diff options
Diffstat (limited to 'scripts/xy_grid.py')
| -rw-r--r-- | scripts/xy_grid.py | 206 |
1 files changed, 140 insertions, 66 deletions
diff --git a/scripts/xy_grid.py b/scripts/xy_grid.py index c89ca1a9..eff0c942 100644 --- a/scripts/xy_grid.py +++ b/scripts/xy_grid.py @@ -10,8 +10,9 @@ import numpy as np import modules.scripts as scripts
import gradio as gr
-from modules import images, hypernetwork
-from modules.processing import process_images, Processed, get_correct_sampler
+from modules import images
+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
@@ -27,6 +28,9 @@ 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)
@@ -73,14 +77,51 @@ def apply_sampler(p, x, xs): 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)
+ p.sd_model = shared.sd_model
+
+
+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):
- hypernetwork.load_hypernetwork(x)
+ 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):
@@ -113,38 +154,44 @@ def str_permutations(x): return x
-AxisOption = namedtuple("AxisOption", ["label", "type", "apply", "format_value"])
-AxisOptionImg2Img = namedtuple("AxisOptionImg2Img", ["label", "type", "apply", "format_value"])
+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("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),
- 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_processed = 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
@@ -152,22 +199,54 @@ 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_processed is None:
- first_processed = 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)
+
+ processed_result.images[0] = grid
+
+ return processed_result
+
- first_processed.images = [grid]
+class SharedSettingsStackHelper(object):
+ def __enter__(self):
+ self.CLIP_stop_at_last_layers = opts.CLIP_stop_at_last_layers
+ self.hypernetwork = opts.sd_hypernetwork
+ self.model = shared.sd_model
+
+ def __exit__(self, exc_type, exc_value, tb):
+ modules.sd_models.reload_model_weights(self.model)
- return first_processed
+ hypernetwork.load_hypernetwork(self.hypernetwork)
+ hypernetwork.apply_strength()
+
+ opts.data["CLIP_stop_at_last_layers"] = self.CLIP_stop_at_last_layers
re_range = re.compile(r"\s*([+-]?\s*\d+)\s*-\s*([+-]?\s*\d+)(?:\s*\(([+-]\d+)\s*\))?\s*")
@@ -188,19 +267,21 @@ 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, 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)
- p.batch_size = 1
+ if not opts.return_grid:
+ p.batch_size = 1
def process_axis(opt, vals):
if opt.label == 'Nothing':
@@ -256,17 +337,10 @@ class Script(scripts.Script): valslist = list(permutations(valslist))
valslist = [opt.type(x) for x in valslist]
-
+
# Confirm options are valid before starting
- if opt.label == "Sampler":
- samplers_dict = build_samplers_dict(p)
- for sampler_val in valslist:
- if sampler_val.lower() not in samplers_dict.keys():
- raise RuntimeError(f"Unknown sampler: {sampler_val}")
- elif opt.label == "Checkpoint name":
- for ckpt_val in valslist:
- if modules.sd_models.get_closet_checkpoint_match(ckpt_val) is None:
- raise RuntimeError(f"Checkpoint for {ckpt_val} not found")
+ if opt.confirm:
+ opt.confirm(p, valslist)
return valslist
@@ -277,7 +351,7 @@ 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
@@ -293,6 +367,9 @@ 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)
@@ -303,22 +380,19 @@ class Script(scripts.Script): return process_images(pc)
- processed = draw_xy_grid(
- p,
- xs=xs,
- ys=ys,
- 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
- )
+ with SharedSettingsStackHelper():
+ processed = draw_xy_grid(
+ p,
+ xs=xs,
+ ys=ys,
+ 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,
+ include_lone_images=include_lone_images
+ )
if opts.grid_save:
images.save_image(processed.images[0], p.outpath_grids, "xy_grid", prompt=p.prompt, seed=processed.seed, grid=True, p=p)
- # restore checkpoint in case it was changed by axes
- modules.sd_models.reload_model_weights(shared.sd_model)
-
- hypernetwork.load_hypernetwork(opts.sd_hypernetwork)
-
return processed
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