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Repository: d8ahazard/sd_smartprocess
Branch: main
Commit: 61b0e747f11a
Files: 72
Total size: 2.4 MB
Directory structure:
gitextract_qqaeml4x/
├── .gitattributes
├── .gitignore
├── README.md
├── characters/
│ ├── nsfw.txt
│ └── sfw.txt
├── clipcrop.py
├── configs/
│ └── upscaler.yaml
├── dbimutils.py
├── file_manager.py
├── install.py
├── interrogators/
│ ├── blip_interrogator.py
│ ├── booru_interrogator.py
│ ├── data/
│ │ ├── artists.txt
│ │ ├── flavors.txt
│ │ ├── mediums.txt
│ │ └── movements.txt
│ ├── idefics2_interrogator.py
│ ├── interrogator.py
│ ├── llava2_interrogator.py
│ ├── moondream_interrogator.py
│ └── wolf_interrogator.py
├── javascript/
│ └── smart_process.js
├── model_download.py
├── mplug_owl2/
│ ├── __init__.py
│ ├── constants.py
│ ├── conversation.py
│ ├── evaluate/
│ │ ├── EVALUATION.md
│ │ ├── __init__.py
│ │ ├── evaluate_caption.py
│ │ ├── evaluate_mmbench.py
│ │ ├── evaluate_mme.py
│ │ ├── evaluate_mmmu.py
│ │ ├── evaluate_vqa.py
│ │ ├── mmbench_converter.py
│ │ ├── vqa.py
│ │ └── vqa_eval.py
│ ├── local_serve/
│ │ ├── __init__.py
│ │ ├── local_web_server.py
│ │ └── model_worker.py
│ ├── mm_utils.py
│ ├── model/
│ │ ├── __init__.py
│ │ ├── builder.py
│ │ ├── configuration_mplug_owl2.py
│ │ ├── configuration_qwen.py
│ │ ├── convert_mplug_owl2_weight_to_hf.py
│ │ ├── modeling_attn_mask_utils.py
│ │ ├── modeling_llama2.py
│ │ ├── modeling_mplug_owl2.py
│ │ ├── modeling_qwen.py
│ │ ├── multiway.py
│ │ ├── utils.py
│ │ └── visual_encoder.py
│ ├── serve/
│ │ ├── __init__.py
│ │ ├── cli.py
│ │ ├── controller.py
│ │ ├── gradio_web_server.py
│ │ ├── model_worker.py
│ │ └── register_workers.py
│ ├── train/
│ │ ├── llama_flash_attn_monkey_patch.py
│ │ ├── mplug_owl2_trainer.py
│ │ ├── train.py
│ │ └── train_mem.py
│ └── utils.py
├── process_params.py
├── processors.py
├── requirements.txt
├── scripts/
│ └── process_main.py
├── smartprocess.py
├── style.css
├── super_resolution.py
└── upscalers/
└── spandrel/
├── spandrel_srformer_model.py
└── spandrel_upscaler_base.py
================================================
FILE CONTENTS
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================================================
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================================================
FILE: README.md
================================================
# Stable Diffusion WebUI Smart Pre-Processing Extension
## What is this??
As the name would imply, this is an extension for
the [Stable-Diffusion WebUI](https://github.com/AUTOMATIC1111/stable-diffusion-webui) by @Automatic1111
## What does this do?
It does a few things, actually.
For starters, it utilizes a combination of BLIP/CLIP and YOLOv5 to provide "smart cropping" for images. The primary
subject of each image is identified, the center of that subject is determined, and then the application tries it's best
to crop the image so as to keep as much of the subject as possible within the dimensions specified.
Second, it allows storing the determined image caption directly to the image filename, versus having to create a txt
file along side every image. You can still create a txt file, use existing captions, or not do any captioning at all.
Third, I've provided face restoration and upscaling options for input images. You can select from GFPGAN and Codeformer
for face restoration, and any of the provided upscalers from the "extras' tab to refine/smooth/add detail to your final
output images.
Last, but not least, it offers a rudimentary way to swap the "class" of a captioned image with the specific keyword in
the image. So, if you're trying to train a subject called "xyz" and "xyz" is a dog, you can easily swap "dog" (and "a
dog") wth "xyz" in your captions. Neato!
## Smart Cropping
As I said above, smart cropping utilizes a combination of YOLOV5 object recognition and BLIP/CLIP (and DeepDanBooru)
captioning to automatically determine the most prominent subject in a photo, and automatically crop the subject as
completely as possible. You can also specify a specific subject (dog/cat/woman/house) for the software to find, and skip
the YOLOV5 detection entirely.
<img src="https://user-images.githubusercontent.com/1633844/198178259-e1ade3d6-386e-41b8-9c93-0eca19c82d3d.png" width="550" height="741" />
If a subject is not found, the image will be downscaled and cropped from the center.
## Smart Captioning
This uses all the same features as set in user preferences, with the additional options to save to txt or append to the
image file name.
Additionally, you can swap the generic "class" of the image with a specific subject keyword. This feature may not be
perfect in all cases, but it should still go a long way in speeding up the captioning process.
You can also specify a maximum caption length, which will split the caption by spaces and append words until the maximum
length is reached.
## Post Processing
It's basically a simplified version of the "extras" tab. The idea is that you can do facial restoration and/or use a
model like swinIR or LDSR to smooth or add details to an image. If an image is "actually" upscaled beyond the target
crop size, it will be downscaled again back to the original size.
================================================
FILE: characters/nsfw.txt
================================================
Human: <image>
Human: Give a short one sentence caption for this image with NO punctuation. DO NOT USE ANY PUNCTUATION OR COMMAS. DO NOT USE COMMAS!!!
AI:
================================================
FILE: characters/sfw.txt
================================================
Revised and SFW prompt:
Act like a stable diffusion SD prompt generator, your focus is to convey warm and playful scenarios in vivid detail, include lush descriptions, the quality of your prompts must be only the best creative and varied. Your subjects must be adults only. When you see "young" in a prompt, assume the subject is in their early 20s. The SD prompts must be formatted in a way the AI can understand.
Stable Diffusion prompts are made of components which are comprised of keywords separated by commas, keywords can be single words or multi-word keywords, and they have a specific order. A typical format for the components looks like this: [Adjectives], [Type], [Framing], [Shot], [subject], [Descriptor], [Descriptor], [Descriptor], [Expression], [Pose], [Action], [Environment], [Details], [Lighting], [Medium], [Aesthetics], [Visual], [Clothing].
Here are some keywords commonly used for each of the components, but always mix them with new ones that are coherent to each component:
Adjectives: Masterpiece, award-winning, Most beautiful, Epic, Majestic, best quality, fantastic, highly detailed, ultra-detailed, 8K, realistic
Type: photo, selfie, 8k photo, professional photo
Framing: Dutch angle, Wide Angle, low angle, high angle, perspective, on eye level, POV, close-up
Shot: mid shot, full shot, portrait, from behind, long shot, cowboy shot, from above, from below
Subject: woman, man
Descriptor: elegant, well-dressed, casual attire, formal attire, stylish, modern, vintage, sporty, artistic, natural, sophisticated, trendy, chic
Expression: satisfied, happy, excited, surprised, mouth open, eyes closed, sleeping, smiling, frowning, crying
Pose: seated, standing, laying down, hands raised, walking, jumping, dancing, running
Action: laughing, chatting, cooking, reading, painting, dancing, sleeping, crying, sitting, standing, lying down
Environment: park, cafe, library, office, beach, mall, rooftop, forest, kitchen, bedroom, outer space, mountains, cityscape
Details: Cloudless sky, glittering night, sparkling rain, shining lights, obscure darkness, red lights, neon lighting
Lighting: light, dim light, two-tone lighting, dynamic lighting, rim light, studio light, diffuse lighting, dark lighting, soft lighting, fantasy lighting, natural light
Medium: photograph, painting, illustration, sketch, render
Aesthetics: vintage, retro, modern, rustic, minimalist, bohemian, industrial, romantic, fantasy, sci-fi, noir, gothic, ethereal
Visual: contrast, cyan hue, Kodachrome, Fujifilm Superia, warm colours, saturation, vibrance, filters coolness, chromatic aberration, cinematic, RAW color
Clothing: casual, formal, vintage, modern, sporty, chic, elegant, streetwear, boho, classic, punk, gothic, trendy
Use the components to build coherent prompts. Use these keywords but also create your own. Generate variations of the keywords that are coherent to each component and fit the instruction. Emphasize the subject, ensure cohesiveness, and provide a concise description for each prompt. Only reply with full single prompts separated by a line break, do not add a numbered list, quotes, or a section breakdown.
For example:
[a photo of a beautiful woman standing outside at night, masterpiece, professional photo, Dutch angle, full shot, woman, elegant, stylish, happy, standing, laughing, park, glittering night, light, photograph, vintage, contrast, casual]
Human: <image>
Human: Caption this image.
AI:
================================================
FILE: clipcrop.py
================================================
# Original project: https://github.com/Vishnunkumar/clipcrop/blob/main/clipcrop/clipcrop.py
import os.path
import sys
import cv2
import numpy
import numpy as np
import torch
from PIL import Image
from clip import clip
import modules.paths
from modules import shared, modelloader
def clip_boxes(boxes, shape):
# Clip boxes (xyxy) to image shape (height, width)
if isinstance(boxes, torch.Tensor): # faster individually
boxes[:, 0].clamp_(0, shape[1]) # x1
boxes[:, 1].clamp_(0, shape[0]) # y1
boxes[:, 2].clamp_(0, shape[1]) # x2
boxes[:, 3].clamp_(0, shape[0]) # y2
else: # np.array (faster grouped)
boxes[:, [0, 2]] = boxes[:, [0, 2]].clip(0, shape[1]) # x1, x2
boxes[:, [1, 3]] = boxes[:, [1, 3]].clip(0, shape[0]) # y1, y2
def find_position(parent: Image, child: Image):
w = child.width
h = child.height
res = cv2.matchTemplate(np.array(parent), np.array(child), cv2.TM_CCOEFF_NORMED)
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
# If the method is TM_SQDIFF or TM_SQDIFF_NORMED, take minimum
top_left = max_loc
center_x = top_left[0] + (w / 2)
center_y = top_left[1] + (h / 2)
return center_x, center_y
class CropClip:
def __init__(self):
# Model
model_name = 'yolov5m6v7.pt'
model_url = 'https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m6.pt'
model_dir = os.path.join(modules.paths.models_path, "yolo")
model_path = modelloader.load_models(model_dir, model_url, None, '.pt', model_name)
was_safe_unpickle = shared.cmd_opts.disable_safe_unpickle
shared.cmd_opts.disable_safe_unpickle = True
self.model = torch.hub.load('ultralytics/yolov5', 'custom', model_path[0])
self.clip = None
self.preprocess = None
shared.cmd_opts.disable_safe_unpickle = was_safe_unpickle
# Prevent BLIP crossfire breakage
try:
del sys.modules['models']
except:
pass
def get_center(self, image: Image, prompt: str):
# Load image into YOLO parser
results = self.model(image) # includes NMS
# Crop each image result to an array
cropped = results.crop(False)
l = []
for crop in cropped:
l.append(Image.fromarray(crop["im"]))
if len(l) == 0:
l = [image]
device = shared.device
# Take out cropped YOLO images, and get the features?
if not self.model or not self.preprocess:
self.clip, self.preprocess = clip.load("ViT-B/32", device=device)
images = torch.stack([self.preprocess(im) for im in l]).to(device)
with torch.no_grad():
image_features = self.clip.encode_image(images)
image_features /= image_features.norm(dim=-1, keepdim=True)
image_features.cpu().numpy()
image_mean = torch.tensor([0.48145466, 0.4578275, 0.40821073]).cuda()
image_std = torch.tensor([0.26862954, 0.26130258, 0.27577711]).cuda()
images = [self.preprocess(im) for im in l]
image_input = torch.tensor(np.stack(images)).cuda()
image_input -= image_mean[:, None, None]
image_input /= image_std[:, None, None]
with torch.no_grad():
image_features = self.clip.encode_image(image_input).float()
image_features /= image_features.norm(dim=-1, keepdim=True)
def similarity_top(similarity_list, N):
results = zip(range(len(similarity_list)), similarity_list)
results = sorted(results, key=lambda x: x[1], reverse=True)
top_images = []
scores = []
for index, score in results[:N]:
scores.append(score)
top_images.append(l[index])
return scores, top_images
# @title Crop
with torch.no_grad():
# Encode and normalize the description using CLIP
text_encoded = self.clip.encode_text(clip.tokenize(prompt).to(device))
text_encoded /= text_encoded.norm(dim=-1, keepdim=True)
# Retrieve the description vector and the photo vectors
similarity = text_encoded.cpu().numpy() @ image_features.cpu().numpy().T
similarity = similarity[0]
scores, imgs = similarity_top(similarity, N=3)
max_area = 0
out = None
for img in imgs:
img_area = img.width * img.height
if img_area > max_area:
max_area = img_area
out = img
if not out:
out = image
res = cv2.matchTemplate(numpy.array(image), numpy.array(out), cv2.TM_SQDIFF)
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
# If the method is TM_SQDIFF or TM_SQDIFF_NORMED, take minimum
top_left = min_loc
bottom_right = (top_left[0] + out.width, top_left[1] + out.height)
return [top_left[0], bottom_right[0], top_left[1], bottom_right[1]]
def unload(self):
if self.model is not None:
self.model.to('cpu')
if self.clip:
self.clip.to('cpu')
if self.preprocess:
self.preprocess.to('cpu')
def load(self):
if self.model is not None:
self.model.to(shared.device)
if self.clip:
self.clip.to(shared.device)
if self.preprocess:
self.preprocess.to(shared.device)
================================================
FILE: configs/upscaler.yaml
================================================
model:
base_learning_rate: 1.0e-04
target: ldm.models.diffusion.ddpm.LatentUpscaleDiffusion
params:
parameterization: "v"
low_scale_key: "lr"
linear_start: 0.0001
linear_end: 0.02
num_timesteps_cond: 1
log_every_t: 200
timesteps: 1000
first_stage_key: "jpg"
cond_stage_key: "txt"
image_size: 128
channels: 4
cond_stage_trainable: false
conditioning_key: "hybrid-adm"
monitor: val/loss_simple_ema
scale_factor: 0.08333
use_ema: False
low_scale_config:
target: ldm.modules.diffusionmodules.upscaling.ImageConcatWithNoiseAugmentation
params:
noise_schedule_config: # image space
linear_start: 0.0001
linear_end: 0.02
max_noise_level: 350
unet_config:
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
params:
use_checkpoint: True
num_classes: 1000 # timesteps for noise conditioning (here constant, just need one)
image_size: 128
in_channels: 7
out_channels: 4
model_channels: 256
attention_resolutions: [ 2,4,8]
num_res_blocks: 2
channel_mult: [ 1, 2, 2, 4]
disable_self_attentions: [True, True, True, False]
disable_middle_self_attn: False
num_heads: 8
use_spatial_transformer: True
transformer_depth: 1
context_dim: 1024
legacy: False
use_linear_in_transformer: True
first_stage_config:
target: ldm.models.autoencoder.AutoencoderKL
params:
embed_dim: 4
ddconfig:
# attn_type: "vanilla-xformers" this model needs efficient attention to be feasible on HR data, also the decoder seems to break in half precision (UNet is fine though)
double_z: True
z_channels: 4
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult: [ 1,2,4 ] # num_down = len(ch_mult)-1
num_res_blocks: 2
attn_resolutions: [ ]
dropout: 0.0
lossconfig:
target: torch.nn.Identity
cond_stage_config:
target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder
params:
freeze: True
layer: "penultimate"
================================================
FILE: dbimutils.py
================================================
# DanBooru IMage Utility functions, borrowed from
# https://github.com/toriato/stable-diffusion-webui-wd14-tagger/blob/master/tagger/dbimutils.py
import cv2
import numpy as np
from PIL import Image
def smart_imread(img, flag=cv2.IMREAD_UNCHANGED):
if img.endswith(".gif"):
img = Image.open(img)
img = img.convert("RGB")
img = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
else:
img = cv2.imread(img, flag)
return img
def smart_24bit(img):
if img.dtype is np.dtype(np.uint16):
img = (img / 257).astype(np.uint8)
if len(img.shape) == 2:
img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
elif img.shape[2] == 4:
trans_mask = img[:, :, 3] == 0
img[trans_mask] = [255, 255, 255, 255]
img = cv2.cvtColor(img, cv2.COLOR_BGRA2BGR)
return img
def make_square(img, target_size):
old_size = img.shape[:2]
desired_size = max(old_size)
desired_size = max(desired_size, target_size)
delta_w = desired_size - old_size[1]
delta_h = desired_size - old_size[0]
top, bottom = delta_h // 2, delta_h - (delta_h // 2)
left, right = delta_w // 2, delta_w - (delta_w // 2)
color = [255, 255, 255]
new_im = cv2.copyMakeBorder(
img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color
)
return new_im
def smart_resize(img, size):
# Assumes the image has already gone through make_square
if img.shape[0] > size:
img = cv2.resize(img, (size, size), interpolation=cv2.INTER_AREA)
elif img.shape[0] < size:
img = cv2.resize(img, (size, size), interpolation=cv2.INTER_CUBIC)
return img
================================================
FILE: file_manager.py
================================================
import os
import re
from typing import List
import PIL
from PIL.Image import Image
def clean_string(s):
"""
Remove non-alphanumeric characters except spaces, and normalize spacing.
Args:
s: The string to clean.
Returns: A cleaned string.
"""
# Remove non-alphanumeric characters except spaces
cleaned = re.sub(r'[^a-zA-Z0-9\s]', '', s)
# Check for a sentence with just the same word repeated
if len(set(cleaned.split())) == 1:
cleaned = cleaned.split()[0]
# Replace multiple spaces with a single space
cleaned = re.sub(r'\s+', ' ', cleaned).strip()
return cleaned
class ImageData:
image_path: str = ""
temp_image_path: str = ""
caption: str = ""
tags: List[str] = []
selected: bool = False
filtered: bool = False
id = None
def __init__(self, image_path):
self.image_path = image_path
self.caption = self.read_caption()
self.tags = self.split_caption()
# Generate a random id
self.id = os.urandom(32).hex()
def read_caption(self):
existing_caption_txt_filename = os.path.splitext(self.image_path)[0] + '.txt'
if os.path.exists(existing_caption_txt_filename):
with open(existing_caption_txt_filename, 'r', encoding="utf8") as file:
existing_caption_txt = file.read()
existing_caption_txt = existing_caption_txt.strip()
else:
image_name = os.path.splitext(os.path.basename(self.image_path))[0]
existing_caption_txt = clean_string(image_name)
return existing_caption_txt
def split_caption(self):
tags = self.caption.split(",")
tags = [tag.strip() for tag in tags]
tags = [tag for tag in tags if tag != ""]
return tags
def update_image(self, image: Image, save_file: bool = False):
if save_file:
img_path = os.path.splitext(self.image_path)[0] + '.png'
if img_path != self.image_path and os.path.exists(self.image_path):
os.remove(self.image_path)
self.image_path = img_path
else:
self.temp_image_path = os.path.splitext(self.image_path)[0] + '_temp.png'
img_path = self.temp_image_path
image.save(img_path)
def update_caption(self, caption: str, save_file: bool = False):
if save_file:
caption_txt_filename = os.path.splitext(self.image_path)[0] + '.txt'
with open(caption_txt_filename, 'w', encoding="utf8") as file:
file.write(caption)
self.caption = caption
self.tags = self.split_caption()
def get_image(self):
return PIL.Image.open(self.image_path).convert("RGB")
class FileManager:
file_path: str = ""
_instance = None
files: List[ImageData] = []
included_tags: List[str] = []
excluded_tags: List[str] = []
included_strings: List[str] = []
excluded_strings: List[str] = []
current_image = None
def __init__(self):
self.files = []
def __new__(cls):
if FileManager._instance is None:
FileManager._instance = object.__new__(cls)
return FileManager._instance
def clear(self):
self.files = []
self.included_tags = []
self.excluded_tags = []
self.included_strings = []
self.excluded_strings = []
self.current_image = None
def load_files(self):
from extensions.sd_smartprocess.smartprocess import is_image
self.clear()
# Walk through all files in the directory that contains the images
for root, dirs, files in os.walk(self.file_path):
for file in files:
file = os.path.join(root, file)
if is_image(file) and "_backup" not in file:
image_data = ImageData(file)
self.files.append(image_data)
self.update_filters()
def filtered_files(self, for_gallery: bool = False):
if not for_gallery:
return [file for file in self.files if file.filtered]
else:
return [(file.image_path, file.caption) for file in self.files if file.filtered]
def all_files(self, for_gallery: bool = False) -> List[ImageData]:
if not for_gallery:
return self.files
else:
return [(file.image_path, file.caption) for file in self.files]
def selected_files(self, for_gallery: bool = False) -> List[ImageData]:
if not for_gallery:
return [file for file in self.files if file.selected]
else:
return [(file.image_path, file.caption) for file in self.files if file.selected]
def filtered_and_selected_files(self, for_gallery: bool = False) -> List[ImageData]:
if not for_gallery:
return [file for file in self.files if file.filtered and file.selected and file.filtered]
else:
return [(file.image_path, file.caption) for file in self.files if file.selected and file.filtered]
def update_files(self, files: List[ImageData]):
for file in files:
self.update_file(file)
def update_file(self, file: ImageData):
# Search for the file with the same ID and update it if found
for i, existing_file in enumerate(self.files):
if existing_file.id == file.id:
self.files[i] = file
break
else:
# The file was not found in the list, append it
self.files.append(file)
def update_filters(self):
"""
Filters a collection of files based on specified inclusion and exclusion criteria for tags and filter strings.
The function filters files based on tags extracted from file captions and matches these tags against specified
inclusion and exclusion criteria. The criteria can be a set of plain tags, wildcard patterns, or regex expressions.
Parameters:
- use_all_files (bool): Determines whether to filter from all files or only selected files.
If True, filters from all files; otherwise, filters from selected files.
Globals:
- filter_tags_include (list): Tags required to include a file.
- filter_tags_exclude (list): Tags that lead to exclusion of a file.
- filter_string_include (list): Filter strings for inclusion; supports wildcards and regex.
- filter_string_exclude (list): Filter strings for exclusion; supports wildcards and regex.
- all_files (list): List of all files, where each file is a tuple (filename, caption).
- selected_files (list): List of selected files, where each file is a tuple (filename, caption).
Returns:
- filtered_files (list): List of files filtered based on the specified criteria.
Examples:
1. Plain Tag Matching:
- To include files with a tag 'holiday', add 'holiday' to filter_tags_include.
- To exclude files with a tag 'work', add 'work' to filter_tags_exclude.
2. Wildcard Patterns:
- To include files with tags starting with 'trip', add 'trip*' to filter_string_include.
- To exclude files with tags ending with '2023', add '*2023' to filter_string_exclude.
3. Regex Expressions:
- To include files with tags that have any number followed by 'days', add '\\d+days' to filter_string_include.
- To exclude files with tags formatted like dates (e.g., '2023-04-01'), add '\\d{4}-\\d{2}-\\d{2}' to filter_string_exclude.
Note:
- The function treats tags as case-sensitive.
- Wildcard '*' matches any sequence of characters (including none).
- Regex patterns should follow Python's 're' module syntax.
"""
def matches_pattern(pattern, string):
# Convert wildcard to regex if necessary
if '*' in pattern:
pattern = '^' + pattern.replace('*', '.*') + '$'
return re.match(pattern, string) is not None
else:
if " " not in pattern:
parts = string.split(" ")
return pattern in parts
else:
return pattern in string
def should_include(tag, filter_tags, filter_strings):
if len(filter_tags) == 0 and len(filter_strings) == 0:
return True
tag_match = False
filter_match = False
if tag in filter_tags and len(filter_tags) > 0:
tag_match = True
if len(filter_strings) > 0:
for filter_string in filter_strings:
if matches_pattern(filter_string, tag):
filter_match = True
break
return tag_match or filter_match
def should_exclude(tag, filter_tags, filter_strings):
if tag in filter_tags:
return True
for filter_string in filter_strings:
if matches_pattern(filter_string, tag):
return True
return False
files = self.files
updated_files = []
for file in files:
tags = file.tags
out_tags = []
for tag in tags:
include = True
if should_exclude(tag, self.excluded_tags, self.excluded_strings):
include = False
elif not should_include(tag, self.included_tags, self.included_strings):
include = False
if include:
out_tags.append(tag)
file.filtered = len(out_tags) > 0
updated_files.append(file)
self.files = files
def all_captions(self):
return [file.caption for file in self.files]
================================================
FILE: install.py
================================================
import os
import sys
from launch import run, git_clone, repo_dir
name = "Smart Crop"
req_file = os.path.join(os.path.dirname(os.path.realpath(__file__)), "requirements.txt")
print(f"loading {name} reqs from {req_file}")
run(f'"{sys.executable}" -m pip install -r "{req_file}"', f"Checking {name} requirements.",
f"Couldn't install {name} requirements.")
print("Cloning BLIP repo...")
blip_repo = "https://github.com/pharmapsychotic/BLIP"
git_clone(blip_repo, repo_dir('BLIP'), "BLIP")
# git clone https://github.com/X-PLUG/mPLUG-Owl.git
# cd mPLUG-Owl/mPLUG-Owl2
# mplug_repo = "https://github.com/X-PLUG/mPLUG-Owl.git"
# mplug_owl_path = repo_dir('mplug_owl_src')
# git_clone(mplug_repo, mplug_owl_path, "mPLUG-Owl")
# mplug_owl2_sub1_path = os.path.join(mplug_owl_path, "mPLUG-Owl", "mplug_owl")
# mplug_owl2_sub2_path = os.path.join(mplug_owl_path, "mPLUG-Owl2", "mplug_owl2")
#
# sys.path.append(mplug_owl2_sub1_path)
# sys.path.append(mplug_owl2_sub2_path)
#
================================================
FILE: interrogators/blip_interrogator.py
================================================
from PIL.Image import Image
from extensions.sd_smartprocess.interrogators.interrogator import Interrogator
from transformers import AutoProcessor, Blip2ForConditionalGeneration
import torch
from extensions.sd_smartprocess.model_download import fetch_model
from extensions.sd_smartprocess.process_params import ProcessParams
class BLIPInterrogator(Interrogator):
_instance = None # Class variable to hold the singleton instance
params = {"max_tokens": 75, "load_in_8bit": False}
def __init__(self, params: ProcessParams):
super().__init__(params)
self.processor = None
self.model = None
self.current_device = None
self.load_8bit = params.load_in_8bit
def __new__(cls, params: ProcessParams):
if cls._instance is None:
cls._instance = super(BLIPInterrogator, cls).__new__(cls)
try:
cls._instance._init(params)
except Exception as e:
cls._instance = None
raise e
cls.initial_prompt = params.blip_initial_prompt
cls._load_8bit = params.load_in_8bit
return cls._instance
def _init(self, params: ProcessParams):
super().__init__(params)
self.model = None
self.processor = None
self.load_8bit = params.load_in_8bit
def interrogate(self, image: Image, params: ProcessParams, unload: bool = False) -> str:
self.load()
self.params = params
max_tokens = params.max_tokens
inputs = self.processor(image, return_tensors="pt").to(self.device, torch.float16)
generated_ids = self.model.generate(**inputs, max_new_tokens=max_tokens)
generated_text = self.processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip()
if unload:
self.unload()
return generated_text
def unload(self):
if self.current_device != "cpu":
try:
self.model.to("cpu")
self.current_device = "cpu"
except:
pass
def load(self):
try:
if self.model is None:
model_path = fetch_model('Salesforce/blip2-opt-6.7b', "blip2")
self.processor = AutoProcessor.from_pretrained(model_path)
self.model = Blip2ForConditionalGeneration.from_pretrained(model_path, torch_dtype=torch.float16, load_in_8bit=self.load_8bit)
self.device = "cuda" if torch.cuda.is_available() else "cpu"
if self.device != self.current_device:
self.model.to(self.device)
self.current_device = self.device
except:
pass
================================================
FILE: interrogators/booru_interrogator.py
================================================
import re
import numpy as np
import torch
from PIL.Image import Image
import modules.deepbooru
from extensions.sd_smartprocess.interrogators.interrogator import Interrogator, re_special
from extensions.sd_smartprocess.process_params import ProcessParams
from modules import images, devices
class BooruInterrogator(Interrogator):
params = {"min_score": 0.75}
def __init__(self, params: ProcessParams) -> None:
super().__init__(params)
self.tags = None
self.booru = modules.deepbooru.DeepDanbooru()
self.model = self.booru.model
def unload(self):
self.booru.stop()
def load(self):
self.booru.start()
pass
def interrogate(self, image: Image, params: ProcessParams, unload: bool = False) -> str:
self.load()
self.params = params
min_score = params.booru_min_score
pic = images.resize_image(2, image, 512, 512)
a = np.expand_dims(np.array(pic, dtype=np.float32), 0) / 255
with torch.no_grad(), devices.autocast():
# Move the model to the same device as the input tensor
self.model.to(devices.device)
# Convert input to the correct type (half-precision if needed)
x = torch.from_numpy(a).to(devices.device).type_as(self.model.n_Conv_0.weight)
# Forward pass through the model
y = self.model(x)[0].detach().cpu().numpy()
probability_dict = {}
for tag, probability in zip(self.model.tags, y):
if tag.startswith("rating:"):
continue
probability_dict[tag] = probability
tags = [tag for tag, _ in sorted(probability_dict.items(), key=lambda x: -x[1])]
output = {}
for tag in tags:
probability = probability_dict[tag]
tag_outformat = tag
tag_outformat = re.sub(re_special, r'\\\1', tag_outformat)
output[tag_outformat] = probability
out_tags = []
for tag in sorted(output, key=output.get, reverse=True):
if output[tag] >= min_score:
# print(f"DBTag {tag} score is {tags[tag]}")
out_tags.append(tag)
output = ", ".join(out_tags)
if unload:
self.unload()
return output
================================================
FILE: interrogators/data/artists.txt
================================================
A. B. Jackson
A. J. Casson
A. R. Middleton Todd
A.B. Frost
A.D.M. Cooper
Aaron Bohrod
Aaron Douglas
Aaron Jasinski
Aaron Miller
Aaron Nagel
Abbott Handerson Thayer
Abdullah Gërguri
Abdur Rahman Chughtai
Abidin Dino
Abraham Begeyn
Abraham Bloemaert
Abraham Bosschaert
Abraham de Vries
Abraham Hondius
Abraham Mignon
Abraham Storck
Abraham van Beijeren
Abraham van Calraet
Abraham van den Tempel
Abraham Willaerts
Abram Arkhipov
Achille Leonardi
Ada Gladys Killins
Ada Hill Walker
Adam Bruce Thomson
Adam Chmielowski
Adam Dario Keel
Adam Elsheimer
Adam Manyoki
Adam Marczyński
Adam Paquette
Adam Pijnacker
Adam Rex
Adam Saks
Adam Szentpétery
Adam Willaerts
Adélaïde Labille-Guiard
Adélaïde Victoire Hall
Adolf Bierbrauer
Adolf Born
Adolf Dietrich
Adolf Fényes
Adolf Hirémy-Hirschl
Adolf Hölzel
Adolf Schrödter
Adolf Ulric Wertmüller
Adolf Wölfli
Adolfo Müller-Ury
Adolph Gottlieb
Adolph Menzel
Adolphe Willette
Adriaen Brouwer
Adriaen Coorte
Adriaen Hanneman
Adriaen Isenbrant
Adriaen van de Velde
Adriaen van de Venne
Adriaen van der Werff
Adriaen van Ostade
adrian ghenie
Adrian Zingg
Adrienn Henczné Deák
Aelbert Cuyp
Aert de Gelder
Aert van der Neer
Aertgen van Leyden
Afewerk Tekle
Affandi
Agnes Lawrence Pelton
Agnes Martin
Agnolo Bronzino
Agnolo Gaddi
Agostino Carracci
Aguri Uchida
Agustín Fernández
Ahmed Karahisari
Ahmed Yacoubi
Ai Weiwei
Ai Xuan
Ai-Mitsu
Aileen Eagleton
Aimé Barraud
Akihiko Yoshida
Akira Toriyama
Akseli Gallen-Kallela
Al Feldstein
Al Williamson
Aladár Körösfői-Kriesch
Alain Tasso
Alan Bean
Alan Davis
Alan Lee
Alan Pollack
Alan Sutherland
Alasdair Grant Taylor
Alasdair Gray
Albert Anker
albert aublet
Albert Bertelsen
Albert Bierstadt
Albert Dorne
Albert Dubois-Pillet
Albert Edelfelt
Albert Gleizes
Albert Guillaume
Albert Henry Krehbiel
Albert Irvin
Albert J. Welti
Albert Joseph Moore
Albert Joseph Pénot
Albert Keller
Albert Kotin
Albert Marquet
Albert Namatjira
Albert Nemethy
Albert Paris Gütersloh
Albert Swinden
Albert Welti
Alberto Burri
Alberto Giacometti
Alberto Morrocco
Alberto Seveso
Alberto Sughi
Alberto Vargas
Albin Egger-Lienz
Albrecht Altdorfer
Albrecht Dürer
Aldus Manutius
Alejandro Burdisio
Alejandro Obregón
Aleksander Gierymski
Aleksander Gine
Aleksander Kobzdej
Aleksander Kotsis
Aleksander Orłowski
Aleksandr Gerasimov
Aleksandr Ivanovich Laktionov
Aleksi Briclot
Alén Diviš
Alena Aenami
Alessandro Allori
Alessandro Galli Bibiena
Alesso Baldovinetti
Alex Grey
Alex Horley
Alex Horley-Orlandelli
Alex Katz
Alex Petruk APe
Alex Ross
Alex Toth
Alexander Archipenko
Alexander Bogen
Alexander Brook
Alexander Calder
Alexander Carse
Alexander Deyneka
Alexander Fedosav
Alexander Ivanov
Alexander Jansson
Alexander Johnston
Alexander Kanoldt
Alexander Kucharsky
Alexander Litovchenko
Alexander Mann
Alexander McQueen
Alexander Milne Calder
Alexander Nasmyth
Alexander Robertson
Alexander Rodchenko
Alexander Roslin
Alexander Runciman
Alexander Scott
Alexander Sharpe Ross
Alexander Stirling Calder
Alexander V. Kuprin
Alexandre Benois
Alexandre Cabanel
Alexandre Falguière
Alexandre-Évariste Fragonard
Alexei Kondratyevich Savrasov
Alexej von Jawlensky
Alexey Merinov
Alexey Venetsianov
Alexis Grimou
Alexis Simon Belle
Alfons Karpiński
Alfons von Czibulka
Alfons Walde
Alfonse Mucha
Alfred Charles Parker
Alfred East
Alfred Edward Chalon
Alfred Eisenstaedt
Alfred Freddy Krupa
Alfred Janes
Alfred Jensen
Alfred Krupa
Alfred Kubin
Alfred Leslie
Alfred Leyman
Alfred Manessier
Alfred Richard Gurrey
Alfred Sisley
Alfred Thompson Bricher
Alfred Wallis
Alfredo Volpi
Algernon Talmage
Alice Bailly
Alice Mason
Alice Neel
Alice Prin
Alice Rahon
Alison Debenham
Alison Geissler
Alison Kinnaird
Alison Watt
Allaert van Everdingen
Allan Brooks
Allan Linder
Allan Ramsay
Allen Butler Talcott
Allen Jones
Allen Tupper True
Alma Thomas
Almada Negreiros
Almeida Júnior
Alonso Vázquez
Aloysius O'Kelly
Alphonse Legros
Alphonse Mucha
Alson S. Clark
Altichiero
Alton Tobey
Altoon Sultan
Alvan Fisher
Amadeo de Souza Cardoso
Amalia Lindegren
Amanda Sage
amano
Ambreen Butt
Ambrose McCarthy Patterson
Ambrosius Benson
Ambrosius Bosschaert
Ambrosius Bosschaert II
Ambrosius Holbein
Amédée Ozenfant
Amedeo Modigliani
Amelia Peláez
Amelia Robertson Hill
Americo Makk
Amir Zand
Ammi Phillips
Amos Ferguson
Amos Sewell
amy sol
Amy Weber
An Gyeon
An Zhengwen
Anato Finnstark
Ancell Stronach
Anders Zorn
Andor Basch
André Bauchant
André Beauneveu
André Castro
André Charles Biéler
Andre de Krayewski
Andre Derain
André Derain
André François
André Kertész
André Lhote
André Masson
André Pijet
André Thomkins
Andrea del Sarto
Andrea del Verrocchio
Andrea Kowch
Andrea Mantegna
Andrea Orcagna
Andrea Pozzo
Andreas Achenbach
Andreas Gursky
Andreas Rocha
Andrée Ruellan
Andrei Kolkoutine
andrei riabovitchev
Andrei Rublev
Andrei Ryabushkin
Andrew Allan
Andrew Bell
Andrew Boog Faithfull
Andrew Domachowski
Andrew Ferez
Andrew Geddes
Andrew Henderson
Andrew Law
Andrew Loomis
Andrew Robertson
Andrew Robinson
Andrew Stevovich
Andrew Wyeth
Andrey Esionov
andrey ryabovichev
Andrey Yefimovich Martynov
Andries Both
Andries Stock
Android Jones
Andrzej Pronaszko
Andrzej Wróblewski
Andy Goldsworthy
Andy Warhol
Aneurin Jones
Ángel Botello
Angelica Kauffman
Aníbal Villacís
Aniello Falcone
Anish Kapoor
Anita Kunz
Anita Malfatti
Anka Zhuravleva
Ann Thetis Blacker
Anna Ancher
Anna and Elena Balbusso
Anna Boch
Anna Dittmann
Anna Findlay
Anna Füssli
Anna Haifisch
Anna Hotchkis
Anna Katharina Block
Anna Maria Barbara Abesch
Anna Mary Robertson Moses
Annabel Eyres
Annabel Kidston
Anne Dunn
Anne Geddes
Anne Nasmyth
Anne Redpath
Anne Rigney
Anne Ryan
Anne Said
Anne Savage
Anne Stokes
Anni Albers
Annibale Carracci
Annie Abernethie Pirie Quibell
Annie Leibovitz
Annie Rose Laing
Ansel Adams
Anselm Kiefer
Anson Maddocks
Antanas Sutkus
Anthony Angarola
Anthony Devas
Anthony Palumbo
Anthony S Waters
Anthony van Dyck
Antoine Blanchard
Antoine Ignace Melling
Antoine Le Nain
Antoine Verney-Carron
Antoine Wiertz
Anton Ažbe
Anton Fadeev
Anton Graff
Anton Lehmden
Anton Mauve
Anton Möller
Anton Otto Fischer
Anton Pieck
Anton Räderscheidt
Anton Solomoukha
Antonello da Messina
Antoni Brodowski
Antoni Pitxot
Antoni Tàpies
Antonie Palamedesz
Antonin Artaud
Antonín Chittussi
Antonín Slavíček
Antonio Canova
Antonio Cavallucci
Antonio Ciseri
Antonio de la Gandara
Antonio Donghi
Antonio Galli Bibiena
Antonio Mancini
Antônio Parreiras
Antonio Rotta
Antonio Saura
Apelles
Apollinary Vasnetsov
Apollonia Saintclair
Aquirax Uno
Arabella Rankin
Araceli Gilbert
Aramenta Dianthe Vail
Archibald Motley
Archibald Robertson
Archibald Skirving
Archibald Standish Hartrick
Arcimboldo
Arent Arentsz
Arie Smit
Arik Brauer
Aristide Maillol
Arkhip Kuindzhi
Arlington Nelson Lindenmuth
Armand Guillaumin
Armin Baumgarten
Armin Hansen
Arnie Swekel
Arnold Blanch
Arnold Böcklin
Arnold Bronckhorst
Arnold Brügger
Arnold Franz Brasz
Arnold Mesches
Arnold Newman
Arshile Gorky
Art & Language
Art Brenner
Art Fitzpatrick
Art Frahm
Art Green
Art of Brom
Art Spiegelman
Artemisia Gentileschi
Artgerm
Arthur Adams
Arthur B. Carles
Arthur Boyd
Arthur Burdett Frost
Arthur Dove
Arthur Garfield Dove
Arthur Hughes
Arthur Lismer
Arthur Melville
Arthur Merric Boyd
Arthur Pan
Arthur Quartley
Arthur Rackham
Arthur Sarkissian
Arthur Streeton
Arthur Webster Emerson
artist
Artur Grottger
Artur Tarnowski
Arturo Rivera
Arvid Nyholm
Asaf Hanuka
Asai Chū
Asger Jorn
Ásgrímur Jónsson
Asher Brown Durand
ashley wood
Atey Ghailan
Attila Meszlenyi
Aubrey Beardsley
Audrey Kawasaki
August Friedrich Schenck
August Lemmer
August Macke
August Querfurt
August Sander
Auguste Baud-Bovy
Auguste Herbin
Augustin Meinrad Bächtiger
Augustus Dunbier
Augustus Earle
Augustus John
Augustus Vincent Tack
Augustyn Mirys
Aurél Bernáth
Auseklis Ozols
Austin Briggs
Austin English
Austin Osman Spare
Avgust Černigoj
Avigdor Arikha
Awataguchi Takamitsu
Ay-O
Aya Goda
Ayako Rokkaku
Ayami Kojima
Ayshia Taşkın
Baiōken Eishun
Balázs Diószegi
Balcomb Greene
Bálint Kiss
Balthasar van der Ast
Balthus
Banksy
Bapu
Barbara Balmer
Barbara Greg
Barbara Longhi
Barbara Nasmyth
Barbara Nessim
Barclay Shaw
Barent Fabritius
Barkley Hendricks
Barnett Newman
Barron Storey
Barthel Bruyn the Elder
Barthel Bruyn the Younger
Barthélemy d'Eyck
Barthélemy Menn
Bartholomeus Breenbergh
Bartholomeus Strobel
Bartholomeus van Bassen
Bartholomeus van der Helst
Bartolomé Esteban Murillo
Bartolomeo Cesi
Bartolomeo Vivarini
Bascove
Basil Blackshaw
Bastien L. Deharme
Bastien Lecouffe-Deharme
Basuki Abdullah
Bauhaus
Bayard Wu
Beatrice Ethel Lithiby
Beatrice Huntington
Beatrix Potter
Beauford Delaney
Bedwyr Williams
Beeple
Beksinski
Béla Apáti Abkarovics
Bela Čikoš Sesija
Béla Czóbel
Béla Iványi-Grünwald
Béla Kondor
Béla Nagy Abodi
Béla Pállik
Ben Enwonwu
Ben Nicholson
Ben Shahn
Ben Stahl
Ben Templesmith
Ben Thompson
Ben Zoeller
Bencho Obreshkov
Béni Ferenczy
Benito Quinquela Martín
Benjamin Block
Benjamin Franklin
Benjamin Gerritsz Cuyp
Benjamin Marra
Benjamin West
Benjamin Williams Leader
Benoit B. Mandelbrot
Bernard Accama
Bernard Buffet
Bernard D’Andrea
Bernard Fleetwood-Walker
Bernard Meninsky
Bernard van Orley
Bernardino Mei
Bernardo Bellotto
Bernardo Cavallino
Bernardo Daddi
Bernardo Strozzi
Bernat Sanjuan
Bernd Fasching
Bernie D’Andrea
Bernie Wrightson
Bernt Tunold
Bert Hardy
Bert Stern
Bertalan Karlovszky
Bertalan Pór
Bertalan Székely
Berthe Morisot
Bertram Brooker
Bessie Wheeler
Beta Vukanović
Bettina Heinen-Ayech
Betty Churcher
Betty Merken
Betye Saar
Bholekar Srihari
Bhupen Khakhar
Bian Jingzhao
Bian Shoumin
Bikash Bhattacharjee
Bill Lewis
Bill Sienkiewicz
Bill Traylor
Bill Ward
Bill Watterson
Billie Waters
Billy Childish
Bjørn Wiinblad
Blanche Hoschedé Monet
Bob Byerley
Bob Eggleton
Bob Ringwood
Bob Ross
Bob Singer
Bob Thompson
Boetius Adamsz Bolswert
Bogi Fabian
Bohumil Kubista
Boleslaw Cybis
Bonnard Pierre
Boris Kustodiev
Boris Vallejo
Boris Vladimirski
Bouchta El Hayani
Bourgeois
Božidar Jakac
Bracha L. Ettinger
Brad Holland
Brad Kunkle
Bradley Walker Tomlin
Brassaï
Brenda Chamberlain
Brett Whiteley
Breyten Breytenbach
Brian 'Chippy' Dugan
Brian Alfred
Brian and Wendy Froud
Brian Bolland
Brian Despain
Brian Dunlop
Brian Fies
Brian Froud
Brian Snøddy
Brian Thomas
Briana Mora
Brice Marden
Bridget Bate Tichenor
Bridget Riley
Brigette Barrager
Brigid Derham
Brom
Brooke Shaden
Brothers Hildebrandt
Bruce Davidson
Bruce Gilden
Bruce McLean
Bruce Munro
Bruce Nauman
Bruce Onobrakpeya
Bruce Pennington
Bruce Timm
Bruno Liljefors
Bryan Organ
Buckminster Fuller
Bunny Yeager
Byeon Sang-byeok
Byron Galvez
Caesar Andrade Faini
Caesar van Everdingen
Cafer Bater
Cagnaccio di San Pietro
Cam Sykes
Camille Bombois
Camille Bouvagne
Camille Corot
Camille Pissarro
Camille Souter
Camille-Pierre Pambu Bodo
Camilo Egas
Camilo Mori
Canaletto
Candido Bido
Cándido López
Candido Portinari
Cao Buxing
Cao Zhibai
Caravaggio
Carel Fabritius
Carel Weight
Carel Willink
Carey Morris
Carl Arnold Gonzenbach
Carl Barks
Carl Critchlow
Carl Eugen Keel
Carl Eytel
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Ronald Davis
Ronald Rae
Ronnie Landfield
Rory McEwen
Rosa Bonheur
Rosalie Emslie
Rose Henriques
Rose Maynard Barton
Rose O’Neill
Rosemary Allan
Ross Bleckner
Ross Tran
rossdraws
Rowena Meeks Abdy
Roy Dalgarno
Roy De Maistre
Roy DeCarava
Roy Lichtenstein
Roy Newell
Roy Petley
Roz Chast
Ruan Jia
Rube Goldberg
Rubens Peale
Rudolf Ernst
Rudolf Freund
Rudolf Hausner
Rudolf Koller
Rudolf Schlichter
Rudolf von Alt
Rudolph Belarski
Rudolph F. Ingerle
Rudy Siswanto
Rufino Tamayo
Rumiko Takahashi
Rupert Bunny
Rupert Shephard
Russell Chatham
Russell Dongjun Lu
Russell Drysdale
Russell Patterson
Ruth Abrahams
Ruth Abrams
Ruth Brandt
Ruth Collet
Ruth Deckard
Ruth Hollingsworth
Ruth Jên
Ruth Orkin
Ruth Sanderson
Ruth Simpson
Ruud Janssen
Ryan Barger
Ryan Pancoast
Ryan Stegman
Ryan Yee
Ryohei Hase
Ryōhei Koiso
Ryoji Ikeda
Ryūkōsai Jokei
Ryūsei Kishida
Ryuzaburo Umehara
S J "Lamorna" Birch
Sadamichi Hirasawa
Sadao Watanabe
Sailor Moon
Saitō Kiyoshi
Sakai Hōitsu
Sally Haley
Salomon de Bray
Salomon Koninck
Salomon van Abbé
Salomon van Ruysdael
Salvador Dali
Salvador Dalí
Sam Black
Sam Bosma
Sam Charles
Sam Dillemans
Sam Francis
Sam Havadtoy
Sam Spratt
Samu Börtsök
Samuel Birmann
Samuel Colman
Samuel Dirksz van Hoogstraten
Samuel F. B. Morse
Samuel Hieronymus Grimm
Samuel Peploe
Samuel Prout
Samuel Scott
Samuel Shelley
Samuel Silva
Samuel Washington Weis
Sándor Bihari
Sándor Bortnyik
Sándor Brodszky
Sándor Liezen-Mayer
Sandra Chevrier
Sandro Botticelli
Saneatsu Mushanokōji
Sanford Robinson Gifford
Santiago Caruso
Santiago Martínez Delgado
Santiago Rusiñol
Sara Saftleven
Sarah Gough Adamson
Sarah Louisa Kilpack
Sarah Lucas
Sarah Morris
Sardar Sobha Singh
Sargent Johnson
Sarper Baran
Sasha Putrya
Satake Yoshiatsu
Satoshi Kon
Saul Steinberg
Saul Tepper
Saul Yaffie
Saurabh Jethani
Sava Šumanović
Sawa Sekkyō
Scarlett Hooft Graafland
Schelte a Bolswert
Scott Gustafson
Scott Listfield
Scott M. Fischer
Scott Naismith
Scott Samuel Summers
Sean Scully
Seb McKinnon
Sebastian Spreng
Sebastian Vrancx
Sebastiano Ricci
Sengai
senior artist
senior character artist
senior environment artist
Serafino De Tivoli
Serge Poliakoff
Serge Sudeikin
Sergei Sviatchenko
Sergio Burzi
Sergio Larraín
Serhii Vasylkivsky
Sesshū Tōyō
Seuss Dr
Shaddy Safadi
Shang Xi
Shani Rhys James
Shao Mi
Sheikh Hamdullah
Sheila McClean
Sheila Mullen
Sheilah Beckett
Shekhar Gurera
Shen Che-Tsai
Shen Quan
Shen Shichong
Shen Zhou
Sheng Mao
Sheng Maoye
Shi Rui
Shi Zhonggui
Shiba Kōkan
Shibata Zeshin
Shigeru Aoki
Shin Saimdang
Shin Yun-bok
Shingei
Shinji Aramaki
Shinoda Toko
Shirley Teed
Shitao
Shog Janit
Shōzaburō Watanabe
Shōzō Shimamoto
Shūbun Tenshō
Shukei Sesson
Shunbaisai Hokuei
Shunkōsai Hokushū
Sidney Nolan
Sidney Richard Percy
Sidney Simon
Siegfried Haas
Sigmar Polke
Sigmund Freudenberger
Sigrid Hjertén
Sigurd Swane
Silvestro Lega
Silvia Dimitrova
Silvia Pelissero
Sim Sa-jeong
Simon Bisley
Simon de Vlieger
Simon Gaon
Simon Marmion
Simon Stalenhag
Simon Stålenhag
Simon Ushakov
Simon Vouet
Simone Martini
Sin Wi
Siona Shimshi
Sir Alfred Munnings
Sir Jacob Epstein
Sir John Tenniel
Sir William Orpen
Sir William Russell Flint
Slava Raškaj
Slawomir Maniak
Slobodan Pejić
Sō Shiseki
Sōami
Sofonisba Anguissola
Sohrab Sepehri
Solomon Gessner
Soma Orlai Petrich
Song Maojin
Song Xu
Sonia Delaunay
Sonia Delaunay-Terk
Sophia Beale
Sophie Anderson
Sophie Gengembre Anderson
Sophie Pemberton
Sophie Taeuber-Arp
Sōtarō Yasui
sparth
Spencer Gore
Stan and Jan Berenstain
Stan Galli
Stan Stokes
Stanhope Forbes
Stanislas Lépine
Stanislav Zhukovsky
Stanisław Ignacy Witkiewicz
Stanisław Masłowski
Stanislaw Samostrzelnik
Stanisław Tondos
Stanisław Witkiewicz
Stanisław Wyspiański
stanley artgerm
Stanley Bahe
Stanley Matthew Mitruk
Stanley Spencer
Stanley Twardowicz
Stanton Macdonald-Wright
Stefan Gierowski
Stefan Lochner
Ștefan Luchian
Stella Schmolle
Stephan Martiniere
Stephan Martinière
stephen bliss
Stephen Bone
Stephen Gammell
Stephen Gilbert
Stephen Greene
Stephen Little
Stephen Pace
Stevan Dohanos
Steve Argyle
Steve Brodner
Steve Dillon
Steve Ditko
Steve Hanks
Steve McCurry
Steve Prescott
Steven Belledin
Steven Campbell
Steven James Petruccio
Stokely Webster
Storm Thorgerson
Stuart Davis
Studio Ghibli
Sudip Roy
Sugimura Jihei
Sun Junze
Sun Kehong
Sun Long
sung choi
Sunil Das
Susan Crile
Susan Heidi
Susan Weil
Susy Pilgrim Waters
Suzanne Duchamp-Crotti
Suzanne Valadon
Suzuki Harunobu
Suzy Rice
Sven Erixson
Sven Nordqvist
Svend Rasmussen Svendsen
Svetlin Velinov
Svetoslav Roerich
Syd Barrett
Syd Mead
Sydney Carline
Sydney Prior Hall
sylvain sarrailh
Sylvester Shchedrin
Sylvia Molloy
Sylvia Sleigh
Sylvia Snowden
Sylvia Wishart
Szymon Czechowicz
T. C. Steele
T. K. Padmini
T. S. Sullivant
Tadanori Yokoo
Tadao Ando
Tadashi Nakayama
Tadashige Ono
Taddeo Gaddi
Tadeusz Ajdukiewicz
Tadeusz Brzozowski
Tadeusz Dominik
Tadeusz Kantor
Tadeusz Makowski
Tadeusz Pruszkówski
Tahir Salahov
Taiyō Matsumoto
Takahashi Yuichi
Takashi Murakami
Takato Yamamoto
Takehisa Yumeji
Takeshi Obata
Takeuchi Seihō
Tamara de Lempicka
Tamara Lempicka
Tamas Galambos
Tan Ting-pho
Tanaka Isson
Tang Di
Tang Sin Yun Sandara
Tang Yifen
Tang Yin
Tani Bunchō
Tanomura Chikuden
Taravat Jalali Farahani
Taro Okamoto
Taro Yamamoto
Tarsila do Amaral
Tatiana Hordiienko
Tatsuyuki Tanaka
Tawaraya Sōtatsu
Ted DeGrazia
Ted Nasmith
Telemaco Signorini
Teobaldo Nina Mamani
Teresa Copnall
Teresa Fasolino
Terese Nielsen
Terrell James
Terry Marks
Terry Morris
Terry Oakes
Terry Redlin
Tetsugoro Yorozu
Tex Avery
the Brothers Hildebrandt
The Family Circus
The Mazeking
theCHAMBA
Theo Constanté
Theo van Doesburg
Theodor Philipsen
Théodore Chassériau
Theodore Earl Butler
Théodore Géricault
Theodore Major
Theodore Robinson
Théodore Rousseau
Théodule Ribot
Theophanes the Greek
Théophile Steinlen
Thierry Bisch
Thomas Aquinas Daly
Thomas Baines
Thomas Barker
Thomas Blackshear
Thomas Bock
Thomas Campbell
Thomas Cantrell Dugdale
Thomas Carr
Thomas Cole
Thomas Cornell
Thomas Corsan Morton
Thomas Couture
Thomas Crane
Thomas Dalziel
Thomas de Keyser
Thomas Dewing
Thomas Doughty
Thomas Eakins
Thomas Fogarty
Thomas Frederick Worrall
Thomas Furlong
Thomas Gainsborough
Thomas Gambier Parry
Thomas Häfner
Thomas Hart Benton
Thomas Hill
Thomas Kinkade
Thomas Kluge
Thomas Lawrence
Thomas M. Baxa
Thomas Mann Baynes
Thomas Millie Dow
Thomas Moran
Thomas Nast
Thomas Rowlandson
Thomas Scholes
Thomas Stothard
Thomas Struth
Thomas Stuart Burnett
Thomas Symington Halliday
Thomas Tudor
Thomas Wijck
Thornton Oakley
Thornton Willis
Thota Vaikuntham
Thyrza Anne Leyshon
Tibor Czorba
Tibor Rényi
Tilo Baumgärtel
Tim and Greg Hildebrandt
Tim Biskup
Tim Doyle
Tim Hildebrandt
Tim Okamura
Tim White
Tina Blondell
Tina Modotti
Tintoretto
Titian
Titian Peale
Titus Lunter
Tivadar Alconiere
Tivadar Csontváry Kosztka
Tobias Stimmer
Todd Lockwood
Tōichi Katō
Tom Bagshaw
Tom Bonson
Tom Carapic
Tom Chambers
Tom Gourdie
Tom La Padula
Tom Lovell
Tom Palin
Tom Phillips
Tom Roberts
Tom Scott RSA
Tom Thomson
Tom Wänerstrand
Tom Wesselmann
Tom Whalen
Tomàs Barceló
tomasz alen kopera
Tomasz Jedruszek
Tomek Setowski
Tomer Hanuka
Tomi Ungerer
Tomioka Tessai
Tommaso Dolabella
Tommaso Masaccio
Tommaso Redi
Tomokazu Matsuyama
Tony DiTerlizzi
Tony Sart
Tony Szczudlo
Tony Tuckson
Tooth Wu
Torii Kiyomasu
Torii Kiyomasu II
Torii Kiyomitsu
Torii Kiyomoto
Torii Kiyonaga
Torii Kiyonobu I
Toriyama Sekien
Toros Roslin
Tosa Mitsunobu
Tosa Mitsuoki
Tōshi Yoshida
Toshiko Okanoue
Tōshūsai Sharaku
Toss Woollaston
Totte Mannes
Tove Jansson
Toyen
Toyohara Chikanobu
Toyohara Kunichika
Tracey Emin
Tracey Moberly
Tracy Harris
Tran Nguyen
Trevor Brown
Troels Wörsel
Tsubasa Nakai
Tsuchida Bakusen
Tsuchiya Koitsu
Tsuguharu Foujita
Tsuji Kakō
Tsukioka Yoshitoshi
Tsuruko Yamazaki
Tuomas Korpi
Tuvia Beeri
Tyler Edlin
Tyler Jacobson
Tytus Czyżewski
Ueda Fumito
Uemura Shōen
Ugo Nespolo
Ulrich Leman
Ulrika Pasch
Umberto Boccioni
Un'ichi Hiratsuka
Unkoku Togan
Uragami Gyokudō
Urakusai Nagahide
Ursula Edgcumbe
Ursula Wood
Utagawa Hirokage
Utagawa Hiroshige II
Utagawa Kunimasa
Utagawa Kunimasu
Utagawa Kunisada
Utagawa Kunisada II
Utagawa Kunisada III
Utagawa Kuniyoshi
Utagawa Toyoharu
Utagawa Toyokuni
Utagawa Yoshiiku
Utagawa Yoshitaki
Utagawa Yoshitora
Utagawa Yoshitsuya
Uwe Wittwer
Václav Brožík
Vadym Meller
Valentin Aleksandrovich Serov
Valentine Hugo
Valéria Dénes
Valerie Petts
Van Gogh
Vanessa Beecroft
Vanessa Bell
Vasile Hutopila
Vasily Andreevich Tropinin
Vasily Perov
Vasily Polenov
Vasily Surikov
Vasily Vereshchagin
Vassily Maximov
Veikko Törmänen
Veno Pilon
Vermeer
Verónica Ruiz de Velasco
Vicente Juan Masip
Victo Ngai
Victor Adame Minguez
Victor Brauner
Victor Enrich
Víctor Manuel García Valdés
Victor Meirelles
Victor Moscoso
Victor Mosquera
Victor Nizovtsev
Victor Noble Rainbird
Victor Surbek
Victor Vasarely
Victor Wang
Victoria Francés
Victorine Foot
Vija Celmins
Viktor de Jeney
Viktor Madarász
Viktor Oliva
Viktor Vasnetsov
Vilhelm Bissen
Vilhelm Kyhn
Vilhelm Lundstrøm
Villard de Honnecourt
Vilmos Aba-Novák
Vincent Di Fate
Vincent Evans
Vincent Lefevre
Vincent Pepi
Vincent Proce
Vincent Van Gogh
Vincenzo Cabianca
Vincenzo Irolli
Vint Lawrence
Viola Paterson
Violet Fuller
Violet Oakley
Virgil Finlay
Virginia Lee Burton
Vito D'Ancona
Vittore Carpaccio
Vivian Maier
Vivien Blackett
Vladimir Baranov-Rossine
Vladimir Borovikovsky
Vladimir Kush
Vladimir Makovsky
Vladimir Novak
Vladimir Tatlin
Vladimir Tretchikoff
Vladimír Vašíček
Vlady Kibalchich Russakov
Vlaho Bukovac
Volkan Baga
Vytautas Kasiulis
W. Lindsay Cable
Wadim Kashin
Walasse Ting
Waldo Peirce
Walenty Wańkowicz
Wally Wood
Walt Disney
Walt Reed
Walter Bayes
Walter Beach Humphrey
Walter Crane
Walter Emerson Baum
Walter Haskell Hinton
Walter Humphrey
Walter Leighton Clark
Walter Osborne
Walter Percy Day
Walter Sickert
Walter Stuempfig
Walther Jervolino
Wang Duo
Wang E
Wang Fu
Wang Guxiang
Wang Hui
Wang Jian
Wang Lü
Wang Meng
Wang Mian
Wang Shimin
Wang Shishen
Wang Wei
Wang Wu
Wang Ximeng
Wang Yi
Wang Yuan
Wang Yuanqi
Wang Zhenpeng
Wang Zhongyu
Warhol
Warren Eugene Brandon
Warren Mahy
Warwick Goble
Washington Allston
Wassily Kandinsky
Watanabe Kazan
Watanabe Shōtei
Wayne Barlowe
Wayne England
Wayne Reynolds
Wayne Thiebaud
Weiwei
Wen Boren
Wen Jia
Wen Tong
Wen Zhengming
Wen Zhenheng
Wenceslas Hollar
Wendell Minor
Wendy Froud
Wenzel Lorenz Reiner
Werner Andermatt
Werner Gutzeit
Werner Tübke
Wes Anderson
Wes Wilson
Wesley Burt
Whitney Darrow
Whitney Sherman
Wifredo Lam
Wilfredo Lam
Wilhelm Bendz
Wilhelm Freddie
Wilhelm Hammershøi
Wilhelm Heise
Wilhelm Leibl
Wilhelm Marstrand
Wilhelm Sasnal
Wilhelm Schnarrenberger
Wilhelm Trübner
Wilhelmina Weber Furlong
Will Barnet
Will Eisner
Will Ellis
Willard Metcalf
Willard Mullin
Willem Claeszoon Heda
Willem Cornelisz Duyster
Willem de Kooning
Willem de Poorter
Willem Drost
Willem Hondius
Willem Jacobsz Delff
Willem Kalf
Willem Labeij
Willem Maris
Willem Pieterszoon Buytewech
Willem van Aelst
Willem van de Velde the Elder
Willem van de Velde the Younger
Willem van der Vliet
Willem van Haecht
Willem van Mieris
William Berra
William Birnie Rhind
William Blake
William Blake Richmond
William Bliss Baker
William Bonnar
William Brodie
William Coldstream
William Congdon
William Conor
William Crosbie
William Crozier
William Dargie
William Didier-Pouget
William Dobell
William Dobson
William Dring
William Edouard Scott
William Edward West
William Etty
William Fettes Douglas
William Forsyth
William Gear
William Geissler
William George Gillies
William Glackens
William Gouw Ferguson
William Grant Stevenson
William Gropper
William H. Mosby
William Harnett
William Hoare
William Hogarth
William Holman Hunt
William Holmes Sullivan
William Home Lizars
William Jacob Baer
William Jennys
William John Thomson
William Kentridge
William Langson Lathrop
William MacTaggart
William McGregor Paxton
William McTaggart
William Merritt Chase
William Michael Harnett
William Miller
William Morris
William Mossman
William Mustart Lockhart
William Nicholson
William Powhida
William Quiller Orchardson
William Simpson
William Steig
William Stott
William Stout
William Thon
William Trost Richards
William Turner
William Turner of Oxford
William Twigg-Smith
William Woodward
William Yellowlees
William York Macgregor
William Zorach
William-Adolphe Bouguereau
Willian Murai
Willie Ito
Willy Bo Richardson
Willy Finch
Wilson Irvine
Winona Nelson
Winslow Homer
Winsor McCay
Winston Churchill
Witold Pruszkówski
Witold Wojtkiewicz
Władysław Czachórski
Władysław Malecki
Władysław Podkowiński
Władysław Ślewiński
Wladyslaw Strzeminski
Wlastimil Hofman
Wlodzimierz Tetmajer
WLOP
Wojciech Gerson
Wojciech Korneli Stattler
Wojciech Kossak
Wojciech Siudmak
Wojciech Weiss
Wolf Huber
Wolf Kahn
Wolf Vostell
Wolfgang Letti
wolfgang lettl
Wolfgang Zelmer
Wouter Pietersz Crabeth
Wu Bin
Wu Changshuo
Wu Daozi
Wu Guanzhong
Wu Hong
Wu Li
Wu Shixian
Wu Wei
Wu Zhen
Wu Zuoren
Wuzhun Shifan
Wyke Bayliss
Wylie Beckert
Wyndham Lewis
Xanthus Russell Smith
Xavier Blum Pinto
Xi Gang
Xia Chang
Xia Gui
Xia Shuwen
Xia Yong
Xiang Shengmo
Xiao Yuncong
Xie He
Xie Huan
Xie Shichen
Xie Sun
Xu Beihong
Xu Wei
Xu Xi
Xuande Emperor
Xul Solar
Yaacov Agam
Yahoo Kusama
Yamagata Hiro
Yamaguchi Kayo
Yamamoto Shōun
Yan Hui
Yan Liben
Yanagawa Nobusada
Yanagawa Shigenobu
Yang Borun
Yang Buzhi
Yang J
Yang Jin
Yang Weizhen
Yanjun Cheng
Yao Tingmei
Yasar Vurdem
Yasuo Kuniyoshi
Yasushi Sugiyama
Yasutomo Oka
Yayoi Kusama
Yayou Kusama
Ye Xin
Yehia Dessouki
Yeong-Hao Han
Yerkaland
Yi Inmun
Yi Insang
Yi Jaegwan
Yi Yuanji
Yoann Lossel
yoji shinkawa
Yokoyama Taikan
Yosa Buson
Yoshida Hanbei
Yoshihiko Wada
Yoshio Markino
Yoshitaka Amano
Yoshitoshi ABe
Yoshitoshi Mori
Yoshiyuki Tomino
Youri Messen-Jaschin
Youssef Howayek
Yousuf Karsh
Yu Zhiding
Yuan Jiang
Yuan Yao
Yue Minjun
Yuki Ogura
Yukihiko Yasuda
Yukimasa Ida
Yuko Shimizu
Yuko Tatsushima
Yumihiko Amano
Yun Du-seo
Yun Shouping
Yun-Fei Ji
Yuri Ivanovich Pimenov
Yuumei
Yves Klein
Yves Tanguay
Yves Tanguy
Yvonne Jacquette
Zacharias Martin Aagaard
Zack Snyder
Zack Stella
Zaha Hadid
Zahari Zograf
Zao Wou-Ki
Zbigniew Nowosadzki
Zdeněk Burian
Zdzislaw Beksinski
zdzisław beksinski
Zdzisław Beksiński
Zeen Chin
Zelma Blakely
Zeng Jing
Zha Shibiao
Zhan Ziqian
Zhang Han
Zhang Kechun
Zhang Lu
Zhang Sengyao
Zhang Shengwen
Zhang Shunzi
Zhang Shuqi
Zhang Wo
Zhang Xiaogang
Zhang Xuan
Zhang Yan
Zhang Yin
Zhang Zeduan
Zhang Zongcang
Zhao Mengfu
Zhao Yong
Zhao Yuan
Zhao Zhiqian
Zhao Zuo
Zheng Xie
Zhichao Cai
Zhou Chen
Zhou Fang
Zhou Jichang
Zhou Shuxi
Zhou Wenjing
Zhu Da
Zhu Derun
Zinaida Serebriakova
Zlatyu Boyadzhiev
Zoë Mozert
Zofia Stryjenska
Zoltan Boros
Zoltán Joó
Zoltán Nuridsány
Zoran Mušič
Zou Yigui
Zou Zhe
Zsolt Bodoni
Zsuzsa Máthé
Zvest Apollonio
Zygmunt Vogel
Zygmunt Waliszewski
Þórarinn B. Þorláksson
================================================
FILE: interrogators/data/flavors.txt
================================================
highly detailed
sharp focus
intricate
digital painting
illustration
octane render
smooth
8 k
elegant
8k
cinematic
4k
fantasy
cinematic lighting
detailed
4 k
photorealistic
unreal engine
masterpiece
realistic
hd
dramatic lighting
volumetric lighting
high detail
beautiful
hyperrealistic
hyper detailed
portrait
high quality
hyper realistic
ultra realistic
unreal engine 5
matte painting
ultra detailed
award winning
matte
intricate details
epic
hdr
extremely detailed
very detailed
oil painting
dynamic lighting
hyperdetailed
oil on canvas
high resolution
sharp
studio lighting
depth of field
hyper realism
full body
colorful
dark
cyberpunk
octane
centered
high contrast
bokeh
global illumination
dramatic
symmetrical
d & d
intricate detail
atmospheric
d&d
golden ratio
wide angle
anime
golden hour
detailed face
rule of thirds
stunning
vibrant colors
soft lighting
ray tracing
photography
epic composition
radiant light
8k resolution
cinematic composition
horror
vibrant
black and white
close up
medium shot
ornate
photo realistic
photo
vivid colors
8 k resolution
trending on art station
futuristic
high details
high definition
volumetric light
wide shot
movie still
establishing shot
surreal
gothic
3 d render
unreal 5
photograph
sunset
painting
sci-fi
rim light
hearthstone
beautiful lighting
ambient lighting
sci - fi
moody
digital illustration
symmetrical face
epic lighting
fog
sakimichan
beautiful face
soft light
fine details
dark fantasy
gorgeous
deep focus
night
3 d
eerie
daz
shallow depth of field
3d
atmospheric lighting
symmetry
cute
studio quality
scifi
chiaroscuro
insanely detailed
white background
natural light
details
hard edges
character design
artistic
asymmetrical
rpg portrait
sunny day
ethereal
bright colors
raytracing
perfect face
god rays
psychedelic
35mm
dramatic light
dynamic pose
3 5 mm
steampunk
focus
extreme detail
full body shot
lifelike
organic painting
backlit
3d render
octane rendered
neon
foggy
uhd
4k resolution
cinematic shot
breathtaking
furaffinity
trending
rutkowski
muted colors
award - winning
scary
pretty face
super detailed
smooth render
vray
vivid
anime style
volumetric
blender
film grain
rendered in octane
creepy
headshot
film still
low angle
ominous
cinematic light
high detailed
final fantasy
dnd
bloom
ultra wide angle
subsurface scattering
smoke
cosplay
very coherent
stylized
ultra hd
unreal engine 5 rendered
face
blue eyes
aesthetic
lens flare
intricate linework
art
flowers
mist
reflections
long hair
wallpaper
vintage
focused
art station
insanely detailed and intricate
smiling
extremely high detail
landscape
profile picture
watercolor
hq
closeup
ultrarealistic
cartoon
muscular
dreamy
high fantasy
award winning photo
minimalist
4 k resolution
male
raw
award-winning
black background
manga
rain
poster
realistic shaded perfect face
professional photography
clouds
perfect composition
painterly
grainy
soft focus
iso 200
dslr
render
award winning photography
magical
abstract
1/160s
post-processing
stanley artgerm lau
clean
bright
realistic face
dystopian
detailed illustration
dynamic
shadows
intricate complexity
50mm
magic
no blur
vaporwave
moody lighting
extreme details
bold shapes
atmosphere
dark background
female
beautiful composition
bouguereau
glowing lights
cel shaded
painted
sense of awe
full of colour
symmetrical facial features
forest
macro
detailed painting
symmetrical balance
ultradetailed
glow
rpg
motion blur
full body portrait
pale skin
anime key visual
pastel colors
mysterious
synthwave
dark atmosphere
neo-gothic
realistic lighting
mystical
science fiction
cinematic atmosphere
stars
behance hd
dungeons and dragons
f/1.4
zbrush
fine detail
sci fi
colourful
anatomically correct
retro
highly detailed portrait
warm lighting
ultra realistic illustration
movie poster
unreal engine 5 highly rendered
cryengine
front view
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milo manara
film noirs
squashed berries
nick alm
cyberpunk futuristic neon
wet - on - wet technique
2021 award winning painting
black and white photography
mythology
one point perspective
carpet
sports photography
incredible quality
various poses
renderman
norman rockwel
brutal shapes
moody atmosphere
day
masterpiece!!
cohesive
extremely detailed woman
fantasy forest landscape
techno artwork
ukiyo - e
sword and sorcery
hasselblad nikon
imogen poots
gems
frank lloyd wright
rational painting
id magazine
earl nore
mcu
pond
illustration style
close-up shot
anaglyph effect
dynamic angle
steampunk style
clear shapes
barefoot
attack on titan
retro futuristic
hand drawn
cctv
highly rendered
thick lines
realistic military carrier
splash painting
fully frontal view
closeup fantasy with water magic
anatomically correct vulpine
lowbrow art
glowing skin
vivid color scheme
2d game art
eldritch horror
extreme close up
video game art
psychological
whole body
sony a7r iv
japanese pottery
realistic oil painting
very very very very detailed
third person
top down view
feminine ethereal
doomed
flower crown
cowboy hat
silver with gold trim
drunk
western comic style
greg hildebrandt and mark brooks
clear details
blue head
fluffy face
only with red
gray
y 2 k aesthetic
bright lighting
pulp art
fully detailed
strong contrast
high level texture render
red lighting
aperture f2.8
scenic
8k photography
darth vader
glistening
dramatic painting
fullshot
highly detailed attributes
8k highly detailed
by tom bagshaw
pixelated
dark cinematic
award winning illustration
godlike
bathed in light
'action lines '!!!
smiling softly
detailed picture
unnerving
handsome face
volumetric shadows and lighting
terrified
artstaion
game concept art
cloth
mystical night
sargent
centered composition
greeble
j.dickenson
luis ricardo falero
photo journalism
michael jackson
no makeup
color image
hibbary
hyperspace
24mm f/1.4
refracted lighting
grim - lighting
skeuomorphic
mamiya
in a scenic background
rutkowski |
octane render 4 k
ritual
background
stone
gloomy and foggy atmosphere
controversial poster art
ghibli studio
ghibli style
leather jacket
pinup
no beard
short beard
shot on 7 0 mm
cold colors
oscar winner
highly detailed faces
dramatic sky
flat design
elf
massive
undercut hairstyle
perfect hands
symmetrical face and body
komorebi
highly detailed atmosphere
luxury advertisement
ayahuasca
glitch effects
ornate headdress
sharp focus!
orthodox saint
high definiton
f. 1 4
yellow hue
1 2 0 0 dpi scan
evangelion
in color
imax 7 0 mm footage
3 d sculpture
cgsociety 4k
shaman
visor
sharply focused
symmetrical portrait
crispy quality
in the rain
dramatic studio lighting
distant mountains
4 k digital camera
mucha style
color graded
curly
hyperrealism 8k
cryengine 8k uhd
yuji himukai
books
black ink on white paper
gigantic
hat
studio lights
loneliness
occult art
ultrasharp focus
sofubi
big trees
beside the river
gta : dubai
iso 100
dense fog
intricate art nouveau
ultra-detail
70mm
computers
fantasy matte painting
pixel
hyperrealistic painting
high contrast colors
disgusting
pixel sorting
evil smile
patriotic
national geographic photoshoot
sculpted in zbrush
soft smile
greg rutknowski
enchanted and magic forest
8 k frostbite 3 engine
elaborate costume
neo vaporwave
v
8 4 mm
huge scale
military
metal gear solid
brushstrokes
medieval fantasy
twitter
creepy atmosphere
wires everywhere
painted portrait
fantasy armor
serious expression
impasto
neoclassic
volumetric light clouds
intense emotion
intricate artwork masterpiece
horizontal symmetry
dark natasha
incredible wide screenshot
long wavy blond hair
small nose with freckles
jen zee
4k”
soft bokeh
simplistic
stunning skies
food
hands
magazine cover
oil painting on canvas
historic artworks society
wholesome
dungeons and dragons portrait
outer space
elon musk
promotional photo
ripped flesh
hyperrealistic oil painting
solemn
render in unreal engine 5
art germ
iron smelting pits
3d primitives
ultra - hd
vignetting
soft hair
blue tint expired film
highly detailed closeup
action photography
horizon zero dawn
hd photography
intelligent
logo design
flcl
light leak
dim light
beautiful photo
detailed perfect face
statue
carl gustav carus
steam punk
astounding stories
arcade
techno
black hole
3d with depth of field
modelsociety
yan gisuka
modern warfare
award winning picture
digital photo
extra details
no noise
rich cinematic atmosphere
glamour shot
3d finalrender
bob larkin and tomer hanuka
loving
androgynous
film lighting
hdr color
masterpiece!!!
victo ngai! muted colors
northern renaissance art
three quarter view
outdoor fine art photography
scene
intricate high details
zombie
stunning inking lines
fine lines
cartoonish
earrings
intimate
romanian heritage
daniel gerhartz
noble
substance 3d painter
baroque style
glowing red eyes
cozy wallpaper
deep
opulent
temple
deep space
4k hdr
anger
outside
h r giger
white horns
fantasy dark art
gouache on canvas
centerd
beautiful dark landscape
iridiscense
cyberpunk concept art
1 8 6 9
cottagecore!!
fibonacci rhythm
bjork aesthetic
movie concept art
semi - realism
satoshi kon anime
meat
mysterious atmosphere
rubber suit
cinematic portrait
3d rendering
branches
advertising photography
exoskeleton
particle effects
vintage fantasy art
geometric curves
wrinkles
captivating
3 d rendering
drips
mountains in the background
shiny crisp finish
angels
depth field
awe-inspiring
beautiful digital painting
candle light
esoteric
sesame street
ashford black marble
colorful watercolor
detailed facial expression
very stylish
female portrait
tankobon
manga illustration
semirealism
reflective detailed textures
from china
breath of the wild art style
gradient yellow
2 k
reclining
crisp and sharp
art supplies
1 / 1 2 5
lifelike texture
tekkon kinreet
epic stunning
award-winning photography
volume light
lovecraftian horror
alex gray
rtx reflections
bust
hot petite
samurai
indoors
insaneley detailed
colour
comic book cover
ornate frilly dress
on fire
gustav klimt 4k
benjamin lacombe
low angle fish eye lens
hero
middle shot
dieselpunk atmosphere
tennis ball
waterhouse
1 5 0 mp
trevor brown style
desk
mysticism
no filter
coral
superhero
cinema4d
glowing aura
braided hair
2 0 2 2
lumnious
alphonse mucha background
rainy night
michelin star
bright clouds
aged paper
yellow mist
octane render in maya
expressing joy
arrogant
sun in the background
greek god
psytrance artwork
dark eyeliner
high depth
kentaro miura manga art style
mike mignogna
beak
8k”
photoshopped
iamag
portrait!!
battle angel alita
physically accurate
bleak
underexposed
greig fraser
nier automata
gaming
small nose
orchid
maya render
fruits
intense stare
real world
40nm lens
fantasy character concept
fantasycore
stranger things
ghosts
humanoid robot
style of studio ghibli
deco fashion
style artgerm
polish poster art
electric breeze
cinematographic shot
luminescence
daguerreotype
slime
historical record
aliens
gta v
textures
piercing gaze
fantasy 4k
fantasy character
open mouth
hiperrealistic
smooth lines
luminism
dark tones
accurate face
timestamp
beautiful with eerie vibes
full body character concept art
lascivious pose
lush landscape
beautiful face!!!!
tape deck
destiny fanart
substance
portrait of a digital shaman
apocalypse landscape
worms eye view
vertical
music
guitar
octane engine
bird's eye view
game asset
8 k dop dof hdr
epic painting
shell
smirk
3d art
provocative
hair
80mm
jewellery
angelic photograph
sorcerer
ominous sky
award winning digital art
yoko taro
d&d concept art
purple hair
day time
figure photography
very high detail
out of focus
glitter accents on figure
looking down
john park
flourescent colors
low angle uhd 8 k
mirror dripping droplet!
bird
dynamic background
4 k photorealism
very detailed oil painting
postapocalyptic
botticelli
cat ears
basil wolverton
low key
traditional corsican
hot
artstation hd
mad max
landscape photography
grey hair
dynamic shadows
shot with sigma f / 4. 2
art nouveau aesthetic
skin texture
creepy lighting
50 mm
rule of third
detailed concept art
full figure
sharp eyes
shinjuku
head and shoulders masterpiece
wearing headphones
aura of light
upper half portrait
goddess art
tom whalen 8 k
industrial factory
full head and shoulders
llama with dreadlocks
natalie shau
burning halo
[ 4 k photorealism ]!!
delicious
flares
35mm macro lens
red and white
transcendent
emil melmoth
harper's bazaar
view from behind
duotone
1960s
volumetrics
mosaic
slimy
clockwork
beautiful face and flawless skin
incredible lighting
watermark
white marble
bioluminescent skin
overhead wires
tintype photo
mandala
partly cloudy
the end of an organism
8 k 3 d
gradient white to gold
pink twintail hair and cyan eyes
cuirass
biomechanical cyborg
portrait of a medieval old king
splash
depressing
geometrical drawings
britt marling style 3/4
hyper realistic photography
darkwave
betta fish
strong bokeh
blue lighting
simplified
dark forest background
space station
cumulus clouds
polaroid photograph
post-apocalyptic
acrylic art
dark forest
neoclassical
alphonso mucha
very very elegant
military uniform
dieter rams
robotic enhancements
anatomically accurate
pig
3 d high definition
midsommar
8 5 mm lens
ground-level view
first light
award winning details
ron english and eiichiro oda
energetic composition
fisheye!!! lens
soft features
blond hair
detailed hair
thick neon lights
face close up
scary atmosphere
glistening skin
victorian era
2 5 0 mm sharp lens
smirking
upscaled
denis sarazhin
sprite
stylized portrait
transparent background
allen williams
practical effects
rusty
full height
godsend
coronarender
sepia tone
bronze
crisp details
digital world
playstation 5 screenshot
acrylic palette knife
moebius starwatcher comic
black clothes
fransico goya
colored in
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bronze age
layout frame
geometric ornament
bridges
huge
35mm photography
pov
steel
faded colors
washy brush
ultrahd
natural sunlight
dark brown skin
an aesthetically pleasing
large entirely-black eyes
bangkuart
old black and white photo
space dandy style
dangerous
long braided hair
marble and gold
black and white illustration
back lit
joshua middleton comic art
kodak color film stock
sensual gloomy style
masterful
creative commons attribution
fashion design
dim dusk lighting
blue cyborg eyes
red and orange glow
showing off his muscles
wearing 1 8 5 0 s era clothes
smooth eric zener cam de leon
tintype
blade runner artifacts
cinematic feel
lush plants and flowers
detailed digital painting
full body art
color theory
snakes
animation pixar style
3 point lighting
f/22
y 2 k
liepke
f1.8
(eos 5ds r
sharp features
sharpened
curves
hd mixed media
clear skin
4k realistic photo
analogue photo quality
religious art
flat lighting
food pov
wrapped thermal background
simon roy
drark
1 6 6 7
octane highly render
dark eyes
perfect factions
highly detailed body
fantastical
distance
tasteful
gundam
bokeh effect
biomechanical details
detailed shading
eyes are glowing red lightbulbs
very funny
horroristic shadows
in the forest
by greg rutkowski
fountain
adobe illustrator
as trending on artstation
layers
pale beige sky
pencil marks
petals
dan mumford. concept art
canon eos
rich deep colours
goldenwolf
looming
saul goodman
magic circle
david a trampier
dystopia core
katsuhiro
bold line art
colorfully
mara demon
highly detailed exquisite fanart
cyber punk
car photography
nc wyeth
lanterns
fluffy clouds
instax
very colorful
traditional
barlowe
4k digital art
boots
floral
straight lines
3d octane render
comic book illustration
gsociety
scales
golden eyes
occlusion
straight hair
dutch golden age
(steampunk)
3d collage
night lights
accurate and detailed
mandelbot fractal
bold natural colors
fog volumes
belsinski
moonlight showing injuries
colorful hair
highly detailed 4 k art
craig mullins 8 k
canon 3 0 0 mm
1 9 9 0
gustave dore and marco mazzoni
candle
giantess shot
photo taken of an epic intricate
takashi takeuchi
tonemapping
flirty
village
magic details
wide
vivid hues
1950s
antique renewel
level design
beer
photo manipulation
gta v cover art
extra detailed
harsh lighting
hyperrealistic render
1920s
anthropomorphized
colossal
light bloom
atmospheric light
warhammer 40000
mermaid
white frame
long - shot
awesome exposition
brilliantly coloured
wiwek
fps
art history 8 k
smooth fuschia skin
kan liu
detailed cartoon
sofurry
organic cyborg
award winning render
tired
outer worlds
tiltshift
americana
high - quality
flying cars
pastels
zoom
rubble
extremely realistic
queen
cyber neon lighting
medium shot close up
hyper - realism
gradient filter
tendrils
opalescent
acid
promo shoot
gracious
a beautiful
cybernetic scifi
messy hair
gold eyes
spray paint
fallout
iridescent beetles
heaven
cloudy background
medium long shot
noir art house
valentina remenar
dark moody lighting
gold jewelry
moody cinematography
real camera
thin waist
long loose red hair
intricate fantasy painting
autumn lights
devianart craze
realism | beeple
imogen poots as a paladin
disney weta portrait
upward shot
elegant art
stormy
grey skin
alexandre chaudret
beautiful fine face features
horizon
1 6 mm
extreme detail skin
beautiful brush strokes
futuristic design
warm color palette
recursion
lamps
summer day
drew struzan illustration art
in legnica
bright lights
wearing a suit
mid day
chris tulloch mccabe
fine art photography
alex grey and beksinski
junk tv
vivienne westwood
siwoo kim
coherent design
crisp 8 k line art
3 color screen print
maxim cover
perfectly defined features
museum diffuse lighting
john stephens
stan winston studios
arm reaching out of thick fog
ceremonial clouds
magic doorway
destiny art
spinning hands and feet
vore
rossetti bouguereau
female dwarven noblewoman
detailed armor
retrofuturistic
quantum wavetracing
tarot cards
smoky
precise
color photography
standing pose
medieval setting
sea of souls
thin porcelain
documentary
hd photo
highly detailed features
battle scene
animatronic
end of the world
anime arts
italian futurism
85mm nd 5
spines
production quality cinema model
global illumination lighting
stars in the sky
outsider art
v - ray
masterful composition!!!
pork
large entirely - black eyes
fake detail
colored gels
in style of zootopia
pixar portrait 8 k photo
reflective surfaces
serene landscape
detailed photography
f 2. 8
hyper-realism
illustration painting
demon
hyper realistic 4k
diffuse light
white shirt
sharp lens
iron man
balanced composition
flowerpunk
ray tracing reflection
matt painting
city landscape
radiant skin
full character
lion
ultradetail face
fame of thrones
maya ali
micro pov
fantastic reality
realistic military gear
stunningly beautiful
tokujin yoshioka
ultra - wide angle
magical composition
flower face
machine parts embedded into face
vray smooth
tokyo
smoking a cigarette
3-point perspective
twisted
items
catholicpunk
illusion surreal art
heavy metal
sharp lighting
vintage photograph
dramatic composition
illumination
night lighting
fashion magazine
biblical
bust portrait
postcyberpunk
beautiful sunset
high detail concept art
videogame
epic angle and pose
no hands visible
candid photography
sharp lineart
fantasy digital painting
extremely coherent
subtle shadows
children illustration
line brush
unreal engine 4 k
kodak film stock
still frame
worth1000.com
black leather jacket
clean composition
shapes
design on a white background
game assets
exotic
thin
isometric art
raven
high-tech
ultra fine detail
beams of light
high resolution photograph
art gallery
greek pool
sly expression
sigma 85mm
cliffs
sand dunes
critical role
aesthetic!
85mm nd 4
shot in canon
in style of midjourney
mysterious vibes
painted warhammer miniature
borderlands
vladimir putin
golden hour lighting
prophet graphic novel
luxury materials
solid shapes
wallpaper - 1 0 2 4
of a beautiful female warframe
colours
- n 9
very ethereal
tornado
cape
b & w
isometric perspective
inspiration
bionic
typography
poltergeist
soft lighting 8k resolution
barbarian
bottom view
8k concept art
lots of details
lush plants
large motifs
night scene
boris vallejo)))
clear facial features
high detail of the face
soft rim light
big
set in half-life 2
crisp lines
fire magic
kimono
intricate fashion clothing
elegant trend
apocalypse now
qi sheng luo
background hyper detailed
sharp spines
ground view shot
loreta lux
matrix
printerest
far shot
miro petrov
epic fantasy card game art
anthropomorphic shiba inu
ureal engine
broken
hibiscus flowers
photorealistic camera shot
material
short brown hair
trending on twitter
tesseract
supernova
deiv calviz
4k photograph
rob rey and kentarõ miura style
electrical case display
space art concept
3d rim light
wlop and rossdraws
gta 5 cover art
skyscrapers
datamosh
paolo eleuteri serpieri
vivid colour
90s
manga and anime 2000
award wining
dramatic hair color
malachite
photorealistic rendering
necromancer
optimistic
psycho pass
pentax
green and pink
environmental light
ornamentation
high face symmetry
an american romanticism painting
ultra sharp focus
very very beautiful furry art
intricate lines
colourised
atlantean warrior
85mm f1.8
architectural
inviting
high detail texture
far away
distorted
ephemeral
3 5 mm photo
three point lighting
black skin
symmetrical body
4k detailed
mingchen shen
laser
hi res
orthographic
bright blue eyes
accurate facial details
code
middle aged
adriana lima
in the woods
very very intricate
colorful lights
neon glow
bastion game
octane render. by makoto shinkai
bioluminiscent creatures
color blocking
keyshot render
clean design
hyper maximalist
warm azure tones
photography of kurzgesagt
3 d scene
craigslist photo
dlsr photography
rich texture
bernie fuchs
maria panfilova
andrea savchenko
mike kime
ludovic plouffe
oliver cook
atmospheric cool colorgrade
animated film
portra 400
water reflection
lumen reflections
alien planet
dead trees
magazine collage
1 9 9 0 s
detailed photograph
assets
dark enlightenment
shootout
sandstorm
imax 7 0 mm
morandi color scheme
galactic
warm coloured
large
foliage
sony
detailed skin
hd 4k
soft diffused light
pointy ears
casting a spell
vray tracing
1980
dull colors
his whole head fits in the frame
witcher 3
surrealist painting
global lighting
field
chris dyer
smooth lighting
official fanart
stopped in time
intricate abstract
feature
midnight hour
procreate
full body and head
corals
mutant
beauty portrait
luiz escanuela
ancient warrior
realistic water splashes
full body concept
greg kutkowski
rb 6 s
dynamic perspective pose
alex kanevsky
stunning 3 d render
real flesh texture
white lashes
uncut
butterfly squid
ultra mega super hyper realistic
vertical portrait
sapphire
brain
tiara
skin tinted a warm tone
meme
propaganda poster
paradise
brunette
high detailed face
villain
planet
ultra - realistic and detailed
azure blue watery eyes
anime masterpiece
full portrait
afternoon
green plants
brilliant
mouth slightly open
ultrarealism
windows
detailed face and eyes
hyper sharp
hyperdetalied
amazing lighting
4k octane render
ink on paper
singularity
lighthearted
mega detailed
professional artist
full of detail
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glossy reflections
magic hour
hyper realistic lighting
surgical supplies
mucha : :
imogen poots paladin
cyber armor
incredibly lifelike attributes
indigo and venetian red
artsation
airbrushed
8 mm
digital 2d
painterly style
night city
city lights
in a white room
concept matte
key lighting
intricate digital art
gears of war
fluorescent
tree of life
dystopian mood
fit
astronaut
breathtaking detailed
lush trees
hellish
central composition
photorealistic painting
very dark
nick knight
medieval portrait
bright tones
very sharp focus
unstirred paint
adventurous
dark scene
ultra realistic vfx
craig mulins
pepe the frog
comedy
fantasy world
wisdom
ultra - detail
kodak gold 2 0 0
no face
high delicate defined details
one single tribe member
donoto giancola
full figure poster
scenery wallpaper aesthetic
white biomechanical details
in style of pan ren wei
portrait photo of a backdrop
cyberpunk futuristic
on alien planet
smoking a cigar
oled
cosplay photo
ektachrome
lo - fi
sideview
vector line art
high depth of field
halloween
ancient greece
rpg character
getty images
retro technology
janis rozentals
recursive
sharp focus ilustration
realistic faces
very long shot
grandiose
john j. park
realistic style
masculine figure
high technology
computer
drama
ana de armas
clay
detailed surroundings
gigachad
heavy film grain
wild vegetation
sci - fi art
laurie greasly
high - resolution
gina heyer
clean elegant painting
hyper detailed masterpiece
irridescent
colorful swirly ripples
miro
full body character portrait
still from a ridley scott movie
cinematic forest lighting
an ultra realistic
game asset sheet
mandelbulb 3 d
soft illumination
impossible
wearing a hoodie
venetian red
fisheye!!!!! lens
porcelain
car
full - hd
new york city
microscopic
highly detailed animal monster
lightrays
demons
4 d
basil gogos
4 k photo
swords
8 k photography
eugene grasset
highly details
8k photo
runes
paint texture
realistic cinematic lighting
wide-angle lens
hiroaki tsutsumi style
beautiful matte painting
high detail photo
cel-shaded
noon
seen from above
cables everywhere
profile portrait
felt
detailed facial expressions
avenue
hdr colors
volumetric wool felting
portrait dnd
one single mask
guyver
acanthus scrolls
neil richards
digital art”
scott m fischer
brass
vibrant iridescent colors
golden hair
contrasting
dichromatism
1 9 5 0 s
tv
anamorphic bokeh
heavy contrast
grainy image
moon rays
glamorous
macro photo
rocky
seifuku
desert background
feminine figure
photorealistic face
artillery
disco elysium
cctv footage
4k image
red and black color palette
jrpg
half skull face
leeloo
glare
fight
couch
rendered in arnold
stage
nier:automata
smog
serenity
megacity
uhd image
fineart
karst landscape
post - apocalyptic
8 k wallpaper
david rubin
semi - realistic anime
metallic armor
strong ambient occlusion
3 d cinematic lighting
maxim magazine cover
goddess of illusion
low spacial lighting : :
ufo
dark fractal background
isometric 3 d
felix kelly
hilarious
battle armor
high octane render
high speed
muted color scheme
photorealistic photography
urban
liminal spaces
taken with polaroid kodak portra
jeremy lipking
pool
volumetric lightening
modular
flemish baroque
insanely integrate
art nouveau style
pleated miniskirt
avatar
close-up portrait
epic art
anime background
ripped
plant
bright light
hdr 8k
ultra wide lens shot
realistic colors
foggy sky
zdzislaw beksisnski
radiant halo of light
dark blue sky
five fingers
oil canvas
pale blue armor
paranormal
character art portrait
synthwave neon retro
mystical shadows
erak note
rule of thirds golden ratio
2 d anime
adrian borda
to hell with you
legends of runeterra
sharp focc
jim warren
rich
at dawn
gurney
paradox
hdr shot
high budget
fantasy art style
of a family standing in a park
body parts
illusion
wallpaper 4k
church
8k ultra realistic
view from front
animals
detailled
bioshock
graphic
extremely beautiful
youthful
house
high quality art
8k detail post-processing
alfons mucha
gouache painting
highly intricate details
professional studio lighting
misty forest
photorealistic 8k
pomegranade
repin
breathtaking stars
luminous lighting
emissive lighting
8 5 mm f / 1. 4
extremely high contrast!
soft vintage glow
thomas kinkade cfg _ scale 8
pupils visible
intense shadows
explosion of data fragments
hyper realistic texture
life and death mixing together
comics
infinity pool
long blue hair
translucent white skin
j. dickenson
radiant
suit
style digital painting
riot entertainment
origami
ashes
very very very beautiful
rough
beautiful aesthetic
clear faces
trending on patreon
colorful flowers
diagram
minecraft
busy
tilted frame
table
cluttered
vibrantly lush neon lighting
master prime lenses
towers
concept art illustration
kimi no na wa
award winning oil painting
genius party
footage from space camera
bees
shrek
dishonored
plumbus
intricate and highly detailed
dark fantasy style art
crimson
scenic full shot
f / 2 2
symmetry!!!
blue moon
stunning sunny lighting
hard backlight
wide wide angle
matt wisniewski style
highly detailed matte painting
sitting
unzoom
studio quality lighting
cars
arcane style
photobash
flash
living room
photorealistic concept art
3 5 mm photograph
3 d animation
on a white background
starfish
3d fractals
supernatural
ellen jewett
london
round nose
dynamic action
soft dramatic lighting
long blond hair
blue-eyed
diablo
legendary
irving penn
dr seuss
portrait shot
direct lighting
fantasy portrait
huge anime eyes
dynamic range
cats
outline
environment art
shy
a master piece of storytelling
fairy
editorial photo
futuristic look
feminine face
silver armor
foggy background
thin lines
cyberface
ommatidia
unreal engine rendered
floral tattoos
extremely detailed shading
fantasy style art
ugly!!!!!!
kehinde wiley
trending on weasyl
gorgeous faces
merging with tree in a forest
ghost of kiev
old english
glowing lights intricate
19th century
blade runner style
nightscape
clay sculpture
detailed clothing
machine
medusa
pop
vibrant deep colors
photorealistic art
big nose
sfumato
fashion model
proper shading
epic scale ultrawide angle
grid
led lights
ecstatic
at sunset
hellscape
young man
powerfull
furry fandom
incredible details
from star trek 2021
super details
awesome pose
dots
canvas print
jon lothian
magical aura
hyperrealistic fantasy art
reflexions
studio dramatic light
mugshot!!!!!
realistic face details
awful
ethereal lights
jeremy mann painting
walter everett
lorica segmentata
turquoise gold details
8k 3d realistic
fine art portrait photography
movie action still frame
sci-fi concept art
1 4 5 0
f 3 2
the last supper
vapor wave
1 9 th century
stormy sky
epic digital art
light and shadow effects
paris
hood
very wide shot
houses
barometric projection
summer evening
sweet
hazy atmosphere
photo portrait
triumphant
wise
eternity
thorns
hope
agostino arrivabene
body
chairs
beautifully detailed
points
coral reef
with gradients
y2k aesthetic
spotlights
danilo torres
adi meyers
thomas reimann
hearthstone style
oriental
french comic art
morning light showing injuries
4 k photograph
nikon 3 5 mm
dad energy
pen - and - ink illustration
raymarching
purple sun
beautiful faces
in dark cave
extreme far view
railgun
markings on his face
octane render ] ”
princess
drone shot
funko pop
shepard fairey
david choe
law contrasts
very cute
weapons
collage art
shading
rob rey
loose brush strokes
los angeles
rendered in vray
dust in the air
hi - res
cinematografic
wedding dress
close up photo
album cover art
gustave dore lithography
wearing a t-shirt
thick eyebrows
green grass
eyelashes
ray tracing global illumination
ink illustration
metroid
ultra quality
young adult
james mcdermott
russia
diaphanous cloth
first person view
dark blue
wolf
borderlands style
eagle
detailed terrain
mechanical parts
wlup
buddhism
detailed detailed detailed
black swans
brown and cyan color scheme
very hyper realistic
on high-quality paper
8 k post - production
incrinate
dark teal lighting
psycho stupid fuck it insane
activision blizzard style
rosetti
podium
life magazine photo
renaissance nimbus overhead
glowing pink face
chie yoshii
black and white drawing
cory chase
detailed jewelry
grainy photo
on a desert road
pan ren wei
high energy
iridescence
freak show
documentary photography
realistic hair
red mouth
atlantis
elder scrolls
the matrix
microchips
black and red
romanian
brawl stars
d&d character art
9 0 s
crowded
sad expression
closeup shot
depressed
scarlett johansson
multiverse
very high resolution
war photography
accurately portrayed
wavy black hair
environmental portrait
religion
megan fox
arthouse. y greg rutkowski
asymmetry
horse
epic landscape
spikes
flesh texture
parts
single head
neurons
studio ghibli film
d&d!
serov
surikov
jung gi kim
stuning 3 d render
katayama bokuyo
berries dripping
on the surface of mars
2 d sprite
visible paint texture
he is about 3 0 years old
baroque object
elize theron
black gold
funk
( eos 5 ds r
artem demura and alphonse mucha
historical photo
intricate jewelry
from the distance
shoujo manga
glowing hair
2 0 0 1 anime
form
mature
real photography
digital 2 d
gross
photorealistic eyes
unreal engine 4
carved soap
unrealengine
octane 3d render
150mm
awe
neon genesis evangelion
blue skies
dunwall city
ilustration
well rendered
geoff darrow
nausicaa
style of greg rutkowski
2d art
human face
happiness
moody dynamic lighting
fast
long wavy hair
cyan
image on the store website
joshua middleton comic cover art
realistic anime
heroine
highly detailed sculpture
black tank top
reflective lighting
artgerm and j. dickenson
cyber neon lights
moment
anime frames
[ cinematic
mix of styles
james gurney and greg rutkowski
rule of third!!!!
style of john berkey
anubis-reptilian
void
groovy
jean baptiste monge
intricate face
asao urata
star
hd photograph
gucci
simple design
grey eyes
mandelbulb
faberge
lossless
grin
people walking
85mm lens
professionally post-processed
hall of mirrors
james cameron
beeple and james jean
immaculate scale
silver haired
limited palette
photorealistic image
rich pastel color palette
uncluttered
bling
vast
15mm
clear skies
fighting pose
glitches
favorite
full-shot
gorilla
5 fingers
breaking bad
godzilla
muscular body
dotart
determined expression
adolfo hohenstein
god of thunder
victoria's secret
happy mood
morning hour
spinning records
default pose neutral expression
intricate and intense oil paint
high resolution render
highlights
high détail
slimey
8 k uhd artwork
on the ocean water
crisp hd image
wrecked buildings
destroyed flipped wrecked cars
fine face
flat shading mucha
aesthetic lighting
henry asencio
danny fox and hilma af klint
chemically propelled
intricate filigree
neutral expression
epic battle
stylized portrait h 704
illustrator
astral
beautiful background
1 8 8 9
silly
blue mist
artistic photography
profile posing
2 d illustration
edgar maxence
cgisociety
portrait painting
tech
uhd 8k
italian
quiet
full body image
short puffy pants made of silk
expressive face
warcraft
android netrunner
space suit
frederik heyman
artsy
venus
particles light
oil panting
red dress
beautiful macro close-up imagery
dmt entity
athena
harsh flash
symmetry features
head slightly tilted
david lynch
postapocalyptic vibes
terragen
ultra view angle view
photorealistoc
backlit glow
high resolution 4 k
cookbook photo
extremely intricate
retrowave color scheme
clean line
zdislaw beksinski
tank girl
psychic
luis rollo
( ( sunbeams ) )
in 1 9 9 5
mark brookes
watery eyes
jisu choe
more and more scars
detailed acrylic
emeralds
omnious
1 0 8 0 p
fluid simulation
directed gaze
tron
bonfire
cinematic film still
buttons
futuristic landscape
silent hill
static
digital oil painting
adobe after effects
stunning moody cinematography
stunning photo
surreal background
redhead
users
puddles of water
full body photo
marvel cinematic universe
highly detailed upper body
overexposed
total recall tech
magnum photos
matisse
kinetic
dream like
anime inspired
white fur
maze
hologram
sofa
mage
long tail
lithograph
valorant
evil grin
old master
black and white color scheme
german expressionism
long tongue
fine inking lines
post - production
kazuya takahashi
super powers
4 5 mm
zerochan art
8 k scan
swirly vibrant ripples
dark blue + dark orange
symmetrical! intricate
leading lines
8 5 mm f 1. 4
cult - classic - comic
skin grain detail
tartan hoody
proportionally accurate
warpaint aesthetic
digital reality
devil
very beautiful face
photorealistic 3 d render
hyperdetailed 3d matte painting
very anime
in peter elson color scheme
stanley lau
night photography
circuits
guns
face closeup
process
deep sea
hollow knight
high detail photograph
extremely detailed face
painted texture maps
trending in art station
long curly hair
attention to detail
dmt art
anime artstyle
photorealistic digital art
greebles
pastel color scheme
mirror
graphic art
threatening
california
official artwork
ruined
small head
maler collier
moons
big forehead
omnious sky
photorealistic!
sonic the hedgehog
john wick
alice russell glenny
dizzy
rick and morty
broken glass
mario martinez
best selling artist
subtle muted cinematic colors
fantasy art station
material study
style of john kenn mortensen
ren heng
utamaro
andy warhol style
winged insects and stems
rococo mechanical
un restaurant avec une terrasse
des boutiques avec des neons
highly detailed”
art print
concept art world
morbid
wide-shot
nixeu
view from below
sitting on a chair
front
movie screenshot
low - light photograph
stubble
opal
metaphysical
high quality graphics
carved marble
groundbreaking
satan
dark color palate
divine proportion
3do magazine
jim henson creature shop
cursed
dithered gradients
caustic
industry
blue tint
amazing digital art
dlsr
fancy
plate armor
old photography
beautiful color
vibrant lighting
sharpen
neon highlights
long exposure shot
neon color
clear lines
ancient ruins
attractive features
tv show
cave painting
paper collage
phil noto
driving a hotrod
vibrant and vivid
slightly smiling
healing
baroque elements
thoughtful
caught on trail cam
rabbit
kramskoi
as seen on artgerm
huifeng huang
facial features )
kenton nelson
1 9 8 1
h. p. lovecraft
hazel green eyes
1 9 9 0 s comic book style
hyper - detailed photo
pomegranates
ultra high pixel detail
perfect future
digital paining
terence nielsen
set in 1 8 6 0
walking down the catwalk
fashion show photo
fractal crystals
hd resolution
light particles
rendered in blender
dark theme
mountain landscape
unreal engine lumen
good
digital photograph
mycelium
svg vector
uncanny valley
money
werewolf
yoshiyuki sadamoto
wide - angle
symbol
art on instagram
high - key lighting
blue eyes. deep focus
capcom
cinematic pose
david friedrich
f8
moody scene
other worlds
on the beach
rack focus
cunning expression
alphonse mucha and craig mullins
fireplace
yusuke murata and makoto shinkai
peter kemp
soothing
f16
hyperdimensional
cyberpunk setting
extremly detailed
appealing
postapocalyptic grunge
michelangelo da caravaggio
meditative
cyberwave
beautiful studio lighting
cyberspace
the expanse tv series
hatsune miku
highly realistically detailed
tom crui
gitextract_qqaeml4x/
├── .gitattributes
├── .gitignore
├── README.md
├── characters/
│ ├── nsfw.txt
│ └── sfw.txt
├── clipcrop.py
├── configs/
│ └── upscaler.yaml
├── dbimutils.py
├── file_manager.py
├── install.py
├── interrogators/
│ ├── blip_interrogator.py
│ ├── booru_interrogator.py
│ ├── data/
│ │ ├── artists.txt
│ │ ├── flavors.txt
│ │ ├── mediums.txt
│ │ └── movements.txt
│ ├── idefics2_interrogator.py
│ ├── interrogator.py
│ ├── llava2_interrogator.py
│ ├── moondream_interrogator.py
│ └── wolf_interrogator.py
├── javascript/
│ └── smart_process.js
├── model_download.py
├── mplug_owl2/
│ ├── __init__.py
│ ├── constants.py
│ ├── conversation.py
│ ├── evaluate/
│ │ ├── EVALUATION.md
│ │ ├── __init__.py
│ │ ├── evaluate_caption.py
│ │ ├── evaluate_mmbench.py
│ │ ├── evaluate_mme.py
│ │ ├── evaluate_mmmu.py
│ │ ├── evaluate_vqa.py
│ │ ├── mmbench_converter.py
│ │ ├── vqa.py
│ │ └── vqa_eval.py
│ ├── local_serve/
│ │ ├── __init__.py
│ │ ├── local_web_server.py
│ │ └── model_worker.py
│ ├── mm_utils.py
│ ├── model/
│ │ ├── __init__.py
│ │ ├── builder.py
│ │ ├── configuration_mplug_owl2.py
│ │ ├── configuration_qwen.py
│ │ ├── convert_mplug_owl2_weight_to_hf.py
│ │ ├── modeling_attn_mask_utils.py
│ │ ├── modeling_llama2.py
│ │ ├── modeling_mplug_owl2.py
│ │ ├── modeling_qwen.py
│ │ ├── multiway.py
│ │ ├── utils.py
│ │ └── visual_encoder.py
│ ├── serve/
│ │ ├── __init__.py
│ │ ├── cli.py
│ │ ├── controller.py
│ │ ├── gradio_web_server.py
│ │ ├── model_worker.py
│ │ └── register_workers.py
│ ├── train/
│ │ ├── llama_flash_attn_monkey_patch.py
│ │ ├── mplug_owl2_trainer.py
│ │ ├── train.py
│ │ └── train_mem.py
│ └── utils.py
├── process_params.py
├── processors.py
├── requirements.txt
├── scripts/
│ └── process_main.py
├── smartprocess.py
├── style.css
├── super_resolution.py
└── upscalers/
└── spandrel/
├── spandrel_srformer_model.py
└── spandrel_upscaler_base.py
SYMBOL INDEX (596 symbols across 50 files)
FILE: clipcrop.py
function clip_boxes (line 16) | def clip_boxes(boxes, shape):
function find_position (line 28) | def find_position(parent: Image, child: Image):
class CropClip (line 40) | class CropClip:
method __init__ (line 41) | def __init__(self):
method get_center (line 59) | def get_center(self, image: Image, prompt: str):
method unload (line 127) | def unload(self):
method load (line 135) | def load(self):
FILE: dbimutils.py
function smart_imread (line 9) | def smart_imread(img, flag=cv2.IMREAD_UNCHANGED):
function smart_24bit (line 19) | def smart_24bit(img):
function make_square (line 32) | def make_square(img, target_size):
function smart_resize (line 49) | def smart_resize(img, size):
FILE: file_manager.py
function clean_string (line 10) | def clean_string(s):
class ImageData (line 28) | class ImageData:
method __init__ (line 37) | def __init__(self, image_path):
method read_caption (line 44) | def read_caption(self):
method split_caption (line 55) | def split_caption(self):
method update_image (line 61) | def update_image(self, image: Image, save_file: bool = False):
method update_caption (line 72) | def update_caption(self, caption: str, save_file: bool = False):
method get_image (line 80) | def get_image(self):
class FileManager (line 84) | class FileManager:
method __init__ (line 94) | def __init__(self):
method __new__ (line 97) | def __new__(cls):
method clear (line 102) | def clear(self):
method load_files (line 110) | def load_files(self):
method filtered_files (line 123) | def filtered_files(self, for_gallery: bool = False):
method all_files (line 129) | def all_files(self, for_gallery: bool = False) -> List[ImageData]:
method selected_files (line 135) | def selected_files(self, for_gallery: bool = False) -> List[ImageData]:
method filtered_and_selected_files (line 141) | def filtered_and_selected_files(self, for_gallery: bool = False) -> Li...
method update_files (line 147) | def update_files(self, files: List[ImageData]):
method update_file (line 151) | def update_file(self, file: ImageData):
method update_filters (line 161) | def update_filters(self):
method all_captions (line 253) | def all_captions(self):
FILE: interrogators/blip_interrogator.py
class BLIPInterrogator (line 11) | class BLIPInterrogator(Interrogator):
method __init__ (line 15) | def __init__(self, params: ProcessParams):
method __new__ (line 22) | def __new__(cls, params: ProcessParams):
method _init (line 34) | def _init(self, params: ProcessParams):
method interrogate (line 40) | def interrogate(self, image: Image, params: ProcessParams, unload: boo...
method unload (line 51) | def unload(self):
method load (line 59) | def load(self):
FILE: interrogators/booru_interrogator.py
class BooruInterrogator (line 13) | class BooruInterrogator(Interrogator):
method __init__ (line 16) | def __init__(self, params: ProcessParams) -> None:
method unload (line 22) | def unload(self):
method load (line 25) | def load(self):
method interrogate (line 29) | def interrogate(self, image: Image, params: ProcessParams, unload: boo...
FILE: interrogators/idefics2_interrogator.py
class Idefics2Interrogator (line 37) | class Idefics2Interrogator(Interrogator):
method __init__ (line 42) | def __init__(self, params: ProcessParams):
method interrogate (line 57) | def interrogate(self, image: Image, params: ProcessParams = None, unlo...
method _to_cpu (line 93) | def _to_cpu(self):
method _to_gpu (line 114) | def _to_gpu(self):
method unload (line 128) | def unload(self):
method load (line 133) | def load(self):
FILE: interrogators/interrogator.py
class Interrogator (line 16) | class Interrogator:
method __init__ (line 17) | def __init__(self, params: ProcessParams) -> None:
method interrogate (line 24) | def interrogate(self, image: Image, params: ProcessParams, unload: boo...
method unload (line 27) | def unload(self):
method load (line 34) | def load(self):
class InterrogatorRegistry (line 45) | class InterrogatorRegistry:
method __new__ (line 48) | def __new__(cls):
method _init (line 54) | def _init(self):
method register (line 57) | def register(self, interrogator: Interrogator):
method get_interrogator (line 60) | def get_interrogator(self, interrogator_name: str) -> Interrogator:
method get_interrogators (line 63) | def get_interrogators(self):
method unload (line 66) | def unload(self):
method load (line 70) | def load(self):
method list_interrogators (line 75) | def list_interrogators(return_cls=False):
FILE: interrogators/llava2_interrogator.py
class LLAVA2Interrogator (line 43) | class LLAVA2Interrogator(Interrogator):
method __init__ (line 48) | def __init__(self, params: ProcessParams):
method interrogate (line 62) | def interrogate(self, image: Image, params: ProcessParams = None, unlo...
method _to_cpu (line 113) | def _to_cpu(self):
method _to_gpu (line 134) | def _to_gpu(self):
method unload (line 147) | def unload(self):
method load (line 152) | def load(self):
FILE: interrogators/moondream_interrogator.py
class MoonDreamInterrogator (line 10) | class MoonDreamInterrogator(Interrogator):
method __init__ (line 13) | def __init__(self, params: ProcessParams):
method interrogate (line 20) | def interrogate(self, image: Image, params: ProcessParams = None, unlo...
method _to_gpu (line 29) | def _to_gpu(self):
method _to_cpu (line 32) | def _to_cpu(self):
method load (line 35) | def load(self):
FILE: interrogators/wolf_interrogator.py
class MoatInterrogator (line 27) | class MoatInterrogator(Interrogator):
method __init__ (line 30) | def __init__(self, params: ProcessParams) -> None:
method _setup (line 34) | def _setup(self):
method interrogate (line 38) | def interrogate(self, image: Image, params: ProcessParams, unload: boo...
class SwinInterrogator (line 45) | class SwinInterrogator(Interrogator):
method __init__ (line 48) | def __init__(self, params: ProcessParams) -> None:
method _setup (line 52) | def _setup(self):
method interrogate (line 56) | def interrogate(self, image: Image, params: ProcessParams, unload: boo...
class ConvInterrogator (line 63) | class ConvInterrogator(Interrogator):
method __init__ (line 66) | def __init__(self, params: ProcessParams) -> None:
method _setup (line 70) | def _setup(self):
method interrogate (line 74) | def interrogate(self, image: Image, params: ProcessParams, unload: boo...
class Conv2Interrogator (line 81) | class Conv2Interrogator(Interrogator):
method __init__ (line 84) | def __init__(self, params: ProcessParams) -> None:
method _setup (line 88) | def _setup(self):
method interrogate (line 92) | def interrogate(self, image: Image, params: ProcessParams, unload: boo...
class VitInterrogator (line 99) | class VitInterrogator(Interrogator):
method __init__ (line 102) | def __init__(self, params: ProcessParams) -> None:
method _setup (line 106) | def _setup(self):
method interrogate (line 110) | def interrogate(self, image: Image, params: ProcessParams, unload: boo...
function predict (line 117) | def predict(
function load_model (line 174) | def load_model(model_path, device):
function smart_imread (line 183) | def smart_imread(img, flag=cv2.IMREAD_UNCHANGED):
function smart_24bit (line 193) | def smart_24bit(img):
function make_square (line 206) | def make_square(img, target_size):
function smart_resize (line 223) | def smart_resize(img, size):
function load_labels (line 232) | def load_labels() -> list[str]:
FILE: javascript/smart_process.js
function start_smart_process (line 1) | function start_smart_process() {
function atEnd (line 10) | function atEnd() {
FILE: model_download.py
function fetch_model (line 10) | def fetch_model(model_repo, model_type, single_file=False):
function disable_safe_unpickle (line 24) | def disable_safe_unpickle():
function enable_safe_unpickle (line 36) | def enable_safe_unpickle():
FILE: mplug_owl2/conversation.py
class SeparatorStyle (line 6) | class SeparatorStyle(Enum):
class Conversation (line 17) | class Conversation:
method get_prompt (line 30) | def get_prompt(self):
method append_message (line 119) | def append_message(self, role, message):
method get_images (line 122) | def get_images(self, return_pil=False):
method to_gradio_chatbot (line 172) | def to_gradio_chatbot(self):
method copy (line 203) | def copy(self):
method dict (line 214) | def dict(self):
FILE: mplug_owl2/evaluate/evaluate_caption.py
class CaptionDataset (line 27) | class CaptionDataset(torch.utils.data.Dataset):
method __init__ (line 29) | def __init__(self, train, test, prompt, image_processor, few_shot=0):
method __len__ (line 38) | def __len__(self):
method __getitem__ (line 41) | def __getitem__(self, idx):
function collate_fn (line 56) | def collate_fn(inputs, tokenizer):
class InferenceSampler (line 75) | class InferenceSampler(torch.utils.data.sampler.Sampler):
method __init__ (line 77) | def __init__(self, size):
method _get_local_indices (line 86) | def _get_local_indices(total_size, world_size, rank):
method __iter__ (line 95) | def __iter__(self):
method __len__ (line 98) | def __len__(self):
FILE: mplug_owl2/evaluate/evaluate_mmbench.py
function mapping_to_annotation (line 51) | def mapping_to_annotation(results, raw_annotation):
function generate_submission_file (line 69) | def generate_submission_file(results, raw_annotation):
function collate_fn (line 86) | def collate_fn(batches, tokenizer):
class VQADataset (line 107) | class VQADataset(torch.utils.data.Dataset):
method __init__ (line 109) | def __init__(self, test, prompt, image_processor):
method __len__ (line 116) | def __len__(self):
method __getitem__ (line 119) | def __getitem__(self, idx):
class InferenceSampler (line 144) | class InferenceSampler(torch.utils.data.sampler.Sampler):
method __init__ (line 146) | def __init__(self, size):
method _get_local_indices (line 155) | def _get_local_indices(total_size, world_size, rank):
method __iter__ (line 164) | def __iter__(self):
method __len__ (line 167) | def __len__(self):
FILE: mplug_owl2/evaluate/evaluate_mme.py
class calculate_metrics (line 36) | class calculate_metrics:
method divide_chunks (line 37) | def divide_chunks(self, l, n=2):
method parse_pred_ans (line 44) | def parse_pred_ans(self, pred_ans):
method compute_metric (line 61) | def compute_metric(self, gts, preds):
method process_result (line 107) | def process_result(self, outputs):
function collate_fn (line 172) | def collate_fn(batches, tokenizer):
class VQADataset (line 196) | class VQADataset(torch.utils.data.Dataset):
method __init__ (line 198) | def __init__(self, test, base_dir, prompt, image_processor):
method __len__ (line 218) | def __len__(self):
method __getitem__ (line 221) | def __getitem__(self, idx):
class InferenceSampler (line 250) | class InferenceSampler(torch.utils.data.sampler.Sampler):
method __init__ (line 252) | def __init__(self, size):
method _get_local_indices (line 261) | def _get_local_indices(total_size, world_size, rank):
method __iter__ (line 270) | def __iter__(self):
method __len__ (line 273) | def __len__(self):
FILE: mplug_owl2/evaluate/evaluate_mmmu.py
function parse_multi_choice_response (line 72) | def parse_multi_choice_response(response, all_choices, index2ans):
function check_is_number (line 127) | def check_is_number(string):
function normalize_str (line 138) | def normalize_str(string):
function extract_numbers (line 162) | def extract_numbers(string):
function parse_open_response (line 184) | def parse_open_response(response):
function eval_multi_choice (line 237) | def eval_multi_choice(gold_i, pred_i):
function eval_open (line 253) | def eval_open(gold_i, pred_i):
function evaluate (line 281) | def evaluate(samples):
function collate_fn (line 306) | def collate_fn(batches, tokenizer):
class VQADataset (line 329) | class VQADataset(torch.utils.data.Dataset):
method __init__ (line 331) | def __init__(self, split, image_processor, eval_split='dev'):
method __len__ (line 345) | def __len__(self):
method __getitem__ (line 348) | def __getitem__(self, idx):
class InferenceSampler (line 391) | class InferenceSampler(torch.utils.data.sampler.Sampler):
method __init__ (line 393) | def __init__(self, size):
method _get_local_indices (line 402) | def _get_local_indices(total_size, world_size, rank):
method __iter__ (line 411) | def __iter__(self):
method __len__ (line 414) | def __len__(self):
FILE: mplug_owl2/evaluate/evaluate_vqa.py
function collate_fn (line 55) | def collate_fn(batches, tokenizer):
class VQADataset (line 77) | class VQADataset(torch.utils.data.Dataset):
method __init__ (line 79) | def __init__(self, train, test, prompt, image_processor, few_shot):
method __len__ (line 88) | def __len__(self):
method __getitem__ (line 91) | def __getitem__(self, idx):
class InferenceSampler (line 108) | class InferenceSampler(torch.utils.data.sampler.Sampler):
method __init__ (line 110) | def __init__(self, size):
method _get_local_indices (line 119) | def _get_local_indices(total_size, world_size, rank):
method __iter__ (line 128) | def __iter__(self):
method __len__ (line 131) | def __len__(self):
FILE: mplug_owl2/evaluate/mmbench_converter.py
function decode_base64_to_image (line 15) | def decode_base64_to_image(base64_string):
FILE: mplug_owl2/evaluate/vqa.py
class VQA (line 31) | class VQA:
method __init__ (line 33) | def __init__(self, annotation_file=None, question_file=None):
method createIndex (line 55) | def createIndex(self):
method info (line 73) | def info(self):
method getQuesIds (line 81) | def getQuesIds(self, imgIds=[], quesTypes=[], ansTypes=[]):
method getImgIds (line 114) | def getImgIds(self, quesIds=[], quesTypes=[], ansTypes=[]):
method loadQA (line 143) | def loadQA(self, ids=[]):
method showQA (line 154) | def showQA(self, anns):
method loadRes (line 168) | def loadRes(self, resFile, quesFile):
FILE: mplug_owl2/evaluate/vqa_eval.py
class VQAEval (line 18) | class VQAEval:
method __init__ (line 20) | def __init__(self, vqa=None, vqaRes=None, n=2):
method evaluate (line 194) | def evaluate(self, quesIds=None):
method processPunctuation (line 254) | def processPunctuation(self, inText):
method processDigitArticle (line 265) | def processDigitArticle(self, inText):
method setAccuracy (line 280) | def setAccuracy(self, accQA, accQuesType, accAnsType):
method setEvalQA (line 298) | def setEvalQA(self, quesId, acc):
method setEvalQuesType (line 301) | def setEvalQuesType(self, quesId, quesType, acc):
method setEvalAnsType (line 306) | def setEvalAnsType(self, quesId, ansType, acc):
method updateProgress (line 311) | def updateProgress(self, progress):
class EvalAIAnswerProcessor (line 337) | class EvalAIAnswerProcessor:
method __init__ (line 508) | def __init__(self, *args, **kwargs):
method word_tokenize (line 511) | def word_tokenize(self, word):
method process_punctuation (line 516) | def process_punctuation(self, in_text):
method process_digit_article (line 528) | def process_digit_article(self, in_text):
method __call__ (line 543) | def __call__(self, item):
FILE: mplug_owl2/local_serve/local_web_server.py
function get_conv_log_filename (line 26) | def get_conv_log_filename():
function load_demo (line 41) | def load_demo(url_params, request: gr.Request):
function vote_last_response (line 47) | def vote_last_response(state, vote_type, request: gr.Request):
function upvote_last_response (line 58) | def upvote_last_response(state, request: gr.Request):
function downvote_last_response (line 64) | def downvote_last_response(state, request: gr.Request):
function flag_last_response (line 70) | def flag_last_response(state, request: gr.Request):
function regenerate (line 76) | def regenerate(state, image_process_mode, request: gr.Request):
function clear_history (line 86) | def clear_history(request: gr.Request):
function add_text (line 92) | def add_text(state, text, image, image_process_mode, request: gr.Request):
function http_bot (line 118) | def http_bot(state, temperature, top_p, max_new_tokens, request: gr.Requ...
function build_demo (line 246) | def build_demo(embed_mode):
FILE: mplug_owl2/local_serve/model_worker.py
class ModelWorker (line 29) | class ModelWorker:
method __init__ (line 30) | def __init__(self, model_path, model_base, model_name, load_8bit, load...
method generate_stream (line 50) | def generate_stream(self, params):
method generate_stream_gate (line 119) | def generate_stream_gate(self, params):
FILE: mplug_owl2/mm_utils.py
function load_image_from_base64 (line 11) | def load_image_from_base64(image):
function expand2square (line 15) | def expand2square(pil_img, background_color):
function process_images (line 29) | def process_images(images, image_processor, model_cfg=None):
function tokenizer_image_token (line 53) | def tokenizer_image_token(prompt, tokenizer, image_token_index=IMAGE_TOK...
function get_model_name_from_path (line 76) | def get_model_name_from_path(model_path):
class KeywordsStoppingCriteria (line 85) | class KeywordsStoppingCriteria(StoppingCriteria):
method __init__ (line 86) | def __init__(self, keywords, tokenizer, input_ids):
method __call__ (line 100) | def __call__(self, output_ids: torch.LongTensor, scores: torch.FloatTe...
FILE: mplug_owl2/model/builder.py
function load_pretrained_model (line 30) | def load_pretrained_model(model_path, model_base, model_name, load_8bit=...
FILE: mplug_owl2/model/configuration_mplug_owl2.py
class LlamaConfig (line 15) | class LlamaConfig(PretrainedConfig):
method __init__ (line 99) | def __init__(
method _rope_scaling_validation (line 152) | def _rope_scaling_validation(self):
class MplugOwlVisionConfig (line 174) | class MplugOwlVisionConfig(PretrainedConfig):
method __init__ (line 216) | def __init__(
method from_pretrained (line 255) | def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os....
class MplugOwlVisualAbstractorConfig (line 271) | class MplugOwlVisualAbstractorConfig(PretrainedConfig):
method __init__ (line 274) | def __init__(
method from_pretrained (line 305) | def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os....
class MPLUGOwl2Config (line 327) | class MPLUGOwl2Config(LlamaConfig):
method __init__ (line 329) | def __init__(self, visual_config=None, **kwargs):
class MPLUGOwl2QwenConfig (line 339) | class MPLUGOwl2QwenConfig(QWenConfig):
method __init__ (line 341) | def __init__(self, visual_config=None, **kwargs):
FILE: mplug_owl2/model/configuration_qwen.py
class QWenConfig (line 9) | class QWenConfig(PretrainedConfig):
method __init__ (line 13) | def __init__(
FILE: mplug_owl2/model/convert_mplug_owl2_weight_to_hf.py
function compute_intermediate_size (line 65) | def compute_intermediate_size(n):
function read_json (line 69) | def read_json(path):
function write_json (line 74) | def write_json(text, path):
function write_model (line 79) | def write_model(model_path,
function write_tokenizer (line 346) | def write_tokenizer(tokenizer_path, input_tokenizer_path):
function main (line 354) | def main():
FILE: mplug_owl2/model/modeling_attn_mask_utils.py
class AttentionMaskConverter (line 19) | class AttentionMaskConverter:
method __init__ (line 35) | def __init__(self, is_causal: bool, sliding_window: Optional[int] = No...
method to_causal_4d (line 44) | def to_causal_4d(
method to_4d (line 77) | def to_4d(
method _make_causal_mask (line 120) | def _make_causal_mask(
method _expand_mask (line 150) | def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Opti...
function _prepare_4d_causal_attention_mask (line 164) | def _prepare_4d_causal_attention_mask(
function _prepare_4d_attention_mask (line 204) | def _prepare_4d_attention_mask(mask: torch.Tensor, dtype: torch.dtype, t...
function _create_4d_causal_attention_mask (line 220) | def _create_4d_causal_attention_mask(
FILE: mplug_owl2/model/modeling_llama2.py
function repeat_kv (line 22) | def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
class LlamaAttention (line 34) | class LlamaAttention(nn.Module):
method __init__ (line 37) | def __init__(self, config: LlamaConfig):
method _init_rope (line 65) | def _init_rope(self):
method _shape (line 92) | def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
method forward (line 95) | def forward(
class LlamaDecoderLayer (line 169) | class LlamaDecoderLayer(nn.Module):
method __init__ (line 170) | def __init__(self, config: LlamaConfig, annoying_param):
method forward (line 186) | def forward(
function model_forward (line 243) | def model_forward(
function causal_model_forward (line 373) | def causal_model_forward(
function replace_llama_modality_adaptive (line 468) | def replace_llama_modality_adaptive():
FILE: mplug_owl2/model/modeling_mplug_owl2.py
class MPLUGOwl2MetaModel (line 32) | class MPLUGOwl2MetaModel:
method __init__ (line 33) | def __init__(self, config):
method get_vision_tower (line 42) | def get_vision_tower(self):
method get_visual_abstractor (line 48) | def get_visual_abstractor(self):
class MPLUGOwl2MetaForCausalLM (line 55) | class MPLUGOwl2MetaForCausalLM(ABC):
method get_model (line 57) | def get_model(self):
method encode_images (line 60) | def encode_images(self, images):
method prepare_inputs_labels_for_multimodal (line 65) | def prepare_inputs_labels_for_multimodal(
function _expand_mask (line 215) | def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Option...
function _make_causal_mask (line 231) | def _make_causal_mask(
class MPLUGOwl2LlamaModel (line 252) | class MPLUGOwl2LlamaModel(MPLUGOwl2MetaModel, LlamaModel):
method __init__ (line 255) | def __init__(self, config: MPLUGOwl2Config):
method _prepare_decoder_attention_mask (line 258) | def _prepare_decoder_attention_mask(
class MPLUGOwl2QWenModel (line 285) | class MPLUGOwl2QWenModel(MPLUGOwl2MetaModel, QWenModel):
method __init__ (line 288) | def __init__(self, config: MPLUGOwl2QwenConfig):
class MPLUGOwl2LlamaForCausalLM (line 292) | class MPLUGOwl2LlamaForCausalLM(LlamaForCausalLM, MPLUGOwl2MetaForCausal...
method __init__ (line 295) | def __init__(self, config):
method encode_images (line 304) | def encode_images(self, images):
method get_model (line 309) | def get_model(self):
method forward (line 312) | def forward(
method prepare_inputs_for_generation (line 374) | def prepare_inputs_for_generation(
class MPLUGOwl2QWenForCausalLM (line 397) | class MPLUGOwl2QWenForCausalLM(QWenLMHeadModel, MPLUGOwl2MetaForCausalLM):
method __init__ (line 400) | def __init__(self, config):
method get_model (line 458) | def get_model(self):
method forward (line 461) | def forward(
FILE: mplug_owl2/model/modeling_qwen.py
function _import_flash_attn (line 78) | def _import_flash_attn():
function quantize_cache_v (line 118) | def quantize_cache_v(fdata, bits, qmax, qmin):
function dequantize_cache_torch (line 140) | def dequantize_cache_torch(qdata, scale, zero):
class FlashSelfAttention (line 144) | class FlashSelfAttention(torch.nn.Module):
method __init__ (line 145) | def __init__(
method unpad_input (line 162) | def unpad_input(self, hidden_states, attention_mask):
method pad_input (line 171) | def pad_input(self, hidden_states, indices, batch, seqlen):
method forward (line 177) | def forward(self, q, k, v, attention_mask=None):
class QWenAttention (line 242) | class QWenAttention(nn.Module):
method __init__ (line 243) | def __init__(self, config):
method _attn (line 325) | def _attn(self, query, key, value, causal_mask=None, attention_mask=No...
method _split_heads (line 396) | def _split_heads(self, tensor, num_heads, attn_head_size):
method _merge_heads (line 401) | def _merge_heads(self, tensor, num_heads, attn_head_size):
method forward (line 406) | def forward(
class QWenMLP (line 564) | class QWenMLP(nn.Module):
method __init__ (line 565) | def __init__(self, config):
method forward (line 576) | def forward(self, hidden_states):
class QWenBlock (line 584) | class QWenBlock(nn.Module):
method __init__ (line 585) | def __init__(self, config):
method forward (line 602) | def forward(
class QWenPreTrainedModel (line 648) | class QWenPreTrainedModel(PreTrainedModel):
method __init__ (line 656) | def __init__(self, *inputs, **kwargs):
method _init_weights (line 659) | def _init_weights(self, module):
method _set_gradient_checkpointing (line 682) | def _set_gradient_checkpointing(self, module, value=False):
class QWenModel (line 687) | class QWenModel(QWenPreTrainedModel):
method __init__ (line 690) | def __init__(self, config):
method get_input_embeddings (line 737) | def get_input_embeddings(self):
method embed_tokens (line 740) | def embed_tokens(self, input_ids):
method set_input_embeddings (line 743) | def set_input_embeddings(self, new_embeddings):
method get_ntk_alpha (line 746) | def get_ntk_alpha(self, true_seq_len):
method forward (line 752) | def forward(
class QWenLMHeadModel (line 943) | class QWenLMHeadModel(QWenPreTrainedModel):
method __init__ (line 947) | def __init__(self, config):
method get_output_embeddings (line 1004) | def get_output_embeddings(self):
method set_output_embeddings (line 1007) | def set_output_embeddings(self, new_embeddings):
method prepare_inputs_for_generation (line 1010) | def prepare_inputs_for_generation(
method forward (line 1036) | def forward(
method _reorder_cache (line 1100) | def _reorder_cache(
method generate (line 1180) | def generate(
class RotaryEmbedding (line 1227) | class RotaryEmbedding(torch.nn.Module):
method __init__ (line 1228) | def __init__(self, dim, base=10000):
method update_rotary_pos_emb_cache (line 1242) | def update_rotary_pos_emb_cache(self, seqlen, ntk_alpha=1.0):
method forward (line 1265) | def forward(self, max_seq_len, ntk_alpha=1.0):
function _rotate_half (line 1271) | def _rotate_half(x):
function apply_rotary_pos_emb (line 1279) | def apply_rotary_pos_emb(t, freqs):
class RMSNorm (line 1303) | class RMSNorm(torch.nn.Module):
method __init__ (line 1304) | def __init__(self, dim: int, eps: float = 1e-6):
method _norm (line 1309) | def _norm(self, x):
method forward (line 1312) | def forward(self, x):
FILE: mplug_owl2/model/multiway.py
class MultiwayNetwork (line 7) | class MultiwayNetwork(nn.Module):
method __init__ (line 9) | def __init__(self, module_provider, num_multiway=2, out_features=None):
method forward (line 14) | def forward(self, hidden_states, multiway_indices):
FILE: mplug_owl2/model/utils.py
function auto_upgrade (line 4) | def auto_upgrade(config):
FILE: mplug_owl2/model/visual_encoder.py
function get_abs_pos (line 14) | def get_abs_pos(abs_pos, tgt_size):
function get_2d_sincos_pos_embed (line 34) | def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False):
function get_2d_sincos_pos_embed_from_grid (line 52) | def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
function get_1d_sincos_pos_embed_from_grid (line 63) | def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
class MplugOwlVisionEmbeddings (line 85) | class MplugOwlVisionEmbeddings(nn.Module):
method __init__ (line 86) | def __init__(self, config):
method forward (line 113) | def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor:
class MplugOwlVisionAttention (line 129) | class MplugOwlVisionAttention(nn.Module):
method __init__ (line 132) | def __init__(self, config):
method _shape (line 149) | def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
method forward (line 152) | def forward(
class MplugOwlMLP (line 237) | class MplugOwlMLP(nn.Module):
method __init__ (line 238) | def __init__(self, config):
method forward (line 246) | def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
class MplugOwlVisionEncoderLayer (line 253) | class MplugOwlVisionEncoderLayer(nn.Module):
method __init__ (line 254) | def __init__(self, config):
method forward (line 262) | def forward(
class MplugOwlVisionEncoder (line 301) | class MplugOwlVisionEncoder(nn.Module):
method __init__ (line 311) | def __init__(self, config):
method forward (line 317) | def forward(
class MplugOwlVisionModel (line 393) | class MplugOwlVisionModel(PreTrainedModel):
method __init__ (line 396) | def __init__(self, config):
method _get_no_split_modules (line 410) | def _get_no_split_modules(self, device_map: str):
method forward (line 418) | def forward(
method get_input_embeddings (line 465) | def get_input_embeddings(self):
class MplugOwlVisualAbstractorMLP (line 469) | class MplugOwlVisualAbstractorMLP(nn.Module):
method __init__ (line 470) | def __init__(self, config):
method forward (line 481) | def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
class MplugOwlVisualAbstractorMultiHeadAttention (line 488) | class MplugOwlVisualAbstractorMultiHeadAttention(nn.Module):
method __init__ (line 489) | def __init__(self, config):
method save_attn_gradients (line 527) | def save_attn_gradients(self, attn_gradients):
method get_attn_gradients (line 530) | def get_attn_gradients(self):
method save_attention_map (line 533) | def save_attention_map(self, attention_map):
method get_attention_map (line 536) | def get_attention_map(self):
method transpose_for_scores (line 539) | def transpose_for_scores(self, x):
method forward (line 544) | def forward(
class MplugOwlVisualAbstractorCrossOutput (line 606) | class MplugOwlVisualAbstractorCrossOutput(nn.Module):
method __init__ (line 607) | def __init__(self, config):
method forward (line 614) | def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Ten...
class MplugOwlVisualAbstractorAttention (line 620) | class MplugOwlVisualAbstractorAttention(nn.Module):
method __init__ (line 621) | def __init__(self, config):
method prune_heads (line 639) | def prune_heads(self, heads):
method forward (line 657) | def forward(
class MplugOwlVisualAbstractorLayer (line 687) | class MplugOwlVisualAbstractorLayer(nn.Module):
method __init__ (line 688) | def __init__(self, config, layer_idx):
method forward (line 698) | def forward(
class MplugOwlVisualAbstractorEncoder (line 723) | class MplugOwlVisualAbstractorEncoder(nn.Module):
method __init__ (line 724) | def __init__(self, config):
method forward (line 732) | def forward(
class MplugOwlVisualAbstractorModel (line 787) | class MplugOwlVisualAbstractorModel(PreTrainedModel):
method __init__ (line 788) | def __init__(self, config, language_hidden_size):
method _get_no_split_modules (line 800) | def _get_no_split_modules(self, device_map: str):
method _prune_heads (line 808) | def _prune_heads(self, heads_to_prune):
method get_extended_attention_mask (line 816) | def get_extended_attention_mask(
method forward (line 860) | def forward(
FILE: mplug_owl2/serve/cli.py
function disable_torch_init (line 17) | def disable_torch_init():
function load_image (line 26) | def load_image(image_file):
function main (line 35) | def main(args):
FILE: mplug_owl2/serve/controller.py
class DispatchMethod (line 28) | class DispatchMethod(Enum):
method from_str (line 33) | def from_str(cls, name):
class WorkerInfo (line 43) | class WorkerInfo:
function heart_beat_controller (line 51) | def heart_beat_controller(controller):
class Controller (line 57) | class Controller:
method __init__ (line 58) | def __init__(self, dispatch_method: str):
method register_worker (line 69) | def register_worker(self, worker_name: str, check_heart_beat: bool,
method get_worker_status (line 88) | def get_worker_status(self, worker_name: str):
method remove_worker (line 101) | def remove_worker(self, worker_name: str):
method refresh_all_workers (line 104) | def refresh_all_workers(self):
method list_models (line 112) | def list_models(self):
method get_worker_address (line 120) | def get_worker_address(self, model_name: str):
method receive_heart_beat (line 173) | def receive_heart_beat(self, worker_name: str, queue_length: int):
method remove_stable_workers_by_expiration (line 183) | def remove_stable_workers_by_expiration(self):
method worker_api_generate_stream (line 193) | def worker_api_generate_stream(self, params):
method worker_api_get_status (line 220) | def worker_api_get_status(self):
function register_worker (line 243) | async def register_worker(request: Request):
function refresh_all_workers (line 251) | async def refresh_all_workers():
function list_models (line 256) | async def list_models():
function get_worker_address (line 262) | async def get_worker_address(request: Request):
function receive_heart_beat (line 269) | async def receive_heart_beat(request: Request):
function worker_api_generate_stream (line 277) | async def worker_api_generate_stream(request: Request):
function worker_api_get_status (line 284) | async def worker_api_get_status(request: Request):
FILE: mplug_owl2/serve/gradio_web_server.py
function get_conv_log_filename (line 32) | def get_conv_log_filename():
function get_model_list (line 38) | def get_model_list():
function load_demo (line 58) | def load_demo(url_params, request: gr.Request):
function load_demo_refresh_model_list (line 72) | def load_demo_refresh_model_list(request: gr.Request):
function vote_last_response (line 83) | def vote_last_response(state, vote_type, model_selector, request: gr.Req...
function upvote_last_response (line 95) | def upvote_last_response(state, model_selector, request: gr.Request):
function downvote_last_response (line 101) | def downvote_last_response(state, model_selector, request: gr.Request):
function flag_last_response (line 107) | def flag_last_response(state, model_selector, request: gr.Request):
function regenerate (line 113) | def regenerate(state, image_process_mode, request: gr.Request):
function clear_history (line 123) | def clear_history(request: gr.Request):
function add_text (line 129) | def add_text(state, text, image, image_process_mode, request: gr.Request):
function http_bot (line 156) | def http_bot(state, model_selector, temperature, top_p, max_new_tokens, ...
function build_demo (line 298) | def build_demo(embed_mode):
FILE: mplug_owl2/serve/model_worker.py
function heart_beat_worker (line 37) | def heart_beat_worker(controller):
class ModelWorker (line 44) | class ModelWorker:
method __init__ (line 45) | def __init__(self, controller_addr, worker_addr,
method register_to_controller (line 75) | def register_to_controller(self):
method send_heart_beat (line 87) | def send_heart_beat(self):
method get_queue_length (line 108) | def get_queue_length(self):
method get_status (line 115) | def get_status(self):
method generate_stream (line 123) | def generate_stream(self, params):
method generate_stream_gate (line 192) | def generate_stream_gate(self, params):
function release_model_semaphore (line 220) | def release_model_semaphore(fn=None):
function generate_stream (line 227) | async def generate_stream(request: Request):
function get_status (line 243) | async def get_status(request: Request):
FILE: mplug_owl2/train/llama_flash_attn_monkey_patch.py
function forward (line 16) | def forward(
function _prepare_decoder_attention_mask (line 100) | def _prepare_decoder_attention_mask(
function replace_llama_attn_with_flash_attn (line 107) | def replace_llama_attn_with_flash_attn():
FILE: mplug_owl2/train/mplug_owl2_trainer.py
function maybe_zero_3 (line 18) | def maybe_zero_3(param, ignore_status=False, name=None):
function get_mm_adapter_state_maybe_zero_3 (line 32) | def get_mm_adapter_state_maybe_zero_3(named_params, keys_to_match):
function split_to_even_chunks (line 38) | def split_to_even_chunks(indices, lengths, num_chunks):
function get_modality_length_grouped_indices (line 60) | def get_modality_length_grouped_indices(lengths, batch_size, world_size,...
function get_length_grouped_indices (line 88) | def get_length_grouped_indices(lengths, batch_size, world_size, generato...
class LengthGroupedSampler (line 99) | class LengthGroupedSampler(Sampler):
method __init__ (line 105) | def __init__(
method __len__ (line 122) | def __len__(self):
method __iter__ (line 125) | def __iter__(self):
class MPLUGOwl2Trainer (line 133) | class MPLUGOwl2Trainer(Trainer):
method _get_train_sampler (line 135) | def _get_train_sampler(self) -> Optional[torch.utils.data.Sampler]:
method create_optimizer (line 150) | def create_optimizer(self):
method _save_checkpoint (line 239) | def _save_checkpoint(self, model, trial, metrics=None):
method _save (line 242) | def _save(self, output_dir: Optional[str] = None, state_dict=None):
FILE: mplug_owl2/train/train.py
function rank0_print (line 44) | def rank0_print(*args):
class ModelArguments (line 50) | class ModelArguments:
class DataArguments (line 56) | class DataArguments:
class TrainingArguments (line 67) | class TrainingArguments(transformers.TrainingArguments):
function maybe_zero_3 (line 104) | def maybe_zero_3(param, ignore_status=False, name=None):
function get_peft_state_maybe_zero_3 (line 119) | def get_peft_state_maybe_zero_3(named_params, bias):
function get_peft_state_non_lora_maybe_zero_3 (line 144) | def get_peft_state_non_lora_maybe_zero_3(named_params, require_grad_only...
function get_mm_adapter_state_maybe_zero_3 (line 152) | def get_mm_adapter_state_maybe_zero_3(named_params, keys_to_match):
function find_all_linear_names (line 158) | def find_all_linear_names(model):
function safe_save_model_for_hf_trainer (line 173) | def safe_save_model_for_hf_trainer(trainer: transformers.Trainer,
function smart_tokenizer_and_embedding_resize (line 192) | def smart_tokenizer_and_embedding_resize(
function _tokenize_fn (line 217) | def _tokenize_fn(strings: Sequence[str],
function _mask_targets (line 244) | def _mask_targets(target, tokenized_lens, speakers):
function _add_speaker_and_signal (line 255) | def _add_speaker_and_signal(header, source, get_conversation=True):
function preprocess_multimodal (line 276) | def preprocess_multimodal(
function preprocess_v1 (line 297) | def preprocess_v1(
function preprocess_plain (line 379) | def preprocess_plain(
function preprocess (line 401) | def preprocess(
class LazySupervisedDataset (line 444) | class LazySupervisedDataset(Dataset):
method __init__ (line 447) | def __init__(self, data_path: str,
method __len__ (line 458) | def __len__(self):
method lengths (line 462) | def lengths(self):
method modality_lengths (line 471) | def modality_lengths(self):
method next_rand (line 528) | def next_rand(self):
method __getitem__ (line 532) | def __getitem__(self, i) -> Dict[str, torch.Tensor]:
class DataCollatorForSupervisedDataset (line 590) | class DataCollatorForSupervisedDataset(object):
method __call__ (line 595) | def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]:
function make_supervised_data_module (line 623) | def make_supervised_data_module(tokenizer: transformers.PreTrainedTokeni...
function train (line 635) | def train():
FILE: mplug_owl2/utils.py
function build_logger (line 17) | def build_logger(logger_name, logger_filename):
class StreamToLogger (line 60) | class StreamToLogger(object):
method __init__ (line 64) | def __init__(self, logger, log_level=logging.INFO):
method __getattr__ (line 70) | def __getattr__(self, attr):
method write (line 73) | def write(self, buf):
method flush (line 87) | def flush(self):
function disable_torch_init (line 93) | def disable_torch_init():
function violates_moderation (line 102) | def violates_moderation(text):
function pretty_print_semaphore (line 123) | def pretty_print_semaphore(semaphore):
FILE: process_params.py
class ProcessParams (line 8) | class ProcessParams:
method clip_params (line 62) | def clip_params(self):
method pre_only (line 78) | def pre_only(self):
method cap_only (line 83) | def cap_only(self):
method post_only (line 89) | def post_only(self):
method from_dict (line 95) | def from_dict(cls, d):
FILE: processors.py
class Processor (line 17) | class Processor:
method __init__ (line 18) | def __init__(self):
method unload (line 22) | def unload(self):
method process (line 29) | def process(self, images: List[Image.Image]) -> List[Any]:
class ClipProcessor (line 33) | class ClipProcessor(Processor):
method __init__ (line 34) | def __init__(
method process (line 66) | def process(self, images: List[Image.Image], short:bool=False) -> List...
method unload (line 77) | def unload(self):
class BooruProcessor (line 85) | class BooruProcessor(Processor):
method __init__ (line 86) | def __init__(self, min_score: float):
method process (line 93) | def process(self, images: List[Image.Image]) -> List[List[str]]:
method unload (line 106) | def unload(self):
class CropProcessor (line 113) | class CropProcessor(Processor):
method __init__ (line 114) | def __init__(self, subject_class: str, pad: bool, crop: bool):
method process (line 124) | def process(self, images: List[Image.Image], captions: List[str] = Non...
method _process_img (line 135) | def _process_img(self, img, short_caption):
method unload (line 191) | def unload(self):
class UpscaleProcessor (line 197) | class UpscaleProcessor(Processor):
method __init__ (line 198) | def __init__(self):
method process (line 204) | def process(self, images: List[Image.Image], captions: List[str] = Non...
method unload (line 215) | def unload(self):
FILE: scripts/process_main.py
function list_scalers (line 60) | def list_scalers():
function generate_caption_section (line 94) | def generate_caption_section():
function sort_tags_dict (line 168) | def sort_tags_dict():
function create_process_ui (line 183) | def create_process_ui():
FILE: smartprocess.py
function printi (line 33) | def printi(message):
function get_backup_path (line 38) | def get_backup_path(file_path, params: ProcessParams):
function save_pic (line 52) | def save_pic(img, src_name, img_index, params: ProcessParams):
function save_img_caption (line 64) | def save_img_caption(image_path: str, img_caption: str, params: ProcessP...
function list_features (line 76) | def list_features():
function is_image (line 97) | def is_image(path: Union[Path, str], feats=None):
function cleanup (line 108) | def cleanup():
function vram_usage (line 118) | def vram_usage():
function unload_system (line 127) | def unload_system():
function load_system (line 145) | def load_system():
function get_crop_clip (line 156) | def get_crop_clip():
function get_image_interrogators (line 168) | def get_image_interrogators(params: ProcessParams, all_captioners):
function clean_string (line 187) | def clean_string(s):
function read_caption (line 214) | def read_caption(image):
function build_caption (line 225) | def build_caption(image, captions_list, tags_to_ignore, caption_length, ...
function calculate_job_length (line 293) | def calculate_job_length(files, crop, caption, captioners, flip, restore...
function crop_smart (line 309) | def crop_smart(img: Image, interrogator: BLIPInterrogator, cc: CropClip,...
function crop_center (line 348) | def crop_center(img: Image, max_size: int):
function crop_empty (line 362) | def crop_empty(img: Image):
function crop_contain (line 412) | def crop_contain(img, params: ProcessParams):
function get_blip_interrogator (line 426) | def get_blip_interrogator(params: ProcessParams):
function process_pre (line 439) | def process_pre(files: List[ImageData], params: ProcessParams) -> List[I...
function process_captions (line 497) | def process_captions(files: List[ImageData], params: ProcessParams, all_...
function process_post (line 569) | def process_post(files: ImageData, params: ProcessParams, all_files: boo...
function do_process (line 694) | def do_process(params: ProcessParams, all_files=False) -> Tuple[List[Ima...
FILE: super_resolution.py
function initialize_model (line 21) | def initialize_model():
function make_batch_sd (line 53) | def make_batch_sd(
function make_noise_augmentation (line 70) | def make_noise_augmentation(model, batch, noise_level=None):
function paint (line 77) | def paint(sampler, image, prompt, seed, scale, h, w, steps, num_samples=...
function pad_image (line 139) | def pad_image(input_image):
function predict (line 147) | def predict(sampler, input_image, prompt, steps, num_samples, scale, see...
function super_resolution (line 168) | def super_resolution(self, images, steps=50, target_scale=2, half_attent...
FILE: upscalers/spandrel/spandrel_srformer_model.py
class SpandrelSRFormerModel (line 22) | class SpandrelSRFormerModel(SpandrelUpscaler):
method __init__ (line 26) | def __init__(self, create_dirs=False):
FILE: upscalers/spandrel/spandrel_upscaler_base.py
function convert_google_drive_link (line 29) | def convert_google_drive_link(url: str) -> str:
function download_file (line 40) | def download_file(url: str, filename: Path | str) -> None:
function extract_file_from_zip (line 59) | def extract_file_from_zip(
function image_to_tensor (line 72) | def image_to_tensor(img: np.ndarray, device: str, half) -> torch.Tensor:
function tensor_to_image (line 87) | def tensor_to_image(tensor: torch.Tensor) -> np.ndarray:
function image_inference_tensor (line 96) | def image_inference_tensor(
function image_inference (line 104) | def image_inference(model: ImageModelDescriptor, image: np.ndarray, devi...
function get_h_w_c (line 108) | def get_h_w_c(image: np.ndarray) -> tuple[int, int, int]:
class SpandrelUpscaler (line 114) | class SpandrelUpscaler(Upscaler):
method __init__ (line 120) | def __init__(self, create_dirs=False):
method do_upscale (line 126) | def do_upscale(self, img: PIL.Image, selected_model: str):
method load_model (line 130) | def load_model(self, path: str):
method preprocess (line 139) | def preprocess(self, image: Image) -> Image:
method postprocess (line 161) | def postprocess(self, image: Image, original_width: int, original_heig...
method internal_upscale (line 180) | def internal_upscale(self, image: Image):
method unload (line 215) | def unload(self):
method load (line 222) | def load(self):
Condensed preview — 72 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (2,618K chars).
[
{
"path": ".gitattributes",
"chars": 66,
"preview": "# Auto detect text files and perform LF normalization\n* text=auto\n"
},
{
"path": ".gitignore",
"chars": 2763,
"preview": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packagi"
},
{
"path": "README.md",
"chars": 2860,
"preview": "# Stable Diffusion WebUI Smart Pre-Processing Extension\n\n## What is this??\n\nAs the name would imply, this is an extensio"
},
{
"path": "characters/nsfw.txt",
"chars": 154,
"preview": "Human: <image>\nHuman: Give a short one sentence caption for this image with NO punctuation. DO NOT USE ANY PUNCTUATION O"
},
{
"path": "characters/sfw.txt",
"chars": 3441,
"preview": "Revised and SFW prompt:\n\nAct like a stable diffusion SD prompt generator, your focus is to convey warm and playful scena"
},
{
"path": "clipcrop.py",
"chars": 5436,
"preview": "# Original project: https://github.com/Vishnunkumar/clipcrop/blob/main/clipcrop/clipcrop.py\nimport os.path\nimport sys\n\ni"
},
{
"path": "configs/upscaler.yaml",
"chars": 2250,
"preview": "model:\n base_learning_rate: 1.0e-04\n target: ldm.models.diffusion.ddpm.LatentUpscaleDiffusion\n params:\n parameteri"
},
{
"path": "dbimutils.py",
"chars": 1653,
"preview": "# DanBooru IMage Utility functions, borrowed from\n# https://github.com/toriato/stable-diffusion-webui-wd14-tagger/blob/m"
},
{
"path": "file_manager.py",
"chars": 9843,
"preview": "import os\nimport re\nfrom typing import List\n\nimport PIL\nfrom PIL.Image import Image\n\n\n\ndef clean_string(s):\n \"\"\"\n "
},
{
"path": "install.py",
"chars": 971,
"preview": "import os\nimport sys\n\nfrom launch import run, git_clone, repo_dir\n\nname = \"Smart Crop\"\nreq_file = os.path.join(os.path.d"
},
{
"path": "interrogators/blip_interrogator.py",
"chars": 2677,
"preview": "from PIL.Image import Image\n\nfrom extensions.sd_smartprocess.interrogators.interrogator import Interrogator\nfrom transfo"
},
{
"path": "interrogators/booru_interrogator.py",
"chars": 2289,
"preview": "import re\n\nimport numpy as np\nimport torch\nfrom PIL.Image import Image\n\nimport modules.deepbooru\nfrom extensions.sd_smar"
},
{
"path": "interrogators/data/artists.txt",
"chars": 81710,
"preview": "A. B. Jackson\nA. J. Casson\nA. R. Middleton Todd\nA.B. Frost\nA.D.M. Cooper\nAaron Bohrod\nAaron Douglas\nAaron Jasinski\nAaron"
},
{
"path": "interrogators/data/flavors.txt",
"chars": 1802549,
"preview": "highly detailed\nsharp focus\nintricate\ndigital painting\nillustration\noctane render\nsmooth\n8 k\nelegant\n8k\ncinematic\n4k\nfan"
},
{
"path": "interrogators/data/mediums.txt",
"chars": 1605,
"preview": "a 3D render\na black and white photo\na bronze sculpture\na cartoon\na cave painting\na character portrait\na charcoal drawing"
},
{
"path": "interrogators/data/movements.txt",
"chars": 2680,
"preview": "abstract art\nabstract expressionism\nabstract illusionism\nacademic art\naction painting\naestheticism\nafrofuturism\naltermod"
},
{
"path": "interrogators/idefics2_interrogator.py",
"chars": 6598,
"preview": "import gc\nimport logging\nfrom datetime import datetime\n\nimport torch\nfrom PIL import Image\nfrom transformers import Auto"
},
{
"path": "interrogators/interrogator.py",
"chars": 3237,
"preview": "# Borrowed from https://github.com/toriato/stable-diffusion-webui-wd14-tagger/blob/master/tagger/interrogator.py\nimport "
},
{
"path": "interrogators/llava2_interrogator.py",
"chars": 6971,
"preview": "import gc\nimport logging\nfrom datetime import datetime\n\nimport torch\nfrom PIL import Image\nfrom transformers import Text"
},
{
"path": "interrogators/moondream_interrogator.py",
"chars": 1658,
"preview": "from PIL.Image import Image\n\nfrom extensions.sd_smartprocess.interrogators.interrogator import Interrogator\nfrom model_d"
},
{
"path": "interrogators/wolf_interrogator.py",
"chars": 7774,
"preview": "import logging\n\nimport PIL\nimport cv2\nimport huggingface_hub\nimport numpy as np\nimport pandas as pd\nfrom PIL.Image impor"
},
{
"path": "javascript/smart_process.js",
"chars": 411,
"preview": "function start_smart_process() {\n let progress = gradioApp().getElementById(\"sp_progress\");\n let gallery = gradioA"
},
{
"path": "model_download.py",
"chars": 1476,
"preview": "import os\n\nimport torch\nfrom huggingface_hub import snapshot_download\n\nfrom modules.paths_internal import models_path\nfr"
},
{
"path": "mplug_owl2/__init__.py",
"chars": 44,
"preview": "from .model import MPLUGOwl2LlamaForCausalLM"
},
{
"path": "mplug_owl2/constants.py",
"chars": 192,
"preview": "CONTROLLER_HEART_BEAT_EXPIRATION = 30\nWORKER_HEART_BEAT_INTERVAL = 15\n\nLOGDIR = \"./demo_logs\"\n\n# Model Constants\nIGNORE_"
},
{
"path": "mplug_owl2/conversation.py",
"chars": 12760,
"preview": "import dataclasses\nfrom enum import auto, Enum\nfrom typing import List, Tuple\nfrom mplug_owl2.constants import DEFAULT_I"
},
{
"path": "mplug_owl2/evaluate/EVALUATION.md",
"chars": 7786,
"preview": "# Evaluation\n## Important\nWe use `batch_size = 1` for evaluation due to the batch inference issue in LLaMA if you do not"
},
{
"path": "mplug_owl2/evaluate/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "mplug_owl2/evaluate/evaluate_caption.py",
"chars": 7197,
"preview": "import argparse\nimport itertools\nimport json\nimport os\nimport random\nimport time\nfrom functools import partial\n\nimport t"
},
{
"path": "mplug_owl2/evaluate/evaluate_mmbench.py",
"chars": 9452,
"preview": "import argparse\nimport itertools\nimport json\nimport os\nimport random\nimport time\nfrom functools import partial\nfrom typi"
},
{
"path": "mplug_owl2/evaluate/evaluate_mme.py",
"chars": 13960,
"preview": "import argparse\nimport itertools\nimport json\nimport os\nimport random\nimport time\nfrom functools import partial\nfrom typi"
},
{
"path": "mplug_owl2/evaluate/evaluate_mmmu.py",
"chars": 20287,
"preview": "import argparse\nimport itertools\nimport json\nimport os\nimport random\nimport time\nfrom functools import partial\nfrom typi"
},
{
"path": "mplug_owl2/evaluate/evaluate_vqa.py",
"chars": 9045,
"preview": "import argparse\nimport itertools\nimport json\nimport os\nimport random\nimport time\nfrom functools import partial\nfrom typi"
},
{
"path": "mplug_owl2/evaluate/mmbench_converter.py",
"chars": 1351,
"preview": "import pandas as pd\nimport io\nimport base64\nimport json\nfrom PIL import Image\n\n'''\nThis scripts convert mmbench_dev tsv "
},
{
"path": "mplug_owl2/evaluate/vqa.py",
"chars": 8506,
"preview": "\"\"\"Copyright (c) 2022, salesforce.com, inc.\n\nAll rights reserved.\nSPDX-License-Identifier: BSD-3-Clause\nFor full license"
},
{
"path": "mplug_owl2/evaluate/vqa_eval.py",
"chars": 17348,
"preview": "\"\"\"Copyright (c) 2022, salesforce.com, inc.\n\nAll rights reserved.\nSPDX-License-Identifier: BSD-3-Clause\nFor full license"
},
{
"path": "mplug_owl2/local_serve/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "mplug_owl2/local_serve/local_web_server.py",
"chars": 15440,
"preview": "import argparse\nimport datetime\nimport json\nimport os\nimport time\n\nimport gradio as gr\nimport requests\n\nfrom mplug_owl2."
},
{
"path": "mplug_owl2/local_serve/model_worker.py",
"chars": 5670,
"preview": "\"\"\"\nA model worker executes the model.\n\"\"\"\nimport argparse\nimport asyncio\nimport json\nimport time\nimport threading\nimpor"
},
{
"path": "mplug_owl2/mm_utils.py",
"chars": 4456,
"preview": "from PIL import Image\nfrom io import BytesIO\nimport base64\n\nimport torch\nfrom transformers import StoppingCriteria\nfrom "
},
{
"path": "mplug_owl2/model/__init__.py",
"chars": 159,
"preview": "from .modeling_mplug_owl2 import MPLUGOwl2LlamaForCausalLM, MPLUGOwl2QWenForCausalLM\nfrom .configuration_mplug_owl2 impo"
},
{
"path": "mplug_owl2/model/builder.py",
"chars": 7499,
"preview": "# Copyright 2023 Haotian Liu\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not "
},
{
"path": "mplug_owl2/model/configuration_mplug_owl2.py",
"chars": 16102,
"preview": "# Copyright (c) Alibaba.\n#\n# This source code is licensed under the license found in the\n# LICENSE file in the root dire"
},
{
"path": "mplug_owl2/model/configuration_qwen.py",
"chars": 2401,
"preview": "# Copyright (c) Alibaba Cloud.\n#\n# This source code is licensed under the license found in the\n# LICENSE file in the roo"
},
{
"path": "mplug_owl2/model/convert_mplug_owl2_weight_to_hf.py",
"chars": 23109,
"preview": "# Copyright 2023 DAMO Academy and The HuggingFace Inc. team. All rights reserved.\n#\n# Licensed under the Apache License,"
},
{
"path": "mplug_owl2/model/modeling_attn_mask_utils.py",
"chars": 10121,
"preview": "# Copyright 2023 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"Lic"
},
{
"path": "mplug_owl2/model/modeling_llama2.py",
"chars": 20496,
"preview": "import inspect\nimport math\nimport warnings\nfrom functools import partial\nfrom typing import List, Optional, Tuple, Union"
},
{
"path": "mplug_owl2/model/modeling_mplug_owl2.py",
"chars": 25761,
"preview": "# Copyright 2023 Haotian Liu & Qinghao Ye (Modified from LLaVA)\n#\n# Licensed under the Apache License, Version 2.0"
},
{
"path": "mplug_owl2/model/modeling_qwen.py",
"chars": 53549,
"preview": "# Copyright (c) Alibaba Cloud.\n#\n# This source code is licensed under the license found in the\n# LICENSE file in the roo"
},
{
"path": "mplug_owl2/model/multiway.py",
"chars": 1347,
"preview": "\nimport torch\nimport torch.utils.checkpoint\nfrom torch import nn\n\n\nclass MultiwayNetwork(nn.Module):\n\n def __init__(s"
},
{
"path": "mplug_owl2/model/utils.py",
"chars": 946,
"preview": "from transformers import AutoConfig\n\n\ndef auto_upgrade(config):\n cfg = AutoConfig.from_pretrained(config)\n if 'mpl"
},
{
"path": "mplug_owl2/model/visual_encoder.py",
"chars": 40369,
"preview": "import math\nfrom typing import Any, Optional, Tuple, Union\n\nfrom transformers.modeling_outputs import BaseModelOutput, B"
},
{
"path": "mplug_owl2/serve/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "mplug_owl2/serve/cli.py",
"chars": 4554,
"preview": "import argparse\nimport torch\n\nfrom mplug_owl2.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN\nfrom mplug_owl2.co"
},
{
"path": "mplug_owl2/serve/controller.py",
"chars": 9945,
"preview": "\"\"\"\nA controller manages distributed workers.\nIt sends worker addresses to clients.\n\"\"\"\nimport argparse\nimport asyncio\ni"
},
{
"path": "mplug_owl2/serve/gradio_web_server.py",
"chars": 17787,
"preview": "import argparse\nimport datetime\nimport json\nimport os\nimport time\n\nimport gradio as gr\nimport requests\n\nfrom mplug_owl2."
},
{
"path": "mplug_owl2/serve/model_worker.py",
"chars": 10389,
"preview": "\"\"\"\nA model worker executes the model.\n\"\"\"\nimport argparse\nimport asyncio\nimport json\nimport time\nimport threading\nimpor"
},
{
"path": "mplug_owl2/serve/register_workers.py",
"chars": 733,
"preview": "\"\"\"\nManually register workers.\n\nUsage:\npython3 -m fastchat.serve.register_worker --controller http://localhost:21001 --w"
},
{
"path": "mplug_owl2/train/llama_flash_attn_monkey_patch.py",
"chars": 4515,
"preview": "from typing import Optional, Tuple\nimport warnings\n\nimport torch\n\nimport transformers\nfrom transformers.models.llama.mod"
},
{
"path": "mplug_owl2/train/mplug_owl2_trainer.py",
"chars": 10647,
"preview": "import os\nimport torch\n\nfrom torch.utils.data import Sampler\n\nfrom transformers import Trainer\nfrom transformers.trainer"
},
{
"path": "mplug_owl2/train/train.py",
"chars": 31796,
"preview": "# Adopted from https://github.com/lm-sys/FastChat. Below is the original copyright:\n# Adopted from tatsu-lab@stanford_al"
},
{
"path": "mplug_owl2/train/train_mem.py",
"chars": 507,
"preview": "# Adopted from https://github.com/lm-sys/FastChat. Below is the original copyright:\n# Adopted from tatsu-lab@stanford_al"
},
{
"path": "mplug_owl2/utils.py",
"chars": 3989,
"preview": "import datetime\nimport logging\nimport logging.handlers\nimport os\nimport sys\n\nimport requests\n\nfrom mplug_owl2.constants "
},
{
"path": "process_params.py",
"chars": 3444,
"preview": "from dataclasses import dataclass, field\nfrom typing import List, Dict\n\nfrom extensions.sd_smartprocess.file_manager imp"
},
{
"path": "processors.py",
"chars": 7694,
"preview": "import gc\nimport random\nfrom typing import List, Any\n\nimport torch\nfrom PIL import Image\nfrom tqdm import tqdm\n\nfrom ext"
},
{
"path": "requirements.txt",
"chars": 180,
"preview": "ipython==8.18.0\nspandrel>=0.3.1\nseaborn==0.13.2\ntensorflow\nopen_clip_torch\nicecream==2.1.3\nchardet\ncchardet\npeft\nflask\nr"
},
{
"path": "scripts/process_main.py",
"chars": 45776,
"preview": "import os\nimport shutil\nimport sys\nfrom importlib import import_module\nfrom typing import Tuple, Union\n\nimport gradio as"
},
{
"path": "smartprocess.py",
"chars": 27218,
"preview": "import gc\nimport os\nimport re\nimport sys\nimport traceback\nfrom io import StringIO\nfrom math import sqrt\nfrom pathlib imp"
},
{
"path": "style.css",
"chars": 836,
"preview": "#sp_src {\n width: calc(100% - 84px);\n min-width: calc(100% - 84px);\n}\n\n#sp_clear_src, #sp_load_src {\n margin-to"
},
{
"path": "super_resolution.py",
"chars": 8131,
"preview": "import gc\nimport os.path\n\nimport safetensors.torch\nimport torch\nimport numpy as np\nfrom PIL import Image\nfrom omegaconf "
},
{
"path": "upscalers/spandrel/spandrel_srformer_model.py",
"chars": 2386,
"preview": "import os.path\n\nfrom extensions.sd_smartprocess.upscalers.spandrel.spandrel_upscaler_base import SpandrelUpscaler\nfrom m"
},
{
"path": "upscalers/spandrel/spandrel_upscaler_base.py",
"chars": 7401,
"preview": "from __future__ import annotations\n\nimport logging\nimport os\nimport re\nimport time\nimport traceback\nimport zipfile\nfrom "
}
]
About this extraction
This page contains the full source code of the d8ahazard/sd_smartprocess GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 72 files (2.4 MB), approximately 624.4k tokens, and a symbol index with 596 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.