diff options
Diffstat (limited to 'scripts')
| -rw-r--r-- | scripts/img2imgalt.py | 36 | ||||
| -rw-r--r-- | scripts/xy_grid.py | 68 |
2 files changed, 82 insertions, 22 deletions
diff --git a/scripts/img2imgalt.py b/scripts/img2imgalt.py index 313a55d2..d438175c 100644 --- a/scripts/img2imgalt.py +++ b/scripts/img2imgalt.py @@ -120,15 +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):
+ 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):
diff --git a/scripts/xy_grid.py b/scripts/xy_grid.py index 3bb080bf..8c7da6bb 100644 --- a/scripts/xy_grid.py +++ b/scripts/xy_grid.py @@ -107,6 +107,10 @@ def apply_hypernetwork(p, x, xs): 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"]:
@@ -165,23 +169,28 @@ axis_options = [ 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),
- AxisOptionImg2Img("Denoising", float, apply_field("denoising_strength"), format_value_add_label, None), # as it is now all AxisOptionImg2Img items must go after AxisOption ones
+ 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
@@ -189,22 +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_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)
- first_processed.images = [grid]
+ processed_result.images[0] = grid
- return first_processed
+ return processed_result
re_range = re.compile(r"\s*([+-]?\s*\d+)\s*-\s*([+-]?\s*\d+)(?:\s*\(([+-]\d+)\s*\))?\s*")
@@ -229,11 +255,12 @@ class Script(scripts.Script): 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)
@@ -311,7 +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
@@ -344,7 +371,8 @@ 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:
@@ -354,6 +382,8 @@ class Script(scripts.Script): 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
|