Commit b6cfaaa2 authored by evshiron's avatar evshiron
Browse files

Merge branch 'master' into fix/encode-pnginfo

parents 73e1cd6f 62e3d71a

modules/api/api.py

+4 −0
Original line number Diff line number Diff line
@@ -231,6 +231,10 @@ class Api: 
        return options



    def set_config(self, req: OptionsModel):

        # currently req has all options fields even if you send a dict like { "send_seed": false }, which means it will

        # overwrite all options with default values.

        raise RuntimeError('Setting options via API is not supported')



        reqDict = vars(req)

        for o in reqDict:

            setattr(shared.opts, o, reqDict[o])

modules/api/models.py

+13 −13
Original line number Diff line number Diff line 
import inspect

from pydantic import BaseModel, Field, create_model

from typing import Any, Optional, Union

from typing import Any, Optional

from typing_extensions import Literal

from inflection import underscore

from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img
@@ -186,7 +186,7 @@ for key in _options: 
    if(_options[key].dest != 'help'):

        flag = _options[key]

        _type = str

        if(_options[key].default != None): _type = type(_options[key].default) 

        if _options[key].default is not None: _type = type(_options[key].default)

        flags.update({flag.dest: (_type,Field(default=flag.default, description=flag.help))})



FlagsModel = create_model("Flags", **flags)
@@ -198,9 +198,9 @@ class SamplerItem(BaseModel): 


class UpscalerItem(BaseModel):

    name: str = Field(title="Name")

    model_name: str | None = Field(title="Model Name")

    model_path: str | None = Field(title="Path")

    model_url: str | None = Field(title="URL")

    model_name: Optional[str] = Field(title="Model Name")

    model_path: Optional[str] = Field(title="Path")

    model_url: Optional[str] = Field(title="URL")



class SDModelItem(BaseModel):

    title: str = Field(title="Title")
@@ -211,21 +211,21 @@ class SDModelItem(BaseModel): 


class HypernetworkItem(BaseModel):

    name: str = Field(title="Name")

    path: str | None = Field(title="Path")

    path: Optional[str] = Field(title="Path")



class FaceRestorerItem(BaseModel):

    name: str = Field(title="Name")

    cmd_dir: str | None = Field(title="Path")

    cmd_dir: Optional[str] = Field(title="Path")



class RealesrganItem(BaseModel):

    name: str = Field(title="Name")

    path: str | None = Field(title="Path")

    scale: int | None = Field(title="Scale")

    path: Optional[str] = Field(title="Path")

    scale: Optional[int] = Field(title="Scale")



class PromptStyleItem(BaseModel):

    name: str = Field(title="Name")

    prompt: str | None = Field(title="Prompt")

    negative_prompt: str | None = Field(title="Negative Prompt")

    prompt: Optional[str] = Field(title="Prompt")

    negative_prompt: Optional[str] = Field(title="Negative Prompt")



class ArtistItem(BaseModel):

    name: str = Field(title="Name")

modules/extensions.py

+5 −2
Original line number Diff line number Diff line
@@ -34,8 +34,11 @@ class Extension: 
        if repo is None or repo.bare:

            self.remote = None

        else:

            try:

                self.remote = next(repo.remote().urls, None)

                self.status = 'unknown'

            except Exception:

                self.remote = None



    def list_files(self, subdir, extension):

        from modules import scripts

modules/hypernetworks/hypernetwork.py

+53 −6
Original line number Diff line number Diff line
@@ -22,6 +22,8 @@ from collections import defaultdict, deque 
from statistics import stdev, mean





optimizer_dict = {optim_name : cls_obj for optim_name, cls_obj in inspect.getmembers(torch.optim, inspect.isclass) if optim_name != "Optimizer"}



class HypernetworkModule(torch.nn.Module):

    multiplier = 1.0

    activation_dict = {
@@ -142,6 +144,8 @@ class Hypernetwork: 
        self.use_dropout = use_dropout

        self.activate_output = activate_output

        self.last_layer_dropout = kwargs['last_layer_dropout'] if 'last_layer_dropout' in kwargs else True

        self.optimizer_name = None

        self.optimizer_state_dict = None



        for size in enable_sizes or []:

            self.layers[size] = (
@@ -163,6 +167,7 @@ class Hypernetwork: 


    def save(self, filename):

        state_dict = {}

        optimizer_saved_dict = {}



        for k, v in self.layers.items():

            state_dict[k] = (v[0].state_dict(), v[1].state_dict())
@@ -179,7 +184,14 @@ class Hypernetwork: 
        state_dict['activate_output'] = self.activate_output

        state_dict['last_layer_dropout'] = self.last_layer_dropout



        if self.optimizer_name is not None:

            optimizer_saved_dict['optimizer_name'] = self.optimizer_name



        torch.save(state_dict, filename)

        if shared.opts.save_optimizer_state and self.optimizer_state_dict:

            optimizer_saved_dict['hash'] = sd_models.model_hash(filename)

            optimizer_saved_dict['optimizer_state_dict'] = self.optimizer_state_dict

            torch.save(optimizer_saved_dict, filename + '.optim')



    def load(self, filename):

        self.filename = filename
@@ -202,6 +214,18 @@ class Hypernetwork: 
        print(f"Activate last layer is set to {self.activate_output}")

        self.last_layer_dropout = state_dict.get('last_layer_dropout', False)



        optimizer_saved_dict = torch.load(self.filename + '.optim', map_location = 'cpu') if os.path.exists(self.filename + '.optim') else {}

        self.optimizer_name = optimizer_saved_dict.get('optimizer_name', 'AdamW')

        print(f"Optimizer name is {self.optimizer_name}")

        if sd_models.model_hash(filename) == optimizer_saved_dict.get('hash', None):

            self.optimizer_state_dict = optimizer_saved_dict.get('optimizer_state_dict', None)

        else:

            self.optimizer_state_dict = None

        if self.optimizer_state_dict:

            print("Loaded existing optimizer from checkpoint")

        else:

            print("No saved optimizer exists in checkpoint")



        for size, sd in state_dict.items():

            if type(size) == int:

                self.layers[size] = (
@@ -219,11 +243,11 @@ class Hypernetwork: 


def list_hypernetworks(path):

    res = {}

    for filename in glob.iglob(os.path.join(path, '**/*.pt'), recursive=True):

    for filename in sorted(glob.iglob(os.path.join(path, '**/*.pt'), recursive=True)):

        name = os.path.splitext(os.path.basename(filename))[0]

        # Prevent a hypothetical "None.pt" from being listed.

        if name != "None":

            res[name] = filename

            res[name + f"({sd_models.model_hash(filename)})"] = filename

    return res
@@ -358,6 +382,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log 
    shared.state.textinfo = "Initializing hypernetwork training..."

    shared.state.job_count = steps



    hypernetwork_name = hypernetwork_name.rsplit('(', 1)[0]

    filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt')



    log_directory = os.path.join(log_directory, datetime.datetime.now().strftime("%Y-%m-%d"), hypernetwork_name)
@@ -404,8 +429,22 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log 
    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)



    # Here we use optimizer from saved HN, or we can specify as UI option.

    if hypernetwork.optimizer_name in optimizer_dict:

        optimizer = optimizer_dict[hypernetwork.optimizer_name](params=weights, lr=scheduler.learn_rate)

        optimizer_name = hypernetwork.optimizer_name

    else:

        print(f"Optimizer type {hypernetwork.optimizer_name} is not defined!")

        optimizer = torch.optim.AdamW(params=weights, lr=scheduler.learn_rate)

        optimizer_name = 'AdamW'



    if hypernetwork.optimizer_state_dict:  # This line must be changed if Optimizer type can be different from saved optimizer.

        try:

            optimizer.load_state_dict(hypernetwork.optimizer_state_dict)

        except RuntimeError as e:

            print("Cannot resume from saved optimizer!")

            print(e)



    steps_without_grad = 0
@@ -467,7 +506,11 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log 
            # Before saving, change name to match current checkpoint.

            hypernetwork_name_every = f'{hypernetwork_name}-{steps_done}'

            last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name_every}.pt')

            hypernetwork.optimizer_name = optimizer_name

            if shared.opts.save_optimizer_state:

                hypernetwork.optimizer_state_dict = optimizer.state_dict()

            save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, last_saved_file)

            hypernetwork.optimizer_state_dict = None  # dereference it after saving, to save memory.



        textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), {

            "loss": f"{previous_mean_loss:.7f}",
@@ -530,8 +573,12 @@ Last saved image: {html.escape(last_saved_image)}<br/> 
    report_statistics(loss_dict)



    filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt')

    hypernetwork.optimizer_name = optimizer_name

    if shared.opts.save_optimizer_state:

        hypernetwork.optimizer_state_dict = optimizer.state_dict()

    save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename)



    del optimizer

    hypernetwork.optimizer_state_dict = None  # dereference it after saving, to save memory.

    return hypernetwork, filename



def save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename):

modules/hypernetworks/ui.py

+1 −1
Original line number Diff line number Diff line
@@ -9,7 +9,7 @@ from modules import devices, sd_hijack, shared 
from modules.hypernetworks import hypernetwork



not_available = ["hardswish", "multiheadattention"]

keys = ["linear"] + list(x for x in hypernetwork.HypernetworkModule.activation_dict.keys() if x not in not_available)

keys = list(x for x in hypernetwork.HypernetworkModule.activation_dict.keys() if x not in not_available)



def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False):

    # Remove illegal characters from name.

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