refactor
Haoming
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commit
20a6fb3166
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@ -1,3 +1,6 @@
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### v2.3.0 - 2024 Sep.20
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- Refactor
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### v2.2.6 - 2024 Sep.18
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- Allow disabling `do_not_save_to_config` to use **Defaults**
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347
README.md
347
README.md
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@ -1,28 +1,99 @@
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# SD Webui Vectorscope CC
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This is an Extension for the [Automatic1111 Webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui), which performs a kind of **Offset Noise** natively,
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allowing you to adjust the brightness, contrast, and color of the generations.
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This is an Extension for the [Automatic1111 Webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui), which performs a kind of **Offset Noise** natively during inference, allowing you to adjust the brightness, contrast, and color of the generations.
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> Also supports both old & new [Forge](https://github.com/lllyasviel/stable-diffusion-webui-forge)
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> [Sample Images](#sample-images)
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## Example Images
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<p align="center">
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<img src="samples/00.jpg" width=256><br>
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<code>Base Image w/o Extension</code>
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</p>
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<details>
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<summary>Infotext</summary>
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- **Checkpoint:** [realisticVisionV51](https://civitai.com/models/4201?modelVersionId=130072)
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- **Positive Prompt:** `(high quality, best quality), a 4k cinematic photo of a gentleman in suit, street in a city at night, (depth of field, bokeh)`
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- **Negative Prompt:** `(low quality, worst quality:1.2), [EasyNegative, EasyNegativeV2]`
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```cpp
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Steps: 32, Sampler: DPM++ 2M Karras, CFG scale: 7.5, Seed: 3709157017, Size: 512x512, Denoising strength: 0.5
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Clip skip: 2, Token merging ratio: 0.2, Token merging ratio hr: 0.2, RNG: CPU, NGMS: 4
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Hires upscale: 2, Hires steps: 16, Hires upscaler: 2xNomosUni_esrgan_multijpg
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```
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</details>
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<table>
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<thead>
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<tr align="center">
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<td><b>Vibrant</b></td>
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<td><b>Cold</b></td>
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<td><b>"Movie when Mexico"</b></td>
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</tr>
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</thead>
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<tbody>
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<tr align="center">
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<td><img src="samples/01.jpg" width=512></td>
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<td><img src="samples/02.jpg" width=512></td>
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<td><img src="samples/03.jpg" width=512></td>
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</tr>
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<tr align="left">
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<td>
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<ul>
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<li><b>Alt:</b> <code>True</code></li>
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<li><b>Saturation:</b> <code>1.75</code></li>
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<li><b>Noise:</b> <code>Ones</code></li>
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<li><b>Scaling:</b> <code>1 - Cos</code></li>
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</ul>
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</td>
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<td>
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<ul>
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<li><b>Brightness:</b> <code>-5.0</code></li>
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<li><b>Contrast:</b> <code>2.5</code></li>
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<li><b>Saturation:</b> <code>0.75</code></li>
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<li><b>R:</b> <code>-3.0</code></li>
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<li><b>B:</b> <code>3.0</code></li>
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<li><b>Noise:</b> <code>Ones</code></li>
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<li><b>Scaling:</b> <code>1 - Sin</code></li>
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</ul>
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</td>
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<td>
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<ul>
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<li><b>Brightness:</b> <code>2.5</code></li>
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<li><b>Contrast:</b> <code>-2.5</code></li>
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<li><b>Saturation:</b> <code>1.25</code></li>
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<li><b>R:</b> <code>1.5</code></li>
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<li><b>G:</b> <code>3.0</code></li>
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<li><b>B:</b> <code>-4.0</code></li>
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<li><b>Noise:</b> <code>Ones</code></li>
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<li><b>Scaling:</b> <code>1 - Sin</code></li>
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</ul>
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</td>
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</tr>
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</tbody>
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</table>
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## How to Use
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After installing this Extension, you will see a new section in both **txt2img** and **img2img** tabs.
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Refer to the parameters and sample images below and play around with the values.
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> **Note:** Since this modifies the underlying latent noise, the composition may change drastically
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> **Note:** Since this Extension modifies the underlying latent tensor, the composition may change drastically depending on the parameters
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#### Parameters
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- **Enable:** Turn on / off this Extension
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- **Alt:** Modify an alternative Tensor instead, causing the effects to be significantly stronger
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- **Brightness:** Adjust the overall brightness of the image
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- **Contrast:** Adjust the overall contrast of the image
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- **Saturation:** Adjust the overall saturation of the image
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### Basic Parameters
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- **Enable:** Enable the Extension 💀
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- **Alt:** Cause the Extension effects to be stronger
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<details>
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<summary><i>Technical Detail</i></summary>
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- This parameter makes the Extension modify the `denoised` Tensor instead of the `x` Tensor
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</details>
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- **Brightness**, **Contrast**, **Saturation**: Adjust the overall `brightness` / `contrast` / `saturation` of the image
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#### Color Channels
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- Comes with a Color Wheel for visualization
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- You can also click and drag on the Color Wheel to select a color directly
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> The color picker isn't 100% accurate for **SDXL**, due to 3 layers of conversions...
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<table>
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<thead align="center">
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@ -51,25 +122,28 @@ Refer to the parameters and sample images below and play around with the values.
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</tbody>
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</table>
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#### Buttons
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- **Reset:** Reset all settings to the default values
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- **Randomize:** Randomize `Brightness`, `Contrast`, `Saturation`, `R`, `G`, `B`
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- The Extension also comes with a Color Wheel for visualization, which you can also click on to pick a color directly
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> The color picker isn't 100% accurate due to multiple layers of conversions...
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#### Style Presets
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- Use the `Dropdown` to select a Style then click **Apply Style** to apply
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- To apply a Style, select from the `Dropdown` then click **Apply Style**
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- To save a Style, enter a name in the `Textbox` then click **Save Style**
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- To delete a Style, enter the name in the `Textbox` then click **Delete Style**
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- *Deleted Style is still in the `styles.json` in case you wish to retrieve it*
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- *Style that was deleted is still in the `styles.json` in case you wish to retrieve it*
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- Click **Refresh Style** to update the `Dropdown` if you edited the `styles.json` manually
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#### Advanced Settings
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- **Process Hires. fix:** By default, this Extension only functions during the **txt2img** phase, so that **Hires. fix** may "fix" the artifacts introduced during **txt2img**. Enable this to process **Hires. fix** phase too.
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- This option does not affect **img2img**
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- **Process ADetailer:** Enable to process **[ADetailer](https://github.com/Bing-su/adetailer)** phase too.
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### Advanced Parameters
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- **Process Hires. fix:** Enable this option to process during the **Hires. fix** phase too
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- By default, this Extension only functions during the regular phase of the `txt2img` mode
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- **Process ADetailer:** Enable this option to process during the **[ADetailer](https://github.com/Bing-su/adetailer)** phase too
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- Will usually cause a square of inconsistent colors
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- **Randomize using Seed:** Enable this option to use the current generation `seed` to randomize the basic parameters
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- Randomized results will be printed in the console
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#### Noise Settings
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> let `x` denote the Tensor ; let `y` denote the operations
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> let **`x`** denote the latent Tensor ; let **`y`** denote the operations
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- **Straight:** All operations are calculated on the same Tensor
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- `x += x * y`
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@ -77,9 +151,9 @@ Refer to the parameters and sample images below and play around with the values.
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- `x += x' * y`
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- **Ones:** All operations are calculated on a Tensor filled with ones
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- `x += 1 * y`
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- **N.Random:** All operations are calculated on a Tensor filled with random values from normal distribution
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- **N.Random:** All operations are calculated on a Tensor filled with random values in normal distribution
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- `x += randn() * y`
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- **U.Random:** All operations are calculated on a Tensor filled with random values from uniform distribution
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- **U.Random:** All operations are calculated on a Tensor filled with random values in uniform distribution
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- `x += rand() * y`
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- **Multi-Res:** All operations are calculated on a Tensor generated with multi-res noise algorithm
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- `x += multires() * y`
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@ -87,153 +161,194 @@ Refer to the parameters and sample images below and play around with the values.
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- `x += abs(F) * y`
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<p align="center">
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<img src="samples/Method.jpg">
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<img src="samples/method.jpg">
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</p>
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<details>
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<summary>Infotext</summary>
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- **Checkpoint:** [realisticVisionV51](https://civitai.com/models/4201?modelVersionId=130072)
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- **Positive Prompt:** `(high quality, best quality), a 4k photo of a cute dog running in the snow, mountains, day, (depth of field, bokeh)`
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- **Negative Prompt:** `(low quality, worst quality:1.2), [EasyNegative, EasyNegativeV2]`
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- **Brightness:** `2.5`
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- **Contrast:** `2.5`
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- **Alt:** `True`
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- **Scaling:** `1 - Cos`
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```cpp
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Steps: 24, Sampler: DPM++ 2M Karras, CFG scale: 7.5, Seed: 1257068736, Size: 512x512, Denoising strength: 0.5
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Clip skip: 2, Token merging ratio: 0.2, Token merging ratio hr: 0.2, RNG: CPU, NGMS: 4
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Hires upscale: 1.5, Hires steps: 16, Hires upscaler: 2xNomosUni_esrgan_multijpg
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```
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</details>
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#### Scaling Settings
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By default, this Extension offsets the noise by the same amount each step.
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But due to the denoising process, this may produce undesired outcomes such as blurriness at high **Brightness** or noises at low **Brightness**.
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Therefore, I added a scaling option to modify the offset amount throughout the process.
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By default, this Extension offsets the noise by the same amount every step. But depending on the `Sampler` and `Scheduler` used, and whether `Alt.` was enabled or not, the effects might be too strong during the early or the later phase of the process, which in turn causes artifacts.
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> Essentially, the "magnitude" of the default Tensor gets smaller every step, so offsetting by the same amount will have stronger effects at the later steps.
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- **Flat:** Default behavior. Same amount each step.
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- **Cos:** Cosine scaling. *(High -> Low)*
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- **Sin:** Sine scaling. *(Low -> High)*
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- **1 - Cos:** *(Low -> High)*
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- **1 - Sin:** *(High -> Low)*
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- **Flat:** Default behavior
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- **Cos:** Cosine scaling `(High -> Low)`
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- **Sin:** Sine scaling `(Low -> High)`
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- **1 - Cos:** `(Low -> High)`
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- **1 - Sin:** `(High -> Low)`
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<p align="center">
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<img src="samples/Scaling.jpg">
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<img src="samples/scaling.jpg">
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</p>
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## Sample Images
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- **Checkpoint:** [Animagine XL V3](https://civitai.com/models/260267)
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- **Pos. Prompt:** `[high quality, best quality], 1girl, solo, casual, night, street, city, <lora:SDXL_Lightning_8steps:1>`
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- **Neg. Prompt:** `lowres, [low quality, worst quality], jpeg`
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- `Euler A SGMUniform`; `10 steps`; `2.0 CFG`; **Seed:** `2836968120`
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- `Multi-Res Abs.` ; `Cos`
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<details>
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<summary>Infotext</summary>
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<p align="center">
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<code>Disabled</code><br>
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<img src="samples/00.jpg" width=768>
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</p>
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- **Checkpoint:** [realisticVisionV51](https://civitai.com/models/4201?modelVersionId=130072)
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- **Positive Prompt:** `(high quality, best quality), a 4k photo of a cute cat standing at a flower field in a park, day, (depth of field, bokeh)`
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- **Negative Prompt:** `(low quality, worst quality:1.2), [EasyNegative, EasyNegativeV2]`
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- **Alt:** `True`
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- **Noise:** `Straight Abs.`
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<p align="center">
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<code>Brightness: 2.0 ; Contrast: -0.5 ; Saturation: 1.5<br>
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R: 2.5; G: 1.5; B: -3</code><br>
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<img src="samples/01.jpg" width=768>
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</p>
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```cpp
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Steps: 24, Sampler: DPM++ 2M Karras, CFG scale: 7.5, Seed: 3515074713, Size: 512x512, Denoising strength: 0.5
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Clip skip: 2, Token merging ratio: 0.2, Token merging ratio hr: 0.2, RNG: CPU, NGMS: 4
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Hires upscale: 1.5, Hires steps: 12, Hires upscaler: 2xNomosUni_esrgan_multijpg
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```
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<p align="center">
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<code>Brightness: -2.5 ; Contrast: 1 ; Saturation: 0.75<br>
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R: -1.5; G: -1.5; B: 4</code><br>
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<img src="samples/02.jpg" width=768>
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</p>
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</details>
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### Buttons
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- **Reset:** Reset all `Basic` and `Advanced` parameters to the default values
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- **Randomize:** Randomize the `Brightness`, `Contrast`, `Saturation`, `R`, `G`, `B` parameters
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## Roadmap
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- [X] Extension Released
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- [X] Extension Released!
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- [X] Add Support for **X/Y/Z Plot**
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- [X] Implement different **Noise** functions
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- [X] Add **Randomize** button
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- [X] **Style** Presets
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- [X] Implement **Randomize** button
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- [X] Implement **Style** Presets
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- [X] Implement **Color Wheel** & **Color Picker**
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- [X] Implement better scaling algorithms
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- [X] Add API Docs
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- [X] Append Parameters onto Metadata
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- You can enable this in the **Infotext** section of the **Settings** tab
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- [X] Add Infotext Support *(by. [catboxanon](https://github.com/catboxanon))*
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- [X] ADD **HDR** Script
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- [X] Add SDXL Support
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- [ ] Add Gradient features
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<p align="center">
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<code>X/Y/Z Plot Support</code><br>
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<img src="samples/XYZ.jpg">
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</p>
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<p align="center">
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<code>Randomize</code><br>
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<img src="samples/Random.jpg"><br>
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The value is used as the random seed<br>You can refer to the console to see the randomized values</p>
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- [X] Append Parameters to Infotext
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- [X] Improved Infotext Support *(by. [catboxanon](https://github.com/catboxanon))*
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- [X] Add **HDR** Script
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- [X] Add Support for **SDXL**
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- [ ] Implement Gradient features
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## API
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You can use this Extension via [API](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/API) by adding an entry to the `alwayson_scripts` of your payload.
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An [example](samples/api_example.json) is provided.
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The `args` are sent in the following order in an `array`:
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You can use this Extension via [API](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/API) by adding an entry to the `alwayson_scripts` of your payload. An [example](samples/api_example.json) is provided. The `args` are sent in the following order in an `array`:
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- **Enable:** `bool`
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- **Alt:** `bool`
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- **Brightness:** `float`
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- **Contrast:** `float`
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- **Saturation:** `float`
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- **R:** `float`
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- **G:** `float`
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- **B:** `float`
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- **Process Hires. Fix:** `bool`
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- **Noise Settings:** `str`
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- **Scaling Settings:** `str`
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<table>
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<thead>
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<tr align="center">
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<td><b>Parameter</b></td>
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<td><b>Type</b></td>
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</tr>
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</thead>
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<tbody>
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<tr align="center">
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<td>Enable</td>
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<td><code>bool</code></td>
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</tr>
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<tr align="center">
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<td>Alt.</td>
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<td><code>bool</code></td>
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</tr>
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<tr align="center">
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<td>Brightness</td>
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<td><code>float</code></td>
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</tr>
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<tr align="center">
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<td>Contrast</td>
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<td><code>float</code></td>
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</tr>
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<tr align="center">
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<td>Saturation</td>
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<td><code>float</code></td>
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</tr>
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<tr align="center">
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<td>R</td>
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<td><code>float</code></td>
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</tr>
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<tr align="center">
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<td>G</td>
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<td><code>float</code></td>
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</tr>
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<tr align="center">
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<td>B</td>
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<td><code>float</code></td>
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</tr>
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<tr align="center">
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<td>Hires. fix</td>
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<td><code>bool</code></td>
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</tr>
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<tr align="center">
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<td>ADetailer</td>
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<td><code>bool</code></td>
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</tr>
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<tr align="center">
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<td>Randomize</td>
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<td><code>bool</code></td>
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</tr>
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<tr align="center">
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<td>Noise Method</td>
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<td><code>str</code></td>
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</tr>
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<tr align="center">
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<td>Scaling</td>
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<td><code>str</code></td>
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</tr>
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</tbody>
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</table>
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## Known Issues
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- Does **not** work certain samplers *(See [Wiki](https://github.com/Haoming02/sd-webui-vectorscope-cc/wiki/Vectorscope-CC-Wiki#effects-with-different-samplers))*
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- Has little effect when used with certain **LoRA**s
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- In rare occasions, this Extension has little effects when used with certain **LoRA**s
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- Works better / worse with certain `Samplers`
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<!--- *(See [Wiki](https://github.com/Haoming02/sd-webui-vectorscope-cc/wiki/Vectorscope-CC-Wiki#effects-with-different-samplers))* --->
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## HDR
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<p align="right"><i><b>BETA</b></i></p>
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> [Discussion Thread](https://github.com/Haoming02/sd-webui-vectorscope-cc/issues/16)
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|
||||
- In the **Script** `Dropdown` at the bottom, there is now a new option: **`High Dynamic Range`**
|
||||
In the **Script** `Dropdown` at the bottom, there is now a new **`High Dynamic Range`** option:
|
||||
|
||||
- This script will generate multiple images *("Brackets")* of varying brightness, then merge them into 1 HDR image
|
||||
- *Do provide feedback in the thread!*
|
||||
- **Highly Recommended** to use a deterministic sampler and high enough steps. `Euler` *(**not** `Euler a`)* worked well in my experience.
|
||||
- **(Recommended)** Use a deterministic sampler and high enough steps. `Euler` *(**not** `Euler a`)* works well in my experience
|
||||
|
||||
#### Settings
|
||||
- **Brackets:** The numer of images to generate
|
||||
- **Gaps:** The brightness difference between each image
|
||||
- **Automatically Merge:** When enabled, this will merge the images using an `OpenCV` algorithm and save to the `HDR` folder in the `outputs` folder; When disabled, this will return all images to the result section, for when you have a more advanced program such as Photoshop to do the merging.
|
||||
- All the images are still saved to the `outputs` folder regardless
|
||||
- **Automatically Merge:** When enabled, this will merge the images using an `OpenCV` algorithm and save to the `HDR` folder in the `outputs` folder
|
||||
- Disable this if you want to merge them yourself using better external program
|
||||
|
||||
<hr>
|
||||
|
||||
<details>
|
||||
<summary>Offset Noise TL;DR</summary>
|
||||
|
||||
The most common *version* of **Offset Noise** you may have heard of is from this [blog post](https://www.crosslabs.org/blog/diffusion-with-offset-noise),
|
||||
where it was discovered that the noise functions used during **training** were flawed, causing `Stable Diffusion` to always generate images with an average of `0.5` *(**ie.** grey)*.
|
||||
The most common *version* of **Offset Noise** you may have heard of is from this [blog post](https://www.crosslabs.org/blog/diffusion-with-offset-noise), where it was discovered that the noise functions used during **training** were flawed, causing `Stable Diffusion` to always generate images with an average of `0.5` *(**ie.** grey)*.
|
||||
|
||||
> **ie.** Even if you prompt for dark/night or bright/snow, the overall image still looks "grey"
|
||||
> **ie.** Even if you prompt for dark/night or bright/snow, the average of the image is still "grey"
|
||||
|
||||
> [Technical Explanations](https://youtu.be/cVxQmbf3q7Q)
|
||||
|
||||
However, this Extension instead tries to offset the latent noise during the **inference** phase.
|
||||
Therefore, you do not need to use models that were specially trained, as this can work on any model.
|
||||
However, this Extension instead tries to offset the latent noise during the **inference** phase. Therefore, you do not need to use models that were specially trained, as this can work on any model.
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary>How does this work?</summary>
|
||||
|
||||
After reading through and messing around with the code,
|
||||
I found out that it is possible to directly modify the Tensors
|
||||
representing the latent noise used by the Stable Diffusion process.
|
||||
After reading through and messing around with the code, I found out that it is possible to directly modify the Tensors representing the latent noise used by the Stable Diffusion process.
|
||||
|
||||
The dimensions of the Tensors is `(X, 4, H / 8, W / 8)`, which can be thought of like this:
|
||||
|
||||
> **X** batch of noise images, with **4** channels, each with **(W / 8) x (H / 8)** values
|
||||
The dimensions of the Tensors is `(X, 4, H / 8, W / 8)`, which represents **X** batch of noise images, with **4** channels, each with **(W / 8) x (H / 8)** values
|
||||
|
||||
> **eg.** Generating a single 512x768 image will create a Tensor of size (1, 4, 96, 64)
|
||||
|
||||
Then, I tried to play around with the values of each channel and ended up discovering these relationships.
|
||||
Essentially, the 4 channels correspond to the **CMYK** color format,
|
||||
hence why you can control the brightness as well as the colors.
|
||||
Then, I tried to play around with the values of each channel and ended up discovering these relationships. Essentially, the 4 channels correspond to the **CMYK** color format for `SD1` *(**Y'CbCr** for `SDXL`)*, hence why you can control the brightness as well as the colors.
|
||||
|
||||
</details>
|
||||
|
||||
<hr>
|
||||
|
||||
#### Vectorscope?
|
||||
The Extension is named this way because the color interactions remind me of the `Vectorscope` found in **Premiere Pro**'s **Lumetri Color**.
|
||||
Those who are experienced in Color Correction should be rather familiar with this Extension.
|
||||
The Extension is named this way because the color interactions remind me of the `Vectorscope` found in **Premiere Pro**'s **Lumetri Color**. Those who are experienced in Color Correction should be rather familiar with this Extension.
|
||||
|
||||
<p align="center"><img src="scripts/vectorscope.png" width=256></p>
|
||||
|
||||
|
|
|
|||
|
|
@ -92,26 +92,17 @@ onUiLoaded(() => {
|
|||
wheel.id = `cc-img-${mode}`;
|
||||
|
||||
const sliders = [
|
||||
[
|
||||
document.getElementById(`cc-r-${mode}`).querySelector('input[type=number]'),
|
||||
document.getElementById(`cc-r-${mode}`).querySelector('input[type=range]')
|
||||
],
|
||||
[
|
||||
document.getElementById(`cc-g-${mode}`).querySelector('input[type=number]'),
|
||||
document.getElementById(`cc-g-${mode}`).querySelector('input[type=range]')
|
||||
],
|
||||
[
|
||||
document.getElementById(`cc-b-${mode}`).querySelector('input[type=number]'),
|
||||
document.getElementById(`cc-b-${mode}`).querySelector('input[type=range]')
|
||||
]
|
||||
document.getElementById(`cc-r-${mode}`).querySelectorAll('input'),
|
||||
document.getElementById(`cc-g-${mode}`).querySelectorAll('input'),
|
||||
document.getElementById(`cc-b-${mode}`).querySelectorAll('input'),
|
||||
];
|
||||
|
||||
const temp = document.getElementById(`cc-temp-${mode}`);
|
||||
|
||||
const dot = temp.querySelector('img');
|
||||
dot.id = `cc-dot-${mode}`;
|
||||
dot.style.left = 'calc(50% - 12px)';
|
||||
dot.style.top = 'calc(50% - 12px)';
|
||||
dot.id = `cc-dot-${mode}`;
|
||||
|
||||
container.appendChild(dot);
|
||||
temp.remove();
|
||||
|
|
|
|||
|
|
@ -0,0 +1,4 @@
|
|||
"""
|
||||
Author: Haoming02
|
||||
License: MIT
|
||||
"""
|
||||
|
|
@ -38,7 +38,10 @@ class NoiseMethods:
|
|||
@staticmethod
|
||||
@torch.inference_mode()
|
||||
def multires_noise(
|
||||
latent: torch.Tensor, use_zero: bool, iterations: int = 8, discount: float = 0.4
|
||||
latent: torch.Tensor,
|
||||
use_zero: bool,
|
||||
iterations: int = 10,
|
||||
discount: float = 0.8,
|
||||
):
|
||||
"""
|
||||
Credit: Kohya_SS
|
||||
|
|
@ -46,21 +49,18 @@ class NoiseMethods:
|
|||
"""
|
||||
|
||||
noise = NoiseMethods.zeros(latent) if use_zero else NoiseMethods.ones(latent)
|
||||
b, c, w, h = noise.shape
|
||||
|
||||
device = latent.device
|
||||
|
||||
b, c, w, h = noise.shape
|
||||
upsampler = torch.nn.Upsample(size=(w, h), mode="bilinear").to(device)
|
||||
|
||||
for batch in range(b):
|
||||
for i in range(iterations):
|
||||
r = random() * 2 + 2
|
||||
|
||||
wn = max(1, int(w / (r**i)))
|
||||
hn = max(1, int(h / (r**i)))
|
||||
|
||||
noise[batch] += (
|
||||
upsampler(torch.randn(1, c, hn, wn).to(device)) * discount**i
|
||||
)[0]
|
||||
noise += upsampler(torch.randn(b, c, wn, hn).to(device)) * discount**i
|
||||
|
||||
if wn == 1 or hn == 1:
|
||||
break
|
||||
|
|
@ -79,6 +79,7 @@ def RGB_2_CbCr(r: float, g: float, b: float) -> tuple[float, float]:
|
|||
original_callback = KDiffusionSampler.callback_state
|
||||
|
||||
|
||||
@torch.no_grad()
|
||||
@torch.inference_mode()
|
||||
@wraps(original_callback)
|
||||
def cc_callback(self, d):
|
||||
|
|
@ -96,7 +97,7 @@ def cc_callback(self, d):
|
|||
mode = str(self.vec_cc["mode"])
|
||||
method = str(self.vec_cc["method"])
|
||||
source: torch.Tensor = d[mode]
|
||||
target: torch.Tensor = None
|
||||
target = None
|
||||
|
||||
if "Straight" in method:
|
||||
target = d[mode].detach().clone()
|
||||
|
|
@ -150,7 +151,6 @@ def cc_callback(self, d):
|
|||
source[i][3] *= sat
|
||||
|
||||
else:
|
||||
# But why...
|
||||
cb, cr = RGB_2_CbCr(r, g, b)
|
||||
|
||||
for i in range(batchSize):
|
||||
|
|
@ -170,12 +170,11 @@ def cc_callback(self, d):
|
|||
return original_callback(self, d)
|
||||
|
||||
|
||||
KDiffusionSampler.callback_state = cc_callback
|
||||
|
||||
|
||||
def restore_callback():
|
||||
KDiffusionSampler.callback_state = original_callback
|
||||
|
||||
|
||||
on_script_unloaded(restore_callback)
|
||||
on_ui_settings(settings)
|
||||
def hook_callbacks():
|
||||
KDiffusionSampler.callback_state = cc_callback
|
||||
on_script_unloaded(restore_callback)
|
||||
on_ui_settings(settings)
|
||||
|
|
|
|||
|
|
@ -9,16 +9,19 @@ DOT = os.path.join(scripts.basedir(), "scripts", "dot.png")
|
|||
def create_colorpicker(is_img: bool):
|
||||
m: str = "img" if is_img else "txt"
|
||||
|
||||
gr.Image(
|
||||
whl = gr.Image(
|
||||
value=WHEEL,
|
||||
interactive=False,
|
||||
container=False,
|
||||
elem_id=f"cc-colorwheel-{m}",
|
||||
)
|
||||
|
||||
gr.Image(
|
||||
dot = gr.Image(
|
||||
value=DOT,
|
||||
interactive=False,
|
||||
container=False,
|
||||
elem_id=f"cc-temp-{m}",
|
||||
)
|
||||
|
||||
whl.do_not_save_to_config = True
|
||||
dot.do_not_save_to_config = True
|
||||
|
|
|
|||
|
|
@ -2,6 +2,7 @@ import random
|
|||
|
||||
|
||||
class Param:
|
||||
|
||||
def __init__(self, minimum: float, maximum: float, default: float):
|
||||
self.minimum = minimum
|
||||
self.maximum = maximum
|
||||
|
|
|
|||
|
|
@ -13,10 +13,9 @@ def apply_scaling(
|
|||
b: float,
|
||||
) -> list:
|
||||
|
||||
if alg == "Flat":
|
||||
mod = 1.0
|
||||
|
||||
else:
|
||||
if alg != "Flat":
|
||||
ratio = float(current_step / total_steps)
|
||||
rad = ratio * pi / 2
|
||||
|
||||
|
|
|
|||
|
|
@ -10,6 +10,7 @@ EMPTY_STYLE = {"styles": {}, "deleted": {}}
|
|||
|
||||
|
||||
class StyleManager:
|
||||
|
||||
def __init__(self):
|
||||
self.STYLE_SHEET: dict = None
|
||||
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
from modules import scripts
|
||||
|
||||
|
||||
def grid_reference():
|
||||
def _grid_reference():
|
||||
for data in scripts.scripts_data:
|
||||
if data.script_class.__module__ in (
|
||||
"scripts.xyz_grid",
|
||||
|
|
@ -40,7 +40,7 @@ def xyz_support(cache: dict):
|
|||
def choices_scaling():
|
||||
return ["Flat", "Cos", "Sin", "1 - Cos", "1 - Sin"]
|
||||
|
||||
xyz_grid = grid_reference()
|
||||
xyz_grid = _grid_reference()
|
||||
|
||||
extra_axis_options = [
|
||||
xyz_grid.AxisOption(
|
||||
|
|
|
|||
BIN
samples/00.jpg
BIN
samples/00.jpg
Binary file not shown.
|
Before Width: | Height: | Size: 148 KiB After Width: | Height: | Size: 507 KiB |
BIN
samples/01.jpg
BIN
samples/01.jpg
Binary file not shown.
|
Before Width: | Height: | Size: 195 KiB After Width: | Height: | Size: 480 KiB |
BIN
samples/02.jpg
BIN
samples/02.jpg
Binary file not shown.
|
Before Width: | Height: | Size: 114 KiB After Width: | Height: | Size: 574 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 492 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 694 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 238 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 587 KiB |
BIN
samples/XYZ.jpg
BIN
samples/XYZ.jpg
Binary file not shown.
|
Before Width: | Height: | Size: 1.6 MiB |
|
|
@ -1,25 +1,27 @@
|
|||
{
|
||||
"prompt": "(masterpiece, best quality), 1girl, solo, night",
|
||||
"negative_prompt": "(low quality, worst quality:1.2)",
|
||||
"seed": -1,
|
||||
"prompt": "a photo of a dog",
|
||||
"negative_prompt": "(low quality, worst quality)",
|
||||
"sampler_name": "Euler a",
|
||||
"sampler_index": "euler",
|
||||
"batch_size": 1,
|
||||
"steps": 24,
|
||||
"cfg_scale": 8.5,
|
||||
"cfg_scale": 6.0,
|
||||
"batch_size": 1,
|
||||
"seed": -1,
|
||||
"width": 512,
|
||||
"height": 512,
|
||||
"alwayson_scripts": {
|
||||
"Vectorscope CC": {
|
||||
"vectorscope cc": {
|
||||
"args": [
|
||||
true,
|
||||
true,
|
||||
-2.0,
|
||||
-2.5,
|
||||
1.5,
|
||||
1.25,
|
||||
0.0,
|
||||
0.85,
|
||||
0.0,
|
||||
0.0,
|
||||
1.0,
|
||||
false,
|
||||
false,
|
||||
false,
|
||||
"Straight Abs.",
|
||||
"Flat"
|
||||
|
|
|
|||
Binary file not shown.
|
After Width: | Height: | Size: 964 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 1.1 MiB |
144
scripts/cc.py
144
scripts/cc.py
|
|
@ -2,20 +2,21 @@ from modules.sd_samplers_kdiffusion import KDiffusionSampler
|
|||
from modules import shared, scripts
|
||||
|
||||
from lib_cc.colorpicker import create_colorpicker
|
||||
from lib_cc.callback import hook_callbacks
|
||||
from lib_cc.style import StyleManager
|
||||
from lib_cc.xyz import xyz_support
|
||||
from lib_cc import callback
|
||||
from lib_cc import const
|
||||
|
||||
from random import seed
|
||||
import gradio as gr
|
||||
|
||||
|
||||
VERSION = "2.2.6"
|
||||
VERSION = "2.3.0"
|
||||
|
||||
|
||||
style_manager = StyleManager()
|
||||
style_manager.load_styles()
|
||||
hook_callbacks()
|
||||
|
||||
|
||||
class VectorscopeCC(scripts.Script):
|
||||
|
|
@ -45,24 +46,24 @@ class VectorscopeCC(scripts.Script):
|
|||
with gr.Row():
|
||||
bri = gr.Slider(
|
||||
label="Brightness",
|
||||
value=const.Brightness.default,
|
||||
minimum=const.Brightness.minimum,
|
||||
maximum=const.Brightness.maximum,
|
||||
step=0.05,
|
||||
value=const.Brightness.default,
|
||||
)
|
||||
con = gr.Slider(
|
||||
label="Contrast",
|
||||
value=const.Contrast.default,
|
||||
minimum=const.Contrast.minimum,
|
||||
maximum=const.Contrast.maximum,
|
||||
step=0.05,
|
||||
value=const.Contrast.default,
|
||||
)
|
||||
sat = gr.Slider(
|
||||
label="Saturation",
|
||||
value=const.Saturation.default,
|
||||
minimum=const.Saturation.minimum,
|
||||
maximum=const.Saturation.maximum,
|
||||
step=0.05,
|
||||
value=const.Saturation.default,
|
||||
)
|
||||
|
||||
with gr.Row():
|
||||
|
|
@ -70,42 +71,33 @@ class VectorscopeCC(scripts.Script):
|
|||
r = gr.Slider(
|
||||
label="R",
|
||||
info="Cyan | Red",
|
||||
value=const.Color.default,
|
||||
minimum=const.Color.minimum,
|
||||
maximum=const.Color.maximum,
|
||||
step=0.05,
|
||||
value=const.Color.default,
|
||||
elem_id=f"cc-r-{mode}",
|
||||
)
|
||||
g = gr.Slider(
|
||||
label="G",
|
||||
info="Magenta | Green",
|
||||
value=const.Color.default,
|
||||
minimum=const.Color.minimum,
|
||||
maximum=const.Color.maximum,
|
||||
step=0.05,
|
||||
value=const.Color.default,
|
||||
elem_id=f"cc-g-{mode}",
|
||||
)
|
||||
b = gr.Slider(
|
||||
label="B",
|
||||
info="Yellow | Blue",
|
||||
value=const.Color.default,
|
||||
minimum=const.Color.minimum,
|
||||
maximum=const.Color.maximum,
|
||||
step=0.05,
|
||||
value=const.Color.default,
|
||||
elem_id=f"cc-b-{mode}",
|
||||
)
|
||||
|
||||
r.input(
|
||||
None,
|
||||
inputs=[r, g, b],
|
||||
_js=f"(r, g, b) => {{ VectorscopeCC.updateCursor(r, g, b, {m}); }}",
|
||||
)
|
||||
g.input(
|
||||
None,
|
||||
inputs=[r, g, b],
|
||||
_js=f"(r, g, b) => {{ VectorscopeCC.updateCursor(r, g, b, {m}); }}",
|
||||
)
|
||||
b.input(
|
||||
for c in (r, g, b):
|
||||
c.input(
|
||||
None,
|
||||
inputs=[r, g, b],
|
||||
_js=f"(r, g, b) => {{ VectorscopeCC.updateCursor(r, g, b, {m}); }}",
|
||||
|
|
@ -125,7 +117,9 @@ class VectorscopeCC(scripts.Script):
|
|||
refresh_btn = gr.Button(value="Refresh Style", scale=2)
|
||||
|
||||
with gr.Row(elem_classes="style-rows"):
|
||||
style_name = gr.Textbox(label="Style Name", scale=3)
|
||||
style_name = gr.Textbox(
|
||||
label="Style Name", lines=1, max_lines=1, scale=3
|
||||
)
|
||||
save_btn = gr.Button(
|
||||
value="Save Style", elem_id=f"cc-save-{mode}", scale=2
|
||||
)
|
||||
|
|
@ -145,12 +139,15 @@ class VectorscopeCC(scripts.Script):
|
|||
|
||||
with gr.Accordion("Advanced Settings", open=False):
|
||||
with gr.Row():
|
||||
doHR = gr.Checkbox(label="Process Hires. fix")
|
||||
doHR = gr.Checkbox(
|
||||
label="Process Hires. fix",
|
||||
visible=(not is_img2img),
|
||||
)
|
||||
doAD = gr.Checkbox(label="Process Adetailer")
|
||||
doRN = gr.Checkbox(label="Randomize using Seed")
|
||||
|
||||
method = gr.Radio(
|
||||
[
|
||||
choices=(
|
||||
"Straight",
|
||||
"Straight Abs.",
|
||||
"Cross",
|
||||
|
|
@ -160,13 +157,13 @@ class VectorscopeCC(scripts.Script):
|
|||
"U.Random",
|
||||
"Multi-Res",
|
||||
"Multi-Res Abs.",
|
||||
],
|
||||
),
|
||||
label="Noise Settings",
|
||||
value="Straight Abs.",
|
||||
)
|
||||
|
||||
scaling = gr.Radio(
|
||||
["Flat", "Cos", "Sin", "1 - Cos", "1 - Sin"],
|
||||
choices=("Flat", "Cos", "Sin", "1 - Cos", "1 - Sin"),
|
||||
label="Scaling Settings",
|
||||
value="Flat",
|
||||
)
|
||||
|
|
@ -188,7 +185,7 @@ class VectorscopeCC(scripts.Script):
|
|||
|
||||
apply_btn.click(
|
||||
fn=style_manager.get_style,
|
||||
inputs=style_choice,
|
||||
inputs=[style_choice],
|
||||
outputs=[*comps],
|
||||
).then(
|
||||
None,
|
||||
|
|
@ -199,18 +196,18 @@ class VectorscopeCC(scripts.Script):
|
|||
save_btn.click(
|
||||
fn=lambda *args: gr.update(choices=style_manager.save_style(*args)),
|
||||
inputs=[style_name, *comps],
|
||||
outputs=style_choice,
|
||||
outputs=[style_choice],
|
||||
)
|
||||
|
||||
delete_btn.click(
|
||||
fn=lambda name: gr.update(choices=style_manager.delete_style(name)),
|
||||
inputs=style_name,
|
||||
outputs=style_choice,
|
||||
inputs=[style_name],
|
||||
outputs=[style_choice],
|
||||
)
|
||||
|
||||
refresh_btn.click(
|
||||
fn=lambda _: gr.update(choices=style_manager.load_styles()),
|
||||
outputs=style_choice,
|
||||
fn=lambda: gr.update(choices=style_manager.load_styles()),
|
||||
outputs=[style_choice],
|
||||
)
|
||||
|
||||
with gr.Row():
|
||||
|
|
@ -248,7 +245,7 @@ class VectorscopeCC(scripts.Script):
|
|||
outputs=[*comps],
|
||||
show_progress="hidden",
|
||||
).then(
|
||||
None,
|
||||
fn=None,
|
||||
inputs=[r, g, b],
|
||||
_js=f"(r, g, b) => {{ VectorscopeCC.updateCursor(r, g, b, {m}); }}",
|
||||
)
|
||||
|
|
@ -258,7 +255,7 @@ class VectorscopeCC(scripts.Script):
|
|||
outputs=[bri, con, sat, r, g, b],
|
||||
show_progress="hidden",
|
||||
).then(
|
||||
None,
|
||||
fn=None,
|
||||
inputs=[r, g, b],
|
||||
_js=f"(r, g, b) => {{ VectorscopeCC.updateCursor(r, g, b, {m}); }}",
|
||||
)
|
||||
|
|
@ -308,52 +305,45 @@ class VectorscopeCC(scripts.Script):
|
|||
seeds: list[int],
|
||||
subseeds: list[int],
|
||||
):
|
||||
if "Enable" in self.xyzCache.keys():
|
||||
enable = self.xyzCache["Enable"].lower().strip() == "true"
|
||||
|
||||
enable = self.xyzCache.pop("Enable", str(enable)).lower().strip() == "true"
|
||||
|
||||
if not enable:
|
||||
if len(self.xyzCache) > 0:
|
||||
if "Enable" not in self.xyzCache.keys():
|
||||
print("\n[Vec.CC] x [X/Y/Z Plot] Extension is not Enabled!\n")
|
||||
self.xyzCache.clear()
|
||||
|
||||
KDiffusionSampler.vec_cc = {"enable": False}
|
||||
setattr(KDiffusionSampler, "vec_cc", {"enable": False})
|
||||
return p
|
||||
|
||||
method = str(self.xyzCache.pop("Method", method))
|
||||
|
||||
if method == "Disabled":
|
||||
setattr(KDiffusionSampler, "vec_cc", {"enable": False})
|
||||
return p
|
||||
|
||||
if "Random" in self.xyzCache.keys():
|
||||
print("[X/Y/Z Plot] x [Vec.CC] Randomize is Enabled.")
|
||||
if len(self.xyzCache) > 1:
|
||||
print("Some parameters will not apply!")
|
||||
print("Some parameters will be overridden!")
|
||||
|
||||
cc_seed = int(self.xyzCache.pop("Random"))
|
||||
else:
|
||||
cc_seed = int(seeds[0]) if doRN else None
|
||||
|
||||
if "Alt" in self.xyzCache.keys():
|
||||
latent = self.xyzCache["Alt"].lower().strip() == "true"
|
||||
latent = self.xyzCache.pop("Alt", str(latent)).lower().strip() == "true"
|
||||
doHR = self.xyzCache.pop("DoHR", str(doHR)).lower().strip() == "true"
|
||||
scaling = str(self.xyzCache.pop("Scaling", scaling))
|
||||
|
||||
if "DoHR" in self.xyzCache.keys():
|
||||
doHR = self.xyzCache["DoHR"].lower().strip() == "true"
|
||||
bri = float(self.xyzCache.pop("Brightness", bri))
|
||||
con = float(self.xyzCache.pop("Contrast", con))
|
||||
sat = float(self.xyzCache.pop("Saturation", sat))
|
||||
|
||||
if "Random" in self.xyzCache.keys():
|
||||
cc_seed = int(self.xyzCache["Random"])
|
||||
r = float(self.xyzCache.pop("R", r))
|
||||
g = float(self.xyzCache.pop("G", g))
|
||||
b = float(self.xyzCache.pop("B", b))
|
||||
|
||||
bri = float(self.xyzCache.get("Brightness", bri))
|
||||
con = float(self.xyzCache.get("Contrast", con))
|
||||
sat = float(self.xyzCache.get("Saturation", sat))
|
||||
|
||||
r = float(self.xyzCache.get("R", r))
|
||||
g = float(self.xyzCache.get("G", g))
|
||||
b = float(self.xyzCache.get("B", b))
|
||||
|
||||
method = str(self.xyzCache.get("Method", method))
|
||||
scaling = str(self.xyzCache.get("Scaling", scaling))
|
||||
|
||||
self.xyzCache.clear()
|
||||
|
||||
if method == "Disabled":
|
||||
KDiffusionSampler.vec_cc = {"enable": False}
|
||||
return p
|
||||
|
||||
steps: int = getattr(p, "firstpass_steps", None) or p.steps
|
||||
assert len(self.xyzCache) == 0
|
||||
|
||||
if cc_seed:
|
||||
seed(cc_seed)
|
||||
|
|
@ -366,13 +356,13 @@ class VectorscopeCC(scripts.Script):
|
|||
g = const.Color.rand()
|
||||
b = const.Color.rand()
|
||||
|
||||
print(f"\n-> Seed: {cc_seed}")
|
||||
print(f"Brightness:\t{bri}")
|
||||
print(f"Contrast:\t{con}")
|
||||
print(f"Saturation:\t{sat}")
|
||||
print(f"R:\t\t{r}")
|
||||
print(f"G:\t\t{g}")
|
||||
print(f"B:\t\t{b}\n")
|
||||
print(f"\n[Seed: {cc_seed}]")
|
||||
print(f"> Brightness: {bri}")
|
||||
print(f"> Contrast: {con}")
|
||||
print(f"> Saturation: {sat}")
|
||||
print(f"> R: {r}")
|
||||
print(f"> G: {g}")
|
||||
print(f"> B: {b}\n")
|
||||
|
||||
if getattr(shared.opts, "cc_metadata", True):
|
||||
p.extra_generation_params.update(
|
||||
|
|
@ -394,6 +384,8 @@ class VectorscopeCC(scripts.Script):
|
|||
}
|
||||
)
|
||||
|
||||
steps: int = getattr(p, "firstpass_steps", None) or p.steps
|
||||
|
||||
bri /= steps
|
||||
con /= steps
|
||||
sat = pow(sat, 1.0 / steps)
|
||||
|
|
@ -403,7 +395,10 @@ class VectorscopeCC(scripts.Script):
|
|||
|
||||
mode: str = "x" if latent else "denoised"
|
||||
|
||||
KDiffusionSampler.vec_cc = {
|
||||
setattr(
|
||||
KDiffusionSampler,
|
||||
"vec_cc",
|
||||
{
|
||||
"enable": True,
|
||||
"mode": mode,
|
||||
"bri": bri,
|
||||
|
|
@ -417,6 +412,15 @@ class VectorscopeCC(scripts.Script):
|
|||
"doAD": doAD,
|
||||
"scaling": scaling,
|
||||
"step": steps,
|
||||
}
|
||||
},
|
||||
)
|
||||
|
||||
return p
|
||||
|
||||
def before_hr(self, p, enable: bool, *args, **kwargs):
|
||||
|
||||
if enable:
|
||||
steps: int = getattr(p, "hr_second_pass_steps", None) or p.steps
|
||||
KDiffusionSampler.vec_cc["step"] = steps
|
||||
|
||||
return p
|
||||
|
|
|
|||
|
|
@ -1,4 +1,5 @@
|
|||
from modules.processing import process_images, get_fixed_seed
|
||||
from modules.shared import state
|
||||
from modules import scripts
|
||||
from copy import copy
|
||||
import gradio as gr
|
||||
|
|
@ -6,39 +7,36 @@ import numpy as np
|
|||
import cv2 as cv
|
||||
|
||||
|
||||
# https://docs.opencv.org/4.8.0/d2/df0/tutorial_py_hdr.html
|
||||
def merge_HDR(imgs: list, path: str, depth: str, fmt: str, gamma: float) -> np.ndarray:
|
||||
def _merge_HDR(imgs: list, path: str, depth: str, fmt: str, gamma: float):
|
||||
"""https://docs.opencv.org/4.8.0/d2/df0/tutorial_py_hdr.html"""
|
||||
|
||||
import datetime
|
||||
import math
|
||||
import os
|
||||
|
||||
output_folder = os.path.join(path, "hdr")
|
||||
os.makedirs(output_folder, exist_ok=True)
|
||||
out_dir = os.path.join(os.path.dirname(path), "hdr")
|
||||
os.makedirs(out_dir, exist_ok=True)
|
||||
print(f'\nSaving HDR Outputs to "{out_dir}"\n')
|
||||
|
||||
imgs_np = [np.asarray(img).astype(np.uint8) for img in imgs]
|
||||
imgs_np = [np.asarray(img, dtype=np.uint8) for img in imgs]
|
||||
|
||||
merge = cv.createMergeMertens()
|
||||
hdr = merge.process(imgs_np)
|
||||
hdr += math.ceil(0 - np.min(hdr) * 1000) / 1000
|
||||
|
||||
# print(f'{np.min(hdr)}, {np.max(hdr)}')
|
||||
# shift min to 0.0
|
||||
hdr += math.ceil(0.0 - np.min(hdr) * 1000) / 1000
|
||||
# print(f"({np.min(hdr)}, {np.max(hdr)}")
|
||||
|
||||
target = 65535 if depth == "16bpc" else 255
|
||||
precision = "uint16" if depth == "16bpc" else "uint8"
|
||||
precision = np.uint16 if depth == "16bpc" else np.uint8
|
||||
|
||||
hdr = np.power(hdr, (1 / gamma))
|
||||
|
||||
ldr = np.clip(hdr * target, 0, target).astype(precision)
|
||||
rgb = cv.cvtColor(ldr, cv.COLOR_BGR2RGB)
|
||||
|
||||
cv.imwrite(
|
||||
os.path.join(
|
||||
output_folder, f'{datetime.datetime.now().strftime("%H-%M-%S")}{fmt}'
|
||||
),
|
||||
rgb,
|
||||
)
|
||||
|
||||
return ldr
|
||||
time = datetime.datetime.now().strftime("%H-%M-%S")
|
||||
cv.imwrite(os.path.join(out_dir, f"{time}{fmt}"), rgb)
|
||||
|
||||
|
||||
class VectorHDR(scripts.Script):
|
||||
|
|
@ -50,10 +48,22 @@ class VectorHDR(scripts.Script):
|
|||
return True
|
||||
|
||||
def ui(self, is_img2img):
|
||||
|
||||
with gr.Row():
|
||||
count = gr.Slider(label="Brackets", minimum=3, maximum=9, step=2, value=5)
|
||||
count = gr.Slider(
|
||||
label="Brackets",
|
||||
minimum=3,
|
||||
maximum=9,
|
||||
step=2,
|
||||
value=5,
|
||||
)
|
||||
|
||||
gap = gr.Slider(
|
||||
label="Gaps", minimum=0.50, maximum=2.50, step=0.25, value=1.25
|
||||
label="Gaps",
|
||||
minimum=0.50,
|
||||
maximum=2.50,
|
||||
step=0.25,
|
||||
value=1.25,
|
||||
)
|
||||
|
||||
with gr.Accordion(
|
||||
|
|
@ -61,6 +71,7 @@ class VectorHDR(scripts.Script):
|
|||
elem_id=f'vec-hdr-{"img" if is_img2img else "txt"}',
|
||||
open=False,
|
||||
):
|
||||
|
||||
auto = gr.Checkbox(label="Automatically Merge", value=True)
|
||||
|
||||
with gr.Row():
|
||||
|
|
@ -76,7 +87,7 @@ class VectorHDR(scripts.Script):
|
|||
value=1.2,
|
||||
)
|
||||
|
||||
for comp in [count, gap, auto, depth, fmt, gamma]:
|
||||
for comp in (count, gap, auto, depth, fmt, gamma):
|
||||
comp.do_not_save_to_config = True
|
||||
|
||||
return [count, gap, auto, depth, fmt, gamma]
|
||||
|
|
@ -84,7 +95,8 @@ class VectorHDR(scripts.Script):
|
|||
def run(
|
||||
self, p, count: int, gap: float, auto: bool, depth: str, fmt: str, gamma: float
|
||||
):
|
||||
center = count // 2
|
||||
center: int = count // 2
|
||||
brackets = brightness_brackets(count, gap)
|
||||
|
||||
p.seed = get_fixed_seed(p.seed)
|
||||
p.scripts.script("vectorscope cc").xyzCache.update({"Enable": "False"})
|
||||
|
|
@ -95,9 +107,12 @@ class VectorHDR(scripts.Script):
|
|||
imgs = [None] * count
|
||||
imgs[center] = baseline.images[0]
|
||||
|
||||
brackets = brightness_brackets(count, gap)
|
||||
|
||||
for it in range(count):
|
||||
|
||||
if state.skipped or state.interrupted or state.stopping_generation:
|
||||
print("HDR Process Skipped...")
|
||||
return baseline
|
||||
|
||||
if it == center:
|
||||
continue
|
||||
|
||||
|
|
@ -115,14 +130,13 @@ class VectorHDR(scripts.Script):
|
|||
proc = process_images(pc)
|
||||
imgs[it] = proc.images[0]
|
||||
|
||||
if not auto:
|
||||
baseline.images = imgs
|
||||
else:
|
||||
baseline.images = [merge_HDR(imgs, p.outpath_samples, depth, fmt, gamma)]
|
||||
if auto:
|
||||
_merge_HDR(imgs, p.outpath_samples, depth, fmt, gamma)
|
||||
|
||||
baseline.images = imgs
|
||||
return baseline
|
||||
|
||||
|
||||
def brightness_brackets(count: int, gap: int) -> list[int]:
|
||||
def brightness_brackets(count: int, gap: float) -> list[float]:
|
||||
half = count // 2
|
||||
return [gap * (i - half) for i in range(count)]
|
||||
|
|
|
|||
Loading…
Reference in New Issue