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import base64
import gc
import os
from io import BytesIO
import gradio as gr
import torch
from fastapi import FastAPI, HTTPException
from PIL import Image
from .api import (encode_file_to_base64, save_base64_image, save_base64_video,
save_url_image, save_url_video)
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
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,
enable_teacache=None, teacache_threshold=None,
num_skip_start_steps=None, teacache_offload=None, 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, enable_teacache=enable_teacache, teacache_threshold=teacache_threshold, num_skip_start_steps=num_skip_start_steps,
teacache_offload=teacache_offload, weight_dtype=weight_dtype, savedir_sample=savedir_sample,
)
def generate(self, datas):
try:
base_model_path = datas.get('base_model_path', 'none')
lora_model_path = datas.get('lora_model_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)
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(start_image)
start_image = [Image.open(start_image)]
else:
start_image = base64.b64decode(start_image)
start_image = [Image.open(BytesIO(start_image))]
if end_image is not None:
if end_image.startswith('http'):
end_image = save_url_image(end_image)
end_image = [Image.open(end_image)]
else:
end_image = base64.b64decode(end_image)
end_image = [Image.open(BytesIO(end_image))]
if validation_video is not None:
if validation_video.startswith('http'):
validation_video = save_url_video(validation_video)
else:
validation_video = save_base64_video(validation_video)
if validation_video_mask is not None:
if validation_video_mask.startswith('http'):
validation_video_mask = save_url_image(validation_video_mask)
else:
validation_video_mask = save_base64_image(validation_video_mask)
if control_video is not None:
if control_video.startswith('http'):
control_video = save_url_video(control_video)
else:
control_video = save_base64_video(control_video)
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,
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)}"
return {"message": comment}
import torch.distributed as dist
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": save_sample_path}
return None
except Exception as e:
self.logger.error(f"Error generating image: {str(e)}")
raise HTTPException(status_code=500, detail=str(e))
class MultiNodesEngine:
def __init__(
self,
world_size,
Controller,
GPU_memory_mode,
scheduler_dict,
model_name,
model_type,
config_path,
ulysses_degree,
ring_degree,
enable_teacache,
teacache_threshold,
num_skip_start_steps,
teacache_offload,
weight_dtype,
savedir_sample
):
# 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, enable_teacache=enable_teacache, teacache_threshold=teacache_threshold, num_skip_start_steps=num_skip_start_steps,
teacache_offload=teacache_offload, 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 |