Here is Draggan AI photo Editor online to demonstrate Image Morphing, which is showing how to morph two images. It took about 300 seconds to finish the whole process and many steps of learning and training. The result is happy ending and impressive.
*train a art style lora
- Usage Instructions:
- Upload two images that correspond to each other and provide the necessary prompts. It is recommended to adjust the [Output Path] accordingly.
- Click the “Run!” button to initiate the process.
Alternatively: - Upload two images that correspond to each other and provide the required prompts.
- Click the “Train LoRA A/B” button to train two separate LoRAs for each image.
- If you have already trained LoRA A or LoRA B previously, you can skip this step and enter the specific LoRA path in the LoRA settings.
- Trained LoRAs are automatically saved to [Output Path]/lora_0.ckpt and [Output Path]/lora_1.ckpt by default.
- Modify the settings below if desired.
- Click “Run w/o LoRA training” to proceed with the process.
Draggan AI Photo Editor Online
Breaking the Barriers of Diffusion Models: Introducing DiffMorpher
Diffusion models have revolutionized image generation, surpassing previous generative models in terms of quality. However, these models face a significant challenge when it comes to smoothly interpolating between two image samples, unlike GANs. This limitation arises from the highly unstructured latent space of diffusion models. A seamless interpolation between images holds immense potential for image morphing applications. In this groundbreaking study, we introduce DiffMorpher, the first-ever approach that enables smooth and natural image interpolation using diffusion models.
Capturing Semantics and Ensuring Smooth Transitions
DiffMorpher tackles the challenge by capturing the semantics of two given images through the fitting of two Latent ODE-RNN Alignments (LoRAs) separately. By interpolating between the LoRA parameters and the latent noises, DiffMorpher ensures a smooth semantic transition between images. Remarkably, this process occurs organically without the need for manual annotation, as the correspondence between the images automatically emerges.
Enhancements for Enhanced Smoothness
To further enhance the smoothness between consecutive images, DiffMorpher introduces an attention interpolation and injection technique, an adaptive normalization adjustment method, and a new sampling schedule. These techniques work in harmony to refine the image morphing effects, resulting in starkly better results compared to previous methods. DiffMorpher bridges a crucial functional gap, bringing diffusion models closer to GANs in terms of image morphing capabilities.
A Leap in Image Morphing Quality
Through extensive experiments, DiffMorpher showcases its superiority in achieving exceptional image morphing effects across various object categories. It sets a new standard for image interpolation, unlocking possibilities that were previously unattainable with diffusion models alone. DiffMorpher’s breakthrough paves the way for advanced applications in image manipulation and synthesis.
DiffMorpher introduces a groundbreaking approach to overcome the limitations of diffusion models in smooth image interpolation. By capturing semantics through LoRAs and leveraging innovative techniques, DiffMorpher achieves remarkable image morphing effects surpassing previous methods. This advancement bridges the gap between diffusion models and GANs, opening new horizons for image manipulation and synthesis. DiffMorpher represents a significant leap forward in the evolution of image morphing quality and functionality.