Spaces:
Sleeping
Sleeping
| title: "ERA SESSION20 - Stable Diffusion: Generative Art with Guidance" | |
| emoji: ๐ | |
| colorFrom: indigo | |
| colorTo: pink | |
| sdk: gradio | |
| sdk_version: 3.48.0 | |
| app_file: app.py | |
| pinned: false | |
| license: mit | |
| **Styles Used:** | |
| 1. [Oil style](https://huggingface.co/sd-concepts-library/oil-style) | |
| 2. [Xyz](https://huggingface.co/sd-concepts-library/xyz) | |
| 3. [Allante](https://huggingface.co/sd-concepts-library/style-of-marc-allante) | |
| 4. [Moebius](https://huggingface.co/sd-concepts-library/moebius) | |
| 5. [Polygons](https://huggingface.co/sd-concepts-library/low-poly-hd-logos-icons) | |
| ### Result of Experiments with different styles: | |
| **Prompt:** `"a cat and dog in the style of cs"` \ | |
| _"cs" in the prompt refers to "custom style" whose embedding is replaced by each of the concept embeddings shown below_ | |
|  | |
| --- | |
| **Prompt:** `"dolphin swimming on Mars in the style of cs"` | |
|  | |
| ### Result of Experiments with Guidance loss functions: | |
| **Prompt:** `"a mouse in the style of cs"` | |
| **Loss Function:** | |
| ```python | |
| def loss_fn(images): | |
| return images.mean() | |
| ``` | |
|  | |
| --- | |
| ```python | |
| def loss_fn(images): | |
| return -images.median()/3 | |
| ``` | |
|  | |
| --- | |
| ```python | |
| def loss_fn(images): | |
| error = (images - images.min()) / 255*(images.max() - images.min()) | |
| return error.mean() | |
| ``` | |
|  | |
| --- | |
| **Prompt:** `"angry german shephard in the style of cs"` | |
| ```python | |
| def loss_fn(images): | |
| error1 = torch.abs(images[:, 0] - 0.9) | |
| error2 = torch.abs(images[:, 1] - 0.9) | |
| error3 = torch.abs(images[:, 2] - 0.9) | |
| return ( | |
| torch.sin(error1.mean()) + torch.sin(error2.mean()) + torch.sin(error3.mean()) | |
| ) / 3 | |
| ``` | |
|  | |
| --- | |
| **Prompt:** `"A campfire (oil on canvas)"` | |
| ```python | |
| def loss_fn(images): | |
| error1 = torch.abs(images[:, 0] - 0.9) | |
| error2 = torch.abs(images[:, 1] - 0.9) | |
| error3 = torch.abs(images[:, 2] - 0.9) | |
| return ( | |
| torch.sin((error1 * error2 * error3)).mean() | |
| + torch.cos((error1 * error2 * error3)).mean() | |
| ) | |
| ``` | |
|  | |
| --- | |
| ```python | |
| def loss_fn(images): | |
| error1 = torch.abs(images[:, 0] - 0.9) | |
| error2 = torch.abs(images[:, 1] - 0.9) | |
| error3 = torch.abs(images[:, 2] - 0.9) | |
| return ( | |
| torch.sin(error1.mean()) + torch.sin(error2.mean()) + torch.sin(error3.mean()) | |
| ) / 3 | |
| ``` | |
|  | |