test

2 files changed, 55 insertions, 36 deletions

diff --git a/scripts/img2imgalt.py b/scripts/img2imgalt.py
index f9894cb0..313a55d2 100644
--- a/scripts/img2imgalt.py
+++ b/scripts/img2imgalt.py
@@ -129,8 +129,6 @@ class Script(scripts.Script):
         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

 

 

         def sample_extra(conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):

@@ -154,7 +152,7 @@ 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)

             

diff --git a/scripts/xy_grid.py b/scripts/xy_grid.py
index cddb192a..3bb080bf 100644
--- a/scripts/xy_grid.py
+++ b/scripts/xy_grid.py
@@ -77,14 +77,42 @@ 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)

 

 

+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 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):

@@ -121,29 +149,29 @@ 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),

-    AxisOption("Clip skip", int, apply_clip_skip, 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("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

 ]

 

 

@@ -197,7 +225,7 @@ 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)

@@ -269,17 +297,10 @@ 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