mirror of
https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
synced 2024-12-29 02:45:05 +08:00
remove Train/Preprocessing tab and put all its functionality into extras batch images mode
This commit is contained in:
AUTOMATIC1111
parent
4a666381bf
commit
11d23e8ca5
@ -170,6 +170,23 @@ function submit_img2img() {
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return res;
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}
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function submit_extras() {
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showSubmitButtons('extras', false);
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var id = randomId();
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requestProgress(id, gradioApp().getElementById('extras_gallery_container'), gradioApp().getElementById('extras_gallery'), function() {
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showSubmitButtons('extras', true);
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});
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var res = create_submit_args(arguments);
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res[0] = id;
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console.log(res);
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return res;
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}
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function restoreProgressTxt2img() {
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showRestoreProgressButton("txt2img", false);
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var id = localGet("txt2img_task_id");
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@ -22,7 +22,6 @@ from modules.api import models
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from modules.shared import opts
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from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
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from modules.textual_inversion.textual_inversion import create_embedding, train_embedding
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from modules.textual_inversion.preprocess import preprocess
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from modules.hypernetworks.hypernetwork import create_hypernetwork, train_hypernetwork
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from PIL import PngImagePlugin, Image
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from modules.sd_models_config import find_checkpoint_config_near_filename
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@ -235,7 +234,6 @@ class Api:
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self.add_api_route("/sdapi/v1/refresh-vae", self.refresh_vae, methods=["POST"])
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self.add_api_route("/sdapi/v1/create/embedding", self.create_embedding, methods=["POST"], response_model=models.CreateResponse)
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self.add_api_route("/sdapi/v1/create/hypernetwork", self.create_hypernetwork, methods=["POST"], response_model=models.CreateResponse)
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self.add_api_route("/sdapi/v1/preprocess", self.preprocess, methods=["POST"], response_model=models.PreprocessResponse)
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self.add_api_route("/sdapi/v1/train/embedding", self.train_embedding, methods=["POST"], response_model=models.TrainResponse)
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self.add_api_route("/sdapi/v1/train/hypernetwork", self.train_hypernetwork, methods=["POST"], response_model=models.TrainResponse)
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self.add_api_route("/sdapi/v1/memory", self.get_memory, methods=["GET"], response_model=models.MemoryResponse)
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@ -675,19 +673,6 @@ class Api:
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finally:
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shared.state.end()
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def preprocess(self, args: dict):
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try:
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shared.state.begin(job="preprocess")
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preprocess(**args) # quick operation unless blip/booru interrogation is enabled
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shared.state.end()
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return models.PreprocessResponse(info='preprocess complete')
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except KeyError as e:
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return models.PreprocessResponse(info=f"preprocess error: invalid token: {e}")
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except Exception as e:
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return models.PreprocessResponse(info=f"preprocess error: {e}")
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finally:
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shared.state.end()
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def train_embedding(self, args: dict):
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try:
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shared.state.begin(job="train_embedding")
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@ -202,9 +202,6 @@ class TrainResponse(BaseModel):
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class CreateResponse(BaseModel):
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info: str = Field(title="Create info", description="Response string from create embedding or hypernetwork task.")
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class PreprocessResponse(BaseModel):
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info: str = Field(title="Preprocess info", description="Response string from preprocessing task.")
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fields = {}
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for key, metadata in opts.data_labels.items():
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value = opts.data.get(key)
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@ -6,7 +6,7 @@ from modules import shared, images, devices, scripts, scripts_postprocessing, ui
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from modules.shared import opts
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def run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir, show_extras_results, *args, save_output: bool = True):
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def run_postprocessing(id_task, extras_mode, image, image_folder, input_dir, output_dir, show_extras_results, *args, save_output: bool = True):
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devices.torch_gc()
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shared.state.begin(job="extras")
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@ -29,11 +29,7 @@ def run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir,
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image_list = shared.listfiles(input_dir)
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for filename in image_list:
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try:
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image = Image.open(filename)
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except Exception:
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continue
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yield image, filename
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yield filename, filename
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else:
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assert image, 'image not selected'
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yield image, None
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@ -45,37 +41,85 @@ def run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir,
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infotext = ''
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for image_data, name in get_images(extras_mode, image, image_folder, input_dir):
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data_to_process = list(get_images(extras_mode, image, image_folder, input_dir))
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shared.state.job_count = len(data_to_process)
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for image_placeholder, name in data_to_process:
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image_data: Image.Image
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shared.state.nextjob()
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shared.state.textinfo = name
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shared.state.skipped = False
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if shared.state.interrupted:
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break
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if isinstance(image_placeholder, str):
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try:
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image_data = Image.open(image_placeholder)
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except Exception:
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continue
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else:
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image_data = image_placeholder
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shared.state.assign_current_image(image_data)
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parameters, existing_pnginfo = images.read_info_from_image(image_data)
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if parameters:
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existing_pnginfo["parameters"] = parameters
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pp = scripts_postprocessing.PostprocessedImage(image_data.convert("RGB"))
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initial_pp = scripts_postprocessing.PostprocessedImage(image_data.convert("RGB"))
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scripts.scripts_postproc.run(pp, args)
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scripts.scripts_postproc.run(initial_pp, args)
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if opts.use_original_name_batch and name is not None:
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basename = os.path.splitext(os.path.basename(name))[0]
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forced_filename = basename
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else:
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basename = ''
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forced_filename = None
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if shared.state.skipped:
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continue
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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])
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used_suffixes = {}
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for pp in [initial_pp, *initial_pp.extra_images]:
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suffix = pp.get_suffix(used_suffixes)
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if opts.enable_pnginfo:
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pp.image.info = existing_pnginfo
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pp.image.info["postprocessing"] = infotext
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if opts.use_original_name_batch and name is not None:
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basename = os.path.splitext(os.path.basename(name))[0]
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forced_filename = basename + suffix
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else:
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basename = ''
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forced_filename = None
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if save_output:
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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)
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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])
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if extras_mode != 2 or show_extras_results:
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outputs.append(pp.image)
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if opts.enable_pnginfo:
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pp.image.info = existing_pnginfo
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pp.image.info["postprocessing"] = infotext
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if save_output:
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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)
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if pp.caption:
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caption_filename = os.path.splitext(fullfn)[0] + ".txt"
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if os.path.isfile(caption_filename):
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with open(caption_filename, encoding="utf8") as file:
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existing_caption = file.read().strip()
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else:
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existing_caption = ""
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action = shared.opts.postprocessing_existing_caption_action
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if action == 'Prepend' and existing_caption:
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caption = f"{existing_caption} {pp.caption}"
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elif action == 'Append' and existing_caption:
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caption = f"{pp.caption} {existing_caption}"
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elif action == 'Keep' and existing_caption:
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caption = existing_caption
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else:
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caption = pp.caption
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caption = caption.strip()
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if caption:
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with open(caption_filename, "w", encoding="utf8") as file:
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file.write(caption)
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if extras_mode != 2 or show_extras_results:
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outputs.append(pp.image)
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image_data.close()
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@ -99,9 +143,11 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
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"upscaler_2_visibility": extras_upscaler_2_visibility,
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},
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"GFPGAN": {
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"enable": True,
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"gfpgan_visibility": gfpgan_visibility,
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},
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"CodeFormer": {
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"enable": True,
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"codeformer_visibility": codeformer_visibility,
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"codeformer_weight": codeformer_weight,
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},
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@ -1,13 +1,56 @@
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import dataclasses
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import os
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import gradio as gr
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from modules import errors, shared
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@dataclasses.dataclass
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class PostprocessedImageSharedInfo:
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target_width: int = None
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target_height: int = None
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class PostprocessedImage:
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def __init__(self, image):
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self.image = image
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self.info = {}
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self.shared = PostprocessedImageSharedInfo()
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self.extra_images = []
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self.nametags = []
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self.disable_processing = False
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self.caption = None
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def get_suffix(self, used_suffixes=None):
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used_suffixes = {} if used_suffixes is None else used_suffixes
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suffix = "-".join(self.nametags)
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if suffix:
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suffix = "-" + suffix
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if suffix not in used_suffixes:
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used_suffixes[suffix] = 1
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return suffix
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for i in range(1, 100):
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proposed_suffix = suffix + "-" + str(i)
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if proposed_suffix not in used_suffixes:
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used_suffixes[proposed_suffix] = 1
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return proposed_suffix
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return suffix
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def create_copy(self, new_image, *, nametags=None, disable_processing=False):
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pp = PostprocessedImage(new_image)
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pp.shared = self.shared
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pp.nametags = self.nametags.copy()
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pp.info = self.info.copy()
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pp.disable_processing = disable_processing
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if nametags is not None:
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pp.nametags += nametags
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return pp
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class ScriptPostprocessing:
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@ -42,10 +85,17 @@ class ScriptPostprocessing:
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pass
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def image_changed(self):
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def process_firstpass(self, pp: PostprocessedImage, **args):
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"""
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Called for all scripts before calling process(). Scripts can examine the image here and set fields
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of the pp object to communicate things to other scripts.
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args contains a dictionary with all values returned by components from ui()
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"""
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pass
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def image_changed(self):
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pass
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def wrap_call(func, filename, funcname, *args, default=None, **kwargs):
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@ -118,16 +168,42 @@ class ScriptPostprocessingRunner:
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return inputs
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def run(self, pp: PostprocessedImage, args):
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for script in self.scripts_in_preferred_order():
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shared.state.job = script.name
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scripts = []
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for script in self.scripts_in_preferred_order():
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script_args = args[script.args_from:script.args_to]
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process_args = {}
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for (name, _component), value in zip(script.controls.items(), script_args):
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process_args[name] = value
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script.process(pp, **process_args)
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scripts.append((script, process_args))
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for script, process_args in scripts:
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script.process_firstpass(pp, **process_args)
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all_images = [pp]
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for script, process_args in scripts:
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if shared.state.skipped:
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break
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shared.state.job = script.name
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for single_image in all_images.copy():
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if not single_image.disable_processing:
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script.process(single_image, **process_args)
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for extra_image in single_image.extra_images:
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if not isinstance(extra_image, PostprocessedImage):
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extra_image = single_image.create_copy(extra_image)
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all_images.append(extra_image)
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single_image.extra_images.clear()
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pp.extra_images = all_images[1:]
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def create_args_for_run(self, scripts_args):
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if not self.ui_created:
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@ -357,6 +357,7 @@ options_templates.update(options_section(('postprocessing', "Postprocessing", "p
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'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()]}),
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'postprocessing_operation_order': OptionInfo([], "Postprocessing operation order", ui_components.DropdownMulti, lambda: {"choices": [x.name for x in shared_items.postprocessing_scripts()]}),
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'upscaling_max_images_in_cache': OptionInfo(5, "Maximum number of images in upscaling cache", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
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'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"),
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}))
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options_templates.update(options_section((None, "Hidden options"), {
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@ -1,232 +0,0 @@
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import os
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from PIL import Image, ImageOps
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import math
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import tqdm
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from modules import shared, images, deepbooru
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from modules.textual_inversion import autocrop
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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):
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try:
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if process_caption:
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shared.interrogator.load()
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if process_caption_deepbooru:
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deepbooru.model.start()
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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)
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finally:
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if process_caption:
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shared.interrogator.send_blip_to_ram()
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if process_caption_deepbooru:
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deepbooru.model.stop()
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def listfiles(dirname):
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return os.listdir(dirname)
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class PreprocessParams:
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src = None
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dstdir = None
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subindex = 0
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flip = False
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process_caption = False
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process_caption_deepbooru = False
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preprocess_txt_action = None
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def save_pic_with_caption(image, index, params: PreprocessParams, existing_caption=None):
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caption = ""
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if params.process_caption:
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caption += shared.interrogator.generate_caption(image)
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if params.process_caption_deepbooru:
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if caption:
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caption += ", "
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caption += deepbooru.model.tag_multi(image)
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filename_part = params.src
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filename_part = os.path.splitext(filename_part)[0]
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filename_part = os.path.basename(filename_part)
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basename = f"{index:05}-{params.subindex}-{filename_part}"
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image.save(os.path.join(params.dstdir, f"{basename}.png"))
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if params.preprocess_txt_action == 'prepend' and existing_caption:
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caption = f"{existing_caption} {caption}"
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elif params.preprocess_txt_action == 'append' and existing_caption:
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caption = f"{caption} {existing_caption}"
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elif params.preprocess_txt_action == 'copy' and existing_caption:
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caption = existing_caption
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caption = caption.strip()
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if caption:
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with open(os.path.join(params.dstdir, f"{basename}.txt"), "w", encoding="utf8") as file:
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file.write(caption)
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params.subindex += 1
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def save_pic(image, index, params, existing_caption=None):
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save_pic_with_caption(image, index, params, existing_caption=existing_caption)
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if params.flip:
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save_pic_with_caption(ImageOps.mirror(image), index, params, existing_caption=existing_caption)
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def split_pic(image, inverse_xy, width, height, overlap_ratio):
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if inverse_xy:
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from_w, from_h = image.height, image.width
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to_w, to_h = height, width
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else:
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from_w, from_h = image.width, image.height
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to_w, to_h = width, height
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h = from_h * to_w // from_w
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if inverse_xy:
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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()
|
||||
@ -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'
|
||||
|
||||
107
modules/ui.py
107
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()
|
||||
|
||||
@ -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)
|
||||
|
||||
@ -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
|
||||
|
||||
30
scripts/postprocessing_caption.py
Normal file
30
scripts/postprocessing_caption.py
Normal file
@ -0,0 +1,30 @@
|
||||
from modules import scripts_postprocessing, ui_components, deepbooru, shared
|
||||
import gradio as gr
|
||||
|
||||
|
||||
class ScriptPostprocessingCeption(scripts_postprocessing.ScriptPostprocessing):
|
||||
name = "Caption"
|
||||
order = 4000
|
||||
|
||||
def ui(self):
|
||||
with ui_components.InputAccordion(False, label="Caption") as enable:
|
||||
option = gr.CheckboxGroup(value=["Deepbooru"], choices=["Deepbooru", "BLIP"], show_label=False)
|
||||
|
||||
return {
|
||||
"enable": enable,
|
||||
"option": option,
|
||||
}
|
||||
|
||||
def process(self, pp: scripts_postprocessing.PostprocessedImage, enable, option):
|
||||
if not enable:
|
||||
return
|
||||
|
||||
captions = [pp.caption]
|
||||
|
||||
if "Deepbooru" in option:
|
||||
captions.append(deepbooru.model.tag(pp.image))
|
||||
|
||||
if "BLIP" in option:
|
||||
captions.append(shared.interrogator.generate_caption(pp.image))
|
||||
|
||||
pp.caption = ", ".join([x for x in captions if x])
|
||||
@ -1,28 +1,28 @@
|
||||
from PIL import Image
|
||||
import numpy as np
|
||||
|
||||
from modules import scripts_postprocessing, codeformer_model
|
||||
from modules import scripts_postprocessing, codeformer_model, ui_components
|
||||
import gradio as gr
|
||||
|
||||
from modules.ui_components import FormRow
|
||||
|
||||
|
||||
class ScriptPostprocessingCodeFormer(scripts_postprocessing.ScriptPostprocessing):
|
||||
name = "CodeFormer"
|
||||
order = 3000
|
||||
|
||||
def ui(self):
|
||||
with FormRow():
|
||||
codeformer_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer visibility", value=0, elem_id="extras_codeformer_visibility")
|
||||
codeformer_weight = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer weight (0 = maximum effect, 1 = minimum effect)", value=0, elem_id="extras_codeformer_weight")
|
||||
with ui_components.InputAccordion(False, label="CodeFormer") as enable:
|
||||
with gr.Row():
|
||||
codeformer_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Visibility", value=1.0, elem_id="extras_codeformer_visibility")
|
||||
codeformer_weight = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Weight (0 = maximum effect, 1 = minimum effect)", value=0, elem_id="extras_codeformer_weight")
|
||||
|
||||
return {
|
||||
"enable": enable,
|
||||
"codeformer_visibility": codeformer_visibility,
|
||||
"codeformer_weight": codeformer_weight,
|
||||
}
|
||||
|
||||
def process(self, pp: scripts_postprocessing.PostprocessedImage, codeformer_visibility, codeformer_weight):
|
||||
if codeformer_visibility == 0:
|
||||
def process(self, pp: scripts_postprocessing.PostprocessedImage, enable, codeformer_visibility, codeformer_weight):
|
||||
if codeformer_visibility == 0 or not enable:
|
||||
return
|
||||
|
||||
restored_img = codeformer_model.codeformer.restore(np.array(pp.image, dtype=np.uint8), w=codeformer_weight)
|
||||
|
||||
32
scripts/postprocessing_create_flipped_copies.py
Normal file
32
scripts/postprocessing_create_flipped_copies.py
Normal file
@ -0,0 +1,32 @@
|
||||
from PIL import ImageOps, Image
|
||||
|
||||
from modules import scripts_postprocessing, ui_components
|
||||
import gradio as gr
|
||||
|
||||
|
||||
class ScriptPostprocessingCreateFlippedCopies(scripts_postprocessing.ScriptPostprocessing):
|
||||
name = "Create flipped copies"
|
||||
order = 4000
|
||||
|
||||
def ui(self):
|
||||
with ui_components.InputAccordion(False, label="Create flipped copies") as enable:
|
||||
with gr.Row():
|
||||
option = gr.CheckboxGroup(value=["Horizontal"], choices=["Horizontal", "Vertical", "Both"], show_label=False)
|
||||
|
||||
return {
|
||||
"enable": enable,
|
||||
"option": option,
|
||||
}
|
||||
|
||||
def process(self, pp: scripts_postprocessing.PostprocessedImage, enable, option):
|
||||
if not enable:
|
||||
return
|
||||
|
||||
if "Horizontal" in option:
|
||||
pp.extra_images.append(ImageOps.mirror(pp.image))
|
||||
|
||||
if "Vertical" in option:
|
||||
pp.extra_images.append(pp.image.transpose(Image.Transpose.FLIP_TOP_BOTTOM))
|
||||
|
||||
if "Both" in option:
|
||||
pp.extra_images.append(pp.image.transpose(Image.Transpose.FLIP_TOP_BOTTOM).transpose(Image.Transpose.FLIP_LEFT_RIGHT))
|
||||
54
scripts/postprocessing_focal_crop.py
Normal file
54
scripts/postprocessing_focal_crop.py
Normal file
@ -0,0 +1,54 @@
|
||||
|
||||
from modules import scripts_postprocessing, ui_components, errors
|
||||
import gradio as gr
|
||||
|
||||
from modules.textual_inversion import autocrop
|
||||
|
||||
|
||||
class ScriptPostprocessingFocalCrop(scripts_postprocessing.ScriptPostprocessing):
|
||||
name = "Auto focal point crop"
|
||||
order = 4000
|
||||
|
||||
def ui(self):
|
||||
with ui_components.InputAccordion(False, label="Auto focal point crop") as enable:
|
||||
face_weight = gr.Slider(label='Focal point face weight', value=0.9, minimum=0.0, maximum=1.0, step=0.05, elem_id="postprocess_focal_crop_face_weight")
|
||||
entropy_weight = gr.Slider(label='Focal point entropy weight', value=0.15, minimum=0.0, maximum=1.0, step=0.05, elem_id="postprocess_focal_crop_entropy_weight")
|
||||
edges_weight = gr.Slider(label='Focal point edges weight', value=0.5, minimum=0.0, maximum=1.0, step=0.05, elem_id="postprocess_focal_crop_edges_weight")
|
||||
debug = gr.Checkbox(label='Create debug image', elem_id="train_process_focal_crop_debug")
|
||||
|
||||
return {
|
||||
"enable": enable,
|
||||
"face_weight": face_weight,
|
||||
"entropy_weight": entropy_weight,
|
||||
"edges_weight": edges_weight,
|
||||
"debug": debug,
|
||||
}
|
||||
|
||||
def process(self, pp: scripts_postprocessing.PostprocessedImage, enable, face_weight, entropy_weight, edges_weight, debug):
|
||||
if not enable:
|
||||
return
|
||||
|
||||
if not pp.shared.target_width or not pp.shared.target_height:
|
||||
return
|
||||
|
||||
dnn_model_path = None
|
||||
try:
|
||||
dnn_model_path = autocrop.download_and_cache_models()
|
||||
except Exception:
|
||||
errors.report("Unable to load face detection model for auto crop selection. Falling back to lower quality haar method.", exc_info=True)
|
||||
|
||||
autocrop_settings = autocrop.Settings(
|
||||
crop_width=pp.shared.target_width,
|
||||
crop_height=pp.shared.target_height,
|
||||
face_points_weight=face_weight,
|
||||
entropy_points_weight=entropy_weight,
|
||||
corner_points_weight=edges_weight,
|
||||
annotate_image=debug,
|
||||
dnn_model_path=dnn_model_path,
|
||||
)
|
||||
|
||||
result, *others = autocrop.crop_image(pp.image, autocrop_settings)
|
||||
|
||||
pp.image = result
|
||||
pp.extra_images = [pp.create_copy(x, nametags=["focal-crop-debug"], disable_processing=True) for x in others]
|
||||
|
||||
@ -1,26 +1,25 @@
|
||||
from PIL import Image
|
||||
import numpy as np
|
||||
|
||||
from modules import scripts_postprocessing, gfpgan_model
|
||||
from modules import scripts_postprocessing, gfpgan_model, ui_components
|
||||
import gradio as gr
|
||||
|
||||
from modules.ui_components import FormRow
|
||||
|
||||
|
||||
class ScriptPostprocessingGfpGan(scripts_postprocessing.ScriptPostprocessing):
|
||||
name = "GFPGAN"
|
||||
order = 2000
|
||||
|
||||
def ui(self):
|
||||
with FormRow():
|
||||
gfpgan_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="GFPGAN visibility", value=0, elem_id="extras_gfpgan_visibility")
|
||||
with ui_components.InputAccordion(False, label="GFPGAN") as enable:
|
||||
gfpgan_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Visibility", value=1.0, elem_id="extras_gfpgan_visibility")
|
||||
|
||||
return {
|
||||
"enable": enable,
|
||||
"gfpgan_visibility": gfpgan_visibility,
|
||||
}
|
||||
|
||||
def process(self, pp: scripts_postprocessing.PostprocessedImage, gfpgan_visibility):
|
||||
if gfpgan_visibility == 0:
|
||||
def process(self, pp: scripts_postprocessing.PostprocessedImage, enable, gfpgan_visibility):
|
||||
if gfpgan_visibility == 0 or not enable:
|
||||
return
|
||||
|
||||
restored_img = gfpgan_model.gfpgan_fix_faces(np.array(pp.image, dtype=np.uint8))
|
||||
|
||||
71
scripts/postprocessing_split_oversized.py
Normal file
71
scripts/postprocessing_split_oversized.py
Normal file
@ -0,0 +1,71 @@
|
||||
import math
|
||||
|
||||
from modules import scripts_postprocessing, ui_components
|
||||
import gradio as gr
|
||||
|
||||
|
||||
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
|
||||
|
||||
|
||||
class ScriptPostprocessingSplitOversized(scripts_postprocessing.ScriptPostprocessing):
|
||||
name = "Split oversized images"
|
||||
order = 4000
|
||||
|
||||
def ui(self):
|
||||
with ui_components.InputAccordion(False, label="Split oversized images") as enable:
|
||||
with gr.Row():
|
||||
split_threshold = gr.Slider(label='Threshold', value=0.5, minimum=0.0, maximum=1.0, step=0.05, elem_id="postprocess_split_threshold")
|
||||
overlap_ratio = gr.Slider(label='Overlap ratio', value=0.2, minimum=0.0, maximum=0.9, step=0.05, elem_id="postprocess_overlap_ratio")
|
||||
|
||||
return {
|
||||
"enable": enable,
|
||||
"split_threshold": split_threshold,
|
||||
"overlap_ratio": overlap_ratio,
|
||||
}
|
||||
|
||||
def process(self, pp: scripts_postprocessing.PostprocessedImage, enable, split_threshold, overlap_ratio):
|
||||
if not enable:
|
||||
return
|
||||
|
||||
width = pp.shared.target_width
|
||||
height = pp.shared.target_height
|
||||
|
||||
if not width or not height:
|
||||
return
|
||||
|
||||
if pp.image.height > pp.image.width:
|
||||
ratio = (pp.image.width * height) / (pp.image.height * width)
|
||||
inverse_xy = False
|
||||
else:
|
||||
ratio = (pp.image.height * width) / (pp.image.width * height)
|
||||
inverse_xy = True
|
||||
|
||||
if ratio >= 1.0 and ratio > split_threshold:
|
||||
return
|
||||
|
||||
result, *others = split_pic(pp.image, inverse_xy, width, height, overlap_ratio)
|
||||
|
||||
pp.image = result
|
||||
pp.extra_images = [pp.create_copy(x) for x in others]
|
||||
|
||||
@ -81,6 +81,14 @@ class ScriptPostprocessingUpscale(scripts_postprocessing.ScriptPostprocessing):
|
||||
|
||||
return image
|
||||
|
||||
def process_firstpass(self, pp: scripts_postprocessing.PostprocessedImage, upscale_mode=1, upscale_by=2.0, upscale_to_width=None, upscale_to_height=None, upscale_crop=False, upscaler_1_name=None, upscaler_2_name=None, upscaler_2_visibility=0.0):
|
||||
if upscale_mode == 1:
|
||||
pp.shared.target_width = upscale_to_width
|
||||
pp.shared.target_height = upscale_to_height
|
||||
else:
|
||||
pp.shared.target_width = int(pp.image.width * upscale_by)
|
||||
pp.shared.target_height = int(pp.image.height * upscale_by)
|
||||
|
||||
def process(self, pp: scripts_postprocessing.PostprocessedImage, upscale_mode=1, upscale_by=2.0, upscale_to_width=None, upscale_to_height=None, upscale_crop=False, upscaler_1_name=None, upscaler_2_name=None, upscaler_2_visibility=0.0):
|
||||
if upscaler_1_name == "None":
|
||||
upscaler_1_name = None
|
||||
@ -126,6 +134,10 @@ class ScriptPostprocessingUpscaleSimple(ScriptPostprocessingUpscale):
|
||||
"upscaler_name": upscaler_name,
|
||||
}
|
||||
|
||||
def process_firstpass(self, pp: scripts_postprocessing.PostprocessedImage, upscale_by=2.0, upscaler_name=None):
|
||||
pp.shared.target_width = int(pp.image.width * upscale_by)
|
||||
pp.shared.target_height = int(pp.image.height * upscale_by)
|
||||
|
||||
def process(self, pp: scripts_postprocessing.PostprocessedImage, upscale_by=2.0, upscaler_name=None):
|
||||
if upscaler_name is None or upscaler_name == "None":
|
||||
return
|
||||
|
||||
64
scripts/processing_autosized_crop.py
Normal file
64
scripts/processing_autosized_crop.py
Normal file
@ -0,0 +1,64 @@
|
||||
from PIL import Image
|
||||
|
||||
from modules import scripts_postprocessing, ui_components
|
||||
import gradio as gr
|
||||
|
||||
|
||||
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)
|
||||
|
||||
|
||||
class ScriptPostprocessingAutosizedCrop(scripts_postprocessing.ScriptPostprocessing):
|
||||
name = "Auto-sized crop"
|
||||
order = 4000
|
||||
|
||||
def ui(self):
|
||||
with ui_components.InputAccordion(False, label="Auto-sized crop") as enable:
|
||||
gr.Markdown('Each image is center-cropped with an automatically chosen width and height.')
|
||||
with gr.Row():
|
||||
mindim = gr.Slider(minimum=64, maximum=2048, step=8, label="Dimension lower bound", value=384, elem_id="postprocess_multicrop_mindim")
|
||||
maxdim = gr.Slider(minimum=64, maximum=2048, step=8, label="Dimension upper bound", value=768, elem_id="postprocess_multicrop_maxdim")
|
||||
with gr.Row():
|
||||
minarea = gr.Slider(minimum=64 * 64, maximum=2048 * 2048, step=1, label="Area lower bound", value=64 * 64, elem_id="postprocess_multicrop_minarea")
|
||||
maxarea = gr.Slider(minimum=64 * 64, maximum=2048 * 2048, step=1, label="Area upper bound", value=640 * 640, elem_id="postprocess_multicrop_maxarea")
|
||||
with gr.Row():
|
||||
objective = gr.Radio(["Maximize area", "Minimize error"], value="Maximize area", label="Resizing objective", elem_id="postprocess_multicrop_objective")
|
||||
threshold = gr.Slider(minimum=0, maximum=1, step=0.01, label="Error threshold", value=0.1, elem_id="postprocess_multicrop_threshold")
|
||||
|
||||
return {
|
||||
"enable": enable,
|
||||
"mindim": mindim,
|
||||
"maxdim": maxdim,
|
||||
"minarea": minarea,
|
||||
"maxarea": maxarea,
|
||||
"objective": objective,
|
||||
"threshold": threshold,
|
||||
}
|
||||
|
||||
def process(self, pp: scripts_postprocessing.PostprocessedImage, enable, mindim, maxdim, minarea, maxarea, objective, threshold):
|
||||
if not enable:
|
||||
return
|
||||
|
||||
cropped = multicrop_pic(pp.image, mindim, maxdim, minarea, maxarea, objective, threshold)
|
||||
if cropped is not None:
|
||||
pp.image = cropped
|
||||
else:
|
||||
print(f"skipped {pp.image.width}x{pp.image.height} image (can't find suitable size within error threshold)")
|
||||
Loading…
Reference in New Issue
Block a user