if ulysses_degree * ring_degree > 1:
import torch.distributed as dist
if dist.get_rank() == 0:
save_results()
else:
save_results() |