brings back sdxl turbo example as image_to_image

06_gpu_and_ml/stable_diffusion/image_to_image.py

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+# ---
+# output-directory: "/tmp/stable-diffusion"
+# tags: ["use-case-image-video-3d"]
+# ---
+
+# # Transform images with SDXL Turbo
+
+# In this example, we run the SDXL Turbo model in _image-to-image_ mode:
+# the model takes in a prompt and an image and transforms the image to better match the prompt.
+
+# For example, the model transformed the image on the left into the image on the right based on the prompt
+# _dog wizard, gandalf, lord of the rings, detailed, fantasy, cute, adorable, Pixar, Disney, 8k_.
+
+# ![](https://modal-cdn.com/cdnbot/sd-im2im-dog-8sanham3_915c7d4c.webp)
+
+# SDXL Turbo is a distilled model designed for fast, interactive image synthesis.
+# Learn more about it [here](https://stability.ai/news/stability-ai-sdxl-turbo).
+
+# ## Define a container image
+
+# First, we define the environment the model inference will run in,
+# the [container image](https://modal.com/docs/guide/custom-container).
+
+from io import BytesIO
+from pathlib import Path
+
+import modal
+
+image = (
+    modal.Image.debian_slim(python_version="3.12")
+    .pip_install(
+        "accelerate~=0.25.0",  # Allows `device_map="auto"``, for computation of optimized device_map
+        "diffusers~=0.24.0",  # Provides model libraries
+        "huggingface-hub[hf-transfer]~=0.25.2",  # Lets us download models from Hugging Face's Hub
+        "Pillow~=10.1.0",  # Image manipulation in Python
+        "safetensors~=0.4.1",  # Enables safetensor format as opposed to using unsafe pickle format
+        "transformers~=4.35.2",  # This is needed for `import torch`
+    )
+    .env({"HF_HUB_ENABLE_HF_TRANSFER": "1"})  # allow faster model downloads
+)
+
+app = modal.App("image-to-image", image=image)
+
+with image.imports():
+    import torch
+    from diffusers import AutoPipelineForImage2Image
+    from diffusers.utils import load_image
+    from huggingface_hub import snapshot_download
+    from PIL import Image
+
+
+# ## Downloading, setting up, and running SDXL Turbo
+
+# The Modal `Cls` defined below contains all the logic to download, set up, and run SDXL Turbo.
+
+# The [container lifecycle](https://modal.com/docs/guide/lifecycle-functions#container-lifecycle-beta) decorators
+# `@build` and `@enter` ensure we download the model when building our container image and load it into memory
+# when we start up a new instance of our `Cls`.
+
+# The `inference` method runs the actual model inference. It takes in an image as a collection of `bytes` and a string `prompt` and returns
+# a new image (also as a collection of `bytes`).
+
+# To avoid excessive cold-starts, we set the `container_idle_timeout` to 240 seconds, meaning once a GPU has loaded the model it will stay
+# online for 4 minutes before spinning down.
+
+
+@app.cls(gpu=modal.gpu.A10G(), container_idle_timeout=240)
+class Model:
+    @modal.build()
+    def download_models(self):
+        # Ignore files that we don't need to speed up download time.
+        ignore = [
+            "*.bin",
+            "*.onnx_data",
+            "*/diffusion_pytorch_model.safetensors",
+        ]
+
+        snapshot_download("stabilityai/sdxl-turbo", ignore_patterns=ignore)
+
+    @modal.enter()
+    def enter(self):
+        self.pipe = AutoPipelineForImage2Image.from_pretrained(
+            "stabilityai/sdxl-turbo",
+            torch_dtype=torch.float16,
+            variant="fp16",
+            device_map="auto",
+        )
+
+    @modal.method()
+    def inference(
+        self, image_bytes: bytes, prompt: str, strength: float = 0.9
+    ) -> bytes:
+        init_image = load_image(Image.open(BytesIO(image_bytes))).resize(
+            (512, 512)
+        )
+        num_inference_steps = 4
+        # "When using SDXL-Turbo for image-to-image generation, make sure that num_inference_steps * strength is larger or equal to 1"
+        # See: https://huggingface.co/stabilityai/sdxl-turbo
+        assert num_inference_steps * strength >= 1
+
+        image = self.pipe(
+            prompt,
+            image=init_image,
+            num_inference_steps=num_inference_steps,
+            strength=strength,
+            guidance_scale=0.0,
+        ).images[0]
+
+        byte_stream = BytesIO()
+        image.save(byte_stream, format="PNG")
+        image_bytes = byte_stream.getvalue()
+
+        return image_bytes
+
+
+# ## Running the model from the command line
+
+# You can run the model from the command line with
+
+# ```bash
+# modal run image_to_image.py
+# ```
+
+# Use `--help` for additional details.
+
+
+@app.local_entrypoint()
+def main(
+    image_path=Path(__file__).parent / "demo_images/dog.png",
+    prompt="dog wizard, gandalf, lord of the rings, detailed, fantasy, cute, adorable, Pixar, Disney, 8k",
+    strength=0.9,  # increase to favor the prompt over the baseline image
+):
+    print(f"🎨 reading input image from {image_path}")
+    input_image_bytes = Path(image_path).read_bytes()
+    print(f"🎨 editing image with prompt {prompt}")
+    output_image_bytes = Model().inference.remote(input_image_bytes, prompt)
+
+    dir = Path("/tmp/stable-diffusion")
+    dir.mkdir(exist_ok=True, parents=True)
+
+    output_path = dir / "output.png"
+    print(f"🎨 saving output image to {output_path}")
+    output_path.write_bytes(output_image_bytes)