Merge branch 'master' into dev/deepdanbooru

3 files changed, 19 insertions, 7 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 11613ca3..a6468e09 100644
--- a/scripts/outpainting_mk_2.py
+++ b/scripts/outpainting_mk_2.py
@@ -85,8 +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/xy_grid.py b/scripts/xy_grid.py
index 1237e754..c0c364df 100644
--- a/scripts/xy_grid.py
+++ b/scripts/xy_grid.py
@@ -1,8 +1,9 @@
 from collections import namedtuple

 from copy import copy

-from itertools import permutations

+from itertools import permutations, chain

 import random

-

+import csv

+from io import StringIO

 from PIL import Image

 import numpy as np

 

@@ -76,6 +77,11 @@ def apply_checkpoint(p, x, xs):
     modules.sd_models.reload_model_weights(shared.sd_model, info)

 

 

+def apply_hypernetwork(p, x, xs):

+    hn = shared.hypernetworks.get(x, None)

+    opts.data["sd_hypernetwork"] = hn.name if hn is not None else 'None'

+

+

 def format_value_add_label(p, opt, x):

     if type(x) == float:

         x = round(x, 8)

@@ -121,6 +127,7 @@ axis_options = [
     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("Hypernetwork", str, apply_hypernetwork, format_value),

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

     AxisOption("Sigma min", float, apply_field("s_tmin"), format_value_add_label),

     AxisOption("Sigma max", float, apply_field("s_tmax"), format_value_add_label),

@@ -168,7 +175,6 @@ re_range_float = re.compile(r"\s*([+-]?\s*\d+(?:.\d*)?)\s*-\s*([+-]?\s*\d+(?:.\d
 re_range_count = re.compile(r"\s*([+-]?\s*\d+)\s*-\s*([+-]?\s*\d+)(?:\s*\[(\d+)\s*\])?\s*")

 re_range_count_float = re.compile(r"\s*([+-]?\s*\d+(?:.\d*)?)\s*-\s*([+-]?\s*\d+(?:.\d*)?)(?:\s*\[(\d+(?:.\d*)?)\s*\])?\s*")

 

-

 class Script(scripts.Script):

     def title(self):

         return "X/Y plot"

@@ -193,11 +199,13 @@ class Script(scripts.Script):
         modules.processing.fix_seed(p)

         p.batch_size = 1

 

+        initial_hn = opts.sd_hypernetwork

+

         def process_axis(opt, vals):

             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 = []

@@ -300,4 +308,6 @@ class Script(scripts.Script):
         # restore checkpoint in case it was changed by axes

         modules.sd_models.reload_model_weights(shared.sd_model)

 

+        opts.data["sd_hypernetwork"] = initial_hn

+

         return processed