diff options
Diffstat (limited to 'modules')
| -rw-r--r-- | modules/deepbooru.py | 4 | ||||
| -rw-r--r-- | modules/sd_models.py | 3 | ||||
| -rw-r--r-- | modules/sd_samplers.py | 40 | ||||
| -rw-r--r-- | modules/shared.py | 6 |
4 files changed, 34 insertions, 19 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 d26e48dc..27ef4ff8 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -106,20 +106,29 @@ def setup_img2img_steps(p, steps=None): return steps, t_enc
-def single_sample_to_image(sample):
- x_sample = processing.decode_first_stage(shared.sd_model, sample.unsqueeze(0))[0]
+def single_sample_to_image(sample, approximation=False):
+ if approximation:
+ # https://discuss.huggingface.co/t/decoding-latents-to-rgb-without-upscaling/23204/2
+ coefs = torch.tensor(
+ [[ 0.298, 0.207, 0.208],
+ [ 0.187, 0.286, 0.173],
+ [-0.158, 0.189, 0.264],
+ [-0.184, -0.271, -0.473]]).to(sample.device)
+ x_sample = torch.einsum("lxy,lr -> rxy", sample, coefs)
+ else:
+ x_sample = processing.decode_first_stage(shared.sd_model, sample.unsqueeze(0))[0]
x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0)
x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
x_sample = x_sample.astype(np.uint8)
return Image.fromarray(x_sample)
-def sample_to_image(samples, index=0):
- return single_sample_to_image(samples[index])
+def sample_to_image(samples, index=0, approximation=False):
+ return single_sample_to_image(samples[index], approximation)
-def samples_to_image_grid(samples):
- return images.image_grid([single_sample_to_image(sample) for sample in samples])
+def samples_to_image_grid(samples, approximation=False):
+ return images.image_grid([single_sample_to_image(sample, approximation) for sample in samples])
def store_latent(decoded):
@@ -127,7 +136,7 @@ def store_latent(decoded): if opts.show_progress_every_n_steps > 0 and shared.state.sampling_step % opts.show_progress_every_n_steps == 0:
if not shared.parallel_processing_allowed:
- shared.state.current_image = sample_to_image(decoded)
+ shared.state.current_image = sample_to_image(decoded, approximation=opts.show_progress_approximate)
class InterruptedException(BaseException):
@@ -288,6 +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
@@ -329,12 +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 8ea3b441..eb3e5aec 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -212,9 +212,9 @@ class State: import modules.sd_samplers
if opts.show_progress_grid:
- self.current_image = modules.sd_samplers.samples_to_image_grid(self.current_latent)
+ self.current_image = modules.sd_samplers.samples_to_image_grid(self.current_latent, approximation=opts.show_progress_approximate)
else:
- self.current_image = modules.sd_samplers.sample_to_image(self.current_latent)
+ self.current_image = modules.sd_samplers.sample_to_image(self.current_latent, approximation=opts.show_progress_approximate)
self.current_image_sampling_step = self.sampling_step
@@ -386,11 +386,13 @@ 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"), {
"show_progressbar": OptionInfo(True, "Show progressbar"),
"show_progress_every_n_steps": OptionInfo(0, "Show image creation progress every N sampling steps. Set to 0 to disable. Set to -1 to show after completion of batch.", gr.Slider, {"minimum": -1, "maximum": 32, "step": 1}),
+ "show_progress_approximate": OptionInfo(False, "Calculate small previews using fast linear approximation instead of VAE"),
"show_progress_grid": OptionInfo(True, "Show previews of all images generated in a batch as a grid"),
"return_grid": OptionInfo(True, "Show grid in results for web"),
"do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),
|