sd-webui/stable-diffusion-webui
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Merge remote-tracking branch 'origin/dev' into dev

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# Conflicts:
#	scripts/img2img.py
#	scripts/sd_utils.py
#	scripts/txt2img.py
This commit is contained in:
ZeroCool940711 2022-09-14 11:39:21 -07:00
commit 3675b566a4
7 changed files with 165 additions and 122 deletions
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1
configs/webui/webui_streamlit.yaml
View File

@ -32,6 +32,7 @@ general:
precision: "autocast" precision: "autocast"
optimized: False optimized: False
optimized_turbo: False optimized_turbo: False
optimized_config: "optimizedSD/v1-inference.yaml"
update_preview: True update_preview: True
update_preview_frequency: 5 update_preview_frequency: 5

6
scripts/img2img.py
View File

@ -163,12 +163,12 @@ def img2img(prompt: str = '', init_info: any = None, init_info_mask: any = None,
mask = torch.from_numpy(mask).to(st.session_state["device"]) mask = torch.from_numpy(mask).to(st.session_state["device"])
if st.session_state['defaults'].general.optimized: if st.session_state['defaults'].general.optimized:
modelFS.to(st.session_state["device"] ) st.session_state.modelFS.to(st.session_state["device"] )
init_image = 2. * image - 1. init_image = 2. * image - 1.
init_image = init_image.to(st.session_state["device"]) init_image = init_image.to(st.session_state["device"])
init_latent = (st.session_state["model"] if not st.session_state['defaults'].general.optimized else st.session_state.modelFS).get_first_stage_encoding((st.session_state["model"] if not st.session_state['defaults'].general.optimized else modelFS).encode_first_stage(init_image)) # move to latent space init_latent = (st.session_state["model"] if not st.session_state['defaults'].general.optimized else st.session_state.modelFS).get_first_stage_encoding((st.session_state["model"] if not st.session_state['defaults'].general.optimized else modelFS).encode_first_stage(init_image)) # move to latent space
if st.session_state['defaults'].general.optimized: if st.session_state['defaults'].general.optimized:
mem = torch.cuda.memory_allocated()/1e6 mem = torch.cuda.memory_allocated()/1e6
st.session_state.modelFS.to("cpu") st.session_state.modelFS.to("cpu")

141
scripts/sd_utils.py
View File

@ -161,44 +161,35 @@ def load_models(continue_prev_run = False, use_GFPGAN=False, use_RealESRGAN=Fals
else: else:
if "RealESRGAN" in st.session_state: if "RealESRGAN" in st.session_state:
del st.session_state["RealESRGAN"] del st.session_state["RealESRGAN"]
if "model" in st.session_state: if "model" in st.session_state:
if "model" in st.session_state and st.session_state["custom_model"] == custom_model: if "model" in st.session_state and st.session_state["custom_model"] == custom_model:
# TODO: check if the optimized mode was changed? # TODO: check if the optimized mode was changed?
print("Model already loaded") print("Model already loaded")
return
else: else:
try: try:
del st.session_state.model del st.session_state.model
del st.session_state.modelCS del st.session_state.modelCS
del st.session_state.modelFS del st.session_state.modelFS
except KeyError: except KeyError:
pass pass
config = OmegaConf.load(st.session_state["defaults"].general.default_model_config) # At this point the model is either
# is not loaded yet or have been evicted:
# load new model into memory
st.session_state.custom_model = custom_model
if custom_model == st.session_state["defaults"].general.default_model: config, device, model, modelCS, modelFS = load_sd_model(custom_model)
model = load_model_from_config(config, st.session_state["defaults"].general.default_model_path)
else:
model = load_model_from_config(config, os.path.join("models","custom", f"{custom_model}.ckpt"))
st.session_state["custom_model"] = custom_model st.session_state.device = device
st.session_state["device"] = torch.device(f"cuda:{defaults.general.gpu}") if torch.cuda.is_available() else torch.device("cpu") st.session_state.model = model
st.session_state["model"] = (model if st.session_state["defaults"].general.no_half else model.half()).to(st.session_state["device"] ) st.session_state.modelCS = modelCS
else: st.session_state.modelFS = modelFS
config = OmegaConf.load(st.session_state["defaults"].general.default_model_config)
if custom_model == st.session_state["defaults"].general.default_model: print("Model loaded.")
model = load_model_from_config(config, st.session_state["defaults"].general.default_model_path)
else:
model = load_model_from_config(config, os.path.join("models","custom", f"{custom_model}.ckpt"))
st.session_state["custom_model"] = custom_model
st.session_state["device"] = torch.device(f"cuda:{st.session_state['defaults'].general.gpu}") if torch.cuda.is_available() else torch.device("cpu")
st.session_state["model"] = (model if st.session_state['defaults'].general.no_half else model.half()).to(st.session_state["device"] )
print("Model loaded.")
def load_model_from_config(config, ckpt, verbose=False): def load_model_from_config(config, ckpt, verbose=False):
@ -222,6 +213,7 @@ def load_model_from_config(config, ckpt, verbose=False):
model.eval() model.eval()
return model return model
def load_sd_from_config(ckpt, verbose=False): def load_sd_from_config(ckpt, verbose=False):
print(f"Loading model from {ckpt}") print(f"Loading model from {ckpt}")
pl_sd = torch.load(ckpt, map_location="cpu") pl_sd = torch.load(ckpt, map_location="cpu")
@ -243,21 +235,21 @@ class MemUsageMonitor(threading.Thread):
def run(self): def run(self):
try: try:
pynvml.nvmlInit() pynvml.nvmlInit()
print(f"[{self.name}] Recording max memory usage...\n")
handle = pynvml.nvmlDeviceGetHandleByIndex(st.session_state['defaults'].general.gpu)
self.total = pynvml.nvmlDeviceGetMemoryInfo(handle).total
while not self.stop_flag:
m = pynvml.nvmlDeviceGetMemoryInfo(handle)
self.max_usage = max(self.max_usage, m.used)
# print(self.max_usage)
time.sleep(0.1)
print(f"[{self.name}] Stopped recording.\n")
pynvml.nvmlShutdown()
except: except:
print(f"[{self.name}] Unable to initialize NVIDIA management. No memory stats. \n") print(f"[{self.name}] Unable to initialize NVIDIA management. No memory stats. \n")
return return
print(f"[{self.name}] Recording max memory usage...\n")
# Missing context
#handle = pynvml.nvmlDeviceGetHandleByIndex(st.session_state['defaults'].general.gpu)
handle = pynvml.nvmlDeviceGetHandleByIndex(0)
self.total = pynvml.nvmlDeviceGetMemoryInfo(handle).total
while not self.stop_flag:
m = pynvml.nvmlDeviceGetMemoryInfo(handle)
self.max_usage = max(self.max_usage, m.used)
# print(self.max_usage)
time.sleep(0.1)
print(f"[{self.name}] Stopped recording.\n")
pynvml.nvmlShutdown()
def read(self): def read(self):
return self.max_usage, self.total return self.max_usage, self.total
@ -685,18 +677,26 @@ def try_loading_LDSR(model_name: str,checking=False):
#try_loading_LDSR('model',checking=True) #try_loading_LDSR('model',checking=True)
def load_SD_model():
if st.session_state['defaults'].general.optimized: # Loads Stable Diffusion model by name
sd = load_sd_from_config(st.session_state['defaults'].general.default_model_path) def load_sd_model(model_name: str) -> [any, any, any, any, any]:
ckpt_path = st.session_state.defaults.general.default_model_path
if model_name != st.session_state.defaults.general.default_model:
ckpt_path = os.path.join("models", "custom", f"{model_name}.ckpt")
if st.session_state.defaults.general.optimized:
config = OmegaConf.load(st.session_state.defaults.general.optimized_config)
sd = load_sd_from_config(ckpt_path)
li, lo = [], [] li, lo = [], []
for key, v_ in sd.items(): for key, v_ in sd.items():
sp = key.split('.') sp = key.split('.')
if(sp[0]) == 'model': if (sp[0]) == 'model':
if('input_blocks' in sp): if 'input_blocks' in sp:
li.append(key) li.append(key)
elif('middle_block' in sp): elif 'middle_block' in sp:
li.append(key) li.append(key)
elif('time_embed' in sp): elif 'time_embed' in sp:
li.append(key) li.append(key)
else: else:
lo.append(key) lo.append(key)
@ -705,14 +705,14 @@ def load_SD_model():
for key in lo: for key in lo:
sd['model2.' + key[6:]] = sd.pop(key) sd['model2.' + key[6:]] = sd.pop(key)
config = OmegaConf.load("optimizedSD/v1-inference.yaml") device = torch.device(f"cuda:{st.session_state.defaults.general.gpu}") \
device = torch.device(f"cuda:{opt.gpu}") if torch.cuda.is_available() else torch.device("cpu") if torch.cuda.is_available() else torch.device("cpu")
model = instantiate_from_config(config.modelUNet) model = instantiate_from_config(config.modelUNet)
_, _ = model.load_state_dict(sd, strict=False) _, _ = model.load_state_dict(sd, strict=False)
model.cuda() model.cuda()
model.eval() model.eval()
model.turbo = st.session_state['defaults'].general.optimized_turbo model.turbo = st.session_state.defaults.general.optimized_turbo
modelCS = instantiate_from_config(config.modelCondStage) modelCS = instantiate_from_config(config.modelCondStage)
_, _ = modelCS.load_state_dict(sd, strict=False) _, _ = modelCS.load_state_dict(sd, strict=False)
@ -725,22 +725,25 @@ def load_SD_model():
del sd del sd
if not st.session_state['defaults'].general.no_half: if not st.session_state.defaults.general.no_half:
model = model.half() model = model.half()
modelCS = modelCS.half() modelCS = modelCS.half()
modelFS = modelFS.half() modelFS = modelFS.half()
return model,modelCS,modelFS,device, config
return config, device, model, modelCS, modelFS
else: else:
config = OmegaConf.load(st.session_state['defaults'].general.default_model_config) config = OmegaConf.load(st.session_state.defaults.general.default_model_config)
model = load_model_from_config(config, st.session_state['defaults'].general.default_model_path) model = load_model_from_config(config, ckpt_path)
device = torch.device(f"cuda:{opt.gpu}") if torch.cuda.is_available() else torch.device("cpu") device = torch.device(f"cuda:{st.session_state.defaults.general.gpu}") \
model = (model if st.session_state['defaults'].general.no_half else model.half()).to(device) if torch.cuda.is_available() else torch.device("cpu")
return model, device,config model = (model if st.session_state.defaults.general.no_half
else model.half()).to(device)
# return config, device, model, None, None
#
# @codedealer: No usages
def ModelLoader(models,load=False,unload=False,imgproc_realesrgan_model_name='RealESRGAN_x4plus'): def ModelLoader(models,load=False,unload=False,imgproc_realesrgan_model_name='RealESRGAN_x4plus'):
#get global variables #get global variables
global_vars = globals() global_vars = globals()
@ -754,8 +757,8 @@ def ModelLoader(models,load=False,unload=False,imgproc_realesrgan_model_name='Re
if m == 'model': if m == 'model':
del global_vars[m+'FS'] del global_vars[m+'FS']
del global_vars[m+'CS'] del global_vars[m+'CS']
if m =='model': if m == 'model':
m='Stable Diffusion' m = 'Stable Diffusion'
print('Unloaded ' + m) print('Unloaded ' + m)
if load: if load:
for m in models: for m in models:
@ -796,11 +799,11 @@ def generation_callback(img, i=0):
# It can probably be done in a better way for someone who knows what they're doing. I don't. # It can probably be done in a better way for someone who knows what they're doing. I don't.
#print (img,isinstance(img, torch.Tensor)) #print (img,isinstance(img, torch.Tensor))
if isinstance(img, torch.Tensor): if isinstance(img, torch.Tensor):
x_samples_ddim = (st.session_state["model"] if not st.session_state['defaults'].general.optimized else modelFS).decode_first_stage(img) x_samples_ddim = (st.session_state["model"] if not st.session_state['defaults'].general.optimized else st.session_state.modelFS).decode_first_stage(img)
else: else:
# When using the k Diffusion samplers they return a dict instead of a tensor that look like this: # When using the k Diffusion samplers they return a dict instead of a tensor that look like this:
# {'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised} # {'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}
x_samples_ddim = (st.session_state["model"] if not st.session_state['defaults'].general.optimized else modelFS).decode_first_stage(img["denoised"]) x_samples_ddim = (st.session_state["model"] if not st.session_state['defaults'].general.optimized else st.session_state.modelFS).decode_first_stage(img["denoised"])
x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0) x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
@ -1034,10 +1037,10 @@ def draw_prompt_matrix(im, width, height, all_prompts):
def check_prompt_length(prompt, comments): def check_prompt_length(prompt, comments):
"""this function tests if prompt is too long, and if so, adds a message to comments""" """this function tests if prompt is too long, and if so, adds a message to comments"""
tokenizer = (st.session_state["model"] if not st.session_state['defaults'].general.optimized else modelCS).cond_stage_model.tokenizer tokenizer = (st.session_state["model"] if not st.session_state['defaults'].general.optimized else st.session_state.modelCS).cond_stage_model.tokenizer
max_length = (st.session_state["model"] if not st.session_state['defaults'].general.optimized else modelCS).cond_stage_model.max_length max_length = (st.session_state["model"] if not st.session_state['defaults'].general.optimized else st.session_state.modelCS).cond_stage_model.max_length
info = (st.session_state["model"] if not st.session_state['defaults'].general.optimized else modelCS).cond_stage_model.tokenizer([prompt], truncation=True, max_length=max_length, info = (st.session_state["model"] if not st.session_state['defaults'].general.optimized else st.session_state.modelCS).cond_stage_model.tokenizer([prompt], truncation=True, max_length=max_length,
return_overflowing_tokens=True, padding="max_length", return_tensors="pt") return_overflowing_tokens=True, padding="max_length", return_tensors="pt")
ovf = info['overflowing_tokens'][0] ovf = info['overflowing_tokens'][0]
overflowing_count = ovf.shape[0] overflowing_count = ovf.shape[0]
@ -1331,9 +1334,9 @@ def process_images(
print(prompt) print(prompt)
if st.session_state['defaults'].general.optimized: if st.session_state['defaults'].general.optimized:
modelCS.to(st.session_state['defaults'].general.gpu) st.session_state.modelCS.to(st.session_state['defaults'].general.gpu)
uc = (st.session_state["model"] if not st.session_state['defaults'].general.optimized else modelCS).get_learned_conditioning(len(prompts) * [""]) uc = (st.session_state["model"] if not st.session_state['defaults'].general.optimized else st.session_state.modelCS).get_learned_conditioning(len(prompts) * [""])
if isinstance(prompts, tuple): if isinstance(prompts, tuple):
prompts = list(prompts) prompts = list(prompts)
@ -1347,16 +1350,16 @@ def process_images(
c = torch.zeros_like(uc) # i dont know if this is correct.. but it works c = torch.zeros_like(uc) # i dont know if this is correct.. but it works
for i in range(0, len(weighted_subprompts)): for i in range(0, len(weighted_subprompts)):
# note if alpha negative, it functions same as torch.sub # note if alpha negative, it functions same as torch.sub
c = torch.add(c, (st.session_state["model"] if not st.session_state['defaults'].general.optimized else modelCS).get_learned_conditioning(weighted_subprompts[i][0]), alpha=weighted_subprompts[i][1]) c = torch.add(c, (st.session_state["model"] if not st.session_state['defaults'].general.optimized else st.session_state.modelCS).get_learned_conditioning(weighted_subprompts[i][0]), alpha=weighted_subprompts[i][1])
else: # just behave like usual else: # just behave like usual
c = (st.session_state["model"] if not st.session_state['defaults'].general.optimized else modelCS).get_learned_conditioning(prompts) c = (st.session_state["model"] if not st.session_state['defaults'].general.optimized else st.session_state.modelCS).get_learned_conditioning(prompts)
shape = [opt_C, height // opt_f, width // opt_f] shape = [opt_C, height // opt_f, width // opt_f]
if st.session_state['defaults'].general.optimized: if st.session_state['defaults'].general.optimized:
mem = torch.cuda.memory_allocated()/1e6 mem = torch.cuda.memory_allocated()/1e6
modelCS.to("cpu") st.session_state.modelCS.to("cpu")
while(torch.cuda.memory_allocated()/1e6 >= mem): while(torch.cuda.memory_allocated()/1e6 >= mem):
time.sleep(1) time.sleep(1)
@ -1385,9 +1388,9 @@ def process_images(
samples_ddim = func_sample(init_data=init_data, x=x, conditioning=c, unconditional_conditioning=uc, sampler_name=sampler_name) samples_ddim = func_sample(init_data=init_data, x=x, conditioning=c, unconditional_conditioning=uc, sampler_name=sampler_name)
if st.session_state['defaults'].general.optimized: if st.session_state['defaults'].general.optimized:
modelFS.to(st.session_state['defaults'].general.gpu) st.session_state.modelFS.to(st.session_state['defaults'].general.gpu)
x_samples_ddim = (st.session_state["model"] if not st.session_state['defaults'].general.optimized else modelFS).decode_first_stage(samples_ddim) x_samples_ddim = (st.session_state["model"] if not st.session_state['defaults'].general.optimized else st.session_state.modelFS).decode_first_stage(samples_ddim)
x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0) x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
for i, x_sample in enumerate(x_samples_ddim): for i, x_sample in enumerate(x_samples_ddim):
@ -1521,7 +1524,7 @@ def process_images(
if st.session_state['defaults'].general.optimized: if st.session_state['defaults'].general.optimized:
mem = torch.cuda.memory_allocated()/1e6 mem = torch.cuda.memory_allocated()/1e6
modelFS.to("cpu") st.session_state.modelFS.to("cpu")
while(torch.cuda.memory_allocated()/1e6 >= mem): while(torch.cuda.memory_allocated()/1e6 >= mem):
time.sleep(1) time.sleep(1)

22
scripts/txt2img.py
View File

@ -162,10 +162,20 @@ def layout():
cfg_scale = st.slider("CFG (Classifier Free Guidance Scale):", min_value=1.0, max_value=30.0, value=st.session_state['defaults'].txt2img.cfg_scale, step=0.5, help="How strongly the image should follow the prompt.") cfg_scale = st.slider("CFG (Classifier Free Guidance Scale):", min_value=1.0, max_value=30.0, value=st.session_state['defaults'].txt2img.cfg_scale, step=0.5, help="How strongly the image should follow the prompt.")
seed = st.text_input("Seed:", value=st.session_state['defaults'].txt2img.seed, help=" The seed to use, if left blank a random seed will be generated.") seed = st.text_input("Seed:", value=st.session_state['defaults'].txt2img.seed, help=" The seed to use, if left blank a random seed will be generated.")
batch_count = st.slider("Batch count.", min_value=1, max_value=100, value=st.session_state['defaults'].txt2img.batch_count, step=1, help="How many iterations or batches of images to generate in total.") batch_count = st.slider("Batch count.", min_value=1, max_value=100, value=st.session_state['defaults'].txt2img.batch_count, step=1, help="How many iterations or batches of images to generate in total.")
#batch_size = st.slider("Batch size", min_value=1, max_value=250, value=defaults.txt2img.batch_size, step=1,
#help="How many images are at once in a batch.\ bs_slider_max_value = 5
#It increases the VRAM usage a lot but if you have enough VRAM it can reduce the time it takes to finish generation as more images are generated at once.\ if st.session_state.defaults.general.optimized:
#Default: 1") bs_slider_max_value = 100
batch_size = st.slider(
"Batch size",
min_value=1,
max_value=bs_slider_max_value,
value=st.session_state.defaults.txt2img.batch_size,
step=1,
help="How many images are at once in a batch.\
It increases the VRAM usage a lot but if you have enough VRAM it can reduce the time it takes to finish generation as more images are generated at once.\
Default: 1")
with st.expander("Preview Settings"): with st.expander("Preview Settings"):
st.session_state["update_preview"] = st.checkbox("Update Image Preview", value=st.session_state['defaults'].txt2img.update_preview, st.session_state["update_preview"] = st.checkbox("Update Image Preview", value=st.session_state['defaults'].txt2img.update_preview,
@ -244,9 +254,9 @@ def layout():
load_models(False, use_GFPGAN, use_RealESRGAN, RealESRGAN_model) load_models(False, use_GFPGAN, use_RealESRGAN, RealESRGAN_model)
try: try:
output_images, seeds, info, stats = txt2img(prompt, st.session_state.sampling_steps, sampler_name, RealESRGAN_model, batch_count, 1, output_images, seeds, info, stats = txt2img(prompt, st.session_state.sampling_steps, sampler_name, RealESRGAN_model, batch_count, batch_size,
cfg_scale, seed, height, width, separate_prompts, normalize_prompt_weights, save_individual_images, cfg_scale, seed, height, width, separate_prompts, normalize_prompt_weights, save_individual_images,
save_grid, group_by_prompt, save_as_jpg, use_GFPGAN, use_RealESRGAN, RealESRGAN_model, fp=st.session_state["defaults"].general.fp, save_grid, group_by_prompt, save_as_jpg, use_GFPGAN, use_RealESRGAN, RealESRGAN_model, fp=st.session_state.defaults.general.fp,
variant_amount=variant_amount, variant_seed=variant_seed, write_info_files=write_info_files) variant_amount=variant_amount, variant_seed=variant_seed, write_info_files=write_info_files)
message.success('Render Complete: ' + info + '; Stats: ' + stats, icon="✅") message.success('Render Complete: ' + info + '; Stats: ' + stats, icon="✅")

57
update_to_latest.cmd Normal file
View File

@ -0,0 +1,57 @@
@echo off
cd %~dp0
:: Duplicate code to find miniconda
IF EXIST custom-conda-path.txt (
FOR /F %%i IN (custom-conda-path.txt) DO set v_custom_path=%%i
)
set v_paths=%ProgramData%\miniconda3
set v_paths=%v_paths%;%USERPROFILE%\miniconda3
set v_paths=%v_paths%;%ProgramData%\anaconda3
set v_paths=%v_paths%;%USERPROFILE%\anaconda3
for %%a in (%v_paths%) do (
IF NOT "%v_custom_path%"=="" (
set v_paths=%v_custom_path%;%v_paths%
)
)
for %%a in (%v_paths%) do (
if EXIST "%%a\Scripts\activate.bat" (
SET v_conda_path=%%a
echo anaconda3/miniconda3 detected in %%a
)
)
IF "%v_conda_path%"=="" (
echo anaconda3/miniconda3 not found. Install from here https://docs.conda.io/en/latest/miniconda.html
exit /b 1
)
:: Update
echo Stashing local changes and pulling latest update...
call git stash
call git pull
echo If you want to restore changes you made before updating, run "git stash pop".
call "%v_conda_path%\Scripts\activate.bat"
for /f "delims=" %%a in ('git log -1 --format^="%%H" -- environment.yaml') DO set v_cur_hash=%%a
set /p "v_last_hash="<"z_version_env.tmp"
echo %v_cur_hash%>z_version_env.tmp
echo Current environment.yaml hash: %v_cur_hash%
echo Previous environment.yaml hash: %v_last_hash%
if "%v_last_hash%" == "%v_cur_hash%" (
echo environment.yaml unchanged. dependencies should be up to date.
echo if you still have unresolved dependencies, delete "z_version_env.tmp"
) else (
echo environment.yaml changed. updating dependencies
call conda env create --name "%v_conda_env_name%" -f environment.yaml
call conda env update --name "%v_conda_env_name%" -f environment.yaml
)
::cmd /k

30
webui-streamlit.cmd
View File

@ -23,17 +23,17 @@ set v_paths=%v_paths%;%ProgramData%\anaconda3
set v_paths=%v_paths%;%USERPROFILE%\anaconda3 set v_paths=%v_paths%;%USERPROFILE%\anaconda3
for %%a in (%v_paths%) do ( for %%a in (%v_paths%) do (
IF NOT "%v_custom_path%"=="" ( IF NOT "%v_custom_path%"=="" (
set v_paths=%v_custom_path%;%v_paths% set v_paths=%v_custom_path%;%v_paths%
) )
) )
for %%a in (%v_paths%) do ( for %%a in (%v_paths%) do (
if EXIST "%%a\Scripts\activate.bat" ( if EXIST "%%a\Scripts\activate.bat" (
SET v_conda_path=%%a SET v_conda_path=%%a
echo anaconda3/miniconda3 detected in %%a echo anaconda3/miniconda3 detected in %%a
goto :CONDA_FOUND goto :CONDA_FOUND
) )
) )
IF "%v_conda_path%"=="" ( IF "%v_conda_path%"=="" (
@ -42,24 +42,10 @@ IF "%v_conda_path%"=="" (
) )
:CONDA_FOUND :CONDA_FOUND
echo Stashing local changes and pulling latest update...
call git stash
call git pull
call "%v_conda_path%\Scripts\activate.bat"
for /f "delims=" %%a in ('git log -1 --format^="%%H" -- environment.yaml') DO set v_cur_hash=%%a if not exist "z_version_env.tmp" (
set /p "v_last_hash="<"z_version_env.tmp" :: first time running, we need to update
echo %v_cur_hash%>z_version_env.tmp call "update_to_latest.cmd"
echo Current environment.yaml hash: %v_cur_hash%
echo Previous environment.yaml hash: %v_last_hash%
if "%v_last_hash%" == "%v_cur_hash%" (
echo environment.yaml version doesn't change
) else (
echo environment.yaml changed, updating dependencies
call conda env create --name "%v_conda_env_name%" -f environment.yaml
call conda env update --name "%v_conda_env_name%" -f environment.yaml
) )
call "%v_conda_path%\Scripts\activate.bat" "%v_conda_env_name%" call "%v_conda_path%\Scripts\activate.bat" "%v_conda_env_name%"

30
webui.cmd
View File

@ -23,17 +23,17 @@ set v_paths=%v_paths%;%ProgramData%\anaconda3
set v_paths=%v_paths%;%USERPROFILE%\anaconda3 set v_paths=%v_paths%;%USERPROFILE%\anaconda3
for %%a in (%v_paths%) do ( for %%a in (%v_paths%) do (
IF NOT "%v_custom_path%"=="" ( IF NOT "%v_custom_path%"=="" (
set v_paths=%v_custom_path%;%v_paths% set v_paths=%v_custom_path%;%v_paths%
) )
) )
for %%a in (%v_paths%) do ( for %%a in (%v_paths%) do (
if EXIST "%%a\Scripts\activate.bat" ( if EXIST "%%a\Scripts\activate.bat" (
SET v_conda_path=%%a SET v_conda_path=%%a
echo anaconda3/miniconda3 detected in %%a echo anaconda3/miniconda3 detected in %%a
goto :CONDA_FOUND goto :CONDA_FOUND
) )
) )
IF "%v_conda_path%"=="" ( IF "%v_conda_path%"=="" (
@ -42,24 +42,10 @@ IF "%v_conda_path%"=="" (
) )
:CONDA_FOUND :CONDA_FOUND
echo Stashing local changes and pulling latest update...
call git stash
call git pull
call "%v_conda_path%\Scripts\activate.bat"
for /f "delims=" %%a in ('git log -1 --format^="%%H" -- environment.yaml') DO set v_cur_hash=%%a if not exist "z_version_env.tmp" (
set /p "v_last_hash="<"z_version_env.tmp" :: first time running, we need to update
echo %v_cur_hash%>z_version_env.tmp call "update_to_latest.cmd"
echo Current environment.yaml hash: %v_cur_hash%
echo Previous environment.yaml hash: %v_last_hash%
if "%v_last_hash%" == "%v_cur_hash%" (
echo environment.yaml version doesn't change
) else (
echo environment.yaml changed, updating dependencies
call conda env create --name "%v_conda_env_name%" -f environment.yaml
call conda env update --name "%v_conda_env_name%" -f environment.yaml
) )
call "%v_conda_path%\Scripts\activate.bat" "%v_conda_env_name%" call "%v_conda_path%\Scripts\activate.bat" "%v_conda_env_name%"