mirror of
https://github.com/vladmandic/automatic
synced 2026-09-20 01:31:13 +02:00
Vladimir Mandic
GitHub
@@ -8,7 +8,7 @@ import torch
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import modules.face_restoration
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import modules.shared
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from modules import shared, devices, modelloader
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from modules.paths import script_path, models_path
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from modules.paths import models_path
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# codeformer people made a choice to include modified basicsr library to their project which makes
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# it utterly impossible to use it alongside with other libraries that also use basicsr, like GFPGAN.
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@@ -7,9 +7,11 @@ import git
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from modules import paths, shared
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extensions = []
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extensions_dir = os.path.join(paths.script_path, "extensions")
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extensions_dir = os.path.join(paths.data_path, "extensions")
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extensions_builtin_dir = os.path.join(paths.script_path, "extensions-builtin")
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if not os.path.exists(extensions_dir):
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os.makedirs(extensions_dir)
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def active():
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return [x for x in extensions if x.enabled]
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@@ -1,4 +1,4 @@
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from modules import extra_networks
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from modules import extra_networks, shared, extra_networks
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from modules.hypernetworks import hypernetwork
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@@ -7,6 +7,12 @@ class ExtraNetworkHypernet(extra_networks.ExtraNetwork):
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super().__init__('hypernet')
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def activate(self, p, params_list):
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additional = shared.opts.sd_hypernetwork
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if additional != "" and additional in shared.hypernetworks and len([x for x in params_list if x.items[0] == additional]) == 0:
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p.all_prompts = [x + f"<hypernet:{additional}:{shared.opts.extra_networks_default_multiplier}>" for x in p.all_prompts]
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params_list.append(extra_networks.ExtraNetworkParams(items=[additional, shared.opts.extra_networks_default_multiplier]))
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names = []
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multipliers = []
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for params in params_list:
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+14
-7
@@ -6,7 +6,7 @@ import shutil
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import torch
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import tqdm
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from modules import shared, images, sd_models, sd_vae
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from modules import shared, images, sd_models, sd_vae, sd_models_config
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from modules.ui_common import plaintext_to_html
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import gradio as gr
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import safetensors.torch
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@@ -37,7 +37,7 @@ def run_pnginfo(image):
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def create_config(ckpt_result, config_source, a, b, c):
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def config(x):
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res = sd_models.find_checkpoint_config(x) if x else None
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res = sd_models_config.find_checkpoint_config_near_filename(x) if x else None
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return res if res != shared.sd_default_config else None
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if config_source == 0:
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@@ -132,6 +132,7 @@ def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_
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tertiary_model_info = sd_models.checkpoints_list[tertiary_model_name] if theta_func1 else None
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result_is_inpainting_model = False
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result_is_instruct_pix2pix_model = False
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if theta_func2:
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shared.state.textinfo = f"Loading B"
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@@ -185,14 +186,19 @@ def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_
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if a.shape != b.shape and a.shape[0:1] + a.shape[2:] == b.shape[0:1] + b.shape[2:]:
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if a.shape[1] == 4 and b.shape[1] == 9:
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raise RuntimeError("When merging inpainting model with a normal one, A must be the inpainting model.")
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if a.shape[1] == 4 and b.shape[1] == 8:
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raise RuntimeError("When merging instruct-pix2pix model with a normal one, A must be the instruct-pix2pix model.")
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assert a.shape[1] == 9 and b.shape[1] == 4, f"Bad dimensions for merged layer {key}: A={a.shape}, B={b.shape}"
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theta_0[key][:, 0:4, :, :] = theta_func2(a[:, 0:4, :, :], b, multiplier)
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result_is_inpainting_model = True
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if a.shape[1] == 8 and b.shape[1] == 4:#If we have an Instruct-Pix2Pix model...
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theta_0[key][:, 0:4, :, :] = theta_func2(a[:, 0:4, :, :], b, multiplier)#Merge only the vectors the models have in common. Otherwise we get an error due to dimension mismatch.
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result_is_instruct_pix2pix_model = True
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else:
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assert a.shape[1] == 9 and b.shape[1] == 4, f"Bad dimensions for merged layer {key}: A={a.shape}, B={b.shape}"
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theta_0[key][:, 0:4, :, :] = theta_func2(a[:, 0:4, :, :], b, multiplier)
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result_is_inpainting_model = True
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else:
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theta_0[key] = theta_func2(a, b, multiplier)
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theta_0[key] = to_half(theta_0[key], save_as_half)
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shared.state.sampling_step += 1
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@@ -226,6 +232,7 @@ def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_
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filename = filename_generator() if custom_name == '' else custom_name
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filename += ".inpainting" if result_is_inpainting_model else ""
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filename += ".instruct-pix2pix" if result_is_instruct_pix2pix_model else ""
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filename += "." + checkpoint_format
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output_modelname = os.path.join(ckpt_dir, filename)
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@@ -6,12 +6,12 @@ import re
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from pathlib import Path
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import gradio as gr
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from modules.shared import script_path
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from modules.paths import data_path
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from modules import shared, ui_tempdir, script_callbacks
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import tempfile
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from PIL import Image
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re_param_code = r'\s*([\w ]+):\s*("(?:\\|\"|[^\"])+"|[^,]*)(?:,|$)'
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re_param_code = r'\s*([\w ]+):\s*("(?:\\"[^,]|\\"|\\|[^\"])+"|[^,]*)(?:,|$)'
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re_param = re.compile(re_param_code)
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re_params = re.compile(r"^(?:" + re_param_code + "){3,}$")
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re_imagesize = re.compile(r"^(\d+)x(\d+)$")
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@@ -289,7 +289,7 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
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def connect_paste(button, paste_fields, input_comp, jsfunc=None):
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def paste_func(prompt):
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if not prompt and not shared.cmd_opts.hide_ui_dir_config:
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filename = os.path.join(script_path, "params.txt")
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filename = os.path.join(data_path, "params.txt")
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if os.path.exists(filename):
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with open(filename, "r", encoding="utf8") as file:
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prompt = file.read()
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@@ -6,12 +6,11 @@ import facexlib
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import gfpgan
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import modules.face_restoration
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from modules import shared, devices, modelloader
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from modules.paths import models_path
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from modules import paths, shared, devices, modelloader
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model_dir = "GFPGAN"
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user_path = None
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model_path = os.path.join(models_path, model_dir)
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model_path = os.path.join(paths.models_path, model_dir)
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model_url = "https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth"
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have_gfpgan = False
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loaded_gfpgan_model = None
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+3
-1
@@ -4,8 +4,10 @@ import os.path
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import filelock
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from modules.paths import data_path
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cache_filename = "cache.json"
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cache_filename = os.path.join(data_path, "cache.json")
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cache_data = None
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+19
-3
@@ -16,11 +16,18 @@ import modules.images as images
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import modules.scripts
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def process_batch(p, input_dir, output_dir, args):
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def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args):
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processing.fix_seed(p)
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images = shared.listfiles(input_dir)
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is_inpaint_batch = False
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if inpaint_mask_dir:
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inpaint_masks = shared.listfiles(inpaint_mask_dir)
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is_inpaint_batch = len(inpaint_masks) > 0
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if is_inpaint_batch:
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print(f"\nInpaint batch is enabled. {len(inpaint_masks)} masks found.")
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print(f"Will process {len(images)} images, creating {p.n_iter * p.batch_size} new images for each.")
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save_normally = output_dir == ''
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@@ -43,6 +50,15 @@ def process_batch(p, input_dir, output_dir, args):
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img = ImageOps.exif_transpose(img)
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p.init_images = [img] * p.batch_size
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if is_inpaint_batch:
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# try to find corresponding mask for an image using simple filename matching
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mask_image_path = os.path.join(inpaint_mask_dir, os.path.basename(image))
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# if not found use first one ("same mask for all images" use-case)
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if not mask_image_path in inpaint_masks:
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mask_image_path = inpaint_masks[0]
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mask_image = Image.open(mask_image_path)
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p.image_mask = mask_image
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proc = modules.scripts.scripts_img2img.run(p, *args)
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if proc is None:
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proc = process_images(p)
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@@ -59,7 +75,7 @@ def process_batch(p, input_dir, output_dir, args):
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processed_image.save(os.path.join(output_dir, filename))
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def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_styles, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, steps: int, sampler_index: int, mask_blur: int, mask_alpha: float, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, *args):
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def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_styles, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, steps: int, sampler_index: int, mask_blur: int, mask_alpha: float, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, img2img_batch_inpaint_mask_dir: str, *args):
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is_batch = mode == 5
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if mode == 0: # img2img
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@@ -139,7 +155,7 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s
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if is_batch:
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assert not shared.cmd_opts.hide_ui_dir_config, "Launched with --hide-ui-dir-config, batch img2img disabled"
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process_batch(p, img2img_batch_input_dir, img2img_batch_output_dir, args)
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process_batch(p, img2img_batch_input_dir, img2img_batch_output_dir, img2img_batch_inpaint_mask_dir, args)
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processed = Processed(p, [], p.seed, "")
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else:
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@@ -12,7 +12,7 @@ from torchvision import transforms
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from torchvision.transforms.functional import InterpolationMode
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import modules.shared as shared
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from modules import devices, paths, lowvram, modelloader, errors
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from modules import devices, paths, shared, lowvram, modelloader, errors
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blip_image_eval_size = 384
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clip_model_name = 'ViT-L/14'
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+9
-1
@@ -4,7 +4,15 @@ import sys
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import modules.safe
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script_path = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
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models_path = os.path.join(script_path, "models")
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# Parse the --data-dir flag first so we can use it as a base for our other argument default values
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parser = argparse.ArgumentParser(add_help=False)
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parser.add_argument("--data-dir", type=str, default=os.path.dirname(os.path.dirname(os.path.realpath(__file__))), help="base path where all user data is stored",)
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cmd_opts_pre = parser.parse_known_args()[0]
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data_path = cmd_opts_pre.data_dir
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models_path = os.path.join(data_path, "models")
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# data_path = cmd_opts_pre.data
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sys.path.insert(0, script_path)
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# search for directory of stable diffusion in following places
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@@ -17,6 +17,7 @@ from modules import devices, prompt_parser, masking, sd_samplers, lowvram, gener
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from modules.sd_hijack import model_hijack
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from modules.shared import opts, cmd_opts, state
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import modules.shared as shared
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import modules.paths as paths
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import modules.face_restoration
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import modules.images as images
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import modules.styles
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@@ -584,7 +585,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
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if not p.disable_extra_networks:
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extra_networks.activate(p, extra_network_data)
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with open(os.path.join(shared.script_path, "params.txt"), "w", encoding="utf8") as file:
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with open(os.path.join(paths.data_path, "params.txt"), "w", encoding="utf8") as file:
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processed = Processed(p, [], p.seed, "")
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file.write(processed.infotext(p, 0))
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@@ -1,16 +1,14 @@
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import os
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import sys
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import traceback
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import importlib.util
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from types import ModuleType
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def load_module(path):
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with open(path, "r", encoding="utf8") as file:
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text = file.read()
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compiled = compile(text, path, 'exec')
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module = ModuleType(os.path.basename(path))
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exec(compiled, module.__dict__)
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module_spec = importlib.util.spec_from_file_location(os.path.basename(path), path)
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module = importlib.util.module_from_spec(module_spec)
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module_spec.loader.exec_module(module)
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return module
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@@ -131,6 +131,8 @@ class StableDiffusionModelHijack:
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m.cond_stage_model.wrapped.model.token_embedding = m.cond_stage_model.wrapped.model.token_embedding.wrapped
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m.cond_stage_model = m.cond_stage_model.wrapped
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undo_optimizations()
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self.apply_circular(False)
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self.layers = None
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self.clip = None
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@@ -5,9 +5,9 @@ import gc
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import time
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def should_hijack_ip2p(checkpoint_info):
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from modules import sd_models
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from modules import sd_models_config
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ckpt_basename = os.path.basename(checkpoint_info.filename).lower()
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cfg_basename = os.path.basename(sd_models.find_checkpoint_config(checkpoint_info)).lower()
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cfg_basename = os.path.basename(sd_models_config.find_checkpoint_config_near_filename(checkpoint_info)).lower()
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return "pix2pix" in ckpt_basename and not "pix2pix" in cfg_basename
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|
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@@ -12,13 +12,13 @@ import ldm.modules.midas as midas
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from ldm.util import instantiate_from_config
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|
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from modules import shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors, hashes, sd_models_config
|
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from modules import paths, shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors, hashes, sd_models_config
|
||||
from modules.paths import models_path
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from modules.sd_hijack_inpainting import do_inpainting_hijack
|
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from modules.timer import Timer
|
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|
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model_dir = "Stable-diffusion"
|
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model_path = os.path.abspath(os.path.join(models_path, model_dir))
|
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model_path = os.path.abspath(os.path.join(paths.models_path, model_dir))
|
||||
|
||||
checkpoints_list = {}
|
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checkpoint_alisases = {}
|
||||
@@ -231,12 +231,10 @@ def get_checkpoint_state_dict(checkpoint_info: CheckpointInfo, timer):
|
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|
||||
|
||||
def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer):
|
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title = checkpoint_info.title
|
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sd_model_hash = checkpoint_info.calculate_shorthash()
|
||||
timer.record("calculate hash")
|
||||
|
||||
if checkpoint_info.title != title:
|
||||
shared.opts.data["sd_model_checkpoint"] = checkpoint_info.title
|
||||
shared.opts.data["sd_model_checkpoint"] = checkpoint_info.title
|
||||
|
||||
if state_dict is None:
|
||||
state_dict = get_checkpoint_state_dict(checkpoint_info, timer)
|
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@@ -307,7 +305,7 @@ def enable_midas_autodownload():
|
||||
location automatically.
|
||||
"""
|
||||
|
||||
midas_path = os.path.join(models_path, 'midas')
|
||||
midas_path = os.path.join(paths.models_path, 'midas')
|
||||
|
||||
# stable-diffusion-stability-ai hard-codes the midas model path to
|
||||
# a location that differs from where other scripts using this model look.
|
||||
|
||||
@@ -1,7 +1,9 @@
|
||||
import re
|
||||
import os
|
||||
|
||||
from modules import shared, paths
|
||||
import torch
|
||||
|
||||
from modules import shared, paths, sd_disable_initialization
|
||||
|
||||
sd_configs_path = shared.sd_configs_path
|
||||
sd_repo_configs_path = os.path.join(paths.paths['Stable Diffusion'], "configs", "stable-diffusion")
|
||||
@@ -10,17 +12,57 @@ sd_repo_configs_path = os.path.join(paths.paths['Stable Diffusion'], "configs",
|
||||
config_default = shared.sd_default_config
|
||||
config_sd2 = os.path.join(sd_repo_configs_path, "v2-inference.yaml")
|
||||
config_sd2v = os.path.join(sd_repo_configs_path, "v2-inference-v.yaml")
|
||||
config_sd2_inpainting = os.path.join(sd_repo_configs_path, "v2-inpainting-inference.yaml")
|
||||
config_depth_model = os.path.join(sd_repo_configs_path, "v2-midas-inference.yaml")
|
||||
config_inpainting = os.path.join(sd_configs_path, "v1-inpainting-inference.yaml")
|
||||
config_instruct_pix2pix = os.path.join(sd_configs_path, "instruct-pix2pix.yaml")
|
||||
config_alt_diffusion = os.path.join(sd_configs_path, "alt-diffusion-inference.yaml")
|
||||
|
||||
re_parametrization_v = re.compile(r'-v\b')
|
||||
|
||||
def is_using_v_parameterization_for_sd2(state_dict):
|
||||
"""
|
||||
Detects whether unet in state_dict is using v-parameterization. Returns True if it is. You're welcome.
|
||||
"""
|
||||
|
||||
import ldm.modules.diffusionmodules.openaimodel
|
||||
from modules import devices
|
||||
|
||||
device = devices.cpu
|
||||
|
||||
with sd_disable_initialization.DisableInitialization():
|
||||
unet = ldm.modules.diffusionmodules.openaimodel.UNetModel(
|
||||
use_checkpoint=True,
|
||||
use_fp16=False,
|
||||
image_size=32,
|
||||
in_channels=4,
|
||||
out_channels=4,
|
||||
model_channels=320,
|
||||
attention_resolutions=[4, 2, 1],
|
||||
num_res_blocks=2,
|
||||
channel_mult=[1, 2, 4, 4],
|
||||
num_head_channels=64,
|
||||
use_spatial_transformer=True,
|
||||
use_linear_in_transformer=True,
|
||||
transformer_depth=1,
|
||||
context_dim=1024,
|
||||
legacy=False
|
||||
)
|
||||
unet.eval()
|
||||
|
||||
with torch.no_grad():
|
||||
unet_sd = {k.replace("model.diffusion_model.", ""): v for k, v in state_dict.items() if "model.diffusion_model." in k}
|
||||
unet.load_state_dict(unet_sd, strict=True)
|
||||
unet.to(device=device, dtype=torch.float)
|
||||
|
||||
test_cond = torch.ones((1, 2, 1024), device=device) * 0.5
|
||||
x_test = torch.ones((1, 4, 8, 8), device=device) * 0.5
|
||||
|
||||
out = (unet(x_test, torch.asarray([999], device=device), context=test_cond) - x_test).mean().item()
|
||||
|
||||
return out < -1
|
||||
|
||||
|
||||
def guess_model_config_from_state_dict(sd, filename):
|
||||
fn = os.path.basename(filename)
|
||||
|
||||
sd2_cond_proj_weight = sd.get('cond_stage_model.model.transformer.resblocks.0.attn.in_proj_weight', None)
|
||||
diffusion_model_input = sd.get('model.diffusion_model.input_blocks.0.0.weight', None)
|
||||
|
||||
@@ -28,7 +70,9 @@ def guess_model_config_from_state_dict(sd, filename):
|
||||
return config_depth_model
|
||||
|
||||
if sd2_cond_proj_weight is not None and sd2_cond_proj_weight.shape[1] == 1024:
|
||||
if re.search(re_parametrization_v, fn) or "v2-1_768" in fn:
|
||||
if diffusion_model_input.shape[1] == 9:
|
||||
return config_sd2_inpainting
|
||||
elif is_using_v_parameterization_for_sd2(sd):
|
||||
return config_sd2v
|
||||
else:
|
||||
return config_sd2
|
||||
|
||||
+2
-3
@@ -3,13 +3,12 @@ import safetensors.torch
|
||||
import os
|
||||
import collections
|
||||
from collections import namedtuple
|
||||
from modules import shared, devices, script_callbacks, sd_models
|
||||
from modules.paths import models_path
|
||||
from modules import paths, shared, devices, script_callbacks, sd_models
|
||||
import glob
|
||||
from copy import deepcopy
|
||||
|
||||
|
||||
vae_path = os.path.abspath(os.path.join(models_path, "VAE"))
|
||||
vae_path = os.path.abspath(os.path.join(paths.models_path, "VAE"))
|
||||
vae_ignore_keys = {"model_ema.decay", "model_ema.num_updates"}
|
||||
vae_dict = {}
|
||||
|
||||
|
||||
+3
-2
@@ -405,7 +405,6 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
|
||||
"enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"),
|
||||
"comma_padding_backtrack": OptionInfo(20, "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1 }),
|
||||
"CLIP_stop_at_last_layers": OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}),
|
||||
"extra_networks_default_multiplier": OptionInfo(1.0, "Multiplier for extra networks", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
||||
"upcast_attn": OptionInfo(False, "Upcast cross attention layer to float32"),
|
||||
}))
|
||||
|
||||
@@ -431,7 +430,9 @@ options_templates.update(options_section(('interrogate', "Interrogate Options"),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('extra_networks', "Extra Networks"), {
|
||||
"extra_networks_default_view": OptionInfo("cards", "Default view for Extra Networks", gr.Dropdown, { "choices": ["cards", "thumbs"] }),
|
||||
"extra_networks_default_view": OptionInfo("cards", "Default view for Extra Networks", gr.Dropdown, {"choices": ["cards", "thumbs"]}),
|
||||
"extra_networks_default_multiplier": OptionInfo(1.0, "Multiplier for extra networks", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
||||
"sd_hypernetwork": OptionInfo("None", "Add hypernetwork to prompt", gr.Dropdown, lambda: {"choices": [""] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('ui', "User interface"), {
|
||||
|
||||
@@ -6,8 +6,7 @@ import sys
|
||||
import tqdm
|
||||
import time
|
||||
|
||||
from modules import shared, images, deepbooru
|
||||
from modules.paths import models_path
|
||||
from modules import paths, shared, images, deepbooru
|
||||
from modules.shared import opts, cmd_opts
|
||||
from modules.textual_inversion import autocrop
|
||||
|
||||
@@ -199,7 +198,7 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pre
|
||||
|
||||
dnn_model_path = None
|
||||
try:
|
||||
dnn_model_path = autocrop.download_and_cache_models(os.path.join(models_path, "opencv"))
|
||||
dnn_model_path = autocrop.download_and_cache_models(os.path.join(paths.models_path, "opencv"))
|
||||
except Exception as e:
|
||||
print("Unable to load face detection model for auto crop selection. Falling back to lower quality haar method.", e)
|
||||
|
||||
|
||||
+19
-6
@@ -21,7 +21,7 @@ from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, wrap_grad
|
||||
|
||||
from modules import sd_hijack, sd_models, localization, script_callbacks, ui_extensions, deepbooru, sd_vae, extra_networks, postprocessing, ui_components, ui_common, ui_postprocessing
|
||||
from modules.ui_components import FormRow, FormGroup, ToolButton, FormHTML
|
||||
from modules.paths import script_path
|
||||
from modules.paths import script_path, data_path
|
||||
|
||||
from modules.shared import opts, cmd_opts, restricted_opts
|
||||
|
||||
@@ -91,6 +91,7 @@ save_style_symbol = '\U0001f4be' # 💾
|
||||
apply_style_symbol = '\U0001f4cb' # 📋
|
||||
clear_prompt_symbol = '\U0001F5D1' # 🗑️
|
||||
extra_networks_symbol = '\U0001F3B4' # 🎴
|
||||
switch_values_symbol = '\U000021C5' # ⇅
|
||||
|
||||
|
||||
def plaintext_to_html(text):
|
||||
@@ -466,6 +467,7 @@ def create_ui():
|
||||
height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="txt2img_height")
|
||||
|
||||
if opts.dimensions_and_batch_together:
|
||||
res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="txt2img_res_switch_btn")
|
||||
with gr.Column(elem_id="txt2img_column_batch"):
|
||||
batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="txt2img_batch_count")
|
||||
batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="txt2img_batch_size")
|
||||
@@ -567,6 +569,8 @@ def create_ui():
|
||||
txt2img_prompt.submit(**txt2img_args)
|
||||
submit.click(**txt2img_args)
|
||||
|
||||
res_switch_btn.click(lambda w, h: (h, w), inputs=[width, height], outputs=[width, height])
|
||||
|
||||
txt_prompt_img.change(
|
||||
fn=modules.images.image_data,
|
||||
inputs=[
|
||||
@@ -691,9 +695,15 @@ def create_ui():
|
||||
|
||||
with gr.TabItem('Batch', id='batch', elem_id="img2img_batch_tab") as tab_batch:
|
||||
hidden = '<br>Disabled when launched with --hide-ui-dir-config.' if shared.cmd_opts.hide_ui_dir_config else ''
|
||||
gr.HTML(f"<p style='padding-bottom: 1em;' class=\"text-gray-500\">Process images in a directory on the same machine where the server is running.<br>Use an empty output directory to save pictures normally instead of writing to the output directory.{hidden}</p>")
|
||||
gr.HTML(
|
||||
f"<p style='padding-bottom: 1em;' class=\"text-gray-500\">Process images in a directory on the same machine where the server is running." +
|
||||
f"<br>Use an empty output directory to save pictures normally instead of writing to the output directory." +
|
||||
f"<br>Add inpaint batch mask directory to enable inpaint batch processing."
|
||||
f"{hidden}</p>"
|
||||
)
|
||||
img2img_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, elem_id="img2img_batch_input_dir")
|
||||
img2img_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, elem_id="img2img_batch_output_dir")
|
||||
img2img_batch_inpaint_mask_dir = gr.Textbox(label="Inpaint batch mask directory (required for inpaint batch processing only)", **shared.hide_dirs, elem_id="img2img_batch_inpaint_mask_dir")
|
||||
|
||||
def copy_image(img):
|
||||
if isinstance(img, dict) and 'image' in img:
|
||||
@@ -728,6 +738,7 @@ def create_ui():
|
||||
height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="img2img_height")
|
||||
|
||||
if opts.dimensions_and_batch_together:
|
||||
res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="img2img_res_switch_btn")
|
||||
with gr.Column(elem_id="img2img_column_batch"):
|
||||
batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="img2img_batch_count")
|
||||
batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="img2img_batch_size")
|
||||
@@ -838,6 +849,7 @@ def create_ui():
|
||||
inpainting_mask_invert,
|
||||
img2img_batch_input_dir,
|
||||
img2img_batch_output_dir,
|
||||
img2img_batch_inpaint_mask_dir
|
||||
] + custom_inputs,
|
||||
outputs=[
|
||||
img2img_gallery,
|
||||
@@ -865,6 +877,7 @@ def create_ui():
|
||||
|
||||
img2img_prompt.submit(**img2img_args)
|
||||
submit.click(**img2img_args)
|
||||
res_switch_btn.click(lambda w, h: (h, w), inputs=[width, height], outputs=[width, height])
|
||||
|
||||
img2img_interrogate.click(
|
||||
fn=lambda *args: process_interrogate(interrogate, *args),
|
||||
@@ -1497,8 +1510,8 @@ def create_ui():
|
||||
with open(cssfile, "r", encoding="utf8") as file:
|
||||
css += file.read() + "\n"
|
||||
|
||||
if os.path.exists(os.path.join(script_path, "user.css")):
|
||||
with open(os.path.join(script_path, "user.css"), "r", encoding="utf8") as file:
|
||||
if os.path.exists(os.path.join(data_path, "user.css")):
|
||||
with open(os.path.join(data_path, "user.css"), "r", encoding="utf8") as file:
|
||||
css += file.read() + "\n"
|
||||
|
||||
if not cmd_opts.no_progressbar_hiding:
|
||||
@@ -1679,14 +1692,14 @@ def create_ui():
|
||||
|
||||
|
||||
def reload_javascript():
|
||||
head = f'<script type="text/javascript" src="file={os.path.abspath("script.js")}"></script>\n'
|
||||
head = f'<script type="text/javascript" src="file={os.path.abspath("script.js")}?{os.path.getmtime("script.js")}"></script>\n'
|
||||
|
||||
inline = f"{localization.localization_js(shared.opts.localization)};"
|
||||
if cmd_opts.theme is not None:
|
||||
inline += f"set_theme('{cmd_opts.theme}');"
|
||||
|
||||
for script in modules.scripts.list_scripts("javascript", ".js"):
|
||||
head += f'<script type="text/javascript" src="file={script.path}"></script>\n'
|
||||
head += f'<script type="text/javascript" src="file={script.path}?{os.path.getmtime(script.path)}"></script>\n'
|
||||
|
||||
head += f'<script type="text/javascript">{inline}</script>\n'
|
||||
|
||||
|
||||
+19
-11
@@ -13,7 +13,7 @@ import shutil
|
||||
import errno
|
||||
|
||||
from modules import extensions, shared, paths
|
||||
|
||||
from modules.call_queue import wrap_gradio_gpu_call
|
||||
|
||||
available_extensions = {"extensions": []}
|
||||
|
||||
@@ -50,12 +50,17 @@ def apply_and_restart(disable_list, update_list):
|
||||
shared.state.need_restart = True
|
||||
|
||||
|
||||
def check_updates():
|
||||
def check_updates(id_task, disable_list):
|
||||
check_access()
|
||||
|
||||
for ext in extensions.extensions:
|
||||
if ext.remote is None:
|
||||
continue
|
||||
disabled = json.loads(disable_list)
|
||||
assert type(disabled) == list, f"wrong disable_list data for apply_and_restart: {disable_list}"
|
||||
|
||||
exts = [ext for ext in extensions.extensions if ext.remote is not None and ext.name not in disabled]
|
||||
shared.state.job_count = len(exts)
|
||||
|
||||
for ext in exts:
|
||||
shared.state.textinfo = ext.name
|
||||
|
||||
try:
|
||||
ext.check_updates()
|
||||
@@ -63,7 +68,9 @@ def check_updates():
|
||||
print(f"Error checking updates for {ext.name}:", file=sys.stderr)
|
||||
print(traceback.format_exc(), file=sys.stderr)
|
||||
|
||||
return extension_table()
|
||||
shared.state.nextjob()
|
||||
|
||||
return extension_table(), ""
|
||||
|
||||
|
||||
def extension_table():
|
||||
@@ -132,7 +139,7 @@ def install_extension_from_url(dirname, url):
|
||||
normalized_url = normalize_git_url(url)
|
||||
assert len([x for x in extensions.extensions if normalize_git_url(x.remote) == normalized_url]) == 0, 'Extension with this URL is already installed'
|
||||
|
||||
tmpdir = os.path.join(paths.script_path, "tmp", dirname)
|
||||
tmpdir = os.path.join(paths.data_path, "tmp", dirname)
|
||||
|
||||
try:
|
||||
shutil.rmtree(tmpdir, True)
|
||||
@@ -273,12 +280,13 @@ def create_ui():
|
||||
with gr.Tabs(elem_id="tabs_extensions") as tabs:
|
||||
with gr.TabItem("Installed"):
|
||||
|
||||
with gr.Row():
|
||||
with gr.Row(elem_id="extensions_installed_top"):
|
||||
apply = gr.Button(value="Apply and restart UI", variant="primary")
|
||||
check = gr.Button(value="Check for updates")
|
||||
extensions_disabled_list = gr.Text(elem_id="extensions_disabled_list", visible=False).style(container=False)
|
||||
extensions_update_list = gr.Text(elem_id="extensions_update_list", visible=False).style(container=False)
|
||||
|
||||
info = gr.HTML()
|
||||
extensions_table = gr.HTML(lambda: extension_table())
|
||||
|
||||
apply.click(
|
||||
@@ -289,10 +297,10 @@ def create_ui():
|
||||
)
|
||||
|
||||
check.click(
|
||||
fn=check_updates,
|
||||
fn=wrap_gradio_gpu_call(check_updates, extra_outputs=[gr.update()]),
|
||||
_js="extensions_check",
|
||||
inputs=[],
|
||||
outputs=[extensions_table],
|
||||
inputs=[info, extensions_disabled_list],
|
||||
outputs=[extensions_table, info],
|
||||
)
|
||||
|
||||
with gr.TabItem("Available"):
|
||||
|
||||
+2
-3
@@ -11,7 +11,6 @@ from modules import modelloader, shared
|
||||
|
||||
LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS)
|
||||
NEAREST = (Image.Resampling.NEAREST if hasattr(Image, 'Resampling') else Image.NEAREST)
|
||||
from modules.paths import models_path
|
||||
|
||||
|
||||
class Upscaler:
|
||||
@@ -39,7 +38,7 @@ class Upscaler:
|
||||
self.mod_scale = None
|
||||
|
||||
if self.model_path is None and self.name:
|
||||
self.model_path = os.path.join(models_path, self.name)
|
||||
self.model_path = os.path.join(shared.models_path, self.name)
|
||||
if self.model_path and create_dirs:
|
||||
os.makedirs(self.model_path, exist_ok=True)
|
||||
|
||||
@@ -143,4 +142,4 @@ class UpscalerNearest(Upscaler):
|
||||
def __init__(self, dirname=None):
|
||||
super().__init__(False)
|
||||
self.name = "Nearest"
|
||||
self.scalers = [UpscalerData("Nearest", None, self)]
|
||||
self.scalers = [UpscalerData("Nearest", None, self)]
|
||||
|
||||
Reference in New Issue
Block a user