mirror of
https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
synced 2025-08-03 19:02:27 +00:00
Merge remote-tracking branch 'upstream/dev' into extras--allow-png-rgba--dev
This commit is contained in:
Art Gourieff
@@ -102,7 +102,7 @@ def get_matched_noise(_np_src_image, np_mask_rgb, noise_q=1, color_variation=0.0
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shaped_noise_fft = _fft2(noise_rgb)
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shaped_noise_fft[:, :, :] = np.absolute(shaped_noise_fft[:, :, :]) ** 2 * (src_dist ** noise_q) * src_phase # perform the actual shaping
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brightness_variation = 0. # color_variation # todo: temporarily tieing brightness variation to color variation for now
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brightness_variation = 0. # color_variation # todo: temporarily tying brightness variation to color variation for now
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contrast_adjusted_np_src = _np_src_image[:] * (brightness_variation + 1.) - brightness_variation * 2.
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# scikit-image is used for histogram matching, very convenient!
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@@ -1,30 +0,0 @@
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from modules import scripts_postprocessing, ui_components, deepbooru, shared
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import gradio as gr
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class ScriptPostprocessingCeption(scripts_postprocessing.ScriptPostprocessing):
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name = "Caption"
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order = 4040
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def ui(self):
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with ui_components.InputAccordion(False, label="Caption") as enable:
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option = gr.CheckboxGroup(value=["Deepbooru"], choices=["Deepbooru", "BLIP"], show_label=False)
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return {
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"enable": enable,
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"option": option,
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}
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def process(self, pp: scripts_postprocessing.PostprocessedImage, enable, option):
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if not enable:
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return
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captions = [pp.caption]
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if "Deepbooru" in option:
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captions.append(deepbooru.model.tag(pp.image))
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if "BLIP" in option:
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captions.append(shared.interrogator.interrogate(pp.image.convert("RGB")))
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pp.caption = ", ".join([x for x in captions if x])
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@@ -1,32 +0,0 @@
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from PIL import ImageOps, Image
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from modules import scripts_postprocessing, ui_components
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import gradio as gr
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class ScriptPostprocessingCreateFlippedCopies(scripts_postprocessing.ScriptPostprocessing):
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name = "Create flipped copies"
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order = 4030
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def ui(self):
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with ui_components.InputAccordion(False, label="Create flipped copies") as enable:
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with gr.Row():
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option = gr.CheckboxGroup(value=["Horizontal"], choices=["Horizontal", "Vertical", "Both"], show_label=False)
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return {
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"enable": enable,
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"option": option,
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}
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def process(self, pp: scripts_postprocessing.PostprocessedImage, enable, option):
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if not enable:
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return
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if "Horizontal" in option:
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pp.extra_images.append(ImageOps.mirror(pp.image))
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if "Vertical" in option:
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pp.extra_images.append(pp.image.transpose(Image.Transpose.FLIP_TOP_BOTTOM))
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if "Both" in option:
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pp.extra_images.append(pp.image.transpose(Image.Transpose.FLIP_TOP_BOTTOM).transpose(Image.Transpose.FLIP_LEFT_RIGHT))
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@@ -1,54 +0,0 @@
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from modules import scripts_postprocessing, ui_components, errors
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import gradio as gr
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from modules.textual_inversion import autocrop
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class ScriptPostprocessingFocalCrop(scripts_postprocessing.ScriptPostprocessing):
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name = "Auto focal point crop"
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order = 4010
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def ui(self):
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with ui_components.InputAccordion(False, label="Auto focal point crop") as enable:
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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")
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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")
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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")
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debug = gr.Checkbox(label='Create debug image', elem_id="train_process_focal_crop_debug")
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return {
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"enable": enable,
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"face_weight": face_weight,
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"entropy_weight": entropy_weight,
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"edges_weight": edges_weight,
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"debug": debug,
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}
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def process(self, pp: scripts_postprocessing.PostprocessedImage, enable, face_weight, entropy_weight, edges_weight, debug):
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if not enable:
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return
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if not pp.shared.target_width or not pp.shared.target_height:
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return
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dnn_model_path = None
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try:
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dnn_model_path = autocrop.download_and_cache_models()
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except Exception:
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errors.report("Unable to load face detection model for auto crop selection. Falling back to lower quality haar method.", exc_info=True)
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autocrop_settings = autocrop.Settings(
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crop_width=pp.shared.target_width,
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crop_height=pp.shared.target_height,
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face_points_weight=face_weight,
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entropy_points_weight=entropy_weight,
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corner_points_weight=edges_weight,
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annotate_image=debug,
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dnn_model_path=dnn_model_path,
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)
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result, *others = autocrop.crop_image(pp.image, autocrop_settings)
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pp.image = result
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pp.extra_images = [pp.create_copy(x, nametags=["focal-crop-debug"], disable_processing=True) for x in others]
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@@ -1,71 +0,0 @@
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import math
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from modules import scripts_postprocessing, ui_components
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import gradio as gr
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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))
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else:
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image = image.resize((to_w, h))
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split_count = math.ceil((h - to_h * overlap_ratio) / (to_h * (1.0 - overlap_ratio)))
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y_step = (h - to_h) / (split_count - 1)
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for i in range(split_count):
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y = int(y_step * i)
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if inverse_xy:
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splitted = image.crop((y, 0, y + to_h, to_w))
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else:
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splitted = image.crop((0, y, to_w, y + to_h))
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yield splitted
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class ScriptPostprocessingSplitOversized(scripts_postprocessing.ScriptPostprocessing):
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name = "Split oversized images"
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order = 4000
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def ui(self):
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with ui_components.InputAccordion(False, label="Split oversized images") as enable:
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with gr.Row():
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split_threshold = gr.Slider(label='Threshold', value=0.5, minimum=0.0, maximum=1.0, step=0.05, elem_id="postprocess_split_threshold")
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overlap_ratio = gr.Slider(label='Overlap ratio', value=0.2, minimum=0.0, maximum=0.9, step=0.05, elem_id="postprocess_overlap_ratio")
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return {
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"enable": enable,
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"split_threshold": split_threshold,
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"overlap_ratio": overlap_ratio,
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}
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def process(self, pp: scripts_postprocessing.PostprocessedImage, enable, split_threshold, overlap_ratio):
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if not enable:
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return
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width = pp.shared.target_width
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height = pp.shared.target_height
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if not width or not height:
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return
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if pp.image.height > pp.image.width:
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ratio = (pp.image.width * height) / (pp.image.height * width)
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inverse_xy = False
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else:
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ratio = (pp.image.height * width) / (pp.image.width * height)
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inverse_xy = True
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if ratio >= 1.0 or ratio > split_threshold:
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return
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result, *others = split_pic(pp.image, inverse_xy, width, height, overlap_ratio)
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pp.image = result
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pp.extra_images = [pp.create_copy(x) for x in others]
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@@ -1,10 +1,12 @@
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import re
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from PIL import Image
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import numpy as np
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from modules import scripts_postprocessing, shared
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import gradio as gr
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from modules.ui_components import FormRow, ToolButton
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from modules.ui_components import FormRow, ToolButton, InputAccordion
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from modules.ui import switch_values_symbol
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upscale_cache = {}
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@@ -17,7 +19,14 @@ class ScriptPostprocessingUpscale(scripts_postprocessing.ScriptPostprocessing):
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def ui(self):
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selected_tab = gr.Number(value=0, visible=False)
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with gr.Column():
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with InputAccordion(True, label="Upscale", elem_id="extras_upscale") as upscale_enabled:
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with FormRow():
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extras_upscaler_1 = gr.Dropdown(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name)
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with FormRow():
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extras_upscaler_2 = gr.Dropdown(label='Upscaler 2', elem_id="extras_upscaler_2", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name)
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extras_upscaler_2_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Upscaler 2 visibility", value=0.0, elem_id="extras_upscaler_2_visibility")
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with FormRow():
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with gr.Tabs(elem_id="extras_resize_mode"):
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with gr.TabItem('Scale by', elem_id="extras_scale_by_tab") as tab_scale_by:
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@@ -32,18 +41,24 @@ class ScriptPostprocessingUpscale(scripts_postprocessing.ScriptPostprocessing):
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upscaling_res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="upscaling_res_switch_btn", tooltip="Switch width/height")
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upscaling_crop = gr.Checkbox(label='Crop to fit', value=True, elem_id="extras_upscaling_crop")
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with FormRow():
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extras_upscaler_1 = gr.Dropdown(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name)
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def on_selected_upscale_method(upscale_method):
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if not shared.opts.set_scale_by_when_changing_upscaler:
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return gr.update()
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with FormRow():
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extras_upscaler_2 = gr.Dropdown(label='Upscaler 2', elem_id="extras_upscaler_2", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name)
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extras_upscaler_2_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Upscaler 2 visibility", value=0.0, elem_id="extras_upscaler_2_visibility")
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match = re.search(r'(\d)[xX]|[xX](\d)', upscale_method)
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if not match:
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return gr.update()
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return gr.update(value=int(match.group(1) or match.group(2)))
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upscaling_res_switch_btn.click(lambda w, h: (h, w), inputs=[upscaling_resize_w, upscaling_resize_h], outputs=[upscaling_resize_w, upscaling_resize_h], show_progress=False)
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tab_scale_by.select(fn=lambda: 0, inputs=[], outputs=[selected_tab])
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tab_scale_to.select(fn=lambda: 1, inputs=[], outputs=[selected_tab])
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extras_upscaler_1.change(on_selected_upscale_method, inputs=[extras_upscaler_1], outputs=[upscaling_resize], show_progress="hidden")
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return {
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"upscale_enabled": upscale_enabled,
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"upscale_mode": selected_tab,
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"upscale_by": upscaling_resize,
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"upscale_to_width": upscaling_resize_w,
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@@ -81,7 +96,7 @@ class ScriptPostprocessingUpscale(scripts_postprocessing.ScriptPostprocessing):
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return image
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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):
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def process_firstpass(self, pp: scripts_postprocessing.PostprocessedImage, upscale_enabled=True, 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):
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if upscale_mode == 1:
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pp.shared.target_width = upscale_to_width
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pp.shared.target_height = upscale_to_height
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@@ -89,7 +104,10 @@ class ScriptPostprocessingUpscale(scripts_postprocessing.ScriptPostprocessing):
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pp.shared.target_width = int(pp.image.width * upscale_by)
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pp.shared.target_height = int(pp.image.height * upscale_by)
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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):
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def process(self, pp: scripts_postprocessing.PostprocessedImage, upscale_enabled=True, 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):
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if not upscale_enabled:
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return
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if upscaler_1_name == "None":
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upscaler_1_name = None
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@@ -1,64 +0,0 @@
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from PIL import Image
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from modules import scripts_postprocessing, ui_components
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import gradio as gr
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def center_crop(image: Image, w: int, h: int):
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iw, ih = image.size
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if ih / h < iw / w:
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sw = w * ih / h
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box = (iw - sw) / 2, 0, iw - (iw - sw) / 2, ih
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else:
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sh = h * iw / w
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box = 0, (ih - sh) / 2, iw, ih - (ih - sh) / 2
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return image.resize((w, h), Image.Resampling.LANCZOS, box)
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def multicrop_pic(image: Image, mindim, maxdim, minarea, maxarea, objective, threshold):
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iw, ih = image.size
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err = lambda w, h: 1 - (lambda x: x if x < 1 else 1 / x)(iw / ih / (w / h))
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wh = max(((w, h) for w in range(mindim, maxdim + 1, 64) for h in range(mindim, maxdim + 1, 64)
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if minarea <= w * h <= maxarea and err(w, h) <= threshold),
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key=lambda wh: (wh[0] * wh[1], -err(*wh))[::1 if objective == 'Maximize area' else -1],
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default=None
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)
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return wh and center_crop(image, *wh)
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class ScriptPostprocessingAutosizedCrop(scripts_postprocessing.ScriptPostprocessing):
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name = "Auto-sized crop"
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order = 4020
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def ui(self):
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with ui_components.InputAccordion(False, label="Auto-sized crop") as enable:
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gr.Markdown('Each image is center-cropped with an automatically chosen width and height.')
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with gr.Row():
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mindim = gr.Slider(minimum=64, maximum=2048, step=8, label="Dimension lower bound", value=384, elem_id="postprocess_multicrop_mindim")
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maxdim = gr.Slider(minimum=64, maximum=2048, step=8, label="Dimension upper bound", value=768, elem_id="postprocess_multicrop_maxdim")
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with gr.Row():
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minarea = gr.Slider(minimum=64 * 64, maximum=2048 * 2048, step=1, label="Area lower bound", value=64 * 64, elem_id="postprocess_multicrop_minarea")
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maxarea = gr.Slider(minimum=64 * 64, maximum=2048 * 2048, step=1, label="Area upper bound", value=640 * 640, elem_id="postprocess_multicrop_maxarea")
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with gr.Row():
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objective = gr.Radio(["Maximize area", "Minimize error"], value="Maximize area", label="Resizing objective", elem_id="postprocess_multicrop_objective")
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threshold = gr.Slider(minimum=0, maximum=1, step=0.01, label="Error threshold", value=0.1, elem_id="postprocess_multicrop_threshold")
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return {
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"enable": enable,
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"mindim": mindim,
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"maxdim": maxdim,
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"minarea": minarea,
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"maxarea": maxarea,
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"objective": objective,
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"threshold": threshold,
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}
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def process(self, pp: scripts_postprocessing.PostprocessedImage, enable, mindim, maxdim, minarea, maxarea, objective, threshold):
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if not enable:
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return
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cropped = multicrop_pic(pp.image, mindim, maxdim, minarea, maxarea, objective, threshold)
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if cropped is not None:
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pp.image = cropped
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else:
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print(f"skipped {pp.image.width}x{pp.image.height} image (can't find suitable size within error threshold)")
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@@ -45,7 +45,7 @@ def apply_prompt(p, x, xs):
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def apply_order(p, x, xs):
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token_order = []
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# Initally grab the tokens from the prompt, so they can be replaced in order of earliest seen
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# Initially grab the tokens from the prompt, so they can be replaced in order of earliest seen
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for token in x:
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token_order.append((p.prompt.find(token), token))
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