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桜華
2023-02-18 21:36:33 +08:00
parent a751623e4d
commit 26472323f9
1 changed files with 45 additions and 40 deletions
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server.py
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@@ -1,4 +1,7 @@
import argparse, os import time
import requests
import argparse
import os
import cv2 import cv2
import torch import torch
import numpy as np import numpy as np
@@ -20,6 +23,7 @@ from ldm.models.diffusion.dpm_solver import DPMSolverSampler
torch.set_grad_enabled(False) torch.set_grad_enabled(False)
def chunk(it, size): def chunk(it, size):
it = iter(it) it = iter(it)
return iter(lambda: tuple(islice(it, size)), ()) return iter(lambda: tuple(islice(it, size)), ())
@@ -210,15 +214,14 @@ def put_watermark(img, wm_encoder=None):
img = Image.fromarray(img[:, :, ::-1]) img = Image.fromarray(img[:, :, ::-1])
return img return img
import time
import requests
# 对任务状态的修改 # 对任务状态的修改
def update_task_status(task:dict, status:str, progress:int): def update_task_status(task: dict, status: str, progress: int):
task["status"] = status task["status"] = status
task["progress"] = progress task["progress"] = progress
requests.put(f"http://localhost:3000/api/drawing/{task['id']}", json=task) requests.put(f"http://localhost:3000/api/drawing/{task['id']}", json=task)
def main_dev(opt): def main_dev(opt):
model_name = '' # 默认模型 model_name = '' # 默认模型
model = None # 默认模型 model = None # 默认模型
@@ -227,17 +230,21 @@ def main_dev(opt):
while True: while True:
time.sleep(2) # 延时1s执行, 避免cpu占用过高 time.sleep(2) # 延时1s执行, 避免cpu占用过高
data = requests.get("http://localhost:3000/api/drawing").json() # 从局域网中获取一组参数 data = requests.get("http://localhost:3000/api/drawing").json() # 从局域网中获取一组参数
print(data) # [{'model': '768-v-ema', 'prompt': '一只猫', 'watermark': '0'}, {'model': '768-v-ema', 'prompt': '一只狗', 'watermark': '0'}] print(data)
# 遍历 data 返回dict # 遍历 data 返回dict
for item in data: for item in data:
print(item) print(item)
update_task_status(item, "running", 0) # 更新任务状态为运行中 update_task_status(item, "running", 0) # 更新任务状态为运行中
# 设置参数 # 设置参数
if 'prompt' in item: opt.prompt = item['prompt'] # 描述 if 'prompt' in item:
if 'n_samples' in item: opt.n_samples = item['n_samples'] # 列数 opt.prompt = item['prompt'] # 描述
if 'n_rows' in item: opt.n_rows = item['n_rows'] # 行数 if 'n_samples' in item:
if 'scale' in item: opt.scale = item['scale'] # 比例 opt.n_samples = item['n_samples'] # 列数
if 'n_rows' in item:
opt.n_rows = item['n_rows'] # 行数
if 'scale' in item:
opt.scale = item['scale'] # 比例
# 如果模型不同，重新加载模型(注意释放内存) # 如果模型不同，重新加载模型(注意释放内存)
if item['ckpt'] != model_name: if item['ckpt'] != model_name:
@@ -300,8 +307,7 @@ def main_dev(opt):
if opt.bf16 and not opt.torchscript and not opt.ipex: if opt.bf16 and not opt.torchscript and not opt.ipex:
raise ValueError('Bfloat16 is supported only for torchscript+ipex') raise ValueError('Bfloat16 is supported only for torchscript+ipex')
if opt.bf16 and unet.dtype != torch.bfloat16: if opt.bf16 and unet.dtype != torch.bfloat16:
raise ValueError("Use configs/stable-diffusion/intel/ configs with bf16 enabled if " + raise ValueError("Use configs/stable-diffusion/intel/ configs with bf16 enabled if you'd like to use bfloat16 with CPU.")
"you'd like to use bfloat16 with CPU.")
if unet.dtype == torch.float16 and device == torch.device("cpu"): if unet.dtype == torch.float16 and device == torch.device("cpu"):
raise ValueError("Use configs/stable-diffusion/intel/ configs for your model if you'd like to run it on CPU.") raise ValueError("Use configs/stable-diffusion/intel/ configs for your model if you'd like to run it on CPU.")
if opt.ipex: if opt.ipex:
@@ -317,8 +323,7 @@ def main_dev(opt):
with torch.no_grad(), additional_context: with torch.no_grad(), additional_context:
# get UNET scripted # get UNET scripted
if unet.use_checkpoint: if unet.use_checkpoint:
raise ValueError("Gradient checkpoint won't work with tracing. " + raise ValueError("Gradient checkpoint won't work with tracing. Use configs/stable-diffusion/intel/ configs for your model or disable checkpoint in your config.")
"Use configs/stable-diffusion/intel/ configs for your model or disable checkpoint in your config.")
img_in = torch.ones(2, 4, 96, 96, dtype=torch.float32) img_in = torch.ones(2, 4, 96, 96, dtype=torch.float32)
t_in = torch.ones(2, dtype=torch.int64) t_in = torch.ones(2, dtype=torch.int64)
context = torch.ones(2, 77, 1024, dtype=torch.float32) context = torch.ones(2, 77, 1024, dtype=torch.float32)
@@ -354,9 +359,9 @@ def main_dev(opt):
print("Running a forward pass for decoder") print("Running a forward pass for decoder")
for _ in range(3): for _ in range(3):
x_samples_ddim = model.decode_first_stage(samples_ddim) x_samples_ddim = model.decode_first_stage(samples_ddim)
precision_scope = autocast if opt.precision=="autocast" or opt.bf16 else nullcontext precision_scope = autocast if opt.precision == "autocast" or opt.bf16 else nullcontext
with torch.no_grad(), precision_scope(opt.device), model.ema_scope(): with torch.no_grad(), precision_scope(opt.device), model.ema_scope():
all_samples = list() #all_samples = list()
# 执行指定的次数 # 执行指定的次数
for n in trange(item['number'], desc="Sampling"): for n in trange(item['number'], desc="Sampling"):
print("Sampling:", n) print("Sampling:", n)
@@ -380,15 +385,16 @@ def main_dev(opt):
x_samples = model.decode_first_stage(samples) x_samples = model.decode_first_stage(samples)
x_samples = torch.clamp((x_samples + 1.0) / 2.0, min=0.0, max=1.0) x_samples = torch.clamp((x_samples + 1.0) / 2.0, min=0.0, max=1.0)
for x_sample in x_samples: for x_sample in x_samples:
print("Sample count:", sample_count)
x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c') x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
img = Image.fromarray(x_sample.astype(np.uint8)) img = Image.fromarray(x_sample.astype(np.uint8))
img = put_watermark(img, wm_encoder) img = put_watermark(img, wm_encoder)
img.save(os.path.join(sample_path, f"{base_count:05}.png")) img.save(os.path.join(sample_path, f"{base_count:05}.png"))
base_count += 1 base_count += 1
sample_count += 1 sample_count += 1
all_samples.append(x_samples) #all_samples.append(x_samples)
print("Sample count:", sample_count) print("Sample count:", sample_count)
#for n in trange(opt.n_iter, desc="Sampling"): # for n in trange(opt.n_iter, desc="Sampling"):
# for prompts in tqdm(data, desc="data"): # for prompts in tqdm(data, desc="data"):
# uc = None # uc = None
# if opt.scale != 1.0: # if opt.scale != 1.0:
@@ -416,24 +422,23 @@ def main_dev(opt):
# base_count += 1 # base_count += 1
# sample_count += 1 # sample_count += 1
# all_samples.append(x_samples) # all_samples.append(x_samples)
## additionally, save as grid # additionally, save as grid
#grid = torch.stack(all_samples, 0) #grid = torch.stack(all_samples, 0)
#grid = rearrange(grid, 'n b c h w -> (n b) c h w') #grid = rearrange(grid, 'n b c h w -> (n b) c h w')
#grid = make_grid(grid, nrow=n_rows) #grid = make_grid(grid, nrow=n_rows)
## to image # to image
#grid = 255. * rearrange(grid, 'c h w -> h w c').cpu().numpy() #grid = 255. * rearrange(grid, 'c h w -> h w c').cpu().numpy()
#grid = Image.fromarray(grid.astype(np.uint8)) #grid = Image.fromarray(grid.astype(np.uint8))
#grid = put_watermark(grid, wm_encoder) #grid = put_watermark(grid, wm_encoder)
#grid.save(os.path.join(outpath, f'grid-{grid_count:04}.png')) #grid.save(os.path.join(outpath, f'grid-{grid_count:04}.png'))
#grid_count += 1 #grid_count += 1
print(f"Your samples are ready and waiting for you here: \n{outpath} \n", f" \nEnjoy.") print(f"Your samples are ready and waiting for you here: \n{outpath} \n", f" \nEnjoy.")
# 修改任务状态为完成 update_task_status(task=item, status='done', progress=1) # 修改任务状态为完成
update_task_status(task=item, status='done', progress=1) print("任务结束, 等待10s后退出..")
# 任务结束, 等待20s后退出 time.sleep(10)
print("任务结束, 等待20s后退出..")
time.sleep(20)
break break
if __name__ == "__main__": if __name__ == "__main__":
opt = parse_args() opt = parse_args()
main_dev(opt) main_dev(opt)