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-rw-r--r--javascript/hints.js2
-rw-r--r--modules/api/api.py7
-rw-r--r--modules/devices.py15
-rw-r--r--modules/generation_parameters_copypaste.py4
-rw-r--r--modules/hypernetworks/hypernetwork.py7
-rw-r--r--modules/img2img.py30
-rw-r--r--modules/interrogate.py3
-rw-r--r--modules/processing.py4
-rw-r--r--modules/safe.py18
-rw-r--r--modules/sd_hijack.py7
-rw-r--r--modules/sd_samplers.py22
-rw-r--r--modules/shared.py3
-rw-r--r--modules/swinir_model.py6
-rw-r--r--modules/textual_inversion/autocrop.py6
-rw-r--r--modules/textual_inversion/dataset.py4
-rw-r--r--modules/textual_inversion/textual_inversion.py5
-rw-r--r--modules/ui.py15
-rw-r--r--modules/ui_extensions.py2
18 files changed, 103 insertions, 57 deletions
diff --git a/javascript/hints.js b/javascript/hints.js
index ac417ff6..57db35be 100644
--- a/javascript/hints.js
+++ b/javascript/hints.js
@@ -94,6 +94,8 @@ titles = {
"Add difference": "Result = A + (B - C) * M",
"Learning rate": "how fast should the training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG: 0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.",
+
+ "Clip skip": "Early stopping parameter for CLIP model; 1 is stop at last layer as usual, 2 is stop at penultimate layer, etc."
}
diff --git a/modules/api/api.py b/modules/api/api.py
index 1de3f98f..54ee7cb0 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -152,7 +152,10 @@ class Api:
)
if populate.sampler_name:
populate.sampler_index = None # prevent a warning later on
- p = StableDiffusionProcessingImg2Img(**vars(populate))
+
+ args = vars(populate)
+ args.pop('include_init_images', None) # this is meant to be done by "exclude": True in model, but it's for a reason that I cannot determine.
+ p = StableDiffusionProcessingImg2Img(**args)
imgs = []
for img in init_images:
@@ -170,7 +173,7 @@ class Api:
b64images = list(map(encode_pil_to_base64, processed.images))
- if (not img2imgreq.include_init_images):
+ if not img2imgreq.include_init_images:
img2imgreq.init_images = None
img2imgreq.mask = None
diff --git a/modules/devices.py b/modules/devices.py
index f00079c6..046460fa 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -66,24 +66,15 @@ dtype_vae = torch.float16
def randn(seed, shape):
- # Pytorch currently doesn't handle setting randomness correctly when the metal backend is used.
- if device.type == 'mps':
- generator = torch.Generator(device=cpu)
- generator.manual_seed(seed)
- noise = torch.randn(shape, generator=generator, device=cpu).to(device)
- return noise
-
torch.manual_seed(seed)
+ if device.type == 'mps':
+ return torch.randn(shape, device=cpu).to(device)
return torch.randn(shape, device=device)
def randn_without_seed(shape):
- # Pytorch currently doesn't handle setting randomness correctly when the metal backend is used.
if device.type == 'mps':
- generator = torch.Generator(device=cpu)
- noise = torch.randn(shape, generator=generator, device=cpu).to(device)
- return noise
-
+ return torch.randn(shape, device=cpu).to(device)
return torch.randn(shape, device=device)
diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index 01980dca..44fe1a6c 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -184,6 +184,10 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
else:
res[k] = v
+ # Missing CLIP skip means it was set to 1 (the default)
+ if "Clip skip" not in res:
+ res["Clip skip"] = "1"
+
return res
diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 8466887f..c406ffb3 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -433,7 +433,10 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step,
dl = modules.textual_inversion.dataset.PersonalizedDataLoader(ds, latent_sampling_method=latent_sampling_method, batch_size=ds.batch_size, pin_memory=pin_memory)
+ old_parallel_processing_allowed = shared.parallel_processing_allowed
+
if unload:
+ shared.parallel_processing_allowed = False
shared.sd_model.cond_stage_model.to(devices.cpu)
shared.sd_model.first_stage_model.to(devices.cpu)
@@ -495,7 +498,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step,
if shared.state.interrupted:
break
- with torch.autocast("cuda"):
+ with devices.autocast():
x = batch.latent_sample.to(devices.device, non_blocking=pin_memory)
if tag_drop_out != 0 or shuffle_tags:
shared.sd_model.cond_stage_model.to(devices.device)
@@ -612,10 +615,12 @@ Last saved image: {html.escape(last_saved_image)}<br/>
if shared.opts.save_optimizer_state:
hypernetwork.optimizer_state_dict = optimizer.state_dict()
save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename)
+
del optimizer
hypernetwork.optimizer_state_dict = None # dereference it after saving, to save memory.
shared.sd_model.cond_stage_model.to(devices.device)
shared.sd_model.first_stage_model.to(devices.device)
+ shared.parallel_processing_allowed = old_parallel_processing_allowed
return hypernetwork, filename
diff --git a/modules/img2img.py b/modules/img2img.py
index 7e58994a..830cfa15 100644
--- a/modules/img2img.py
+++ b/modules/img2img.py
@@ -4,7 +4,7 @@ import sys
import traceback
import numpy as np
-from PIL import Image, ImageOps, ImageChops
+from PIL import Image, ImageOps, ImageFilter, ImageEnhance
from modules import devices, sd_samplers
from modules.processing import Processed, StableDiffusionProcessingImg2Img, process_images
@@ -40,7 +40,7 @@ def process_batch(p, input_dir, output_dir, args):
img = Image.open(image)
# Use the EXIF orientation of photos taken by smartphones.
- img = ImageOps.exif_transpose(img)
+ img = ImageOps.exif_transpose(img)
p.init_images = [img] * p.batch_size
proc = modules.scripts.scripts_img2img.run(p, *args)
@@ -59,18 +59,30 @@ def process_batch(p, input_dir, output_dir, args):
processed_image.save(os.path.join(output_dir, filename))
-def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, init_img, init_img_with_mask, init_img_inpaint, init_mask_inpaint, mask_mode, steps: int, sampler_index: int, mask_blur: int, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, *args):
+def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, init_img, init_img_with_mask, init_img_with_mask_orig, init_img_inpaint, init_mask_inpaint, mask_mode, steps: int, sampler_index: int, mask_blur: int, mask_alpha: float, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, *args):
is_inpaint = mode == 1
is_batch = mode == 2
if is_inpaint:
# Drawn mask
if mask_mode == 0:
- image = init_img_with_mask['image']
- mask = init_img_with_mask['mask']
- alpha_mask = ImageOps.invert(image.split()[-1]).convert('L').point(lambda x: 255 if x > 0 else 0, mode='1')
- mask = ImageChops.lighter(alpha_mask, mask.convert('L')).convert('L')
- image = image.convert('RGB')
+ image = init_img_with_mask
+ is_mask_sketch = isinstance(image, dict)
+ is_mask_paint = not is_mask_sketch
+ if is_mask_sketch:
+ # Sketch: mask iff. not transparent
+ image, mask = image["image"], image["mask"]
+ pred = np.array(mask)[..., -1] > 0
+ else:
+ # Color-sketch: mask iff. painted over
+ orig = init_img_with_mask_orig or image
+ pred = np.any(np.array(image) != np.array(orig), axis=-1)
+ mask = Image.fromarray(pred.astype(np.uint8) * 255, "L")
+ if is_mask_paint:
+ mask = ImageEnhance.Brightness(mask).enhance(1 - mask_alpha / 100)
+ blur = ImageFilter.GaussianBlur(mask_blur)
+ image = Image.composite(image.filter(blur), orig, mask.filter(blur))
+ image = image.convert("RGB")
# Uploaded mask
else:
image = init_img_inpaint
@@ -82,7 +94,7 @@ def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, pro
# Use the EXIF orientation of photos taken by smartphones.
if image is not None:
- image = ImageOps.exif_transpose(image)
+ image = ImageOps.exif_transpose(image)
assert 0. <= denoising_strength <= 1., 'can only work with strength in [0.0, 1.0]'
diff --git a/modules/interrogate.py b/modules/interrogate.py
index 9769aa34..40c6b082 100644
--- a/modules/interrogate.py
+++ b/modules/interrogate.py
@@ -148,8 +148,7 @@ class InterrogateModels:
clip_image = self.clip_preprocess(pil_image).unsqueeze(0).type(self.dtype).to(devices.device_interrogate)
- precision_scope = torch.autocast if shared.cmd_opts.precision == "autocast" else contextlib.nullcontext
- with torch.no_grad(), precision_scope("cuda"):
+ with torch.no_grad(), devices.autocast():
image_features = self.clip_model.encode_image(clip_image).type(self.dtype)
image_features /= image_features.norm(dim=-1, keepdim=True)
diff --git a/modules/processing.py b/modules/processing.py
index 0a73ccbb..3d2c4dc9 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -530,8 +530,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
with devices.autocast():
samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, prompts=prompts)
- samples_ddim = samples_ddim.to(devices.dtype_vae)
- x_samples_ddim = decode_first_stage(p.sd_model, samples_ddim)
+ x_samples_ddim = [decode_first_stage(p.sd_model, samples_ddim[i:i+1].to(dtype=devices.dtype_vae))[0].cpu() for i in range(samples_ddim.size(0))]
+ x_samples_ddim = torch.stack(x_samples_ddim).float()
x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
del samples_ddim
diff --git a/modules/safe.py b/modules/safe.py
index a9209e38..10460ad0 100644
--- a/modules/safe.py
+++ b/modules/safe.py
@@ -62,14 +62,12 @@ class RestrictedUnpickler(pickle.Unpickler):
raise Exception(f"global '{module}/{name}' is forbidden")
-allowed_zip_names = ["archive/data.pkl", "archive/version"]
-allowed_zip_names_re = re.compile(r"^archive/data/\d+$")
-
+# Regular expression that accepts 'dirname/version', 'dirname/data.pkl', and 'dirname/data/<number>'
+allowed_zip_names_re = re.compile(r"^([^/]+)/((data/\d+)|version|(data\.pkl))$")
+data_pkl_re = re.compile(r"^([^/]+)/data\.pkl$")
def check_zip_filenames(filename, names):
for name in names:
- if name in allowed_zip_names:
- continue
if allowed_zip_names_re.match(name):
continue
@@ -82,8 +80,14 @@ def check_pt(filename, extra_handler):
# new pytorch format is a zip file
with zipfile.ZipFile(filename) as z:
check_zip_filenames(filename, z.namelist())
-
- with z.open('archive/data.pkl') as file:
+
+ # find filename of data.pkl in zip file: '<directory name>/data.pkl'
+ data_pkl_filenames = [f for f in z.namelist() if data_pkl_re.match(f)]
+ if len(data_pkl_filenames) == 0:
+ raise Exception(f"data.pkl not found in {filename}")
+ if len(data_pkl_filenames) > 1:
+ raise Exception(f"Multiple data.pkl found in {filename}")
+ with z.open(data_pkl_filenames[0]) as file:
unpickler = RestrictedUnpickler(file)
unpickler.extra_handler = extra_handler
unpickler.load()
diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index b824b5bf..95a17093 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -17,6 +17,7 @@ from modules.sd_hijack_optimizations import invokeAI_mps_available
import ldm.modules.attention
import ldm.modules.diffusionmodules.model
+import ldm.modules.diffusionmodules.openaimodel
import ldm.models.diffusion.ddim
import ldm.models.diffusion.plms
import ldm.modules.encoders.modules
@@ -182,11 +183,7 @@ def register_buffer(self, name, attr):
if type(attr) == torch.Tensor:
if attr.device != devices.device:
-
- if devices.has_mps():
- attr = attr.to(device="mps", dtype=torch.float32)
- else:
- attr = attr.to(devices.device)
+ attr = attr.to(device=devices.device, dtype=(torch.float32 if devices.device.type == 'mps' else None))
setattr(self, name, attr)
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 5fefb227..4c123d3b 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -6,6 +6,7 @@ import tqdm
from PIL import Image
import inspect
import k_diffusion.sampling
+import torchsde._brownian.brownian_interval
import ldm.models.diffusion.ddim
import ldm.models.diffusion.plms
from modules import prompt_parser, devices, processing, images
@@ -364,7 +365,23 @@ class TorchHijack:
if noise.shape == x.shape:
return noise
- return torch.randn_like(x)
+ if x.device.type == 'mps':
+ return torch.randn_like(x, device=devices.cpu).to(x.device)
+ else:
+ return torch.randn_like(x)
+
+
+# MPS fix for randn in torchsde
+def torchsde_randn(size, dtype, device, seed):
+ if device.type == 'mps':
+ generator = torch.Generator(devices.cpu).manual_seed(int(seed))
+ return torch.randn(size, dtype=dtype, device=devices.cpu, generator=generator).to(device)
+ else:
+ generator = torch.Generator(device).manual_seed(int(seed))
+ return torch.randn(size, dtype=dtype, device=device, generator=generator)
+
+
+torchsde._brownian.brownian_interval._randn = torchsde_randn
class KDiffusionSampler:
@@ -415,8 +432,7 @@ class KDiffusionSampler:
self.model_wrap.step = 0
self.eta = p.eta or opts.eta_ancestral
- if self.sampler_noises is not None:
- k_diffusion.sampling.torch = TorchHijack(self.sampler_noises)
+ k_diffusion.sampling.torch = TorchHijack(self.sampler_noises if self.sampler_noises is not None else [])
extra_params_kwargs = {}
for param_name in self.extra_params:
diff --git a/modules/shared.py b/modules/shared.py
index c36ee211..8202d8e5 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -72,6 +72,7 @@ parser.add_argument("--ui-settings-file", type=str, help="filename to use for ui
parser.add_argument("--gradio-debug", action='store_true', help="launch gradio with --debug option")
parser.add_argument("--gradio-auth", type=str, help='set gradio authentication like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None)
parser.add_argument("--gradio-img2img-tool", type=str, help='gradio image uploader tool: can be either editor for ctopping, or color-sketch for drawing', choices=["color-sketch", "editor"], default="editor")
+parser.add_argument("--gradio-inpaint-tool", type=str, choices=["sketch", "color-sketch"], default="sketch", help="gradio inpainting editor: can be either sketch to only blur/noise the input, or color-sketch to paint over it")
parser.add_argument("--opt-channelslast", action='store_true', help="change memory type for stable diffusion to channels last")
parser.add_argument("--styles-file", type=str, help="filename to use for styles", default=os.path.join(script_path, 'styles.csv'))
parser.add_argument("--autolaunch", action='store_true', help="open the webui URL in the system's default browser upon launch", default=False)
@@ -371,7 +372,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
"enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"),
"comma_padding_backtrack": OptionInfo(20, "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1 }),
"filter_nsfw": OptionInfo(False, "Filter NSFW content"),
- 'CLIP_stop_at_last_layers': OptionInfo(1, "Stop At last layers of CLIP model", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}),
+ 'CLIP_stop_at_last_layers': OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}),
"random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}),
}))
diff --git a/modules/swinir_model.py b/modules/swinir_model.py
index facd262d..483eabd4 100644
--- a/modules/swinir_model.py
+++ b/modules/swinir_model.py
@@ -13,10 +13,6 @@ from modules.swinir_model_arch import SwinIR as net
from modules.swinir_model_arch_v2 import Swin2SR as net2
from modules.upscaler import Upscaler, UpscalerData
-precision_scope = (
- torch.autocast if cmd_opts.precision == "autocast" else contextlib.nullcontext
-)
-
class UpscalerSwinIR(Upscaler):
def __init__(self, dirname):
@@ -112,7 +108,7 @@ def upscale(
img = np.moveaxis(img, 2, 0) / 255
img = torch.from_numpy(img).float()
img = img.unsqueeze(0).to(devices.device_swinir)
- with torch.no_grad(), precision_scope("cuda"):
+ with torch.no_grad(), devices.autocast():
_, _, h_old, w_old = img.size()
h_pad = (h_old // window_size + 1) * window_size - h_old
w_pad = (w_old // window_size + 1) * window_size - w_old
diff --git a/modules/textual_inversion/autocrop.py b/modules/textual_inversion/autocrop.py
index 9859974a..68e1103c 100644
--- a/modules/textual_inversion/autocrop.py
+++ b/modules/textual_inversion/autocrop.py
@@ -276,8 +276,8 @@ def poi_average(pois, settings):
weight += poi.weight
x += poi.x * poi.weight
y += poi.y * poi.weight
- avg_x = round(x / weight)
- avg_y = round(y / weight)
+ avg_x = round(weight and x / weight)
+ avg_y = round(weight and y / weight)
return PointOfInterest(avg_x, avg_y)
@@ -338,4 +338,4 @@ class Settings:
self.face_points_weight = face_points_weight
self.annotate_image = annotate_image
self.destop_view_image = False
- self.dnn_model_path = dnn_model_path \ No newline at end of file
+ self.dnn_model_path = dnn_model_path
diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index e5725f33..2dc64c3c 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -82,7 +82,7 @@ class PersonalizedBase(Dataset):
torchdata = torch.from_numpy(npimage).permute(2, 0, 1).to(device=device, dtype=torch.float32)
latent_sample = None
- with torch.autocast("cuda"):
+ with devices.autocast():
latent_dist = model.encode_first_stage(torchdata.unsqueeze(dim=0))
if latent_sampling_method == "once" or (latent_sampling_method == "deterministic" and not isinstance(latent_dist, DiagonalGaussianDistribution)):
@@ -101,7 +101,7 @@ class PersonalizedBase(Dataset):
entry.cond_text = self.create_text(filename_text)
if include_cond and not (self.tag_drop_out != 0 or self.shuffle_tags):
- with torch.autocast("cuda"):
+ with devices.autocast():
entry.cond = cond_model([entry.cond_text]).to(devices.cpu).squeeze(0)
self.dataset.append(entry)
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 4eb75cb5..e28c357a 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -269,6 +269,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
# dataset loading may take a while, so input validations and early returns should be done before this
shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
+ old_parallel_processing_allowed = shared.parallel_processing_allowed
pin_memory = shared.opts.pin_memory
@@ -279,6 +280,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
dl = modules.textual_inversion.dataset.PersonalizedDataLoader(ds, latent_sampling_method=latent_sampling_method, batch_size=ds.batch_size, pin_memory=pin_memory)
if unload:
+ shared.parallel_processing_allowed = False
shared.sd_model.first_stage_model.to(devices.cpu)
embedding.vec.requires_grad = True
@@ -316,7 +318,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
if shared.state.interrupted:
break
- with torch.autocast("cuda"):
+ with devices.autocast():
# c = stack_conds(batch.cond).to(devices.device)
# mask = torch.tensor(batch.emb_index).to(devices.device, non_blocking=pin_memory)
# print(mask)
@@ -450,6 +452,7 @@ Last saved image: {html.escape(last_saved_image)}<br/>
pbar.leave = False
pbar.close()
shared.sd_model.first_stage_model.to(devices.device)
+ shared.parallel_processing_allowed = old_parallel_processing_allowed
return embedding, filename
diff --git a/modules/ui.py b/modules/ui.py
index 00809361..2eb0b684 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -792,11 +792,22 @@ def create_ui():
init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool=cmd_opts.gradio_img2img_tool).style(height=480)
with gr.TabItem('Inpaint', id='inpaint'):
- init_img_with_mask = gr.Image(label="Image for inpainting with mask", show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool="sketch", image_mode="RGBA").style(height=480)
+ init_img_with_mask_orig = gr.State(None)
+ init_img_with_mask = gr.Image(label="Image for inpainting with mask", show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool=cmd_opts.gradio_inpaint_tool, image_mode="RGBA").style(height=480)
+ def update_orig(image, state):
+ if image is not None:
+ same_size = state is not None and state.size == image.size
+ has_exact_match = np.any(np.all(np.array(image) == np.array(state), axis=-1))
+ edited = same_size and has_exact_match
+ return image if not edited or state is None else state
+
+ init_img_with_mask.change(update_orig, [init_img_with_mask, init_img_with_mask_orig], init_img_with_mask_orig)
init_img_inpaint = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_base")
init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_mask")
+ show_mask_alpha = cmd_opts.gradio_inpaint_tool == "color-sketch"
+ mask_alpha = gr.Slider(label="Mask transparency", interactive=show_mask_alpha, visible=show_mask_alpha)
mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4)
with gr.Row():
@@ -884,12 +895,14 @@ def create_ui():
img2img_prompt_style2,
init_img,
init_img_with_mask,
+ init_img_with_mask_orig,
init_img_inpaint,
init_mask_inpaint,
mask_mode,
steps,
sampler_index,
mask_blur,
+ mask_alpha,
inpainting_fill,
restore_faces,
tiling,
diff --git a/modules/ui_extensions.py b/modules/ui_extensions.py
index 030f011e..42667941 100644
--- a/modules/ui_extensions.py
+++ b/modules/ui_extensions.py
@@ -17,7 +17,7 @@ available_extensions = {"extensions": []}
def check_access():
- assert not shared.cmd_opts.disable_extension_access, "extension access disabed because of commandline flags"
+ assert not shared.cmd_opts.disable_extension_access, "extension access disabled because of command line flags"
def apply_and_restart(disable_list, update_list):
generated by cgit v1.2.1 (git 2.18.0) at 2024-09-22 06:39:32 +0000