mirror of
https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
synced 2026-01-09 02:31:57 -08:00
Merge branch 'AUTOMATIC1111:master' into master
This commit is contained in:
不会画画的中医不是好程序员
GitHub
commit
1e18a5ffcc
42 changed files with 1223 additions and 291 deletions
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@ -10,13 +10,11 @@ from basicsr.utils.download_util import load_file_from_url
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import modules.upscaler
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from modules import devices, modelloader
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from modules.bsrgan_model_arch import RRDBNet
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from modules.paths import models_path
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class UpscalerBSRGAN(modules.upscaler.Upscaler):
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def __init__(self, dirname):
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self.name = "BSRGAN"
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self.model_path = os.path.join(models_path, self.name)
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self.model_name = "BSRGAN 4x"
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self.model_url = "https://github.com/cszn/KAIR/releases/download/v1.0/BSRGAN.pth"
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self.user_path = dirname
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73
modules/deepbooru.py
Normal file
73
modules/deepbooru.py
Normal file
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@ -0,0 +1,73 @@
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import os.path
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from concurrent.futures import ProcessPoolExecutor
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from multiprocessing import get_context
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def _load_tf_and_return_tags(pil_image, threshold):
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import deepdanbooru as dd
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import tensorflow as tf
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import numpy as np
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this_folder = os.path.dirname(__file__)
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model_path = os.path.abspath(os.path.join(this_folder, '..', 'models', 'deepbooru'))
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if not os.path.exists(os.path.join(model_path, 'project.json')):
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# there is no point importing these every time
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import zipfile
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from basicsr.utils.download_util import load_file_from_url
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load_file_from_url(r"https://github.com/KichangKim/DeepDanbooru/releases/download/v3-20211112-sgd-e28/deepdanbooru-v3-20211112-sgd-e28.zip",
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model_path)
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with zipfile.ZipFile(os.path.join(model_path, "deepdanbooru-v3-20211112-sgd-e28.zip"), "r") as zip_ref:
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zip_ref.extractall(model_path)
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os.remove(os.path.join(model_path, "deepdanbooru-v3-20211112-sgd-e28.zip"))
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tags = dd.project.load_tags_from_project(model_path)
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model = dd.project.load_model_from_project(
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model_path, compile_model=True
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)
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width = model.input_shape[2]
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height = model.input_shape[1]
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image = np.array(pil_image)
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image = tf.image.resize(
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image,
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size=(height, width),
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method=tf.image.ResizeMethod.AREA,
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preserve_aspect_ratio=True,
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)
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image = image.numpy() # EagerTensor to np.array
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image = dd.image.transform_and_pad_image(image, width, height)
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image = image / 255.0
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image_shape = image.shape
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image = image.reshape((1, image_shape[0], image_shape[1], image_shape[2]))
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y = model.predict(image)[0]
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result_dict = {}
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for i, tag in enumerate(tags):
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result_dict[tag] = y[i]
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result_tags_out = []
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result_tags_print = []
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for tag in tags:
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if result_dict[tag] >= threshold:
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if tag.startswith("rating:"):
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continue
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result_tags_out.append(tag)
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result_tags_print.append(f'{result_dict[tag]} {tag}')
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print('\n'.join(sorted(result_tags_print, reverse=True)))
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return ', '.join(result_tags_out).replace('_', ' ').replace(':', ' ')
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def subprocess_init_no_cuda():
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import os
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os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
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def get_deepbooru_tags(pil_image, threshold=0.5):
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context = get_context('spawn')
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with ProcessPoolExecutor(initializer=subprocess_init_no_cuda, mp_context=context) as executor:
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f = executor.submit(_load_tf_and_return_tags, pil_image, threshold, )
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ret = f.result() # will rethrow any exceptions
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return ret
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@ -5,9 +5,8 @@ import torch
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from PIL import Image
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from basicsr.utils.download_util import load_file_from_url
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import modules.esrgam_model_arch as arch
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import modules.esrgan_model_arch as arch
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from modules import shared, modelloader, images, devices
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from modules.paths import models_path
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from modules.upscaler import Upscaler, UpscalerData
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from modules.shared import opts
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@ -76,7 +75,6 @@ class UpscalerESRGAN(Upscaler):
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self.model_name = "ESRGAN_4x"
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self.scalers = []
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self.user_path = dirname
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self.model_path = os.path.join(models_path, self.name)
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super().__init__()
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model_paths = self.find_models(ext_filter=[".pt", ".pth"])
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scalers = []
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@ -111,7 +109,7 @@ class UpscalerESRGAN(Upscaler):
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print("Unable to load %s from %s" % (self.model_path, filename))
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return None
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pretrained_net = torch.load(filename, map_location='cpu' if shared.device.type == 'mps' else None)
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pretrained_net = torch.load(filename, map_location='cpu' if devices.device_esrgan.type == 'mps' else None)
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crt_model = arch.RRDBNet(3, 3, 64, 23, gc=32)
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pretrained_net = fix_model_layers(crt_model, pretrained_net)
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@ -29,7 +29,7 @@ def run_extras(extras_mode, image, image_folder, gfpgan_visibility, codeformer_v
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if extras_mode == 1:
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#convert file to pillow image
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for img in image_folder:
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image = Image.fromarray(np.array(Image.open(img)))
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image = Image.open(img)
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imageArr.append(image)
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imageNameArr.append(os.path.splitext(img.orig_name)[0])
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else:
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@ -98,6 +98,10 @@ def run_extras(extras_mode, image, image_folder, gfpgan_visibility, codeformer_v
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no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo,
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forced_filename=image_name if opts.use_original_name_batch else None)
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if opts.enable_pnginfo:
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image.info = existing_pnginfo
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image.info["extras"] = info
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outputs.append(image)
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devices.torch_gc()
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@ -169,9 +173,9 @@ def run_modelmerger(primary_model_name, secondary_model_name, interp_method, int
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print(f"Loading {secondary_model_info.filename}...")
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secondary_model = torch.load(secondary_model_info.filename, map_location='cpu')
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theta_0 = primary_model['state_dict']
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theta_1 = secondary_model['state_dict']
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theta_0 = sd_models.get_state_dict_from_checkpoint(primary_model)
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theta_1 = sd_models.get_state_dict_from_checkpoint(secondary_model)
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theta_funcs = {
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"Weighted Sum": weighted_sum,
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98
modules/hypernetwork.py
Normal file
98
modules/hypernetwork.py
Normal file
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@ -0,0 +1,98 @@
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import glob
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import os
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import sys
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import traceback
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import torch
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from ldm.util import default
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from modules import devices, shared
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import torch
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from torch import einsum
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from einops import rearrange, repeat
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class HypernetworkModule(torch.nn.Module):
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def __init__(self, dim, state_dict):
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super().__init__()
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self.linear1 = torch.nn.Linear(dim, dim * 2)
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self.linear2 = torch.nn.Linear(dim * 2, dim)
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self.load_state_dict(state_dict, strict=True)
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self.to(devices.device)
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def forward(self, x):
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return x + (self.linear2(self.linear1(x)))
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class Hypernetwork:
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filename = None
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name = None
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def __init__(self, filename):
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self.filename = filename
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self.name = os.path.splitext(os.path.basename(filename))[0]
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self.layers = {}
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state_dict = torch.load(filename, map_location='cpu')
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for size, sd in state_dict.items():
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self.layers[size] = (HypernetworkModule(size, sd[0]), HypernetworkModule(size, sd[1]))
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def list_hypernetworks(path):
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res = {}
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for filename in glob.iglob(os.path.join(path, '**/*.pt'), recursive=True):
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name = os.path.splitext(os.path.basename(filename))[0]
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res[name] = filename
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return res
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def load_hypernetwork(filename):
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path = shared.hypernetworks.get(filename, None)
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if path is not None:
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print(f"Loading hypernetwork {filename}")
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try:
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shared.loaded_hypernetwork = Hypernetwork(path)
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except Exception:
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print(f"Error loading hypernetwork {path}", file=sys.stderr)
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print(traceback.format_exc(), file=sys.stderr)
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else:
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if shared.loaded_hypernetwork is not None:
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print(f"Unloading hypernetwork")
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shared.loaded_hypernetwork = None
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def attention_CrossAttention_forward(self, x, context=None, mask=None):
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h = self.heads
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q = self.to_q(x)
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context = default(context, x)
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hypernetwork = shared.loaded_hypernetwork
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hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
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if hypernetwork_layers is not None:
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k = self.to_k(hypernetwork_layers[0](context))
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v = self.to_v(hypernetwork_layers[1](context))
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else:
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k = self.to_k(context)
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v = self.to_v(context)
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q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
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sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
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if mask is not None:
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mask = rearrange(mask, 'b ... -> b (...)')
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max_neg_value = -torch.finfo(sim.dtype).max
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mask = repeat(mask, 'b j -> (b h) () j', h=h)
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sim.masked_fill_(~mask, max_neg_value)
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# attention, what we cannot get enough of
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attn = sim.softmax(dim=-1)
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out = einsum('b i j, b j d -> b i d', attn, v)
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out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
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return self.to_out(out)
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@ -349,6 +349,38 @@ def get_next_sequence_number(path, basename):
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def save_image(image, path, basename, seed=None, prompt=None, extension='png', info=None, short_filename=False, no_prompt=False, grid=False, pnginfo_section_name='parameters', p=None, existing_info=None, forced_filename=None, suffix="", save_to_dirs=None):
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'''Save an image.
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Args:
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image (`PIL.Image`):
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The image to be saved.
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path (`str`):
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The directory to save the image. Note, the option `save_to_dirs` will make the image to be saved into a sub directory.
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basename (`str`):
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The base filename which will be applied to `filename pattern`.
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seed, prompt, short_filename,
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extension (`str`):
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Image file extension, default is `png`.
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pngsectionname (`str`):
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Specify the name of the section which `info` will be saved in.
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info (`str` or `PngImagePlugin.iTXt`):
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PNG info chunks.
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existing_info (`dict`):
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Additional PNG info. `existing_info == {pngsectionname: info, ...}`
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no_prompt:
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TODO I don't know its meaning.
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p (`StableDiffusionProcessing`)
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forced_filename (`str`):
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If specified, `basename` and filename pattern will be ignored.
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save_to_dirs (bool):
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If true, the image will be saved into a subdirectory of `path`.
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Returns: (fullfn, txt_fullfn)
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fullfn (`str`):
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The full path of the saved imaged.
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txt_fullfn (`str` or None):
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If a text file is saved for this image, this will be its full path. Otherwise None.
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'''
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if short_filename or prompt is None or seed is None:
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file_decoration = ""
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elif opts.save_to_dirs:
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@ -424,7 +456,10 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
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piexif.insert(exif_bytes(), fullfn_without_extension + ".jpg")
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if opts.save_txt and info is not None:
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with open(f"{fullfn_without_extension}.txt", "w", encoding="utf8") as file:
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txt_fullfn = f"{fullfn_without_extension}.txt"
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with open(txt_fullfn, "w", encoding="utf8") as file:
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file.write(info + "\n")
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else:
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txt_fullfn = None
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return fullfn
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return fullfn, txt_fullfn
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@ -32,6 +32,8 @@ def process_batch(p, input_dir, output_dir, args):
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for i, image in enumerate(images):
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state.job = f"{i+1} out of {len(images)}"
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if state.skipped:
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state.skipped = False
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if state.interrupted:
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break
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@ -140,11 +140,11 @@ class InterrogateModels:
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res = caption
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cilp_image = self.clip_preprocess(pil_image).unsqueeze(0).type(self.dtype).to(shared.device)
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clip_image = self.clip_preprocess(pil_image).unsqueeze(0).type(self.dtype).to(shared.device)
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precision_scope = torch.autocast if shared.cmd_opts.precision == "autocast" else contextlib.nullcontext
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with torch.no_grad(), precision_scope("cuda"):
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image_features = self.clip_model.encode_image(cilp_image).type(self.dtype)
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image_features = self.clip_model.encode_image(clip_image).type(self.dtype)
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image_features /= image_features.norm(dim=-1, keepdim=True)
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|
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@ -7,13 +7,11 @@ from basicsr.utils.download_util import load_file_from_url
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from modules.upscaler import Upscaler, UpscalerData
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from modules.ldsr_model_arch import LDSR
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from modules import shared
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from modules.paths import models_path
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class UpscalerLDSR(Upscaler):
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def __init__(self, user_path):
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self.name = "LDSR"
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self.model_path = os.path.join(models_path, self.name)
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self.user_path = user_path
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self.model_url = "https://heibox.uni-heidelberg.de/f/578df07c8fc04ffbadf3/?dl=1"
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self.yaml_url = "https://heibox.uni-heidelberg.de/f/31a76b13ea27482981b4/?dl=1"
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@ -1,6 +1,7 @@
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import argparse
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import os
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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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@ -12,6 +13,7 @@ possible_sd_paths = [os.path.join(script_path, 'repositories/stable-diffusion'),
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for possible_sd_path in possible_sd_paths:
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if os.path.exists(os.path.join(possible_sd_path, 'ldm/models/diffusion/ddpm.py')):
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sd_path = os.path.abspath(possible_sd_path)
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break
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assert sd_path is not None, "Couldn't find Stable Diffusion in any of: " + str(possible_sd_paths)
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|
|
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@ -46,6 +46,12 @@ def apply_color_correction(correction, image):
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return image
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def get_correct_sampler(p):
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if isinstance(p, modules.processing.StableDiffusionProcessingTxt2Img):
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return sd_samplers.samplers
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elif isinstance(p, modules.processing.StableDiffusionProcessingImg2Img):
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return sd_samplers.samplers_for_img2img
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class StableDiffusionProcessing:
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def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt="", styles=None, seed=-1, subseed=-1, subseed_strength=0, seed_resize_from_h=-1, seed_resize_from_w=-1, seed_enable_extras=True, sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512, restore_faces=False, tiling=False, do_not_save_samples=False, do_not_save_grid=False, extra_generation_params=None, overlay_images=None, negative_prompt=None, eta=None):
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self.sd_model = sd_model
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@ -123,6 +129,7 @@ class Processed:
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self.index_of_first_image = index_of_first_image
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self.styles = p.styles
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self.job_timestamp = state.job_timestamp
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self.clip_skip = opts.CLIP_stop_at_last_layers
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self.eta = p.eta
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self.ddim_discretize = p.ddim_discretize
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@ -169,6 +176,7 @@ class Processed:
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"infotexts": self.infotexts,
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"styles": self.styles,
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"job_timestamp": self.job_timestamp,
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"clip_skip": self.clip_skip,
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}
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return json.dumps(obj)
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|
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@ -266,14 +274,18 @@ def fix_seed(p):
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def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration=0, position_in_batch=0):
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index = position_in_batch + iteration * p.batch_size
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clip_skip = getattr(p, 'clip_skip', opts.CLIP_stop_at_last_layers)
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generation_params = {
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"Steps": p.steps,
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"Sampler": sd_samplers.samplers[p.sampler_index].name,
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||||
"Sampler": get_correct_sampler(p)[p.sampler_index].name,
|
||||
"CFG scale": p.cfg_scale,
|
||||
"Seed": all_seeds[index],
|
||||
"Face restoration": (opts.face_restoration_model if p.restore_faces else None),
|
||||
"Size": f"{p.width}x{p.height}",
|
||||
"Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash),
|
||||
"Model": (None if not opts.add_model_name_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')),
|
||||
"Hypernet": (None if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.name.replace(',', '').replace(':', '')),
|
||||
"Batch size": (None if p.batch_size < 2 else p.batch_size),
|
||||
"Batch pos": (None if p.batch_size < 2 else position_in_batch),
|
||||
"Variation seed": (None if p.subseed_strength == 0 else all_subseeds[index]),
|
||||
|
|
@ -281,6 +293,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
|
|||
"Seed resize from": (None if p.seed_resize_from_w == 0 or p.seed_resize_from_h == 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),
|
||||
"Denoising strength": getattr(p, 'denoising_strength', None),
|
||||
"Eta": (None if p.sampler is None or p.sampler.eta == p.sampler.default_eta else p.sampler.eta),
|
||||
"Clip skip": None if clip_skip <= 1 else clip_skip,
|
||||
}
|
||||
|
||||
generation_params.update(p.extra_generation_params)
|
||||
|
|
@ -312,6 +325,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
|
|||
os.makedirs(p.outpath_grids, exist_ok=True)
|
||||
|
||||
modules.sd_hijack.model_hijack.apply_circular(p.tiling)
|
||||
modules.sd_hijack.model_hijack.clear_comments()
|
||||
|
||||
comments = {}
|
||||
|
||||
|
|
@ -341,7 +355,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
|
|||
infotexts = []
|
||||
output_images = []
|
||||
|
||||
with torch.no_grad():
|
||||
with torch.no_grad(), p.sd_model.ema_scope():
|
||||
with devices.autocast():
|
||||
p.init(all_prompts, all_seeds, all_subseeds)
|
||||
|
||||
|
|
@ -349,6 +363,9 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
|
|||
state.job_count = p.n_iter
|
||||
|
||||
for n in range(p.n_iter):
|
||||
if state.skipped:
|
||||
state.skipped = False
|
||||
|
||||
if state.interrupted:
|
||||
break
|
||||
|
||||
|
|
@ -375,9 +392,9 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
|
|||
with devices.autocast():
|
||||
samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength)
|
||||
|
||||
if state.interrupted:
|
||||
if state.interrupted or state.skipped:
|
||||
|
||||
# if we are interruped, sample returns just noise
|
||||
# if we are interrupted, sample returns just noise
|
||||
# use the image collected previously in sampler loop
|
||||
samples_ddim = shared.state.current_latent
|
||||
|
||||
|
|
@ -436,7 +453,8 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
|
|||
|
||||
text = infotext(n, i)
|
||||
infotexts.append(text)
|
||||
image.info["parameters"] = text
|
||||
if opts.enable_pnginfo:
|
||||
image.info["parameters"] = text
|
||||
output_images.append(image)
|
||||
|
||||
del x_samples_ddim
|
||||
|
|
@ -455,7 +473,8 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
|
|||
if opts.return_grid:
|
||||
text = infotext()
|
||||
infotexts.insert(0, text)
|
||||
grid.info["parameters"] = text
|
||||
if opts.enable_pnginfo:
|
||||
grid.info["parameters"] = text
|
||||
output_images.insert(0, grid)
|
||||
index_of_first_image = 1
|
||||
|
||||
|
|
|
|||
|
|
@ -13,13 +13,14 @@ import lark
|
|||
|
||||
schedule_parser = lark.Lark(r"""
|
||||
!start: (prompt | /[][():]/+)*
|
||||
prompt: (emphasized | scheduled | plain | WHITESPACE)*
|
||||
prompt: (emphasized | scheduled | alternate | plain | WHITESPACE)*
|
||||
!emphasized: "(" prompt ")"
|
||||
| "(" prompt ":" prompt ")"
|
||||
| "[" prompt "]"
|
||||
scheduled: "[" [prompt ":"] prompt ":" [WHITESPACE] NUMBER "]"
|
||||
alternate: "[" prompt ("|" prompt)+ "]"
|
||||
WHITESPACE: /\s+/
|
||||
plain: /([^\\\[\]():]|\\.)+/
|
||||
plain: /([^\\\[\]():|]|\\.)+/
|
||||
%import common.SIGNED_NUMBER -> NUMBER
|
||||
""")
|
||||
|
||||
|
|
@ -59,6 +60,8 @@ def get_learned_conditioning_prompt_schedules(prompts, steps):
|
|||
tree.children[-1] *= steps
|
||||
tree.children[-1] = min(steps, int(tree.children[-1]))
|
||||
l.append(tree.children[-1])
|
||||
def alternate(self, tree):
|
||||
l.extend(range(1, steps+1))
|
||||
CollectSteps().visit(tree)
|
||||
return sorted(set(l))
|
||||
|
||||
|
|
@ -67,6 +70,8 @@ def get_learned_conditioning_prompt_schedules(prompts, steps):
|
|||
def scheduled(self, args):
|
||||
before, after, _, when = args
|
||||
yield before or () if step <= when else after
|
||||
def alternate(self, args):
|
||||
yield next(args[(step - 1)%len(args)])
|
||||
def start(self, args):
|
||||
def flatten(x):
|
||||
if type(x) == str:
|
||||
|
|
@ -239,6 +244,15 @@ def reconstruct_multicond_batch(c: MulticondLearnedConditioning, current_step):
|
|||
|
||||
conds_list.append(conds_for_batch)
|
||||
|
||||
# if prompts have wildly different lengths above the limit we'll get tensors fo different shapes
|
||||
# and won't be able to torch.stack them. So this fixes that.
|
||||
token_count = max([x.shape[0] for x in tensors])
|
||||
for i in range(len(tensors)):
|
||||
if tensors[i].shape[0] != token_count:
|
||||
last_vector = tensors[i][-1:]
|
||||
last_vector_repeated = last_vector.repeat([token_count - tensors[i].shape[0], 1])
|
||||
tensors[i] = torch.vstack([tensors[i], last_vector_repeated])
|
||||
|
||||
return conds_list, torch.stack(tensors).to(device=param.device, dtype=param.dtype)
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -8,14 +8,12 @@ from basicsr.utils.download_util import load_file_from_url
|
|||
from realesrgan import RealESRGANer
|
||||
|
||||
from modules.upscaler import Upscaler, UpscalerData
|
||||
from modules.paths import models_path
|
||||
from modules.shared import cmd_opts, opts
|
||||
|
||||
|
||||
class UpscalerRealESRGAN(Upscaler):
|
||||
def __init__(self, path):
|
||||
self.name = "RealESRGAN"
|
||||
self.model_path = os.path.join(models_path, self.name)
|
||||
self.user_path = path
|
||||
super().__init__()
|
||||
try:
|
||||
|
|
|
|||
93
modules/safe.py
Normal file
93
modules/safe.py
Normal file
|
|
@ -0,0 +1,93 @@
|
|||
# this code is adapted from the script contributed by anon from /h/
|
||||
|
||||
import io
|
||||
import pickle
|
||||
import collections
|
||||
import sys
|
||||
import traceback
|
||||
|
||||
import torch
|
||||
import numpy
|
||||
import _codecs
|
||||
import zipfile
|
||||
|
||||
|
||||
# PyTorch 1.13 and later have _TypedStorage renamed to TypedStorage
|
||||
TypedStorage = torch.storage.TypedStorage if hasattr(torch.storage, 'TypedStorage') else torch.storage._TypedStorage
|
||||
|
||||
|
||||
def encode(*args):
|
||||
out = _codecs.encode(*args)
|
||||
return out
|
||||
|
||||
|
||||
class RestrictedUnpickler(pickle.Unpickler):
|
||||
def persistent_load(self, saved_id):
|
||||
assert saved_id[0] == 'storage'
|
||||
return TypedStorage()
|
||||
|
||||
def find_class(self, module, name):
|
||||
if module == 'collections' and name == 'OrderedDict':
|
||||
return getattr(collections, name)
|
||||
if module == 'torch._utils' and name in ['_rebuild_tensor_v2', '_rebuild_parameter']:
|
||||
return getattr(torch._utils, name)
|
||||
if module == 'torch' and name in ['FloatStorage', 'HalfStorage', 'IntStorage', 'LongStorage', 'DoubleStorage']:
|
||||
return getattr(torch, name)
|
||||
if module == 'torch.nn.modules.container' and name in ['ParameterDict']:
|
||||
return getattr(torch.nn.modules.container, name)
|
||||
if module == 'numpy.core.multiarray' and name == 'scalar':
|
||||
return numpy.core.multiarray.scalar
|
||||
if module == 'numpy' and name == 'dtype':
|
||||
return numpy.dtype
|
||||
if module == '_codecs' and name == 'encode':
|
||||
return encode
|
||||
if module == "pytorch_lightning.callbacks" and name == 'model_checkpoint':
|
||||
import pytorch_lightning.callbacks
|
||||
return pytorch_lightning.callbacks.model_checkpoint
|
||||
if module == "pytorch_lightning.callbacks.model_checkpoint" and name == 'ModelCheckpoint':
|
||||
import pytorch_lightning.callbacks.model_checkpoint
|
||||
return pytorch_lightning.callbacks.model_checkpoint.ModelCheckpoint
|
||||
if module == "__builtin__" and name == 'set':
|
||||
return set
|
||||
|
||||
# Forbid everything else.
|
||||
raise pickle.UnpicklingError(f"global '{module}/{name}' is forbidden")
|
||||
|
||||
|
||||
def check_pt(filename):
|
||||
try:
|
||||
|
||||
# new pytorch format is a zip file
|
||||
with zipfile.ZipFile(filename) as z:
|
||||
with z.open('archive/data.pkl') as file:
|
||||
unpickler = RestrictedUnpickler(file)
|
||||
unpickler.load()
|
||||
|
||||
except zipfile.BadZipfile:
|
||||
|
||||
# if it's not a zip file, it's an olf pytorch format, with five objects written to pickle
|
||||
with open(filename, "rb") as file:
|
||||
unpickler = RestrictedUnpickler(file)
|
||||
for i in range(5):
|
||||
unpickler.load()
|
||||
|
||||
|
||||
def load(filename, *args, **kwargs):
|
||||
from modules import shared
|
||||
|
||||
try:
|
||||
if not shared.cmd_opts.disable_safe_unpickle:
|
||||
check_pt(filename)
|
||||
|
||||
except Exception:
|
||||
print(f"Error verifying pickled file from {filename}:", file=sys.stderr)
|
||||
print(traceback.format_exc(), file=sys.stderr)
|
||||
print(f"\nThe file may be malicious, so the program is not going to read it.", file=sys.stderr)
|
||||
print(f"You can skip this check with --disable-safe-unpickle commandline argument.", file=sys.stderr)
|
||||
return None
|
||||
|
||||
return unsafe_torch_load(filename, *args, **kwargs)
|
||||
|
||||
|
||||
unsafe_torch_load = torch.load
|
||||
torch.load = load
|
||||
|
|
@ -9,14 +9,12 @@ from basicsr.utils.download_util import load_file_from_url
|
|||
|
||||
import modules.upscaler
|
||||
from modules import devices, modelloader
|
||||
from modules.paths import models_path
|
||||
from modules.scunet_model_arch import SCUNet as net
|
||||
|
||||
|
||||
class UpscalerScuNET(modules.upscaler.Upscaler):
|
||||
def __init__(self, dirname):
|
||||
self.name = "ScuNET"
|
||||
self.model_path = os.path.join(models_path, self.name)
|
||||
self.model_name = "ScuNET GAN"
|
||||
self.model_name2 = "ScuNET PSNR"
|
||||
self.model_url = "https://github.com/cszn/KAIR/releases/download/v1.0/scunet_color_real_gan.pth"
|
||||
|
|
|
|||
|
|
@ -40,7 +40,7 @@ class WMSA(nn.Module):
|
|||
Returns:
|
||||
attn_mask: should be (1 1 w p p),
|
||||
"""
|
||||
# supporting sqaure.
|
||||
# supporting square.
|
||||
attn_mask = torch.zeros(h, w, p, p, p, p, dtype=torch.bool, device=self.relative_position_params.device)
|
||||
if self.type == 'W':
|
||||
return attn_mask
|
||||
|
|
@ -65,7 +65,7 @@ class WMSA(nn.Module):
|
|||
x = rearrange(x, 'b (w1 p1) (w2 p2) c -> b w1 w2 p1 p2 c', p1=self.window_size, p2=self.window_size)
|
||||
h_windows = x.size(1)
|
||||
w_windows = x.size(2)
|
||||
# sqaure validation
|
||||
# square validation
|
||||
# assert h_windows == w_windows
|
||||
|
||||
x = rearrange(x, 'b w1 w2 p1 p2 c -> b (w1 w2) (p1 p2) c', p1=self.window_size, p2=self.window_size)
|
||||
|
|
|
|||
|
|
@ -8,7 +8,7 @@ from torch import einsum
|
|||
from torch.nn.functional import silu
|
||||
|
||||
import modules.textual_inversion.textual_inversion
|
||||
from modules import prompt_parser, devices, sd_hijack_optimizations, shared
|
||||
from modules import prompt_parser, devices, sd_hijack_optimizations, shared, hypernetwork
|
||||
from modules.shared import opts, device, cmd_opts
|
||||
|
||||
import ldm.modules.attention
|
||||
|
|
@ -18,23 +18,37 @@ attention_CrossAttention_forward = ldm.modules.attention.CrossAttention.forward
|
|||
diffusionmodules_model_nonlinearity = ldm.modules.diffusionmodules.model.nonlinearity
|
||||
diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.AttnBlock.forward
|
||||
|
||||
|
||||
def apply_optimizations():
|
||||
undo_optimizations()
|
||||
|
||||
ldm.modules.diffusionmodules.model.nonlinearity = silu
|
||||
|
||||
if cmd_opts.opt_split_attention_v1:
|
||||
if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and torch.cuda.get_device_capability(shared.device) == (8, 6)):
|
||||
print("Applying xformers cross attention optimization.")
|
||||
ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward
|
||||
ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward
|
||||
elif cmd_opts.opt_split_attention_v1:
|
||||
print("Applying v1 cross attention optimization.")
|
||||
ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
|
||||
elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()):
|
||||
print("Applying cross attention optimization.")
|
||||
ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward
|
||||
ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward
|
||||
|
||||
|
||||
def undo_optimizations():
|
||||
ldm.modules.attention.CrossAttention.forward = attention_CrossAttention_forward
|
||||
ldm.modules.attention.CrossAttention.forward = hypernetwork.attention_CrossAttention_forward
|
||||
ldm.modules.diffusionmodules.model.nonlinearity = diffusionmodules_model_nonlinearity
|
||||
ldm.modules.diffusionmodules.model.AttnBlock.forward = diffusionmodules_model_AttnBlock_forward
|
||||
|
||||
|
||||
def get_target_prompt_token_count(token_count):
|
||||
if token_count < 75:
|
||||
return 75
|
||||
|
||||
return math.ceil(token_count / 10) * 10
|
||||
|
||||
|
||||
class StableDiffusionModelHijack:
|
||||
fixes = None
|
||||
comments = []
|
||||
|
|
@ -80,10 +94,12 @@ class StableDiffusionModelHijack:
|
|||
for layer in [layer for layer in self.layers if type(layer) == torch.nn.Conv2d]:
|
||||
layer.padding_mode = 'circular' if enable else 'zeros'
|
||||
|
||||
def clear_comments(self):
|
||||
self.comments = []
|
||||
|
||||
def tokenize(self, text):
|
||||
max_length = self.clip.max_length - 2
|
||||
_, remade_batch_tokens, _, _, _, token_count = self.clip.process_text([text])
|
||||
return remade_batch_tokens[0], token_count, max_length
|
||||
return remade_batch_tokens[0], token_count, get_target_prompt_token_count(token_count)
|
||||
|
||||
|
||||
class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
|
||||
|
|
@ -92,7 +108,6 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
|
|||
self.wrapped = wrapped
|
||||
self.hijack: StableDiffusionModelHijack = hijack
|
||||
self.tokenizer = wrapped.tokenizer
|
||||
self.max_length = wrapped.max_length
|
||||
self.token_mults = {}
|
||||
|
||||
tokens_with_parens = [(k, v) for k, v in self.tokenizer.get_vocab().items() if '(' in k or ')' in k or '[' in k or ']' in k]
|
||||
|
|
@ -114,7 +129,6 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
|
|||
def tokenize_line(self, line, used_custom_terms, hijack_comments):
|
||||
id_start = self.wrapped.tokenizer.bos_token_id
|
||||
id_end = self.wrapped.tokenizer.eos_token_id
|
||||
maxlen = self.wrapped.max_length
|
||||
|
||||
if opts.enable_emphasis:
|
||||
parsed = prompt_parser.parse_prompt_attention(line)
|
||||
|
|
@ -146,19 +160,12 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
|
|||
used_custom_terms.append((embedding.name, embedding.checksum()))
|
||||
i += embedding_length_in_tokens
|
||||
|
||||
if len(remade_tokens) > maxlen - 2:
|
||||
vocab = {v: k for k, v in self.wrapped.tokenizer.get_vocab().items()}
|
||||
ovf = remade_tokens[maxlen - 2:]
|
||||
overflowing_words = [vocab.get(int(x), "") for x in ovf]
|
||||
overflowing_text = self.wrapped.tokenizer.convert_tokens_to_string(''.join(overflowing_words))
|
||||
hijack_comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n")
|
||||
|
||||
token_count = len(remade_tokens)
|
||||
remade_tokens = remade_tokens + [id_end] * (maxlen - 2 - len(remade_tokens))
|
||||
remade_tokens = [id_start] + remade_tokens[0:maxlen - 2] + [id_end]
|
||||
prompt_target_length = get_target_prompt_token_count(token_count)
|
||||
tokens_to_add = prompt_target_length - len(remade_tokens) + 1
|
||||
|
||||
multipliers = multipliers + [1.0] * (maxlen - 2 - len(multipliers))
|
||||
multipliers = [1.0] + multipliers[0:maxlen - 2] + [1.0]
|
||||
remade_tokens = [id_start] + remade_tokens + [id_end] * tokens_to_add
|
||||
multipliers = [1.0] + multipliers + [1.0] * tokens_to_add
|
||||
|
||||
return remade_tokens, fixes, multipliers, token_count
|
||||
|
||||
|
|
@ -175,7 +182,8 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
|
|||
if line in cache:
|
||||
remade_tokens, fixes, multipliers = cache[line]
|
||||
else:
|
||||
remade_tokens, fixes, multipliers, token_count = self.tokenize_line(line, used_custom_terms, hijack_comments)
|
||||
remade_tokens, fixes, multipliers, current_token_count = self.tokenize_line(line, used_custom_terms, hijack_comments)
|
||||
token_count = max(current_token_count, token_count)
|
||||
|
||||
cache[line] = (remade_tokens, fixes, multipliers)
|
||||
|
||||
|
|
@ -189,7 +197,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
|
|||
def process_text_old(self, text):
|
||||
id_start = self.wrapped.tokenizer.bos_token_id
|
||||
id_end = self.wrapped.tokenizer.eos_token_id
|
||||
maxlen = self.wrapped.max_length
|
||||
maxlen = self.wrapped.max_length # you get to stay at 77
|
||||
used_custom_terms = []
|
||||
remade_batch_tokens = []
|
||||
overflowing_words = []
|
||||
|
|
@ -261,17 +269,29 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
|
|||
batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text(text)
|
||||
|
||||
self.hijack.fixes = hijack_fixes
|
||||
self.hijack.comments = hijack_comments
|
||||
self.hijack.comments += hijack_comments
|
||||
|
||||
if len(used_custom_terms) > 0:
|
||||
self.hijack.comments.append("Used embeddings: " + ", ".join([f'{word} [{checksum}]' for word, checksum in used_custom_terms]))
|
||||
|
||||
tokens = torch.asarray(remade_batch_tokens).to(device)
|
||||
outputs = self.wrapped.transformer(input_ids=tokens)
|
||||
z = outputs.last_hidden_state
|
||||
target_token_count = get_target_prompt_token_count(token_count) + 2
|
||||
|
||||
position_ids_array = [min(x, 75) for x in range(target_token_count-1)] + [76]
|
||||
position_ids = torch.asarray(position_ids_array, device=devices.device).expand((1, -1))
|
||||
|
||||
remade_batch_tokens_of_same_length = [x + [self.wrapped.tokenizer.eos_token_id] * (target_token_count - len(x)) for x in remade_batch_tokens]
|
||||
tokens = torch.asarray(remade_batch_tokens_of_same_length).to(device)
|
||||
|
||||
outputs = self.wrapped.transformer(input_ids=tokens, position_ids=position_ids, output_hidden_states=-opts.CLIP_stop_at_last_layers)
|
||||
if opts.CLIP_stop_at_last_layers > 1:
|
||||
z = outputs.hidden_states[-opts.CLIP_stop_at_last_layers]
|
||||
z = self.wrapped.transformer.text_model.final_layer_norm(z)
|
||||
else:
|
||||
z = outputs.last_hidden_state
|
||||
|
||||
# restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise
|
||||
batch_multipliers = torch.asarray(batch_multipliers).to(device)
|
||||
batch_multipliers_of_same_length = [x + [1.0] * (target_token_count - len(x)) for x in batch_multipliers]
|
||||
batch_multipliers = torch.asarray(batch_multipliers_of_same_length).to(device)
|
||||
original_mean = z.mean()
|
||||
z *= batch_multipliers.reshape(batch_multipliers.shape + (1,)).expand(z.shape)
|
||||
new_mean = z.mean()
|
||||
|
|
|
|||
|
|
@ -1,22 +1,46 @@
|
|||
import math
|
||||
import sys
|
||||
import traceback
|
||||
|
||||
import torch
|
||||
from torch import einsum
|
||||
|
||||
from ldm.util import default
|
||||
from einops import rearrange
|
||||
|
||||
from modules import shared
|
||||
|
||||
if shared.cmd_opts.xformers or shared.cmd_opts.force_enable_xformers:
|
||||
try:
|
||||
import xformers.ops
|
||||
import functorch
|
||||
xformers._is_functorch_available = True
|
||||
shared.xformers_available = True
|
||||
except Exception:
|
||||
print("Cannot import xformers", file=sys.stderr)
|
||||
print(traceback.format_exc(), file=sys.stderr)
|
||||
|
||||
|
||||
# see https://github.com/basujindal/stable-diffusion/pull/117 for discussion
|
||||
def split_cross_attention_forward_v1(self, x, context=None, mask=None):
|
||||
h = self.heads
|
||||
|
||||
q = self.to_q(x)
|
||||
q_in = self.to_q(x)
|
||||
context = default(context, x)
|
||||
k = self.to_k(context)
|
||||
v = self.to_v(context)
|
||||
|
||||
hypernetwork = shared.loaded_hypernetwork
|
||||
hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
|
||||
|
||||
if hypernetwork_layers is not None:
|
||||
k_in = self.to_k(hypernetwork_layers[0](context))
|
||||
v_in = self.to_v(hypernetwork_layers[1](context))
|
||||
else:
|
||||
k_in = self.to_k(context)
|
||||
v_in = self.to_v(context)
|
||||
del context, x
|
||||
|
||||
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
|
||||
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in))
|
||||
del q_in, k_in, v_in
|
||||
|
||||
r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device)
|
||||
for i in range(0, q.shape[0], 2):
|
||||
|
|
@ -29,6 +53,7 @@ def split_cross_attention_forward_v1(self, x, context=None, mask=None):
|
|||
|
||||
r1[i:end] = einsum('b i j, b j d -> b i d', s2, v[i:end])
|
||||
del s2
|
||||
del q, k, v
|
||||
|
||||
r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h)
|
||||
del r1
|
||||
|
|
@ -42,8 +67,19 @@ def split_cross_attention_forward(self, x, context=None, mask=None):
|
|||
|
||||
q_in = self.to_q(x)
|
||||
context = default(context, x)
|
||||
k_in = self.to_k(context) * self.scale
|
||||
v_in = self.to_v(context)
|
||||
|
||||
hypernetwork = shared.loaded_hypernetwork
|
||||
hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
|
||||
|
||||
if hypernetwork_layers is not None:
|
||||
k_in = self.to_k(hypernetwork_layers[0](context))
|
||||
v_in = self.to_v(hypernetwork_layers[1](context))
|
||||
else:
|
||||
k_in = self.to_k(context)
|
||||
v_in = self.to_v(context)
|
||||
|
||||
k_in *= self.scale
|
||||
|
||||
del context, x
|
||||
|
||||
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in))
|
||||
|
|
@ -92,6 +128,25 @@ def split_cross_attention_forward(self, x, context=None, mask=None):
|
|||
|
||||
return self.to_out(r2)
|
||||
|
||||
def xformers_attention_forward(self, x, context=None, mask=None):
|
||||
h = self.heads
|
||||
q_in = self.to_q(x)
|
||||
context = default(context, x)
|
||||
hypernetwork = shared.loaded_hypernetwork
|
||||
hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
|
||||
if hypernetwork_layers is not None:
|
||||
k_in = self.to_k(hypernetwork_layers[0](context))
|
||||
v_in = self.to_v(hypernetwork_layers[1](context))
|
||||
else:
|
||||
k_in = self.to_k(context)
|
||||
v_in = self.to_v(context)
|
||||
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b n h d', h=h), (q_in, k_in, v_in))
|
||||
del q_in, k_in, v_in
|
||||
out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None)
|
||||
|
||||
out = rearrange(out, 'b n h d -> b n (h d)', h=h)
|
||||
return self.to_out(out)
|
||||
|
||||
def cross_attention_attnblock_forward(self, x):
|
||||
h_ = x
|
||||
h_ = self.norm(h_)
|
||||
|
|
@ -154,3 +209,16 @@ def cross_attention_attnblock_forward(self, x):
|
|||
h3 += x
|
||||
|
||||
return h3
|
||||
|
||||
def xformers_attnblock_forward(self, x):
|
||||
try:
|
||||
h_ = x
|
||||
h_ = self.norm(h_)
|
||||
q1 = self.q(h_).contiguous()
|
||||
k1 = self.k(h_).contiguous()
|
||||
v = self.v(h_).contiguous()
|
||||
out = xformers.ops.memory_efficient_attention(q1, k1, v)
|
||||
out = self.proj_out(out)
|
||||
return x + out
|
||||
except NotImplementedError:
|
||||
return cross_attention_attnblock_forward(self, x)
|
||||
|
|
|
|||
|
|
@ -5,7 +5,6 @@ from collections import namedtuple
|
|||
import torch
|
||||
from omegaconf import OmegaConf
|
||||
|
||||
|
||||
from ldm.util import instantiate_from_config
|
||||
|
||||
from modules import shared, modelloader, devices
|
||||
|
|
@ -14,7 +13,7 @@ from modules.paths import models_path
|
|||
model_dir = "Stable-diffusion"
|
||||
model_path = os.path.abspath(os.path.join(models_path, model_dir))
|
||||
|
||||
CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash', 'model_name'])
|
||||
CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash', 'model_name', 'config'])
|
||||
checkpoints_list = {}
|
||||
|
||||
try:
|
||||
|
|
@ -63,14 +62,20 @@ def list_models():
|
|||
if os.path.exists(cmd_ckpt):
|
||||
h = model_hash(cmd_ckpt)
|
||||
title, short_model_name = modeltitle(cmd_ckpt, h)
|
||||
checkpoints_list[title] = CheckpointInfo(cmd_ckpt, title, h, short_model_name)
|
||||
checkpoints_list[title] = CheckpointInfo(cmd_ckpt, title, h, short_model_name, shared.cmd_opts.config)
|
||||
shared.opts.data['sd_model_checkpoint'] = title
|
||||
elif cmd_ckpt is not None and cmd_ckpt != shared.default_sd_model_file:
|
||||
print(f"Checkpoint in --ckpt argument not found (Possible it was moved to {model_path}: {cmd_ckpt}", file=sys.stderr)
|
||||
for filename in model_list:
|
||||
h = model_hash(filename)
|
||||
title, short_model_name = modeltitle(filename, h)
|
||||
checkpoints_list[title] = CheckpointInfo(filename, title, h, short_model_name)
|
||||
|
||||
basename, _ = os.path.splitext(filename)
|
||||
config = basename + ".yaml"
|
||||
if not os.path.exists(config):
|
||||
config = shared.cmd_opts.config
|
||||
|
||||
checkpoints_list[title] = CheckpointInfo(filename, title, h, short_model_name, config)
|
||||
|
||||
|
||||
def get_closet_checkpoint_match(searchString):
|
||||
|
|
@ -116,13 +121,24 @@ def select_checkpoint():
|
|||
return checkpoint_info
|
||||
|
||||
|
||||
def load_model_weights(model, checkpoint_file, sd_model_hash):
|
||||
def get_state_dict_from_checkpoint(pl_sd):
|
||||
if "state_dict" in pl_sd:
|
||||
return pl_sd["state_dict"]
|
||||
|
||||
return pl_sd
|
||||
|
||||
|
||||
def load_model_weights(model, checkpoint_info):
|
||||
checkpoint_file = checkpoint_info.filename
|
||||
sd_model_hash = checkpoint_info.hash
|
||||
|
||||
print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")
|
||||
|
||||
pl_sd = torch.load(checkpoint_file, map_location="cpu")
|
||||
if "global_step" in pl_sd:
|
||||
print(f"Global Step: {pl_sd['global_step']}")
|
||||
sd = pl_sd["state_dict"]
|
||||
|
||||
sd = get_state_dict_from_checkpoint(pl_sd)
|
||||
|
||||
model.load_state_dict(sd, strict=False)
|
||||
|
||||
|
|
@ -134,17 +150,29 @@ def load_model_weights(model, checkpoint_file, sd_model_hash):
|
|||
|
||||
devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16
|
||||
|
||||
vae_file = os.path.splitext(checkpoint_file)[0] + ".vae.pt"
|
||||
if os.path.exists(vae_file):
|
||||
print(f"Loading VAE weights from: {vae_file}")
|
||||
vae_ckpt = torch.load(vae_file, map_location="cpu")
|
||||
vae_dict = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss"}
|
||||
|
||||
model.first_stage_model.load_state_dict(vae_dict)
|
||||
|
||||
model.sd_model_hash = sd_model_hash
|
||||
model.sd_model_checkpint = checkpoint_file
|
||||
model.sd_model_checkpoint = checkpoint_file
|
||||
model.sd_checkpoint_info = checkpoint_info
|
||||
|
||||
|
||||
def load_model():
|
||||
from modules import lowvram, sd_hijack
|
||||
checkpoint_info = select_checkpoint()
|
||||
|
||||
sd_config = OmegaConf.load(shared.cmd_opts.config)
|
||||
if checkpoint_info.config != shared.cmd_opts.config:
|
||||
print(f"Loading config from: {checkpoint_info.config}")
|
||||
|
||||
sd_config = OmegaConf.load(checkpoint_info.config)
|
||||
sd_model = instantiate_from_config(sd_config.model)
|
||||
load_model_weights(sd_model, checkpoint_info.filename, checkpoint_info.hash)
|
||||
load_model_weights(sd_model, checkpoint_info)
|
||||
|
||||
if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
|
||||
lowvram.setup_for_low_vram(sd_model, shared.cmd_opts.medvram)
|
||||
|
|
@ -163,9 +191,13 @@ def reload_model_weights(sd_model, info=None):
|
|||
from modules import lowvram, devices, sd_hijack
|
||||
checkpoint_info = info or select_checkpoint()
|
||||
|
||||
if sd_model.sd_model_checkpint == checkpoint_info.filename:
|
||||
if sd_model.sd_model_checkpoint == checkpoint_info.filename:
|
||||
return
|
||||
|
||||
if sd_model.sd_checkpoint_info.config != checkpoint_info.config:
|
||||
shared.sd_model = load_model()
|
||||
return shared.sd_model
|
||||
|
||||
if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
|
||||
lowvram.send_everything_to_cpu()
|
||||
else:
|
||||
|
|
@ -173,7 +205,7 @@ def reload_model_weights(sd_model, info=None):
|
|||
|
||||
sd_hijack.model_hijack.undo_hijack(sd_model)
|
||||
|
||||
load_model_weights(sd_model, checkpoint_info.filename, checkpoint_info.hash)
|
||||
load_model_weights(sd_model, checkpoint_info)
|
||||
|
||||
sd_hijack.model_hijack.hijack(sd_model)
|
||||
|
||||
|
|
|
|||
|
|
@ -106,7 +106,7 @@ def extended_tdqm(sequence, *args, desc=None, **kwargs):
|
|||
seq = sequence if cmd_opts.disable_console_progressbars else tqdm.tqdm(sequence, *args, desc=state.job, file=shared.progress_print_out, **kwargs)
|
||||
|
||||
for x in seq:
|
||||
if state.interrupted:
|
||||
if state.interrupted or state.skipped:
|
||||
break
|
||||
|
||||
yield x
|
||||
|
|
@ -142,6 +142,16 @@ class VanillaStableDiffusionSampler:
|
|||
assert all([len(conds) == 1 for conds in conds_list]), 'composition via AND is not supported for DDIM/PLMS samplers'
|
||||
cond = tensor
|
||||
|
||||
# for DDIM, shapes must match, we can't just process cond and uncond independently;
|
||||
# filling unconditional_conditioning with repeats of the last vector to match length is
|
||||
# not 100% correct but should work well enough
|
||||
if unconditional_conditioning.shape[1] < cond.shape[1]:
|
||||
last_vector = unconditional_conditioning[:, -1:]
|
||||
last_vector_repeated = last_vector.repeat([1, cond.shape[1] - unconditional_conditioning.shape[1], 1])
|
||||
unconditional_conditioning = torch.hstack([unconditional_conditioning, last_vector_repeated])
|
||||
elif unconditional_conditioning.shape[1] > cond.shape[1]:
|
||||
unconditional_conditioning = unconditional_conditioning[:, :cond.shape[1]]
|
||||
|
||||
if self.mask is not None:
|
||||
img_orig = self.sampler.model.q_sample(self.init_latent, ts)
|
||||
x_dec = img_orig * self.mask + self.nmask * x_dec
|
||||
|
|
@ -171,7 +181,7 @@ class VanillaStableDiffusionSampler:
|
|||
|
||||
self.initialize(p)
|
||||
|
||||
# existing code fails with cetain step counts, like 9
|
||||
# existing code fails with certain step counts, like 9
|
||||
try:
|
||||
self.sampler.make_schedule(ddim_num_steps=steps, ddim_eta=self.eta, ddim_discretize=p.ddim_discretize, verbose=False)
|
||||
except Exception:
|
||||
|
|
@ -194,7 +204,7 @@ class VanillaStableDiffusionSampler:
|
|||
|
||||
steps = steps or p.steps
|
||||
|
||||
# existing code fails with cetin step counts, like 9
|
||||
# existing code fails with certain step counts, like 9
|
||||
try:
|
||||
samples_ddim, _ = self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)
|
||||
except Exception:
|
||||
|
|
@ -221,18 +231,29 @@ class CFGDenoiser(torch.nn.Module):
|
|||
|
||||
x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x])
|
||||
sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma])
|
||||
cond_in = torch.cat([tensor, uncond])
|
||||
|
||||
if shared.batch_cond_uncond:
|
||||
x_out = self.inner_model(x_in, sigma_in, cond=cond_in)
|
||||
if tensor.shape[1] == uncond.shape[1]:
|
||||
cond_in = torch.cat([tensor, uncond])
|
||||
|
||||
if shared.batch_cond_uncond:
|
||||
x_out = self.inner_model(x_in, sigma_in, cond=cond_in)
|
||||
else:
|
||||
x_out = torch.zeros_like(x_in)
|
||||
for batch_offset in range(0, x_out.shape[0], batch_size):
|
||||
a = batch_offset
|
||||
b = a + batch_size
|
||||
x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=cond_in[a:b])
|
||||
else:
|
||||
x_out = torch.zeros_like(x_in)
|
||||
for batch_offset in range(0, x_out.shape[0], batch_size):
|
||||
batch_size = batch_size*2 if shared.batch_cond_uncond else batch_size
|
||||
for batch_offset in range(0, tensor.shape[0], batch_size):
|
||||
a = batch_offset
|
||||
b = a + batch_size
|
||||
x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=cond_in[a:b])
|
||||
b = min(a + batch_size, tensor.shape[0])
|
||||
x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=tensor[a:b])
|
||||
|
||||
denoised_uncond = x_out[-batch_size:]
|
||||
x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=uncond)
|
||||
|
||||
denoised_uncond = x_out[-uncond.shape[0]:]
|
||||
denoised = torch.clone(denoised_uncond)
|
||||
|
||||
for i, conds in enumerate(conds_list):
|
||||
|
|
@ -254,7 +275,7 @@ def extended_trange(sampler, count, *args, **kwargs):
|
|||
seq = range(count) if cmd_opts.disable_console_progressbars else tqdm.trange(count, *args, desc=state.job, file=shared.progress_print_out, **kwargs)
|
||||
|
||||
for x in seq:
|
||||
if state.interrupted:
|
||||
if state.interrupted or state.skipped:
|
||||
break
|
||||
|
||||
if sampler.stop_at is not None and x > sampler.stop_at:
|
||||
|
|
|
|||
|
|
@ -13,7 +13,7 @@ import modules.memmon
|
|||
import modules.sd_models
|
||||
import modules.styles
|
||||
import modules.devices as devices
|
||||
from modules import sd_samplers
|
||||
from modules import sd_samplers, hypernetwork
|
||||
from modules.paths import models_path, script_path, sd_path
|
||||
|
||||
sd_model_file = os.path.join(script_path, 'model.ckpt')
|
||||
|
|
@ -43,6 +43,9 @@ parser.add_argument("--realesrgan-models-path", type=str, help="Path to director
|
|||
parser.add_argument("--scunet-models-path", type=str, help="Path to directory with ScuNET model file(s).", default=os.path.join(models_path, 'ScuNET'))
|
||||
parser.add_argument("--swinir-models-path", type=str, help="Path to directory with SwinIR model file(s).", default=os.path.join(models_path, 'SwinIR'))
|
||||
parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with LDSR model file(s).", default=os.path.join(models_path, 'LDSR'))
|
||||
parser.add_argument("--xformers", action='store_true', help="enable xformers for cross attention layers")
|
||||
parser.add_argument("--force-enable-xformers", action='store_true', help="enable xformers for cross attention layers regardless of whether the checking code thinks you can run it; do not make bug reports if this fails to work")
|
||||
parser.add_argument("--deepdanbooru", action='store_true', help="enable deepdanbooru interrogator")
|
||||
parser.add_argument("--opt-split-attention", action='store_true', help="force-enables cross-attention layer optimization. By default, it's on for torch.cuda and off for other torch devices.")
|
||||
parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization")
|
||||
parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find")
|
||||
|
|
@ -62,6 +65,7 @@ parser.add_argument("--autolaunch", action='store_true', help="open the webui UR
|
|||
parser.add_argument("--use-textbox-seed", action='store_true', help="use textbox for seeds in UI (no up/down, but possible to input long seeds)", default=False)
|
||||
parser.add_argument("--disable-console-progressbars", action='store_true', help="do not output progressbars to console", default=False)
|
||||
parser.add_argument("--enable-console-prompts", action='store_true', help="print prompts to console when generating with txt2img and img2img", default=False)
|
||||
parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False)
|
||||
|
||||
|
||||
cmd_opts = parser.parse_args()
|
||||
|
|
@ -73,11 +77,15 @@ device = devices.device
|
|||
|
||||
batch_cond_uncond = cmd_opts.always_batch_cond_uncond or not (cmd_opts.lowvram or cmd_opts.medvram)
|
||||
parallel_processing_allowed = not cmd_opts.lowvram and not cmd_opts.medvram
|
||||
|
||||
xformers_available = False
|
||||
config_filename = cmd_opts.ui_settings_file
|
||||
|
||||
hypernetworks = hypernetwork.list_hypernetworks(os.path.join(models_path, 'hypernetworks'))
|
||||
loaded_hypernetwork = None
|
||||
|
||||
|
||||
class State:
|
||||
skipped = False
|
||||
interrupted = False
|
||||
job = ""
|
||||
job_no = 0
|
||||
|
|
@ -90,6 +98,9 @@ class State:
|
|||
current_image_sampling_step = 0
|
||||
textinfo = None
|
||||
|
||||
def skip(self):
|
||||
self.skipped = True
|
||||
|
||||
def interrupt(self):
|
||||
self.interrupted = True
|
||||
|
||||
|
|
@ -112,8 +123,6 @@ prompt_styles = modules.styles.StyleDatabase(styles_filename)
|
|||
interrogator = modules.interrogate.InterrogateModels("interrogate")
|
||||
|
||||
face_restorers = []
|
||||
# This was moved to webui.py with the other model "setup" calls.
|
||||
# modules.sd_models.list_models()
|
||||
|
||||
|
||||
def realesrgan_models_names():
|
||||
|
|
@ -122,18 +131,19 @@ def realesrgan_models_names():
|
|||
|
||||
|
||||
class OptionInfo:
|
||||
def __init__(self, default=None, label="", component=None, component_args=None, onchange=None):
|
||||
def __init__(self, default=None, label="", component=None, component_args=None, onchange=None, show_on_main_page=False):
|
||||
self.default = default
|
||||
self.label = label
|
||||
self.component = component
|
||||
self.component_args = component_args
|
||||
self.onchange = onchange
|
||||
self.section = None
|
||||
self.show_on_main_page = show_on_main_page
|
||||
|
||||
|
||||
def options_section(section_identifer, options_dict):
|
||||
def options_section(section_identifier, options_dict):
|
||||
for k, v in options_dict.items():
|
||||
v.section = section_identifer
|
||||
v.section = section_identifier
|
||||
|
||||
return options_dict
|
||||
|
||||
|
|
@ -205,7 +215,8 @@ options_templates.update(options_section(('system', "System"), {
|
|||
}))
|
||||
|
||||
options_templates.update(options_section(('sd', "Stable Diffusion"), {
|
||||
"sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}),
|
||||
"sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, show_on_main_page=True),
|
||||
"sd_hypernetwork": OptionInfo("None", "Stable Diffusion finetune hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}),
|
||||
"img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
|
||||
"save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
|
||||
"img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising)."),
|
||||
|
|
@ -214,6 +225,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
|
|||
"use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."),
|
||||
"enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"),
|
||||
"filter_nsfw": OptionInfo(False, "Filter NSFW content"),
|
||||
'CLIP_stop_at_last_layers': OptionInfo(1, "Stop At last layers of CLIP model", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}),
|
||||
"random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}),
|
||||
}))
|
||||
|
||||
|
|
@ -228,13 +240,14 @@ options_templates.update(options_section(('interrogate', "Interrogate Options"),
|
|||
|
||||
options_templates.update(options_section(('ui', "User interface"), {
|
||||
"show_progressbar": OptionInfo(True, "Show progressbar"),
|
||||
"show_progress_every_n_steps": OptionInfo(0, "Show show image creation progress every N sampling steps. Set 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 32, "step": 1}),
|
||||
"show_progress_every_n_steps": OptionInfo(0, "Show image creation progress every N sampling steps. Set 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 32, "step": 1}),
|
||||
"return_grid": OptionInfo(True, "Show grid in results for web"),
|
||||
"do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),
|
||||
"add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"),
|
||||
"add_model_name_to_info": OptionInfo(False, "Add model name to generation information"),
|
||||
"font": OptionInfo("", "Font for image grids that have text"),
|
||||
"js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
|
||||
"js_modal_lightbox_initialy_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
|
||||
"js_modal_lightbox_initially_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
|
||||
"show_progress_in_title": OptionInfo(True, "Show generation progress in window title."),
|
||||
}))
|
||||
|
||||
|
|
|
|||
|
|
@ -8,7 +8,6 @@ from basicsr.utils.download_util import load_file_from_url
|
|||
from tqdm import tqdm
|
||||
|
||||
from modules import modelloader
|
||||
from modules.paths import models_path
|
||||
from modules.shared import cmd_opts, opts, device
|
||||
from modules.swinir_model_arch import SwinIR as net
|
||||
from modules.upscaler import Upscaler, UpscalerData
|
||||
|
|
@ -25,7 +24,6 @@ class UpscalerSwinIR(Upscaler):
|
|||
"/003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR" \
|
||||
"-L_x4_GAN.pth "
|
||||
self.model_name = "SwinIR 4x"
|
||||
self.model_path = os.path.join(models_path, self.name)
|
||||
self.user_path = dirname
|
||||
super().__init__()
|
||||
scalers = []
|
||||
|
|
|
|||
|
|
@ -166,7 +166,7 @@ class SwinTransformerBlock(nn.Module):
|
|||
|
||||
Args:
|
||||
dim (int): Number of input channels.
|
||||
input_resolution (tuple[int]): Input resulotion.
|
||||
input_resolution (tuple[int]): Input resolution.
|
||||
num_heads (int): Number of attention heads.
|
||||
window_size (int): Window size.
|
||||
shift_size (int): Shift size for SW-MSA.
|
||||
|
|
|
|||
156
modules/ui.py
156
modules/ui.py
|
|
@ -25,6 +25,8 @@ import gradio.routes
|
|||
from modules import sd_hijack
|
||||
from modules.paths import script_path
|
||||
from modules.shared import opts, cmd_opts
|
||||
if cmd_opts.deepdanbooru:
|
||||
from modules.deepbooru import get_deepbooru_tags
|
||||
import modules.shared as shared
|
||||
from modules.sd_samplers import samplers, samplers_for_img2img
|
||||
from modules.sd_hijack import model_hijack
|
||||
|
|
@ -39,7 +41,7 @@ from modules.images import save_image
|
|||
import modules.textual_inversion.ui
|
||||
import modules.images_history as img_his
|
||||
|
||||
# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the bowser will not show any UI
|
||||
# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI
|
||||
mimetypes.init()
|
||||
mimetypes.add_type('application/javascript', '.js')
|
||||
|
||||
|
|
@ -99,11 +101,12 @@ def send_gradio_gallery_to_image(x):
|
|||
return image_from_url_text(x[0])
|
||||
|
||||
|
||||
def save_files(js_data, images, index):
|
||||
def save_files(js_data, images, do_make_zip, index):
|
||||
import csv
|
||||
filenames = []
|
||||
fullfns = []
|
||||
|
||||
#quick dictionary to class object conversion. Its neccesary due apply_filename_pattern requiring it
|
||||
#quick dictionary to class object conversion. Its necessary due apply_filename_pattern requiring it
|
||||
class MyObject:
|
||||
def __init__(self, d=None):
|
||||
if d is not None:
|
||||
|
|
@ -138,14 +141,29 @@ def save_files(js_data, images, index):
|
|||
is_grid = image_index < p.index_of_first_image
|
||||
i = 0 if is_grid else (image_index - p.index_of_first_image)
|
||||
|
||||
fullfn = save_image(image, path, "", seed=p.all_seeds[i], prompt=p.all_prompts[i], extension=extension, info=p.infotexts[image_index], grid=is_grid, p=p, save_to_dirs=save_to_dirs)
|
||||
fullfn, txt_fullfn = save_image(image, path, "", seed=p.all_seeds[i], prompt=p.all_prompts[i], extension=extension, info=p.infotexts[image_index], grid=is_grid, p=p, save_to_dirs=save_to_dirs)
|
||||
|
||||
filename = os.path.relpath(fullfn, path)
|
||||
filenames.append(filename)
|
||||
fullfns.append(fullfn)
|
||||
if txt_fullfn:
|
||||
filenames.append(os.path.basename(txt_fullfn))
|
||||
fullfns.append(txt_fullfn)
|
||||
|
||||
writer.writerow([data["prompt"], data["seed"], data["width"], data["height"], data["sampler"], data["cfg_scale"], data["steps"], filenames[0], data["negative_prompt"]])
|
||||
|
||||
return '', '', plaintext_to_html(f"Saved: {filenames[0]}")
|
||||
# Make Zip
|
||||
if do_make_zip:
|
||||
zip_filepath = os.path.join(path, "images.zip")
|
||||
|
||||
from zipfile import ZipFile
|
||||
with ZipFile(zip_filepath, "w") as zip_file:
|
||||
for i in range(len(fullfns)):
|
||||
with open(fullfns[i], mode="rb") as f:
|
||||
zip_file.writestr(filenames[i], f.read())
|
||||
fullfns.insert(0, zip_filepath)
|
||||
|
||||
return gr.File.update(value=fullfns, visible=True), '', '', plaintext_to_html(f"Saved: {filenames[0]}")
|
||||
|
||||
|
||||
def wrap_gradio_call(func, extra_outputs=None):
|
||||
|
|
@ -192,6 +210,7 @@ def wrap_gradio_call(func, extra_outputs=None):
|
|||
# last item is always HTML
|
||||
res[-1] += f"<div class='performance'><p class='time'>Time taken: <wbr>{elapsed_text}</p>{vram_html}</div>"
|
||||
|
||||
shared.state.skipped = False
|
||||
shared.state.interrupted = False
|
||||
shared.state.job_count = 0
|
||||
|
||||
|
|
@ -292,6 +311,11 @@ def interrogate(image):
|
|||
return gr_show(True) if prompt is None else prompt
|
||||
|
||||
|
||||
def interrogate_deepbooru(image):
|
||||
prompt = get_deepbooru_tags(image)
|
||||
return gr_show(True) if prompt is None else prompt
|
||||
|
||||
|
||||
def create_seed_inputs():
|
||||
with gr.Row():
|
||||
with gr.Box():
|
||||
|
|
@ -412,24 +436,36 @@ def create_toprow(is_img2img):
|
|||
|
||||
with gr.Column(scale=1):
|
||||
with gr.Row():
|
||||
skip = gr.Button('Skip', elem_id=f"{id_part}_skip")
|
||||
interrupt = gr.Button('Interrupt', elem_id=f"{id_part}_interrupt")
|
||||
submit = gr.Button('Generate', elem_id=f"{id_part}_generate", variant='primary')
|
||||
|
||||
skip.click(
|
||||
fn=lambda: shared.state.skip(),
|
||||
inputs=[],
|
||||
outputs=[],
|
||||
)
|
||||
|
||||
interrupt.click(
|
||||
fn=lambda: shared.state.interrupt(),
|
||||
inputs=[],
|
||||
outputs=[],
|
||||
)
|
||||
|
||||
with gr.Row():
|
||||
with gr.Row(scale=1):
|
||||
if is_img2img:
|
||||
interrogate = gr.Button('Interrogate', elem_id="interrogate")
|
||||
interrogate = gr.Button('Interrogate\nCLIP', elem_id="interrogate")
|
||||
if cmd_opts.deepdanbooru:
|
||||
deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru")
|
||||
else:
|
||||
deepbooru = None
|
||||
else:
|
||||
interrogate = None
|
||||
deepbooru = None
|
||||
prompt_style_apply = gr.Button('Apply style', elem_id="style_apply")
|
||||
save_style = gr.Button('Create style', elem_id="style_create")
|
||||
|
||||
return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, interrogate, prompt_style_apply, save_style, paste, token_counter, token_button
|
||||
return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, interrogate, deepbooru, prompt_style_apply, save_style, paste, token_counter, token_button
|
||||
|
||||
|
||||
def setup_progressbar(progressbar, preview, id_part, textinfo=None):
|
||||
|
|
@ -458,7 +494,7 @@ def create_ui(wrap_gradio_gpu_call):
|
|||
import modules.txt2img
|
||||
|
||||
with gr.Blocks(analytics_enabled=False) as txt2img_interface:
|
||||
txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, txt2img_prompt_style_apply, txt2img_save_style, paste, token_counter, token_button = create_toprow(is_img2img=False)
|
||||
txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, paste, token_counter, token_button = create_toprow(is_img2img=False)
|
||||
dummy_component = gr.Label(visible=False)
|
||||
|
||||
with gr.Row(elem_id='txt2img_progress_row'):
|
||||
|
|
@ -513,6 +549,12 @@ def create_ui(wrap_gradio_gpu_call):
|
|||
button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder'
|
||||
open_txt2img_folder = gr.Button(folder_symbol, elem_id=button_id)
|
||||
|
||||
with gr.Row():
|
||||
do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False)
|
||||
|
||||
with gr.Row():
|
||||
download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False)
|
||||
|
||||
with gr.Group():
|
||||
html_info = gr.HTML()
|
||||
generation_info = gr.Textbox(visible=False)
|
||||
|
|
@ -563,13 +605,15 @@ def create_ui(wrap_gradio_gpu_call):
|
|||
|
||||
save.click(
|
||||
fn=wrap_gradio_call(save_files),
|
||||
_js="(x, y, z) => [x, y, selected_gallery_index()]",
|
||||
_js="(x, y, z, w) => [x, y, z, selected_gallery_index()]",
|
||||
inputs=[
|
||||
generation_info,
|
||||
txt2img_gallery,
|
||||
do_make_zip,
|
||||
html_info,
|
||||
],
|
||||
outputs=[
|
||||
download_files,
|
||||
html_info,
|
||||
html_info,
|
||||
html_info,
|
||||
|
|
@ -611,7 +655,7 @@ def create_ui(wrap_gradio_gpu_call):
|
|||
|
||||
|
||||
with gr.Blocks(analytics_enabled=False) as img2img_interface:
|
||||
img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_prompt_style_apply, img2img_save_style, paste, token_counter, token_button = create_toprow(is_img2img=True)
|
||||
img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, paste, token_counter, token_button = create_toprow(is_img2img=True)
|
||||
|
||||
with gr.Row(elem_id='img2img_progress_row'):
|
||||
with gr.Column(scale=1):
|
||||
|
|
@ -695,6 +739,12 @@ def create_ui(wrap_gradio_gpu_call):
|
|||
button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder'
|
||||
open_img2img_folder = gr.Button(folder_symbol, elem_id=button_id)
|
||||
|
||||
with gr.Row():
|
||||
do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False)
|
||||
|
||||
with gr.Row():
|
||||
download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False)
|
||||
|
||||
with gr.Group():
|
||||
html_info = gr.HTML()
|
||||
generation_info = gr.Textbox(visible=False)
|
||||
|
|
@ -769,15 +819,24 @@ def create_ui(wrap_gradio_gpu_call):
|
|||
outputs=[img2img_prompt],
|
||||
)
|
||||
|
||||
if cmd_opts.deepdanbooru:
|
||||
img2img_deepbooru.click(
|
||||
fn=interrogate_deepbooru,
|
||||
inputs=[init_img],
|
||||
outputs=[img2img_prompt],
|
||||
)
|
||||
|
||||
save.click(
|
||||
fn=wrap_gradio_call(save_files),
|
||||
_js="(x, y, z) => [x, y, selected_gallery_index()]",
|
||||
_js="(x, y, z, w) => [x, y, z, selected_gallery_index()]",
|
||||
inputs=[
|
||||
generation_info,
|
||||
img2img_gallery,
|
||||
html_info
|
||||
do_make_zip,
|
||||
html_info,
|
||||
],
|
||||
outputs=[
|
||||
download_files,
|
||||
html_info,
|
||||
html_info,
|
||||
html_info,
|
||||
|
|
@ -941,7 +1000,7 @@ def create_ui(wrap_gradio_gpu_call):
|
|||
custom_name = gr.Textbox(label="Custom Name (Optional)")
|
||||
interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Interpolation Amount', value=0.3)
|
||||
interp_method = gr.Radio(choices=["Weighted Sum", "Sigmoid", "Inverse Sigmoid"], value="Weighted Sum", label="Interpolation Method")
|
||||
save_as_half = gr.Checkbox(value=False, label="Safe as float16")
|
||||
save_as_half = gr.Checkbox(value=False, label="Save as float16")
|
||||
modelmerger_merge = gr.Button(elem_id="modelmerger_merge", label="Merge", variant='primary')
|
||||
|
||||
with gr.Column(variant='panel'):
|
||||
|
|
@ -975,9 +1034,9 @@ def create_ui(wrap_gradio_gpu_call):
|
|||
process_dst = gr.Textbox(label='Destination directory')
|
||||
|
||||
with gr.Row():
|
||||
process_flip = gr.Checkbox(label='Flip')
|
||||
process_split = gr.Checkbox(label='Split into two')
|
||||
process_caption = gr.Checkbox(label='Add caption')
|
||||
process_flip = gr.Checkbox(label='Create flipped copies')
|
||||
process_split = gr.Checkbox(label='Split oversized images into two')
|
||||
process_caption = gr.Checkbox(label='Use BLIP caption as filename')
|
||||
|
||||
with gr.Row():
|
||||
with gr.Column(scale=3):
|
||||
|
|
@ -1097,6 +1156,15 @@ def create_ui(wrap_gradio_gpu_call):
|
|||
component_dict = {}
|
||||
|
||||
def open_folder(f):
|
||||
if not os.path.isdir(f):
|
||||
print(f"""
|
||||
WARNING
|
||||
An open_folder request was made with an argument that is not a folder.
|
||||
This could be an error or a malicious attempt to run code on your computer.
|
||||
Requested path was: {f}
|
||||
""", file=sys.stderr)
|
||||
return
|
||||
|
||||
if not shared.cmd_opts.hide_ui_dir_config:
|
||||
path = os.path.normpath(f)
|
||||
if platform.system() == "Windows":
|
||||
|
|
@ -1110,10 +1178,13 @@ def create_ui(wrap_gradio_gpu_call):
|
|||
changed = 0
|
||||
|
||||
for key, value, comp in zip(opts.data_labels.keys(), args, components):
|
||||
if not opts.same_type(value, opts.data_labels[key].default):
|
||||
return f"Bad value for setting {key}: {value}; expecting {type(opts.data_labels[key].default).__name__}"
|
||||
if comp != dummy_component and not opts.same_type(value, opts.data_labels[key].default):
|
||||
return f"Bad value for setting {key}: {value}; expecting {type(opts.data_labels[key].default).__name__}", opts.dumpjson()
|
||||
|
||||
for key, value, comp in zip(opts.data_labels.keys(), args, components):
|
||||
if comp == dummy_component:
|
||||
continue
|
||||
|
||||
comp_args = opts.data_labels[key].component_args
|
||||
if comp_args and isinstance(comp_args, dict) and comp_args.get('visible') is False:
|
||||
continue
|
||||
|
|
@ -1140,6 +1211,21 @@ def create_ui(wrap_gradio_gpu_call):
|
|||
images_history = img_his.create_history_tabs(gr, opts, wrap_gradio_call(modules.extras.run_pnginfo), images_history_switch_dict)
|
||||
|
||||
|
||||
def run_settings_single(value, key):
|
||||
if not opts.same_type(value, opts.data_labels[key].default):
|
||||
return gr.update(visible=True), opts.dumpjson()
|
||||
|
||||
oldval = opts.data.get(key, None)
|
||||
opts.data[key] = value
|
||||
|
||||
if oldval != value:
|
||||
if opts.data_labels[key].onchange is not None:
|
||||
opts.data_labels[key].onchange()
|
||||
|
||||
opts.save(shared.config_filename)
|
||||
|
||||
return gr.update(value=value), opts.dumpjson()
|
||||
|
||||
with gr.Blocks(analytics_enabled=False) as settings_interface:
|
||||
settings_submit = gr.Button(value="Apply settings", variant='primary')
|
||||
result = gr.HTML()
|
||||
|
|
@ -1147,6 +1233,8 @@ def create_ui(wrap_gradio_gpu_call):
|
|||
settings_cols = 3
|
||||
items_per_col = int(len(opts.data_labels) * 0.9 / settings_cols)
|
||||
|
||||
quicksettings_list = []
|
||||
|
||||
cols_displayed = 0
|
||||
items_displayed = 0
|
||||
previous_section = None
|
||||
|
|
@ -1169,10 +1257,14 @@ def create_ui(wrap_gradio_gpu_call):
|
|||
|
||||
gr.HTML(elem_id="settings_header_text_{}".format(item.section[0]), value='<h1 class="gr-button-lg">{}</h1>'.format(item.section[1]))
|
||||
|
||||
component = create_setting_component(k)
|
||||
component_dict[k] = component
|
||||
components.append(component)
|
||||
items_displayed += 1
|
||||
if item.show_on_main_page:
|
||||
quicksettings_list.append((i, k, item))
|
||||
components.append(dummy_component)
|
||||
else:
|
||||
component = create_setting_component(k)
|
||||
component_dict[k] = component
|
||||
components.append(component)
|
||||
items_displayed += 1
|
||||
|
||||
request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications")
|
||||
request_notifications.click(
|
||||
|
|
@ -1186,7 +1278,6 @@ def create_ui(wrap_gradio_gpu_call):
|
|||
reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary')
|
||||
restart_gradio = gr.Button(value='Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)', variant='primary')
|
||||
|
||||
|
||||
def reload_scripts():
|
||||
modules.scripts.reload_script_body_only()
|
||||
|
||||
|
|
@ -1235,7 +1326,11 @@ def create_ui(wrap_gradio_gpu_call):
|
|||
css += css_hide_progressbar
|
||||
|
||||
with gr.Blocks(css=css, analytics_enabled=False, title="Stable Diffusion") as demo:
|
||||
|
||||
with gr.Row(elem_id="quicksettings"):
|
||||
for i, k, item in quicksettings_list:
|
||||
component = create_setting_component(k)
|
||||
component_dict[k] = component
|
||||
|
||||
settings_interface.gradio_ref = demo
|
||||
|
||||
with gr.Tabs() as tabs:
|
||||
|
|
@ -1252,7 +1347,16 @@ def create_ui(wrap_gradio_gpu_call):
|
|||
inputs=components,
|
||||
outputs=[result, text_settings],
|
||||
)
|
||||
|
||||
|
||||
for i, k, item in quicksettings_list:
|
||||
component = component_dict[k]
|
||||
|
||||
component.change(
|
||||
fn=lambda value, k=k: run_settings_single(value, key=k),
|
||||
inputs=[component],
|
||||
outputs=[component, text_settings],
|
||||
)
|
||||
|
||||
def modelmerger(*args):
|
||||
try:
|
||||
results = modules.extras.run_modelmerger(*args)
|
||||
|
|
|
|||
|
|
@ -36,10 +36,11 @@ class Upscaler:
|
|||
self.half = not modules.shared.cmd_opts.no_half
|
||||
self.pre_pad = 0
|
||||
self.mod_scale = None
|
||||
if self.name is not None and create_dirs:
|
||||
|
||||
if self.model_path is None and self.name:
|
||||
self.model_path = os.path.join(models_path, self.name)
|
||||
if not os.path.exists(self.model_path):
|
||||
os.makedirs(self.model_path)
|
||||
if self.model_path and create_dirs:
|
||||
os.makedirs(self.model_path, exist_ok=True)
|
||||
|
||||
try:
|
||||
import cv2
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue