Merge branch 'master' into master

1 files changed, 180 insertions, 50 deletions

diff --git a/scripts/xy_grid.py b/scripts/xy_grid.py
index 146663b0..88ad3bf7 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

 

@@ -9,7 +11,8 @@ import modules.scripts as scripts
 import gradio as gr

 

 from modules import images

-from modules.processing import process_images, Processed

+from modules.hypernetworks import hypernetwork

+from modules.processing import process_images, Processed, get_correct_sampler, StableDiffusionProcessingTxt2Img

 from modules.shared import opts, cmd_opts, state

 import modules.shared as shared

 import modules.sd_samplers

@@ -25,31 +28,101 @@ def apply_field(field):
 

 

 def apply_prompt(p, x, xs):

+    if xs[0] not in p.prompt and xs[0] not in p.negative_prompt:

+        raise RuntimeError(f"Prompt S/R did not find {xs[0]} in prompt or negative prompt.")

+

     p.prompt = p.prompt.replace(xs[0], x)

     p.negative_prompt = p.negative_prompt.replace(xs[0], x)

 

 

-samplers_dict = {}

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

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

-    for alias in sampler.aliases:

-        samplers_dict[alias.lower()] = i

+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

+    

+

+def build_samplers_dict(p):

+    samplers_dict = {}

+    for i, sampler in enumerate(get_correct_sampler(p)):

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

+        for alias in sampler.aliases:

+            samplers_dict[alias.lower()] = i

+    return samplers_dict

 

 

 def apply_sampler(p, x, xs):

-    sampler_index = samplers_dict.get(x.lower(), None)

+    sampler_index = build_samplers_dict(p).get(x.lower(), None)

     if sampler_index is None:

         raise RuntimeError(f"Unknown sampler: {x}")

 

     p.sampler_index = sampler_index

 

 

+def confirm_samplers(p, xs):

+    samplers_dict = build_samplers_dict(p)

+    for x in xs:

+        if x.lower() not in samplers_dict.keys():

+            raise RuntimeError(f"Unknown sampler: {x}")

+

+

 def apply_checkpoint(p, x, xs):

     info = modules.sd_models.get_closet_checkpoint_match(x)

-    assert info is not None, f'Checkpoint for {x} not found'

+    if info is None:

+        raise RuntimeError(f"Unknown checkpoint: {x}")

     modules.sd_models.reload_model_weights(shared.sd_model, info)

 

 

+def confirm_checkpoints(p, xs):

+    for x in xs:

+        if modules.sd_models.get_closet_checkpoint_match(x) is None:

+            raise RuntimeError(f"Unknown checkpoint: {x}")

+

+

+def apply_hypernetwork(p, x, xs):

+    if x.lower() in ["", "none"]:

+        name = None

+    else:

+        name = hypernetwork.find_closest_hypernetwork_name(x)

+        if not name:

+            raise RuntimeError(f"Unknown hypernetwork: {x}")

+    hypernetwork.load_hypernetwork(name)

+

+

+def apply_hypernetwork_strength(p, x, xs):

+    hypernetwork.apply_strength(x)

+

+

+def confirm_hypernetworks(p, xs):

+    for x in xs:

+        if x.lower() in ["", "none"]:

+            continue

+        if not hypernetwork.find_closest_hypernetwork_name(x):

+            raise RuntimeError(f"Unknown hypernetwork: {x}")

+

+

+def apply_clip_skip(p, x, xs):

+    opts.data["CLIP_stop_at_last_layers"] = x

+

+

 def format_value_add_label(p, opt, x):

     if type(x) == float:

         x = round(x, 8)

@@ -60,46 +133,64 @@ 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 ""

 

 

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

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

+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", "confirm"])

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

 

 

 axis_options = [

-    AxisOption("Nothing", str, do_nothing, format_nothing),

-    AxisOption("Seed", int, apply_field("seed"), format_value_add_label),

-    AxisOption("Var. seed", int, apply_field("subseed"), format_value_add_label),

-    AxisOption("Var. strength", float, apply_field("subseed_strength"), format_value_add_label),

-    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("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),

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

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

-    AxisOption("Sigma noise", float, apply_field("s_noise"), format_value_add_label),

-    AxisOption("Eta", float, apply_field("eta"), format_value_add_label),

-    AxisOptionImg2Img("Denoising", float, apply_field("denoising_strength"), format_value_add_label),  # as it is now all AxisOptionImg2Img items must go after AxisOption ones

+    AxisOption("Nothing", str, do_nothing, format_nothing, None),

+    AxisOption("Seed", int, apply_field("seed"), format_value_add_label, None),

+    AxisOption("Var. seed", int, apply_field("subseed"), format_value_add_label, None),

+    AxisOption("Var. strength", float, apply_field("subseed_strength"), format_value_add_label, None),

+    AxisOption("Steps", int, apply_field("steps"), format_value_add_label, None),

+    AxisOption("CFG Scale", float, apply_field("cfg_scale"), format_value_add_label, None),

+    AxisOption("Prompt S/R", str, apply_prompt, format_value, None),

+    AxisOption("Prompt order", str_permutations, apply_order, format_value_join_list, None),

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

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

+    AxisOption("Hypernetwork", str, apply_hypernetwork, format_value, confirm_hypernetworks),

+    AxisOption("Hypernet str.", float, apply_hypernetwork_strength, format_value_add_label, None),

+    AxisOption("Sigma Churn", float, apply_field("s_churn"), format_value_add_label, None),

+    AxisOption("Sigma min", float, apply_field("s_tmin"), format_value_add_label, None),

+    AxisOption("Sigma max", float, apply_field("s_tmax"), format_value_add_label, None),

+    AxisOption("Sigma noise", float, apply_field("s_noise"), format_value_add_label, None),

+    AxisOption("Eta", float, apply_field("eta"), format_value_add_label, None),

+    AxisOption("Clip skip", int, apply_clip_skip, format_value_add_label, None),

+    AxisOption("Denoising", float, apply_field("denoising_strength"), format_value_add_label, None),

 ]

 

 

-def draw_xy_grid(p, xs, ys, x_labels, y_labels, cell, draw_legend):

-    res = []

-

+def draw_xy_grid(p, xs, ys, x_labels, y_labels, cell, draw_legend, include_lone_images):

     ver_texts = [[images.GridAnnotation(y)] for y in y_labels]

     hor_texts = [[images.GridAnnotation(x)] for x in x_labels]

 

-    first_pocessed = None

+    # Temporary list of all the images that are generated to be populated into the grid.

+    # Will be filled with empty images for any individual step that fails to process properly

+    image_cache = []

+

+    processed_result = None

+    cell_mode = "P"

+    cell_size = (1,1)

 

     state.job_count = len(xs) * len(ys) * p.n_iter

 

@@ -107,22 +198,39 @@ def draw_xy_grid(p, xs, ys, x_labels, y_labels, cell, draw_legend):
         for ix, x in enumerate(xs):

             state.job = f"{ix + iy * len(xs) + 1} out of {len(xs) * len(ys)}"

 

-            processed = cell(x, y)

-            if first_pocessed is None:

-                first_pocessed = processed

-

+            processed:Processed = cell(x, y)

             try:

-              res.append(processed.images[0])

+                # this dereference will throw an exception if the image was not processed

+                # (this happens in cases such as if the user stops the process from the UI)

+                processed_image = processed.images[0]

+                

+                if processed_result is None:

+                    # Use our first valid processed result as a template container to hold our full results

+                    processed_result = copy(processed)

+                    cell_mode = processed_image.mode

+                    cell_size = processed_image.size

+                    processed_result.images = [Image.new(cell_mode, cell_size)]

+

+                image_cache.append(processed_image)

+                if include_lone_images:

+                    processed_result.images.append(processed_image)

+                    processed_result.all_prompts.append(processed.prompt)

+                    processed_result.all_seeds.append(processed.seed)

+                    processed_result.infotexts.append(processed.infotexts[0])

             except:

-              res.append(Image.new(res[0].mode, res[0].size))

+                image_cache.append(Image.new(cell_mode, cell_size))

+

+    if not processed_result:

+        print("Unexpected error: draw_xy_grid failed to return even a single processed image")

+        return Processed()

 

-    grid = images.image_grid(res, rows=len(ys))

+    grid = images.image_grid(image_cache, rows=len(ys))

     if draw_legend:

-        grid = images.draw_grid_annotations(grid, res[0].width, res[0].height, hor_texts, ver_texts)

+        grid = images.draw_grid_annotations(grid, cell_size[0], cell_size[1], hor_texts, ver_texts)

 

-    first_pocessed.images = [grid]

+    processed_result.images[0] = grid

 

-    return first_pocessed

+    return processed_result

 

 

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

@@ -143,23 +251,30 @@ class Script(scripts.Script):
             x_values = gr.Textbox(label="X values", visible=False, lines=1)

 

         with gr.Row():

-            y_type = gr.Dropdown(label="Y type", choices=[x.label for x in current_axis_options], value=current_axis_options[4].label, visible=False, type="index", elem_id="y_type")

+            y_type = gr.Dropdown(label="Y type", choices=[x.label for x in current_axis_options], value=current_axis_options[0].label, visible=False, type="index", elem_id="y_type")

             y_values = gr.Textbox(label="Y values", visible=False, lines=1)

         

         draw_legend = gr.Checkbox(label='Draw legend', value=True)

+        include_lone_images = gr.Checkbox(label='Include Separate Images', value=False)

         no_fixed_seeds = gr.Checkbox(label='Keep -1 for seeds', value=False)

 

-        return [x_type, x_values, y_type, y_values, draw_legend, no_fixed_seeds]

+        return [x_type, x_values, y_type, y_values, draw_legend, include_lone_images, no_fixed_seeds]

+

+    def run(self, p, x_type, x_values, y_type, y_values, draw_legend, include_lone_images, no_fixed_seeds):

+        if not no_fixed_seeds:

+            modules.processing.fix_seed(p)

+

+        if not opts.return_grid:

+            p.batch_size = 1

 

-    def run(self, p, x_type, x_values, y_type, y_values, draw_legend, no_fixed_seeds):

-        modules.processing.fix_seed(p)

-        p.batch_size = 1

+

+        CLIP_stop_at_last_layers = opts.CLIP_stop_at_last_layers

 

         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 = []

@@ -168,7 +283,6 @@ class Script(scripts.Script):
                     m = re_range.fullmatch(val)

                     mc = re_range_count.fullmatch(val)

                     if m is not None:

-

                         start = int(m.group(1))

                         end = int(m.group(2))+1

                         step = int(m.group(3)) if m.group(3) is not None else 1

@@ -206,9 +320,15 @@ 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]

 

+            # Confirm options are valid before starting

+            if opt.confirm:

+                opt.confirm(p, valslist)

+

             return valslist

 

         x_opt = axis_options[x_type]

@@ -218,7 +338,7 @@ class Script(scripts.Script):
         ys = process_axis(y_opt, y_values)

 

         def fix_axis_seeds(axis_opt, axis_list):

-            if axis_opt.label == 'Seed':

+            if axis_opt.label in ['Seed','Var. seed']:

                 return [int(random.randrange(4294967294)) if val is None or val == '' or val == -1 else val for val in axis_list]

             else:

                 return axis_list

@@ -234,6 +354,9 @@ class Script(scripts.Script):
         else:

             total_steps = p.steps * len(xs) * len(ys)

 

+        if isinstance(p, StableDiffusionProcessingTxt2Img) and p.enable_hr:

+            total_steps *= 2

+

         print(f"X/Y plot will create {len(xs) * len(ys) * p.n_iter} images on a {len(xs)}x{len(ys)} grid. (Total steps to process: {total_steps * p.n_iter})")

         shared.total_tqdm.updateTotal(total_steps * p.n_iter)

 

@@ -251,7 +374,8 @@ class Script(scripts.Script):
             x_labels=[x_opt.format_value(p, x_opt, x) for x in xs],

             y_labels=[y_opt.format_value(p, y_opt, y) for y in ys],

             cell=cell,

-            draw_legend=draw_legend

+            draw_legend=draw_legend,

+            include_lone_images=include_lone_images

         )

 

         if opts.grid_save:

@@ -260,4 +384,10 @@ class Script(scripts.Script):
         # restore checkpoint in case it was changed by axes

         modules.sd_models.reload_model_weights(shared.sd_model)

 

+        hypernetwork.load_hypernetwork(opts.sd_hypernetwork)

+        hypernetwork.apply_strength()

+

+

+        opts.data["CLIP_stop_at_last_layers"] = CLIP_stop_at_last_layers

+

         return processed