@@ -24,325 +24,32 @@ This pipeline was contributed by [clarencechen](https://github.com/clarencechen)
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@@ -24,325 +24,32 @@ This pipeline was contributed by [clarencechen](https://github.com/clarencechen)
## Tips
## Tips
* The pipeline can generate masks that can be fed into other inpainting pipelines. Check out the code examples below to know more.
* The pipeline can generate masks that can be fed into other inpainting pipelines.
* In order to generate an image using this pipeline, both an image mask (manually specified or generated using `generate_mask`)
* In order to generate an image using this pipeline, both an image mask (source and target prompts can be manually specified or generated, and passed to [`~StableDiffusionDiffEditPipeline.generate_mask`])
and a set of partially inverted latents (generated using `invert`) _must_ be provided as arguments when calling the pipeline to generate the final edited image.
and a set of partially inverted latents (generated using [`~StableDiffusionDiffEditPipeline.invert`]) _must_ be provided as arguments when calling the pipeline to generate the final edited image.
Refer to the code examples below for more details.
* The function [`~StableDiffusionDiffEditPipeline.generate_mask`] exposes two prompt arguments, `source_prompt` and `target_prompt`
* The function `generate_mask` exposes two prompt arguments, `source_prompt` and `target_prompt`,
that let you control the locations of the semantic edits in the final image to be generated. Let's say,
that let you control the locations of the semantic edits in the final image to be generated. Let's say,
you wanted to translate from "cat" to "dog". In this case, the edit direction will be "cat -> dog". To reflect
you wanted to translate from "cat" to "dog". In this case, the edit direction will be "cat -> dog". To reflect
this in the generated mask, you simply have to set the embeddings related to the phrases including "cat" to
this in the generated mask, you simply have to set the embeddings related to the phrases including "cat" to
`source_prompt_embeds` and "dog" to `target_prompt_embeds`. Refer to the code example below for more details.
`source_prompt` and "dog" to `target_prompt`.
* When generating partially inverted latents using `invert`, assign a caption or text embedding describing the
* When generating partially inverted latents using `invert`, assign a caption or text embedding describing the
overall image to the `prompt` argument to help guide the inverse latent sampling process. In most cases, the
overall image to the `prompt` argument to help guide the inverse latent sampling process. In most cases, the
source concept is sufficently descriptive to yield good results, but feel free to explore alternatives.
source concept is sufficently descriptive to yield good results, but feel free to explore alternatives.
Please refer to [this code example](#generating-image-captions-for-inversion) for more details.
* When calling the pipeline to generate the final edited image, assign the source concept to `negative_prompt`
* When calling the pipeline to generate the final edited image, assign the source concept to `negative_prompt`
and the target concept to `prompt`. Taking the above example, you simply have to set the embeddings related to
and the target concept to `prompt`. Taking the above example, you simply have to set the embeddings related to
the phrases including "cat" to `negative_prompt_embeds` and "dog" to `prompt_embeds`. Refer to the code example
the phrases including "cat" to `negative_prompt` and "dog" to `prompt`.
below for more details.
* If you wanted to reverse the direction in the example above, i.e., "dog -> cat", then it's recommended to:
* If you wanted to reverse the direction in the example above, i.e., "dog -> cat", then it's recommended to:
* Swap the `source_prompt` and `target_prompt` in the arguments to `generate_mask`.
* Swap the `source_prompt` and `target_prompt` in the arguments to `generate_mask`.
* Change the input prompt for `invert` to include "dog".
* Change the input prompt in [`~StableDiffusionDiffEditPipeline.invert`] to include "dog".
* Swap the `prompt` and `negative_prompt` in the arguments to call the pipeline to generate the final edited image.
* Swap the `prompt` and `negative_prompt` in the arguments to call the pipeline to generate the final edited image.
* Note that the source and target prompts, or their corresponding embeddings, can also be automatically generated. Please, refer to [this discussion](#generating-source-and-target-embeddings) for more details.
* The source and target prompts, or their corresponding embeddings, can also be automatically generated. Please refer to the [DiffEdit](/using-diffusers/diffedit) guide for more details.
## Usage example
### Based on an input image with a caption
When the pipeline is conditioned on an input image, we first obtain partially inverted latents from the input image using a
`DDIMInverseScheduler` with the help of a caption. Then we generate an editing mask to identify relevant regions in the image using the source and target prompts. Finally,
the inverted noise and generated mask is used to start the generation process.
Now, generate the image with the inverted latents and semantically generated mask:
```py
image=pipeline(
prompt=target_prompt,
mask_image=mask_image,
image_latents=inv_latents,
generator=generator,
negative_prompt=source_prompt,
).images[0]
image.save("edited_image.png")
```
## Generating image captions for inversion
The authors originally used the source concept prompt as the caption for generating the partially inverted latents. However, we can also leverage open source and public image captioning models for the same purpose.
Below, we provide an end-to-end example with the [BLIP](https://huggingface.co/docs/transformers/model_doc/blip) model
We encourage you to play around with the different parameters supported by the
`generate()` method ([documentation](https://huggingface.co/docs/transformers/main/en/main_classes/text_generation#transformers.generation_tf_utils.TFGenerationMixin.generate)) for the generation quality you are looking for.
**4. Load the embedding model**:
Here, we need to use the same text encoder model used by the subsequent Stable Diffusion model.
Image editing typically requires providing a mask of the area to be edited. DiffEdit automatically generates the mask for you based on a text query, making it easier overall to create a mask without image editing software. The DiffEdit algorithm works in three steps:
1. the diffusion model denoises an image conditioned on some query text and reference text which produces different noise estimates for different areas of the image; the difference is used to infer a mask to identify which area of the image needs to be changed to match the query text
2. the input image is encoded into latent space with DDIM
3. the latents are decoded with the diffusion model conditioned on the text query, using the mask as a guide such that pixels outside the mask remain the same as in the input image
This guide will show you how to use DiffEdit to edit images without manually creating a mask.
Before you begin, make sure you have the following libraries installed:
```py
# uncomment to install the necessary libraries in Colab
The [`StableDiffusionDiffEditPipeline`] requires an image mask and a set of partially inverted latents. The image mask is generated from the [`~StableDiffusionDiffEditPipeline.generate_mask`] function, and includes two parameters, `source_prompt` and `target_prompt`. These parameters determine what to edit in the image. For example, if you want to change a bowl of *fruits* to a bowl of *pears*, then:
```py
source_prompt="a bowl of fruits"
target_prompt="a bowl of pears"
```
The partially inverted latents are generated from the [`~StableDiffusionDiffEditPipeline.invert`] function, and it is generally a good idea to include a `prompt` or *caption* describing the image to help guide the inverse latent sampling process. The caption can often be your `source_prompt`, but feel free to experiment with other text descriptions!
Let's load the pipeline, scheduler, inverse scheduler, and enable some optimizations to reduce memory usage:
Use the [`~StableDiffusionDiffEditPipeline.generate_mask`] function to generate the image mask. You'll need to pass it the `source_prompt` and `target_prompt` to specify what to edit in the image:
```py
source_prompt="a bowl of fruits"
target_prompt="a basket of pears"
mask_image=pipeline.generate_mask(
image=raw_image,
source_prompt=source_prompt,
target_prompt=target_prompt,
)
```
Next, create the inverted latents and pass it a caption describing the image:
Finally, pass the image mask and inverted latents to the pipeline. The `target_prompt` becomes the `prompt` now, and the `source_prompt` is used as the `negative_prompt`:
The source and target embeddings can be automatically generated with the [Flan-T5](https://huggingface.co/docs/transformers/model_doc/flan-t5) model instead of creating them manually.
Load the Flan-T5 model and tokenizer from the 🤗 Transformers library:
Check out the [generation strategy](https://huggingface.co/docs/transformers/main/en/generation_strategies) guide if you're interested in learning more about strategies for generating different quality text.
</Tip>
Load the text encoder model used by the [`StableDiffusionDiffEditPipeline`] to encode the text. You'll use the text encoder to compute the text embeddings:
Finally, pass the embeddings to the [`~StableDiffusionDiffEditPipeline.generate_mask`] and [`~StableDiffusionDiffEditPipeline.invert`] functions, and pipeline to generate the image:
```diff
from diffusers import DDIMInverseScheduler, DDIMScheduler
While you can use the `source_prompt` as a caption to help generate the partially inverted latents, you can also use the [BLIP](https://huggingface.co/docs/transformers/model_doc/blip) model to automatically generate a caption.
Load the BLIP model and processor from the 🤗 Transformers library:
