Unverified Commit 649d79a8 authored by guaneec's avatar guaneec Committed by GitHub
Browse files

Merge branch 'master' into hn-activation

parents 877d94f9 757264c4

localizations/fr-FR.json

0 → 100644
+415 −0

File added.

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modules/api/api.py

+9 −4
Original line number Diff line number Diff line
@@ -7,6 +7,7 @@ import uvicorn 
from fastapi import Body, APIRouter, HTTPException

from fastapi.responses import JSONResponse

from pydantic import BaseModel, Field, Json

from typing import List

import json

import io

import base64
@@ -15,12 +16,12 @@ from PIL import Image 
sampler_to_index = lambda name: next(filter(lambda row: name.lower() == row[1].name.lower(), enumerate(all_samplers)), None)



class TextToImageResponse(BaseModel):

    images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")

    images: List[str] = Field(default=None, title="Image", description="The generated image in base64 format.")

    parameters: Json

    info: Json



class ImageToImageResponse(BaseModel):

    images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")

    images: List[str] = Field(default=None, title="Image", description="The generated image in base64 format.")

    parameters: Json

    info: Json
@@ -65,7 +66,7 @@ class Api: 
            i.save(buffer, format="png")

            b64images.append(base64.b64encode(buffer.getvalue()))



        return TextToImageResponse(images=b64images, parameters=json.dumps(vars(txt2imgreq)), info=json.dumps(processed.info))

        return TextToImageResponse(images=b64images, parameters=json.dumps(vars(txt2imgreq)), info=processed.js())
@@ -111,7 +112,11 @@ class Api: 
            i.save(buffer, format="png")

            b64images.append(base64.b64encode(buffer.getvalue()))



        return ImageToImageResponse(images=b64images, parameters=json.dumps(vars(img2imgreq)), info=json.dumps(processed.info))

        if (not img2imgreq.include_init_images):

            img2imgreq.init_images = None

            img2imgreq.mask = None



        return ImageToImageResponse(images=b64images, parameters=json.dumps(vars(img2imgreq)), info=processed.js())



    def extrasapi(self):

        raise NotImplementedError

modules/api/models.py

+5 −3
Original line number Diff line number Diff line
@@ -31,6 +31,7 @@ class ModelDef(BaseModel): 
    field_alias: str

    field_type: Any

    field_value: Any

    field_exclude: bool = False





class PydanticModelGenerator:
@@ -78,7 +79,8 @@ class PydanticModelGenerator: 
                field=underscore(fields["key"]), 

                field_alias=fields["key"], 

                field_type=fields["type"],

                field_value=fields["default"]))

                field_value=fields["default"],

                field_exclude=fields["exclude"] if "exclude" in fields else False))



    def generate_model(self):

        """
@@ -86,7 +88,7 @@ class PydanticModelGenerator: 
        from the json and overrides provided at initialization

        """

        fields = {

            d.field: (d.field_type, Field(default=d.field_value, alias=d.field_alias)) for d in self._model_def

            d.field: (d.field_type, Field(default=d.field_value, alias=d.field_alias, exclude=d.field_exclude)) for d in self._model_def

        }

        DynamicModel = create_model(self._model_name, **fields)

        DynamicModel.__config__.allow_population_by_field_name = True
@@ -102,5 +104,5 @@ StableDiffusionTxt2ImgProcessingAPI = PydanticModelGenerator( 
StableDiffusionImg2ImgProcessingAPI = PydanticModelGenerator(

    "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": "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()
 
 No newline at end of file

modules/hypernetworks/hypernetwork.py

+39 −10
Original line number Diff line number Diff line
@@ -5,6 +5,7 @@ import html 
import os

import sys

import traceback

import inspect



import modules.textual_inversion.dataset

import torch
@@ -15,10 +16,12 @@ from modules import devices, processing, sd_models, shared 
from modules.textual_inversion import textual_inversion

from modules.textual_inversion.learn_schedule import LearnRateScheduler

from torch import einsum

from torch.nn.init import normal_, xavier_normal_, xavier_uniform_, kaiming_normal_, kaiming_uniform_, zeros_



from collections import defaultdict, deque

from statistics import stdev, mean





class HypernetworkModule(torch.nn.Module):

    multiplier = 1.0

    activation_dict = {
@@ -26,9 +29,12 @@ class HypernetworkModule(torch.nn.Module): 
        "leakyrelu": torch.nn.LeakyReLU,

        "elu": torch.nn.ELU,

        "swish": torch.nn.Hardswish,

        "tanh": torch.nn.Tanh,

        "sigmoid": torch.nn.Sigmoid,

    }

    activation_dict.update({cls_name.lower(): cls_obj for cls_name, cls_obj in inspect.getmembers(torch.nn.modules.activation) if inspect.isclass(cls_obj) and cls_obj.__module__ == 'torch.nn.modules.activation'})



    def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, add_layer_norm=False, use_dropout=False, activate_output=False):

    def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, weight_init='Normal', add_layer_norm=False, use_dropout=False, activate_output=False):

        super().__init__()



        assert layer_structure is not None, "layer_structure must not be None"
@@ -65,9 +71,24 @@ class HypernetworkModule(torch.nn.Module): 
        else:

            for layer in self.linear:

                if type(layer) == torch.nn.Linear or type(layer) == torch.nn.LayerNorm:

                    layer.weight.data.normal_(mean=0.0, std=0.01)

                    layer.bias.data.zero_()



                    w, b = layer.weight.data, layer.bias.data

                    if weight_init == "Normal" or type(layer) == torch.nn.LayerNorm:

                        normal_(w, mean=0.0, std=0.01)

                        normal_(b, mean=0.0, std=0.005)

                    elif weight_init == 'XavierUniform':

                        xavier_uniform_(w)

                        zeros_(b)

                    elif weight_init == 'XavierNormal':

                        xavier_normal_(w)

                        zeros_(b)

                    elif weight_init == 'KaimingUniform':

                        kaiming_uniform_(w, nonlinearity='leaky_relu' if 'leakyrelu' == activation_func else 'relu')

                        zeros_(b)

                    elif weight_init == 'KaimingNormal':

                        kaiming_normal_(w, nonlinearity='leaky_relu' if 'leakyrelu' == activation_func else 'relu')

                        zeros_(b)

                    else:

                        raise KeyError(f"Key {weight_init} is not defined as initialization!")

        self.to(devices.device)



    def fix_old_state_dict(self, state_dict):
@@ -105,7 +126,7 @@ class Hypernetwork: 
    filename = None

    name = None



    def __init__(self, name=None, enable_sizes=None, layer_structure=None, activation_func=None, add_layer_norm=False, use_dropout=False, activate_output=False):

    def __init__(self, name=None, enable_sizes=None, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False, activate_output=False)

        self.filename = None

        self.name = name

        self.layers = {}
@@ -114,14 +135,15 @@ class Hypernetwork: 
        self.sd_checkpoint_name = None

        self.layer_structure = layer_structure

        self.activation_func = activation_func

        self.weight_init = weight_init

        self.add_layer_norm = add_layer_norm

        self.use_dropout = use_dropout

        self.activate_output = activate_output



        for size in enable_sizes or []:

            self.layers[size] = (

                HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.add_layer_norm, self.use_dropout, self.activate_output),

                HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.add_layer_norm, self.use_dropout, self.activate_output),

                HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout, self.activate_output),

                HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout, self.activate_output),

            )



    def weights(self):
@@ -145,6 +167,7 @@ class Hypernetwork: 
        state_dict['layer_structure'] = self.layer_structure

        state_dict['activation_func'] = self.activation_func

        state_dict['is_layer_norm'] = self.add_layer_norm

        state_dict['weight_initialization'] = self.weight_init

        state_dict['use_dropout'] = self.use_dropout

        state_dict['sd_checkpoint'] = self.sd_checkpoint

        state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name
@@ -160,16 +183,22 @@ class Hypernetwork: 
        state_dict = torch.load(filename, map_location='cpu')



        self.layer_structure = state_dict.get('layer_structure', [1, 2, 1])

        print(self.layer_structure)

        self.activation_func = state_dict.get('activation_func', None)

        print(f"Activation function is {self.activation_func}")

        self.weight_init = state_dict.get('weight_initialization', 'Normal')

        print(f"Weight initialization is {self.weight_init}")

        self.add_layer_norm = state_dict.get('is_layer_norm', False)

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

        print(f"Layer norm is set to {self.add_layer_norm}")

        self.use_dropout = state_dict.get('use_dropout', False

        print(f"Dropout usage is set to {self.use_dropout}" )

        self.activate_output = state_dict.get('activate_output', True)



        for size, sd in state_dict.items():

            if type(size) == int:

                self.layers[size] = (

                    HypernetworkModule(size, sd[0], self.layer_structure, self.activation_func, self.add_layer_norm, self.use_dropout, self.activate_output),

                    HypernetworkModule(size, sd[1], self.layer_structure, self.activation_func, self.add_layer_norm, self.use_dropout, self.activate_output),

                    HypernetworkModule(size, sd[0], self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout, self.activate_output),

                    HypernetworkModule(size, sd[1], self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout, self.activate_output),

                )



        self.name = state_dict.get('name', self.name)

modules/hypernetworks/ui.py

+3 −1
Original line number Diff line number Diff line
@@ -8,8 +8,9 @@ import modules.textual_inversion.textual_inversion 
from modules import devices, sd_hijack, shared

from modules.hypernetworks import hypernetwork



keys = list(hypernetwork.HypernetworkModule.activation_dict.keys())



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

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.

    name = "".join( x for x in name if (x.isalnum() or x in "._- "))
@@ -25,6 +26,7 @@ def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, 
        enable_sizes=[int(x) for x in enable_sizes],

        layer_structure=layer_structure,

        activation_func=activation_func,

        weight_init=weight_init,

        add_layer_norm=add_layer_norm,

        use_dropout=use_dropout,

    )

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