# Parameters

## [Stable Diffusion](https://stable-diffusion-art.com/install-windows/)

<figure><img src="/files/Qp56YgaRNTC0UU54LtlW" alt=""><figcaption><p>Reverse diffusion works by subtracting the predicted noise from the image successively.</p></figcaption></figure>

### **Weighting Syntax**

Stable Diffusion supports both **parentheses** and **numerical weights** for emphasis:

* **Emphasis:**
  * `(keyword)` increases emphasis slightly.
  * `((keyword))` increases it more significantly.
  * `(((keyword)))` or additional parentheses for even stronger emphasis.
* **De-emphasis:**
  * `[keyword]` reduces the importance of the keyword.
* **Numerical Weights:**
  * Use `keyword:weight` to directly set the weight.
  * Example: `a cat:1.5, a dog:0.5` gives more prominence to "cat" than "dog."
  * Typical weights range from `0.0` (ignore) to `2.0` (high emphasis).

<details>

<summary>Combining Weighted Terms</summary>

Stable Diffusion allows complex prompts with weighted phrases:

* **Example Prompt:**

  <pre data-overflow="wrap"><code>A beautiful ((sunset over the ocean)):1.5, [a city skyline]:0.7, colorful skies:1.2
  </code></pre>

  * This prioritizes the sunset while de-emphasizing the city skyline.

</details>

<details>

<summary>Impact of Weighting</summary>

Weights directly influence the model’s focus, but the results depend on:

* **Model Training Data**: Some concepts or terms may already be prominent or weak due to the dataset.
* **Prompt Complexity**: Overloading the prompt with high-emphasis terms can dilute results.
* **Inherent Ambiguity**: The model interprets prompts probabilistically, so multiple runs might slightly differ.

</details>

<details>

<summary>Using AND/OR for Advanced Control</summary>

You can separate concepts using `AND` for better multi-object composition:

* **Example:**

  ```
  A futuristic city:1.2 AND a dragon flying in the sky:0.8
  ```

  * This creates a balance between the city and dragon elements

</details>

<details>

<summary>Examples</summary>

* **Detailed Portrait:**

  <pre data-overflow="wrap"><code>A ((realistic portrait)) of a woman with blue eyes:1.3, wearing a red scarf:1.0 --negative (blurry), (cartoonish)
  </code></pre>
* **Fantasy Scene:**

  <pre data-overflow="wrap"><code>A magical forest with glowing mushrooms:1.5 AND a fairy with delicate wings:1.2 --negative (dark shadows), (low resolution)
  </code></pre>

</details>

## [MidJourney](https://www.midjourney.com/)

<figure><img src="/files/7VTh20LFjJuciyvuErIt" alt=""><figcaption></figcaption></figure>

In MidJourney, **weighting syntax** allows users to emphasize or de-emphasize certain elements in their image prompts. This can guide the AI on which parts of the prompt are more important or should have a greater influence on the final output.

### **Weighting**

{% tabs %}
{% tab title="What" %}
**Double Colons (`::`)**:

* Place `::` followed by a numerical weight after a specific term or phrase.
* Add a space **after** the numerical weight.
* Example: `a golden retriever::2 sitting in a field of flowers::0.5`
  * The **golden retriever** is emphasized with weight 2 (more influence).
  * The **field of flowers** is de-emphasized with weight 0.5 (less influence).
    {% endtab %}

{% tab title="Why" %}
Weighting allows you to emphasize or de-emphasize specific parts of your prompt by assigning a numerical weight to them. This helps you guide the AI to prioritize certain elements over others in the image composition.
{% endtab %}

{% tab title="How" %}
Weights are specified using a `::` followed by a number. The number determines how much influence the preceding word or phrase has on the output.
{% endtab %}

{% tab title="Example" %}

```plaintext
term1::weight1 term2::weight2 term3::weight3
```

```plaintext
a futuristic city::2, a sunset::0.5, a flying car::1
```

{% endtab %}

{% tab title="Notes" %}

* **Default Weight:** If no weight is specified, it defaults to 1.&#x20;
* **Higher Weight:** A number greater than 1 (e.g., ::2, ::3) increases influence.&#x20;
* **Lower Weight:** A number less than 1 (e.g., ::0.5) decreases influence.&#x20;
* **Zero Weight:** A weight of ::0 effectively ignores that term.
* Avoid assigning very high weights (e.g., `::10`) unless you want the image to focus exclusively on that element.
* MidJourney automatically normalizes the overall influence of weighted terms in a prompt. If one term is heavily weighted, other terms with default or lower weights may be downplayed.
  {% endtab %}
  {% endtabs %}

### Examples Prompts

{% tabs %}
{% tab title="Balanced" %}

```
a dragon::1, a castle::1, a knight::1
```

All elements have equal influence.
{% endtab %}

{% tab title="Emphasized" %}

```plaintext
a dragon::2, a castle::1, a knight::0.5
```

The dragon is the primary focus, the knight is de-emphasized.
{% endtab %}

{% tab title="Dominant Background" %}

```plaintext
a sunset::3, a beach::1, palm trees::0.8
```

The sunset dominates, while the beach and palm trees are secondary.
{% endtab %}

{% tab title="Negative Parameter" %}

```plaintext
a futuristic city::2 a flying car::1 --no skyscrapers
```

You can use `--no` with weights to exclude or downplay certain elements. For instance:

This tells MidJourney to focus on the city and car but avoid including skyscrapers.
{% endtab %}
{% endtabs %}

### **Combining Image Input with Text Weights**

{% tabs %}
{% tab title="How?" %}
When combining image references and text prompts, the `--iw` (image weight) parameter adjusts the influence of the uploaded image(s) compared to the text. By default, `--iw` is `1`.
{% endtab %}

{% tab title="Example" %}

```plaintext
[image reference link] a desert landscape::2, futuristic architecture::1 --iw 0.5
```

The text elements are emphasized more than the image input due to the reduced `--iw` value.
{% endtab %}

{% tab title="Use Cases" %}

* **Focus Control:** Highlight specific objects, styles, or themes.
  * Example: `a giant robot::2 city skyline::0.5`
* **Detail Reduction:** Downplay less critical aspects of a scene.
  * Example: `a dense forest::2 a small lake::0.2`
* **Experimentation:** Test different weights to fine-tune output.
  {% endtab %}
  {% endtabs %}

***

![](/files/iadwt1BvQjiz0K1C4cEt)\ <sub>© 2024 by SopakcoSauce. Except as otherwise noted, the content of this page is licensed under</sub> [<sub>CC BY-NC-SA 4.0</sub>](https://creativecommons.org/licenses/by-nc-sa/4.0/)&#x20;


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://sopakcosauce.gitbook.io/sopakcosauce-docs/image-generation-guides/parameters.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.