mirror of
https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
synced 2026-01-04 00:02:39 -08:00
Merge remote-tracking branch 'origin2/dev' into soft-inpainting
# Conflicts: # modules/processing.py
This commit is contained in:
CodeHatchling
commit
38864816fa
47 changed files with 1329 additions and 668 deletions
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@ -22,7 +22,6 @@ from modules.api import models
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from modules.shared import opts
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from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
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from modules.textual_inversion.textual_inversion import create_embedding, train_embedding
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from modules.textual_inversion.preprocess import preprocess
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from modules.hypernetworks.hypernetwork import create_hypernetwork, train_hypernetwork
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from PIL import PngImagePlugin, Image
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from modules.sd_models_config import find_checkpoint_config_near_filename
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@ -235,7 +234,6 @@ class Api:
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self.add_api_route("/sdapi/v1/refresh-vae", self.refresh_vae, methods=["POST"])
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self.add_api_route("/sdapi/v1/create/embedding", self.create_embedding, methods=["POST"], response_model=models.CreateResponse)
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self.add_api_route("/sdapi/v1/create/hypernetwork", self.create_hypernetwork, methods=["POST"], response_model=models.CreateResponse)
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self.add_api_route("/sdapi/v1/preprocess", self.preprocess, methods=["POST"], response_model=models.PreprocessResponse)
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self.add_api_route("/sdapi/v1/train/embedding", self.train_embedding, methods=["POST"], response_model=models.TrainResponse)
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self.add_api_route("/sdapi/v1/train/hypernetwork", self.train_hypernetwork, methods=["POST"], response_model=models.TrainResponse)
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self.add_api_route("/sdapi/v1/memory", self.get_memory, methods=["GET"], response_model=models.MemoryResponse)
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@ -675,19 +673,6 @@ class Api:
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finally:
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shared.state.end()
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def preprocess(self, args: dict):
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try:
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shared.state.begin(job="preprocess")
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preprocess(**args) # quick operation unless blip/booru interrogation is enabled
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shared.state.end()
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return models.PreprocessResponse(info='preprocess complete')
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except KeyError as e:
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return models.PreprocessResponse(info=f"preprocess error: invalid token: {e}")
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except Exception as e:
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return models.PreprocessResponse(info=f"preprocess error: {e}")
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finally:
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shared.state.end()
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def train_embedding(self, args: dict):
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try:
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shared.state.begin(job="train_embedding")
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@ -202,9 +202,6 @@ class TrainResponse(BaseModel):
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class CreateResponse(BaseModel):
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info: str = Field(title="Create info", description="Response string from create embedding or hypernetwork task.")
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class PreprocessResponse(BaseModel):
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info: str = Field(title="Preprocess info", description="Response string from preprocessing task.")
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fields = {}
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for key, metadata in opts.data_labels.items():
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value = opts.data.get(key)
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@ -70,6 +70,7 @@ parser.add_argument("--opt-sdp-no-mem-attention", action='store_true', help="pre
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parser.add_argument("--disable-opt-split-attention", action='store_true', help="prefer no cross-attention layer optimization for automatic choice of optimization")
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parser.add_argument("--disable-nan-check", action='store_true', help="do not check if produced images/latent spaces have nans; useful for running without a checkpoint in CI")
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parser.add_argument("--use-cpu", nargs='+', help="use CPU as torch device for specified modules", default=[], type=str.lower)
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parser.add_argument("--use-ipex", action="store_true", help="use Intel XPU as torch device")
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parser.add_argument("--disable-model-loading-ram-optimization", action='store_true', help="disable an optimization that reduces RAM use when loading a model")
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parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests")
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parser.add_argument("--port", type=int, help="launch gradio with given server port, you need root/admin rights for ports < 1024, defaults to 7860 if available", default=None)
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@ -8,6 +8,13 @@ from modules import errors, shared
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if sys.platform == "darwin":
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from modules import mac_specific
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if shared.cmd_opts.use_ipex:
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from modules import xpu_specific
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def has_xpu() -> bool:
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return shared.cmd_opts.use_ipex and xpu_specific.has_xpu
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def has_mps() -> bool:
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if sys.platform != "darwin":
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@ -30,6 +37,9 @@ def get_optimal_device_name():
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if has_mps():
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return "mps"
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if has_xpu():
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return xpu_specific.get_xpu_device_string()
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return "cpu"
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@ -38,7 +48,7 @@ def get_optimal_device():
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def get_device_for(task):
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if task in shared.cmd_opts.use_cpu:
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if task in shared.cmd_opts.use_cpu or "all" in shared.cmd_opts.use_cpu:
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return cpu
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return get_optimal_device()
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@ -54,6 +64,9 @@ def torch_gc():
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if has_mps():
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mac_specific.torch_mps_gc()
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if has_xpu():
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xpu_specific.torch_xpu_gc()
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def enable_tf32():
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if torch.cuda.is_available():
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@ -1,3 +1,4 @@
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from __future__ import annotations
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import base64
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import io
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import json
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@ -15,9 +16,6 @@ re_imagesize = re.compile(r"^(\d+)x(\d+)$")
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re_hypernet_hash = re.compile("\(([0-9a-f]+)\)$")
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type_of_gr_update = type(gr.update())
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paste_fields = {}
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registered_param_bindings = []
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class ParamBinding:
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def __init__(self, paste_button, tabname, source_text_component=None, source_image_component=None, source_tabname=None, override_settings_component=None, paste_field_names=None):
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@ -30,6 +28,10 @@ class ParamBinding:
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self.paste_field_names = paste_field_names or []
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paste_fields: dict[str, dict] = {}
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registered_param_bindings: list[ParamBinding] = []
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def reset():
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paste_fields.clear()
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registered_param_bindings.clear()
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@ -113,7 +115,6 @@ def register_paste_params_button(binding: ParamBinding):
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def connect_paste_params_buttons():
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binding: ParamBinding
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for binding in registered_param_bindings:
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destination_image_component = paste_fields[binding.tabname]["init_img"]
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fields = paste_fields[binding.tabname]["fields"]
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@ -313,6 +314,9 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
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if "VAE Decoder" not in res:
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res["VAE Decoder"] = "Full"
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skip = set(shared.opts.infotext_skip_pasting)
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res = {k: v for k, v in res.items() if k not in skip}
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return res
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@ -443,3 +447,4 @@ def connect_paste(button, paste_fields, input_comp, override_settings_component,
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outputs=[],
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show_progress=False,
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)
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@ -47,10 +47,20 @@ def Block_get_config(self):
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def BlockContext_init(self, *args, **kwargs):
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if scripts.scripts_current is not None:
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scripts.scripts_current.before_component(self, **kwargs)
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scripts.script_callbacks.before_component_callback(self, **kwargs)
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res = original_BlockContext_init(self, *args, **kwargs)
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add_classes_to_gradio_component(self)
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scripts.script_callbacks.after_component_callback(self, **kwargs)
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if scripts.scripts_current is not None:
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scripts.scripts_current.after_component(self, **kwargs)
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return res
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@ -3,3 +3,14 @@ import sys
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# this will break any attempt to import xformers which will prevent stability diffusion repo from trying to use it
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if "--xformers" not in "".join(sys.argv):
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sys.modules["xformers"] = None
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# Hack to fix a changed import in torchvision 0.17+, which otherwise breaks
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# basicsr; see https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/13985
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try:
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import torchvision.transforms.functional_tensor # noqa: F401
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except ImportError:
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try:
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import torchvision.transforms.functional as functional
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sys.modules["torchvision.transforms.functional_tensor"] = functional
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except ImportError:
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pass # shrug...
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@ -6,6 +6,7 @@ import os
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import shutil
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import sys
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import importlib.util
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import importlib.metadata
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import platform
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import json
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from functools import lru_cache
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@ -119,11 +120,16 @@ def run(command, desc=None, errdesc=None, custom_env=None, live: bool = default_
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def is_installed(package):
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try:
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spec = importlib.util.find_spec(package)
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except ModuleNotFoundError:
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return False
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dist = importlib.metadata.distribution(package)
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except importlib.metadata.PackageNotFoundError:
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try:
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spec = importlib.util.find_spec(package)
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except ModuleNotFoundError:
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return False
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return spec is not None
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return spec is not None
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return dist is not None
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def repo_dir(name):
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@ -310,6 +316,26 @@ def requirements_met(requirements_file):
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def prepare_environment():
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torch_index_url = os.environ.get('TORCH_INDEX_URL', "https://download.pytorch.org/whl/cu118")
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torch_command = os.environ.get('TORCH_COMMAND', f"pip install torch==2.0.1 torchvision==0.15.2 --extra-index-url {torch_index_url}")
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if args.use_ipex:
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if platform.system() == "Windows":
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# The "Nuullll/intel-extension-for-pytorch" wheels were built from IPEX source for Intel Arc GPU: https://github.com/intel/intel-extension-for-pytorch/tree/xpu-main
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# This is NOT an Intel official release so please use it at your own risk!!
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# See https://github.com/Nuullll/intel-extension-for-pytorch/releases/tag/v2.0.110%2Bxpu-master%2Bdll-bundle for details.
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#
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# Strengths (over official IPEX 2.0.110 windows release):
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# - AOT build (for Arc GPU only) to eliminate JIT compilation overhead: https://github.com/intel/intel-extension-for-pytorch/issues/399
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# - Bundles minimal oneAPI 2023.2 dependencies into the python wheels, so users don't need to install oneAPI for the whole system.
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# - Provides a compatible torchvision wheel: https://github.com/intel/intel-extension-for-pytorch/issues/465
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# Limitation:
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# - Only works for python 3.10
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url_prefix = "https://github.com/Nuullll/intel-extension-for-pytorch/releases/download/v2.0.110%2Bxpu-master%2Bdll-bundle"
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torch_command = os.environ.get('TORCH_COMMAND', f"pip install {url_prefix}/torch-2.0.0a0+gite9ebda2-cp310-cp310-win_amd64.whl {url_prefix}/torchvision-0.15.2a0+fa99a53-cp310-cp310-win_amd64.whl {url_prefix}/intel_extension_for_pytorch-2.0.110+gitc6ea20b-cp310-cp310-win_amd64.whl")
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else:
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# Using official IPEX release for linux since it's already an AOT build.
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# However, users still have to install oneAPI toolkit and activate oneAPI environment manually.
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# See https://intel.github.io/intel-extension-for-pytorch/index.html#installation for details.
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torch_index_url = os.environ.get('TORCH_INDEX_URL', "https://pytorch-extension.intel.com/release-whl/stable/xpu/us/")
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torch_command = os.environ.get('TORCH_COMMAND', f"pip install torch==2.0.0a0 intel-extension-for-pytorch==2.0.110+gitba7f6c1 --extra-index-url {torch_index_url}")
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requirements_file = os.environ.get('REQS_FILE', "requirements_versions.txt")
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xformers_package = os.environ.get('XFORMERS_PACKAGE', 'xformers==0.0.20')
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@ -352,6 +378,8 @@ def prepare_environment():
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run(f'"{python}" -m {torch_command}', "Installing torch and torchvision", "Couldn't install torch", live=True)
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startup_timer.record("install torch")
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if args.use_ipex:
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args.skip_torch_cuda_test = True
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if not args.skip_torch_cuda_test and not check_run_python("import torch; assert torch.cuda.is_available()"):
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raise RuntimeError(
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'Torch is not able to use GPU; '
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|
|
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|
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@ -1,6 +1,7 @@
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import logging
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import torch
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from torch import Tensor
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import platform
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from modules.sd_hijack_utils import CondFunc
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from packaging import version
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@ -51,6 +52,17 @@ def cumsum_fix(input, cumsum_func, *args, **kwargs):
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return cumsum_func(input, *args, **kwargs)
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# MPS workaround for https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14046
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def interpolate_with_fp32_fallback(orig_func, *args, **kwargs) -> Tensor:
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try:
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return orig_func(*args, **kwargs)
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except RuntimeError as e:
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if "not implemented for" in str(e) and "Half" in str(e):
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input_tensor = args[0]
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return orig_func(input_tensor.to(torch.float32), *args[1:], **kwargs).to(input_tensor.dtype)
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else:
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print(f"An unexpected RuntimeError occurred: {str(e)}")
|
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|
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if has_mps:
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if platform.mac_ver()[0].startswith("13.2."):
|
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# MPS workaround for https://github.com/pytorch/pytorch/issues/95188, thanks to danieldk (https://github.com/explosion/curated-transformers/pull/124)
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|
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@ -77,6 +89,9 @@ if has_mps:
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# MPS workaround for https://github.com/pytorch/pytorch/issues/96113
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CondFunc('torch.nn.functional.layer_norm', lambda orig_func, x, normalized_shape, weight, bias, eps, **kwargs: orig_func(x.float(), normalized_shape, weight.float() if weight is not None else None, bias.float() if bias is not None else bias, eps).to(x.dtype), lambda _, input, *args, **kwargs: len(args) == 4 and input.device.type == 'mps')
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|
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# MPS workaround for https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14046
|
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CondFunc('torch.nn.functional.interpolate', interpolate_with_fp32_fallback, None)
|
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|
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# MPS workaround for https://github.com/pytorch/pytorch/issues/92311
|
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if platform.processor() == 'i386':
|
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for funcName in ['torch.argmax', 'torch.Tensor.argmax']:
|
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|
|
|
|||
|
|
@ -24,10 +24,15 @@ from pytorch_lightning.utilities.distributed import rank_zero_only
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from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config
|
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from ldm.modules.ema import LitEma
|
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from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution
|
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from ldm.models.autoencoder import VQModelInterface, IdentityFirstStage, AutoencoderKL
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from ldm.models.autoencoder import IdentityFirstStage, AutoencoderKL
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from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
|
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from ldm.models.diffusion.ddim import DDIMSampler
|
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|
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try:
|
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from ldm.models.autoencoder import VQModelInterface
|
||||
except Exception:
|
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class VQModelInterface:
|
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pass
|
||||
|
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__conditioning_keys__ = {'concat': 'c_concat',
|
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'crossattn': 'c_crossattn',
|
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|
|
|
|||
|
|
@ -29,11 +29,7 @@ def run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir,
|
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|
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image_list = shared.listfiles(input_dir)
|
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for filename in image_list:
|
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try:
|
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image = Image.open(filename)
|
||||
except Exception:
|
||||
continue
|
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yield image, filename
|
||||
yield filename, filename
|
||||
else:
|
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assert image, 'image not selected'
|
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yield image, None
|
||||
|
|
@ -45,35 +41,85 @@ def run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir,
|
|||
|
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infotext = ''
|
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|
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for image_data, name in get_images(extras_mode, image, image_folder, input_dir):
|
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data_to_process = list(get_images(extras_mode, image, image_folder, input_dir))
|
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shared.state.job_count = len(data_to_process)
|
||||
|
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for image_placeholder, name in data_to_process:
|
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image_data: Image.Image
|
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|
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shared.state.nextjob()
|
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shared.state.textinfo = name
|
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shared.state.skipped = False
|
||||
|
||||
if shared.state.interrupted:
|
||||
break
|
||||
|
||||
if isinstance(image_placeholder, str):
|
||||
try:
|
||||
image_data = Image.open(image_placeholder)
|
||||
except Exception:
|
||||
continue
|
||||
else:
|
||||
image_data = image_placeholder
|
||||
|
||||
shared.state.assign_current_image(image_data)
|
||||
|
||||
parameters, existing_pnginfo = images.read_info_from_image(image_data)
|
||||
if parameters:
|
||||
existing_pnginfo["parameters"] = parameters
|
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|
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pp = scripts_postprocessing.PostprocessedImage(image_data.convert("RGB"))
|
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initial_pp = scripts_postprocessing.PostprocessedImage(image_data.convert("RGB"))
|
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|
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scripts.scripts_postproc.run(pp, args)
|
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scripts.scripts_postproc.run(initial_pp, args)
|
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|
||||
if opts.use_original_name_batch and name is not None:
|
||||
basename = os.path.splitext(os.path.basename(name))[0]
|
||||
else:
|
||||
basename = ''
|
||||
if shared.state.skipped:
|
||||
continue
|
||||
|
||||
infotext = ", ".join([k if k == v else f'{k}: {generation_parameters_copypaste.quote(v)}' for k, v in pp.info.items() if v is not None])
|
||||
used_suffixes = {}
|
||||
for pp in [initial_pp, *initial_pp.extra_images]:
|
||||
suffix = pp.get_suffix(used_suffixes)
|
||||
|
||||
if opts.enable_pnginfo:
|
||||
pp.image.info = existing_pnginfo
|
||||
pp.image.info["postprocessing"] = infotext
|
||||
if opts.use_original_name_batch and name is not None:
|
||||
basename = os.path.splitext(os.path.basename(name))[0]
|
||||
forced_filename = basename + suffix
|
||||
else:
|
||||
basename = ''
|
||||
forced_filename = None
|
||||
|
||||
if save_output:
|
||||
images.save_image(pp.image, path=outpath, basename=basename, seed=None, prompt=None, extension=opts.samples_format, info=infotext, short_filename=True, no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=None)
|
||||
infotext = ", ".join([k if k == v else f'{k}: {generation_parameters_copypaste.quote(v)}' for k, v in pp.info.items() if v is not None])
|
||||
|
||||
if extras_mode != 2 or show_extras_results:
|
||||
outputs.append(pp.image)
|
||||
if opts.enable_pnginfo:
|
||||
pp.image.info = existing_pnginfo
|
||||
pp.image.info["postprocessing"] = infotext
|
||||
|
||||
if save_output:
|
||||
fullfn, _ = images.save_image(pp.image, path=outpath, basename=basename, extension=opts.samples_format, info=infotext, short_filename=True, no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=forced_filename, suffix=suffix)
|
||||
|
||||
if pp.caption:
|
||||
caption_filename = os.path.splitext(fullfn)[0] + ".txt"
|
||||
if os.path.isfile(caption_filename):
|
||||
with open(caption_filename, encoding="utf8") as file:
|
||||
existing_caption = file.read().strip()
|
||||
else:
|
||||
existing_caption = ""
|
||||
|
||||
action = shared.opts.postprocessing_existing_caption_action
|
||||
if action == 'Prepend' and existing_caption:
|
||||
caption = f"{existing_caption} {pp.caption}"
|
||||
elif action == 'Append' and existing_caption:
|
||||
caption = f"{pp.caption} {existing_caption}"
|
||||
elif action == 'Keep' and existing_caption:
|
||||
caption = existing_caption
|
||||
else:
|
||||
caption = pp.caption
|
||||
|
||||
caption = caption.strip()
|
||||
if caption:
|
||||
with open(caption_filename, "w", encoding="utf8") as file:
|
||||
file.write(caption)
|
||||
|
||||
if extras_mode != 2 or show_extras_results:
|
||||
outputs.append(pp.image)
|
||||
|
||||
image_data.close()
|
||||
|
||||
|
|
@ -82,6 +128,10 @@ def run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir,
|
|||
return outputs, ui_common.plaintext_to_html(infotext), ''
|
||||
|
||||
|
||||
def run_postprocessing_webui(id_task, *args, **kwargs):
|
||||
return run_postprocessing(*args, **kwargs)
|
||||
|
||||
|
||||
def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_dir, show_extras_results, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility, upscale_first: bool, save_output: bool = True):
|
||||
"""old handler for API"""
|
||||
|
||||
|
|
@ -97,9 +147,11 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
|
|||
"upscaler_2_visibility": extras_upscaler_2_visibility,
|
||||
},
|
||||
"GFPGAN": {
|
||||
"enable": True,
|
||||
"gfpgan_visibility": gfpgan_visibility,
|
||||
},
|
||||
"CodeFormer": {
|
||||
"enable": True,
|
||||
"codeformer_visibility": codeformer_visibility,
|
||||
"codeformer_weight": codeformer_weight,
|
||||
},
|
||||
|
|
|
|||
|
|
@ -692,8 +692,8 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
|
|||
"Size": f"{p.width}x{p.height}",
|
||||
"Model hash": p.sd_model_hash if opts.add_model_hash_to_info else None,
|
||||
"Model": p.sd_model_name if opts.add_model_name_to_info else None,
|
||||
"VAE hash": p.sd_vae_hash if opts.add_model_hash_to_info else None,
|
||||
"VAE": p.sd_vae_name if opts.add_model_name_to_info else None,
|
||||
"VAE hash": p.sd_vae_hash if opts.add_vae_hash_to_info else None,
|
||||
"VAE": p.sd_vae_name if opts.add_vae_name_to_info else None,
|
||||
"Variation seed": (None if p.subseed_strength == 0 else (p.all_subseeds[0] if use_main_prompt else all_subseeds[index])),
|
||||
"Variation seed strength": (None if p.subseed_strength == 0 else p.subseed_strength),
|
||||
"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}"),
|
||||
|
|
@ -980,27 +980,26 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
|
|||
if opts.enable_pnginfo:
|
||||
image.info["parameters"] = text
|
||||
output_images.append(image)
|
||||
if save_samples and any([opts.save_mask, opts.save_mask_composite, opts.return_mask, opts.return_mask_composite]):
|
||||
if hasattr(p, 'masks_for_overlay') and p.masks_for_overlay:
|
||||
image_mask = p.masks_for_overlay[i].convert('RGB')
|
||||
image_mask_composite = Image.composite(original_denoised_image.convert('RGBA').convert('RGBa'), Image.new('RGBa', image.size), images.resize_image(2, p.masks_for_overlay[i], image.width, image.height).convert('L')).convert('RGBA')
|
||||
elif hasattr(p, 'mask_for_overlay') and p.mask_for_overlay:
|
||||
image_mask = p.mask_for_overlay.convert('RGB')
|
||||
image_mask_composite = Image.composite(original_denoised_image.convert('RGBA').convert('RGBa'), Image.new('RGBa', image.size), images.resize_image(2, p.mask_for_overlay, image.width, image.height).convert('L')).convert('RGBA')
|
||||
else:
|
||||
image_mask = None
|
||||
image_mask_composite = None
|
||||
|
||||
if image_mask is not None and image_mask_composite is not None:
|
||||
if opts.save_mask:
|
||||
if hasattr(p, 'mask_for_overlay') and p.mask_for_overlay:
|
||||
mask_for_overlay = p.mask_for_overlay
|
||||
elif hasattr(p, 'masks_for_overlay') and p.masks_for_overlay and p.masks_for_overlay[i]:
|
||||
mask_for_overlay = p.masks_for_overlay[i]
|
||||
else:
|
||||
mask_for_overlay = None
|
||||
|
||||
if mask_for_overlay is not None:
|
||||
if opts.return_mask or opts.save_mask:
|
||||
image_mask = mask_for_overlay.convert('RGB')
|
||||
if save_samples and opts.save_mask:
|
||||
images.save_image(image_mask, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-mask")
|
||||
|
||||
if opts.save_mask_composite:
|
||||
images.save_image(image_mask_composite, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-mask-composite")
|
||||
|
||||
if opts.return_mask:
|
||||
output_images.append(image_mask)
|
||||
|
||||
if opts.return_mask_composite or opts.save_mask_composite:
|
||||
image_mask_composite = Image.composite(original_denoised_image.convert('RGBA').convert('RGBa'), Image.new('RGBa', image.size), images.resize_image(2, mask_for_overlay, image.width, image.height).convert('L')).convert('RGBA')
|
||||
if save_samples and opts.save_mask_composite:
|
||||
images.save_image(image_mask_composite, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-mask-composite")
|
||||
if opts.return_mask_composite:
|
||||
output_images.append(image_mask_composite)
|
||||
|
||||
|
|
|
|||
|
|
@ -560,17 +560,25 @@ class ScriptRunner:
|
|||
on_after.clear()
|
||||
|
||||
def create_script_ui(self, script):
|
||||
import modules.api.models as api_models
|
||||
|
||||
script.args_from = len(self.inputs)
|
||||
script.args_to = len(self.inputs)
|
||||
|
||||
try:
|
||||
self.create_script_ui_inner(script)
|
||||
except Exception:
|
||||
errors.report(f"Error creating UI for {script.name}: ", exc_info=True)
|
||||
|
||||
def create_script_ui_inner(self, script):
|
||||
import modules.api.models as api_models
|
||||
|
||||
controls = wrap_call(script.ui, script.filename, "ui", script.is_img2img)
|
||||
|
||||
if controls is None:
|
||||
return
|
||||
|
||||
script.name = wrap_call(script.title, script.filename, "title", default=script.filename).lower()
|
||||
|
||||
api_args = []
|
||||
|
||||
for control in controls:
|
||||
|
|
|
|||
|
|
@ -1,13 +1,56 @@
|
|||
import dataclasses
|
||||
import os
|
||||
import gradio as gr
|
||||
|
||||
from modules import errors, shared
|
||||
|
||||
|
||||
@dataclasses.dataclass
|
||||
class PostprocessedImageSharedInfo:
|
||||
target_width: int = None
|
||||
target_height: int = None
|
||||
|
||||
|
||||
class PostprocessedImage:
|
||||
def __init__(self, image):
|
||||
self.image = image
|
||||
self.info = {}
|
||||
self.shared = PostprocessedImageSharedInfo()
|
||||
self.extra_images = []
|
||||
self.nametags = []
|
||||
self.disable_processing = False
|
||||
self.caption = None
|
||||
|
||||
def get_suffix(self, used_suffixes=None):
|
||||
used_suffixes = {} if used_suffixes is None else used_suffixes
|
||||
suffix = "-".join(self.nametags)
|
||||
if suffix:
|
||||
suffix = "-" + suffix
|
||||
|
||||
if suffix not in used_suffixes:
|
||||
used_suffixes[suffix] = 1
|
||||
return suffix
|
||||
|
||||
for i in range(1, 100):
|
||||
proposed_suffix = suffix + "-" + str(i)
|
||||
|
||||
if proposed_suffix not in used_suffixes:
|
||||
used_suffixes[proposed_suffix] = 1
|
||||
return proposed_suffix
|
||||
|
||||
return suffix
|
||||
|
||||
def create_copy(self, new_image, *, nametags=None, disable_processing=False):
|
||||
pp = PostprocessedImage(new_image)
|
||||
pp.shared = self.shared
|
||||
pp.nametags = self.nametags.copy()
|
||||
pp.info = self.info.copy()
|
||||
pp.disable_processing = disable_processing
|
||||
|
||||
if nametags is not None:
|
||||
pp.nametags += nametags
|
||||
|
||||
return pp
|
||||
|
||||
|
||||
class ScriptPostprocessing:
|
||||
|
|
@ -42,10 +85,17 @@ class ScriptPostprocessing:
|
|||
|
||||
pass
|
||||
|
||||
def image_changed(self):
|
||||
def process_firstpass(self, pp: PostprocessedImage, **args):
|
||||
"""
|
||||
Called for all scripts before calling process(). Scripts can examine the image here and set fields
|
||||
of the pp object to communicate things to other scripts.
|
||||
args contains a dictionary with all values returned by components from ui()
|
||||
"""
|
||||
|
||||
pass
|
||||
|
||||
|
||||
def image_changed(self):
|
||||
pass
|
||||
|
||||
|
||||
def wrap_call(func, filename, funcname, *args, default=None, **kwargs):
|
||||
|
|
@ -118,16 +168,42 @@ class ScriptPostprocessingRunner:
|
|||
return inputs
|
||||
|
||||
def run(self, pp: PostprocessedImage, args):
|
||||
for script in self.scripts_in_preferred_order():
|
||||
shared.state.job = script.name
|
||||
scripts = []
|
||||
|
||||
for script in self.scripts_in_preferred_order():
|
||||
script_args = args[script.args_from:script.args_to]
|
||||
|
||||
process_args = {}
|
||||
for (name, _component), value in zip(script.controls.items(), script_args):
|
||||
process_args[name] = value
|
||||
|
||||
script.process(pp, **process_args)
|
||||
scripts.append((script, process_args))
|
||||
|
||||
for script, process_args in scripts:
|
||||
script.process_firstpass(pp, **process_args)
|
||||
|
||||
all_images = [pp]
|
||||
|
||||
for script, process_args in scripts:
|
||||
if shared.state.skipped:
|
||||
break
|
||||
|
||||
shared.state.job = script.name
|
||||
|
||||
for single_image in all_images.copy():
|
||||
|
||||
if not single_image.disable_processing:
|
||||
script.process(single_image, **process_args)
|
||||
|
||||
for extra_image in single_image.extra_images:
|
||||
if not isinstance(extra_image, PostprocessedImage):
|
||||
extra_image = single_image.create_copy(extra_image)
|
||||
|
||||
all_images.append(extra_image)
|
||||
|
||||
single_image.extra_images.clear()
|
||||
|
||||
pp.extra_images = all_images[1:]
|
||||
|
||||
def create_args_for_run(self, scripts_args):
|
||||
if not self.ui_created:
|
||||
|
|
|
|||
|
|
@ -38,8 +38,12 @@ ldm.models.diffusion.ddpm.print = shared.ldm_print
|
|||
optimizers = []
|
||||
current_optimizer: sd_hijack_optimizations.SdOptimization = None
|
||||
|
||||
ldm_original_forward = patches.patch(__file__, ldm.modules.diffusionmodules.openaimodel.UNetModel, "forward", sd_unet.UNetModel_forward)
|
||||
sgm_original_forward = patches.patch(__file__, sgm.modules.diffusionmodules.openaimodel.UNetModel, "forward", sd_unet.UNetModel_forward)
|
||||
ldm_patched_forward = sd_unet.create_unet_forward(ldm.modules.diffusionmodules.openaimodel.UNetModel.forward)
|
||||
ldm_original_forward = patches.patch(__file__, ldm.modules.diffusionmodules.openaimodel.UNetModel, "forward", ldm_patched_forward)
|
||||
|
||||
sgm_patched_forward = sd_unet.create_unet_forward(sgm.modules.diffusionmodules.openaimodel.UNetModel.forward)
|
||||
sgm_original_forward = patches.patch(__file__, sgm.modules.diffusionmodules.openaimodel.UNetModel, "forward", sgm_patched_forward)
|
||||
|
||||
|
||||
def list_optimizers():
|
||||
new_optimizers = script_callbacks.list_optimizers_callback()
|
||||
|
|
@ -303,8 +307,6 @@ class StableDiffusionModelHijack:
|
|||
self.layers = None
|
||||
self.clip = None
|
||||
|
||||
sd_unet.original_forward = None
|
||||
|
||||
|
||||
def apply_circular(self, enable):
|
||||
if self.circular_enabled == enable:
|
||||
|
|
|
|||
|
|
@ -230,15 +230,19 @@ def select_checkpoint():
|
|||
return checkpoint_info
|
||||
|
||||
|
||||
checkpoint_dict_replacements = {
|
||||
checkpoint_dict_replacements_sd1 = {
|
||||
'cond_stage_model.transformer.embeddings.': 'cond_stage_model.transformer.text_model.embeddings.',
|
||||
'cond_stage_model.transformer.encoder.': 'cond_stage_model.transformer.text_model.encoder.',
|
||||
'cond_stage_model.transformer.final_layer_norm.': 'cond_stage_model.transformer.text_model.final_layer_norm.',
|
||||
}
|
||||
|
||||
checkpoint_dict_replacements_sd2_turbo = { # Converts SD 2.1 Turbo from SGM to LDM format.
|
||||
'conditioner.embedders.0.': 'cond_stage_model.',
|
||||
}
|
||||
|
||||
def transform_checkpoint_dict_key(k):
|
||||
for text, replacement in checkpoint_dict_replacements.items():
|
||||
|
||||
def transform_checkpoint_dict_key(k, replacements):
|
||||
for text, replacement in replacements.items():
|
||||
if k.startswith(text):
|
||||
k = replacement + k[len(text):]
|
||||
|
||||
|
|
@ -249,9 +253,14 @@ def get_state_dict_from_checkpoint(pl_sd):
|
|||
pl_sd = pl_sd.pop("state_dict", pl_sd)
|
||||
pl_sd.pop("state_dict", None)
|
||||
|
||||
is_sd2_turbo = 'conditioner.embedders.0.model.ln_final.weight' in pl_sd and pl_sd['conditioner.embedders.0.model.ln_final.weight'].size()[0] == 1024
|
||||
|
||||
sd = {}
|
||||
for k, v in pl_sd.items():
|
||||
new_key = transform_checkpoint_dict_key(k)
|
||||
if is_sd2_turbo:
|
||||
new_key = transform_checkpoint_dict_key(k, checkpoint_dict_replacements_sd2_turbo)
|
||||
else:
|
||||
new_key = transform_checkpoint_dict_key(k, checkpoint_dict_replacements_sd1)
|
||||
|
||||
if new_key is not None:
|
||||
sd[new_key] = v
|
||||
|
|
|
|||
|
|
@ -11,7 +11,7 @@ from modules.models.diffusion.uni_pc import uni_pc
|
|||
def ddim(model, x, timesteps, extra_args=None, callback=None, disable=None, eta=0.0):
|
||||
alphas_cumprod = model.inner_model.inner_model.alphas_cumprod
|
||||
alphas = alphas_cumprod[timesteps]
|
||||
alphas_prev = alphas_cumprod[torch.nn.functional.pad(timesteps[:-1], pad=(1, 0))].to(torch.float64 if x.device.type != 'mps' else torch.float32)
|
||||
alphas_prev = alphas_cumprod[torch.nn.functional.pad(timesteps[:-1], pad=(1, 0))].to(torch.float64 if x.device.type != 'mps' and x.device.type != 'xpu' else torch.float32)
|
||||
sqrt_one_minus_alphas = torch.sqrt(1 - alphas)
|
||||
sigmas = eta * np.sqrt((1 - alphas_prev.cpu().numpy()) / (1 - alphas.cpu()) * (1 - alphas.cpu() / alphas_prev.cpu().numpy()))
|
||||
|
||||
|
|
@ -43,7 +43,7 @@ def ddim(model, x, timesteps, extra_args=None, callback=None, disable=None, eta=
|
|||
def plms(model, x, timesteps, extra_args=None, callback=None, disable=None):
|
||||
alphas_cumprod = model.inner_model.inner_model.alphas_cumprod
|
||||
alphas = alphas_cumprod[timesteps]
|
||||
alphas_prev = alphas_cumprod[torch.nn.functional.pad(timesteps[:-1], pad=(1, 0))].to(torch.float64 if x.device.type != 'mps' else torch.float32)
|
||||
alphas_prev = alphas_cumprod[torch.nn.functional.pad(timesteps[:-1], pad=(1, 0))].to(torch.float64 if x.device.type != 'mps' and x.device.type != 'xpu' else torch.float32)
|
||||
sqrt_one_minus_alphas = torch.sqrt(1 - alphas)
|
||||
|
||||
extra_args = {} if extra_args is None else extra_args
|
||||
|
|
|
|||
|
|
@ -5,8 +5,7 @@ from modules import script_callbacks, shared, devices
|
|||
unet_options = []
|
||||
current_unet_option = None
|
||||
current_unet = None
|
||||
original_forward = None
|
||||
|
||||
original_forward = None # not used, only left temporarily for compatibility
|
||||
|
||||
def list_unets():
|
||||
new_unets = script_callbacks.list_unets_callback()
|
||||
|
|
@ -84,9 +83,12 @@ class SdUnet(torch.nn.Module):
|
|||
pass
|
||||
|
||||
|
||||
def UNetModel_forward(self, x, timesteps=None, context=None, *args, **kwargs):
|
||||
if current_unet is not None:
|
||||
return current_unet.forward(x, timesteps, context, *args, **kwargs)
|
||||
def create_unet_forward(original_forward):
|
||||
def UNetModel_forward(self, x, timesteps=None, context=None, *args, **kwargs):
|
||||
if current_unet is not None:
|
||||
return current_unet.forward(x, timesteps, context, *args, **kwargs)
|
||||
|
||||
return original_forward(self, x, timesteps, context, *args, **kwargs)
|
||||
return original_forward(self, x, timesteps, context, *args, **kwargs)
|
||||
|
||||
return UNetModel_forward
|
||||
|
||||
|
|
|
|||
|
|
@ -66,6 +66,22 @@ def reload_hypernetworks():
|
|||
shared.hypernetworks = hypernetwork.list_hypernetworks(cmd_opts.hypernetwork_dir)
|
||||
|
||||
|
||||
def get_infotext_names():
|
||||
from modules import generation_parameters_copypaste, shared
|
||||
res = {}
|
||||
|
||||
for info in shared.opts.data_labels.values():
|
||||
if info.infotext:
|
||||
res[info.infotext] = 1
|
||||
|
||||
for tab_data in generation_parameters_copypaste.paste_fields.values():
|
||||
for _, name in tab_data.get("fields") or []:
|
||||
if isinstance(name, str):
|
||||
res[name] = 1
|
||||
|
||||
return list(res)
|
||||
|
||||
|
||||
ui_reorder_categories_builtin_items = [
|
||||
"prompt",
|
||||
"image",
|
||||
|
|
|
|||
|
|
@ -46,8 +46,6 @@ options_templates.update(options_section(('saving-images', "Saving images/grids"
|
|||
"grid_text_inactive_color": OptionInfo("#999999", "Inactive text color for image grids", ui_components.FormColorPicker, {}),
|
||||
"grid_background_color": OptionInfo("#ffffff", "Background color for image grids", ui_components.FormColorPicker, {}),
|
||||
|
||||
"enable_pnginfo": OptionInfo(True, "Save text information about generation parameters as chunks to png files"),
|
||||
"save_txt": OptionInfo(False, "Create a text file next to every image with generation parameters."),
|
||||
"save_images_before_face_restoration": OptionInfo(False, "Save a copy of image before doing face restoration."),
|
||||
"save_images_before_highres_fix": OptionInfo(False, "Save a copy of image before applying highres fix."),
|
||||
"save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
|
||||
|
|
@ -237,6 +235,7 @@ options_templates.update(options_section(('interrogate', "Interrogate"), {
|
|||
|
||||
options_templates.update(options_section(('extra_networks', "Extra Networks", "sd"), {
|
||||
"extra_networks_show_hidden_directories": OptionInfo(True, "Show hidden directories").info("directory is hidden if its name starts with \".\"."),
|
||||
"extra_networks_dir_button_function": OptionInfo(False, "Add a '/' to the beginning of directory buttons").info("Buttons will display the contents of the selected directory without acting as a search filter."),
|
||||
"extra_networks_hidden_models": OptionInfo("When searched", "Show cards for models in hidden directories", gr.Radio, {"choices": ["Always", "When searched", "Never"]}).info('"When searched" option will only show the item when the search string has 4 characters or more'),
|
||||
"extra_networks_default_multiplier": OptionInfo(1.0, "Default multiplier for extra networks", gr.Slider, {"minimum": 0.0, "maximum": 2.0, "step": 0.01}),
|
||||
"extra_networks_card_width": OptionInfo(0, "Card width for Extra Networks").info("in pixels"),
|
||||
|
|
@ -252,47 +251,65 @@ options_templates.update(options_section(('extra_networks', "Extra Networks", "s
|
|||
"sd_hypernetwork": OptionInfo("None", "Add hypernetwork to prompt", gr.Dropdown, lambda: {"choices": ["None", *shared.hypernetworks]}, refresh=shared_items.reload_hypernetworks),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('ui', "User interface", "ui"), {
|
||||
"localization": OptionInfo("None", "Localization", gr.Dropdown, lambda: {"choices": ["None"] + list(localization.localizations.keys())}, refresh=lambda: localization.list_localizations(cmd_opts.localizations_dir)).needs_reload_ui(),
|
||||
"gradio_theme": OptionInfo("Default", "Gradio theme", ui_components.DropdownEditable, lambda: {"choices": ["Default"] + shared_gradio_themes.gradio_hf_hub_themes}).info("you can also manually enter any of themes from the <a href='https://huggingface.co/spaces/gradio/theme-gallery'>gallery</a>.").needs_reload_ui(),
|
||||
"gradio_themes_cache": OptionInfo(True, "Cache gradio themes locally").info("disable to update the selected Gradio theme"),
|
||||
"gallery_height": OptionInfo("", "Gallery height", gr.Textbox).info("an be any valid CSS value").needs_reload_ui(),
|
||||
"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"),
|
||||
"send_seed": OptionInfo(True, "Send seed when sending prompt or image to other interface"),
|
||||
"send_size": OptionInfo(True, "Send size when sending prompt or image to another interface"),
|
||||
"js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
|
||||
"js_modal_lightbox_initially_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
|
||||
"js_modal_lightbox_gamepad": OptionInfo(False, "Navigate image viewer with gamepad"),
|
||||
"js_modal_lightbox_gamepad_repeat": OptionInfo(250, "Gamepad repeat period, in milliseconds"),
|
||||
"show_progress_in_title": OptionInfo(True, "Show generation progress in window title."),
|
||||
options_templates.update(options_section(('ui_prompt_editing', "Prompt editing", "ui"), {
|
||||
"keyedit_precision_attention": OptionInfo(0.1, "Precision for (attention:1.1) when editing the prompt with Ctrl+up/down", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}),
|
||||
"keyedit_precision_extra": OptionInfo(0.05, "Precision for <extra networks:0.9> when editing the prompt with Ctrl+up/down", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}),
|
||||
"keyedit_delimiters": OptionInfo(r".,\/!?%^*;:{}=`~() ", "Word delimiters when editing the prompt with Ctrl+up/down"),
|
||||
"keyedit_delimiters_whitespace": OptionInfo(["Tab", "Carriage Return", "Line Feed"], "Ctrl+up/down whitespace delimiters", gr.CheckboxGroup, lambda: {"choices": ["Tab", "Carriage Return", "Line Feed"]}),
|
||||
"disable_token_counters": OptionInfo(False, "Disable prompt token counters").needs_reload_ui(),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('ui_gallery', "Gallery", "ui"), {
|
||||
"return_grid": OptionInfo(True, "Show grid in gallery"),
|
||||
"do_not_show_images": OptionInfo(False, "Do not show any images in gallery"),
|
||||
"js_modal_lightbox": OptionInfo(True, "Full page image viewer: enable"),
|
||||
"js_modal_lightbox_initially_zoomed": OptionInfo(True, "Full page image viewer: show images zoomed in by default"),
|
||||
"js_modal_lightbox_gamepad": OptionInfo(False, "Full page image viewer: navigate with gamepad"),
|
||||
"js_modal_lightbox_gamepad_repeat": OptionInfo(250, "Full page image viewer: gamepad repeat period").info("in milliseconds"),
|
||||
"gallery_height": OptionInfo("", "Gallery height", gr.Textbox).info("can be any valid CSS value, for example 768px or 20em").needs_reload_ui(),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('ui_alternatives', "UI alternatives", "ui"), {
|
||||
"compact_prompt_box": OptionInfo(False, "Compact prompt layout").info("puts prompt and negative prompt inside the Generate tab, leaving more vertical space for the image on the right").needs_reload_ui(),
|
||||
"samplers_in_dropdown": OptionInfo(True, "Use dropdown for sampler selection instead of radio group").needs_reload_ui(),
|
||||
"dimensions_and_batch_together": OptionInfo(True, "Show Width/Height and Batch sliders in same row").needs_reload_ui(),
|
||||
"keyedit_precision_attention": OptionInfo(0.1, "Ctrl+up/down precision when editing (attention:1.1)", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}),
|
||||
"keyedit_precision_extra": OptionInfo(0.05, "Ctrl+up/down precision when editing <extra networks:0.9>", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}),
|
||||
"keyedit_delimiters": OptionInfo(r".,\/!?%^*;:{}=`~() ", "Ctrl+up/down word delimiters"),
|
||||
"keyedit_delimiters_whitespace": OptionInfo(["Tab", "Carriage Return", "Line Feed"], "Ctrl+up/down whitespace delimiters", gr.CheckboxGroup, lambda: {"choices": ["Tab", "Carriage Return", "Line Feed"]}),
|
||||
"keyedit_move": OptionInfo(True, "Alt+left/right moves prompt elements"),
|
||||
"quicksettings_list": OptionInfo(["sd_model_checkpoint"], "Quicksettings list", ui_components.DropdownMulti, lambda: {"choices": list(shared.opts.data_labels.keys())}).js("info", "settingsHintsShowQuicksettings").info("setting entries that appear at the top of page rather than in settings tab").needs_reload_ui(),
|
||||
"ui_tab_order": OptionInfo([], "UI tab order", ui_components.DropdownMulti, lambda: {"choices": list(shared.tab_names)}).needs_reload_ui(),
|
||||
"hidden_tabs": OptionInfo([], "Hidden UI tabs", ui_components.DropdownMulti, lambda: {"choices": list(shared.tab_names)}).needs_reload_ui(),
|
||||
"ui_reorder_list": OptionInfo([], "txt2img/img2img UI item order", ui_components.DropdownMulti, lambda: {"choices": list(shared_items.ui_reorder_categories())}).info("selected items appear first").needs_reload_ui(),
|
||||
"sd_checkpoint_dropdown_use_short": OptionInfo(False, "Checkpoint dropdown: use filenames without paths").info("models in subdirectories like photo/sd15.ckpt will be listed as just sd15.ckpt"),
|
||||
"hires_fix_show_sampler": OptionInfo(False, "Hires fix: show hires checkpoint and sampler selection").needs_reload_ui(),
|
||||
"hires_fix_show_prompts": OptionInfo(False, "Hires fix: show hires prompt and negative prompt").needs_reload_ui(),
|
||||
"disable_token_counters": OptionInfo(False, "Disable prompt token counters").needs_reload_ui(),
|
||||
"txt2img_settings_accordion": OptionInfo(False, "Settings in txt2img hidden under Accordion").needs_reload_ui(),
|
||||
"img2img_settings_accordion": OptionInfo(False, "Settings in img2img hidden under Accordion").needs_reload_ui(),
|
||||
"compact_prompt_box": OptionInfo(False, "Compact prompt layout").info("puts prompt and negative prompt inside the Generate tab, leaving more vertical space for the image on the right").needs_reload_ui(),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('ui', "User interface", "ui"), {
|
||||
"localization": OptionInfo("None", "Localization", gr.Dropdown, lambda: {"choices": ["None"] + list(localization.localizations.keys())}, refresh=lambda: localization.list_localizations(cmd_opts.localizations_dir)).needs_reload_ui(),
|
||||
"quicksettings_list": OptionInfo(["sd_model_checkpoint"], "Quicksettings list", ui_components.DropdownMulti, lambda: {"choices": list(shared.opts.data_labels.keys())}).js("info", "settingsHintsShowQuicksettings").info("setting entries that appear at the top of page rather than in settings tab").needs_reload_ui(),
|
||||
"ui_tab_order": OptionInfo([], "UI tab order", ui_components.DropdownMulti, lambda: {"choices": list(shared.tab_names)}).needs_reload_ui(),
|
||||
"hidden_tabs": OptionInfo([], "Hidden UI tabs", ui_components.DropdownMulti, lambda: {"choices": list(shared.tab_names)}).needs_reload_ui(),
|
||||
"ui_reorder_list": OptionInfo([], "UI item order for txt2img/img2img tabs", ui_components.DropdownMulti, lambda: {"choices": list(shared_items.ui_reorder_categories())}).info("selected items appear first").needs_reload_ui(),
|
||||
"gradio_theme": OptionInfo("Default", "Gradio theme", ui_components.DropdownEditable, lambda: {"choices": ["Default"] + shared_gradio_themes.gradio_hf_hub_themes}).info("you can also manually enter any of themes from the <a href='https://huggingface.co/spaces/gradio/theme-gallery'>gallery</a>.").needs_reload_ui(),
|
||||
"gradio_themes_cache": OptionInfo(True, "Cache gradio themes locally").info("disable to update the selected Gradio theme"),
|
||||
"show_progress_in_title": OptionInfo(True, "Show generation progress in window title."),
|
||||
"send_seed": OptionInfo(True, "Send seed when sending prompt or image to other interface"),
|
||||
"send_size": OptionInfo(True, "Send size when sending prompt or image to another interface"),
|
||||
}))
|
||||
|
||||
|
||||
options_templates.update(options_section(('infotext', "Infotext", "ui"), {
|
||||
"add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"),
|
||||
"add_model_name_to_info": OptionInfo(True, "Add model name to generation information"),
|
||||
"add_user_name_to_info": OptionInfo(False, "Add user name to generation information when authenticated"),
|
||||
"add_version_to_infotext": OptionInfo(True, "Add program version to generation information"),
|
||||
"infotext_explanation": OptionHTML("""
|
||||
Infotext is what this software calls the text that contains generation parameters and can be used to generate the same picture again.
|
||||
It is displayed in UI below the image. To use infotext, paste it into the prompt and click the ↙️ paste button.
|
||||
"""),
|
||||
"enable_pnginfo": OptionInfo(True, "Write infotext to metadata of the generated image"),
|
||||
"save_txt": OptionInfo(False, "Create a text file with infotext next to every generated image"),
|
||||
|
||||
"add_model_name_to_info": OptionInfo(True, "Add model name to infotext"),
|
||||
"add_model_hash_to_info": OptionInfo(True, "Add model hash to infotext"),
|
||||
"add_vae_name_to_info": OptionInfo(True, "Add VAE name to infotext"),
|
||||
"add_vae_hash_to_info": OptionInfo(True, "Add VAE hash to infotext"),
|
||||
"add_user_name_to_info": OptionInfo(False, "Add user name to infotext when authenticated"),
|
||||
"add_version_to_infotext": OptionInfo(True, "Add program version to infotext"),
|
||||
"disable_weights_auto_swap": OptionInfo(True, "Disregard checkpoint information from pasted infotext").info("when reading generation parameters from text into UI"),
|
||||
"infotext_skip_pasting": OptionInfo([], "Disregard fields from pasted infotext", ui_components.DropdownMulti, lambda: {"choices": shared_items.get_infotext_names()}),
|
||||
"infotext_styles": OptionInfo("Apply if any", "Infer styles from prompts of pasted infotext", gr.Radio, {"choices": ["Ignore", "Apply", "Discard", "Apply if any"]}).info("when reading generation parameters from text into UI)").html("""<ul style='margin-left: 1.5em'>
|
||||
<li>Ignore: keep prompt and styles dropdown as it is.</li>
|
||||
<li>Apply: remove style text from prompt, always replace styles dropdown value with found styles (even if none are found).</li>
|
||||
|
|
@ -341,6 +358,7 @@ options_templates.update(options_section(('postprocessing', "Postprocessing", "p
|
|||
'postprocessing_enable_in_main_ui': OptionInfo([], "Enable postprocessing operations in txt2img and img2img tabs", ui_components.DropdownMulti, lambda: {"choices": [x.name for x in shared_items.postprocessing_scripts()]}),
|
||||
'postprocessing_operation_order': OptionInfo([], "Postprocessing operation order", ui_components.DropdownMulti, lambda: {"choices": [x.name for x in shared_items.postprocessing_scripts()]}),
|
||||
'upscaling_max_images_in_cache': OptionInfo(5, "Maximum number of images in upscaling cache", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
|
||||
'postprocessing_existing_caption_action': OptionInfo("Ignore", "Action for existing captions", gr.Radio, {"choices": ["Ignore", "Keep", "Prepend", "Append"]}).info("when generating captions using postprocessing; Ignore = use generated; Keep = use original; Prepend/Append = combine both"),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section((None, "Hidden options"), {
|
||||
|
|
|
|||
|
|
@ -1,4 +1,5 @@
|
|||
import csv
|
||||
import fnmatch
|
||||
import os
|
||||
import os.path
|
||||
import re
|
||||
|
|
@ -10,6 +11,23 @@ class PromptStyle(typing.NamedTuple):
|
|||
name: str
|
||||
prompt: str
|
||||
negative_prompt: str
|
||||
path: str = None
|
||||
|
||||
|
||||
def clean_text(text: str) -> str:
|
||||
"""
|
||||
Iterating through a list of regular expressions and replacement strings, we
|
||||
clean up the prompt and style text to make it easier to match against each
|
||||
other.
|
||||
"""
|
||||
re_list = [
|
||||
("multiple commas", re.compile("(,+\s+)+,?"), ", "),
|
||||
("multiple spaces", re.compile("\s{2,}"), " "),
|
||||
]
|
||||
for _, regex, replace in re_list:
|
||||
text = regex.sub(replace, text)
|
||||
|
||||
return text.strip(", ")
|
||||
|
||||
|
||||
def merge_prompts(style_prompt: str, prompt: str) -> str:
|
||||
|
|
@ -26,41 +44,64 @@ def apply_styles_to_prompt(prompt, styles):
|
|||
for style in styles:
|
||||
prompt = merge_prompts(style, prompt)
|
||||
|
||||
return prompt
|
||||
return clean_text(prompt)
|
||||
|
||||
|
||||
re_spaces = re.compile(" +")
|
||||
def unwrap_style_text_from_prompt(style_text, prompt):
|
||||
"""
|
||||
Checks the prompt to see if the style text is wrapped around it. If so,
|
||||
returns True plus the prompt text without the style text. Otherwise, returns
|
||||
False with the original prompt.
|
||||
|
||||
|
||||
def extract_style_text_from_prompt(style_text, prompt):
|
||||
stripped_prompt = re.sub(re_spaces, " ", prompt.strip())
|
||||
stripped_style_text = re.sub(re_spaces, " ", style_text.strip())
|
||||
Note that the "cleaned" version of the style text is only used for matching
|
||||
purposes here. It isn't returned; the original style text is not modified.
|
||||
"""
|
||||
stripped_prompt = clean_text(prompt)
|
||||
stripped_style_text = clean_text(style_text)
|
||||
if "{prompt}" in stripped_style_text:
|
||||
left, right = stripped_style_text.split("{prompt}", 2)
|
||||
# Work out whether the prompt is wrapped in the style text. If so, we
|
||||
# return True and the "inner" prompt text that isn't part of the style.
|
||||
try:
|
||||
left, right = stripped_style_text.split("{prompt}", 2)
|
||||
except ValueError as e:
|
||||
# If the style text has multple "{prompt}"s, we can't split it into
|
||||
# two parts. This is an error, but we can't do anything about it.
|
||||
print(f"Unable to compare style text to prompt:\n{style_text}")
|
||||
print(f"Error: {e}")
|
||||
return False, prompt
|
||||
if stripped_prompt.startswith(left) and stripped_prompt.endswith(right):
|
||||
prompt = stripped_prompt[len(left):len(stripped_prompt)-len(right)]
|
||||
prompt = stripped_prompt[len(left) : len(stripped_prompt) - len(right)]
|
||||
return True, prompt
|
||||
else:
|
||||
# Work out whether the given prompt ends with the style text. If so, we
|
||||
# return True and the prompt text up to where the style text starts.
|
||||
if stripped_prompt.endswith(stripped_style_text):
|
||||
prompt = stripped_prompt[:len(stripped_prompt)-len(stripped_style_text)]
|
||||
|
||||
if prompt.endswith(', '):
|
||||
prompt = stripped_prompt[: len(stripped_prompt) - len(stripped_style_text)]
|
||||
if prompt.endswith(", "):
|
||||
prompt = prompt[:-2]
|
||||
|
||||
return True, prompt
|
||||
|
||||
return False, prompt
|
||||
|
||||
|
||||
def extract_style_from_prompts(style: PromptStyle, prompt, negative_prompt):
|
||||
def extract_original_prompts(style: PromptStyle, prompt, negative_prompt):
|
||||
"""
|
||||
Takes a style and compares it to the prompt and negative prompt. If the style
|
||||
matches, returns True plus the prompt and negative prompt with the style text
|
||||
removed. Otherwise, returns False with the original prompt and negative prompt.
|
||||
"""
|
||||
if not style.prompt and not style.negative_prompt:
|
||||
return False, prompt, negative_prompt
|
||||
|
||||
match_positive, extracted_positive = extract_style_text_from_prompt(style.prompt, prompt)
|
||||
match_positive, extracted_positive = unwrap_style_text_from_prompt(
|
||||
style.prompt, prompt
|
||||
)
|
||||
if not match_positive:
|
||||
return False, prompt, negative_prompt
|
||||
|
||||
match_negative, extracted_negative = extract_style_text_from_prompt(style.negative_prompt, negative_prompt)
|
||||
match_negative, extracted_negative = unwrap_style_text_from_prompt(
|
||||
style.negative_prompt, negative_prompt
|
||||
)
|
||||
if not match_negative:
|
||||
return False, prompt, negative_prompt
|
||||
|
||||
|
|
@ -69,25 +110,88 @@ def extract_style_from_prompts(style: PromptStyle, prompt, negative_prompt):
|
|||
|
||||
class StyleDatabase:
|
||||
def __init__(self, path: str):
|
||||
self.no_style = PromptStyle("None", "", "")
|
||||
self.no_style = PromptStyle("None", "", "", None)
|
||||
self.styles = {}
|
||||
self.path = path
|
||||
|
||||
folder, file = os.path.split(self.path)
|
||||
self.default_file = file.split("*")[0] + ".csv"
|
||||
if self.default_file == ".csv":
|
||||
self.default_file = "styles.csv"
|
||||
self.default_path = os.path.join(folder, self.default_file)
|
||||
|
||||
self.prompt_fields = [field for field in PromptStyle._fields if field != "path"]
|
||||
|
||||
self.reload()
|
||||
|
||||
def reload(self):
|
||||
"""
|
||||
Clears the style database and reloads the styles from the CSV file(s)
|
||||
matching the path used to initialize the database.
|
||||
"""
|
||||
self.styles.clear()
|
||||
|
||||
if not os.path.exists(self.path):
|
||||
return
|
||||
path, filename = os.path.split(self.path)
|
||||
|
||||
with open(self.path, "r", encoding="utf-8-sig", newline='') as file:
|
||||
if "*" in filename:
|
||||
fileglob = filename.split("*")[0] + "*.csv"
|
||||
filelist = []
|
||||
for file in os.listdir(path):
|
||||
if fnmatch.fnmatch(file, fileglob):
|
||||
filelist.append(file)
|
||||
# Add a visible divider to the style list
|
||||
half_len = round(len(file) / 2)
|
||||
divider = f"{'-' * (20 - half_len)} {file.upper()}"
|
||||
divider = f"{divider} {'-' * (40 - len(divider))}"
|
||||
self.styles[divider] = PromptStyle(
|
||||
f"{divider}", None, None, "do_not_save"
|
||||
)
|
||||
# Add styles from this CSV file
|
||||
self.load_from_csv(os.path.join(path, file))
|
||||
if len(filelist) == 0:
|
||||
print(f"No styles found in {path} matching {fileglob}")
|
||||
return
|
||||
elif not os.path.exists(self.path):
|
||||
print(f"Style database not found: {self.path}")
|
||||
return
|
||||
else:
|
||||
self.load_from_csv(self.path)
|
||||
|
||||
def load_from_csv(self, path: str):
|
||||
with open(path, "r", encoding="utf-8-sig", newline="") as file:
|
||||
reader = csv.DictReader(file, skipinitialspace=True)
|
||||
for row in reader:
|
||||
# Ignore empty rows or rows starting with a comment
|
||||
if not row or row["name"].startswith("#"):
|
||||
continue
|
||||
# Support loading old CSV format with "name, text"-columns
|
||||
prompt = row["prompt"] if "prompt" in row else row["text"]
|
||||
negative_prompt = row.get("negative_prompt", "")
|
||||
self.styles[row["name"]] = PromptStyle(row["name"], prompt, negative_prompt)
|
||||
# Add style to database
|
||||
self.styles[row["name"]] = PromptStyle(
|
||||
row["name"], prompt, negative_prompt, path
|
||||
)
|
||||
|
||||
def get_style_paths(self) -> list():
|
||||
"""
|
||||
Returns a list of all distinct paths, including the default path, of
|
||||
files that styles are loaded from."""
|
||||
# Update any styles without a path to the default path
|
||||
for style in list(self.styles.values()):
|
||||
if not style.path:
|
||||
self.styles[style.name] = style._replace(path=self.default_path)
|
||||
|
||||
# Create a list of all distinct paths, including the default path
|
||||
style_paths = set()
|
||||
style_paths.add(self.default_path)
|
||||
for _, style in self.styles.items():
|
||||
if style.path:
|
||||
style_paths.add(style.path)
|
||||
|
||||
# Remove any paths for styles that are just list dividers
|
||||
style_paths.remove("do_not_save")
|
||||
|
||||
return list(style_paths)
|
||||
|
||||
def get_style_prompts(self, styles):
|
||||
return [self.styles.get(x, self.no_style).prompt for x in styles]
|
||||
|
|
@ -96,20 +200,53 @@ class StyleDatabase:
|
|||
return [self.styles.get(x, self.no_style).negative_prompt for x in styles]
|
||||
|
||||
def apply_styles_to_prompt(self, prompt, styles):
|
||||
return apply_styles_to_prompt(prompt, [self.styles.get(x, self.no_style).prompt for x in styles])
|
||||
return apply_styles_to_prompt(
|
||||
prompt, [self.styles.get(x, self.no_style).prompt for x in styles]
|
||||
)
|
||||
|
||||
def apply_negative_styles_to_prompt(self, prompt, styles):
|
||||
return apply_styles_to_prompt(prompt, [self.styles.get(x, self.no_style).negative_prompt for x in styles])
|
||||
return apply_styles_to_prompt(
|
||||
prompt, [self.styles.get(x, self.no_style).negative_prompt for x in styles]
|
||||
)
|
||||
|
||||
def save_styles(self, path: str) -> None:
|
||||
# Always keep a backup file around
|
||||
if os.path.exists(path):
|
||||
shutil.copy(path, f"{path}.bak")
|
||||
def save_styles(self, path: str = None) -> None:
|
||||
# The path argument is deprecated, but kept for backwards compatibility
|
||||
_ = path
|
||||
|
||||
with open(path, "w", encoding="utf-8-sig", newline='') as file:
|
||||
writer = csv.DictWriter(file, fieldnames=PromptStyle._fields)
|
||||
writer.writeheader()
|
||||
writer.writerows(style._asdict() for k, style in self.styles.items())
|
||||
# Update any styles without a path to the default path
|
||||
for style in list(self.styles.values()):
|
||||
if not style.path:
|
||||
self.styles[style.name] = style._replace(path=self.default_path)
|
||||
|
||||
# Create a list of all distinct paths, including the default path
|
||||
style_paths = set()
|
||||
style_paths.add(self.default_path)
|
||||
for _, style in self.styles.items():
|
||||
if style.path:
|
||||
style_paths.add(style.path)
|
||||
|
||||
# Remove any paths for styles that are just list dividers
|
||||
style_paths.remove("do_not_save")
|
||||
|
||||
csv_names = [os.path.split(path)[1].lower() for path in style_paths]
|
||||
|
||||
for style_path in style_paths:
|
||||
# Always keep a backup file around
|
||||
if os.path.exists(style_path):
|
||||
shutil.copy(style_path, f"{style_path}.bak")
|
||||
|
||||
# Write the styles to the CSV file
|
||||
with open(style_path, "w", encoding="utf-8-sig", newline="") as file:
|
||||
writer = csv.DictWriter(file, fieldnames=self.prompt_fields)
|
||||
writer.writeheader()
|
||||
for style in (s for s in self.styles.values() if s.path == style_path):
|
||||
# Skip style list dividers, e.g. "STYLES.CSV"
|
||||
if style.name.lower().strip("# ") in csv_names:
|
||||
continue
|
||||
# Write style fields, ignoring the path field
|
||||
writer.writerow(
|
||||
{k: v for k, v in style._asdict().items() if k != "path"}
|
||||
)
|
||||
|
||||
def extract_styles_from_prompt(self, prompt, negative_prompt):
|
||||
extracted = []
|
||||
|
|
@ -120,7 +257,9 @@ class StyleDatabase:
|
|||
found_style = None
|
||||
|
||||
for style in applicable_styles:
|
||||
is_match, new_prompt, new_neg_prompt = extract_style_from_prompts(style, prompt, negative_prompt)
|
||||
is_match, new_prompt, new_neg_prompt = extract_original_prompts(
|
||||
style, prompt, negative_prompt
|
||||
)
|
||||
if is_match:
|
||||
found_style = style
|
||||
prompt = new_prompt
|
||||
|
|
|
|||
|
|
@ -3,6 +3,8 @@ import requests
|
|||
import os
|
||||
import numpy as np
|
||||
from PIL import ImageDraw
|
||||
from modules import paths_internal
|
||||
from pkg_resources import parse_version
|
||||
|
||||
GREEN = "#0F0"
|
||||
BLUE = "#00F"
|
||||
|
|
@ -25,7 +27,6 @@ def crop_image(im, settings):
|
|||
elif is_portrait(settings.crop_width, settings.crop_height):
|
||||
scale_by = settings.crop_height / im.height
|
||||
|
||||
|
||||
im = im.resize((int(im.width * scale_by), int(im.height * scale_by)))
|
||||
im_debug = im.copy()
|
||||
|
||||
|
|
@ -69,6 +70,7 @@ def crop_image(im, settings):
|
|||
|
||||
return results
|
||||
|
||||
|
||||
def focal_point(im, settings):
|
||||
corner_points = image_corner_points(im, settings) if settings.corner_points_weight > 0 else []
|
||||
entropy_points = image_entropy_points(im, settings) if settings.entropy_points_weight > 0 else []
|
||||
|
|
@ -78,118 +80,120 @@ def focal_point(im, settings):
|
|||
|
||||
weight_pref_total = 0
|
||||
if corner_points:
|
||||
weight_pref_total += settings.corner_points_weight
|
||||
weight_pref_total += settings.corner_points_weight
|
||||
if entropy_points:
|
||||
weight_pref_total += settings.entropy_points_weight
|
||||
weight_pref_total += settings.entropy_points_weight
|
||||
if face_points:
|
||||
weight_pref_total += settings.face_points_weight
|
||||
weight_pref_total += settings.face_points_weight
|
||||
|
||||
corner_centroid = None
|
||||
if corner_points:
|
||||
corner_centroid = centroid(corner_points)
|
||||
corner_centroid.weight = settings.corner_points_weight / weight_pref_total
|
||||
pois.append(corner_centroid)
|
||||
corner_centroid = centroid(corner_points)
|
||||
corner_centroid.weight = settings.corner_points_weight / weight_pref_total
|
||||
pois.append(corner_centroid)
|
||||
|
||||
entropy_centroid = None
|
||||
if entropy_points:
|
||||
entropy_centroid = centroid(entropy_points)
|
||||
entropy_centroid.weight = settings.entropy_points_weight / weight_pref_total
|
||||
pois.append(entropy_centroid)
|
||||
entropy_centroid = centroid(entropy_points)
|
||||
entropy_centroid.weight = settings.entropy_points_weight / weight_pref_total
|
||||
pois.append(entropy_centroid)
|
||||
|
||||
face_centroid = None
|
||||
if face_points:
|
||||
face_centroid = centroid(face_points)
|
||||
face_centroid.weight = settings.face_points_weight / weight_pref_total
|
||||
pois.append(face_centroid)
|
||||
face_centroid = centroid(face_points)
|
||||
face_centroid.weight = settings.face_points_weight / weight_pref_total
|
||||
pois.append(face_centroid)
|
||||
|
||||
average_point = poi_average(pois, settings)
|
||||
|
||||
if settings.annotate_image:
|
||||
d = ImageDraw.Draw(im)
|
||||
max_size = min(im.width, im.height) * 0.07
|
||||
if corner_centroid is not None:
|
||||
color = BLUE
|
||||
box = corner_centroid.bounding(max_size * corner_centroid.weight)
|
||||
d.text((box[0], box[1]-15), f"Edge: {corner_centroid.weight:.02f}", fill=color)
|
||||
d.ellipse(box, outline=color)
|
||||
if len(corner_points) > 1:
|
||||
for f in corner_points:
|
||||
d.rectangle(f.bounding(4), outline=color)
|
||||
if entropy_centroid is not None:
|
||||
color = "#ff0"
|
||||
box = entropy_centroid.bounding(max_size * entropy_centroid.weight)
|
||||
d.text((box[0], box[1]-15), f"Entropy: {entropy_centroid.weight:.02f}", fill=color)
|
||||
d.ellipse(box, outline=color)
|
||||
if len(entropy_points) > 1:
|
||||
for f in entropy_points:
|
||||
d.rectangle(f.bounding(4), outline=color)
|
||||
if face_centroid is not None:
|
||||
color = RED
|
||||
box = face_centroid.bounding(max_size * face_centroid.weight)
|
||||
d.text((box[0], box[1]-15), f"Face: {face_centroid.weight:.02f}", fill=color)
|
||||
d.ellipse(box, outline=color)
|
||||
if len(face_points) > 1:
|
||||
for f in face_points:
|
||||
d.rectangle(f.bounding(4), outline=color)
|
||||
d = ImageDraw.Draw(im)
|
||||
max_size = min(im.width, im.height) * 0.07
|
||||
if corner_centroid is not None:
|
||||
color = BLUE
|
||||
box = corner_centroid.bounding(max_size * corner_centroid.weight)
|
||||
d.text((box[0], box[1] - 15), f"Edge: {corner_centroid.weight:.02f}", fill=color)
|
||||
d.ellipse(box, outline=color)
|
||||
if len(corner_points) > 1:
|
||||
for f in corner_points:
|
||||
d.rectangle(f.bounding(4), outline=color)
|
||||
if entropy_centroid is not None:
|
||||
color = "#ff0"
|
||||
box = entropy_centroid.bounding(max_size * entropy_centroid.weight)
|
||||
d.text((box[0], box[1] - 15), f"Entropy: {entropy_centroid.weight:.02f}", fill=color)
|
||||
d.ellipse(box, outline=color)
|
||||
if len(entropy_points) > 1:
|
||||
for f in entropy_points:
|
||||
d.rectangle(f.bounding(4), outline=color)
|
||||
if face_centroid is not None:
|
||||
color = RED
|
||||
box = face_centroid.bounding(max_size * face_centroid.weight)
|
||||
d.text((box[0], box[1] - 15), f"Face: {face_centroid.weight:.02f}", fill=color)
|
||||
d.ellipse(box, outline=color)
|
||||
if len(face_points) > 1:
|
||||
for f in face_points:
|
||||
d.rectangle(f.bounding(4), outline=color)
|
||||
|
||||
d.ellipse(average_point.bounding(max_size), outline=GREEN)
|
||||
d.ellipse(average_point.bounding(max_size), outline=GREEN)
|
||||
|
||||
return average_point
|
||||
|
||||
|
||||
def image_face_points(im, settings):
|
||||
if settings.dnn_model_path is not None:
|
||||
detector = cv2.FaceDetectorYN.create(
|
||||
settings.dnn_model_path,
|
||||
"",
|
||||
(im.width, im.height),
|
||||
0.9, # score threshold
|
||||
0.3, # nms threshold
|
||||
5000 # keep top k before nms
|
||||
)
|
||||
faces = detector.detect(np.array(im))
|
||||
results = []
|
||||
if faces[1] is not None:
|
||||
for face in faces[1]:
|
||||
x = face[0]
|
||||
y = face[1]
|
||||
w = face[2]
|
||||
h = face[3]
|
||||
results.append(
|
||||
PointOfInterest(
|
||||
int(x + (w * 0.5)), # face focus left/right is center
|
||||
int(y + (h * 0.33)), # face focus up/down is close to the top of the head
|
||||
size = w,
|
||||
weight = 1/len(faces[1])
|
||||
)
|
||||
)
|
||||
return results
|
||||
detector = cv2.FaceDetectorYN.create(
|
||||
settings.dnn_model_path,
|
||||
"",
|
||||
(im.width, im.height),
|
||||
0.9, # score threshold
|
||||
0.3, # nms threshold
|
||||
5000 # keep top k before nms
|
||||
)
|
||||
faces = detector.detect(np.array(im))
|
||||
results = []
|
||||
if faces[1] is not None:
|
||||
for face in faces[1]:
|
||||
x = face[0]
|
||||
y = face[1]
|
||||
w = face[2]
|
||||
h = face[3]
|
||||
results.append(
|
||||
PointOfInterest(
|
||||
int(x + (w * 0.5)), # face focus left/right is center
|
||||
int(y + (h * 0.33)), # face focus up/down is close to the top of the head
|
||||
size=w,
|
||||
weight=1 / len(faces[1])
|
||||
)
|
||||
)
|
||||
return results
|
||||
else:
|
||||
np_im = np.array(im)
|
||||
gray = cv2.cvtColor(np_im, cv2.COLOR_BGR2GRAY)
|
||||
np_im = np.array(im)
|
||||
gray = cv2.cvtColor(np_im, cv2.COLOR_BGR2GRAY)
|
||||
|
||||
tries = [
|
||||
[ f'{cv2.data.haarcascades}haarcascade_eye.xml', 0.01 ],
|
||||
[ f'{cv2.data.haarcascades}haarcascade_frontalface_default.xml', 0.05 ],
|
||||
[ f'{cv2.data.haarcascades}haarcascade_profileface.xml', 0.05 ],
|
||||
[ f'{cv2.data.haarcascades}haarcascade_frontalface_alt.xml', 0.05 ],
|
||||
[ f'{cv2.data.haarcascades}haarcascade_frontalface_alt2.xml', 0.05 ],
|
||||
[ f'{cv2.data.haarcascades}haarcascade_frontalface_alt_tree.xml', 0.05 ],
|
||||
[ f'{cv2.data.haarcascades}haarcascade_eye_tree_eyeglasses.xml', 0.05 ],
|
||||
[ f'{cv2.data.haarcascades}haarcascade_upperbody.xml', 0.05 ]
|
||||
]
|
||||
for t in tries:
|
||||
classifier = cv2.CascadeClassifier(t[0])
|
||||
minsize = int(min(im.width, im.height) * t[1]) # at least N percent of the smallest side
|
||||
try:
|
||||
faces = classifier.detectMultiScale(gray, scaleFactor=1.1,
|
||||
minNeighbors=7, minSize=(minsize, minsize), flags=cv2.CASCADE_SCALE_IMAGE)
|
||||
except Exception:
|
||||
continue
|
||||
tries = [
|
||||
[f'{cv2.data.haarcascades}haarcascade_eye.xml', 0.01],
|
||||
[f'{cv2.data.haarcascades}haarcascade_frontalface_default.xml', 0.05],
|
||||
[f'{cv2.data.haarcascades}haarcascade_profileface.xml', 0.05],
|
||||
[f'{cv2.data.haarcascades}haarcascade_frontalface_alt.xml', 0.05],
|
||||
[f'{cv2.data.haarcascades}haarcascade_frontalface_alt2.xml', 0.05],
|
||||
[f'{cv2.data.haarcascades}haarcascade_frontalface_alt_tree.xml', 0.05],
|
||||
[f'{cv2.data.haarcascades}haarcascade_eye_tree_eyeglasses.xml', 0.05],
|
||||
[f'{cv2.data.haarcascades}haarcascade_upperbody.xml', 0.05]
|
||||
]
|
||||
for t in tries:
|
||||
classifier = cv2.CascadeClassifier(t[0])
|
||||
minsize = int(min(im.width, im.height) * t[1]) # at least N percent of the smallest side
|
||||
try:
|
||||
faces = classifier.detectMultiScale(gray, scaleFactor=1.1,
|
||||
minNeighbors=7, minSize=(minsize, minsize),
|
||||
flags=cv2.CASCADE_SCALE_IMAGE)
|
||||
except Exception:
|
||||
continue
|
||||
|
||||
if faces:
|
||||
rects = [[f[0], f[1], f[0] + f[2], f[1] + f[3]] for f in faces]
|
||||
return [PointOfInterest((r[0] +r[2]) // 2, (r[1] + r[3]) // 2, size=abs(r[0]-r[2]), weight=1/len(rects)) for r in rects]
|
||||
if faces:
|
||||
rects = [[f[0], f[1], f[0] + f[2], f[1] + f[3]] for f in faces]
|
||||
return [PointOfInterest((r[0] + r[2]) // 2, (r[1] + r[3]) // 2, size=abs(r[0] - r[2]),
|
||||
weight=1 / len(rects)) for r in rects]
|
||||
return []
|
||||
|
||||
|
||||
|
|
@ -198,7 +202,7 @@ def image_corner_points(im, settings):
|
|||
|
||||
# naive attempt at preventing focal points from collecting at watermarks near the bottom
|
||||
gd = ImageDraw.Draw(grayscale)
|
||||
gd.rectangle([0, im.height*.9, im.width, im.height], fill="#999")
|
||||
gd.rectangle([0, im.height * .9, im.width, im.height], fill="#999")
|
||||
|
||||
np_im = np.array(grayscale)
|
||||
|
||||
|
|
@ -206,7 +210,7 @@ def image_corner_points(im, settings):
|
|||
np_im,
|
||||
maxCorners=100,
|
||||
qualityLevel=0.04,
|
||||
minDistance=min(grayscale.width, grayscale.height)*0.06,
|
||||
minDistance=min(grayscale.width, grayscale.height) * 0.06,
|
||||
useHarrisDetector=False,
|
||||
)
|
||||
|
||||
|
|
@ -215,8 +219,8 @@ def image_corner_points(im, settings):
|
|||
|
||||
focal_points = []
|
||||
for point in points:
|
||||
x, y = point.ravel()
|
||||
focal_points.append(PointOfInterest(x, y, size=4, weight=1/len(points)))
|
||||
x, y = point.ravel()
|
||||
focal_points.append(PointOfInterest(x, y, size=4, weight=1 / len(points)))
|
||||
|
||||
return focal_points
|
||||
|
||||
|
|
@ -225,13 +229,13 @@ def image_entropy_points(im, settings):
|
|||
landscape = im.height < im.width
|
||||
portrait = im.height > im.width
|
||||
if landscape:
|
||||
move_idx = [0, 2]
|
||||
move_max = im.size[0]
|
||||
move_idx = [0, 2]
|
||||
move_max = im.size[0]
|
||||
elif portrait:
|
||||
move_idx = [1, 3]
|
||||
move_max = im.size[1]
|
||||
move_idx = [1, 3]
|
||||
move_max = im.size[1]
|
||||
else:
|
||||
return []
|
||||
return []
|
||||
|
||||
e_max = 0
|
||||
crop_current = [0, 0, settings.crop_width, settings.crop_height]
|
||||
|
|
@ -241,14 +245,14 @@ def image_entropy_points(im, settings):
|
|||
e = image_entropy(crop)
|
||||
|
||||
if (e > e_max):
|
||||
e_max = e
|
||||
crop_best = list(crop_current)
|
||||
e_max = e
|
||||
crop_best = list(crop_current)
|
||||
|
||||
crop_current[move_idx[0]] += 4
|
||||
crop_current[move_idx[1]] += 4
|
||||
|
||||
x_mid = int(crop_best[0] + settings.crop_width/2)
|
||||
y_mid = int(crop_best[1] + settings.crop_height/2)
|
||||
x_mid = int(crop_best[0] + settings.crop_width / 2)
|
||||
y_mid = int(crop_best[1] + settings.crop_height / 2)
|
||||
|
||||
return [PointOfInterest(x_mid, y_mid, size=25, weight=1.0)]
|
||||
|
||||
|
|
@ -294,22 +298,23 @@ def is_square(w, h):
|
|||
return w == h
|
||||
|
||||
|
||||
def download_and_cache_models(dirname):
|
||||
download_url = 'https://github.com/opencv/opencv_zoo/blob/91fb0290f50896f38a0ab1e558b74b16bc009428/models/face_detection_yunet/face_detection_yunet_2022mar.onnx?raw=true'
|
||||
model_file_name = 'face_detection_yunet.onnx'
|
||||
model_dir_opencv = os.path.join(paths_internal.models_path, 'opencv')
|
||||
if parse_version(cv2.__version__) >= parse_version('4.8'):
|
||||
model_file_path = os.path.join(model_dir_opencv, 'face_detection_yunet_2023mar.onnx')
|
||||
model_url = 'https://github.com/opencv/opencv_zoo/blob/b6e370b10f641879a87890d44e42173077154a05/models/face_detection_yunet/face_detection_yunet_2023mar.onnx?raw=true'
|
||||
else:
|
||||
model_file_path = os.path.join(model_dir_opencv, 'face_detection_yunet.onnx')
|
||||
model_url = 'https://github.com/opencv/opencv_zoo/blob/91fb0290f50896f38a0ab1e558b74b16bc009428/models/face_detection_yunet/face_detection_yunet_2022mar.onnx?raw=true'
|
||||
|
||||
os.makedirs(dirname, exist_ok=True)
|
||||
|
||||
cache_file = os.path.join(dirname, model_file_name)
|
||||
if not os.path.exists(cache_file):
|
||||
print(f"downloading face detection model from '{download_url}' to '{cache_file}'")
|
||||
response = requests.get(download_url)
|
||||
with open(cache_file, "wb") as f:
|
||||
def download_and_cache_models():
|
||||
if not os.path.exists(model_file_path):
|
||||
os.makedirs(model_dir_opencv, exist_ok=True)
|
||||
print(f"downloading face detection model from '{model_url}' to '{model_file_path}'")
|
||||
response = requests.get(model_url)
|
||||
with open(model_file_path, "wb") as f:
|
||||
f.write(response.content)
|
||||
|
||||
if os.path.exists(cache_file):
|
||||
return cache_file
|
||||
return None
|
||||
return model_file_path
|
||||
|
||||
|
||||
class PointOfInterest:
|
||||
|
|
|
|||
|
|
@ -1,232 +0,0 @@
|
|||
import os
|
||||
from PIL import Image, ImageOps
|
||||
import math
|
||||
import tqdm
|
||||
|
||||
from modules import paths, shared, images, deepbooru
|
||||
from modules.textual_inversion import autocrop
|
||||
|
||||
|
||||
def preprocess(id_task, process_src, process_dst, process_width, process_height, preprocess_txt_action, process_keep_original_size, process_flip, process_split, process_caption, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2, process_focal_crop=False, process_focal_crop_face_weight=0.9, process_focal_crop_entropy_weight=0.15, process_focal_crop_edges_weight=0.5, process_focal_crop_debug=False, process_multicrop=None, process_multicrop_mindim=None, process_multicrop_maxdim=None, process_multicrop_minarea=None, process_multicrop_maxarea=None, process_multicrop_objective=None, process_multicrop_threshold=None):
|
||||
try:
|
||||
if process_caption:
|
||||
shared.interrogator.load()
|
||||
|
||||
if process_caption_deepbooru:
|
||||
deepbooru.model.start()
|
||||
|
||||
preprocess_work(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_keep_original_size, process_flip, process_split, process_caption, process_caption_deepbooru, split_threshold, overlap_ratio, process_focal_crop, process_focal_crop_face_weight, process_focal_crop_entropy_weight, process_focal_crop_edges_weight, process_focal_crop_debug, process_multicrop, process_multicrop_mindim, process_multicrop_maxdim, process_multicrop_minarea, process_multicrop_maxarea, process_multicrop_objective, process_multicrop_threshold)
|
||||
|
||||
finally:
|
||||
|
||||
if process_caption:
|
||||
shared.interrogator.send_blip_to_ram()
|
||||
|
||||
if process_caption_deepbooru:
|
||||
deepbooru.model.stop()
|
||||
|
||||
|
||||
def listfiles(dirname):
|
||||
return os.listdir(dirname)
|
||||
|
||||
|
||||
class PreprocessParams:
|
||||
src = None
|
||||
dstdir = None
|
||||
subindex = 0
|
||||
flip = False
|
||||
process_caption = False
|
||||
process_caption_deepbooru = False
|
||||
preprocess_txt_action = None
|
||||
|
||||
|
||||
def save_pic_with_caption(image, index, params: PreprocessParams, existing_caption=None):
|
||||
caption = ""
|
||||
|
||||
if params.process_caption:
|
||||
caption += shared.interrogator.generate_caption(image)
|
||||
|
||||
if params.process_caption_deepbooru:
|
||||
if caption:
|
||||
caption += ", "
|
||||
caption += deepbooru.model.tag_multi(image)
|
||||
|
||||
filename_part = params.src
|
||||
filename_part = os.path.splitext(filename_part)[0]
|
||||
filename_part = os.path.basename(filename_part)
|
||||
|
||||
basename = f"{index:05}-{params.subindex}-{filename_part}"
|
||||
image.save(os.path.join(params.dstdir, f"{basename}.png"))
|
||||
|
||||
if params.preprocess_txt_action == 'prepend' and existing_caption:
|
||||
caption = f"{existing_caption} {caption}"
|
||||
elif params.preprocess_txt_action == 'append' and existing_caption:
|
||||
caption = f"{caption} {existing_caption}"
|
||||
elif params.preprocess_txt_action == 'copy' and existing_caption:
|
||||
caption = existing_caption
|
||||
|
||||
caption = caption.strip()
|
||||
|
||||
if caption:
|
||||
with open(os.path.join(params.dstdir, f"{basename}.txt"), "w", encoding="utf8") as file:
|
||||
file.write(caption)
|
||||
|
||||
params.subindex += 1
|
||||
|
||||
|
||||
def save_pic(image, index, params, existing_caption=None):
|
||||
save_pic_with_caption(image, index, params, existing_caption=existing_caption)
|
||||
|
||||
if params.flip:
|
||||
save_pic_with_caption(ImageOps.mirror(image), index, params, existing_caption=existing_caption)
|
||||
|
||||
|
||||
def split_pic(image, inverse_xy, width, height, overlap_ratio):
|
||||
if inverse_xy:
|
||||
from_w, from_h = image.height, image.width
|
||||
to_w, to_h = height, width
|
||||
else:
|
||||
from_w, from_h = image.width, image.height
|
||||
to_w, to_h = width, height
|
||||
h = from_h * to_w // from_w
|
||||
if inverse_xy:
|
||||
image = image.resize((h, to_w))
|
||||
else:
|
||||
image = image.resize((to_w, h))
|
||||
|
||||
split_count = math.ceil((h - to_h * overlap_ratio) / (to_h * (1.0 - overlap_ratio)))
|
||||
y_step = (h - to_h) / (split_count - 1)
|
||||
for i in range(split_count):
|
||||
y = int(y_step * i)
|
||||
if inverse_xy:
|
||||
splitted = image.crop((y, 0, y + to_h, to_w))
|
||||
else:
|
||||
splitted = image.crop((0, y, to_w, y + to_h))
|
||||
yield splitted
|
||||
|
||||
# not using torchvision.transforms.CenterCrop because it doesn't allow float regions
|
||||
def center_crop(image: Image, w: int, h: int):
|
||||
iw, ih = image.size
|
||||
if ih / h < iw / w:
|
||||
sw = w * ih / h
|
||||
box = (iw - sw) / 2, 0, iw - (iw - sw) / 2, ih
|
||||
else:
|
||||
sh = h * iw / w
|
||||
box = 0, (ih - sh) / 2, iw, ih - (ih - sh) / 2
|
||||
return image.resize((w, h), Image.Resampling.LANCZOS, box)
|
||||
|
||||
|
||||
def multicrop_pic(image: Image, mindim, maxdim, minarea, maxarea, objective, threshold):
|
||||
iw, ih = image.size
|
||||
err = lambda w, h: 1-(lambda x: x if x < 1 else 1/x)(iw/ih/(w/h))
|
||||
wh = max(((w, h) for w in range(mindim, maxdim+1, 64) for h in range(mindim, maxdim+1, 64)
|
||||
if minarea <= w * h <= maxarea and err(w, h) <= threshold),
|
||||
key= lambda wh: (wh[0]*wh[1], -err(*wh))[::1 if objective=='Maximize area' else -1],
|
||||
default=None
|
||||
)
|
||||
return wh and center_crop(image, *wh)
|
||||
|
||||
|
||||
def preprocess_work(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_keep_original_size, process_flip, process_split, process_caption, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2, process_focal_crop=False, process_focal_crop_face_weight=0.9, process_focal_crop_entropy_weight=0.3, process_focal_crop_edges_weight=0.5, process_focal_crop_debug=False, process_multicrop=None, process_multicrop_mindim=None, process_multicrop_maxdim=None, process_multicrop_minarea=None, process_multicrop_maxarea=None, process_multicrop_objective=None, process_multicrop_threshold=None):
|
||||
width = process_width
|
||||
height = process_height
|
||||
src = os.path.abspath(process_src)
|
||||
dst = os.path.abspath(process_dst)
|
||||
split_threshold = max(0.0, min(1.0, split_threshold))
|
||||
overlap_ratio = max(0.0, min(0.9, overlap_ratio))
|
||||
|
||||
assert src != dst, 'same directory specified as source and destination'
|
||||
|
||||
os.makedirs(dst, exist_ok=True)
|
||||
|
||||
files = listfiles(src)
|
||||
|
||||
shared.state.job = "preprocess"
|
||||
shared.state.textinfo = "Preprocessing..."
|
||||
shared.state.job_count = len(files)
|
||||
|
||||
params = PreprocessParams()
|
||||
params.dstdir = dst
|
||||
params.flip = process_flip
|
||||
params.process_caption = process_caption
|
||||
params.process_caption_deepbooru = process_caption_deepbooru
|
||||
params.preprocess_txt_action = preprocess_txt_action
|
||||
|
||||
pbar = tqdm.tqdm(files)
|
||||
for index, imagefile in enumerate(pbar):
|
||||
params.subindex = 0
|
||||
filename = os.path.join(src, imagefile)
|
||||
try:
|
||||
img = Image.open(filename)
|
||||
img = ImageOps.exif_transpose(img)
|
||||
img = img.convert("RGB")
|
||||
except Exception:
|
||||
continue
|
||||
|
||||
description = f"Preprocessing [Image {index}/{len(files)}]"
|
||||
pbar.set_description(description)
|
||||
shared.state.textinfo = description
|
||||
|
||||
params.src = filename
|
||||
|
||||
existing_caption = None
|
||||
existing_caption_filename = f"{os.path.splitext(filename)[0]}.txt"
|
||||
if os.path.exists(existing_caption_filename):
|
||||
with open(existing_caption_filename, 'r', encoding="utf8") as file:
|
||||
existing_caption = file.read()
|
||||
|
||||
if shared.state.interrupted:
|
||||
break
|
||||
|
||||
if img.height > img.width:
|
||||
ratio = (img.width * height) / (img.height * width)
|
||||
inverse_xy = False
|
||||
else:
|
||||
ratio = (img.height * width) / (img.width * height)
|
||||
inverse_xy = True
|
||||
|
||||
process_default_resize = True
|
||||
|
||||
if process_split and ratio < 1.0 and ratio <= split_threshold:
|
||||
for splitted in split_pic(img, inverse_xy, width, height, overlap_ratio):
|
||||
save_pic(splitted, index, params, existing_caption=existing_caption)
|
||||
process_default_resize = False
|
||||
|
||||
if process_focal_crop and img.height != img.width:
|
||||
|
||||
dnn_model_path = None
|
||||
try:
|
||||
dnn_model_path = autocrop.download_and_cache_models(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)
|
||||
|
||||
autocrop_settings = autocrop.Settings(
|
||||
crop_width = width,
|
||||
crop_height = height,
|
||||
face_points_weight = process_focal_crop_face_weight,
|
||||
entropy_points_weight = process_focal_crop_entropy_weight,
|
||||
corner_points_weight = process_focal_crop_edges_weight,
|
||||
annotate_image = process_focal_crop_debug,
|
||||
dnn_model_path = dnn_model_path,
|
||||
)
|
||||
for focal in autocrop.crop_image(img, autocrop_settings):
|
||||
save_pic(focal, index, params, existing_caption=existing_caption)
|
||||
process_default_resize = False
|
||||
|
||||
if process_multicrop:
|
||||
cropped = multicrop_pic(img, process_multicrop_mindim, process_multicrop_maxdim, process_multicrop_minarea, process_multicrop_maxarea, process_multicrop_objective, process_multicrop_threshold)
|
||||
if cropped is not None:
|
||||
save_pic(cropped, index, params, existing_caption=existing_caption)
|
||||
else:
|
||||
print(f"skipped {img.width}x{img.height} image {filename} (can't find suitable size within error threshold)")
|
||||
process_default_resize = False
|
||||
|
||||
if process_keep_original_size:
|
||||
save_pic(img, index, params, existing_caption=existing_caption)
|
||||
process_default_resize = False
|
||||
|
||||
if process_default_resize:
|
||||
img = images.resize_image(1, img, width, height)
|
||||
save_pic(img, index, params, existing_caption=existing_caption)
|
||||
|
||||
shared.state.nextjob()
|
||||
|
|
@ -3,7 +3,6 @@ import html
|
|||
import gradio as gr
|
||||
|
||||
import modules.textual_inversion.textual_inversion
|
||||
import modules.textual_inversion.preprocess
|
||||
from modules import sd_hijack, shared
|
||||
|
||||
|
||||
|
|
@ -15,12 +14,6 @@ def create_embedding(name, initialization_text, nvpt, overwrite_old):
|
|||
return gr.Dropdown.update(choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())), f"Created: {filename}", ""
|
||||
|
||||
|
||||
def preprocess(*args):
|
||||
modules.textual_inversion.preprocess.preprocess(*args)
|
||||
|
||||
return f"Preprocessing {'interrupted' if shared.state.interrupted else 'finished'}.", ""
|
||||
|
||||
|
||||
def train_embedding(*args):
|
||||
|
||||
assert not shared.cmd_opts.lowvram, 'Training models with lowvram not possible'
|
||||
|
|
|
|||
107
modules/ui.py
107
modules/ui.py
|
|
@ -919,71 +919,6 @@ def create_ui():
|
|||
with gr.Column():
|
||||
create_hypernetwork = gr.Button(value="Create hypernetwork", variant='primary', elem_id="train_create_hypernetwork")
|
||||
|
||||
with gr.Tab(label="Preprocess images", id="preprocess_images"):
|
||||
process_src = gr.Textbox(label='Source directory', elem_id="train_process_src")
|
||||
process_dst = gr.Textbox(label='Destination directory', elem_id="train_process_dst")
|
||||
process_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="train_process_width")
|
||||
process_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="train_process_height")
|
||||
preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', value="ignore", choices=["ignore", "copy", "prepend", "append"], elem_id="train_preprocess_txt_action")
|
||||
|
||||
with gr.Row():
|
||||
process_keep_original_size = gr.Checkbox(label='Keep original size', elem_id="train_process_keep_original_size")
|
||||
process_flip = gr.Checkbox(label='Create flipped copies', elem_id="train_process_flip")
|
||||
process_split = gr.Checkbox(label='Split oversized images', elem_id="train_process_split")
|
||||
process_focal_crop = gr.Checkbox(label='Auto focal point crop', elem_id="train_process_focal_crop")
|
||||
process_multicrop = gr.Checkbox(label='Auto-sized crop', elem_id="train_process_multicrop")
|
||||
process_caption = gr.Checkbox(label='Use BLIP for caption', elem_id="train_process_caption")
|
||||
process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True, elem_id="train_process_caption_deepbooru")
|
||||
|
||||
with gr.Row(visible=False) as process_split_extra_row:
|
||||
process_split_threshold = gr.Slider(label='Split image threshold', value=0.5, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_split_threshold")
|
||||
process_overlap_ratio = gr.Slider(label='Split image overlap ratio', value=0.2, minimum=0.0, maximum=0.9, step=0.05, elem_id="train_process_overlap_ratio")
|
||||
|
||||
with gr.Row(visible=False) as process_focal_crop_row:
|
||||
process_focal_crop_face_weight = gr.Slider(label='Focal point face weight', value=0.9, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_face_weight")
|
||||
process_focal_crop_entropy_weight = gr.Slider(label='Focal point entropy weight', value=0.15, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_entropy_weight")
|
||||
process_focal_crop_edges_weight = gr.Slider(label='Focal point edges weight', value=0.5, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_edges_weight")
|
||||
process_focal_crop_debug = gr.Checkbox(label='Create debug image', elem_id="train_process_focal_crop_debug")
|
||||
|
||||
with gr.Column(visible=False) as process_multicrop_col:
|
||||
gr.Markdown('Each image is center-cropped with an automatically chosen width and height.')
|
||||
with gr.Row():
|
||||
process_multicrop_mindim = gr.Slider(minimum=64, maximum=2048, step=8, label="Dimension lower bound", value=384, elem_id="train_process_multicrop_mindim")
|
||||
process_multicrop_maxdim = gr.Slider(minimum=64, maximum=2048, step=8, label="Dimension upper bound", value=768, elem_id="train_process_multicrop_maxdim")
|
||||
with gr.Row():
|
||||
process_multicrop_minarea = gr.Slider(minimum=64*64, maximum=2048*2048, step=1, label="Area lower bound", value=64*64, elem_id="train_process_multicrop_minarea")
|
||||
process_multicrop_maxarea = gr.Slider(minimum=64*64, maximum=2048*2048, step=1, label="Area upper bound", value=640*640, elem_id="train_process_multicrop_maxarea")
|
||||
with gr.Row():
|
||||
process_multicrop_objective = gr.Radio(["Maximize area", "Minimize error"], value="Maximize area", label="Resizing objective", elem_id="train_process_multicrop_objective")
|
||||
process_multicrop_threshold = gr.Slider(minimum=0, maximum=1, step=0.01, label="Error threshold", value=0.1, elem_id="train_process_multicrop_threshold")
|
||||
|
||||
with gr.Row():
|
||||
with gr.Column(scale=3):
|
||||
gr.HTML(value="")
|
||||
|
||||
with gr.Column():
|
||||
with gr.Row():
|
||||
interrupt_preprocessing = gr.Button("Interrupt", elem_id="train_interrupt_preprocessing")
|
||||
run_preprocess = gr.Button(value="Preprocess", variant='primary', elem_id="train_run_preprocess")
|
||||
|
||||
process_split.change(
|
||||
fn=lambda show: gr_show(show),
|
||||
inputs=[process_split],
|
||||
outputs=[process_split_extra_row],
|
||||
)
|
||||
|
||||
process_focal_crop.change(
|
||||
fn=lambda show: gr_show(show),
|
||||
inputs=[process_focal_crop],
|
||||
outputs=[process_focal_crop_row],
|
||||
)
|
||||
|
||||
process_multicrop.change(
|
||||
fn=lambda show: gr_show(show),
|
||||
inputs=[process_multicrop],
|
||||
outputs=[process_multicrop_col],
|
||||
)
|
||||
|
||||
def get_textual_inversion_template_names():
|
||||
return sorted(textual_inversion.textual_inversion_templates)
|
||||
|
||||
|
|
@ -1084,42 +1019,6 @@ def create_ui():
|
|||
]
|
||||
)
|
||||
|
||||
run_preprocess.click(
|
||||
fn=wrap_gradio_gpu_call(textual_inversion_ui.preprocess, extra_outputs=[gr.update()]),
|
||||
_js="start_training_textual_inversion",
|
||||
inputs=[
|
||||
dummy_component,
|
||||
process_src,
|
||||
process_dst,
|
||||
process_width,
|
||||
process_height,
|
||||
preprocess_txt_action,
|
||||
process_keep_original_size,
|
||||
process_flip,
|
||||
process_split,
|
||||
process_caption,
|
||||
process_caption_deepbooru,
|
||||
process_split_threshold,
|
||||
process_overlap_ratio,
|
||||
process_focal_crop,
|
||||
process_focal_crop_face_weight,
|
||||
process_focal_crop_entropy_weight,
|
||||
process_focal_crop_edges_weight,
|
||||
process_focal_crop_debug,
|
||||
process_multicrop,
|
||||
process_multicrop_mindim,
|
||||
process_multicrop_maxdim,
|
||||
process_multicrop_minarea,
|
||||
process_multicrop_maxarea,
|
||||
process_multicrop_objective,
|
||||
process_multicrop_threshold,
|
||||
],
|
||||
outputs=[
|
||||
ti_output,
|
||||
ti_outcome,
|
||||
],
|
||||
)
|
||||
|
||||
train_embedding.click(
|
||||
fn=wrap_gradio_gpu_call(textual_inversion_ui.train_embedding, extra_outputs=[gr.update()]),
|
||||
_js="start_training_textual_inversion",
|
||||
|
|
@ -1193,12 +1092,6 @@ def create_ui():
|
|||
outputs=[],
|
||||
)
|
||||
|
||||
interrupt_preprocessing.click(
|
||||
fn=lambda: shared.state.interrupt(),
|
||||
inputs=[],
|
||||
outputs=[],
|
||||
)
|
||||
|
||||
loadsave = ui_loadsave.UiLoadsave(cmd_opts.ui_config_file)
|
||||
|
||||
settings = ui_settings.UiSettings()
|
||||
|
|
|
|||
|
|
@ -335,6 +335,11 @@ def normalize_git_url(url):
|
|||
return url
|
||||
|
||||
|
||||
def get_extension_dirname_from_url(url):
|
||||
*parts, last_part = url.split('/')
|
||||
return normalize_git_url(last_part)
|
||||
|
||||
|
||||
def install_extension_from_url(dirname, url, branch_name=None):
|
||||
check_access()
|
||||
|
||||
|
|
@ -346,10 +351,7 @@ def install_extension_from_url(dirname, url, branch_name=None):
|
|||
assert url, 'No URL specified'
|
||||
|
||||
if dirname is None or dirname == "":
|
||||
*parts, last_part = url.split('/')
|
||||
last_part = normalize_git_url(last_part)
|
||||
|
||||
dirname = last_part
|
||||
dirname = get_extension_dirname_from_url(url)
|
||||
|
||||
target_dir = os.path.join(extensions.extensions_dir, dirname)
|
||||
assert not os.path.exists(target_dir), f'Extension directory already exists: {target_dir}'
|
||||
|
|
@ -449,7 +451,8 @@ def get_date(info: dict, key):
|
|||
|
||||
def refresh_available_extensions_from_data(hide_tags, sort_column, filter_text=""):
|
||||
extlist = available_extensions["extensions"]
|
||||
installed_extension_urls = {normalize_git_url(extension.remote): extension.name for extension in extensions.extensions}
|
||||
installed_extensions = {extension.name for extension in extensions.extensions}
|
||||
installed_extension_urls = {normalize_git_url(extension.remote) for extension in extensions.extensions if extension.remote is not None}
|
||||
|
||||
tags = available_extensions.get("tags", {})
|
||||
tags_to_hide = set(hide_tags)
|
||||
|
|
@ -482,7 +485,7 @@ def refresh_available_extensions_from_data(hide_tags, sort_column, filter_text="
|
|||
if url is None:
|
||||
continue
|
||||
|
||||
existing = installed_extension_urls.get(normalize_git_url(url), None)
|
||||
existing = get_extension_dirname_from_url(url) in installed_extensions or normalize_git_url(url) in installed_extension_urls
|
||||
extension_tags = extension_tags + ["installed"] if existing else extension_tags
|
||||
|
||||
if any(x for x in extension_tags if x in tags_to_hide):
|
||||
|
|
|
|||
|
|
@ -151,8 +151,13 @@ class ExtraNetworksPage:
|
|||
continue
|
||||
|
||||
subdir = os.path.abspath(x)[len(parentdir):].replace("\\", "/")
|
||||
while subdir.startswith("/"):
|
||||
subdir = subdir[1:]
|
||||
|
||||
if shared.opts.extra_networks_dir_button_function:
|
||||
if not subdir.startswith("/"):
|
||||
subdir = "/" + subdir
|
||||
else:
|
||||
while subdir.startswith("/"):
|
||||
subdir = subdir[1:]
|
||||
|
||||
is_empty = len(os.listdir(x)) == 0
|
||||
if not is_empty and not subdir.endswith("/"):
|
||||
|
|
|
|||
|
|
@ -1,9 +1,10 @@
|
|||
import gradio as gr
|
||||
from modules import scripts, shared, ui_common, postprocessing, call_queue
|
||||
from modules import scripts, shared, ui_common, postprocessing, call_queue, ui_toprow
|
||||
import modules.generation_parameters_copypaste as parameters_copypaste
|
||||
|
||||
|
||||
def create_ui():
|
||||
dummy_component = gr.Label(visible=False)
|
||||
tab_index = gr.State(value=0)
|
||||
|
||||
with gr.Row(equal_height=False, variant='compact'):
|
||||
|
|
@ -20,11 +21,13 @@ def create_ui():
|
|||
extras_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, placeholder="Leave blank to save images to the default path.", elem_id="extras_batch_output_dir")
|
||||
show_extras_results = gr.Checkbox(label='Show result images', value=True, elem_id="extras_show_extras_results")
|
||||
|
||||
submit = gr.Button('Generate', elem_id="extras_generate", variant='primary')
|
||||
|
||||
script_inputs = scripts.scripts_postproc.setup_ui()
|
||||
|
||||
with gr.Column():
|
||||
toprow = ui_toprow.Toprow(is_compact=True, is_img2img=False, id_part="extras")
|
||||
toprow.create_inline_toprow_image()
|
||||
submit = toprow.submit
|
||||
|
||||
result_images, html_info_x, html_info, html_log = ui_common.create_output_panel("extras", shared.opts.outdir_extras_samples)
|
||||
|
||||
tab_single.select(fn=lambda: 0, inputs=[], outputs=[tab_index])
|
||||
|
|
@ -32,8 +35,10 @@ def create_ui():
|
|||
tab_batch_dir.select(fn=lambda: 2, inputs=[], outputs=[tab_index])
|
||||
|
||||
submit.click(
|
||||
fn=call_queue.wrap_gradio_gpu_call(postprocessing.run_postprocessing, extra_outputs=[None, '']),
|
||||
fn=call_queue.wrap_gradio_gpu_call(postprocessing.run_postprocessing_webui, extra_outputs=[None, '']),
|
||||
_js="submit_extras",
|
||||
inputs=[
|
||||
dummy_component,
|
||||
tab_index,
|
||||
extras_image,
|
||||
image_batch,
|
||||
|
|
@ -45,8 +50,9 @@ def create_ui():
|
|||
outputs=[
|
||||
result_images,
|
||||
html_info_x,
|
||||
html_info,
|
||||
]
|
||||
html_log,
|
||||
],
|
||||
show_progress=False,
|
||||
)
|
||||
|
||||
parameters_copypaste.add_paste_fields("extras", extras_image, None)
|
||||
|
|
|
|||
|
|
@ -34,8 +34,10 @@ class Toprow:
|
|||
|
||||
submit_box = None
|
||||
|
||||
def __init__(self, is_img2img, is_compact=False):
|
||||
id_part = "img2img" if is_img2img else "txt2img"
|
||||
def __init__(self, is_img2img, is_compact=False, id_part=None):
|
||||
if id_part is None:
|
||||
id_part = "img2img" if is_img2img else "txt2img"
|
||||
|
||||
self.id_part = id_part
|
||||
self.is_img2img = is_img2img
|
||||
self.is_compact = is_compact
|
||||
|
|
|
|||
|
|
@ -57,6 +57,9 @@ class Upscaler:
|
|||
dest_h = int((img.height * scale) // 8 * 8)
|
||||
|
||||
for _ in range(3):
|
||||
if img.width >= dest_w and img.height >= dest_h:
|
||||
break
|
||||
|
||||
shape = (img.width, img.height)
|
||||
|
||||
img = self.do_upscale(img, selected_model)
|
||||
|
|
@ -64,9 +67,6 @@ class Upscaler:
|
|||
if shape == (img.width, img.height):
|
||||
break
|
||||
|
||||
if img.width >= dest_w and img.height >= dest_h:
|
||||
break
|
||||
|
||||
if img.width != dest_w or img.height != dest_h:
|
||||
img = img.resize((int(dest_w), int(dest_h)), resample=LANCZOS)
|
||||
|
||||
|
|
|
|||
50
modules/xpu_specific.py
Normal file
50
modules/xpu_specific.py
Normal file
|
|
@ -0,0 +1,50 @@
|
|||
from modules import shared
|
||||
from modules.sd_hijack_utils import CondFunc
|
||||
|
||||
has_ipex = False
|
||||
try:
|
||||
import torch
|
||||
import intel_extension_for_pytorch as ipex # noqa: F401
|
||||
has_ipex = True
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
|
||||
def check_for_xpu():
|
||||
return has_ipex and hasattr(torch, 'xpu') and torch.xpu.is_available()
|
||||
|
||||
|
||||
def get_xpu_device_string():
|
||||
if shared.cmd_opts.device_id is not None:
|
||||
return f"xpu:{shared.cmd_opts.device_id}"
|
||||
return "xpu"
|
||||
|
||||
|
||||
def torch_xpu_gc():
|
||||
with torch.xpu.device(get_xpu_device_string()):
|
||||
torch.xpu.empty_cache()
|
||||
|
||||
|
||||
has_xpu = check_for_xpu()
|
||||
|
||||
if has_xpu:
|
||||
# W/A for https://github.com/intel/intel-extension-for-pytorch/issues/452: torch.Generator API doesn't support XPU device
|
||||
CondFunc('torch.Generator',
|
||||
lambda orig_func, device=None: torch.xpu.Generator(device),
|
||||
lambda orig_func, device=None: device is not None and device.type == "xpu")
|
||||
|
||||
# W/A for some OPs that could not handle different input dtypes
|
||||
CondFunc('torch.nn.functional.layer_norm',
|
||||
lambda orig_func, input, normalized_shape=None, weight=None, *args, **kwargs:
|
||||
orig_func(input.to(weight.data.dtype), normalized_shape, weight, *args, **kwargs),
|
||||
lambda orig_func, input, normalized_shape=None, weight=None, *args, **kwargs:
|
||||
weight is not None and input.dtype != weight.data.dtype)
|
||||
CondFunc('torch.nn.modules.GroupNorm.forward',
|
||||
lambda orig_func, self, input: orig_func(self, input.to(self.weight.data.dtype)),
|
||||
lambda orig_func, self, input: input.dtype != self.weight.data.dtype)
|
||||
CondFunc('torch.nn.modules.linear.Linear.forward',
|
||||
lambda orig_func, self, input: orig_func(self, input.to(self.weight.data.dtype)),
|
||||
lambda orig_func, self, input: input.dtype != self.weight.data.dtype)
|
||||
CondFunc('torch.nn.modules.conv.Conv2d.forward',
|
||||
lambda orig_func, self, input: orig_func(self, input.to(self.weight.data.dtype)),
|
||||
lambda orig_func, self, input: input.dtype != self.weight.data.dtype)
|
||||
Loading…
Add table
Add a link
Reference in a new issue