mirror of
https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
synced 2026-01-04 08:11:43 -08:00
Merge branch 'master' of https://github.com/AUTOMATIC1111/stable-diffusion-webui into TI_optimizations
This commit is contained in:
Fampai
commit
3b0127e698
22 changed files with 1650 additions and 615 deletions
|
|
@ -1,20 +1,25 @@
|
|||
import time
|
||||
import uvicorn
|
||||
from gradio.processing_utils import encode_pil_to_base64, decode_base64_to_file, decode_base64_to_image
|
||||
from fastapi import APIRouter, HTTPException
|
||||
from fastapi import APIRouter, Depends, HTTPException
|
||||
import modules.shared as shared
|
||||
from modules import devices
|
||||
from modules.api.models import *
|
||||
from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
|
||||
from modules.sd_samplers import all_samplers
|
||||
from modules.extras import run_extras, run_pnginfo
|
||||
|
||||
|
||||
def upscaler_to_index(name: str):
|
||||
try:
|
||||
return [x.name.lower() for x in shared.sd_upscalers].index(name.lower())
|
||||
except:
|
||||
raise HTTPException(status_code=400, detail=f"Invalid upscaler, needs to be on of these: {' , '.join([x.name for x in sd_upscalers])}")
|
||||
|
||||
|
||||
sampler_to_index = lambda name: next(filter(lambda row: name.lower() == row[1].name.lower(), enumerate(all_samplers)), None)
|
||||
|
||||
|
||||
def setUpscalers(req: dict):
|
||||
reqDict = vars(req)
|
||||
reqDict['extras_upscaler_1'] = upscaler_to_index(req.upscaler_1)
|
||||
|
|
@ -23,6 +28,7 @@ def setUpscalers(req: dict):
|
|||
reqDict.pop('upscaler_2')
|
||||
return reqDict
|
||||
|
||||
|
||||
class Api:
|
||||
def __init__(self, app, queue_lock):
|
||||
self.router = APIRouter()
|
||||
|
|
@ -33,15 +39,16 @@ class Api:
|
|||
self.app.add_api_route("/sdapi/v1/extra-single-image", self.extras_single_image_api, methods=["POST"], response_model=ExtrasSingleImageResponse)
|
||||
self.app.add_api_route("/sdapi/v1/extra-batch-images", self.extras_batch_images_api, methods=["POST"], response_model=ExtrasBatchImagesResponse)
|
||||
self.app.add_api_route("/sdapi/v1/png-info", self.pnginfoapi, methods=["POST"], response_model=PNGInfoResponse)
|
||||
self.app.add_api_route("/sdapi/v1/progress", self.progressapi, methods=["GET"], response_model=ProgressResponse)
|
||||
|
||||
def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
|
||||
sampler_index = sampler_to_index(txt2imgreq.sampler_index)
|
||||
|
||||
|
||||
if sampler_index is None:
|
||||
raise HTTPException(status_code=404, detail="Sampler not found")
|
||||
|
||||
raise HTTPException(status_code=404, detail="Sampler not found")
|
||||
|
||||
populate = txt2imgreq.copy(update={ # Override __init__ params
|
||||
"sd_model": shared.sd_model,
|
||||
"sd_model": shared.sd_model,
|
||||
"sampler_index": sampler_index[0],
|
||||
"do_not_save_samples": True,
|
||||
"do_not_save_grid": True
|
||||
|
|
@ -49,34 +56,39 @@ class Api:
|
|||
)
|
||||
p = StableDiffusionProcessingTxt2Img(**vars(populate))
|
||||
# Override object param
|
||||
|
||||
shared.state.begin()
|
||||
|
||||
with self.queue_lock:
|
||||
processed = process_images(p)
|
||||
|
||||
|
||||
shared.state.end()
|
||||
|
||||
b64images = list(map(encode_pil_to_base64, processed.images))
|
||||
|
||||
|
||||
return TextToImageResponse(images=b64images, parameters=vars(txt2imgreq), info=processed.js())
|
||||
|
||||
def img2imgapi(self, img2imgreq: StableDiffusionImg2ImgProcessingAPI):
|
||||
sampler_index = sampler_to_index(img2imgreq.sampler_index)
|
||||
|
||||
|
||||
if sampler_index is None:
|
||||
raise HTTPException(status_code=404, detail="Sampler not found")
|
||||
raise HTTPException(status_code=404, detail="Sampler not found")
|
||||
|
||||
|
||||
init_images = img2imgreq.init_images
|
||||
if init_images is None:
|
||||
raise HTTPException(status_code=404, detail="Init image not found")
|
||||
raise HTTPException(status_code=404, detail="Init image not found")
|
||||
|
||||
mask = img2imgreq.mask
|
||||
if mask:
|
||||
mask = decode_base64_to_image(mask)
|
||||
|
||||
|
||||
|
||||
populate = img2imgreq.copy(update={ # Override __init__ params
|
||||
"sd_model": shared.sd_model,
|
||||
"sd_model": shared.sd_model,
|
||||
"sampler_index": sampler_index[0],
|
||||
"do_not_save_samples": True,
|
||||
"do_not_save_grid": True,
|
||||
"do_not_save_grid": True,
|
||||
"mask": mask
|
||||
}
|
||||
)
|
||||
|
|
@ -88,16 +100,20 @@ class Api:
|
|||
imgs = [img] * p.batch_size
|
||||
|
||||
p.init_images = imgs
|
||||
# Override object param
|
||||
|
||||
shared.state.begin()
|
||||
|
||||
with self.queue_lock:
|
||||
processed = process_images(p)
|
||||
|
||||
|
||||
shared.state.end()
|
||||
|
||||
b64images = list(map(encode_pil_to_base64, processed.images))
|
||||
|
||||
if (not img2imgreq.include_init_images):
|
||||
img2imgreq.init_images = None
|
||||
img2imgreq.mask = None
|
||||
|
||||
|
||||
return ImageToImageResponse(images=b64images, parameters=vars(img2imgreq), info=processed.js())
|
||||
|
||||
def extras_single_image_api(self, req: ExtrasSingleImageRequest):
|
||||
|
|
@ -125,7 +141,7 @@ class Api:
|
|||
result = run_extras(extras_mode=1, image="", input_dir="", output_dir="", **reqDict)
|
||||
|
||||
return ExtrasBatchImagesResponse(images=list(map(encode_pil_to_base64, result[0])), html_info=result[1])
|
||||
|
||||
|
||||
def pnginfoapi(self, req: PNGInfoRequest):
|
||||
if(not req.image.strip()):
|
||||
return PNGInfoResponse(info="")
|
||||
|
|
@ -134,6 +150,32 @@ class Api:
|
|||
|
||||
return PNGInfoResponse(info=result[1])
|
||||
|
||||
def progressapi(self, req: ProgressRequest = Depends()):
|
||||
# copy from check_progress_call of ui.py
|
||||
|
||||
if shared.state.job_count == 0:
|
||||
return ProgressResponse(progress=0, eta_relative=0, state=shared.state.dict())
|
||||
|
||||
# avoid dividing zero
|
||||
progress = 0.01
|
||||
|
||||
if shared.state.job_count > 0:
|
||||
progress += shared.state.job_no / shared.state.job_count
|
||||
if shared.state.sampling_steps > 0:
|
||||
progress += 1 / shared.state.job_count * shared.state.sampling_step / shared.state.sampling_steps
|
||||
|
||||
time_since_start = time.time() - shared.state.time_start
|
||||
eta = (time_since_start/progress)
|
||||
eta_relative = eta-time_since_start
|
||||
|
||||
progress = min(progress, 1)
|
||||
|
||||
current_image = None
|
||||
if shared.state.current_image and not req.skip_current_image:
|
||||
current_image = encode_pil_to_base64(shared.state.current_image)
|
||||
|
||||
return ProgressResponse(progress=progress, eta_relative=eta_relative, state=shared.state.dict(), current_image=current_image)
|
||||
|
||||
def launch(self, server_name, port):
|
||||
self.app.include_router(self.router)
|
||||
uvicorn.run(self.app, host=server_name, port=port)
|
||||
|
|
|
|||
|
|
@ -52,17 +52,17 @@ class PydanticModelGenerator:
|
|||
# field_type = str if not overrides.get(k) else overrides[k]["type"]
|
||||
# print(k, v.annotation, v.default)
|
||||
field_type = v.annotation
|
||||
|
||||
|
||||
return Optional[field_type]
|
||||
|
||||
|
||||
def merge_class_params(class_):
|
||||
all_classes = list(filter(lambda x: x is not object, inspect.getmro(class_)))
|
||||
parameters = {}
|
||||
for classes in all_classes:
|
||||
parameters = {**parameters, **inspect.signature(classes.__init__).parameters}
|
||||
return parameters
|
||||
|
||||
|
||||
|
||||
|
||||
self._model_name = model_name
|
||||
self._class_data = merge_class_params(class_instance)
|
||||
self._model_def = [
|
||||
|
|
@ -74,11 +74,11 @@ class PydanticModelGenerator:
|
|||
)
|
||||
for (k,v) in self._class_data.items() if k not in API_NOT_ALLOWED
|
||||
]
|
||||
|
||||
|
||||
for fields in additional_fields:
|
||||
self._model_def.append(ModelDef(
|
||||
field=underscore(fields["key"]),
|
||||
field_alias=fields["key"],
|
||||
field=underscore(fields["key"]),
|
||||
field_alias=fields["key"],
|
||||
field_type=fields["type"],
|
||||
field_value=fields["default"],
|
||||
field_exclude=fields["exclude"] if "exclude" in fields else False))
|
||||
|
|
@ -95,15 +95,15 @@ class PydanticModelGenerator:
|
|||
DynamicModel.__config__.allow_population_by_field_name = True
|
||||
DynamicModel.__config__.allow_mutation = True
|
||||
return DynamicModel
|
||||
|
||||
|
||||
StableDiffusionTxt2ImgProcessingAPI = PydanticModelGenerator(
|
||||
"StableDiffusionProcessingTxt2Img",
|
||||
"StableDiffusionProcessingTxt2Img",
|
||||
StableDiffusionProcessingTxt2Img,
|
||||
[{"key": "sampler_index", "type": str, "default": "Euler"}]
|
||||
).generate_model()
|
||||
|
||||
StableDiffusionImg2ImgProcessingAPI = PydanticModelGenerator(
|
||||
"StableDiffusionProcessingImg2Img",
|
||||
"StableDiffusionProcessingImg2Img",
|
||||
StableDiffusionProcessingImg2Img,
|
||||
[{"key": "sampler_index", "type": str, "default": "Euler"}, {"key": "init_images", "type": list, "default": None}, {"key": "denoising_strength", "type": float, "default": 0.75}, {"key": "mask", "type": str, "default": None}, {"key": "include_init_images", "type": bool, "default": False, "exclude" : True}]
|
||||
).generate_model()
|
||||
|
|
@ -155,4 +155,13 @@ class PNGInfoRequest(BaseModel):
|
|||
image: str = Field(title="Image", description="The base64 encoded PNG image")
|
||||
|
||||
class PNGInfoResponse(BaseModel):
|
||||
info: str = Field(title="Image info", description="A string with all the info the image had")
|
||||
info: str = Field(title="Image info", description="A string with all the info the image had")
|
||||
|
||||
class ProgressRequest(BaseModel):
|
||||
skip_current_image: bool = Field(default=False, title="Skip current image", description="Skip current image serialization")
|
||||
|
||||
class ProgressResponse(BaseModel):
|
||||
progress: float = Field(title="Progress", description="The progress with a range of 0 to 1")
|
||||
eta_relative: float = Field(title="ETA in secs")
|
||||
state: dict = Field(title="State", description="The current state snapshot")
|
||||
current_image: str = Field(default=None, title="Current image", description="The current image in base64 format. opts.show_progress_every_n_steps is required for this to work.")
|
||||
|
|
|
|||
|
|
@ -66,6 +66,7 @@ def integrate_settings_paste_fields(component_dict):
|
|||
|
||||
settings_map = {
|
||||
'sd_hypernetwork': 'Hypernet',
|
||||
'sd_hypernetwork_strength': 'Hypernet strength',
|
||||
'CLIP_stop_at_last_layers': 'Clip skip',
|
||||
'sd_model_checkpoint': 'Model hash',
|
||||
}
|
||||
|
|
|
|||
|
|
@ -209,13 +209,16 @@ def list_hypernetworks(path):
|
|||
res = {}
|
||||
for filename in glob.iglob(os.path.join(path, '**/*.pt'), recursive=True):
|
||||
name = os.path.splitext(os.path.basename(filename))[0]
|
||||
res[name] = filename
|
||||
# Prevent a hypothetical "None.pt" from being listed.
|
||||
if name != "None":
|
||||
res[name] = filename
|
||||
return res
|
||||
|
||||
|
||||
def load_hypernetwork(filename):
|
||||
path = shared.hypernetworks.get(filename, None)
|
||||
if path is not None:
|
||||
# Prevent any file named "None.pt" from being loaded.
|
||||
if path is not None and filename != "None":
|
||||
print(f"Loading hypernetwork {filename}")
|
||||
try:
|
||||
shared.loaded_hypernetwork = Hypernetwork()
|
||||
|
|
@ -332,7 +335,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
|
|||
# images allows training previews to have infotext. Importing it at the top causes a circular import problem.
|
||||
from modules import images
|
||||
|
||||
assert hypernetwork_name, 'hypernetwork not selected'
|
||||
save_hypernetwork_every = save_hypernetwork_every or 0
|
||||
create_image_every = create_image_every or 0
|
||||
textual_inversion.validate_train_inputs(hypernetwork_name, learn_rate, batch_size, data_root, template_file, steps, save_hypernetwork_every, create_image_every, log_directory, name="hypernetwork")
|
||||
|
||||
path = shared.hypernetworks.get(hypernetwork_name, None)
|
||||
shared.loaded_hypernetwork = Hypernetwork()
|
||||
|
|
@ -358,39 +363,44 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
|
|||
else:
|
||||
images_dir = None
|
||||
|
||||
hypernetwork = shared.loaded_hypernetwork
|
||||
checkpoint = sd_models.select_checkpoint()
|
||||
|
||||
ititial_step = hypernetwork.step or 0
|
||||
if ititial_step >= steps:
|
||||
shared.state.textinfo = f"Model has already been trained beyond specified max steps"
|
||||
return hypernetwork, filename
|
||||
|
||||
scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)
|
||||
|
||||
# dataset loading may take a while, so input validations and early returns should be done before this
|
||||
shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
|
||||
with torch.autocast("cuda"):
|
||||
ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size)
|
||||
|
||||
if unload:
|
||||
shared.sd_model.cond_stage_model.to(devices.cpu)
|
||||
shared.sd_model.first_stage_model.to(devices.cpu)
|
||||
|
||||
hypernetwork = shared.loaded_hypernetwork
|
||||
weights = hypernetwork.weights()
|
||||
for weight in weights:
|
||||
weight.requires_grad = True
|
||||
|
||||
size = len(ds.indexes)
|
||||
loss_dict = defaultdict(lambda : deque(maxlen = 1024))
|
||||
losses = torch.zeros((size,))
|
||||
previous_mean_losses = [0]
|
||||
previous_mean_loss = 0
|
||||
print("Mean loss of {} elements".format(size))
|
||||
|
||||
last_saved_file = "<none>"
|
||||
last_saved_image = "<none>"
|
||||
forced_filename = "<none>"
|
||||
|
||||
ititial_step = hypernetwork.step or 0
|
||||
if ititial_step > steps:
|
||||
return hypernetwork, filename
|
||||
|
||||
scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)
|
||||
|
||||
weights = hypernetwork.weights()
|
||||
for weight in weights:
|
||||
weight.requires_grad = True
|
||||
# if optimizer == "AdamW": or else Adam / AdamW / SGD, etc...
|
||||
optimizer = torch.optim.AdamW(weights, lr=scheduler.learn_rate)
|
||||
|
||||
steps_without_grad = 0
|
||||
|
||||
last_saved_file = "<none>"
|
||||
last_saved_image = "<none>"
|
||||
forced_filename = "<none>"
|
||||
|
||||
pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step)
|
||||
for i, entries in pbar:
|
||||
hypernetwork.step = i + ititial_step
|
||||
|
|
@ -443,9 +453,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
|
|||
|
||||
if hypernetwork_dir is not None and steps_done % save_hypernetwork_every == 0:
|
||||
# Before saving, change name to match current checkpoint.
|
||||
hypernetwork.name = f'{hypernetwork_name}-{steps_done}'
|
||||
last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork.name}.pt')
|
||||
hypernetwork.save(last_saved_file)
|
||||
hypernetwork_name_every = f'{hypernetwork_name}-{steps_done}'
|
||||
last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name_every}.pt')
|
||||
save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, last_saved_file)
|
||||
|
||||
textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), {
|
||||
"loss": f"{previous_mean_loss:.7f}",
|
||||
|
|
@ -506,13 +516,23 @@ Last saved image: {html.escape(last_saved_image)}<br/>
|
|||
"""
|
||||
|
||||
report_statistics(loss_dict)
|
||||
checkpoint = sd_models.select_checkpoint()
|
||||
|
||||
hypernetwork.sd_checkpoint = checkpoint.hash
|
||||
hypernetwork.sd_checkpoint_name = checkpoint.model_name
|
||||
# Before saving for the last time, change name back to the base name (as opposed to the save_hypernetwork_every step-suffixed naming convention).
|
||||
hypernetwork.name = hypernetwork_name
|
||||
filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork.name}.pt')
|
||||
hypernetwork.save(filename)
|
||||
filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt')
|
||||
save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename)
|
||||
|
||||
return hypernetwork, filename
|
||||
|
||||
def save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename):
|
||||
old_hypernetwork_name = hypernetwork.name
|
||||
old_sd_checkpoint = hypernetwork.sd_checkpoint if hasattr(hypernetwork, "sd_checkpoint") else None
|
||||
old_sd_checkpoint_name = hypernetwork.sd_checkpoint_name if hasattr(hypernetwork, "sd_checkpoint_name") else None
|
||||
try:
|
||||
hypernetwork.sd_checkpoint = checkpoint.hash
|
||||
hypernetwork.sd_checkpoint_name = checkpoint.model_name
|
||||
hypernetwork.name = hypernetwork_name
|
||||
hypernetwork.save(filename)
|
||||
except:
|
||||
hypernetwork.sd_checkpoint = old_sd_checkpoint
|
||||
hypernetwork.sd_checkpoint_name = old_sd_checkpoint_name
|
||||
hypernetwork.name = old_hypernetwork_name
|
||||
raise
|
||||
|
|
|
|||
|
|
@ -396,6 +396,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
|
|||
"Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash),
|
||||
"Model": (None if not opts.add_model_name_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')),
|
||||
"Hypernet": (None if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.name),
|
||||
"Hypernet strength": (None if shared.loaded_hypernetwork is None or shared.opts.sd_hypernetwork_strength >= 1 else shared.opts.sd_hypernetwork_strength),
|
||||
"Batch size": (None if p.batch_size < 2 else p.batch_size),
|
||||
"Batch pos": (None if p.batch_size < 2 else position_in_batch),
|
||||
"Variation seed": (None if p.subseed_strength == 0 else all_subseeds[index]),
|
||||
|
|
@ -686,15 +687,12 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
|
|||
|
||||
noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
|
||||
|
||||
image_conditioning = self.txt2img_image_conditioning(x)
|
||||
|
||||
# GC now before running the next img2img to prevent running out of memory
|
||||
x = None
|
||||
devices.torch_gc()
|
||||
|
||||
image_conditioning = self.img2img_image_conditioning(
|
||||
decoded_samples,
|
||||
samples,
|
||||
decoded_samples.new_ones(decoded_samples.shape[0], 1, decoded_samples.shape[2], decoded_samples.shape[3])
|
||||
)
|
||||
samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps, image_conditioning=image_conditioning)
|
||||
|
||||
return samples
|
||||
|
|
|
|||
|
|
@ -144,9 +144,38 @@ class State:
|
|||
self.sampling_step = 0
|
||||
self.current_image_sampling_step = 0
|
||||
|
||||
def get_job_timestamp(self):
|
||||
return datetime.datetime.now().strftime("%Y%m%d%H%M%S") # shouldn't this return job_timestamp?
|
||||
def dict(self):
|
||||
obj = {
|
||||
"skipped": self.skipped,
|
||||
"interrupted": self.skipped,
|
||||
"job": self.job,
|
||||
"job_count": self.job_count,
|
||||
"job_no": self.job_no,
|
||||
"sampling_step": self.sampling_step,
|
||||
"sampling_steps": self.sampling_steps,
|
||||
}
|
||||
|
||||
return obj
|
||||
|
||||
def begin(self):
|
||||
self.sampling_step = 0
|
||||
self.job_count = -1
|
||||
self.job_no = 0
|
||||
self.job_timestamp = datetime.datetime.now().strftime("%Y%m%d%H%M%S")
|
||||
self.current_latent = None
|
||||
self.current_image = None
|
||||
self.current_image_sampling_step = 0
|
||||
self.skipped = False
|
||||
self.interrupted = False
|
||||
self.textinfo = None
|
||||
|
||||
devices.torch_gc()
|
||||
|
||||
def end(self):
|
||||
self.job = ""
|
||||
self.job_count = 0
|
||||
|
||||
devices.torch_gc()
|
||||
|
||||
state = State()
|
||||
|
||||
|
|
|
|||
|
|
@ -42,6 +42,8 @@ class PersonalizedBase(Dataset):
|
|||
self.lines = lines
|
||||
|
||||
assert data_root, 'dataset directory not specified'
|
||||
assert os.path.isdir(data_root), "Dataset directory doesn't exist"
|
||||
assert os.listdir(data_root), "Dataset directory is empty"
|
||||
|
||||
cond_model = shared.sd_model.cond_stage_model
|
||||
|
||||
|
|
|
|||
|
|
@ -4,30 +4,37 @@ import tqdm
|
|||
class LearnScheduleIterator:
|
||||
def __init__(self, learn_rate, max_steps, cur_step=0):
|
||||
"""
|
||||
specify learn_rate as "0.001:100, 0.00001:1000, 1e-5:10000" to have lr of 0.001 until step 100, 0.00001 until 1000, 1e-5:10000 until 10000
|
||||
specify learn_rate as "0.001:100, 0.00001:1000, 1e-5:10000" to have lr of 0.001 until step 100, 0.00001 until 1000, and 1e-5 until 10000
|
||||
"""
|
||||
|
||||
pairs = learn_rate.split(',')
|
||||
self.rates = []
|
||||
self.it = 0
|
||||
self.maxit = 0
|
||||
for i, pair in enumerate(pairs):
|
||||
tmp = pair.split(':')
|
||||
if len(tmp) == 2:
|
||||
step = int(tmp[1])
|
||||
if step > cur_step:
|
||||
self.rates.append((float(tmp[0]), min(step, max_steps)))
|
||||
self.maxit += 1
|
||||
if step > max_steps:
|
||||
try:
|
||||
for i, pair in enumerate(pairs):
|
||||
if not pair.strip():
|
||||
continue
|
||||
tmp = pair.split(':')
|
||||
if len(tmp) == 2:
|
||||
step = int(tmp[1])
|
||||
if step > cur_step:
|
||||
self.rates.append((float(tmp[0]), min(step, max_steps)))
|
||||
self.maxit += 1
|
||||
if step > max_steps:
|
||||
return
|
||||
elif step == -1:
|
||||
self.rates.append((float(tmp[0]), max_steps))
|
||||
self.maxit += 1
|
||||
return
|
||||
elif step == -1:
|
||||
else:
|
||||
self.rates.append((float(tmp[0]), max_steps))
|
||||
self.maxit += 1
|
||||
return
|
||||
else:
|
||||
self.rates.append((float(tmp[0]), max_steps))
|
||||
self.maxit += 1
|
||||
return
|
||||
assert self.rates
|
||||
except (ValueError, AssertionError):
|
||||
raise Exception('Invalid learning rate schedule. It should be a number or, for example, like "0.001:100, 0.00001:1000, 1e-5:10000" to have lr of 0.001 until step 100, 0.00001 until 1000, and 1e-5 until 10000.')
|
||||
|
||||
|
||||
def __iter__(self):
|
||||
return self
|
||||
|
|
|
|||
|
|
@ -119,7 +119,7 @@ class EmbeddingDatabase:
|
|||
vec = emb.detach().to(devices.device, dtype=torch.float32)
|
||||
embedding = Embedding(vec, name)
|
||||
embedding.step = data.get('step', None)
|
||||
embedding.sd_checkpoint = data.get('hash', None)
|
||||
embedding.sd_checkpoint = data.get('sd_checkpoint', None)
|
||||
embedding.sd_checkpoint_name = data.get('sd_checkpoint_name', None)
|
||||
self.register_embedding(embedding, shared.sd_model)
|
||||
|
||||
|
|
@ -204,9 +204,30 @@ def write_loss(log_directory, filename, step, epoch_len, values):
|
|||
**values,
|
||||
})
|
||||
|
||||
def validate_train_inputs(model_name, learn_rate, batch_size, data_root, template_file, steps, save_model_every, create_image_every, log_directory, name="embedding"):
|
||||
assert model_name, f"{name} not selected"
|
||||
assert learn_rate, "Learning rate is empty or 0"
|
||||
assert isinstance(batch_size, int), "Batch size must be integer"
|
||||
assert batch_size > 0, "Batch size must be positive"
|
||||
assert data_root, "Dataset directory is empty"
|
||||
assert os.path.isdir(data_root), "Dataset directory doesn't exist"
|
||||
assert os.listdir(data_root), "Dataset directory is empty"
|
||||
assert template_file, "Prompt template file is empty"
|
||||
assert os.path.isfile(template_file), "Prompt template file doesn't exist"
|
||||
assert steps, "Max steps is empty or 0"
|
||||
assert isinstance(steps, int), "Max steps must be integer"
|
||||
assert steps > 0 , "Max steps must be positive"
|
||||
assert isinstance(save_model_every, int), "Save {name} must be integer"
|
||||
assert save_model_every >= 0 , "Save {name} must be positive or 0"
|
||||
assert isinstance(create_image_every, int), "Create image must be integer"
|
||||
assert create_image_every >= 0 , "Create image must be positive or 0"
|
||||
if save_model_every or create_image_every:
|
||||
assert log_directory, "Log directory is empty"
|
||||
|
||||
def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
|
||||
assert embedding_name, 'embedding not selected'
|
||||
save_embedding_every = save_embedding_every or 0
|
||||
create_image_every = create_image_every or 0
|
||||
validate_train_inputs(embedding_name, learn_rate, batch_size, data_root, template_file, steps, save_embedding_every, create_image_every, log_directory, name="embedding")
|
||||
|
||||
shared.state.textinfo = "Initializing textual inversion training..."
|
||||
shared.state.job_count = steps
|
||||
|
|
@ -233,19 +254,30 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
|
|||
os.makedirs(images_embeds_dir, exist_ok=True)
|
||||
else:
|
||||
images_embeds_dir = None
|
||||
|
||||
|
||||
cond_model = shared.sd_model.cond_stage_model
|
||||
|
||||
hijack = sd_hijack.model_hijack
|
||||
|
||||
embedding = hijack.embedding_db.word_embeddings[embedding_name]
|
||||
checkpoint = sd_models.select_checkpoint()
|
||||
|
||||
ititial_step = embedding.step or 0
|
||||
if ititial_step >= steps:
|
||||
shared.state.textinfo = f"Model has already been trained beyond specified max steps"
|
||||
return embedding, filename
|
||||
|
||||
scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)
|
||||
|
||||
# dataset loading may take a while, so input validations and early returns should be done before this
|
||||
shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
|
||||
with torch.autocast("cuda"):
|
||||
ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file, batch_size=batch_size)
|
||||
if unload:
|
||||
shared.sd_model.first_stage_model.to(devices.cpu)
|
||||
|
||||
hijack = sd_hijack.model_hijack
|
||||
|
||||
embedding = hijack.embedding_db.word_embeddings[embedding_name]
|
||||
embedding.vec.requires_grad = True
|
||||
optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate)
|
||||
|
||||
losses = torch.zeros((32,))
|
||||
|
||||
|
|
@ -254,13 +286,6 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
|
|||
forced_filename = "<none>"
|
||||
embedding_yet_to_be_embedded = False
|
||||
|
||||
ititial_step = embedding.step or 0
|
||||
if ititial_step > steps:
|
||||
return embedding, filename
|
||||
|
||||
scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)
|
||||
optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate)
|
||||
|
||||
pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)
|
||||
for i, entries in pbar:
|
||||
embedding.step = i + ititial_step
|
||||
|
|
@ -293,9 +318,9 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
|
|||
|
||||
if embedding_dir is not None and steps_done % save_embedding_every == 0:
|
||||
# Before saving, change name to match current checkpoint.
|
||||
embedding.name = f'{embedding_name}-{steps_done}'
|
||||
last_saved_file = os.path.join(embedding_dir, f'{embedding.name}.pt')
|
||||
embedding.save(last_saved_file)
|
||||
embedding_name_every = f'{embedding_name}-{steps_done}'
|
||||
last_saved_file = os.path.join(embedding_dir, f'{embedding_name_every}.pt')
|
||||
save_embedding(embedding, checkpoint, embedding_name_every, last_saved_file, remove_cached_checksum=True)
|
||||
embedding_yet_to_be_embedded = True
|
||||
|
||||
write_loss(log_directory, "textual_inversion_loss.csv", embedding.step, len(ds), {
|
||||
|
|
@ -382,14 +407,26 @@ Last saved image: {html.escape(last_saved_image)}<br/>
|
|||
</p>
|
||||
"""
|
||||
|
||||
checkpoint = sd_models.select_checkpoint()
|
||||
|
||||
embedding.sd_checkpoint = checkpoint.hash
|
||||
embedding.sd_checkpoint_name = checkpoint.model_name
|
||||
embedding.cached_checksum = None
|
||||
# Before saving for the last time, change name back to base name (as opposed to the save_embedding_every step-suffixed naming convention).
|
||||
embedding.name = embedding_name
|
||||
filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding.name}.pt')
|
||||
embedding.save(filename)
|
||||
filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt')
|
||||
save_embedding(embedding, checkpoint, embedding_name, filename, remove_cached_checksum=True)
|
||||
|
||||
return embedding, filename
|
||||
|
||||
def save_embedding(embedding, checkpoint, embedding_name, filename, remove_cached_checksum=True):
|
||||
old_embedding_name = embedding.name
|
||||
old_sd_checkpoint = embedding.sd_checkpoint if hasattr(embedding, "sd_checkpoint") else None
|
||||
old_sd_checkpoint_name = embedding.sd_checkpoint_name if hasattr(embedding, "sd_checkpoint_name") else None
|
||||
old_cached_checksum = embedding.cached_checksum if hasattr(embedding, "cached_checksum") else None
|
||||
try:
|
||||
embedding.sd_checkpoint = checkpoint.hash
|
||||
embedding.sd_checkpoint_name = checkpoint.model_name
|
||||
if remove_cached_checksum:
|
||||
embedding.cached_checksum = None
|
||||
embedding.name = embedding_name
|
||||
embedding.save(filename)
|
||||
except:
|
||||
embedding.sd_checkpoint = old_sd_checkpoint
|
||||
embedding.sd_checkpoint_name = old_sd_checkpoint_name
|
||||
embedding.name = old_embedding_name
|
||||
embedding.cached_checksum = old_cached_checksum
|
||||
raise
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue