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Running
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Zero
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# This file is modified from https://github.com/xdit-project/xDiT/blob/main/entrypoints/launch.py
import base64
import gc
import hashlib
import io
import os
import tempfile
from io import BytesIO
import gradio as gr
import requests
import torch
import torch.distributed as dist
from fastapi import FastAPI, HTTPException
from PIL import Image
from .api import download_from_url, encode_file_to_base64
try:
import ray
except:
print("Ray is not installed. If you want to use multi gpus api. Please install it by running 'pip install ray'.")
ray = None
def save_base64_video_dist(base64_string):
video_data = base64.b64decode(base64_string)
md5_hash = hashlib.md5(video_data).hexdigest()
filename = f"{md5_hash}.mp4"
temp_dir = tempfile.gettempdir()
file_path = os.path.join(temp_dir, filename)
if dist.is_initialized():
if dist.get_rank() == 0:
with open(file_path, 'wb') as video_file:
video_file.write(video_data)
dist.barrier()
else:
with open(file_path, 'wb') as video_file:
video_file.write(video_data)
return file_path
def save_base64_image_dist(base64_string):
video_data = base64.b64decode(base64_string)
md5_hash = hashlib.md5(video_data).hexdigest()
filename = f"{md5_hash}.jpg"
temp_dir = tempfile.gettempdir()
file_path = os.path.join(temp_dir, filename)
if dist.is_initialized():
if dist.get_rank() == 0:
with open(file_path, 'wb') as video_file:
video_file.write(video_data)
dist.barrier()
else:
with open(file_path, 'wb') as video_file:
video_file.write(video_data)
return file_path
def save_url_video_dist(url):
video_data = download_from_url(url)
if video_data:
return save_base64_video_dist(base64.b64encode(video_data))
return None
def save_url_image_dist(url):
image_data = download_from_url(url)
if image_data:
return save_base64_image_dist(base64.b64encode(image_data))
return None
if ray is not None:
@ray.remote(num_gpus=1)
class MultiNodesGenerator:
def __init__(
self, rank: int, world_size: int, Controller,
GPU_memory_mode, scheduler_dict, model_name=None, model_type="Inpaint",
config_path=None, ulysses_degree=1, ring_degree=1,
fsdp_dit=False, fsdp_text_encoder=False, compile_dit=False,
weight_dtype=None, savedir_sample=None,
):
# Set PyTorch distributed environment variables
os.environ["RANK"] = str(rank)
os.environ["WORLD_SIZE"] = str(world_size)
os.environ["MASTER_ADDR"] = "127.0.0.1"
os.environ["MASTER_PORT"] = "29500"
self.rank = rank
self.controller = Controller(
GPU_memory_mode, scheduler_dict, model_name=model_name, model_type=model_type, config_path=config_path,
ulysses_degree=ulysses_degree, ring_degree=ring_degree,
fsdp_dit=fsdp_dit, fsdp_text_encoder=fsdp_text_encoder, compile_dit=compile_dit,
weight_dtype=weight_dtype, savedir_sample=savedir_sample,
)
def generate(self, datas):
try:
base_model_path = datas.get('base_model_path', 'none')
base_model_2_path = datas.get('base_model_2_path', 'none')
lora_model_path = datas.get('lora_model_path', 'none')
lora_model_2_path = datas.get('lora_model_2_path', 'none')
lora_alpha_slider = datas.get('lora_alpha_slider', 0.55)
prompt_textbox = datas.get('prompt_textbox', None)
negative_prompt_textbox = datas.get('negative_prompt_textbox', '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. ')
sampler_dropdown = datas.get('sampler_dropdown', 'Euler')
sample_step_slider = datas.get('sample_step_slider', 30)
resize_method = datas.get('resize_method', "Generate by")
width_slider = datas.get('width_slider', 672)
height_slider = datas.get('height_slider', 384)
base_resolution = datas.get('base_resolution', 512)
is_image = datas.get('is_image', False)
generation_method = datas.get('generation_method', False)
length_slider = datas.get('length_slider', 49)
overlap_video_length = datas.get('overlap_video_length', 4)
partial_video_length = datas.get('partial_video_length', 72)
cfg_scale_slider = datas.get('cfg_scale_slider', 6)
start_image = datas.get('start_image', None)
end_image = datas.get('end_image', None)
validation_video = datas.get('validation_video', None)
validation_video_mask = datas.get('validation_video_mask', None)
control_video = datas.get('control_video', None)
denoise_strength = datas.get('denoise_strength', 0.70)
seed_textbox = datas.get("seed_textbox", 43)
ref_image = datas.get('ref_image', None)
enable_teacache = datas.get('enable_teacache', True)
teacache_threshold = datas.get('teacache_threshold', 0.10)
num_skip_start_steps = datas.get('num_skip_start_steps', 1)
teacache_offload = datas.get('teacache_offload', False)
cfg_skip_ratio = datas.get('cfg_skip_ratio', 0)
enable_riflex = datas.get('enable_riflex', False)
riflex_k = datas.get('riflex_k', 6)
fps = datas.get('fps', None)
generation_method = "Image Generation" if is_image else generation_method
if start_image is not None:
if start_image.startswith('http'):
start_image = save_url_image_dist(start_image)
start_image = [Image.open(start_image).convert("RGB")]
else:
start_image = base64.b64decode(start_image)
start_image = [Image.open(BytesIO(start_image)).convert("RGB")]
if end_image is not None:
if end_image.startswith('http'):
end_image = save_url_image_dist(end_image)
end_image = [Image.open(end_image).convert("RGB")]
else:
end_image = base64.b64decode(end_image)
end_image = [Image.open(BytesIO(end_image)).convert("RGB")]
if validation_video is not None:
if validation_video.startswith('http'):
validation_video = save_url_video_dist(validation_video)
else:
validation_video = save_base64_video_dist(validation_video)
if validation_video_mask is not None:
if validation_video_mask.startswith('http'):
validation_video_mask = save_url_image_dist(validation_video_mask)
else:
validation_video_mask = save_base64_image_dist(validation_video_mask)
if control_video is not None:
if control_video.startswith('http'):
control_video = save_url_video_dist(control_video)
else:
control_video = save_base64_video_dist(control_video)
if ref_image is not None:
if ref_image.startswith('http'):
ref_image = save_url_image_dist(ref_image)
ref_image = [Image.open(ref_image).convert("RGB")]
else:
ref_image = base64.b64decode(ref_image)
ref_image = [Image.open(BytesIO(ref_image)).convert("RGB")]
try:
save_sample_path, comment = self.controller.generate(
"",
base_model_path,
lora_model_path,
lora_alpha_slider,
prompt_textbox,
negative_prompt_textbox,
sampler_dropdown,
sample_step_slider,
resize_method,
width_slider,
height_slider,
base_resolution,
generation_method,
length_slider,
overlap_video_length,
partial_video_length,
cfg_scale_slider,
start_image,
end_image,
validation_video,
validation_video_mask,
control_video,
denoise_strength,
seed_textbox,
ref_image = ref_image,
enable_teacache = enable_teacache,
teacache_threshold = teacache_threshold,
num_skip_start_steps = num_skip_start_steps,
teacache_offload = teacache_offload,
cfg_skip_ratio = cfg_skip_ratio,
enable_riflex = enable_riflex,
riflex_k = riflex_k,
base_model_2_dropdown = base_model_2_path,
lora_model_2_dropdown = lora_model_2_path,
fps = fps,
is_api = True,
)
except Exception as e:
gc.collect()
torch.cuda.empty_cache()
torch.cuda.ipc_collect()
save_sample_path = ""
comment = f"Error. error information is {str(e)}"
if dist.is_initialized():
if dist.get_rank() == 0:
return {"message": comment, "save_sample_path": None, "base64_encoding": None}
else:
return None
else:
return {"message": comment, "save_sample_path": None, "base64_encoding": None}
if dist.is_initialized():
if dist.get_rank() == 0:
if save_sample_path != "":
return {"message": comment, "save_sample_path": save_sample_path, "base64_encoding": encode_file_to_base64(save_sample_path)}
else:
return {"message": comment, "save_sample_path": None, "base64_encoding": None}
else:
return None
else:
if save_sample_path != "":
return {"message": comment, "save_sample_path": save_sample_path, "base64_encoding": encode_file_to_base64(save_sample_path)}
else:
return {"message": comment, "save_sample_path": None, "base64_encoding": None}
except Exception as e:
print(f"Error generating: {str(e)}")
comment = f"Error generating: {str(e)}"
if dist.is_initialized():
if dist.get_rank() == 0:
return {"message": comment, "save_sample_path": None, "base64_encoding": None}
else:
return None
else:
return {"message": comment, "save_sample_path": None, "base64_encoding": None}
class MultiNodesEngine:
def __init__(
self,
world_size,
Controller,
GPU_memory_mode,
scheduler_dict,
model_name,
model_type,
config_path,
ulysses_degree=1,
ring_degree=1,
fsdp_dit=False,
fsdp_text_encoder=False,
compile_dit=False,
weight_dtype=torch.bfloat16,
savedir_sample="samples"
):
# Ensure Ray is initialized
if not ray.is_initialized():
ray.init()
num_workers = world_size
self.workers = [
MultiNodesGenerator.remote(
rank, world_size, Controller,
GPU_memory_mode, scheduler_dict, model_name=model_name, model_type=model_type, config_path=config_path,
ulysses_degree=ulysses_degree, ring_degree=ring_degree,
fsdp_dit=fsdp_dit, fsdp_text_encoder=fsdp_text_encoder, compile_dit=compile_dit,
weight_dtype=weight_dtype, savedir_sample=savedir_sample,
)
for rank in range(num_workers)
]
print("Update workers done")
async def generate(self, data):
results = ray.get([
worker.generate.remote(data)
for worker in self.workers
])
return next(path for path in results if path is not None)
def multi_nodes_infer_forward_api(_: gr.Blocks, app: FastAPI, engine):
@app.post("/videox_fun/infer_forward")
async def _multi_nodes_infer_forward_api(
datas: dict,
):
try:
result = await engine.generate(datas)
return result
except Exception as e:
if isinstance(e, HTTPException):
raise e
raise HTTPException(status_code=500, detail=str(e))
else:
MultiNodesEngine = None
MultiNodesGenerator = None
multi_nodes_infer_forward_api = None |