Add files

.pre-commit-config.yaml

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+repos:
+- repo: https://github.com/pre-commit/pre-commit-hooks
+  rev: v4.4.0
+  hooks:
+  - id: check-executables-have-shebangs
+  - id: check-json
+  - id: check-merge-conflict
+  - id: check-shebang-scripts-are-executable
+  - id: check-toml
+  - id: check-yaml
+  - id: end-of-file-fixer
+  - id: mixed-line-ending
+    args: ["--fix=lf"]
+  - id: requirements-txt-fixer
+  - id: trailing-whitespace
+- repo: https://github.com/myint/docformatter
+  rev: v1.7.5
+  hooks:
+  - id: docformatter
+    args: ["--in-place"]
+- repo: https://github.com/pycqa/isort
+  rev: 5.12.0
+  hooks:
+    - id: isort
+      args: ["--profile", "black"]
+- repo: https://github.com/pre-commit/mirrors-mypy
+  rev: v1.5.1
+  hooks:
+    - id: mypy
+      args: ["--ignore-missing-imports"]
+      additional_dependencies: ["types-python-slugify", "types-requests", "types-PyYAML"]
+- repo: https://github.com/psf/black
+  rev: 23.7.0
+  hooks:
+    - id: black
+      language_version: python3.10
+      args: ["--line-length", "119"]
+- repo: https://github.com/kynan/nbstripout
+  rev: 0.6.1
+  hooks:
+    - id: nbstripout
+      args: ["--extra-keys", "metadata.interpreter metadata.kernelspec cell.metadata.pycharm"]
+- repo: https://github.com/nbQA-dev/nbQA
+  rev: 1.7.0
+  hooks:
+    - id: nbqa-black
+    - id: nbqa-pyupgrade
+      args: ["--py37-plus"]
+    - id: nbqa-isort
+      args: ["--float-to-top"]

.vscode/settings.json

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+{
+    "[python]": {
+        "editor.defaultFormatter": "ms-python.black-formatter",
+        "editor.formatOnType": true,
+        "editor.codeActionsOnSave": {
+            "source.organizeImports": true
+        }
+    },
+    "black-formatter.args": [
+        "--line-length=119"
+    ],
+    "isort.args": ["--profile", "black"],
+    "flake8.args": [
+        "--max-line-length=119"
+    ],
+    "ruff.args": [
+        "--line-length=119"
+    ],
+    "editor.formatOnSave": true,
+    "files.insertFinalNewline": true
+}

LICENSE

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+MIT License
+
+Copyright (c) 2023 hysts
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

app.py

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+#!/usr/bin/env python
+
+import random
+
+import gradio as gr
+import numpy as np
+import PIL.Image
+import torch
+from diffusers import DDPMScheduler, StableDiffusionXLAdapterPipeline, T2IAdapter
+
+DESCRIPTION = "# T2I-Adapter-SDXL Sketch"
+
+if not torch.cuda.is_available():
+    DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>"
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+if torch.cuda.is_available():
+    model_id = "stabilityai/stable-diffusion-xl-base-1.0"
+    adapter = T2IAdapter.from_pretrained(
+        "Adapter/t2iadapter",
+        subfolder="sketch_sdxl_1.0",
+        torch_dtype=torch.float16,
+        adapter_type="full_adapter_xl",
+    )
+    scheduler = DDPMScheduler.from_pretrained(model_id, subfolder="scheduler")
+    pipe = StableDiffusionXLAdapterPipeline.from_pretrained(
+        model_id,
+        adapter=adapter,
+        safety_checker=None,
+        torch_dtype=torch.float16,
+        variant="fp16",
+        scheduler=scheduler,
+    )
+    pipe.to(device)
+else:
+    pipe = None
+
+MAX_SEED = np.iinfo(np.int32).max
+
+
+def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    return seed
+
+
+def run(
+    image: PIL.Image.Image,
+    prompt: str,
+    negative_prompt: str,
+    num_steps=50,
+    guidance_scale=7.5,
+    seed=0,
+) -> PIL.Image.Image:
+    # Convert the input image, which is a boolean image, to a grayscale image whose value is 0 or 255.
+    image = image.convert("L")
+
+    generator = torch.Generator(device=device).manual_seed(seed)
+    out = pipe(
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        image=image,
+        num_inference_steps=num_steps,
+        generator=generator,
+        guidance_scale=guidance_scale,
+    ).images[0]
+    return out
+
+
+with gr.Blocks() as demo:
+    gr.Markdown(DESCRIPTION)
+    with gr.Row():
+        with gr.Column():
+            image = gr.Image(
+                source="canvas",
+                tool="sketch",
+                type="pil",
+                image_mode="1",
+                invert_colors=True,
+                shape=(1024, 1024),
+                brush_radius=20,
+                height=600,
+            )
+            prompt = gr.Textbox(label="Prompt")
+            run_button = gr.Button("Run")
+            with gr.Accordion("Advanced options", open=False):
+                negative_prompt = gr.Textbox(
+                    label="Negative prompt", value="extra digit, fewer digits, cropped, worst quality, low quality"
+                )
+                num_steps = gr.Slider(
+                    label="Number of steps",
+                    minimum=1,
+                    maximum=100,
+                    step=1,
+                    value=50,
+                )
+                guidance_scale = gr.Slider(
+                    label="Guidance scale",
+                    minimum=0.1,
+                    maximum=30.0,
+                    step=0.1,
+                    value=7.5,
+                )
+                seed = gr.Slider(
+                    label="Seed",
+                    minimum=0,
+                    maximum=MAX_SEED,
+                    step=1,
+                    value=0,
+                )
+                randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+        with gr.Column():
+            result = gr.Image(label="Result", height=600)
+
+    inputs = [
+        image,
+        prompt,
+        negative_prompt,
+        num_steps,
+        guidance_scale,
+        seed,
+    ]
+    prompt.submit(
+        fn=randomize_seed_fn,
+        inputs=[seed, randomize_seed],
+        outputs=seed,
+        queue=False,
+        api_name=False,
+    ).then(
+        fn=run,
+        inputs=inputs,
+        outputs=result,
+        api_name=False,
+    )
+    run_button.click(
+        fn=randomize_seed_fn,
+        inputs=[seed, randomize_seed],
+        outputs=seed,
+        queue=False,
+        api_name=False,
+    ).then(
+        fn=run,
+        inputs=inputs,
+        outputs=result,
+        api_name="run",
+    )
+
+if __name__ == "__main__":
+    demo.queue(max_size=20).launch()

requirements.txt

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+git+https://github.com/huggingface/diffusers
+gradio==3.42.0
+Pillow==10.0.0
+torch==2.0.1
+transformers==4.33.0

style.css

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+h1 {
+  text-align: center;
+}
+
+#duplicate-button {
+  margin: auto;
+  color: #fff;
+  background: #1565c0;
+  border-radius: 100vh;
+}