mirror of
https://github.com/sd-webui/stable-diffusion-webui.git
synced 2024-12-15 07:12:58 +03:00
fix: [streamlit] optimization mode
This commit is contained in:
Thomas Mello
parent
eb85dc4d63
commit
ede81bdc5c
@ -31,6 +31,7 @@ general:
|
||||
precision: "autocast"
|
||||
optimized: False
|
||||
optimized_turbo: False
|
||||
optimized_config: "optimizedSD/v1-inference.yaml"
|
||||
update_preview: True
|
||||
update_preview_frequency: 5
|
||||
|
||||
|
||||
@ -163,15 +163,15 @@ def img2img(prompt: str = '', init_info: any = None, init_info_mask: any = None,
|
||||
mask = torch.from_numpy(mask).to(st.session_state["device"])
|
||||
|
||||
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 = init_image.to(st.session_state["device"])
|
||||
init_latent = (st.session_state["model"] if not st.session_state['defaults'].general.optimized else 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:
|
||||
mem = torch.cuda.memory_allocated()/1e6
|
||||
modelFS.to("cpu")
|
||||
st.session_state.modelFS.to("cpu")
|
||||
while(torch.cuda.memory_allocated()/1e6 >= mem):
|
||||
time.sleep(1)
|
||||
|
||||
|
||||
@ -163,40 +163,33 @@ def load_models(continue_prev_run = False, use_GFPGAN=False, use_RealESRGAN=Fals
|
||||
if "RealESRGAN" in st.session_state:
|
||||
del st.session_state["RealESRGAN"]
|
||||
|
||||
|
||||
|
||||
if "model" in st.session_state:
|
||||
if "model" in st.session_state and st.session_state["custom_model"] == custom_model:
|
||||
# TODO: check if the optimized mode was changed?
|
||||
print("Model already loaded")
|
||||
|
||||
return
|
||||
else:
|
||||
try:
|
||||
del st.session_state["model"]
|
||||
del st.session_state.model
|
||||
del st.session_state.modelCS
|
||||
del st.session_state.modelFS
|
||||
except KeyError:
|
||||
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:
|
||||
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"))
|
||||
config, device, model, modelCS, modelFS = load_sd_model(custom_model)
|
||||
|
||||
st.session_state["custom_model"] = custom_model
|
||||
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 if st.session_state["defaults"].general.no_half else model.half()).to(st.session_state["device"] )
|
||||
else:
|
||||
config = OmegaConf.load(st.session_state["defaults"].general.default_model_config)
|
||||
st.session_state.device = device
|
||||
st.session_state.model = model
|
||||
st.session_state.modelCS = modelCS
|
||||
st.session_state.modelFS = modelFS
|
||||
|
||||
if custom_model == st.session_state["defaults"].general.default_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"] = 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.")
|
||||
print("Model loaded.")
|
||||
|
||||
|
||||
def load_model_from_config(config, ckpt, verbose=False):
|
||||
@ -220,6 +213,7 @@ def load_model_from_config(config, ckpt, verbose=False):
|
||||
model.eval()
|
||||
return model
|
||||
|
||||
|
||||
def load_sd_from_config(ckpt, verbose=False):
|
||||
print(f"Loading model from {ckpt}")
|
||||
pl_sd = torch.load(ckpt, map_location="cpu")
|
||||
@ -681,18 +675,26 @@ def try_loading_LDSR(model_name: str,checking=False):
|
||||
|
||||
#try_loading_LDSR('model',checking=True)
|
||||
|
||||
def load_SD_model():
|
||||
if st.session_state['defaults'].general.optimized:
|
||||
sd = load_sd_from_config(st.session_state['defaults'].general.default_model_path)
|
||||
|
||||
# Loads Stable Diffusion model by name
|
||||
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 = [], []
|
||||
for key, v_ in sd.items():
|
||||
sp = key.split('.')
|
||||
if(sp[0]) == 'model':
|
||||
if('input_blocks' in sp):
|
||||
if (sp[0]) == 'model':
|
||||
if 'input_blocks' in sp:
|
||||
li.append(key)
|
||||
elif('middle_block' in sp):
|
||||
elif 'middle_block' in sp:
|
||||
li.append(key)
|
||||
elif('time_embed' in sp):
|
||||
elif 'time_embed' in sp:
|
||||
li.append(key)
|
||||
else:
|
||||
lo.append(key)
|
||||
@ -701,14 +703,14 @@ def load_SD_model():
|
||||
for key in lo:
|
||||
sd['model2.' + key[6:]] = sd.pop(key)
|
||||
|
||||
config = OmegaConf.load("optimizedSD/v1-inference.yaml")
|
||||
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}") \
|
||||
if torch.cuda.is_available() else torch.device("cpu")
|
||||
|
||||
model = instantiate_from_config(config.modelUNet)
|
||||
_, _ = model.load_state_dict(sd, strict=False)
|
||||
model.cuda()
|
||||
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.load_state_dict(sd, strict=False)
|
||||
@ -721,22 +723,25 @@ def load_SD_model():
|
||||
|
||||
del sd
|
||||
|
||||
if not st.session_state['defaults'].general.no_half:
|
||||
if not st.session_state.defaults.general.no_half:
|
||||
model = model.half()
|
||||
modelCS = modelCS.half()
|
||||
modelFS = modelFS.half()
|
||||
return model,modelCS,modelFS,device, config
|
||||
|
||||
return config, device, model, modelCS, modelFS
|
||||
else:
|
||||
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)
|
||||
config = OmegaConf.load(st.session_state.defaults.general.default_model_config)
|
||||
model = load_model_from_config(config, ckpt_path)
|
||||
|
||||
device = torch.device(f"cuda:{opt.gpu}") if torch.cuda.is_available() else torch.device("cpu")
|
||||
model = (model if st.session_state['defaults'].general.no_half else model.half()).to(device)
|
||||
return model, device,config
|
||||
device = torch.device(f"cuda:{st.session_state.defaults.general.gpu}") \
|
||||
if torch.cuda.is_available() else torch.device("cpu")
|
||||
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'):
|
||||
#get global variables
|
||||
global_vars = globals()
|
||||
@ -750,8 +755,8 @@ def ModelLoader(models,load=False,unload=False,imgproc_realesrgan_model_name='Re
|
||||
if m == 'model':
|
||||
del global_vars[m+'FS']
|
||||
del global_vars[m+'CS']
|
||||
if m =='model':
|
||||
m='Stable Diffusion'
|
||||
if m == 'model':
|
||||
m = 'Stable Diffusion'
|
||||
print('Unloaded ' + m)
|
||||
if load:
|
||||
for m in models:
|
||||
@ -792,11 +797,11 @@ 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.
|
||||
#print (img,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:
|
||||
# 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_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)
|
||||
|
||||
@ -1025,10 +1030,10 @@ def draw_prompt_matrix(im, width, height, all_prompts):
|
||||
def check_prompt_length(prompt, 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
|
||||
max_length = (st.session_state["model"] if not st.session_state['defaults'].general.optimized else modelCS).cond_stage_model.max_length
|
||||
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 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")
|
||||
ovf = info['overflowing_tokens'][0]
|
||||
overflowing_count = ovf.shape[0]
|
||||
@ -1322,9 +1327,9 @@ def process_images(
|
||||
print(prompt)
|
||||
|
||||
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):
|
||||
prompts = list(prompts)
|
||||
@ -1338,16 +1343,16 @@ def process_images(
|
||||
c = torch.zeros_like(uc) # i dont know if this is correct.. but it works
|
||||
for i in range(0, len(weighted_subprompts)):
|
||||
# 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
|
||||
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]
|
||||
|
||||
if st.session_state['defaults'].general.optimized:
|
||||
mem = torch.cuda.memory_allocated()/1e6
|
||||
modelCS.to("cpu")
|
||||
st.session_state.modelCS.to("cpu")
|
||||
while(torch.cuda.memory_allocated()/1e6 >= mem):
|
||||
time.sleep(1)
|
||||
|
||||
@ -1376,9 +1381,9 @@ def process_images(
|
||||
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:
|
||||
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)
|
||||
|
||||
for i, x_sample in enumerate(x_samples_ddim):
|
||||
@ -1512,7 +1517,7 @@ def process_images(
|
||||
|
||||
if st.session_state['defaults'].general.optimized:
|
||||
mem = torch.cuda.memory_allocated()/1e6
|
||||
modelFS.to("cpu")
|
||||
st.session_state.modelFS.to("cpu")
|
||||
while(torch.cuda.memory_allocated()/1e6 >= mem):
|
||||
time.sleep(1)
|
||||
|
||||
|
||||
@ -162,16 +162,26 @@ 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.")
|
||||
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_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.\
|
||||
#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")
|
||||
|
||||
bs_slider_max_value = 5
|
||||
if st.session_state.defaults.general.optimized:
|
||||
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"):
|
||||
st.session_state["update_preview"] = st.checkbox("Update Image Preview", value=st.session_state['defaults'].txt2img.update_preview,
|
||||
help="If enabled the image preview will be updated during the generation instead of at the end. \
|
||||
You can use the Update Preview \Frequency option bellow to customize how frequent it's updated. \
|
||||
By default this is enabled and the frequency is set to 1 step.")
|
||||
help="If enabled the image preview will be updated during the generation instead of at the end. \
|
||||
You can use the Update Preview \Frequency option bellow to customize how frequent it's updated. \
|
||||
By default this is enabled and the frequency is set to 1 step.")
|
||||
|
||||
st.session_state["update_preview_frequency"] = st.text_input("Update Image Preview Frequency", value=st.session_state['defaults'].txt2img.update_preview_frequency,
|
||||
help="Frequency in steps at which the the preview image is updated. By default the frequency \
|
||||
@ -244,9 +254,9 @@ def layout():
|
||||
load_models(False, use_GFPGAN, use_RealESRGAN, RealESRGAN_model)
|
||||
|
||||
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,
|
||||
save_grid, group_by_prompt, save_as_jpg, use_GFPGAN, use_RealESRGAN, RealESRGAN_model, fp=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)
|
||||
|
||||
message.success('Render Complete: ' + info + '; Stats: ' + stats, icon="✅")
|
||||
|
||||
Loading…
Reference in New Issue
Block a user