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