chore: Fix typos

This commit is contained in:
Aidan Holland 2022-10-08 15:12:24 -04:00 committed by AUTOMATIC1111
parent 610a7f4e14
commit 432782163a
10 changed files with 17 additions and 17 deletions

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@ -34,7 +34,7 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web
- Sampling method selection
- Interrupt processing at any time
- 4GB video card support (also reports of 2GB working)
- Correct seeds for batches
- Correct seeds for batches
- Prompt length validation
- get length of prompt in tokens as you type
- get a warning after generation if some text was truncated

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@ -95,7 +95,7 @@ function showGalleryImage(){
e.addEventListener('click', function (evt) {
if(!opts.js_modal_lightbox) return;
modalZoomSet(gradioApp().getElementById('modalImage'), opts.js_modal_lightbox_initialy_zoomed)
modalZoomSet(gradioApp().getElementById('modalImage'), opts.js_modal_lightbox_initially_zoomed)
showModal(evt)
},true);
}

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@ -140,11 +140,11 @@ class InterrogateModels:
res = caption
cilp_image = self.clip_preprocess(pil_image).unsqueeze(0).type(self.dtype).to(shared.device)
clip_image = self.clip_preprocess(pil_image).unsqueeze(0).type(self.dtype).to(shared.device)
precision_scope = torch.autocast if shared.cmd_opts.precision == "autocast" else contextlib.nullcontext
with torch.no_grad(), precision_scope("cuda"):
image_features = self.clip_model.encode_image(cilp_image).type(self.dtype)
image_features = self.clip_model.encode_image(clip_image).type(self.dtype)
image_features /= image_features.norm(dim=-1, keepdim=True)

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@ -386,7 +386,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
if state.interrupted or state.skipped:
# if we are interruped, sample returns just noise
# if we are interrupted, sample returns just noise
# use the image collected previously in sampler loop
samples_ddim = shared.state.current_latent

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@ -40,7 +40,7 @@ class WMSA(nn.Module):
Returns:
attn_mask: should be (1 1 w p p),
"""
# supporting sqaure.
# supporting square.
attn_mask = torch.zeros(h, w, p, p, p, p, dtype=torch.bool, device=self.relative_position_params.device)
if self.type == 'W':
return attn_mask
@ -65,7 +65,7 @@ class WMSA(nn.Module):
x = rearrange(x, 'b (w1 p1) (w2 p2) c -> b w1 w2 p1 p2 c', p1=self.window_size, p2=self.window_size)
h_windows = x.size(1)
w_windows = x.size(2)
# sqaure validation
# square validation
# assert h_windows == w_windows
x = rearrange(x, 'b w1 w2 p1 p2 c -> b (w1 w2) (p1 p2) c', p1=self.window_size, p2=self.window_size)

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@ -147,7 +147,7 @@ def load_model_weights(model, checkpoint_file, sd_model_hash):
model.first_stage_model.load_state_dict(vae_dict)
model.sd_model_hash = sd_model_hash
model.sd_model_checkpint = checkpoint_file
model.sd_model_checkpoint = checkpoint_file
def load_model():
@ -175,7 +175,7 @@ def reload_model_weights(sd_model, info=None):
from modules import lowvram, devices, sd_hijack
checkpoint_info = info or select_checkpoint()
if sd_model.sd_model_checkpint == checkpoint_info.filename:
if sd_model.sd_model_checkpoint == checkpoint_info.filename:
return
if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:

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@ -181,7 +181,7 @@ class VanillaStableDiffusionSampler:
self.initialize(p)
# existing code fails with cetain step counts, like 9
# existing code fails with certain step counts, like 9
try:
self.sampler.make_schedule(ddim_num_steps=steps, ddim_eta=self.eta, ddim_discretize=p.ddim_discretize, verbose=False)
except Exception:
@ -204,7 +204,7 @@ class VanillaStableDiffusionSampler:
steps = steps or p.steps
# existing code fails with cetin step counts, like 9
# existing code fails with certain step counts, like 9
try:
samples_ddim, _ = self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)
except Exception:

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@ -141,9 +141,9 @@ class OptionInfo:
self.section = None
def options_section(section_identifer, options_dict):
def options_section(section_identifier, options_dict):
for k, v in options_dict.items():
v.section = section_identifer
v.section = section_identifier
return options_dict
@ -246,7 +246,7 @@ options_templates.update(options_section(('ui', "User interface"), {
"add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"),
"font": OptionInfo("", "Font for image grids that have text"),
"js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
"js_modal_lightbox_initialy_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
"js_modal_lightbox_initially_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
"show_progress_in_title": OptionInfo(True, "Show generation progress in window title."),
}))

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@ -166,7 +166,7 @@ class SwinTransformerBlock(nn.Module):
Args:
dim (int): Number of input channels.
input_resolution (tuple[int]): Input resulotion.
input_resolution (tuple[int]): Input resolution.
num_heads (int): Number of attention heads.
window_size (int): Window size.
shift_size (int): Shift size for SW-MSA.

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@ -38,7 +38,7 @@ from modules import prompt_parser
from modules.images import save_image
import modules.textual_inversion.ui
# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the bowser will not show any UI
# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI
mimetypes.init()
mimetypes.add_type('application/javascript', '.js')
@ -102,7 +102,7 @@ def save_files(js_data, images, index):
import csv
filenames = []
#quick dictionary to class object conversion. Its neccesary due apply_filename_pattern requiring it
#quick dictionary to class object conversion. Its necessary due apply_filename_pattern requiring it
class MyObject:
def __init__(self, d=None):
if d is not None: