Duplicate from SmilingWolf/wd-v1-4-tags

Co-authored-by: Smiling Wolf <SmilingWolf@users.noreply.huggingface.co>

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bin.* filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zstandard filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

.gitignore

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+images

README.md

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+---
+title: WaifuDiffusion v1.4 Tags
+emoji: 💬
+colorFrom: blue
+colorTo: red
+sdk: gradio
+sdk_version: 3.16.2
+app_file: app.py
+pinned: false
+duplicated_from: SmilingWolf/wd-v1-4-tags
+---
+
+# Configuration
+
+`title`: _string_
+Display title for the Space
+
+`emoji`: _string_
+Space emoji (emoji-only character allowed)
+
+`colorFrom`: _string_
+Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
+
+`colorTo`: _string_
+Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
+
+`sdk`: _string_
+Can be either `gradio`, `streamlit`, or `static`
+
+`sdk_version` : _string_
+Only applicable for `streamlit` SDK.  
+See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
+
+`app_file`: _string_
+Path to your main application file (which contains either `gradio` or `streamlit` Python code, or `static` html code).
+Path is relative to the root of the repository.
+
+`pinned`: _boolean_
+Whether the Space stays on top of your list.

Utils/dbimutils.py

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+# DanBooru IMage Utility functions
+
+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

app.py

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+from __future__ import annotations
+
+import argparse
+import functools
+import html
+import os
+
+import gradio as gr
+import huggingface_hub
+import numpy as np
+import onnxruntime as rt
+import pandas as pd
+import piexif
+import piexif.helper
+import PIL.Image
+
+from Utils import dbimutils
+
+TITLE = "WaifuDiffusion v1.4 Tags"
+DESCRIPTION = """
+Demo for:
+- [SmilingWolf/wd-v1-4-moat-tagger-v2](https://huggingface.co/SmilingWolf/wd-v1-4-moat-tagger-v2)
+- [SmilingWolf/wd-v1-4-swinv2-tagger-v2](https://huggingface.co/SmilingWolf/wd-v1-4-convnext-tagger-v2)
+- [SmilingWolf/wd-v1-4-convnext-tagger-v2](https://huggingface.co/SmilingWolf/wd-v1-4-convnext-tagger-v2)
+- [SmilingWolf/wd-v1-4-convnextv2-tagger-v2](https://huggingface.co/SmilingWolf/wd-v1-4-convnextv2-tagger-v2)
+- [SmilingWolf/wd-v1-4-vit-tagger-v2](https://huggingface.co/SmilingWolf/wd-v1-4-vit-tagger-v2)
+
+Includes "ready to copy" prompt and a prompt analyzer.
+
+Modified from [NoCrypt/DeepDanbooru_string](https://huggingface.co/spaces/NoCrypt/DeepDanbooru_string)  
+Modified from [hysts/DeepDanbooru](https://huggingface.co/spaces/hysts/DeepDanbooru)
+
+PNG Info code forked from [AUTOMATIC1111/stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui)
+
+Example image by [ほし☆☆☆](https://www.pixiv.net/en/users/43565085)
+"""
+
+HF_TOKEN = os.environ["HF_TOKEN"]
+MOAT_MODEL_REPO = "SmilingWolf/wd-v1-4-moat-tagger-v2"
+SWIN_MODEL_REPO = "SmilingWolf/wd-v1-4-swinv2-tagger-v2"
+CONV_MODEL_REPO = "SmilingWolf/wd-v1-4-convnext-tagger-v2"
+CONV2_MODEL_REPO = "SmilingWolf/wd-v1-4-convnextv2-tagger-v2"
+VIT_MODEL_REPO = "SmilingWolf/wd-v1-4-vit-tagger-v2"
+MODEL_FILENAME = "model.onnx"
+LABEL_FILENAME = "selected_tags.csv"
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--score-slider-step", type=float, default=0.05)
+    parser.add_argument("--score-general-threshold", type=float, default=0.35)
+    parser.add_argument("--score-character-threshold", type=float, default=0.85)
+    parser.add_argument("--share", action="store_true")
+    return parser.parse_args()
+
+
+def load_model(model_repo: str, model_filename: str) -> rt.InferenceSession:
+    path = huggingface_hub.hf_hub_download(
+        model_repo, model_filename, use_auth_token=HF_TOKEN
+    )
+    model = rt.InferenceSession(path)
+    return model
+
+
+def change_model(model_name):
+    global loaded_models
+
+    if model_name == "MOAT":
+        model = load_model(MOAT_MODEL_REPO, MODEL_FILENAME)
+    elif model_name == "SwinV2":
+        model = load_model(SWIN_MODEL_REPO, MODEL_FILENAME)
+    elif model_name == "ConvNext":
+        model = load_model(CONV_MODEL_REPO, MODEL_FILENAME)
+    elif model_name == "ConvNextV2":
+        model = load_model(CONV2_MODEL_REPO, MODEL_FILENAME)
+    elif model_name == "ViT":
+        model = load_model(VIT_MODEL_REPO, MODEL_FILENAME)
+
+    loaded_models[model_name] = model
+    return loaded_models[model_name]
+
+
+def load_labels() -> list[str]:
+    path = huggingface_hub.hf_hub_download(
+        MOAT_MODEL_REPO, LABEL_FILENAME, use_auth_token=HF_TOKEN
+    )
+    df = pd.read_csv(path)
+
+    tag_names = df["name"].tolist()
+    rating_indexes = list(np.where(df["category"] == 9)[0])
+    general_indexes = list(np.where(df["category"] == 0)[0])
+    character_indexes = list(np.where(df["category"] == 4)[0])
+    return tag_names, rating_indexes, general_indexes, character_indexes
+
+
+def plaintext_to_html(text):
+    text = (
+        "<p>" + "<br>\n".join([f"{html.escape(x)}" for x in text.split("\n")]) + "</p>"
+    )
+    return text
+
+
+def predict(
+    image: PIL.Image.Image,
+    model_name: str,
+    general_threshold: float,
+    character_threshold: float,
+    tag_names: list[str],
+    rating_indexes: list[np.int64],
+    general_indexes: list[np.int64],
+    character_indexes: list[np.int64],
+):
+    global loaded_models
+
+    rawimage = image
+
+    model = loaded_models[model_name]
+    if model is None:
+        model = change_model(model_name)
+
+    _, height, width, _ = model.get_inputs()[0].shape
+
+    # Alpha to white
+    image = image.convert("RGBA")
+    new_image = PIL.Image.new("RGBA", image.size, "WHITE")
+    new_image.paste(image, mask=image)
+    image = new_image.convert("RGB")
+    image = np.asarray(image)
+
+    # PIL RGB to OpenCV BGR
+    image = image[:, :, ::-1]
+
+    image = dbimutils.make_square(image, height)
+    image = dbimutils.smart_resize(image, height)
+    image = image.astype(np.float32)
+    image = np.expand_dims(image, 0)
+
+    input_name = model.get_inputs()[0].name
+    label_name = model.get_outputs()[0].name
+    probs = model.run([label_name], {input_name: image})[0]
+
+    labels = list(zip(tag_names, probs[0].astype(float)))
+
+    # First 4 labels are actually ratings: pick one with argmax
+    ratings_names = [labels[i] for i in rating_indexes]
+    rating = dict(ratings_names)
+
+    # Then we have general tags: pick any where prediction confidence > threshold
+    general_names = [labels[i] for i in general_indexes]
+    general_res = [x for x in general_names if x[1] > general_threshold]
+    general_res = dict(general_res)
+
+    # Everything else is characters: pick any where prediction confidence > threshold
+    character_names = [labels[i] for i in character_indexes]
+    character_res = [x for x in character_names if x[1] > character_threshold]
+    character_res = dict(character_res)
+
+    b = dict(sorted(general_res.items(), key=lambda item: item[1], reverse=True))
+    a = (
+        ", ".join(list(b.keys()))
+        .replace("_", " ")
+        .replace("(", "\(")
+        .replace(")", "\)")
+    )
+    c = ", ".join(list(b.keys()))
+
+    items = rawimage.info
+    geninfo = ""
+
+    if "exif" in rawimage.info:
+        exif = piexif.load(rawimage.info["exif"])
+        exif_comment = (exif or {}).get("Exif", {}).get(piexif.ExifIFD.UserComment, b"")
+        try:
+            exif_comment = piexif.helper.UserComment.load(exif_comment)
+        except ValueError:
+            exif_comment = exif_comment.decode("utf8", errors="ignore")
+
+        items["exif comment"] = exif_comment
+        geninfo = exif_comment
+
+        for field in [
+            "jfif",
+            "jfif_version",
+            "jfif_unit",
+            "jfif_density",
+            "dpi",
+            "exif",
+            "loop",
+            "background",
+            "timestamp",
+            "duration",
+        ]:
+            items.pop(field, None)
+
+    geninfo = items.get("parameters", geninfo)
+
+    info = f"""
+<p><h4>PNG Info</h4></p>    
+"""
+    for key, text in items.items():
+        info += (
+            f"""
+<div>
+<p><b>{plaintext_to_html(str(key))}</b></p>
+<p>{plaintext_to_html(str(text))}</p>
+</div>
+""".strip()
+            + "\n"
+        )
+
+    if len(info) == 0:
+        message = "Nothing found in the image."
+        info = f"<div><p>{message}<p></div>"
+
+    return (a, c, rating, character_res, general_res, info)
+
+
+def main():
+    global loaded_models
+    loaded_models = {
+        "MOAT": None,
+        "SwinV2": None,
+        "ConvNext": None,
+        "ConvNextV2": None,
+        "ViT": None,
+    }
+
+    args = parse_args()
+
+    change_model("MOAT")
+
+    tag_names, rating_indexes, general_indexes, character_indexes = load_labels()
+
+    func = functools.partial(
+        predict,
+        tag_names=tag_names,
+        rating_indexes=rating_indexes,
+        general_indexes=general_indexes,
+        character_indexes=character_indexes,
+    )
+
+    gr.Interface(
+        fn=func,
+        inputs=[
+            gr.Image(type="pil", label="Input"),
+            gr.Radio(
+                ["MOAT", "SwinV2", "ConvNext", "ConvNextV2", "ViT"],
+                value="MOAT",
+                label="Model",
+            ),
+            gr.Slider(
+                0,
+                1,
+                step=args.score_slider_step,
+                value=args.score_general_threshold,
+                label="General Tags Threshold",
+            ),
+            gr.Slider(
+                0,
+                1,
+                step=args.score_slider_step,
+                value=args.score_character_threshold,
+                label="Character Tags Threshold",
+            ),
+        ],
+        outputs=[
+            gr.Textbox(label="Output (string)"),
+            gr.Textbox(label="Output (raw string)"),
+            gr.Label(label="Rating"),
+            gr.Label(label="Output (characters)"),
+            gr.Label(label="Output (tags)"),
+            gr.HTML(),
+        ],
+        examples=[["power.jpg", "MOAT", 0.35, 0.85]],
+        title=TITLE,
+        description=DESCRIPTION,
+        allow_flagging="never",
+    ).launch(
+        enable_queue=True,
+        share=args.share,
+    )
+
+
+if __name__ == "__main__":
+    main()

requirements.txt

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+pillow>=9.0.0
+piexif>=1.1.3
+onnxruntime>=1.12.0
+opencv-python
+huggingface-hub