mirror of
https://github.com/lllyasviel/stable-diffusion-webui-forge.git
synced 2026-07-21 21:01:24 +08:00
interrogate
This commit is contained in:
Haoming
parent
58667ddd92
commit
b3a96523f3
@ -17,7 +17,7 @@ from fastapi.encoders import jsonable_encoder
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from secrets import compare_digest
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import modules.shared as shared
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from modules import sd_samplers, deepbooru, images, scripts, ui, postprocessing, errors, restart, shared_items, script_callbacks, infotext_utils, sd_models, sd_schedulers
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from modules import sd_samplers, images, scripts, ui, postprocessing, errors, restart, shared_items, script_callbacks, infotext_utils, sd_models, sd_schedulers
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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, process_extra_images
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@ -214,7 +214,6 @@ class Api:
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self.add_api_route("/sdapi/v1/extra-batch-images", self.extras_batch_images_api, methods=["POST"], response_model=models.ExtrasBatchImagesResponse)
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self.add_api_route("/sdapi/v1/png-info", self.pnginfoapi, methods=["POST"], response_model=models.PNGInfoResponse)
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self.add_api_route("/sdapi/v1/progress", self.progressapi, methods=["GET"], response_model=models.ProgressResponse)
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self.add_api_route("/sdapi/v1/interrogate", self.interrogateapi, methods=["POST"])
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self.add_api_route("/sdapi/v1/interrupt", self.interruptapi, methods=["POST"])
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self.add_api_route("/sdapi/v1/skip", self.skip, methods=["POST"])
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self.add_api_route("/sdapi/v1/options", self.get_config, methods=["GET"], response_model=models.OptionsModel)
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@ -379,7 +378,7 @@ class Api:
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def get_base_type(annotation):
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origin = get_origin(annotation)
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if origin is Union: # represents Optional
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args = get_args(annotation) # filter out NoneType
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non_none_args = [arg for arg in args if arg is not type(None)]
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@ -642,25 +641,6 @@ class Api:
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return models.ProgressResponse(progress=progress, eta_relative=eta_relative, state=shared.state.dict(), current_image=current_image, textinfo=shared.state.textinfo, current_task=current_task)
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def interrogateapi(self, interrogatereq: models.InterrogateRequest):
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image_b64 = interrogatereq.image
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if image_b64 is None:
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raise HTTPException(status_code=404, detail="Image not found")
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img = decode_base64_to_image(image_b64)
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img = img.convert('RGB')
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# Override object param
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with self.queue_lock:
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if interrogatereq.model == "clip":
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processed = shared.interrogator.interrogate(img)
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elif interrogatereq.model == "deepdanbooru":
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processed = deepbooru.model.tag(img)
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else:
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raise HTTPException(status_code=404, detail="Model not found")
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return models.InterrogateResponse(caption=processed)
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def interruptapi(self):
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shared.state.interrupt()
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@ -191,13 +191,6 @@ class ProgressResponse(BaseModel):
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current_image: str | None = Field(default=None, title="Current image", description="The current image in base64 format. opts.show_progress_every_n_steps is required for this to work.")
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textinfo: str | None = Field(default=None, title="Info text", description="Info text used by WebUI.")
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class InterrogateRequest(BaseModel):
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image: str = Field(default="", title="Image", description="Image to work on, must be a Base64 string containing the image's data.")
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model: str = Field(default="clip", title="Model", description="The interrogate model used.")
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class InterrogateResponse(BaseModel):
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caption: str | None = Field(default=None, title="Caption", description="The generated caption for the image.")
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class TrainResponse(BaseModel):
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info: str = Field(title="Train info", description="Response string from train embedding or hypernetwork task.")
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@ -57,11 +57,9 @@ parser.add_argument("--esrgan-models-path", type=normalized_filepath, help="Path
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parser.add_argument("--bsrgan-models-path", type=normalized_filepath, help="Path to directory with BSRGAN model file(s).", default=os.path.join(models_path, 'BSRGAN'))
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parser.add_argument("--realesrgan-models-path", type=normalized_filepath, help="Path to directory with RealESRGAN model file(s).", default=os.path.join(models_path, 'RealESRGAN'))
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parser.add_argument("--dat-models-path", type=normalized_filepath, help="Path to directory with DAT model file(s).", default=os.path.join(models_path, 'DAT'))
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parser.add_argument("--clip-models-path", type=normalized_filepath, help="Path to directory with CLIP model file(s), for Interrogate options.", default=None)
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parser.add_argument("--xformers", action='store_true', help="enable xformers for cross attention layers")
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parser.add_argument("--force-enable-xformers", action='store_true', help="enable xformers for cross attention layers regardless of whether the checking code thinks you can run it; do not make bug reports if this fails to work")
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parser.add_argument("--xformers-flash-attention", action='store_true', help="enable xformers with Flash Attention to improve reproducibility (supported for SD2.x or variant only)")
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parser.add_argument("--deepdanbooru", action='store_true', help="does not do anything")
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parser.add_argument("--opt-split-attention", action='store_true', help="prefer Doggettx's cross-attention layer optimization for automatic choice of optimization")
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parser.add_argument("--opt-sub-quad-attention", action='store_true', help="prefer memory efficient sub-quadratic cross-attention layer optimization for automatic choice of optimization")
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parser.add_argument("--sub-quad-q-chunk-size", type=int, help="query chunk size for the sub-quadratic cross-attention layer optimization to use", default=1024)
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@ -1,109 +0,0 @@
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import os
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import re
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import torch
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import numpy as np
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from modules import modelloader, paths, deepbooru_model, images, shared
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from backend import memory_management
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from backend.patcher.base import ModelPatcher
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re_special = re.compile(r'([\\()])')
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class DeepDanbooru:
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def __init__(self):
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self.model = None
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self.load_device = memory_management.text_encoder_device()
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self.offload_device = memory_management.text_encoder_offload_device()
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self.dtype = torch.float32
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if memory_management.should_use_fp16(device=self.load_device):
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self.dtype = torch.float16
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self.patcher = None
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def load(self):
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if self.model is not None:
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return
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files = modelloader.load_models(
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model_path=os.path.join(paths.models_path, "torch_deepdanbooru"),
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model_url='https://github.com/AUTOMATIC1111/TorchDeepDanbooru/releases/download/v1/model-resnet_custom_v3.pt',
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ext_filter=[".pt"],
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download_name='model-resnet_custom_v3.pt',
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)
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self.model = deepbooru_model.DeepDanbooruModel()
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self.model.load_state_dict(torch.load(files[0], map_location="cpu"))
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self.model.eval()
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self.model.to(self.offload_device, self.dtype)
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self.patcher = ModelPatcher(self.model, load_device=self.load_device, offload_device=self.offload_device)
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def start(self):
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self.load()
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memory_management.load_models_gpu([self.patcher])
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def stop(self):
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pass
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def tag(self, pil_image):
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self.start()
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res = self.tag_multi(pil_image)
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self.stop()
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return res
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def tag_multi(self, pil_image, force_disable_ranks=False):
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threshold = shared.opts.interrogate_deepbooru_score_threshold
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use_spaces = shared.opts.deepbooru_use_spaces
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use_escape = shared.opts.deepbooru_escape
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alpha_sort = shared.opts.deepbooru_sort_alpha
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include_ranks = shared.opts.interrogate_return_ranks and not force_disable_ranks
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pic = images.resize_image(2, pil_image.convert("RGB"), 512, 512)
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a = np.expand_dims(np.array(pic, dtype=np.float32), 0) / 255
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with torch.no_grad():
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x = torch.from_numpy(a).to(self.load_device, self.dtype)
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y = self.model(x)[0].detach().cpu().numpy()
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probability_dict = {}
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for tag, probability in zip(self.model.tags, y):
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if probability < threshold:
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continue
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if tag.startswith("rating:"):
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continue
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probability_dict[tag] = probability
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if alpha_sort:
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tags = sorted(probability_dict)
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else:
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tags = [tag for tag, _ in sorted(probability_dict.items(), key=lambda x: -x[1])]
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res = []
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filtertags = {x.strip().replace(' ', '_') for x in shared.opts.deepbooru_filter_tags.split(",")}
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for tag in [x for x in tags if x not in filtertags]:
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probability = probability_dict[tag]
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tag_outformat = tag
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if use_spaces:
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tag_outformat = tag_outformat.replace('_', ' ')
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if use_escape:
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tag_outformat = re.sub(re_special, r'\\\1', tag_outformat)
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if include_ranks:
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tag_outformat = f"({tag_outformat}:{probability:.3f})"
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res.append(tag_outformat)
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return ", ".join(res)
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model = DeepDanbooru()
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@ -1,678 +0,0 @@
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from modules import devices
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# see https://github.com/AUTOMATIC1111/TorchDeepDanbooru for more
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class DeepDanbooruModel(nn.Module):
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def __init__(self):
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super(DeepDanbooruModel, self).__init__()
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self.tags = []
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self.n_Conv_0 = nn.Conv2d(kernel_size=(7, 7), in_channels=3, out_channels=64, stride=(2, 2))
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self.n_MaxPool_0 = nn.MaxPool2d(kernel_size=(3, 3), stride=(2, 2))
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self.n_Conv_1 = nn.Conv2d(kernel_size=(1, 1), in_channels=64, out_channels=256)
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self.n_Conv_2 = nn.Conv2d(kernel_size=(1, 1), in_channels=64, out_channels=64)
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self.n_Conv_3 = nn.Conv2d(kernel_size=(3, 3), in_channels=64, out_channels=64)
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self.n_Conv_4 = nn.Conv2d(kernel_size=(1, 1), in_channels=64, out_channels=256)
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self.n_Conv_5 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=64)
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self.n_Conv_6 = nn.Conv2d(kernel_size=(3, 3), in_channels=64, out_channels=64)
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self.n_Conv_7 = nn.Conv2d(kernel_size=(1, 1), in_channels=64, out_channels=256)
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self.n_Conv_8 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=64)
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self.n_Conv_9 = nn.Conv2d(kernel_size=(3, 3), in_channels=64, out_channels=64)
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self.n_Conv_10 = nn.Conv2d(kernel_size=(1, 1), in_channels=64, out_channels=256)
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self.n_Conv_11 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=512, stride=(2, 2))
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self.n_Conv_12 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=128)
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self.n_Conv_13 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128, stride=(2, 2))
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self.n_Conv_14 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
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self.n_Conv_15 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)
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self.n_Conv_16 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)
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self.n_Conv_17 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
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self.n_Conv_18 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)
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self.n_Conv_19 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)
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self.n_Conv_20 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
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self.n_Conv_21 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)
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self.n_Conv_22 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)
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self.n_Conv_23 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
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self.n_Conv_24 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)
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self.n_Conv_25 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)
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self.n_Conv_26 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
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self.n_Conv_27 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)
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self.n_Conv_28 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)
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self.n_Conv_29 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
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self.n_Conv_30 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)
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self.n_Conv_31 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)
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self.n_Conv_32 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
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self.n_Conv_33 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)
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self.n_Conv_34 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)
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self.n_Conv_35 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
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self.n_Conv_36 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=1024, stride=(2, 2))
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self.n_Conv_37 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=256)
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self.n_Conv_38 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256, stride=(2, 2))
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self.n_Conv_39 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
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self.n_Conv_40 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
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self.n_Conv_41 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
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self.n_Conv_42 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
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self.n_Conv_43 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
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self.n_Conv_44 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
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self.n_Conv_45 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
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self.n_Conv_46 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
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self.n_Conv_47 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
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self.n_Conv_48 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
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self.n_Conv_49 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
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self.n_Conv_50 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
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self.n_Conv_51 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
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self.n_Conv_52 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
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self.n_Conv_53 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
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self.n_Conv_54 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
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self.n_Conv_55 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
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self.n_Conv_56 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
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self.n_Conv_57 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
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self.n_Conv_58 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
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self.n_Conv_59 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
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self.n_Conv_60 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
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self.n_Conv_61 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
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self.n_Conv_62 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
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self.n_Conv_63 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
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self.n_Conv_64 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
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self.n_Conv_65 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
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self.n_Conv_66 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
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self.n_Conv_67 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
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self.n_Conv_68 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
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self.n_Conv_69 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
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self.n_Conv_70 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
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self.n_Conv_71 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
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self.n_Conv_72 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
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self.n_Conv_73 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
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self.n_Conv_74 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
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self.n_Conv_75 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
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self.n_Conv_76 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
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self.n_Conv_77 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
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self.n_Conv_78 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
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self.n_Conv_79 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
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self.n_Conv_80 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
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self.n_Conv_81 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
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self.n_Conv_82 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
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self.n_Conv_83 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
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self.n_Conv_84 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
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self.n_Conv_85 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
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self.n_Conv_86 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
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self.n_Conv_87 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
|
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self.n_Conv_88 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
|
||||
self.n_Conv_89 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
|
||||
self.n_Conv_90 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
|
||||
self.n_Conv_91 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
|
||||
self.n_Conv_92 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
|
||||
self.n_Conv_93 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
|
||||
self.n_Conv_94 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
|
||||
self.n_Conv_95 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
|
||||
self.n_Conv_96 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
|
||||
self.n_Conv_97 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
|
||||
self.n_Conv_98 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256, stride=(2, 2))
|
||||
self.n_Conv_99 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
|
||||
self.n_Conv_100 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=1024, stride=(2, 2))
|
||||
self.n_Conv_101 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
|
||||
self.n_Conv_102 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
|
||||
self.n_Conv_103 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
|
||||
self.n_Conv_104 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
|
||||
self.n_Conv_105 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
|
||||
self.n_Conv_106 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
|
||||
self.n_Conv_107 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
|
||||
self.n_Conv_108 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
|
||||
self.n_Conv_109 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
|
||||
self.n_Conv_110 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
|
||||
self.n_Conv_111 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
|
||||
self.n_Conv_112 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
|
||||
self.n_Conv_113 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
|
||||
self.n_Conv_114 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
|
||||
self.n_Conv_115 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
|
||||
self.n_Conv_116 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
|
||||
self.n_Conv_117 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
|
||||
self.n_Conv_118 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
|
||||
self.n_Conv_119 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
|
||||
self.n_Conv_120 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
|
||||
self.n_Conv_121 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
|
||||
self.n_Conv_122 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
|
||||
self.n_Conv_123 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
|
||||
self.n_Conv_124 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
|
||||
self.n_Conv_125 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
|
||||
self.n_Conv_126 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
|
||||
self.n_Conv_127 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
|
||||
self.n_Conv_128 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
|
||||
self.n_Conv_129 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
|
||||
self.n_Conv_130 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
|
||||
self.n_Conv_131 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
|
||||
self.n_Conv_132 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
|
||||
self.n_Conv_133 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
|
||||
self.n_Conv_134 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
|
||||
self.n_Conv_135 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
|
||||
self.n_Conv_136 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
|
||||
self.n_Conv_137 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
|
||||
self.n_Conv_138 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
|
||||
self.n_Conv_139 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
|
||||
self.n_Conv_140 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
|
||||
self.n_Conv_141 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
|
||||
self.n_Conv_142 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
|
||||
self.n_Conv_143 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
|
||||
self.n_Conv_144 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
|
||||
self.n_Conv_145 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
|
||||
self.n_Conv_146 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
|
||||
self.n_Conv_147 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
|
||||
self.n_Conv_148 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
|
||||
self.n_Conv_149 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
|
||||
self.n_Conv_150 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
|
||||
self.n_Conv_151 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
|
||||
self.n_Conv_152 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
|
||||
self.n_Conv_153 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
|
||||
self.n_Conv_154 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
|
||||
self.n_Conv_155 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
|
||||
self.n_Conv_156 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
|
||||
self.n_Conv_157 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
|
||||
self.n_Conv_158 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=2048, stride=(2, 2))
|
||||
self.n_Conv_159 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=512)
|
||||
self.n_Conv_160 = nn.Conv2d(kernel_size=(3, 3), in_channels=512, out_channels=512, stride=(2, 2))
|
||||
self.n_Conv_161 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=2048)
|
||||
self.n_Conv_162 = nn.Conv2d(kernel_size=(1, 1), in_channels=2048, out_channels=512)
|
||||
self.n_Conv_163 = nn.Conv2d(kernel_size=(3, 3), in_channels=512, out_channels=512)
|
||||
self.n_Conv_164 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=2048)
|
||||
self.n_Conv_165 = nn.Conv2d(kernel_size=(1, 1), in_channels=2048, out_channels=512)
|
||||
self.n_Conv_166 = nn.Conv2d(kernel_size=(3, 3), in_channels=512, out_channels=512)
|
||||
self.n_Conv_167 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=2048)
|
||||
self.n_Conv_168 = nn.Conv2d(kernel_size=(1, 1), in_channels=2048, out_channels=4096, stride=(2, 2))
|
||||
self.n_Conv_169 = nn.Conv2d(kernel_size=(1, 1), in_channels=2048, out_channels=1024)
|
||||
self.n_Conv_170 = nn.Conv2d(kernel_size=(3, 3), in_channels=1024, out_channels=1024, stride=(2, 2))
|
||||
self.n_Conv_171 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=4096)
|
||||
self.n_Conv_172 = nn.Conv2d(kernel_size=(1, 1), in_channels=4096, out_channels=1024)
|
||||
self.n_Conv_173 = nn.Conv2d(kernel_size=(3, 3), in_channels=1024, out_channels=1024)
|
||||
self.n_Conv_174 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=4096)
|
||||
self.n_Conv_175 = nn.Conv2d(kernel_size=(1, 1), in_channels=4096, out_channels=1024)
|
||||
self.n_Conv_176 = nn.Conv2d(kernel_size=(3, 3), in_channels=1024, out_channels=1024)
|
||||
self.n_Conv_177 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=4096)
|
||||
self.n_Conv_178 = nn.Conv2d(kernel_size=(1, 1), in_channels=4096, out_channels=9176, bias=False)
|
||||
|
||||
def forward(self, *inputs):
|
||||
t_358, = inputs
|
||||
t_359 = t_358.permute(*[0, 3, 1, 2])
|
||||
t_359_padded = F.pad(t_359, [2, 3, 2, 3], value=0)
|
||||
t_360 = self.n_Conv_0(t_359_padded.to(self.n_Conv_0.bias.dtype) if devices.unet_needs_upcast else t_359_padded)
|
||||
t_361 = F.relu(t_360)
|
||||
t_361 = F.pad(t_361, [0, 1, 0, 1], value=float('-inf'))
|
||||
t_362 = self.n_MaxPool_0(t_361)
|
||||
t_363 = self.n_Conv_1(t_362)
|
||||
t_364 = self.n_Conv_2(t_362)
|
||||
t_365 = F.relu(t_364)
|
||||
t_365_padded = F.pad(t_365, [1, 1, 1, 1], value=0)
|
||||
t_366 = self.n_Conv_3(t_365_padded)
|
||||
t_367 = F.relu(t_366)
|
||||
t_368 = self.n_Conv_4(t_367)
|
||||
t_369 = torch.add(t_368, t_363)
|
||||
t_370 = F.relu(t_369)
|
||||
t_371 = self.n_Conv_5(t_370)
|
||||
t_372 = F.relu(t_371)
|
||||
t_372_padded = F.pad(t_372, [1, 1, 1, 1], value=0)
|
||||
t_373 = self.n_Conv_6(t_372_padded)
|
||||
t_374 = F.relu(t_373)
|
||||
t_375 = self.n_Conv_7(t_374)
|
||||
t_376 = torch.add(t_375, t_370)
|
||||
t_377 = F.relu(t_376)
|
||||
t_378 = self.n_Conv_8(t_377)
|
||||
t_379 = F.relu(t_378)
|
||||
t_379_padded = F.pad(t_379, [1, 1, 1, 1], value=0)
|
||||
t_380 = self.n_Conv_9(t_379_padded)
|
||||
t_381 = F.relu(t_380)
|
||||
t_382 = self.n_Conv_10(t_381)
|
||||
t_383 = torch.add(t_382, t_377)
|
||||
t_384 = F.relu(t_383)
|
||||
t_385 = self.n_Conv_11(t_384)
|
||||
t_386 = self.n_Conv_12(t_384)
|
||||
t_387 = F.relu(t_386)
|
||||
t_387_padded = F.pad(t_387, [0, 1, 0, 1], value=0)
|
||||
t_388 = self.n_Conv_13(t_387_padded)
|
||||
t_389 = F.relu(t_388)
|
||||
t_390 = self.n_Conv_14(t_389)
|
||||
t_391 = torch.add(t_390, t_385)
|
||||
t_392 = F.relu(t_391)
|
||||
t_393 = self.n_Conv_15(t_392)
|
||||
t_394 = F.relu(t_393)
|
||||
t_394_padded = F.pad(t_394, [1, 1, 1, 1], value=0)
|
||||
t_395 = self.n_Conv_16(t_394_padded)
|
||||
t_396 = F.relu(t_395)
|
||||
t_397 = self.n_Conv_17(t_396)
|
||||
t_398 = torch.add(t_397, t_392)
|
||||
t_399 = F.relu(t_398)
|
||||
t_400 = self.n_Conv_18(t_399)
|
||||
t_401 = F.relu(t_400)
|
||||
t_401_padded = F.pad(t_401, [1, 1, 1, 1], value=0)
|
||||
t_402 = self.n_Conv_19(t_401_padded)
|
||||
t_403 = F.relu(t_402)
|
||||
t_404 = self.n_Conv_20(t_403)
|
||||
t_405 = torch.add(t_404, t_399)
|
||||
t_406 = F.relu(t_405)
|
||||
t_407 = self.n_Conv_21(t_406)
|
||||
t_408 = F.relu(t_407)
|
||||
t_408_padded = F.pad(t_408, [1, 1, 1, 1], value=0)
|
||||
t_409 = self.n_Conv_22(t_408_padded)
|
||||
t_410 = F.relu(t_409)
|
||||
t_411 = self.n_Conv_23(t_410)
|
||||
t_412 = torch.add(t_411, t_406)
|
||||
t_413 = F.relu(t_412)
|
||||
t_414 = self.n_Conv_24(t_413)
|
||||
t_415 = F.relu(t_414)
|
||||
t_415_padded = F.pad(t_415, [1, 1, 1, 1], value=0)
|
||||
t_416 = self.n_Conv_25(t_415_padded)
|
||||
t_417 = F.relu(t_416)
|
||||
t_418 = self.n_Conv_26(t_417)
|
||||
t_419 = torch.add(t_418, t_413)
|
||||
t_420 = F.relu(t_419)
|
||||
t_421 = self.n_Conv_27(t_420)
|
||||
t_422 = F.relu(t_421)
|
||||
t_422_padded = F.pad(t_422, [1, 1, 1, 1], value=0)
|
||||
t_423 = self.n_Conv_28(t_422_padded)
|
||||
t_424 = F.relu(t_423)
|
||||
t_425 = self.n_Conv_29(t_424)
|
||||
t_426 = torch.add(t_425, t_420)
|
||||
t_427 = F.relu(t_426)
|
||||
t_428 = self.n_Conv_30(t_427)
|
||||
t_429 = F.relu(t_428)
|
||||
t_429_padded = F.pad(t_429, [1, 1, 1, 1], value=0)
|
||||
t_430 = self.n_Conv_31(t_429_padded)
|
||||
t_431 = F.relu(t_430)
|
||||
t_432 = self.n_Conv_32(t_431)
|
||||
t_433 = torch.add(t_432, t_427)
|
||||
t_434 = F.relu(t_433)
|
||||
t_435 = self.n_Conv_33(t_434)
|
||||
t_436 = F.relu(t_435)
|
||||
t_436_padded = F.pad(t_436, [1, 1, 1, 1], value=0)
|
||||
t_437 = self.n_Conv_34(t_436_padded)
|
||||
t_438 = F.relu(t_437)
|
||||
t_439 = self.n_Conv_35(t_438)
|
||||
t_440 = torch.add(t_439, t_434)
|
||||
t_441 = F.relu(t_440)
|
||||
t_442 = self.n_Conv_36(t_441)
|
||||
t_443 = self.n_Conv_37(t_441)
|
||||
t_444 = F.relu(t_443)
|
||||
t_444_padded = F.pad(t_444, [0, 1, 0, 1], value=0)
|
||||
t_445 = self.n_Conv_38(t_444_padded)
|
||||
t_446 = F.relu(t_445)
|
||||
t_447 = self.n_Conv_39(t_446)
|
||||
t_448 = torch.add(t_447, t_442)
|
||||
t_449 = F.relu(t_448)
|
||||
t_450 = self.n_Conv_40(t_449)
|
||||
t_451 = F.relu(t_450)
|
||||
t_451_padded = F.pad(t_451, [1, 1, 1, 1], value=0)
|
||||
t_452 = self.n_Conv_41(t_451_padded)
|
||||
t_453 = F.relu(t_452)
|
||||
t_454 = self.n_Conv_42(t_453)
|
||||
t_455 = torch.add(t_454, t_449)
|
||||
t_456 = F.relu(t_455)
|
||||
t_457 = self.n_Conv_43(t_456)
|
||||
t_458 = F.relu(t_457)
|
||||
t_458_padded = F.pad(t_458, [1, 1, 1, 1], value=0)
|
||||
t_459 = self.n_Conv_44(t_458_padded)
|
||||
t_460 = F.relu(t_459)
|
||||
t_461 = self.n_Conv_45(t_460)
|
||||
t_462 = torch.add(t_461, t_456)
|
||||
t_463 = F.relu(t_462)
|
||||
t_464 = self.n_Conv_46(t_463)
|
||||
t_465 = F.relu(t_464)
|
||||
t_465_padded = F.pad(t_465, [1, 1, 1, 1], value=0)
|
||||
t_466 = self.n_Conv_47(t_465_padded)
|
||||
t_467 = F.relu(t_466)
|
||||
t_468 = self.n_Conv_48(t_467)
|
||||
t_469 = torch.add(t_468, t_463)
|
||||
t_470 = F.relu(t_469)
|
||||
t_471 = self.n_Conv_49(t_470)
|
||||
t_472 = F.relu(t_471)
|
||||
t_472_padded = F.pad(t_472, [1, 1, 1, 1], value=0)
|
||||
t_473 = self.n_Conv_50(t_472_padded)
|
||||
t_474 = F.relu(t_473)
|
||||
t_475 = self.n_Conv_51(t_474)
|
||||
t_476 = torch.add(t_475, t_470)
|
||||
t_477 = F.relu(t_476)
|
||||
t_478 = self.n_Conv_52(t_477)
|
||||
t_479 = F.relu(t_478)
|
||||
t_479_padded = F.pad(t_479, [1, 1, 1, 1], value=0)
|
||||
t_480 = self.n_Conv_53(t_479_padded)
|
||||
t_481 = F.relu(t_480)
|
||||
t_482 = self.n_Conv_54(t_481)
|
||||
t_483 = torch.add(t_482, t_477)
|
||||
t_484 = F.relu(t_483)
|
||||
t_485 = self.n_Conv_55(t_484)
|
||||
t_486 = F.relu(t_485)
|
||||
t_486_padded = F.pad(t_486, [1, 1, 1, 1], value=0)
|
||||
t_487 = self.n_Conv_56(t_486_padded)
|
||||
t_488 = F.relu(t_487)
|
||||
t_489 = self.n_Conv_57(t_488)
|
||||
t_490 = torch.add(t_489, t_484)
|
||||
t_491 = F.relu(t_490)
|
||||
t_492 = self.n_Conv_58(t_491)
|
||||
t_493 = F.relu(t_492)
|
||||
t_493_padded = F.pad(t_493, [1, 1, 1, 1], value=0)
|
||||
t_494 = self.n_Conv_59(t_493_padded)
|
||||
t_495 = F.relu(t_494)
|
||||
t_496 = self.n_Conv_60(t_495)
|
||||
t_497 = torch.add(t_496, t_491)
|
||||
t_498 = F.relu(t_497)
|
||||
t_499 = self.n_Conv_61(t_498)
|
||||
t_500 = F.relu(t_499)
|
||||
t_500_padded = F.pad(t_500, [1, 1, 1, 1], value=0)
|
||||
t_501 = self.n_Conv_62(t_500_padded)
|
||||
t_502 = F.relu(t_501)
|
||||
t_503 = self.n_Conv_63(t_502)
|
||||
t_504 = torch.add(t_503, t_498)
|
||||
t_505 = F.relu(t_504)
|
||||
t_506 = self.n_Conv_64(t_505)
|
||||
t_507 = F.relu(t_506)
|
||||
t_507_padded = F.pad(t_507, [1, 1, 1, 1], value=0)
|
||||
t_508 = self.n_Conv_65(t_507_padded)
|
||||
t_509 = F.relu(t_508)
|
||||
t_510 = self.n_Conv_66(t_509)
|
||||
t_511 = torch.add(t_510, t_505)
|
||||
t_512 = F.relu(t_511)
|
||||
t_513 = self.n_Conv_67(t_512)
|
||||
t_514 = F.relu(t_513)
|
||||
t_514_padded = F.pad(t_514, [1, 1, 1, 1], value=0)
|
||||
t_515 = self.n_Conv_68(t_514_padded)
|
||||
t_516 = F.relu(t_515)
|
||||
t_517 = self.n_Conv_69(t_516)
|
||||
t_518 = torch.add(t_517, t_512)
|
||||
t_519 = F.relu(t_518)
|
||||
t_520 = self.n_Conv_70(t_519)
|
||||
t_521 = F.relu(t_520)
|
||||
t_521_padded = F.pad(t_521, [1, 1, 1, 1], value=0)
|
||||
t_522 = self.n_Conv_71(t_521_padded)
|
||||
t_523 = F.relu(t_522)
|
||||
t_524 = self.n_Conv_72(t_523)
|
||||
t_525 = torch.add(t_524, t_519)
|
||||
t_526 = F.relu(t_525)
|
||||
t_527 = self.n_Conv_73(t_526)
|
||||
t_528 = F.relu(t_527)
|
||||
t_528_padded = F.pad(t_528, [1, 1, 1, 1], value=0)
|
||||
t_529 = self.n_Conv_74(t_528_padded)
|
||||
t_530 = F.relu(t_529)
|
||||
t_531 = self.n_Conv_75(t_530)
|
||||
t_532 = torch.add(t_531, t_526)
|
||||
t_533 = F.relu(t_532)
|
||||
t_534 = self.n_Conv_76(t_533)
|
||||
t_535 = F.relu(t_534)
|
||||
t_535_padded = F.pad(t_535, [1, 1, 1, 1], value=0)
|
||||
t_536 = self.n_Conv_77(t_535_padded)
|
||||
t_537 = F.relu(t_536)
|
||||
t_538 = self.n_Conv_78(t_537)
|
||||
t_539 = torch.add(t_538, t_533)
|
||||
t_540 = F.relu(t_539)
|
||||
t_541 = self.n_Conv_79(t_540)
|
||||
t_542 = F.relu(t_541)
|
||||
t_542_padded = F.pad(t_542, [1, 1, 1, 1], value=0)
|
||||
t_543 = self.n_Conv_80(t_542_padded)
|
||||
t_544 = F.relu(t_543)
|
||||
t_545 = self.n_Conv_81(t_544)
|
||||
t_546 = torch.add(t_545, t_540)
|
||||
t_547 = F.relu(t_546)
|
||||
t_548 = self.n_Conv_82(t_547)
|
||||
t_549 = F.relu(t_548)
|
||||
t_549_padded = F.pad(t_549, [1, 1, 1, 1], value=0)
|
||||
t_550 = self.n_Conv_83(t_549_padded)
|
||||
t_551 = F.relu(t_550)
|
||||
t_552 = self.n_Conv_84(t_551)
|
||||
t_553 = torch.add(t_552, t_547)
|
||||
t_554 = F.relu(t_553)
|
||||
t_555 = self.n_Conv_85(t_554)
|
||||
t_556 = F.relu(t_555)
|
||||
t_556_padded = F.pad(t_556, [1, 1, 1, 1], value=0)
|
||||
t_557 = self.n_Conv_86(t_556_padded)
|
||||
t_558 = F.relu(t_557)
|
||||
t_559 = self.n_Conv_87(t_558)
|
||||
t_560 = torch.add(t_559, t_554)
|
||||
t_561 = F.relu(t_560)
|
||||
t_562 = self.n_Conv_88(t_561)
|
||||
t_563 = F.relu(t_562)
|
||||
t_563_padded = F.pad(t_563, [1, 1, 1, 1], value=0)
|
||||
t_564 = self.n_Conv_89(t_563_padded)
|
||||
t_565 = F.relu(t_564)
|
||||
t_566 = self.n_Conv_90(t_565)
|
||||
t_567 = torch.add(t_566, t_561)
|
||||
t_568 = F.relu(t_567)
|
||||
t_569 = self.n_Conv_91(t_568)
|
||||
t_570 = F.relu(t_569)
|
||||
t_570_padded = F.pad(t_570, [1, 1, 1, 1], value=0)
|
||||
t_571 = self.n_Conv_92(t_570_padded)
|
||||
t_572 = F.relu(t_571)
|
||||
t_573 = self.n_Conv_93(t_572)
|
||||
t_574 = torch.add(t_573, t_568)
|
||||
t_575 = F.relu(t_574)
|
||||
t_576 = self.n_Conv_94(t_575)
|
||||
t_577 = F.relu(t_576)
|
||||
t_577_padded = F.pad(t_577, [1, 1, 1, 1], value=0)
|
||||
t_578 = self.n_Conv_95(t_577_padded)
|
||||
t_579 = F.relu(t_578)
|
||||
t_580 = self.n_Conv_96(t_579)
|
||||
t_581 = torch.add(t_580, t_575)
|
||||
t_582 = F.relu(t_581)
|
||||
t_583 = self.n_Conv_97(t_582)
|
||||
t_584 = F.relu(t_583)
|
||||
t_584_padded = F.pad(t_584, [0, 1, 0, 1], value=0)
|
||||
t_585 = self.n_Conv_98(t_584_padded)
|
||||
t_586 = F.relu(t_585)
|
||||
t_587 = self.n_Conv_99(t_586)
|
||||
t_588 = self.n_Conv_100(t_582)
|
||||
t_589 = torch.add(t_587, t_588)
|
||||
t_590 = F.relu(t_589)
|
||||
t_591 = self.n_Conv_101(t_590)
|
||||
t_592 = F.relu(t_591)
|
||||
t_592_padded = F.pad(t_592, [1, 1, 1, 1], value=0)
|
||||
t_593 = self.n_Conv_102(t_592_padded)
|
||||
t_594 = F.relu(t_593)
|
||||
t_595 = self.n_Conv_103(t_594)
|
||||
t_596 = torch.add(t_595, t_590)
|
||||
t_597 = F.relu(t_596)
|
||||
t_598 = self.n_Conv_104(t_597)
|
||||
t_599 = F.relu(t_598)
|
||||
t_599_padded = F.pad(t_599, [1, 1, 1, 1], value=0)
|
||||
t_600 = self.n_Conv_105(t_599_padded)
|
||||
t_601 = F.relu(t_600)
|
||||
t_602 = self.n_Conv_106(t_601)
|
||||
t_603 = torch.add(t_602, t_597)
|
||||
t_604 = F.relu(t_603)
|
||||
t_605 = self.n_Conv_107(t_604)
|
||||
t_606 = F.relu(t_605)
|
||||
t_606_padded = F.pad(t_606, [1, 1, 1, 1], value=0)
|
||||
t_607 = self.n_Conv_108(t_606_padded)
|
||||
t_608 = F.relu(t_607)
|
||||
t_609 = self.n_Conv_109(t_608)
|
||||
t_610 = torch.add(t_609, t_604)
|
||||
t_611 = F.relu(t_610)
|
||||
t_612 = self.n_Conv_110(t_611)
|
||||
t_613 = F.relu(t_612)
|
||||
t_613_padded = F.pad(t_613, [1, 1, 1, 1], value=0)
|
||||
t_614 = self.n_Conv_111(t_613_padded)
|
||||
t_615 = F.relu(t_614)
|
||||
t_616 = self.n_Conv_112(t_615)
|
||||
t_617 = torch.add(t_616, t_611)
|
||||
t_618 = F.relu(t_617)
|
||||
t_619 = self.n_Conv_113(t_618)
|
||||
t_620 = F.relu(t_619)
|
||||
t_620_padded = F.pad(t_620, [1, 1, 1, 1], value=0)
|
||||
t_621 = self.n_Conv_114(t_620_padded)
|
||||
t_622 = F.relu(t_621)
|
||||
t_623 = self.n_Conv_115(t_622)
|
||||
t_624 = torch.add(t_623, t_618)
|
||||
t_625 = F.relu(t_624)
|
||||
t_626 = self.n_Conv_116(t_625)
|
||||
t_627 = F.relu(t_626)
|
||||
t_627_padded = F.pad(t_627, [1, 1, 1, 1], value=0)
|
||||
t_628 = self.n_Conv_117(t_627_padded)
|
||||
t_629 = F.relu(t_628)
|
||||
t_630 = self.n_Conv_118(t_629)
|
||||
t_631 = torch.add(t_630, t_625)
|
||||
t_632 = F.relu(t_631)
|
||||
t_633 = self.n_Conv_119(t_632)
|
||||
t_634 = F.relu(t_633)
|
||||
t_634_padded = F.pad(t_634, [1, 1, 1, 1], value=0)
|
||||
t_635 = self.n_Conv_120(t_634_padded)
|
||||
t_636 = F.relu(t_635)
|
||||
t_637 = self.n_Conv_121(t_636)
|
||||
t_638 = torch.add(t_637, t_632)
|
||||
t_639 = F.relu(t_638)
|
||||
t_640 = self.n_Conv_122(t_639)
|
||||
t_641 = F.relu(t_640)
|
||||
t_641_padded = F.pad(t_641, [1, 1, 1, 1], value=0)
|
||||
t_642 = self.n_Conv_123(t_641_padded)
|
||||
t_643 = F.relu(t_642)
|
||||
t_644 = self.n_Conv_124(t_643)
|
||||
t_645 = torch.add(t_644, t_639)
|
||||
t_646 = F.relu(t_645)
|
||||
t_647 = self.n_Conv_125(t_646)
|
||||
t_648 = F.relu(t_647)
|
||||
t_648_padded = F.pad(t_648, [1, 1, 1, 1], value=0)
|
||||
t_649 = self.n_Conv_126(t_648_padded)
|
||||
t_650 = F.relu(t_649)
|
||||
t_651 = self.n_Conv_127(t_650)
|
||||
t_652 = torch.add(t_651, t_646)
|
||||
t_653 = F.relu(t_652)
|
||||
t_654 = self.n_Conv_128(t_653)
|
||||
t_655 = F.relu(t_654)
|
||||
t_655_padded = F.pad(t_655, [1, 1, 1, 1], value=0)
|
||||
t_656 = self.n_Conv_129(t_655_padded)
|
||||
t_657 = F.relu(t_656)
|
||||
t_658 = self.n_Conv_130(t_657)
|
||||
t_659 = torch.add(t_658, t_653)
|
||||
t_660 = F.relu(t_659)
|
||||
t_661 = self.n_Conv_131(t_660)
|
||||
t_662 = F.relu(t_661)
|
||||
t_662_padded = F.pad(t_662, [1, 1, 1, 1], value=0)
|
||||
t_663 = self.n_Conv_132(t_662_padded)
|
||||
t_664 = F.relu(t_663)
|
||||
t_665 = self.n_Conv_133(t_664)
|
||||
t_666 = torch.add(t_665, t_660)
|
||||
t_667 = F.relu(t_666)
|
||||
t_668 = self.n_Conv_134(t_667)
|
||||
t_669 = F.relu(t_668)
|
||||
t_669_padded = F.pad(t_669, [1, 1, 1, 1], value=0)
|
||||
t_670 = self.n_Conv_135(t_669_padded)
|
||||
t_671 = F.relu(t_670)
|
||||
t_672 = self.n_Conv_136(t_671)
|
||||
t_673 = torch.add(t_672, t_667)
|
||||
t_674 = F.relu(t_673)
|
||||
t_675 = self.n_Conv_137(t_674)
|
||||
t_676 = F.relu(t_675)
|
||||
t_676_padded = F.pad(t_676, [1, 1, 1, 1], value=0)
|
||||
t_677 = self.n_Conv_138(t_676_padded)
|
||||
t_678 = F.relu(t_677)
|
||||
t_679 = self.n_Conv_139(t_678)
|
||||
t_680 = torch.add(t_679, t_674)
|
||||
t_681 = F.relu(t_680)
|
||||
t_682 = self.n_Conv_140(t_681)
|
||||
t_683 = F.relu(t_682)
|
||||
t_683_padded = F.pad(t_683, [1, 1, 1, 1], value=0)
|
||||
t_684 = self.n_Conv_141(t_683_padded)
|
||||
t_685 = F.relu(t_684)
|
||||
t_686 = self.n_Conv_142(t_685)
|
||||
t_687 = torch.add(t_686, t_681)
|
||||
t_688 = F.relu(t_687)
|
||||
t_689 = self.n_Conv_143(t_688)
|
||||
t_690 = F.relu(t_689)
|
||||
t_690_padded = F.pad(t_690, [1, 1, 1, 1], value=0)
|
||||
t_691 = self.n_Conv_144(t_690_padded)
|
||||
t_692 = F.relu(t_691)
|
||||
t_693 = self.n_Conv_145(t_692)
|
||||
t_694 = torch.add(t_693, t_688)
|
||||
t_695 = F.relu(t_694)
|
||||
t_696 = self.n_Conv_146(t_695)
|
||||
t_697 = F.relu(t_696)
|
||||
t_697_padded = F.pad(t_697, [1, 1, 1, 1], value=0)
|
||||
t_698 = self.n_Conv_147(t_697_padded)
|
||||
t_699 = F.relu(t_698)
|
||||
t_700 = self.n_Conv_148(t_699)
|
||||
t_701 = torch.add(t_700, t_695)
|
||||
t_702 = F.relu(t_701)
|
||||
t_703 = self.n_Conv_149(t_702)
|
||||
t_704 = F.relu(t_703)
|
||||
t_704_padded = F.pad(t_704, [1, 1, 1, 1], value=0)
|
||||
t_705 = self.n_Conv_150(t_704_padded)
|
||||
t_706 = F.relu(t_705)
|
||||
t_707 = self.n_Conv_151(t_706)
|
||||
t_708 = torch.add(t_707, t_702)
|
||||
t_709 = F.relu(t_708)
|
||||
t_710 = self.n_Conv_152(t_709)
|
||||
t_711 = F.relu(t_710)
|
||||
t_711_padded = F.pad(t_711, [1, 1, 1, 1], value=0)
|
||||
t_712 = self.n_Conv_153(t_711_padded)
|
||||
t_713 = F.relu(t_712)
|
||||
t_714 = self.n_Conv_154(t_713)
|
||||
t_715 = torch.add(t_714, t_709)
|
||||
t_716 = F.relu(t_715)
|
||||
t_717 = self.n_Conv_155(t_716)
|
||||
t_718 = F.relu(t_717)
|
||||
t_718_padded = F.pad(t_718, [1, 1, 1, 1], value=0)
|
||||
t_719 = self.n_Conv_156(t_718_padded)
|
||||
t_720 = F.relu(t_719)
|
||||
t_721 = self.n_Conv_157(t_720)
|
||||
t_722 = torch.add(t_721, t_716)
|
||||
t_723 = F.relu(t_722)
|
||||
t_724 = self.n_Conv_158(t_723)
|
||||
t_725 = self.n_Conv_159(t_723)
|
||||
t_726 = F.relu(t_725)
|
||||
t_726_padded = F.pad(t_726, [0, 1, 0, 1], value=0)
|
||||
t_727 = self.n_Conv_160(t_726_padded)
|
||||
t_728 = F.relu(t_727)
|
||||
t_729 = self.n_Conv_161(t_728)
|
||||
t_730 = torch.add(t_729, t_724)
|
||||
t_731 = F.relu(t_730)
|
||||
t_732 = self.n_Conv_162(t_731)
|
||||
t_733 = F.relu(t_732)
|
||||
t_733_padded = F.pad(t_733, [1, 1, 1, 1], value=0)
|
||||
t_734 = self.n_Conv_163(t_733_padded)
|
||||
t_735 = F.relu(t_734)
|
||||
t_736 = self.n_Conv_164(t_735)
|
||||
t_737 = torch.add(t_736, t_731)
|
||||
t_738 = F.relu(t_737)
|
||||
t_739 = self.n_Conv_165(t_738)
|
||||
t_740 = F.relu(t_739)
|
||||
t_740_padded = F.pad(t_740, [1, 1, 1, 1], value=0)
|
||||
t_741 = self.n_Conv_166(t_740_padded)
|
||||
t_742 = F.relu(t_741)
|
||||
t_743 = self.n_Conv_167(t_742)
|
||||
t_744 = torch.add(t_743, t_738)
|
||||
t_745 = F.relu(t_744)
|
||||
t_746 = self.n_Conv_168(t_745)
|
||||
t_747 = self.n_Conv_169(t_745)
|
||||
t_748 = F.relu(t_747)
|
||||
t_748_padded = F.pad(t_748, [0, 1, 0, 1], value=0)
|
||||
t_749 = self.n_Conv_170(t_748_padded)
|
||||
t_750 = F.relu(t_749)
|
||||
t_751 = self.n_Conv_171(t_750)
|
||||
t_752 = torch.add(t_751, t_746)
|
||||
t_753 = F.relu(t_752)
|
||||
t_754 = self.n_Conv_172(t_753)
|
||||
t_755 = F.relu(t_754)
|
||||
t_755_padded = F.pad(t_755, [1, 1, 1, 1], value=0)
|
||||
t_756 = self.n_Conv_173(t_755_padded)
|
||||
t_757 = F.relu(t_756)
|
||||
t_758 = self.n_Conv_174(t_757)
|
||||
t_759 = torch.add(t_758, t_753)
|
||||
t_760 = F.relu(t_759)
|
||||
t_761 = self.n_Conv_175(t_760)
|
||||
t_762 = F.relu(t_761)
|
||||
t_762_padded = F.pad(t_762, [1, 1, 1, 1], value=0)
|
||||
t_763 = self.n_Conv_176(t_762_padded)
|
||||
t_764 = F.relu(t_763)
|
||||
t_765 = self.n_Conv_177(t_764)
|
||||
t_766 = torch.add(t_765, t_760)
|
||||
t_767 = F.relu(t_766)
|
||||
t_768 = self.n_Conv_178(t_767)
|
||||
t_769 = F.avg_pool2d(t_768, kernel_size=t_768.shape[-2:])
|
||||
t_770 = torch.squeeze(t_769, 3)
|
||||
t_770 = torch.squeeze(t_770, 2)
|
||||
t_771 = torch.sigmoid(t_770)
|
||||
return t_771
|
||||
|
||||
def load_state_dict(self, state_dict, **kwargs):
|
||||
self.tags = state_dict.get('tags', [])
|
||||
|
||||
super(DeepDanbooruModel, self).load_state_dict({k: v for k, v in state_dict.items() if k != 'tags'})
|
||||
|
||||
@ -50,7 +50,6 @@ def enable_tf32():
|
||||
cpu: torch.device = torch.device("cpu")
|
||||
fp8: bool = False
|
||||
device: torch.device = memory_management.get_torch_device()
|
||||
device_interrogate: torch.device = memory_management.text_encoder_device() # for backward compatibility, not used now
|
||||
device_gfpgan: torch.device = memory_management.get_torch_device() # will be managed by memory management system
|
||||
device_esrgan: torch.device = memory_management.get_torch_device() # will be managed by memory management system
|
||||
device_codeformer: torch.device = memory_management.get_torch_device() # will be managed by memory management system
|
||||
|
||||
@ -1,219 +0,0 @@
|
||||
import os
|
||||
import sys
|
||||
from collections import namedtuple
|
||||
from pathlib import Path
|
||||
import re
|
||||
|
||||
import torch
|
||||
import torch.hub
|
||||
|
||||
from torchvision import transforms
|
||||
from torchvision.transforms.functional import InterpolationMode
|
||||
|
||||
from modules import devices, paths, shared, modelloader, errors
|
||||
from backend import memory_management
|
||||
from backend.patcher.base import ModelPatcher
|
||||
|
||||
|
||||
blip_image_eval_size = 384
|
||||
clip_model_name = 'ViT-L/14'
|
||||
|
||||
Category = namedtuple("Category", ["name", "topn", "items"])
|
||||
|
||||
re_topn = re.compile(r"\.top(\d+)$")
|
||||
|
||||
def category_types():
|
||||
return [f.stem for f in Path(shared.interrogator.content_dir).glob('*.txt')]
|
||||
|
||||
|
||||
def download_default_clip_interrogate_categories(content_dir):
|
||||
print("Downloading CLIP categories...")
|
||||
|
||||
tmpdir = f"{content_dir}_tmp"
|
||||
category_types = ["artists", "flavors", "mediums", "movements"]
|
||||
|
||||
try:
|
||||
os.makedirs(tmpdir, exist_ok=True)
|
||||
for category_type in category_types:
|
||||
torch.hub.download_url_to_file(f"https://raw.githubusercontent.com/pharmapsychotic/clip-interrogator/main/clip_interrogator/data/{category_type}.txt", os.path.join(tmpdir, f"{category_type}.txt"))
|
||||
os.rename(tmpdir, content_dir)
|
||||
|
||||
except Exception as e:
|
||||
errors.display(e, "downloading default CLIP interrogate categories")
|
||||
finally:
|
||||
if os.path.exists(tmpdir):
|
||||
os.removedirs(tmpdir)
|
||||
|
||||
|
||||
class InterrogateModels:
|
||||
blip_model = None
|
||||
clip_model = None
|
||||
clip_preprocess = None
|
||||
dtype = None
|
||||
running_on_cpu = None
|
||||
|
||||
def __init__(self, content_dir):
|
||||
self.loaded_categories = None
|
||||
self.skip_categories = []
|
||||
self.content_dir = content_dir
|
||||
|
||||
self.load_device = memory_management.text_encoder_device()
|
||||
self.offload_device = memory_management.text_encoder_offload_device()
|
||||
self.dtype = torch.float32
|
||||
|
||||
if memory_management.should_use_fp16(device=self.load_device):
|
||||
self.dtype = torch.float16
|
||||
|
||||
self.blip_patcher = None
|
||||
self.clip_patcher = None
|
||||
|
||||
def categories(self):
|
||||
if not os.path.exists(self.content_dir):
|
||||
download_default_clip_interrogate_categories(self.content_dir)
|
||||
|
||||
if self.loaded_categories is not None and self.skip_categories == shared.opts.interrogate_clip_skip_categories:
|
||||
return self.loaded_categories
|
||||
|
||||
self.loaded_categories = []
|
||||
|
||||
if os.path.exists(self.content_dir):
|
||||
self.skip_categories = shared.opts.interrogate_clip_skip_categories
|
||||
category_types = []
|
||||
for filename in Path(self.content_dir).glob('*.txt'):
|
||||
category_types.append(filename.stem)
|
||||
if filename.stem in self.skip_categories:
|
||||
continue
|
||||
m = re_topn.search(filename.stem)
|
||||
topn = 1 if m is None else int(m.group(1))
|
||||
with open(filename, "r", encoding="utf8") as file:
|
||||
lines = [x.strip() for x in file.readlines()]
|
||||
|
||||
self.loaded_categories.append(Category(name=filename.stem, topn=topn, items=lines))
|
||||
|
||||
return self.loaded_categories
|
||||
|
||||
def create_fake_fairscale(self):
|
||||
class FakeFairscale:
|
||||
def checkpoint_wrapper(self):
|
||||
pass
|
||||
|
||||
sys.modules["fairscale.nn.checkpoint.checkpoint_activations"] = FakeFairscale
|
||||
|
||||
def load_blip_model(self):
|
||||
self.create_fake_fairscale()
|
||||
import models.blip
|
||||
|
||||
files = modelloader.load_models(
|
||||
model_path=os.path.join(paths.models_path, "BLIP"),
|
||||
model_url='https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_caption_capfilt_large.pth',
|
||||
ext_filter=[".pth"],
|
||||
download_name='model_base_caption_capfilt_large.pth',
|
||||
)
|
||||
|
||||
blip_model = models.blip.blip_decoder(pretrained=files[0], image_size=blip_image_eval_size, vit='base', med_config=os.path.join(paths.paths["BLIP"], "configs", "med_config.json"))
|
||||
blip_model.eval()
|
||||
|
||||
return blip_model
|
||||
|
||||
def load_clip_model(self):
|
||||
import clip
|
||||
import clip.model
|
||||
|
||||
clip.model.LayerNorm = torch.nn.LayerNorm
|
||||
|
||||
model, preprocess = clip.load(clip_model_name, device="cpu", download_root=shared.cmd_opts.clip_models_path)
|
||||
model.eval()
|
||||
|
||||
return model, preprocess
|
||||
|
||||
def load(self):
|
||||
if self.blip_model is None:
|
||||
self.blip_model = self.load_blip_model()
|
||||
self.blip_model = self.blip_model.to(device=self.offload_device, dtype=self.dtype)
|
||||
self.blip_patcher = ModelPatcher(self.blip_model, load_device=self.load_device, offload_device=self.offload_device)
|
||||
|
||||
if self.clip_model is None:
|
||||
self.clip_model, self.clip_preprocess = self.load_clip_model()
|
||||
self.clip_model = self.clip_model.to(device=self.offload_device, dtype=self.dtype)
|
||||
self.clip_patcher = ModelPatcher(self.clip_model, load_device=self.load_device, offload_device=self.offload_device)
|
||||
|
||||
memory_management.load_models_gpu([self.blip_patcher, self.clip_patcher])
|
||||
return
|
||||
|
||||
def send_clip_to_ram(self):
|
||||
pass
|
||||
|
||||
def send_blip_to_ram(self):
|
||||
pass
|
||||
|
||||
def unload(self):
|
||||
pass
|
||||
|
||||
def rank(self, image_features, text_array, top_count=1):
|
||||
import clip
|
||||
|
||||
devices.torch_gc()
|
||||
|
||||
if shared.opts.interrogate_clip_dict_limit != 0:
|
||||
text_array = text_array[0:int(shared.opts.interrogate_clip_dict_limit)]
|
||||
|
||||
top_count = min(top_count, len(text_array))
|
||||
text_tokens = clip.tokenize(list(text_array), truncate=True).to(self.load_device)
|
||||
text_features = self.clip_model.encode_text(text_tokens).type(self.dtype)
|
||||
text_features /= text_features.norm(dim=-1, keepdim=True)
|
||||
|
||||
similarity = torch.zeros((1, len(text_array))).to(self.load_device)
|
||||
for i in range(image_features.shape[0]):
|
||||
similarity += (100.0 * image_features[i].unsqueeze(0) @ text_features.T).softmax(dim=-1)
|
||||
similarity /= image_features.shape[0]
|
||||
|
||||
top_probs, top_labels = similarity.cpu().topk(top_count, dim=-1)
|
||||
return [(text_array[top_labels[0][i].numpy()], (top_probs[0][i].numpy()*100)) for i in range(top_count)]
|
||||
|
||||
def generate_caption(self, pil_image):
|
||||
gpu_image = transforms.Compose([
|
||||
transforms.Resize((blip_image_eval_size, blip_image_eval_size), interpolation=InterpolationMode.BICUBIC),
|
||||
transforms.ToTensor(),
|
||||
transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))
|
||||
])(pil_image).unsqueeze(0).type(self.dtype).to(self.load_device)
|
||||
|
||||
with torch.no_grad():
|
||||
caption = self.blip_model.generate(gpu_image, sample=False, num_beams=int(shared.opts.interrogate_clip_num_beams), min_length=int(shared.opts.interrogate_clip_min_length), max_length=shared.opts.interrogate_clip_max_length)
|
||||
|
||||
return caption[0]
|
||||
|
||||
def interrogate(self, pil_image):
|
||||
res = ""
|
||||
shared.state.begin(job="interrogate")
|
||||
try:
|
||||
self.load()
|
||||
|
||||
caption = self.generate_caption(pil_image)
|
||||
self.send_blip_to_ram()
|
||||
devices.torch_gc()
|
||||
|
||||
res = caption
|
||||
|
||||
clip_image = self.clip_preprocess(pil_image).unsqueeze(0).type(self.dtype).to(self.load_device)
|
||||
|
||||
with torch.no_grad(), devices.autocast():
|
||||
image_features = self.clip_model.encode_image(clip_image).type(self.dtype)
|
||||
|
||||
image_features /= image_features.norm(dim=-1, keepdim=True)
|
||||
|
||||
for cat in self.categories():
|
||||
matches = self.rank(image_features, cat.items, top_count=cat.topn)
|
||||
for match, score in matches:
|
||||
if shared.opts.interrogate_return_ranks:
|
||||
res += f", ({match}:{score/100:.3f})"
|
||||
else:
|
||||
res += f", {match}"
|
||||
|
||||
except Exception:
|
||||
errors.report("Error interrogating", exc_info=True)
|
||||
res += "<error>"
|
||||
|
||||
self.unload()
|
||||
shared.state.end()
|
||||
|
||||
return res
|
||||
@ -385,14 +385,14 @@ def prepare_environment():
|
||||
# stable_diffusion_xl_repo = os.environ.get('STABLE_DIFFUSION_XL_REPO', "https://github.com/Stability-AI/generative-models.git")
|
||||
# k_diffusion_repo = os.environ.get('K_DIFFUSION_REPO', 'https://github.com/crowsonkb/k-diffusion.git')
|
||||
# huggingface_guess_repo = os.environ.get('HUGGINGFACE_GUESS_REPO', 'https://github.com/lllyasviel/huggingface_guess.git')
|
||||
blip_repo = os.environ.get('BLIP_REPO', 'https://github.com/salesforce/BLIP.git')
|
||||
# blip_repo = os.environ.get('BLIP_REPO', 'https://github.com/salesforce/BLIP.git')
|
||||
|
||||
# assets_commit_hash = os.environ.get('ASSETS_COMMIT_HASH', "6f7db241d2f8ba7457bac5ca9753331f0c266917")
|
||||
# stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "cf1d67a6fd5ea1aa600c4df58e5b47da45f6bdbf")
|
||||
# stable_diffusion_xl_commit_hash = os.environ.get('STABLE_DIFFUSION_XL_COMMIT_HASH', "45c443b316737a4ab6e40413d7794a7f5657c19f")
|
||||
# k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "ab527a9a6d347f364e3d185ba6d714e22d80cb3c")
|
||||
# huggingface_guess_commit_hash = os.environ.get('HUGGINGFACE_GUESS_HASH', "84826248b49bb7ca754c73293299c4d4e23a548d")
|
||||
blip_commit_hash = os.environ.get('BLIP_COMMIT_HASH', "48211a1594f1321b00f14c9f7a5b4813144b2fb9")
|
||||
# blip_commit_hash = os.environ.get('BLIP_COMMIT_HASH', "48211a1594f1321b00f14c9f7a5b4813144b2fb9")
|
||||
|
||||
try:
|
||||
# the existence of this file is a signal to webui.sh/bat that webui needs to be restarted when it stops execution
|
||||
@ -452,7 +452,7 @@ def prepare_environment():
|
||||
# git_clone(stable_diffusion_xl_repo, repo_dir('generative-models'), "Stable Diffusion XL", stable_diffusion_xl_commit_hash)
|
||||
# git_clone(k_diffusion_repo, repo_dir('k-diffusion'), "K-diffusion", k_diffusion_commit_hash)
|
||||
# git_clone(huggingface_guess_repo, repo_dir('huggingface_guess'), "huggingface_guess", huggingface_guess_commit_hash)
|
||||
git_clone(blip_repo, repo_dir('BLIP'), "BLIP", blip_commit_hash)
|
||||
# git_clone(blip_repo, repo_dir('BLIP'), "BLIP", blip_commit_hash)
|
||||
|
||||
startup_timer.record("clone repositores")
|
||||
|
||||
|
||||
@ -8,7 +8,7 @@ sys.path.insert(0, script_path)
|
||||
sd_path = os.path.dirname(__file__)
|
||||
|
||||
path_dirs = [
|
||||
(os.path.join(sd_path, '../repositories/BLIP'), 'models/blip.py', 'BLIP', []),
|
||||
# (os.path.join(sd_path, '../repositories/BLIP'), 'models/blip.py', 'BLIP', []),
|
||||
# (os.path.join(sd_path, '../packages_3rdparty'), 'gguf/quants.py', 'packages_3rdparty', []),
|
||||
# (os.path.join(sd_path, '../repositories/k-diffusion'), 'k_diffusion/sampling.py', 'k_diffusion', ["atstart"]),
|
||||
# (os.path.join(sd_path, '../repositories/huggingface_guess'), 'huggingface_guess/detection.py', 'huggingface_guess', []),
|
||||
|
||||
@ -10,7 +10,7 @@ from typing import TYPE_CHECKING
|
||||
from backend import memory_management
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from modules import shared_state, styles, interrogate, shared_total_tqdm, memmon
|
||||
from modules import shared_state, styles, shared_total_tqdm, memmon
|
||||
|
||||
cmd_opts = shared_cmd_options.cmd_opts
|
||||
parser = shared_cmd_options.parser
|
||||
@ -37,8 +37,6 @@ state: 'shared_state.State' = None
|
||||
|
||||
prompt_styles: 'styles.StyleDatabase' = None
|
||||
|
||||
interrogator: 'interrogate.InterrogateModels' = None
|
||||
|
||||
face_restorers = []
|
||||
|
||||
options_templates: dict = None
|
||||
|
||||
@ -33,9 +33,6 @@ def initialize():
|
||||
from modules import styles
|
||||
shared.prompt_styles = styles.StyleDatabase(shared.styles_filename)
|
||||
|
||||
from modules import interrogate
|
||||
shared.interrogator = interrogate.InterrogateModels("interrogate")
|
||||
|
||||
from modules import shared_total_tqdm
|
||||
shared.total_tqdm = shared_total_tqdm.TotalTQDM()
|
||||
|
||||
|
||||
@ -1,7 +1,7 @@
|
||||
import os
|
||||
import gradio as gr
|
||||
|
||||
from modules import localization, ui_components, shared_items, shared, interrogate, shared_gradio_themes, util, sd_emphasis
|
||||
from modules import localization, ui_components, shared_items, shared, shared_gradio_themes, util, sd_emphasis
|
||||
from modules.paths_internal import models_path, script_path, data_path, sd_configs_path, sd_default_config, sd_model_file, default_sd_model_file, extensions_dir, extensions_builtin_dir, default_output_dir # noqa: F401
|
||||
from modules.shared_cmd_options import cmd_opts
|
||||
from modules.options import options_section, OptionInfo, OptionHTML, categories
|
||||
@ -262,21 +262,6 @@ options_templates.update(options_section(('compatibility', "Compatibility", "sd"
|
||||
"refiner_switch_by_sample_steps": OptionInfo(False, "Switch to refiner by sampling steps instead of model timesteps. Old behavior for refiner.", infotext="Refiner switch by sampling steps")
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('interrogate', "Interrogate"), {
|
||||
"interrogate_keep_models_in_memory": OptionInfo(False, "Keep models in VRAM"),
|
||||
"interrogate_return_ranks": OptionInfo(False, "Include ranks of model tags matches in results.").info("booru only"),
|
||||
"interrogate_clip_num_beams": OptionInfo(1, "BLIP: num_beams", gr.Slider, {"minimum": 1, "maximum": 16, "step": 1}),
|
||||
"interrogate_clip_min_length": OptionInfo(24, "BLIP: minimum description length", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}),
|
||||
"interrogate_clip_max_length": OptionInfo(48, "BLIP: maximum description length", gr.Slider, {"minimum": 1, "maximum": 256, "step": 1}),
|
||||
"interrogate_clip_dict_limit": OptionInfo(1500, "CLIP: maximum number of lines in text file").info("0 = No limit"),
|
||||
"interrogate_clip_skip_categories": OptionInfo([], "CLIP: skip inquire categories", gr.CheckboxGroup, lambda: {"choices": interrogate.category_types()}, refresh=interrogate.category_types),
|
||||
"interrogate_deepbooru_score_threshold": OptionInfo(0.5, "deepbooru: score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
|
||||
"deepbooru_sort_alpha": OptionInfo(True, "deepbooru: sort tags alphabetically").info("if not: sort by score"),
|
||||
"deepbooru_use_spaces": OptionInfo(True, "deepbooru: use spaces in tags").info("if not: use underscores"),
|
||||
"deepbooru_escape": OptionInfo(True, "deepbooru: escape (\\) brackets").info("so they are used as literal brackets and not for emphasis"),
|
||||
"deepbooru_filter_tags": OptionInfo("", "deepbooru: filter out those tags").info("separate by comma"),
|
||||
}))
|
||||
|
||||
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."),
|
||||
|
||||
@ -13,7 +13,7 @@ from PIL import Image, PngImagePlugin # noqa: F401
|
||||
from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, wrap_gradio_call, wrap_gradio_call_no_job # noqa: F401
|
||||
|
||||
from modules import gradio_extensions, sd_schedulers # noqa: F401
|
||||
from modules import sd_hijack, sd_models, script_callbacks, paths_internal, ui_extensions, deepbooru, extra_networks, ui_common, ui_postprocessing, progress, ui_loadsave, shared_items, ui_settings, timer, sysinfo, ui_checkpoint_merger, scripts, sd_samplers, processing, ui_extra_networks, ui_toprow, launch_utils
|
||||
from modules import sd_hijack, sd_models, script_callbacks, paths_internal, ui_extensions, extra_networks, ui_common, ui_postprocessing, progress, ui_loadsave, shared_items, ui_settings, timer, sysinfo, ui_checkpoint_merger, scripts, sd_samplers, processing, ui_extra_networks, ui_toprow, launch_utils
|
||||
from modules.ui_components import FormRow, FormGroup, ToolButton, FormHTML, InputAccordion, ResizeHandleRow
|
||||
from modules.paths import script_path
|
||||
from modules.ui_common import create_refresh_button
|
||||
@ -119,40 +119,6 @@ def resize_from_to_html(width, height, scale_by):
|
||||
return f"resize: from <span class='resolution'>{width}x{height}</span> to <span class='resolution'>{target_width}x{target_height}</span>"
|
||||
|
||||
|
||||
def process_interrogate(interrogation_function, mode, ii_input_dir, ii_output_dir, *ii_singles):
|
||||
mode = int(mode)
|
||||
if mode in (0, 1, 3, 4):
|
||||
return [interrogation_function(ii_singles[mode]), None]
|
||||
elif mode == 2:
|
||||
return [interrogation_function(ii_singles[mode]), None]
|
||||
elif mode == 5:
|
||||
assert not shared.cmd_opts.hide_ui_dir_config, "Launched with --hide-ui-dir-config, batch img2img disabled"
|
||||
images = shared.listfiles(ii_input_dir)
|
||||
print(f"Will process {len(images)} images.")
|
||||
if ii_output_dir != "":
|
||||
os.makedirs(ii_output_dir, exist_ok=True)
|
||||
else:
|
||||
ii_output_dir = ii_input_dir
|
||||
|
||||
for image in images:
|
||||
img = Image.open(image)
|
||||
filename = os.path.basename(image)
|
||||
left, _ = os.path.splitext(filename)
|
||||
print(interrogation_function(img), file=open(os.path.join(ii_output_dir, f"{left}.txt"), 'a', encoding='utf-8'))
|
||||
|
||||
return [gr.update(), None]
|
||||
|
||||
|
||||
def interrogate(image):
|
||||
prompt = shared.interrogator.interrogate(image.convert("RGB"))
|
||||
return gr.update() if prompt is None else prompt
|
||||
|
||||
|
||||
def interrogate_deepbooru(image):
|
||||
prompt = deepbooru.model.tag(image)
|
||||
return gr.update() if prompt is None else prompt
|
||||
|
||||
|
||||
def connect_clear_prompt(button):
|
||||
"""Given clear button, prompt, and token_counter objects, setup clear prompt button click event"""
|
||||
button.click(
|
||||
@ -829,21 +795,6 @@ def create_ui():
|
||||
show_progress=False,
|
||||
)
|
||||
|
||||
interrogate_args = dict(
|
||||
_js="get_img2img_tab_index",
|
||||
inputs=[
|
||||
dummy_component,
|
||||
img2img_batch_input_dir,
|
||||
img2img_batch_output_dir,
|
||||
init_img.background,
|
||||
sketch.background,
|
||||
init_img_with_mask.background,
|
||||
inpaint_color_sketch.background,
|
||||
init_img_inpaint,
|
||||
],
|
||||
outputs=[toprow.prompt, dummy_component],
|
||||
)
|
||||
|
||||
toprow.prompt.submit(**img2img_args)
|
||||
toprow.submit.click(**img2img_args)
|
||||
|
||||
@ -870,16 +821,6 @@ def create_ui():
|
||||
show_progress=False,
|
||||
)
|
||||
|
||||
toprow.button_interrogate.click(
|
||||
fn=lambda *args: process_interrogate(interrogate, *args),
|
||||
**interrogate_args,
|
||||
)
|
||||
|
||||
toprow.button_deepbooru.click(
|
||||
fn=lambda *args: process_interrogate(interrogate_deepbooru, *args),
|
||||
**interrogate_args,
|
||||
)
|
||||
|
||||
steps = scripts.scripts_img2img.script('Sampler').steps
|
||||
|
||||
toprow.ui_styles.dropdown.change(fn=wrap_queued_call(update_token_counter), inputs=[toprow.prompt, steps, toprow.ui_styles.dropdown], outputs=[toprow.token_counter])
|
||||
|
||||
@ -13,9 +13,6 @@ class Toprow:
|
||||
prompt_img = None
|
||||
negative_prompt = None
|
||||
|
||||
button_interrogate = None
|
||||
button_deepbooru = None
|
||||
|
||||
interrupt = None
|
||||
interrupting = None
|
||||
skip = None
|
||||
@ -121,10 +118,6 @@ class Toprow:
|
||||
self.clear_prompt_button = ToolButton(value=clear_prompt_symbol, elem_id=f"{self.id_part}_clear_prompt", tooltip="Clear prompt")
|
||||
self.apply_styles = ToolButton(value=ui_prompt_styles.styles_materialize_symbol, elem_id=f"{self.id_part}_style_apply", tooltip="Apply all selected styles to prompts. Strips comments, if enabled.")
|
||||
|
||||
if self.is_img2img:
|
||||
self.button_interrogate = ToolButton('📎', tooltip='Interrogate CLIP - use CLIP neural network to create a text describing the image, and put it into the prompt field', elem_id="interrogate")
|
||||
self.button_deepbooru = ToolButton('📦', tooltip='Interrogate DeepBooru - use DeepBooru neural network to create a text describing the image, and put it into the prompt field', elem_id="deepbooru")
|
||||
|
||||
self.restore_progress_button = ToolButton(value=restore_progress_symbol, elem_id=f"{self.id_part}_restore_progress", visible=False, tooltip="Restore progress")
|
||||
|
||||
self.token_counter = gr.HTML(value="<span>0/75</span>", elem_id=f"{self.id_part}_token_counter", elem_classes=["token-counter"], visible=False)
|
||||
|
||||
@ -2,7 +2,7 @@ import math
|
||||
|
||||
import gradio as gr
|
||||
import modules.scripts as scripts
|
||||
from modules import deepbooru, images, processing, shared
|
||||
from modules import images, processing, shared
|
||||
from modules.processing import Processed
|
||||
from modules.shared import opts, state
|
||||
|
||||
@ -18,11 +18,10 @@ class Script(scripts.Script):
|
||||
loops = gr.Slider(minimum=1, maximum=32, step=1, label='Loops', value=4, elem_id=self.elem_id("loops"))
|
||||
final_denoising_strength = gr.Slider(minimum=0, maximum=1, step=0.01, label='Final denoising strength', value=0.5, elem_id=self.elem_id("final_denoising_strength"))
|
||||
denoising_curve = gr.Dropdown(label="Denoising strength curve", choices=["Aggressive", "Linear", "Lazy"], value="Linear")
|
||||
append_interrogation = gr.Dropdown(label="Append interrogated prompt at each iteration", choices=["None", "CLIP", "DeepBooru"], value="None")
|
||||
|
||||
return [loops, final_denoising_strength, denoising_curve, append_interrogation]
|
||||
return [loops, final_denoising_strength, denoising_curve]
|
||||
|
||||
def run(self, p, loops, final_denoising_strength, denoising_curve, append_interrogation):
|
||||
def run(self, p, loops, final_denoising_strength, denoising_curve):
|
||||
processing.fix_seed(p)
|
||||
batch_count = p.n_iter
|
||||
p.extra_generation_params = {
|
||||
@ -83,13 +82,6 @@ class Script(scripts.Script):
|
||||
if opts.img2img_color_correction:
|
||||
p.color_corrections = initial_color_corrections
|
||||
|
||||
if append_interrogation != "None":
|
||||
p.prompt = f"{original_prompt}, " if original_prompt else ""
|
||||
if append_interrogation == "CLIP":
|
||||
p.prompt += shared.interrogator.interrogate(p.init_images[0])
|
||||
elif append_interrogation == "DeepBooru":
|
||||
p.prompt += deepbooru.model.tag(p.init_images[0])
|
||||
|
||||
state.job = f"Iteration {i + 1}/{loops}, batch {n + 1}/{batch_count}"
|
||||
|
||||
processed = processing.process_images(p)
|
||||
|
||||
10
style.css
10
style.css
@ -331,14 +331,6 @@ input[type="checkbox"].input-accordion-checkbox{
|
||||
gap: 0.4em;
|
||||
}
|
||||
|
||||
.interrogate-col{
|
||||
min-width: 0 !important;
|
||||
max-width: fit-content;
|
||||
gap: 0.5em;
|
||||
}
|
||||
.interrogate-col > button{
|
||||
flex: 1;
|
||||
}
|
||||
|
||||
.generate-box{
|
||||
position: relative;
|
||||
@ -1396,7 +1388,7 @@ body.resizing .resize-handle {
|
||||
flex-basis: 100%;
|
||||
}
|
||||
/* Buttons for directories. */
|
||||
.extra-network-tree .tree-list-content-dir {}
|
||||
.extra-network-tree .tree-list-content-dir {}
|
||||
|
||||
/* Buttons for files. */
|
||||
.extra-network-tree .tree-list-item--has-subitem .tree-list--subgroup > li:first-child {
|
||||
|
||||
Loading…
Reference in New Issue
Block a user