Generate Images with Depth Guided Stable Diffusion and Rerun
Tags: ai, machinelearning, tutorial, opensource
Published: 2024-05-13 15:33:36

{% cta https://www.rerun.io/examples/generative-vision/depth_guided_stable_diffusion %} Source Code {% endcta %} {% cta https://www.rerun.io/examples %} Explore Other Examples {% endcta %}

Depth Guided Stable Diffusion enriches the image generation process by incorporating depth information, providing a unique way to control the spatial composition of generated images. This approach allows for more nuanced and layered creations, making it especially useful for scenes requiring a sense of three-dimensionality.

# Logging and visualizing with Rerun 
The visualizations in this example were created with the Rerun SDK, demonstrating the integration of depth information in the Stable Diffusion image generation process. Here is the code for generating the visualization in Rerun.

## Prompt 
Visualizing the prompt and negative prompt

```python
rr.log("prompt/text", rr.TextLog(prompt))
rr.log("prompt/text_negative", rr.TextLog(negative_prompt))
```

## Text 
Visualizing the text input ids, the text attention mask and the unconditional input ids

```python
rr.log("prompt/text_input/ids", rr.BarChart(text_input_ids))
rr.log("prompt/text_input/attention_mask", rr.BarChart(text_inputs.attention_mask))
rr.log("prompt/uncond_input/ids", rr.Tensor(uncond_input.input_ids))
```

## Text embeddings 
Visualizing the text embeddings. The text embeddings are generated in response to the specific prompts used while the unconditional text embeddings represent a neutral or baseline state without specific input conditions.

```python
rr.log("prompt/text_embeddings", rr.Tensor(text_embeddings))
rr.log("prompt/uncond_embeddings", rr.Tensor(uncond_embeddings))
```

## Depth map 
Visualizing the pixel values of the depth estimation, estimated depth image, interpolated depth image and normalized depth image
```python
rr.log("depth/input_preprocessed", rr.Tensor(pixel_values))
rr.log("depth/estimated", rr.DepthImage(depth_map))
rr.log("depth/interpolated", rr.DepthImage(depth_map))
rr.log("depth/normalized", rr.DepthImage(depth_map))
```

## Latents 
Log the latents, the representation of the images in the format used by the diffusion model.
```python
rr.log("diffusion/latents", rr.Tensor(latents, dim_names=["b", "c", "h", "w"]))
```

## Denoising loop 
For each step in the denoising loop we set a time sequence with step and timestep and log the latent model input, noise predictions, latents and image. This make is possible for us to see all denoising steps in the Rerun viewer.

```python
rr.set_time_sequence("step", i)
rr.set_time_sequence("timestep", t)
rr.log("diffusion/latent_model_input", rr.Tensor(latent_model_input))
rr.log("diffusion/noise_pred", rr.Tensor(noise_pred, dim_names=["b", "c", "h", "w"]))
rr.log("diffusion/latents", rr.Tensor(latents, dim_names=["b", "c", "h", "w"]))
rr.log("image/diffused", rr.Image(image))
```

## Diffused image 
Finally we log the diffused image generated by the model.
```python
rr.log("image/diffused", rr.Image(image_8))
```

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