Post-training

Post-training strategies like RLHF (Reinforcement Learning from Human Feedback) and DPO (Direct Preference Optimization) can also be applied to image-generation models (e.g., Stable Diffusion, DALL·E, Midjourney) to improve alignment, safety, and aesthetic quality. However, since images are non-textual, the methods differ slightly from those used in language models. Below is how these techniques work for image-generation AI:

1. Key Challenges for Image Models vs. Text Models

2. How RLHF Works for Image Models

Step 1: Supervised Fine-Tuning (SFT)

Train the base model (e.g., Stable Diffusion) on high-quality, curated images.
Helps the model generate better initial outputs before alignment.

Step 2: Reward Model Training

Collect human preference data by showing users multiple generated images and asking:
- Which image is more aesthetically pleasing?
- Which image better follows the prompt?
- Which image is safer (no harmful content)?
Train a reward model (e.g., a neural network) to predict human preferences.

Step 3: RL Fine-Tuning (PPO or Diffusion Policy Optimization)

Use Reinforcement Learning (RL) to fine-tune the image generator to maximize the reward score.
Unlike text models, optimizing in pixel space is computationally expensive, so alternatives include:
- Latent-space optimization (e.g., fine-tuning Stable Diffusion’s latent space).
- Denoising Diffusion Policy Optimization (DDPO) (a variant of PPO for diffusion models).

Example: Improving Aesthetics with RLHF

A model like DALL·E 3 may use RLHF to ensure:
- Generated images match prompts more accurately.
- Images are more visually appealing (better lighting, composition).
- Avoids generating harmful/NSFW content.

3. How DPO Works for Image Models

Since DPO eliminates the need for a separate reward model, it can be more efficient for image alignment.

Step 1: Collect Preference Data

Humans rank pairs of images (A, B) based on:
- Prompt faithfulness (does it match the text?).
- Aesthetics (which looks better?).
- Safety (which is less harmful?).

Step 2: Direct Optimization

Instead of training a reward model, DPO directly adjusts the image generator’s weights to increase the likelihood of preferred images over dispreferred ones.
Works well for diffusion models since it avoids unstable RL training.

Example: Reducing Bias with DPO

If a model generates stereotypical images (e.g., “CEO” always as a man), DPO can:
- Downweight biased images in training.
- Upsample diverse, fairer generations.

4. Other Post-Training Strategies for Image Models

A. Rejection Sampling (Best-of-N Filtering)

Generate multiple images, then pick the best one using:
- A safety classifier (e.g., NSFW filter).
- A reward model (e.g., aesthetic scorer).
Used in Midjourney v6 to improve output quality.

B. Adversarial Training (Red-Teaming)

Test the model with malicious prompts (e.g., requests for violent images).
Fine-tune the model to refuse harmful generations.

Platforms like Stable Diffusion XL allow users to rate images, which are then used for further fine-tuning.

5. Applications & Trade-offs

6. Real-World Examples

OpenAI’s DALL·E 3 – Uses RLHF to improve prompt adherence and safety.
Stable Diffusion XL – Leverages human feedback for better aesthetics.
Midjourney – Uses ranking systems to refine style and quality.

Conclusion

RLHF and DPO can align image models with human preferences, but pixel-space optimization is harder than text.
DPO is more efficient than RLHF for images but requires good preference data.
Hybrid approaches (RLHF + filters) are common in production systems.

Would you like details on implementing this for a specific image model (e.g., Stable Diffusion)?