OmniGen (#2619)

CVS-157332

.ci/spellcheck/.pyspelling.wordlist.txt

@@ -591,6 +591,8 @@ ocr
 OCRBench
 OCRv
 odometry
+OmniGen
+OmniGen's
 OmniParser
 OMZ
 OneFormer

notebooks/omnigen/README.md

@@ -0,0 +1,39 @@
+# Unified image generation using OmniGen and OpenVINO
+
+OmniGen is a unified image generation model that can generate a wide range of images from multi-modal prompts. It is designed to be simple, flexible, and easy to use. Existing image generation models often require loading several additional network modules (such as ControlNet, IP-Adapter, Reference-Net, etc.) and performing extra preprocessing steps (e.g., face detection, pose estimation, cropping, etc.) to generate a satisfactory image. OmniGen can generate various images directly through arbitrarily multi-modal instructions without additional plugins and operations.  it can automatically identify the features (e.g., required object, human pose, depth mapping) in input images according to the text prompt.
+
+Here are the illustrations of OmniGen's capabilities:
+
+* You can control the image generation flexibly via OmniGen
+
+
+  ![exanple_1.png](https://github.com/VectorSpaceLab/OmniGen/raw/main/imgs/demo_cases.png)
+
+* Referring Expression Generation: You can input multiple images and use simple, general language to refer to the objects within those images. OmniGen can automatically recognize the necessary objects in each image and generate new images based on them. No additional operations, such as image cropping or face detection, are required.
+
+  ![example_2.png](https://github.com/VectorSpaceLab/OmniGen/raw/main/imgs/referring.png)
+
+
+You can find more details about a model on [project page](https://vectorspacelab.github.io/OmniGen/), [paper](https://arxiv.org/pdf/2409.11340v1), [original repository](https://github.com/VectorSpaceLab/OmniGen).
+
+This tutorial considers how to run and optimize OmniGen using OpenVINO.
+
+## Notebook contents
+The tutorial consists from following steps:
+
+- Install requirements
+- Convert and Optimize model using OpenVINO and NNCF
+- Run OpenVINO model inference
+- Launch Interactive demo
+
+In this demonstration, you'll try to run OmniGen for various image generation tasks.
+
+The image bellow is illustrates model's result for text to image generation.
+![](https://github.com/user-attachments/assets/ca0929af-f766-4e69-872f-95ceceeac634)
+
+
+## Installation instructions
+This is a self-contained example that relies solely on its own code.</br>
+We recommend running the notebook in a virtual environment. You only need a Jupyter server to start.
+For details, please refer to [Installation Guide](../../README.md).
+<img referrerpolicy="no-referrer-when-downgrade" src="https://static.scarf.sh/a.png?x-pxid=5b5a4db0-7875-4bfb-bdbd-01698b5b1a77&file=notebooks/omnigen/README.md" />

notebooks/omnigen/gradio_helper.py

@@ -0,0 +1,261 @@
+import gradio as gr
+import random
+
+
+def make_demo(pipe):
+    def generate_image(
+        text,
+        img1,
+        img2,
+        img3,
+        height,
+        width,
+        guidance_scale,
+        img_guidance_scale,
+        inference_steps,
+        seed,
+        max_input_image_size,
+        randomize_seed,
+        _=gr.Progress(track_tqdm=True),
+    ):
+        input_images = [img1, img2, img3]
+        # Delete None
+        input_images = [img for img in input_images if img is not None]
+        if len(input_images) == 0:
+            input_images = None
+
+        if randomize_seed:
+            seed = random.randint(0, 10000000)
+
+        output = pipe(
+            prompt=text,
+            input_images=input_images,
+            height=height,
+            width=width,
+            guidance_scale=guidance_scale,
+            img_guidance_scale=img_guidance_scale,
+            num_inference_steps=inference_steps,
+            separate_cfg_infer=True,
+            seed=seed,
+            max_input_image_size=max_input_image_size,
+        )
+        img = output[0]
+        return img
+
+    def get_example():
+        case = [
+            [
+                "A curly-haired man in a red shirt is drinking tea.",
+                None,
+                None,
+                None,
+                256,
+                256,
+                2.5,
+                1.6,
+                20,
+                256,
+            ],
+            [
+                "The woman in <img><|image_1|></img> waves her hand happily in the crowd",
+                "OmniGen/imgs/test_cases/zhang.png",
+                None,
+                None,
+                512,
+                512,
+                2.5,
+                1.9,
+                40,
+                256,
+            ],
+            [
+                "A man in a black shirt is reading a book. The man is the right man in <img><|image_1|></img>.",
+                "OmniGen/imgs/test_cases/two_man.jpg",
+                None,
+                None,
+                256,
+                256,
+                2.5,
+                1.6,
+                20,
+                256,
+            ],
+            [
+                "The flower <img><|image_1|></img> is placed in the vase which is in the middle of <img><|image_2|></img> on a wooden table of a living room",
+                "OmniGen/imgs/test_cases/rose.jpg",
+                "OmniGen/imgs/test_cases/vase.jpg",
+                None,
+                512,
+                512,
+                2.5,
+                1.6,
+                20,
+                512,
+            ],
+            [
+                "<img><|image_1|><img>\n Remove the woman's earrings. Replace the mug with a clear glass filled with sparkling iced cola.",
+                "OmniGen/imgs/demo_cases/t2i_woman_with_book.png",
+                None,
+                None,
+                320,
+                320,
+                2.5,
+                1.6,
+                24,
+                320,
+            ],
+            [
+                "Detect the skeleton of human in this image: <img><|image_1|></img>.",
+                "OmniGen/imgs/test_cases/control.jpg",
+                None,
+                None,
+                512,
+                512,
+                2.0,
+                1.6,
+                20,
+                512,
+            ],
+            [
+                "Generate a new photo using the following picture and text as conditions: <img><|image_1|><img>\n A young boy is sitting on a sofa in the library, holding a book. His hair is neatly combed, and a faint smile plays on his lips, with a few freckles scattered across his cheeks. The library is quiet, with rows of shelves filled with books stretching out behind him.",
+                "OmniGen/imgs/demo_cases/skeletal.png",
+                None,
+                None,
+                288,
+                320,
+                2,
+                1.6,
+                32,
+                320,
+            ],
+            [
+                "Following the depth mapping of this image <img><|image_1|><img>, generate a new photo: A young girl is sitting on a sofa in the library, holding a book. His hair is neatly combed, and a faint smile plays on his lips, with a few freckles scattered across his cheeks. The library is quiet, with rows of shelves filled with books stretching out behind him.",
+                "OmniGen/imgs/demo_cases/edit.png",
+                None,
+                None,
+                512,
+                512,
+                2.0,
+                1.6,
+                15,
+                512,
+            ],
+            [
+                "<img><|image_1|><\/img> What item can be used to see the current time? Please highlight it in blue.",
+                "OmniGen/imgs/test_cases/watch.jpg",
+                None,
+                None,
+                224,
+                224,
+                2.5,
+                1.6,
+                100,
+                224,
+            ],
+            [
+                "According to the following examples, generate an output for the input.\nInput: <img><|image_1|></img>\nOutput: <img><|image_2|></img>\n\nInput: <img><|image_3|></img>\nOutput: ",
+                "OmniGen/imgs/test_cases/icl1.jpg",
+                "OmniGen/imgs/test_cases/icl2.jpg",
+                "OmniGen/imgs/test_cases/icl3.jpg",
+                224,
+                224,
+                2.5,
+                1.6,
+                12,
+                768,
+            ],
+        ]
+        return case
+
+    description = """
+    OmniGen is a unified image generation model that you can use to perform various tasks, including but not limited to text-to-image generation, subject-driven generation, Identity-Preserving Generation, and image-conditioned generation.
+    For multi-modal to image generation, you should pass a string as `prompt`, and a list of image paths as `input_images`. The placeholder in the prompt should be in the format of `<img><|image_*|></img>` (for the first image, the placeholder is <img><|image_1|></img>. for the second image, the the placeholder is <img><|image_2|></img>).
+    For example, use an image of a woman to generate a new image:
+    prompt = "A woman holds a bouquet of flowers and faces the camera. Thw woman is \<img\>\<|image_1|\>\</img\>."
+    Tips:
+    - For image editing task and controlnet task, we recommend setting the height and width of output image as the same as input image. For example, if you want to edit a 512x512 image, you should set the height and width of output image as 512x512. You also can set the `use_input_image_size_as_output` to automatically set the height and width of output image as the same as input image.
+    - If inference time is too long when inputting multiple images, please try to reduce the `max_input_image_size`. 
+    - Oversaturated: If the image appears oversaturated, please reduce the `guidance_scale`.
+    - Low-quality: More detailed prompts will lead to better results. 
+    - Animate Style: If the generated images are in animate style, you can try to add `photo` to the prompt`.
+    - Edit generated image. If you generate an image by omnigen and then want to edit it, you cannot use the same seed to edit this image. For example, use seed=0 to generate image, and should use seed=1 to edit this image.
+    - For image editing tasks, we recommend placing the image before the editing instruction. For example, use `<img><|image_1|></img> remove suit`, rather than `remove suit <img><|image_1|></img>`. 
+    """
+
+    # Gradio
+    with gr.Blocks() as demo:
+        gr.Markdown("# OmniGen: Unified Image Generation")
+        gr.Markdown(description)
+        with gr.Row():
+            with gr.Column():
+                # text prompt
+                prompt_input = gr.Textbox(
+                    label="Enter your prompt, use <img><|image_i|></img> to represent i-th input image", placeholder="Type your prompt here..."
+                )
+
+                with gr.Row(equal_height=True):
+                    # input images
+                    image_input_1 = gr.Image(label="<img><|image_1|></img>", type="filepath")
+                    image_input_2 = gr.Image(label="<img><|image_2|></img>", type="filepath")
+                    image_input_3 = gr.Image(label="<img><|image_3|></img>", type="filepath")
+
+                # slider
+                height_input = gr.Slider(label="Height", minimum=128, maximum=2048, value=256, step=16)
+                width_input = gr.Slider(label="Width", minimum=128, maximum=2048, value=256, step=16)
+
+                guidance_scale_input = gr.Slider(label="Guidance Scale", minimum=1.0, maximum=5.0, value=2.5, step=0.1)
+
+                img_guidance_scale_input = gr.Slider(label="img_guidance_scale", minimum=1.0, maximum=2.0, value=1.6, step=0.1)
+
+                num_inference_steps = gr.Slider(label="Inference Steps", minimum=1, maximum=100, value=20, step=1)
+
+                seed_input = gr.Slider(label="Seed", minimum=0, maximum=2147483647, value=42, step=1)
+                randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+
+                max_input_image_size = gr.Slider(label="max_input_image_size", minimum=128, maximum=2048, value=256, step=16)
+
+                # generate
+                generate_button = gr.Button("Generate Image")
+
+            with gr.Column():
+                # output image
+                output_image = gr.Image(label="Output Image")
+
+        # click
+        generate_button.click(
+            generate_image,
+            inputs=[
+                prompt_input,
+                image_input_1,
+                image_input_2,
+                image_input_3,
+                height_input,
+                width_input,
+                guidance_scale_input,
+                img_guidance_scale_input,
+                num_inference_steps,
+                seed_input,
+                max_input_image_size,
+                randomize_seed,
+            ],
+            outputs=output_image,
+        )
+
+        gr.Examples(
+            examples=get_example(),
+            inputs=[
+                prompt_input,
+                image_input_1,
+                image_input_2,
+                image_input_3,
+                height_input,
+                width_input,
+                guidance_scale_input,
+                img_guidance_scale_input,
+                seed_input,
+                max_input_image_size,
+                randomize_seed,
+            ],
+        )
+
+    return demo