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https://github.com/lllyasviel/stable-diffusion-webui-forge.git
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Gradio 4 + WebUI 1.10
This commit is contained in:
layerdiffusion
@@ -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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@@ -25,7 +25,7 @@ class ScriptPostprocessingCodeFormer(scripts_postprocessing.ScriptPostprocessing
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if codeformer_visibility == 0 or not enable:
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return
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restored_img = codeformer_model.codeformer.restore(np.array(pp.image, dtype=np.uint8), w=codeformer_weight)
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restored_img = codeformer_model.codeformer.restore(np.array(pp.image.convert("RGB"), dtype=np.uint8), w=codeformer_weight)
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res = Image.fromarray(restored_img)
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if codeformer_visibility < 1.0:
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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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@@ -22,7 +22,7 @@ class ScriptPostprocessingGfpGan(scripts_postprocessing.ScriptPostprocessing):
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if gfpgan_visibility == 0 or not enable:
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return
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restored_img = gfpgan_model.gfpgan_fix_faces(np.array(pp.image, dtype=np.uint8))
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restored_img = gfpgan_model.gfpgan_fix_faces(np.array(pp.image.convert("RGB"), dtype=np.uint8))
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res = Image.fromarray(restored_img)
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if gfpgan_visibility < 1.0:
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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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@@ -1,15 +1,28 @@
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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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def limit_size_by_one_dimention(w, h, limit):
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if h > w and h > limit:
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w = limit * w // h
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h = limit
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elif w > limit:
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h = limit * h // w
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w = limit
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return int(w), int(h)
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class ScriptPostprocessingUpscale(scripts_postprocessing.ScriptPostprocessing):
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name = "Upscale"
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order = 1000
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@@ -17,11 +30,22 @@ 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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upscaling_resize = gr.Slider(minimum=1.0, maximum=8.0, step=0.05, label="Resize", value=4, elem_id="extras_upscaling_resize")
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with gr.Row():
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with gr.Column(scale=4):
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upscaling_resize = gr.Slider(minimum=1.0, maximum=8.0, step=0.05, label="Resize", value=4, elem_id="extras_upscaling_resize")
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with gr.Column(scale=1, min_width=160):
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max_side_length = gr.Number(label="Max side length", value=0, elem_id="extras_upscale_max_side_length", tooltip="If any of two sides of the image ends up larger than specified, will downscale it to fit. 0 = no limit.", min_width=160, step=8, minimum=0)
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with gr.TabItem('Scale to', elem_id="extras_scale_to_tab") as tab_scale_to:
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with FormRow():
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@@ -32,20 +56,27 @@ 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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"max_side_length": max_side_length,
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"upscale_to_width": upscaling_resize_w,
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"upscale_to_height": upscaling_resize_h,
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"upscale_crop": upscaling_crop,
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@@ -54,12 +85,18 @@ class ScriptPostprocessingUpscale(scripts_postprocessing.ScriptPostprocessing):
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"upscaler_2_visibility": extras_upscaler_2_visibility,
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}
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def upscale(self, image, info, upscaler, upscale_mode, upscale_by, upscale_to_width, upscale_to_height, upscale_crop):
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def upscale(self, image, info, upscaler, upscale_mode, upscale_by, max_side_length, upscale_to_width, upscale_to_height, upscale_crop):
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if upscale_mode == 1:
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upscale_by = max(upscale_to_width/image.width, upscale_to_height/image.height)
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info["Postprocess upscale to"] = f"{upscale_to_width}x{upscale_to_height}"
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else:
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info["Postprocess upscale by"] = upscale_by
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if max_side_length != 0 and max(*image.size)*upscale_by > max_side_length:
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upscale_mode = 1
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upscale_crop = False
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upscale_to_width, upscale_to_height = limit_size_by_one_dimention(image.width*upscale_by, image.height*upscale_by, max_side_length)
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upscale_by = max(upscale_to_width/image.width, upscale_to_height/image.height)
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info["Max side length"] = max_side_length
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cache_key = (hash(np.array(image.getdata()).tobytes()), upscaler.name, upscale_mode, upscale_by, upscale_to_width, upscale_to_height, upscale_crop)
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cached_image = upscale_cache.pop(cache_key, None)
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@@ -81,7 +118,7 @@ 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, max_side_length=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 +126,13 @@ 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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pp.shared.target_width, pp.shared.target_height = limit_size_by_one_dimention(pp.shared.target_width, pp.shared.target_height, max_side_length)
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def process(self, pp: scripts_postprocessing.PostprocessedImage, upscale_enabled=True, upscale_mode=1, upscale_by=2.0, max_side_length=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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upscaler_1_name = upscaler_1_name
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if upscaler_1_name == "None":
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upscaler_1_name = None
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@@ -99,17 +142,20 @@ class ScriptPostprocessingUpscale(scripts_postprocessing.ScriptPostprocessing):
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if not upscaler1:
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return
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upscaler_2_name = upscaler_2_name
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if upscaler_2_name == "None":
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upscaler_2_name = None
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upscaler2 = next(iter([x for x in shared.sd_upscalers if x.name == upscaler_2_name and x.name != "None"]), None)
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assert upscaler2 or (upscaler_2_name is None), f'could not find upscaler named {upscaler_2_name}'
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upscaled_image = self.upscale(pp.image, pp.info, upscaler1, upscale_mode, upscale_by, upscale_to_width, upscale_to_height, upscale_crop)
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upscaled_image = self.upscale(pp.image, pp.info, upscaler1, upscale_mode, upscale_by, max_side_length, upscale_to_width, upscale_to_height, upscale_crop)
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pp.info["Postprocess upscaler"] = upscaler1.name
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|
||||
if upscaler2 and upscaler_2_visibility > 0:
|
||||
second_upscale = self.upscale(pp.image, pp.info, upscaler2, upscale_mode, upscale_by, upscale_to_width, upscale_to_height, upscale_crop)
|
||||
second_upscale = self.upscale(pp.image, pp.info, upscaler2, upscale_mode, upscale_by, max_side_length, upscale_to_width, upscale_to_height, upscale_crop)
|
||||
if upscaled_image.mode != second_upscale.mode:
|
||||
second_upscale = second_upscale.convert(upscaled_image.mode)
|
||||
upscaled_image = Image.blend(upscaled_image, second_upscale, upscaler_2_visibility)
|
||||
|
||||
pp.info["Postprocess upscaler 2"] = upscaler2.name
|
||||
@@ -145,5 +191,5 @@ class ScriptPostprocessingUpscaleSimple(ScriptPostprocessingUpscale):
|
||||
upscaler1 = next(iter([x for x in shared.sd_upscalers if x.name == upscaler_name]), None)
|
||||
assert upscaler1, f'could not find upscaler named {upscaler_name}'
|
||||
|
||||
pp.image = self.upscale(pp.image, pp.info, upscaler1, 0, upscale_by, 0, 0, False)
|
||||
pp.image = self.upscale(pp.image, pp.info, upscaler1, 0, upscale_by, 0, 0, 0, False)
|
||||
pp.info["Postprocess upscaler"] = upscaler1.name
|
||||
|
||||
@@ -1,64 +0,0 @@
|
||||
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 = 4020
|
||||
|
||||
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)")
|
||||
@@ -11,7 +11,7 @@ import numpy as np
|
||||
import modules.scripts as scripts
|
||||
import gradio as gr
|
||||
|
||||
from modules import images, sd_samplers, processing, sd_models, sd_vae, sd_samplers_kdiffusion, errors
|
||||
from modules import images, sd_samplers, processing, sd_models, sd_vae, sd_schedulers, errors
|
||||
from modules.processing import process_images, Processed, StableDiffusionProcessingTxt2Img
|
||||
from modules.shared import opts, state
|
||||
import modules.shared as shared
|
||||
@@ -45,7 +45,7 @@ def apply_prompt(p, x, xs):
|
||||
def apply_order(p, x, xs):
|
||||
token_order = []
|
||||
|
||||
# Initally grab the tokens from the prompt, so they can be replaced in order of earliest seen
|
||||
# Initially grab the tokens from the prompt, so they can be replaced in order of earliest seen
|
||||
for token in x:
|
||||
token_order.append((p.prompt.find(token), token))
|
||||
|
||||
@@ -95,33 +95,38 @@ def confirm_checkpoints_or_none(p, xs):
|
||||
raise RuntimeError(f"Unknown checkpoint: {x}")
|
||||
|
||||
|
||||
def apply_clip_skip(p, x, xs):
|
||||
opts.data["CLIP_stop_at_last_layers"] = x
|
||||
def confirm_range(min_val, max_val, axis_label):
|
||||
"""Generates a AxisOption.confirm() function that checks all values are within the specified range."""
|
||||
|
||||
def confirm_range_fun(p, xs):
|
||||
for x in xs:
|
||||
if not (max_val >= x >= min_val):
|
||||
raise ValueError(f'{axis_label} value "{x}" out of range [{min_val}, {max_val}]')
|
||||
|
||||
return confirm_range_fun
|
||||
|
||||
|
||||
def apply_upscale_latent_space(p, x, xs):
|
||||
if x.lower().strip() != '0':
|
||||
opts.data["use_scale_latent_for_hires_fix"] = True
|
||||
else:
|
||||
opts.data["use_scale_latent_for_hires_fix"] = False
|
||||
def apply_size(p, x: str, xs) -> None:
|
||||
try:
|
||||
width, _, height = x.partition('x')
|
||||
width = int(width.strip())
|
||||
height = int(height.strip())
|
||||
p.width = width
|
||||
p.height = height
|
||||
except ValueError:
|
||||
print(f"Invalid size in XYZ plot: {x}")
|
||||
|
||||
|
||||
def find_vae(name: str):
|
||||
if name.lower() in ['auto', 'automatic']:
|
||||
return modules.sd_vae.unspecified
|
||||
if name.lower() == 'none':
|
||||
return None
|
||||
else:
|
||||
choices = [x for x in sorted(modules.sd_vae.vae_dict, key=lambda x: len(x)) if name.lower().strip() in x.lower()]
|
||||
if len(choices) == 0:
|
||||
print(f"No VAE found for {name}; using automatic")
|
||||
return modules.sd_vae.unspecified
|
||||
else:
|
||||
return modules.sd_vae.vae_dict[choices[0]]
|
||||
if (name := name.strip().lower()) in ('auto', 'automatic'):
|
||||
return 'Automatic'
|
||||
elif name == 'none':
|
||||
return 'None'
|
||||
return next((k for k in modules.sd_vae.vae_dict if k.lower() == name), print(f'No VAE found for {name}; using Automatic') or 'Automatic')
|
||||
|
||||
|
||||
def apply_vae(p, x, xs):
|
||||
modules.sd_vae.reload_vae_weights(shared.sd_model, vae_file=find_vae(x))
|
||||
p.override_settings['sd_vae'] = find_vae(x)
|
||||
|
||||
|
||||
def apply_styles(p: StableDiffusionProcessingTxt2Img, x: str, _):
|
||||
@@ -129,7 +134,7 @@ def apply_styles(p: StableDiffusionProcessingTxt2Img, x: str, _):
|
||||
|
||||
|
||||
def apply_uni_pc_order(p, x, xs):
|
||||
opts.data["uni_pc_order"] = min(x, p.steps - 1)
|
||||
p.override_settings['uni_pc_order'] = min(x, p.steps - 1)
|
||||
|
||||
|
||||
def apply_face_restore(p, opt, x):
|
||||
@@ -151,12 +156,14 @@ def apply_override(field, boolean: bool = False):
|
||||
if boolean:
|
||||
x = True if x.lower() == "true" else False
|
||||
p.override_settings[field] = x
|
||||
|
||||
return fun
|
||||
|
||||
|
||||
def boolean_choice(reverse: bool = False):
|
||||
def choice():
|
||||
return ["False", "True"] if reverse else ["True", "False"]
|
||||
|
||||
return choice
|
||||
|
||||
|
||||
@@ -201,7 +208,7 @@ def list_to_csv_string(data_list):
|
||||
|
||||
|
||||
def csv_string_to_list_strip(data_str):
|
||||
return list(map(str.strip, chain.from_iterable(csv.reader(StringIO(data_str)))))
|
||||
return list(map(str.strip, chain.from_iterable(csv.reader(StringIO(data_str), skipinitialspace=True))))
|
||||
|
||||
|
||||
class AxisOption:
|
||||
@@ -248,18 +255,20 @@ axis_options = [
|
||||
AxisOption("Sigma min", float, apply_field("s_tmin")),
|
||||
AxisOption("Sigma max", float, apply_field("s_tmax")),
|
||||
AxisOption("Sigma noise", float, apply_field("s_noise")),
|
||||
AxisOption("Schedule type", str, apply_override("k_sched_type"), choices=lambda: list(sd_samplers_kdiffusion.k_diffusion_scheduler)),
|
||||
AxisOption("Schedule type", str, apply_field("scheduler"), choices=lambda: [x.label for x in sd_schedulers.schedulers]),
|
||||
AxisOption("Schedule min sigma", float, apply_override("sigma_min")),
|
||||
AxisOption("Schedule max sigma", float, apply_override("sigma_max")),
|
||||
AxisOption("Schedule rho", float, apply_override("rho")),
|
||||
AxisOption("Beta schedule alpha", float, apply_override("beta_dist_alpha")),
|
||||
AxisOption("Beta schedule beta", float, apply_override("beta_dist_beta")),
|
||||
AxisOption("Eta", float, apply_field("eta")),
|
||||
AxisOption("Clip skip", int, apply_clip_skip),
|
||||
AxisOption("Clip skip", int, apply_override('CLIP_stop_at_last_layers')),
|
||||
AxisOption("Denoising", float, apply_field("denoising_strength")),
|
||||
AxisOption("Initial noise multiplier", float, apply_field("initial_noise_multiplier")),
|
||||
AxisOption("Extra noise", float, apply_override("img2img_extra_noise")),
|
||||
AxisOptionTxt2Img("Hires upscaler", str, apply_field("hr_upscaler"), choices=lambda: [*shared.latent_upscale_modes, *[x.name for x in shared.sd_upscalers]]),
|
||||
AxisOptionImg2Img("Cond. Image Mask Weight", float, apply_field("inpainting_mask_weight")),
|
||||
AxisOption("VAE", str, apply_vae, cost=0.7, choices=lambda: ['None'] + list(sd_vae.vae_dict)),
|
||||
AxisOption("VAE", str, apply_vae, cost=0.7, choices=lambda: ['Automatic', 'None'] + list(sd_vae.vae_dict)),
|
||||
AxisOption("Styles", str, apply_styles, choices=lambda: list(shared.prompt_styles.styles)),
|
||||
AxisOption("UniPC Order", int, apply_uni_pc_order, cost=0.5),
|
||||
AxisOption("Face restore", str, apply_face_restore, format_value=format_value),
|
||||
@@ -271,6 +280,7 @@ axis_options = [
|
||||
AxisOption("Refiner switch at", float, apply_field('refiner_switch_at')),
|
||||
AxisOption("RNG source", str, apply_override("randn_source"), choices=lambda: ["GPU", "CPU", "NV"]),
|
||||
AxisOption("FP8 mode", str, apply_override("fp8_storage"), cost=0.9, choices=lambda: ["Disable", "Enable for SDXL", "Enable"]),
|
||||
AxisOption("Size", str, apply_size),
|
||||
]
|
||||
|
||||
|
||||
@@ -366,16 +376,17 @@ def draw_xyz_grid(p, xs, ys, zs, x_labels, y_labels, z_labels, cell, draw_legend
|
||||
end_index = start_index + len(xs) * len(ys)
|
||||
grid = images.image_grid(processed_result.images[start_index:end_index], rows=len(ys))
|
||||
if draw_legend:
|
||||
grid = images.draw_grid_annotations(grid, processed_result.images[start_index].size[0], processed_result.images[start_index].size[1], hor_texts, ver_texts, margin_size)
|
||||
grid_max_w, grid_max_h = map(max, zip(*(img.size for img in processed_result.images[start_index:end_index])))
|
||||
grid = images.draw_grid_annotations(grid, grid_max_w, grid_max_h, hor_texts, ver_texts, margin_size)
|
||||
processed_result.images.insert(i, grid)
|
||||
processed_result.all_prompts.insert(i, processed_result.all_prompts[start_index])
|
||||
processed_result.all_seeds.insert(i, processed_result.all_seeds[start_index])
|
||||
processed_result.infotexts.insert(i, processed_result.infotexts[start_index])
|
||||
|
||||
sub_grid_size = processed_result.images[0].size
|
||||
z_grid = images.image_grid(processed_result.images[:z_count], rows=1)
|
||||
z_sub_grid_max_w, z_sub_grid_max_h = map(max, zip(*(img.size for img in processed_result.images[:z_count])))
|
||||
if draw_legend:
|
||||
z_grid = images.draw_grid_annotations(z_grid, sub_grid_size[0], sub_grid_size[1], title_texts, [[images.GridAnnotation()]])
|
||||
z_grid = images.draw_grid_annotations(z_grid, z_sub_grid_max_w, z_sub_grid_max_h, title_texts, [[images.GridAnnotation()]])
|
||||
processed_result.images.insert(0, z_grid)
|
||||
# TODO: Deeper aspects of the program rely on grid info being misaligned between metadata arrays, which is not ideal.
|
||||
# processed_result.all_prompts.insert(0, processed_result.all_prompts[0])
|
||||
@@ -387,18 +398,12 @@ def draw_xyz_grid(p, xs, ys, zs, x_labels, y_labels, z_labels, cell, draw_legend
|
||||
|
||||
class SharedSettingsStackHelper(object):
|
||||
def __enter__(self):
|
||||
self.CLIP_stop_at_last_layers = opts.CLIP_stop_at_last_layers
|
||||
self.vae = opts.sd_vae
|
||||
self.uni_pc_order = opts.uni_pc_order
|
||||
pass
|
||||
|
||||
def __exit__(self, exc_type, exc_value, tb):
|
||||
opts.data["sd_vae"] = self.vae
|
||||
opts.data["uni_pc_order"] = self.uni_pc_order
|
||||
modules.sd_models.reload_model_weights()
|
||||
modules.sd_vae.reload_vae_weights()
|
||||
|
||||
opts.data["CLIP_stop_at_last_layers"] = self.CLIP_stop_at_last_layers
|
||||
|
||||
|
||||
re_range = re.compile(r"\s*([+-]?\s*\d+)\s*-\s*([+-]?\s*\d+)(?:\s*\(([+-]\d+)\s*\))?\s*")
|
||||
re_range_float = re.compile(r"\s*([+-]?\s*\d+(?:.\d*)?)\s*-\s*([+-]?\s*\d+(?:.\d*)?)(?:\s*\(([+-]\d+(?:.\d*)?)\s*\))?\s*")
|
||||
@@ -560,7 +565,7 @@ class Script(scripts.Script):
|
||||
mc = re_range_count.fullmatch(val)
|
||||
if m is not None:
|
||||
start = int(m.group(1))
|
||||
end = int(m.group(2))+1
|
||||
end = int(m.group(2)) + 1
|
||||
step = int(m.group(3)) if m.group(3) is not None else 1
|
||||
|
||||
valslist_ext += list(range(start, end, step))
|
||||
@@ -713,11 +718,11 @@ class Script(scripts.Script):
|
||||
ydim = len(ys) if vary_seeds_y else 1
|
||||
|
||||
if vary_seeds_x:
|
||||
pc.seed += ix
|
||||
pc.seed += ix
|
||||
if vary_seeds_y:
|
||||
pc.seed += iy * xdim
|
||||
pc.seed += iy * xdim
|
||||
if vary_seeds_z:
|
||||
pc.seed += iz * xdim * ydim
|
||||
pc.seed += iz * xdim * ydim
|
||||
|
||||
try:
|
||||
res = process_images(pc)
|
||||
@@ -785,18 +790,18 @@ class Script(scripts.Script):
|
||||
z_count = len(zs)
|
||||
|
||||
# Set the grid infotexts to the real ones with extra_generation_params (1 main grid + z_count sub-grids)
|
||||
processed.infotexts[:1+z_count] = grid_infotext[:1+z_count]
|
||||
processed.infotexts[:1 + z_count] = grid_infotext[:1 + z_count]
|
||||
|
||||
if not include_lone_images:
|
||||
# Don't need sub-images anymore, drop from list:
|
||||
processed.images = processed.images[:z_count+1]
|
||||
processed.images = processed.images[:z_count + 1]
|
||||
|
||||
if opts.grid_save:
|
||||
# Auto-save main and sub-grids:
|
||||
grid_count = z_count + 1 if z_count > 1 else 1
|
||||
for g in range(grid_count):
|
||||
# TODO: See previous comment about intentional data misalignment.
|
||||
adj_g = g-1 if g > 0 else g
|
||||
adj_g = g - 1 if g > 0 else g
|
||||
images.save_image(processed.images[g], p.outpath_grids, "xyz_grid", info=processed.infotexts[g], extension=opts.grid_format, prompt=processed.all_prompts[adj_g], seed=processed.all_seeds[adj_g], grid=True, p=processed)
|
||||
if not include_sub_grids: # if not include_sub_grids then skip saving after the first grid
|
||||
break
|
||||
|
||||
Reference in New Issue
Block a user