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import os
import sys
import torch
from diffusers import FlowMatchEulerDiscreteScheduler
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 (AutoencoderKL, CLIPTextModel, CLIPTokenizer,
FluxTransformer2DModel, T5EncoderModel,
T5TokenizerFast)
from videox_fun.pipeline import FluxPipeline
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)
from videox_fun.utils.lora_utils import merge_lora, 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, 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.
#
# 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_cpu_offload_and_qfloat8"
# 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 the fsdp_dit and sequential_cpu_offload.
compile_dit = False
# model path
model_name = "models/Diffusion_Transformer/FLUX.1-dev"
# 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 = [1344, 768]
# 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
prompt = "1girl, black_hair, brown_eyes, earrings, freckles, grey_background, jewelry, lips, long_hair, looking_at_viewer, nose, piercing, realistic, red_lips, solo, upper_body"
negative_prompt = "The video is not of a high quality, it has a low resolution. Watermark present in each frame. The background is solid. Strange body and strange trajectory. Distortion. "
guidance_scale = 1.0
seed = 43
num_inference_steps = 50
lora_weight = 0.55
save_path = "samples/flux-t2i"
device = set_multi_gpus_devices(ulysses_degree, ring_degree)
transformer = FluxTransformer2DModel.from_pretrained(
model_name,
subfolder="transformer",
low_cpu_mem_usage=True,
torch_dtype=weight_dtype,
).to(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)}")
# Get Vae
vae = AutoencoderKL.from_pretrained(
model_name,
subfolder="vae"
).to(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)}")
# Get tokenizer and text_encoder
tokenizer = CLIPTokenizer.from_pretrained(
model_name, subfolder="tokenizer"
)
text_encoder = CLIPTextModel.from_pretrained(
model_name, subfolder="text_encoder", torch_dtype=weight_dtype
)
tokenizer_2 = T5TokenizerFast.from_pretrained(
model_name, subfolder="tokenizer_2"
)
text_encoder_2 = T5EncoderModel.from_pretrained(
model_name, subfolder="text_encoder_2", torch_dtype=weight_dtype
)
# Get Scheduler
Chosen_Scheduler = scheduler_dict = {
"Flow": FlowMatchEulerDiscreteScheduler,
"Flow_Unipc": FlowUniPCMultistepScheduler,
"Flow_DPM++": FlowDPMSolverMultistepScheduler,
}[sampler_name]
scheduler = Chosen_Scheduler.from_pretrained(
model_name,
subfolder="scheduler"
)
pipeline = FluxPipeline(
vae=vae,
tokenizer=tokenizer,
text_encoder=text_encoder,
tokenizer_2=tokenizer_2,
text_encoder_2=text_encoder_2,
transformer=transformer,
scheduler=scheduler,
)
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) + list(transformer.single_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.text_model.encoder.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_cpu_offload_and_qfloat8":
convert_model_weight_to_float8(transformer, exclude_module_name=["img_in", "txt_in", "timestep"], 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=["img_in", "txt_in", "timestep"], 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():
sample = pipeline(
prompt,
negative_prompt = negative_prompt,
height = sample_size[0],
width = sample_size[1],
generator = generator,
true_cfg_scale = guidance_scale,
num_inference_steps = num_inference_steps,
).images
if lora_path is not None:
pipeline = unmerge_lora(pipeline, lora_path, lora_weight, device=device, dtype=weight_dtype)
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 + ".png")
image = sample[0]
image.save(video_path)
if ulysses_degree * ring_degree > 1:
import torch.distributed as dist
if dist.get_rank() == 0:
save_results()
else:
save_results() |