diff options
Diffstat (limited to 'scripts')
| -rw-r--r-- | scripts/img2imgalt.py | 43 | ||||
| -rw-r--r-- | scripts/loopback.py | 4 | ||||
| -rw-r--r-- | scripts/outpainting_mk_2.py | 5 | ||||
| -rw-r--r-- | scripts/prompts_from_file.py | 4 | ||||
| -rw-r--r-- | scripts/sd_upscale.py | 6 | ||||
| -rw-r--r-- | scripts/xy_grid.py | 230 |
6 files changed, 231 insertions, 61 deletions
diff --git a/scripts/img2imgalt.py b/scripts/img2imgalt.py index 0ef137f7..d438175c 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
@@ -121,17 +120,45 @@ class Script(scripts.Script): return is_img2img
def ui(self, is_img2img):
+ info = gr.Markdown('''
+ * `CFG Scale` should be 2 or lower.
+ ''')
+
+ override_sampler = gr.Checkbox(label="Override `Sampling method` to Euler?(this method is built for it)", value=True)
+
+ override_prompt = gr.Checkbox(label="Override `prompt` to the same value as `original prompt`?(and `negative prompt`)", value=True)
original_prompt = gr.Textbox(label="Original prompt", lines=1)
original_negative_prompt = gr.Textbox(label="Original negative prompt", lines=1)
- cfg = gr.Slider(label="Decode CFG scale", minimum=0.0, maximum=15.0, step=0.1, value=1.0)
+
+ override_steps = gr.Checkbox(label="Override `Sampling Steps` to the same value as `Decode steps`?", value=True)
st = gr.Slider(label="Decode steps", minimum=1, maximum=150, step=1, value=50)
+
+ override_strength = gr.Checkbox(label="Override `Denoising strength` to 1?", value=True)
+
+ cfg = gr.Slider(label="Decode CFG scale", minimum=0.0, maximum=15.0, step=0.1, value=1.0)
randomness = gr.Slider(label="Randomness", minimum=0.0, maximum=1.0, step=0.01, value=0.0)
sigma_adjustment = gr.Checkbox(label="Sigma adjustment for finding noise for image", value=False)
- return [original_prompt, original_negative_prompt, cfg, st, randomness, sigma_adjustment]
- def run(self, p, original_prompt, original_negative_prompt, cfg, st, randomness, sigma_adjustment):
- p.batch_size = 1
- p.batch_count = 1
+ return [
+ info,
+ override_sampler,
+ override_prompt, original_prompt, original_negative_prompt,
+ override_steps, st,
+ override_strength,
+ cfg, randomness, sigma_adjustment,
+ ]
+
+ def run(self, p, _, override_sampler, override_prompt, original_prompt, original_negative_prompt, override_steps, st, override_strength, cfg, randomness, sigma_adjustment):
+ # Override
+ if override_sampler:
+ p.sampler_index = [sampler.name for sampler in sd_samplers.samplers].index("Euler")
+ if override_prompt:
+ p.prompt = original_prompt
+ p.negative_prompt = original_negative_prompt
+ if override_steps:
+ p.steps = st
+ if override_strength:
+ p.denoising_strength = 1.0
def sample_extra(conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
@@ -155,11 +182,11 @@ class Script(scripts.Script): rec_noise = find_noise_for_image(p, cond, uncond, cfg, st)
self.cache = Cached(rec_noise, cfg, st, lat, original_prompt, original_negative_prompt, sigma_adjustment)
- rand_noise = processing.create_random_tensors(p.init_latent.shape[1:], [p.seed + x + 1 for x in range(p.init_latent.shape[0])])
+ rand_noise = processing.create_random_tensors(p.init_latent.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, seed_resize_from_h=p.seed_resize_from_h, seed_resize_from_w=p.seed_resize_from_w, p=p)
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/loopback.py b/scripts/loopback.py index e90b58d4..d8c68af8 100644 --- a/scripts/loopback.py +++ b/scripts/loopback.py @@ -38,6 +38,7 @@ class Script(scripts.Script): grids = []
all_images = []
+ original_init_image = p.init_images
state.job_count = loops * batch_count
initial_color_corrections = [processing.setup_color_correction(p.init_images[0])]
@@ -45,6 +46,9 @@ class Script(scripts.Script): for n in range(batch_count):
history = []
+ # Reset to original init image at the start of each batch
+ p.init_images = original_init_image
+
for i in range(loops):
p.n_iter = 1
p.batch_size = 1
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/prompts_from_file.py b/scripts/prompts_from_file.py index 0a862a5b..5732623f 100644 --- a/scripts/prompts_from_file.py +++ b/scripts/prompts_from_file.py @@ -10,7 +10,6 @@ from modules.processing import Processed, process_images from PIL import Image
from modules.shared import opts, cmd_opts, state
-
class Script(scripts.Script):
def title(self):
return "Prompts from file or textbox"
@@ -67,6 +66,9 @@ class Script(scripts.Script): "do_not_save_grid": process_boolean_tag
}
+ def on_show(self, checkbox_txt, file, prompt_txt):
+ return [ gr.Checkbox.update(visible = True), gr.File.update(visible = not checkbox_txt), gr.TextArea.update(visible = checkbox_txt) ]
+
def run(self, p, checkbox_txt, data: bytes, prompt_txt: str):
if (checkbox_txt):
lines = [x.strip() for x in prompt_txt.splitlines()]
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..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
|