Merge branch 'master' into gallery-styling

4 files changed, 54 insertions, 46 deletions

diff --git a/scripts/img2imgalt.py b/scripts/img2imgalt.py
index 0ef137f7..f9894cb0 100644
--- a/scripts/img2imgalt.py
+++ b/scripts/img2imgalt.py
@@ -8,7 +8,6 @@ import gradio as gr
 

 from modules import processing, shared, sd_samplers, prompt_parser

 from modules.processing import Processed

-from modules.sd_samplers import samplers

 from modules.shared import opts, cmd_opts, state

 

 import torch

@@ -159,7 +158,7 @@ class Script(scripts.Script):
             

             combined_noise = ((1 - randomness) * rec_noise + randomness * rand_noise) / ((randomness**2 + (1-randomness)**2) ** 0.5)

             

-            sampler = samplers[p.sampler_index].constructor(p.sd_model)

+            sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, p.sampler_index, p.sd_model)

 

             sigmas = sampler.model_wrap.get_sigmas(p.steps)

             

diff --git a/scripts/outpainting_mk_2.py b/scripts/outpainting_mk_2.py
index 9719bb8f..a6468e09 100644
--- a/scripts/outpainting_mk_2.py
+++ b/scripts/outpainting_mk_2.py
@@ -11,46 +11,8 @@ from modules import images, processing, devices
 from modules.processing import Processed, process_images

 from modules.shared import opts, cmd_opts, state

 

-#  https://github.com/parlance-zz/g-diffuser-bot

-def expand(x, dir, amount, power=0.75):

-    is_left = dir == 3

-    is_right = dir == 1

-    is_up = dir == 0

-    is_down = dir == 2

-

-    if is_left or is_right:

-        noise = np.zeros((x.shape[0], amount, 3), dtype=float)

-        indexes = np.random.random((x.shape[0], amount)) ** power * (1 - np.arange(amount) / amount)

-        if is_right:

-            indexes = 1 - indexes

-        indexes = (indexes * (x.shape[1] - 1)).astype(int)

-

-        for row in range(x.shape[0]):

-            if is_left:

-                noise[row] = x[row][indexes[row]]

-            else:

-                noise[row] = np.flip(x[row][indexes[row]], axis=0)

-

-        x = np.concatenate([noise, x] if is_left else [x, noise], axis=1)

-        return x

-

-    if is_up or is_down:

-        noise = np.zeros((amount, x.shape[1], 3), dtype=float)

-        indexes = np.random.random((x.shape[1], amount)) ** power * (1 - np.arange(amount) / amount)

-        if is_down:

-            indexes = 1 - indexes

-        indexes = (indexes * x.shape[0] - 1).astype(int)

-

-        for row in range(x.shape[1]):

-            if is_up:

-                noise[:, row] = x[:, row][indexes[row]]

-            else:

-                noise[:, row] = np.flip(x[:, row][indexes[row]], axis=0)

-

-        x = np.concatenate([noise, x] if is_up else [x, noise], axis=0)

-        return x

-

 

+# this function is taken from https://github.com/parlance-zz/g-diffuser-bot

 def get_matched_noise(_np_src_image, np_mask_rgb, noise_q=1, color_variation=0.05):

     # helper fft routines that keep ortho normalization and auto-shift before and after fft

     def _fft2(data):

@@ -123,8 +85,11 @@ def get_matched_noise(_np_src_image, np_mask_rgb, noise_q=1, color_variation=0.0
     src_dist = np.absolute(src_fft)

     src_phase = src_fft / src_dist

 

+    # create a generator with a static seed to make outpainting deterministic / only follow global seed

+    rng = np.random.default_rng(0)

+

     noise_window = _get_gaussian_window(width, height, mode=1)  # start with simple gaussian noise

-    noise_rgb = np.random.random_sample((width, height, num_channels))

+    noise_rgb = rng.random((width, height, num_channels))

     noise_grey = (np.sum(noise_rgb, axis=2) / 3.)

     noise_rgb *= color_variation  # the colorfulness of the starting noise is blended to greyscale with a parameter

     for c in range(num_channels):

diff --git a/scripts/sd_upscale.py b/scripts/sd_upscale.py
index 2653e2d4..cb37ff7e 100644
--- a/scripts/sd_upscale.py
+++ b/scripts/sd_upscale.py
@@ -34,7 +34,11 @@ class Script(scripts.Script):
         seed = p.seed

 

         init_img = p.init_images[0]

-        img = upscaler.scaler.upscale(init_img, 2, upscaler.data_path)

+        

+        if(upscaler.name != "None"): 

+            img = upscaler.scaler.upscale(init_img, 2, upscaler.data_path)

+        else:

+            img = init_img

 

         devices.torch_gc()

 

diff --git a/scripts/xy_grid.py b/scripts/xy_grid.py
index 146663b0..6344e612 100644
--- a/scripts/xy_grid.py
+++ b/scripts/xy_grid.py
@@ -1,7 +1,9 @@
 from collections import namedtuple

 from copy import copy

+from itertools import permutations, chain

 import random

-

+import csv

+from io import StringIO

 from PIL import Image

 import numpy as np

 

@@ -29,6 +31,31 @@ def apply_prompt(p, x, xs):
     p.negative_prompt = p.negative_prompt.replace(xs[0], x)

 

 

+def apply_order(p, x, xs):

+    token_order = []

+

+    # Initally grab the tokens from the prompt, so they can be replaced in order of earliest seen

+    for token in x:

+        token_order.append((p.prompt.find(token), token))

+

+    token_order.sort(key=lambda t: t[0])

+

+    prompt_parts = []

+

+    # Split the prompt up, taking out the tokens

+    for _, token in token_order:

+        n = p.prompt.find(token)

+        prompt_parts.append(p.prompt[0:n])

+        p.prompt = p.prompt[n + len(token):]

+

+    # Rebuild the prompt with the tokens in the order we want

+    prompt_tmp = ""

+    for idx, part in enumerate(prompt_parts):

+        prompt_tmp += part

+        prompt_tmp += x[idx]

+    p.prompt = prompt_tmp + p.prompt

+    

+

 samplers_dict = {}

 for i, sampler in enumerate(modules.sd_samplers.samplers):

     samplers_dict[sampler.name.lower()] = i

@@ -60,16 +87,26 @@ def format_value_add_label(p, opt, x):
 def format_value(p, opt, x):

     if type(x) == float:

         x = round(x, 8)

-

     return x

 

+

+def format_value_join_list(p, opt, x):

+    return ", ".join(x)

+

+

 def do_nothing(p, x, xs):

     pass

 

+

 def format_nothing(p, opt, x):

     return ""

 

 

+def str_permutations(x):

+    """dummy function for specifying it in AxisOption's type when you want to get a list of permutations"""

+    return x

+

+

 AxisOption = namedtuple("AxisOption", ["label", "type", "apply", "format_value"])

 AxisOptionImg2Img = namedtuple("AxisOptionImg2Img", ["label", "type", "apply", "format_value"])

 

@@ -82,6 +119,7 @@ axis_options = [
     AxisOption("Steps", int, apply_field("steps"), format_value_add_label),

     AxisOption("CFG Scale", float, apply_field("cfg_scale"), format_value_add_label),

     AxisOption("Prompt S/R", str, apply_prompt, format_value),

+    AxisOption("Prompt order", str_permutations, apply_order, format_value_join_list),

     AxisOption("Sampler", str, apply_sampler, format_value),

     AxisOption("Checkpoint name", str, apply_checkpoint, format_value),

     AxisOption("Sigma Churn", float, apply_field("s_churn"), format_value_add_label),

@@ -159,7 +197,7 @@ class Script(scripts.Script):
             if opt.label == 'Nothing':

                 return [0]

 

-            valslist = [x.strip() for x in vals.split(",")]

+            valslist = [x.strip() for x in chain.from_iterable(csv.reader(StringIO(vals)))]

 

             if opt.type == int:

                 valslist_ext = []

@@ -206,6 +244,8 @@ class Script(scripts.Script):
                         valslist_ext.append(val)

 

                 valslist = valslist_ext

+            elif opt.type == str_permutations:

+                valslist = list(permutations(valslist))

 

             valslist = [opt.type(x) for x in valslist]