Add A Method 'get_weighted_text_embeddings_sd3_3p' To Embedding_funcs.py Please

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Enhancing the Stability Diffusion Embeddings with Multiple Prompts

Introduction

The stability diffusion embeddings (sd_embed) library provides a powerful tool for generating text embeddings using various tokenizers and text encoders. However, the current implementation of the get_weighted_text_embeddings_sd3 method only takes two prompts, prompt and neg_prompt, and passes them to all three tokenizers and text encoders. This limitation can be a hindrance for researchers who require more flexibility in their stable diffusion research tools.

In this article, we will explore how to modify the get_weighted_text_embeddings_sd3 method to accommodate multiple prompts, making it more versatile and useful for a wider range of applications.

Current Implementation

The current implementation of the get_weighted_text_embeddings_sd3 method is as follows:

def get_weighted_text_embeddings_sd3(prompt, neg_prompt, pad_last_block, use_t5_encoder):
    # ...
    embeddings = []
    for tokenizer, text_encoder in zip(tokenizers, text_encoders):
        # ...
        embeddings.append(text_encoder.encode(tokenizer.encode(prompt)))
        embeddings.append(text_encoder.encode(tokenizer.encode(neg_prompt)))
    # ...
    return embeddings

As you can see, the method only takes two prompts, prompt and neg_prompt, and passes them to all three tokenizers and text encoders.

Proposed Modification

To address the limitation of the current implementation, we propose modifying the get_weighted_text_embeddings_sd3 method to get_weighted_text_embeddings_sd3_3p. This new method will take six prompts: prompt, prompt_2, prompt_3, neg_prompt, neg_prompt_2, and neg_prompt_3. Each of these prompts will be passed to each of the three tokenizers and text encoders.

Here is the proposed implementation:

def get_weighted_text_embeddings_sd3_3p(prompt, prompt_2, prompt_3, neg_prompt, neg_prompt_2, neg_prompt_3, pad_last_block, use_t5_encoder):
    # ...
    embeddings = []
    for tokenizer, text_encoder in zip(tokenizers, text_encoders):
        # ...
        embeddings.append(text_encoder.encode(tokenizer.encode(prompt)))
        embeddings.append(text_encoder.encode(tokenizer.encode(prompt_2)))
        embeddings.append(text_encoder.encode(tokenizer.encode(prompt_3)))
        embeddings.append(text_encoder.encode(tokenizer.encode(neg_prompt)))
        embeddings.append(text_encoder.encode(tokenizer.encode(neg_prompt_2)))
        embeddings.append(text_encoder.encode(tokenizer.encode(neg_prompt_3)))
    # ...
    return embeddings

To maintain backwards compatibility, we can create a new method called get_weighted_text_embeddings_sd3 that simply calls the get_weighted_text_embeddings_sd3_3p method with the first two prompts repeated for all three tokenizers and text encoders:

def get_weighted_text_embeddings_sd3(prompt, neg_prompt, pad_last_block, use_t5_encoder):
    return get_weighted_text_embeddings_sd3_3p(prompt, prompt, prompt, neg_prompt, neg_prompt, neg_prompt, pad_last_block, use_t5_encoder)

Benefits of the Proposed Modification

The proposed modification offers several benefits:

  1. Increased flexibility: The new method allows researchers to control the input prompts for each of the three tokenizers and text encoders, making it more versatile and useful for a wider range of applications.
  2. Improved compatibility: The backwards compatibility of the get_weighted_text_embeddings_sd3 method ensures that existing code will continue to work as expected, while the new method provides an additional option for researchers who require more flexibility.
  3. Enhanced stability diffusion research: The ability to control the input prompts for each of the three tokenizers and text encoders can lead to more accurate and stable results in stable diffusion research.

Conclusion

In conclusion, the proposed modification to the get_weighted_text_embeddings_sd3 method offers several benefits, including increased flexibility, improved compatibility, and enhanced stability diffusion research. By modifying the method to get_weighted_text_embeddings_sd3_3p and creating a new method called get_weighted_text_embeddings_sd3 that simply calls the former, we can provide a more versatile and useful tool for researchers working with stable diffusion embeddings.
Frequently Asked Questions: Enhancing the Stability Diffusion Embeddings with Multiple Prompts

Introduction

The stability diffusion embeddings (sd_embed) library has been enhanced with a new method called get_weighted_text_embeddings_sd3_3p, which allows researchers to control the input prompts for each of the three tokenizers and text encoders. This new method provides increased flexibility and improved compatibility with existing code. In this article, we will answer some frequently asked questions about the new method and its benefits.

Q: What is the purpose of the get_weighted_text_embeddings_sd3_3p method?

A: The get_weighted_text_embeddings_sd3_3p method is designed to provide increased flexibility and control over the input prompts for each of the three tokenizers and text encoders. This allows researchers to tailor the input prompts to their specific needs and applications.

Q: How does the get_weighted_text_embeddings_sd3_3p method differ from the get_weighted_text_embeddings_sd3 method?

A: The get_weighted_text_embeddings_sd3_3p method takes six prompts as input, whereas the get_weighted_text_embeddings_sd3 method takes only two prompts. The get_weighted_text_embeddings_sd3_3p method also allows researchers to control the input prompts for each of the three tokenizers and text encoders.

Q: What are the benefits of using the get_weighted_text_embeddings_sd3_3p method?

A: The benefits of using the get_weighted_text_embeddings_sd3_3p method include:

  • Increased flexibility and control over the input prompts
  • Improved compatibility with existing code
  • Enhanced stability diffusion research

Q: How do I use the get_weighted_text_embeddings_sd3_3p method?

A: To use the get_weighted_text_embeddings_sd3_3p method, you will need to pass six prompts as input: prompt, prompt_2, prompt_3, neg_prompt, neg_prompt_2, and neg_prompt_3. You can then use the method to generate weighted text embeddings.

Q: Can I still use the get_weighted_text_embeddings_sd3 method?

A: Yes, you can still use the get_weighted_text_embeddings_sd3 method. The get_weighted_text_embeddings_sd3 method simply calls the get_weighted_text_embeddings_sd3_3p method with the first two prompts repeated for all three tokenizers and text encoders.

Q: What are the implications of using the get_weighted_text_embeddings_sd3_3p method for stability diffusion research?

A: The get_weighted_text_embeddings_sd3_3p method can lead to more accurate and stable results in stability diffusion research by allowing researchers to control the input prompts for each of the three tokenizers and text encoders.

Q: Can I use the get_weighted_text_embeddings_sd3_3p method with other sd_embed methods?

A: Yes, you can use the get_weighted_text_embeddings_sd3_3p method with other sd_embed methods. The get_weighted_text_embeddings_sd3_3p method is designed to be compatible with other sd_embed methods.

Conclusion

In conclusion, the get_weighted_text_embeddings_sd3_3p method provides increased flexibility and control over the input prompts for each of the three tokenizers and text encoders. This can lead to more accurate and stable results in stability diffusion research. We hope this article has answered your questions and provided a better understanding of the benefits and implications of using the get_weighted_text_embeddings_sd3_3p method.