initial commit

.gitattributes

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Dockerfile

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+FROM python:3.12
+COPY --from=ghcr.io/astral-sh/uv:0.4.20 /uv /bin/uv
+
+RUN useradd -m -u 1000 user
+ENV PATH="/home/user/.local/bin:$PATH"
+ENV UV_SYSTEM_PYTHON=1
+
+WORKDIR /app
+
+COPY --chown=user ./requirements.txt requirements.txt
+RUN uv pip install -r requirements.txt
+
+COPY --chown=user . /app
+USER user
+
+CMD ["marimo", "run", "app.py", "--host", "0.0.0.0", "--port", "7860"]

README.md

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+---
+title: marimo text-to-image template
+emoji: 🎨
+colorFrom: blue
+colorTo: green
+sdk: docker
+pinned: true
+license: mit
+short_description: Template for deploying a marimo text-to-image app to HF
+---
+
+Check out marimo at <https://github.com/marimo-team/marimo>
+Check out the configuration reference at <https://huggingface.co/docs/hub/spaces-config-reference>

app.py

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+import marimo
+
+__generated_with = "0.9.14"
+app = marimo.App(width="medium")
+
+
+@app.cell(hide_code=True)
+def __():
+    import random
+
+    import marimo as mo
+    import numpy as np
+    import tqdm
+    import transformers
+
+    # Patch tqdm to work marimo notebooks
+    tqdm.auto.tqdm = tqdm.notebook.tqdm
+
+    import torch
+    from diffusers import DiffusionPipeline
+
+    MAX_SEED = np.iinfo(np.int32).max
+    MAX_IMAGE_SIZE = 1024
+
+    model_repo_id = (
+        "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
+    )
+    return (
+        DiffusionPipeline,
+        MAX_IMAGE_SIZE,
+        MAX_SEED,
+        mo,
+        model_repo_id,
+        np,
+        random,
+        torch,
+        tqdm,
+        transformers,
+    )
+
+
+@app.cell
+def __(mo, model_repo_id):
+    mo.md(f"""# HuggingFace Text-to-Image: **{model_repo_id}**""")
+    return
+
+
+@app.cell(hide_code=True)
+def __():
+    examples = [
+        "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
+        "An astronaut riding a green horse",
+        "A delicious ceviche cheesecake slice",
+    ]
+    return (examples,)
+
+
+@app.cell
+def __(
+    DiffusionPipeline,
+    MAX_SEED,
+    mo,
+    model_repo_id,
+    random,
+    torch,
+):
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+
+    if torch.cuda.is_available():
+        torch_dtype = torch.float16
+    else:
+        torch_dtype = torch.float32
+
+    pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
+    pipe = pipe.to(device)
+
+    def infer(
+        prompt,
+        negative_prompt,
+        seed,
+        randomize_seed,
+        width,
+        height,
+        guidance_scale,
+        num_inference_steps,
+    ):
+        if randomize_seed:
+            seed = random.randint(0, MAX_SEED)
+
+        generator = torch.Generator().manual_seed(seed)
+
+        image = pipe(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            guidance_scale=guidance_scale,
+            num_inference_steps=num_inference_steps,
+            width=width,
+            height=height,
+            generator=generator,
+        ).images[0]
+
+        return image, seed
+
+    mo.output.clear()  # Clear loading tdqm
+    return device, infer, pipe, torch_dtype
+
+
+@app.cell
+def __(mo):
+    get_prompt, set_prompt = mo.state("")
+    return get_prompt, set_prompt
+
+
+@app.cell
+def __(get_prompt, mo, set_prompt):
+    prompt = mo.ui.text_area(
+        placeholder="Enter your prompt",
+        label="Prompt",
+        full_width=True,
+        value=get_prompt(),
+        on_change=set_prompt,
+    )
+    return (prompt,)
+
+
+@app.cell
+def __(examples, mo, set_prompt):
+    def _on_click(example):
+        def handle(v):
+            set_prompt(example)
+
+        return handle
+
+    buttons = mo.ui.array(
+        [
+            mo.ui.button(label=example, on_click=_on_click(example))
+            for example in examples
+        ]
+    )
+
+    example_options = mo.vstack(buttons)
+    return buttons, example_options
+
+
+@app.cell
+def __(MAX_IMAGE_SIZE, MAX_SEED, example_options, mo, prompt):
+    run_button = mo.ui.run_button(label="Run", kind="success", full_width=True)
+
+    negative_prompt = mo.ui.text_area(
+        placeholder="Enter a negative prompt", label="Negative prompt"
+    )
+
+    seed = mo.ui.slider(start=0, stop=MAX_SEED, value=0, label="Seed")
+
+    randomize_seed = mo.ui.checkbox(label="Randomize seed", value=True)
+
+    width = mo.ui.slider(
+        start=256, stop=MAX_IMAGE_SIZE, step=32, value=1024, label="Width"
+    )
+    height = mo.ui.slider(
+        start=256, stop=MAX_IMAGE_SIZE, step=32, value=1024, label="Height"
+    )
+
+    guidance_scale = mo.ui.slider(
+        start=0.0, stop=10.0, step=0.1, value=0.0, label="Guidance scale"
+    )
+
+    num_inference_steps = mo.ui.slider(
+        start=1, stop=50, step=1, value=2, label="Number of inference steps"
+    )
+
+    # Create advanced settings in an accordion
+    advanced_settings = mo.accordion(
+        {
+            "::lucide:list:: Examples": example_options,
+            "::lucide:settings:: Advanced Settings": mo.hstack(
+                [
+                    mo.vstack([negative_prompt, seed, randomize_seed]),
+                    mo.vstack(
+                        [width, height, guidance_scale, num_inference_steps],
+                        align="end",
+                    ),
+                ]
+            ).style(padding="10px"),
+        },
+    )
+
+    # Layout the main interface
+    mo.vstack([prompt, run_button, advanced_settings])
+    return (
+        advanced_settings,
+        guidance_scale,
+        height,
+        negative_prompt,
+        num_inference_steps,
+        randomize_seed,
+        run_button,
+        seed,
+        width,
+    )
+
+
+@app.cell
+def __(mo):
+    get_image, set_image = mo.state(None)
+    return get_image, set_image
+
+
+@app.cell
+def __(
+    guidance_scale,
+    height,
+    infer,
+    mo,
+    negative_prompt,
+    num_inference_steps,
+    prompt,
+    randomize_seed,
+    run_button,
+    seed,
+    set_image,
+    width,
+):
+    mo.stop(not run_button.value)
+
+    _image, _seed = infer(
+        prompt.value,
+        negative_prompt.value,
+        seed.value,
+        randomize_seed.value,
+        width.value,
+        height.value,
+        guidance_scale.value,
+        num_inference_steps.value,
+    )
+    set_image(_image)
+    mo.output.clear()  # Clear loading tdqm
+    return
+
+
+@app.cell
+def __(get_image):
+    get_image()
+    return
+
+
+if __name__ == "__main__":
+    app.run()

development.md

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+# Development
+
+## Testing your Dockerfile locally
+
+```bash
+docker build -t marimo-app .
+docker run -it --rm -p 7860:7860 marimo-app
+```

requirements.txt

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+accelerate==1.1.0
+diffusers==0.31.0
+numpy==2.1.3
+torch==2.5.1
+tqdm==4.66.6
+transformers==4.46.1
+huggingface-hub==0.26.2
+marimo
+# Or a specific version
+# marimo>=0.9.0
+
+# Add other dependencies as needed