py递归未优化, 更换为while
This commit is contained in:
176
server.py
176
server.py
@@ -216,11 +216,21 @@ def put_watermark(img, wm_encoder=None):
|
||||
|
||||
|
||||
# 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, data: list = []):
|
||||
task["status"] = status
|
||||
task["progress"] = progress
|
||||
task["data"] = data
|
||||
requests.put(f"http://localhost:3000/api/drawing/{task['id']}", json=task)
|
||||
|
||||
# 从局域网中获取一组任务(如果列表为空，等待2s后重新获取)
|
||||
def get_tasks():
|
||||
tasks = requests.get("http://localhost:3000/api/drawing?status=waiting").json()
|
||||
if len(tasks) == 0:
|
||||
while len(tasks) == 0:
|
||||
print('no task, wait 2s...')
|
||||
time.sleep(2)
|
||||
tasks = requests.get("http://localhost:3000/api/drawing?status=waiting").json()
|
||||
return tasks
|
||||
|
||||
def main_dev(opt):
|
||||
model_name = '' # 默认模型
|
||||
@@ -228,29 +238,14 @@ def main_dev(opt):
|
||||
config = None # 默认配置
|
||||
device = None # 默认设备
|
||||
while True:
|
||||
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
|
||||
print(data)
|
||||
# 遍历 data 返回dict
|
||||
for item in data:
|
||||
print(item)
|
||||
update_task_status(item, "running", 0) # 更新任务状态为运行中
|
||||
|
||||
# 设置参数
|
||||
if 'prompt' in item:
|
||||
opt.prompt = item['prompt'] # 描述
|
||||
if 'number' in item:
|
||||
opt.n_samples = item['number'] # 列数
|
||||
print(f"n_samples: {opt.n_samples}")
|
||||
#if 'n_rows' in item:
|
||||
# opt.n_rows = item['n_rows'] # 行数
|
||||
if 'scale' in item:
|
||||
opt.scale = item['scale'] # 比例
|
||||
for task in get_tasks(): # 遍历 tasks 返回 dict
|
||||
print('task:', task) # 打印任务
|
||||
update_task_status(task, "running", 0) # 更新任务状态为运行中
|
||||
|
||||
# 如果模型不同，重新加载模型(注意释放内存)
|
||||
if item['ckpt'] != model_name:
|
||||
if task['ckpt'] != model_name:
|
||||
# 获取环境配置
|
||||
model_name = item['ckpt']
|
||||
model_name = task['ckpt']
|
||||
opt.config = f'/data/{model_name}.yaml'
|
||||
opt.ckpt = f'/data/{model_name}.ckpt'
|
||||
opt.device = 'cuda'
|
||||
@@ -261,6 +256,7 @@ def main_dev(opt):
|
||||
print(f"加载模型到显存: {model_name}..")
|
||||
model = load_model_from_config(config, f"{opt.ckpt}", device)
|
||||
print(f"加载到显存完成: {model_name}")
|
||||
|
||||
# 使用指定的模型和配置文件进行推理一组参数
|
||||
if opt.plms:
|
||||
sampler = PLMSSampler(model, device=device)
|
||||
@@ -268,104 +264,46 @@ def main_dev(opt):
|
||||
sampler = DPMSolverSampler(model, device=device)
|
||||
else:
|
||||
sampler = DDIMSampler(model, device=device)
|
||||
|
||||
# 检查输出目录是否存在
|
||||
os.makedirs(opt.outdir, exist_ok=True)
|
||||
outpath = opt.outdir
|
||||
|
||||
# 创建水印编码器
|
||||
wm = "SDV2"
|
||||
wm_encoder = WatermarkEncoder()
|
||||
wm_encoder.set_watermark('bytes', wm.encode('utf-8'))
|
||||
|
||||
# x
|
||||
batch_size = opt.n_samples
|
||||
#n_rows = opt.n_rows if opt.n_rows > 0 else batch_size
|
||||
batch_size = task['number']
|
||||
|
||||
if not opt.from_file:
|
||||
prompt = opt.prompt
|
||||
prompt = task['prompt']
|
||||
assert prompt is not None
|
||||
data = [batch_size * [prompt]]
|
||||
print("data:", data)
|
||||
else:
|
||||
print(f"reading prompts from {opt.from_file}")
|
||||
with open(opt.from_file, "r") as f:
|
||||
data = f.read().splitlines()
|
||||
data = [p for p in data for i in range(opt.repeat)]
|
||||
data = list(chunk(data, batch_size))
|
||||
print("data:", data)
|
||||
# x
|
||||
sample_path = os.path.join(outpath, "samples")
|
||||
os.makedirs(sample_path, exist_ok=True)
|
||||
sample_count = 0
|
||||
base_count = len(os.listdir(sample_path))
|
||||
grid_count = len(os.listdir(outpath)) - 1
|
||||
|
||||
# x
|
||||
start_code = None
|
||||
if opt.fixed_code:
|
||||
start_code = torch.randn([opt.n_samples, opt.C, opt.H // opt.f, opt.W // opt.f], device=device)
|
||||
|
||||
'''
|
||||
# 切换模型
|
||||
if opt.torchscript or opt.ipex:
|
||||
transformer = model.cond_stage_model.model
|
||||
unet = model.model.diffusion_model
|
||||
decoder = model.first_stage_model.decoder
|
||||
additional_context = torch.cpu.amp.autocast() if opt.bf16 else nullcontext()
|
||||
shape = [opt.C, opt.H // opt.f, opt.W // opt.f]
|
||||
if opt.bf16 and not opt.torchscript and not opt.ipex:
|
||||
raise ValueError('Bfloat16 is supported only for torchscript+ipex')
|
||||
if opt.bf16 and unet.dtype != torch.bfloat16:
|
||||
raise ValueError("Use configs/stable-diffusion/intel/ configs with bf16 enabled if you'd like to use bfloat16 with 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.")
|
||||
if opt.ipex:
|
||||
import intel_extension_for_pytorch as ipex
|
||||
bf16_dtype = torch.bfloat16 if opt.bf16 else None
|
||||
transformer = transformer.to(memory_format=torch.channels_last)
|
||||
transformer = ipex.optimize(transformer, level="O1", inplace=True)
|
||||
unet = unet.to(memory_format=torch.channels_last)
|
||||
unet = ipex.optimize(unet, level="O1", auto_kernel_selection=True, inplace=True, dtype=bf16_dtype)
|
||||
decoder = decoder.to(memory_format=torch.channels_last)
|
||||
decoder = ipex.optimize(decoder, level="O1", auto_kernel_selection=True, inplace=True, dtype=bf16_dtype)
|
||||
if opt.torchscript:
|
||||
with torch.no_grad(), additional_context:
|
||||
# get UNET scripted
|
||||
if unet.use_checkpoint:
|
||||
raise ValueError("Gradient checkpoint won't work with tracing. 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)
|
||||
t_in = torch.ones(2, dtype=torch.int64)
|
||||
context = torch.ones(2, 77, 1024, dtype=torch.float32)
|
||||
scripted_unet = torch.jit.trace(unet, (img_in, t_in, context))
|
||||
scripted_unet = torch.jit.optimize_for_inference(scripted_unet)
|
||||
print(type(scripted_unet))
|
||||
model.model.scripted_diffusion_model = scripted_unet
|
||||
# get Decoder for first stage model scripted
|
||||
samples_ddim = torch.ones(1, 4, 96, 96, dtype=torch.float32)
|
||||
scripted_decoder = torch.jit.trace(decoder, (samples_ddim))
|
||||
scripted_decoder = torch.jit.optimize_for_inference(scripted_decoder)
|
||||
print(type(scripted_decoder))
|
||||
model.first_stage_model.decoder = scripted_decoder
|
||||
prompts = data[0]
|
||||
print("Running a forward pass to initialize optimizations")
|
||||
uc = None
|
||||
if opt.scale != 1.0:
|
||||
uc = model.get_learned_conditioning(batch_size * [""])
|
||||
if isinstance(prompts, tuple):
|
||||
prompts = list(prompts)
|
||||
with torch.no_grad(), additional_context:
|
||||
for _ in range(3):
|
||||
c = model.get_learned_conditioning(prompts)
|
||||
samples_ddim, _ = sampler.sample(S=5,
|
||||
conditioning=c,
|
||||
batch_size=batch_size,
|
||||
shape=shape,
|
||||
verbose=False,
|
||||
unconditional_guidance_scale=opt.scale,
|
||||
unconditional_conditioning=uc,
|
||||
eta=opt.ddim_eta,
|
||||
x_T=start_code)
|
||||
print("Running a forward pass for decoder")
|
||||
for _ in range(3):
|
||||
x_samples_ddim = model.decode_first_stage(samples_ddim)
|
||||
start_code = torch.randn([task['number'], opt.C, opt.H // opt.f, opt.W // opt.f], device=device)
|
||||
# 生成图片
|
||||
precision_scope = autocast if opt.precision == "autocast" or opt.bf16 else nullcontext
|
||||
with torch.no_grad(), precision_scope(opt.device), model.ema_scope():
|
||||
#all_samples = list()
|
||||
# 执行指定的任务批次 (row)(item['number'])
|
||||
images = []
|
||||
# 执行指定的任务批次 (row)(task['number'])
|
||||
for n in trange(1, desc="Sampling"):
|
||||
print("Sampling:", data)
|
||||
for prompts in tqdm(data, desc="data"):
|
||||
@@ -378,7 +316,7 @@ def main_dev(opt):
|
||||
shape = [opt.C, opt.H // opt.f, opt.W // opt.f]
|
||||
samples, _ = sampler.sample(S=opt.steps,
|
||||
conditioning=c,
|
||||
batch_size=opt.n_samples,
|
||||
batch_size=task['number'],
|
||||
shape=shape,
|
||||
verbose=False,
|
||||
unconditional_guidance_scale=opt.scale,
|
||||
@@ -389,58 +327,18 @@ def main_dev(opt):
|
||||
x_samples = torch.clamp((x_samples + 1.0) / 2.0, min=0.0, max=1.0)
|
||||
for x_sample in x_samples:
|
||||
print("Sample count:", sample_count)
|
||||
imge_path = os.path.join(sample_path, f"{base_count:05}.png")
|
||||
x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
|
||||
img = Image.fromarray(x_sample.astype(np.uint8))
|
||||
img = put_watermark(img, wm_encoder)
|
||||
img.save(os.path.join(sample_path, f"{base_count:05}.png"))
|
||||
img.save(imge_path)
|
||||
base_count += 1
|
||||
sample_count += 1
|
||||
#all_samples.append(x_samples)
|
||||
images.append(imge_path)
|
||||
print("Sample count:", sample_count)
|
||||
# for n in trange(opt.n_iter, desc="Sampling"):
|
||||
# for prompts in tqdm(data, desc="data"):
|
||||
# uc = None
|
||||
# if opt.scale != 1.0:
|
||||
# uc = model.get_learned_conditioning(batch_size * [""])
|
||||
# if isinstance(prompts, tuple):
|
||||
# prompts = list(prompts)
|
||||
# c = model.get_learned_conditioning(prompts)
|
||||
# shape = [opt.C, opt.H // opt.f, opt.W // opt.f]
|
||||
# samples, _ = sampler.sample(S=opt.steps,
|
||||
# conditioning=c,
|
||||
# batch_size=opt.n_samples,
|
||||
# shape=shape,
|
||||
# verbose=False,
|
||||
# unconditional_guidance_scale=opt.scale,
|
||||
# unconditional_conditioning=uc,
|
||||
# eta=opt.ddim_eta,
|
||||
# x_T=start_code)
|
||||
# x_samples = model.decode_first_stage(samples)
|
||||
# x_samples = torch.clamp((x_samples + 1.0) / 2.0, min=0.0, max=1.0)
|
||||
# for x_sample in x_samples:
|
||||
# x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
|
||||
# img = Image.fromarray(x_sample.astype(np.uint8))
|
||||
# img = put_watermark(img, wm_encoder)
|
||||
# img.save(os.path.join(sample_path, f"{base_count:05}.png"))
|
||||
# base_count += 1
|
||||
# sample_count += 1
|
||||
# all_samples.append(x_samples)
|
||||
# additionally, save as grid
|
||||
#grid = torch.stack(all_samples, 0)
|
||||
#grid = rearrange(grid, 'n b c h w -> (n b) c h w')
|
||||
#grid = make_grid(grid, nrow=n_rows)
|
||||
# to image
|
||||
#grid = 255. * rearrange(grid, 'c h w -> h w c').cpu().numpy()
|
||||
#grid = Image.fromarray(grid.astype(np.uint8))
|
||||
#grid = put_watermark(grid, wm_encoder)
|
||||
#grid.save(os.path.join(outpath, f'grid-{grid_count:04}.png'))
|
||||
#grid_count += 1
|
||||
print(f"Your samples are ready and waiting for you here: \n{outpath} \n", f" \nEnjoy.")
|
||||
update_task_status(task=item, status='done', progress=1) # 修改任务状态为完成
|
||||
'''
|
||||
print("任务结束, 等待10s后退出..")
|
||||
#time.sleep(10)
|
||||
break
|
||||
update_task_status(task=task, status='done', progress=1, data=images) # 修改任务状态为完成
|
||||
print("批次任务结束..")
|
||||
#break
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
Reference in New Issue
Block a user