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-rw-r--r--modules/api/api.py15
-rw-r--r--modules/api/models.py3
-rw-r--r--modules/postprocessing.py92
-rw-r--r--modules/scripts_postprocessing.py86
-rw-r--r--modules/shared_options.py1
-rw-r--r--modules/textual_inversion/preprocess.py232
-rw-r--r--modules/textual_inversion/ui.py7
-rw-r--r--modules/ui.py107
-rw-r--r--modules/ui_postprocessing.py16
-rw-r--r--modules/ui_toprow.py6
10 files changed, 166 insertions, 399 deletions
diff --git a/modules/api/api.py b/modules/api/api.py
index 09083874..b3d74e51 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -22,7 +22,6 @@ from modules.api import models
from modules.shared import opts
from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
from modules.textual_inversion.textual_inversion import create_embedding, train_embedding
-from modules.textual_inversion.preprocess import preprocess
from modules.hypernetworks.hypernetwork import create_hypernetwork, train_hypernetwork
from PIL import PngImagePlugin, Image
from modules.sd_models_config import find_checkpoint_config_near_filename
@@ -235,7 +234,6 @@ class Api:
self.add_api_route("/sdapi/v1/refresh-vae", self.refresh_vae, methods=["POST"])
self.add_api_route("/sdapi/v1/create/embedding", self.create_embedding, methods=["POST"], response_model=models.CreateResponse)
self.add_api_route("/sdapi/v1/create/hypernetwork", self.create_hypernetwork, methods=["POST"], response_model=models.CreateResponse)
- self.add_api_route("/sdapi/v1/preprocess", self.preprocess, methods=["POST"], response_model=models.PreprocessResponse)
self.add_api_route("/sdapi/v1/train/embedding", self.train_embedding, methods=["POST"], response_model=models.TrainResponse)
self.add_api_route("/sdapi/v1/train/hypernetwork", self.train_hypernetwork, methods=["POST"], response_model=models.TrainResponse)
self.add_api_route("/sdapi/v1/memory", self.get_memory, methods=["GET"], response_model=models.MemoryResponse)
@@ -675,19 +673,6 @@ class Api:
finally:
shared.state.end()
- def preprocess(self, args: dict):
- try:
- shared.state.begin(job="preprocess")
- preprocess(**args) # quick operation unless blip/booru interrogation is enabled
- shared.state.end()
- return models.PreprocessResponse(info='preprocess complete')
- except KeyError as e:
- return models.PreprocessResponse(info=f"preprocess error: invalid token: {e}")
- except Exception as e:
- return models.PreprocessResponse(info=f"preprocess error: {e}")
- finally:
- shared.state.end()
-
def train_embedding(self, args: dict):
try:
shared.state.begin(job="train_embedding")
diff --git a/modules/api/models.py b/modules/api/models.py
index a0d80af8..33894b3e 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -202,9 +202,6 @@ class TrainResponse(BaseModel):
class CreateResponse(BaseModel):
info: str = Field(title="Create info", description="Response string from create embedding or hypernetwork task.")
-class PreprocessResponse(BaseModel):
- info: str = Field(title="Preprocess info", description="Response string from preprocessing task.")
-
fields = {}
for key, metadata in opts.data_labels.items():
value = opts.data.get(key)
diff --git a/modules/postprocessing.py b/modules/postprocessing.py
index 0a134ee4..3c85a74c 100644
--- a/modules/postprocessing.py
+++ b/modules/postprocessing.py
@@ -6,7 +6,7 @@ from modules import shared, images, devices, scripts, scripts_postprocessing, ui
from modules.shared import opts
-def run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir, show_extras_results, *args, save_output: bool = True):
+def run_postprocessing(id_task, extras_mode, image, image_folder, input_dir, output_dir, show_extras_results, *args, save_output: bool = True):
devices.torch_gc()
shared.state.begin(job="extras")
@@ -29,11 +29,7 @@ def run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir,
image_list = shared.listfiles(input_dir)
for filename in image_list:
- try:
- image = Image.open(filename)
- except Exception:
- continue
- yield image, filename
+ yield filename, filename
else:
assert image, 'image not selected'
yield image, None
@@ -45,37 +41,85 @@ def run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir,
infotext = ''
- for image_data, name in get_images(extras_mode, image, image_folder, input_dir):
+ data_to_process = list(get_images(extras_mode, image, image_folder, input_dir))
+ shared.state.job_count = len(data_to_process)
+
+ for image_placeholder, name in data_to_process:
image_data: Image.Image
+ shared.state.nextjob()
shared.state.textinfo = name
+ shared.state.skipped = False
+
+ if shared.state.interrupted:
+ break
+
+ if isinstance(image_placeholder, str):
+ try:
+ image_data = Image.open(image_placeholder)
+ except Exception:
+ continue
+ else:
+ image_data = image_placeholder
+
+ shared.state.assign_current_image(image_data)
parameters, existing_pnginfo = images.read_info_from_image(image_data)
if parameters:
existing_pnginfo["parameters"] = parameters
- pp = scripts_postprocessing.PostprocessedImage(image_data.convert("RGB"))
+ initial_pp = scripts_postprocessing.PostprocessedImage(image_data.convert("RGB"))
- scripts.scripts_postproc.run(pp, args)
+ scripts.scripts_postproc.run(initial_pp, args)
- if opts.use_original_name_batch and name is not None:
- basename = os.path.splitext(os.path.basename(name))[0]
- forced_filename = basename
- else:
- basename = ''
- forced_filename = None
+ if shared.state.skipped:
+ continue
+
+ used_suffixes = {}
+ for pp in [initial_pp, *initial_pp.extra_images]:
+ suffix = pp.get_suffix(used_suffixes)
+
+ if opts.use_original_name_batch and name is not None:
+ basename = os.path.splitext(os.path.basename(name))[0]
+ forced_filename = basename + suffix
+ else:
+ basename = ''
+ forced_filename = None
+
+ infotext = ", ".join([k if k == v else f'{k}: {generation_parameters_copypaste.quote(v)}' for k, v in pp.info.items() if v is not None])
+
+ if opts.enable_pnginfo:
+ pp.image.info = existing_pnginfo
+ pp.image.info["postprocessing"] = infotext
+
+ if save_output:
+ fullfn, _ = images.save_image(pp.image, path=outpath, basename=basename, extension=opts.samples_format, info=infotext, short_filename=True, no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=forced_filename, suffix=suffix)
- infotext = ", ".join([k if k == v else f'{k}: {generation_parameters_copypaste.quote(v)}' for k, v in pp.info.items() if v is not None])
+ if pp.caption:
+ caption_filename = os.path.splitext(fullfn)[0] + ".txt"
+ if os.path.isfile(caption_filename):
+ with open(caption_filename, encoding="utf8") as file:
+ existing_caption = file.read().strip()
+ else:
+ existing_caption = ""
- if opts.enable_pnginfo:
- pp.image.info = existing_pnginfo
- pp.image.info["postprocessing"] = infotext
+ action = shared.opts.postprocessing_existing_caption_action
+ if action == 'Prepend' and existing_caption:
+ caption = f"{existing_caption} {pp.caption}"
+ elif action == 'Append' and existing_caption:
+ caption = f"{pp.caption} {existing_caption}"
+ elif action == 'Keep' and existing_caption:
+ caption = existing_caption
+ else:
+ caption = pp.caption
- if save_output:
- images.save_image(pp.image, path=outpath, basename=basename, extension=opts.samples_format, info=infotext, short_filename=True, no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=forced_filename)
+ caption = caption.strip()
+ if caption:
+ with open(caption_filename, "w", encoding="utf8") as file:
+ file.write(caption)
- if extras_mode != 2 or show_extras_results:
- outputs.append(pp.image)
+ if extras_mode != 2 or show_extras_results:
+ outputs.append(pp.image)
image_data.close()
@@ -99,9 +143,11 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
"upscaler_2_visibility": extras_upscaler_2_visibility,
},
"GFPGAN": {
+ "enable": True,
"gfpgan_visibility": gfpgan_visibility,
},
"CodeFormer": {
+ "enable": True,
"codeformer_visibility": codeformer_visibility,
"codeformer_weight": codeformer_weight,
},
diff --git a/modules/scripts_postprocessing.py b/modules/scripts_postprocessing.py
index bac1335d..901cad08 100644
--- a/modules/scripts_postprocessing.py
+++ b/modules/scripts_postprocessing.py
@@ -1,13 +1,56 @@
+import dataclasses
import os
import gradio as gr
from modules import errors, shared
+@dataclasses.dataclass
+class PostprocessedImageSharedInfo:
+ target_width: int = None
+ target_height: int = None
+
+
class PostprocessedImage:
def __init__(self, image):
self.image = image
self.info = {}
+ self.shared = PostprocessedImageSharedInfo()
+ self.extra_images = []
+ self.nametags = []
+ self.disable_processing = False
+ self.caption = None
+
+ def get_suffix(self, used_suffixes=None):
+ used_suffixes = {} if used_suffixes is None else used_suffixes
+ suffix = "-".join(self.nametags)
+ if suffix:
+ suffix = "-" + suffix
+
+ if suffix not in used_suffixes:
+ used_suffixes[suffix] = 1
+ return suffix
+
+ for i in range(1, 100):
+ proposed_suffix = suffix + "-" + str(i)
+
+ if proposed_suffix not in used_suffixes:
+ used_suffixes[proposed_suffix] = 1
+ return proposed_suffix
+
+ return suffix
+
+ def create_copy(self, new_image, *, nametags=None, disable_processing=False):
+ pp = PostprocessedImage(new_image)
+ pp.shared = self.shared
+ pp.nametags = self.nametags.copy()
+ pp.info = self.info.copy()
+ pp.disable_processing = disable_processing
+
+ if nametags is not None:
+ pp.nametags += nametags
+
+ return pp
class ScriptPostprocessing:
@@ -42,10 +85,17 @@ class ScriptPostprocessing:
pass
- def image_changed(self):
- pass
+ def process_firstpass(self, pp: PostprocessedImage, **args):
+ """
+ Called for all scripts before calling process(). Scripts can examine the image here and set fields
+ of the pp object to communicate things to other scripts.
+ args contains a dictionary with all values returned by components from ui()
+ """
+ pass
+ def image_changed(self):
+ pass
def wrap_call(func, filename, funcname, *args, default=None, **kwargs):
@@ -118,16 +168,42 @@ class ScriptPostprocessingRunner:
return inputs
def run(self, pp: PostprocessedImage, args):
- for script in self.scripts_in_preferred_order():
- shared.state.job = script.name
+ scripts = []
+ for script in self.scripts_in_preferred_order():
script_args = args[script.args_from:script.args_to]
process_args = {}
for (name, _component), value in zip(script.controls.items(), script_args):
process_args[name] = value
- script.process(pp, **process_args)
+ scripts.append((script, process_args))
+
+ for script, process_args in scripts:
+ script.process_firstpass(pp, **process_args)
+
+ all_images = [pp]
+
+ for script, process_args in scripts:
+ if shared.state.skipped:
+ break
+
+ shared.state.job = script.name
+
+ for single_image in all_images.copy():
+
+ if not single_image.disable_processing:
+ script.process(single_image, **process_args)
+
+ for extra_image in single_image.extra_images:
+ if not isinstance(extra_image, PostprocessedImage):
+ extra_image = single_image.create_copy(extra_image)
+
+ all_images.append(extra_image)
+
+ single_image.extra_images.clear()
+
+ pp.extra_images = all_images[1:]
def create_args_for_run(self, scripts_args):
if not self.ui_created:
diff --git a/modules/shared_options.py b/modules/shared_options.py
index d8a27180..859dee40 100644
--- a/modules/shared_options.py
+++ b/modules/shared_options.py
@@ -357,6 +357,7 @@ options_templates.update(options_section(('postprocessing', "Postprocessing", "p
'postprocessing_enable_in_main_ui': OptionInfo([], "Enable postprocessing operations in txt2img and img2img tabs", ui_components.DropdownMulti, lambda: {"choices": [x.name for x in shared_items.postprocessing_scripts()]}),
'postprocessing_operation_order': OptionInfo([], "Postprocessing operation order", ui_components.DropdownMulti, lambda: {"choices": [x.name for x in shared_items.postprocessing_scripts()]}),
'upscaling_max_images_in_cache': OptionInfo(5, "Maximum number of images in upscaling cache", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
+ 'postprocessing_existing_caption_action': OptionInfo("Ignore", "Action for existing captions", gr.Radio, {"choices": ["Ignore", "Keep", "Prepend", "Append"]}).info("when generating captions using postprocessing; Ignore = use generated; Keep = use original; Prepend/Append = combine both"),
}))
options_templates.update(options_section((None, "Hidden options"), {
diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
deleted file mode 100644
index 789fa083..00000000
--- a/modules/textual_inversion/preprocess.py
+++ /dev/null
@@ -1,232 +0,0 @@
-import os
-from PIL import Image, ImageOps
-import math
-import tqdm
-
-from modules import shared, images, deepbooru
-from modules.textual_inversion import autocrop
-
-
-def preprocess(id_task, process_src, process_dst, process_width, process_height, preprocess_txt_action, process_keep_original_size, process_flip, process_split, process_caption, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2, process_focal_crop=False, process_focal_crop_face_weight=0.9, process_focal_crop_entropy_weight=0.15, process_focal_crop_edges_weight=0.5, process_focal_crop_debug=False, process_multicrop=None, process_multicrop_mindim=None, process_multicrop_maxdim=None, process_multicrop_minarea=None, process_multicrop_maxarea=None, process_multicrop_objective=None, process_multicrop_threshold=None):
- try:
- if process_caption:
- shared.interrogator.load()
-
- if process_caption_deepbooru:
- deepbooru.model.start()
-
- preprocess_work(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_keep_original_size, process_flip, process_split, process_caption, process_caption_deepbooru, split_threshold, overlap_ratio, process_focal_crop, process_focal_crop_face_weight, process_focal_crop_entropy_weight, process_focal_crop_edges_weight, process_focal_crop_debug, process_multicrop, process_multicrop_mindim, process_multicrop_maxdim, process_multicrop_minarea, process_multicrop_maxarea, process_multicrop_objective, process_multicrop_threshold)
-
- finally:
-
- if process_caption:
- shared.interrogator.send_blip_to_ram()
-
- if process_caption_deepbooru:
- deepbooru.model.stop()
-
-
-def listfiles(dirname):
- return os.listdir(dirname)
-
-
-class PreprocessParams:
- src = None
- dstdir = None
- subindex = 0
- flip = False
- process_caption = False
- process_caption_deepbooru = False
- preprocess_txt_action = None
-
-
-def save_pic_with_caption(image, index, params: PreprocessParams, existing_caption=None):
- caption = ""
-
- if params.process_caption:
- caption += shared.interrogator.generate_caption(image)
-
- if params.process_caption_deepbooru:
- if caption:
- caption += ", "
- caption += deepbooru.model.tag_multi(image)
-
- filename_part = params.src
- filename_part = os.path.splitext(filename_part)[0]
- filename_part = os.path.basename(filename_part)
-
- basename = f"{index:05}-{params.subindex}-{filename_part}"
- image.save(os.path.join(params.dstdir, f"{basename}.png"))
-
- if params.preprocess_txt_action == 'prepend' and existing_caption:
- caption = f"{existing_caption} {caption}"
- elif params.preprocess_txt_action == 'append' and existing_caption:
- caption = f"{caption} {existing_caption}"
- elif params.preprocess_txt_action == 'copy' and existing_caption:
- caption = existing_caption
-
- caption = caption.strip()
-
- if caption:
- with open(os.path.join(params.dstdir, f"{basename}.txt"), "w", encoding="utf8") as file:
- file.write(caption)
-
- params.subindex += 1
-
-
-def save_pic(image, index, params, existing_caption=None):
- save_pic_with_caption(image, index, params, existing_caption=existing_caption)
-
- if params.flip:
- save_pic_with_caption(ImageOps.mirror(image), index, params, existing_caption=existing_caption)
-
-
-def split_pic(image, inverse_xy, width, height, overlap_ratio):
- if inverse_xy:
- from_w, from_h = image.height, image.width
- to_w, to_h = height, width
- else:
- from_w, from_h = image.width, image.height
- to_w, to_h = width, height
- h = from_h * to_w // from_w
- if inverse_xy:
- image = image.resize((h, to_w))
- else:
- image = image.resize((to_w, h))
-
- split_count = math.ceil((h - to_h * overlap_ratio) / (to_h * (1.0 - overlap_ratio)))
- y_step = (h - to_h) / (split_count - 1)
- for i in range(split_count):
- y = int(y_step * i)
- if inverse_xy:
- splitted = image.crop((y, 0, y + to_h, to_w))
- else:
- splitted = image.crop((0, y, to_w, y + to_h))
- yield splitted
-
-# not using torchvision.transforms.CenterCrop because it doesn't allow float regions
-def center_crop(image: Image, w: int, h: int):
- iw, ih = image.size
- if ih / h < iw / w:
- sw = w * ih / h
- box = (iw - sw) / 2, 0, iw - (iw - sw) / 2, ih
- else:
- sh = h * iw / w
- box = 0, (ih - sh) / 2, iw, ih - (ih - sh) / 2
- return image.resize((w, h), Image.Resampling.LANCZOS, box)
-
-
-def multicrop_pic(image: Image, mindim, maxdim, minarea, maxarea, objective, threshold):
- iw, ih = image.size
- err = lambda w, h: 1-(lambda x: x if x < 1 else 1/x)(iw/ih/(w/h))
- wh = max(((w, h) for w in range(mindim, maxdim+1, 64) for h in range(mindim, maxdim+1, 64)
- if minarea <= w * h <= maxarea and err(w, h) <= threshold),
- key= lambda wh: (wh[0]*wh[1], -err(*wh))[::1 if objective=='Maximize area' else -1],
- default=None
- )
- return wh and center_crop(image, *wh)
-
-
-def preprocess_work(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_keep_original_size, process_flip, process_split, process_caption, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2, process_focal_crop=False, process_focal_crop_face_weight=0.9, process_focal_crop_entropy_weight=0.3, process_focal_crop_edges_weight=0.5, process_focal_crop_debug=False, process_multicrop=None, process_multicrop_mindim=None, process_multicrop_maxdim=None, process_multicrop_minarea=None, process_multicrop_maxarea=None, process_multicrop_objective=None, process_multicrop_threshold=None):
- width = process_width
- height = process_height
- src = os.path.abspath(process_src)
- dst = os.path.abspath(process_dst)
- split_threshold = max(0.0, min(1.0, split_threshold))
- overlap_ratio = max(0.0, min(0.9, overlap_ratio))
-
- assert src != dst, 'same directory specified as source and destination'
-
- os.makedirs(dst, exist_ok=True)
-
- files = listfiles(src)
-
- shared.state.job = "preprocess"
- shared.state.textinfo = "Preprocessing..."
- shared.state.job_count = len(files)
-
- params = PreprocessParams()
- params.dstdir = dst
- params.flip = process_flip
- params.process_caption = process_caption
- params.process_caption_deepbooru = process_caption_deepbooru
- params.preprocess_txt_action = preprocess_txt_action
-
- pbar = tqdm.tqdm(files)
- for index, imagefile in enumerate(pbar):
- params.subindex = 0
- filename = os.path.join(src, imagefile)
- try:
- img = Image.open(filename)
- img = ImageOps.exif_transpose(img)
- img = img.convert("RGB")
- except Exception:
- continue
-
- description = f"Preprocessing [Image {index}/{len(files)}]"
- pbar.set_description(description)
- shared.state.textinfo = description
-
- params.src = filename
-
- existing_caption = None
- existing_caption_filename = f"{os.path.splitext(filename)[0]}.txt"
- if os.path.exists(existing_caption_filename):
- with open(existing_caption_filename, 'r', encoding="utf8") as file:
- existing_caption = file.read()
-
- if shared.state.interrupted:
- break
-
- if img.height > img.width:
- ratio = (img.width * height) / (img.height * width)
- inverse_xy = False
- else:
- ratio = (img.height * width) / (img.width * height)
- inverse_xy = True
-
- process_default_resize = True
-
- if process_split and ratio < 1.0 and ratio <= split_threshold:
- for splitted in split_pic(img, inverse_xy, width, height, overlap_ratio):
- save_pic(splitted, index, params, existing_caption=existing_caption)
- process_default_resize = False
-
- if process_focal_crop and img.height != img.width:
-
- dnn_model_path = None
- try:
- dnn_model_path = autocrop.download_and_cache_models()
- except Exception as e:
- print("Unable to load face detection model for auto crop selection. Falling back to lower quality haar method.", e)
-
- autocrop_settings = autocrop.Settings(
- crop_width = width,
- crop_height = height,
- face_points_weight = process_focal_crop_face_weight,
- entropy_points_weight = process_focal_crop_entropy_weight,
- corner_points_weight = process_focal_crop_edges_weight,
- annotate_image = process_focal_crop_debug,
- dnn_model_path = dnn_model_path,
- )
- for focal in autocrop.crop_image(img, autocrop_settings):
- save_pic(focal, index, params, existing_caption=existing_caption)
- process_default_resize = False
-
- if process_multicrop:
- cropped = multicrop_pic(img, process_multicrop_mindim, process_multicrop_maxdim, process_multicrop_minarea, process_multicrop_maxarea, process_multicrop_objective, process_multicrop_threshold)
- if cropped is not None:
- save_pic(cropped, index, params, existing_caption=existing_caption)
- else:
- print(f"skipped {img.width}x{img.height} image {filename} (can't find suitable size within error threshold)")
- process_default_resize = False
-
- if process_keep_original_size:
- save_pic(img, index, params, existing_caption=existing_caption)
- process_default_resize = False
-
- if process_default_resize:
- img = images.resize_image(1, img, width, height)
- save_pic(img, index, params, existing_caption=existing_caption)
-
- shared.state.nextjob()
diff --git a/modules/textual_inversion/ui.py b/modules/textual_inversion/ui.py
index 35c4feef..f149ad1f 100644
--- a/modules/textual_inversion/ui.py
+++ b/modules/textual_inversion/ui.py
@@ -3,7 +3,6 @@ import html
import gradio as gr
import modules.textual_inversion.textual_inversion
-import modules.textual_inversion.preprocess
from modules import sd_hijack, shared
@@ -15,12 +14,6 @@ def create_embedding(name, initialization_text, nvpt, overwrite_old):
return gr.Dropdown.update(choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())), f"Created: {filename}", ""
-def preprocess(*args):
- modules.textual_inversion.preprocess.preprocess(*args)
-
- return f"Preprocessing {'interrupted' if shared.state.interrupted else 'finished'}.", ""
-
-
def train_embedding(*args):
assert not shared.cmd_opts.lowvram, 'Training models with lowvram not possible'
diff --git a/modules/ui.py b/modules/ui.py
index 08e0ad77..d80486dd 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -912,71 +912,6 @@ def create_ui():
with gr.Column():
create_hypernetwork = gr.Button(value="Create hypernetwork", variant='primary', elem_id="train_create_hypernetwork")
- with gr.Tab(label="Preprocess images", id="preprocess_images"):
- process_src = gr.Textbox(label='Source directory', elem_id="train_process_src")
- process_dst = gr.Textbox(label='Destination directory', elem_id="train_process_dst")
- process_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="train_process_width")
- process_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="train_process_height")
- preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', value="ignore", choices=["ignore", "copy", "prepend", "append"], elem_id="train_preprocess_txt_action")
-
- with gr.Row():
- process_keep_original_size = gr.Checkbox(label='Keep original size', elem_id="train_process_keep_original_size")
- process_flip = gr.Checkbox(label='Create flipped copies', elem_id="train_process_flip")
- process_split = gr.Checkbox(label='Split oversized images', elem_id="train_process_split")
- process_focal_crop = gr.Checkbox(label='Auto focal point crop', elem_id="train_process_focal_crop")
- process_multicrop = gr.Checkbox(label='Auto-sized crop', elem_id="train_process_multicrop")
- process_caption = gr.Checkbox(label='Use BLIP for caption', elem_id="train_process_caption")
- process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True, elem_id="train_process_caption_deepbooru")
-
- with gr.Row(visible=False) as process_split_extra_row:
- process_split_threshold = gr.Slider(label='Split image threshold', value=0.5, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_split_threshold")
- process_overlap_ratio = gr.Slider(label='Split image overlap ratio', value=0.2, minimum=0.0, maximum=0.9, step=0.05, elem_id="train_process_overlap_ratio")
-
- with gr.Row(visible=False) as process_focal_crop_row:
- process_focal_crop_face_weight = gr.Slider(label='Focal point face weight', value=0.9, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_face_weight")
- process_focal_crop_entropy_weight = gr.Slider(label='Focal point entropy weight', value=0.15, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_entropy_weight")
- process_focal_crop_edges_weight = gr.Slider(label='Focal point edges weight', value=0.5, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_edges_weight")
- process_focal_crop_debug = gr.Checkbox(label='Create debug image', elem_id="train_process_focal_crop_debug")
-
- with gr.Column(visible=False) as process_multicrop_col:
- gr.Markdown('Each image is center-cropped with an automatically chosen width and height.')
- with gr.Row():
- process_multicrop_mindim = gr.Slider(minimum=64, maximum=2048, step=8, label="Dimension lower bound", value=384, elem_id="train_process_multicrop_mindim")
- process_multicrop_maxdim = gr.Slider(minimum=64, maximum=2048, step=8, label="Dimension upper bound", value=768, elem_id="train_process_multicrop_maxdim")
- with gr.Row():
- process_multicrop_minarea = gr.Slider(minimum=64*64, maximum=2048*2048, step=1, label="Area lower bound", value=64*64, elem_id="train_process_multicrop_minarea")
- process_multicrop_maxarea = gr.Slider(minimum=64*64, maximum=2048*2048, step=1, label="Area upper bound", value=640*640, elem_id="train_process_multicrop_maxarea")
- with gr.Row():
- process_multicrop_objective = gr.Radio(["Maximize area", "Minimize error"], value="Maximize area", label="Resizing objective", elem_id="train_process_multicrop_objective")
- process_multicrop_threshold = gr.Slider(minimum=0, maximum=1, step=0.01, label="Error threshold", value=0.1, elem_id="train_process_multicrop_threshold")
-
- with gr.Row():
- with gr.Column(scale=3):
- gr.HTML(value="")
-
- with gr.Column():
- with gr.Row():
- interrupt_preprocessing = gr.Button("Interrupt", elem_id="train_interrupt_preprocessing")
- run_preprocess = gr.Button(value="Preprocess", variant='primary', elem_id="train_run_preprocess")
-
- process_split.change(
- fn=lambda show: gr_show(show),
- inputs=[process_split],
- outputs=[process_split_extra_row],
- )
-
- process_focal_crop.change(
- fn=lambda show: gr_show(show),
- inputs=[process_focal_crop],
- outputs=[process_focal_crop_row],
- )
-
- process_multicrop.change(
- fn=lambda show: gr_show(show),
- inputs=[process_multicrop],
- outputs=[process_multicrop_col],
- )
-
def get_textual_inversion_template_names():
return sorted(textual_inversion.textual_inversion_templates)
@@ -1077,42 +1012,6 @@ def create_ui():
]
)
- run_preprocess.click(
- fn=wrap_gradio_gpu_call(textual_inversion_ui.preprocess, extra_outputs=[gr.update()]),
- _js="start_training_textual_inversion",
- inputs=[
- dummy_component,
- process_src,
- process_dst,
- process_width,
- process_height,
- preprocess_txt_action,
- process_keep_original_size,
- process_flip,
- process_split,
- process_caption,
- process_caption_deepbooru,
- process_split_threshold,
- process_overlap_ratio,
- process_focal_crop,
- process_focal_crop_face_weight,
- process_focal_crop_entropy_weight,
- process_focal_crop_edges_weight,
- process_focal_crop_debug,
- process_multicrop,
- process_multicrop_mindim,
- process_multicrop_maxdim,
- process_multicrop_minarea,
- process_multicrop_maxarea,
- process_multicrop_objective,
- process_multicrop_threshold,
- ],
- outputs=[
- ti_output,
- ti_outcome,
- ],
- )
-
train_embedding.click(
fn=wrap_gradio_gpu_call(textual_inversion_ui.train_embedding, extra_outputs=[gr.update()]),
_js="start_training_textual_inversion",
@@ -1186,12 +1085,6 @@ def create_ui():
outputs=[],
)
- interrupt_preprocessing.click(
- fn=lambda: shared.state.interrupt(),
- inputs=[],
- outputs=[],
- )
-
loadsave = ui_loadsave.UiLoadsave(cmd_opts.ui_config_file)
settings = ui_settings.UiSettings()
diff --git a/modules/ui_postprocessing.py b/modules/ui_postprocessing.py
index 802e1ce7..fbad0800 100644
--- a/modules/ui_postprocessing.py
+++ b/modules/ui_postprocessing.py
@@ -1,9 +1,10 @@
import gradio as gr
-from modules import scripts, shared, ui_common, postprocessing, call_queue
+from modules import scripts, shared, ui_common, postprocessing, call_queue, ui_toprow
import modules.generation_parameters_copypaste as parameters_copypaste
def create_ui():
+ dummy_component = gr.Label(visible=False)
tab_index = gr.State(value=0)
with gr.Row(equal_height=False, variant='compact'):
@@ -20,11 +21,13 @@ def create_ui():
extras_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, placeholder="Leave blank to save images to the default path.", elem_id="extras_batch_output_dir")
show_extras_results = gr.Checkbox(label='Show result images', value=True, elem_id="extras_show_extras_results")
- submit = gr.Button('Generate', elem_id="extras_generate", variant='primary')
-
script_inputs = scripts.scripts_postproc.setup_ui()
with gr.Column():
+ toprow = ui_toprow.Toprow(is_compact=True, is_img2img=False, id_part="extras")
+ toprow.create_inline_toprow_image()
+ submit = toprow.submit
+
result_images, html_info_x, html_info, html_log = ui_common.create_output_panel("extras", shared.opts.outdir_extras_samples)
tab_single.select(fn=lambda: 0, inputs=[], outputs=[tab_index])
@@ -33,7 +36,9 @@ def create_ui():
submit.click(
fn=call_queue.wrap_gradio_gpu_call(postprocessing.run_postprocessing, extra_outputs=[None, '']),
+ _js="submit_extras",
inputs=[
+ dummy_component,
tab_index,
extras_image,
image_batch,
@@ -45,8 +50,9 @@ def create_ui():
outputs=[
result_images,
html_info_x,
- html_info,
- ]
+ html_log,
+ ],
+ show_progress=False,
)
parameters_copypaste.add_paste_fields("extras", extras_image, None)
diff --git a/modules/ui_toprow.py b/modules/ui_toprow.py
index 985b5a2d..88838f97 100644
--- a/modules/ui_toprow.py
+++ b/modules/ui_toprow.py
@@ -34,8 +34,10 @@ class Toprow:
submit_box = None
- def __init__(self, is_img2img, is_compact=False):
- id_part = "img2img" if is_img2img else "txt2img"
+ def __init__(self, is_img2img, is_compact=False, id_part=None):
+ if id_part is None:
+ id_part = "img2img" if is_img2img else "txt2img"
+
self.id_part = id_part
self.is_img2img = is_img2img
self.is_compact = is_compact
generated by cgit v1.2.1 (git 2.18.0) at 2024-09-20 05:30:45 +0000