diff options
| -rw-r--r-- | modules/deepbooru.py | 4 | ||||
| -rw-r--r-- | modules/sd_models.py | 3 | ||||
| -rw-r--r-- | modules/sd_samplers.py | 17 | ||||
| -rw-r--r-- | modules/shared.py | 1 |
4 files changed, 15 insertions, 10 deletions
diff --git a/modules/deepbooru.py b/modules/deepbooru.py index dfc83357..122fce7f 100644 --- a/modules/deepbooru.py +++ b/modules/deepbooru.py @@ -79,7 +79,9 @@ class DeepDanbooru: res = [] - for tag in tags: + filtertags = set([x.strip().replace(' ', '_') for x in shared.opts.deepbooru_filter_tags.split(",")]) + + for tag in [x for x in tags if x not in filtertags]: probability = probability_dict[tag] tag_outformat = tag if use_spaces: diff --git a/modules/sd_models.py b/modules/sd_models.py index 1254e5ae..6ca06211 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -111,9 +111,6 @@ def model_hash(filename): def select_checkpoint():
model_checkpoint = shared.opts.sd_model_checkpoint
-
- if len(model_checkpoint) == 0:
- model_checkpoint = shared.default_sd_model_file
checkpoint_info = checkpoints_list.get(model_checkpoint, None)
if checkpoint_info is not None:
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index fbb56af4..27ef4ff8 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -297,6 +297,16 @@ class CFGDenoiser(torch.nn.Module): self.init_latent = None
self.step = 0
+ def combine_denoised(self, x_out, conds_list, uncond, cond_scale):
+ denoised_uncond = x_out[-uncond.shape[0]:]
+ denoised = torch.clone(denoised_uncond)
+
+ for i, conds in enumerate(conds_list):
+ for cond_index, weight in conds:
+ denoised[i] += (x_out[cond_index] - denoised_uncond[i]) * (weight * cond_scale)
+
+ return denoised
+
def forward(self, x, sigma, uncond, cond, cond_scale, image_cond):
if state.interrupted or state.skipped:
raise InterruptedException
@@ -338,12 +348,7 @@ class CFGDenoiser(torch.nn.Module): x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond={"c_crossattn": [uncond], "c_concat": [image_cond_in[-uncond.shape[0]:]]})
- denoised_uncond = x_out[-uncond.shape[0]:]
- denoised = torch.clone(denoised_uncond)
-
- for i, conds in enumerate(conds_list):
- for cond_index, weight in conds:
- denoised[i] += (x_out[cond_index] - denoised_uncond[i]) * (weight * cond_scale)
+ denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale)
if self.mask is not None:
denoised = self.init_latent * self.mask + self.nmask * denoised
diff --git a/modules/shared.py b/modules/shared.py index 1067b1d3..eb3e5aec 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -386,6 +386,7 @@ options_templates.update(options_section(('interrogate', "Interrogate Options"), "deepbooru_sort_alpha": OptionInfo(True, "Interrogate: deepbooru sort alphabetically"),
"deepbooru_use_spaces": OptionInfo(False, "use spaces for tags in deepbooru"),
"deepbooru_escape": OptionInfo(True, "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)"),
+ "deepbooru_filter_tags": OptionInfo("", "filter out those tags from deepbooru output (separated by comma)"),
}))
options_templates.update(options_section(('ui', "User interface"), {
|