Multiple conditioning for SD

🧨 Diffusers
1

Hi, I have not worked before with diffusion models. How can I use multiple conditions like 5 images and 5 corresponding descriptions and get 1 image and 1 description as an output?
For the description I believe I should use an external image-to-text model adding 5 descriptions in the context, but I have no clue how to use a diffusion model for a single output for 5 images with conditioning text

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2

I don’t see many diffusion models that take multiple images as input…

Perhaps IP adapters or ControlNet…

3

That’s an exciting challenge! Combining multiple images and their descriptions into a single conditioned output using a diffusion model requires a careful approach to conditioning, image fusion, and prompt engineering. Here’s a step-by-step breakdown of how you can do it:

Step 1: Extract Image Descriptions

Since you’re planning to use image-to-text models externally, here’s the general flow:

  • Use a model like BLIP or CLIP to generate descriptions for each image.
  • Store each description and associate it with its corresponding image.
  • Format them into a context-rich input for your diffusion model.

Step 2: Conditioning the Diffusion Model

To guide the diffusion model towards a fused output:

  1. Concatenate Image Encodings – Use an image encoder (e.g., CLIP or VAE) to process each of the 5 images separately.
  2. Embed Text Descriptions Together – Instead of conditioning on a single prompt, structure the input like:
    "A fusion of the following styles and scenes: 
 - [Description of Image 1]
 - [Description of Image 2]
 - [Description of Image 3]
 - [Description of Image 4]
 - [Description of Image 5]"
  3. Fine-Tune Cross-Attention Layers – If using Stable Diffusion, modify cross-attention layers to attend to multiple images at once.

Step 3: Image Fusion Strategy

You have a few approaches depending on the model’s flexibility:

  • Latent Space Averaging – Compute a weighted mean of latent encodings before feeding them into the UNet.
  • Image Stacking in Conditioning – Stack latent representations and allow the model to decide dominant features.
  • Blend with ControlNet – If you need precise control, consider integrating ControlNet to guide the generation based on shapes, edges, or composition.

Step 4: Generating the Final Output

Once your diffusion model receives the combined latent features and merged text descriptions, generate:

  • A single synthesized image reflecting features from all input images.
  • A final text description either from your image-to-text model or an additional pass through CLIP.

Code Example (Stable Diffusion Approach)

Here’s a simplified Python snippet using Stable Diffusion’s latent averaging method:

import torch
from diffusers import StableDiffusionPipeline
from transformers import CLIPProcessor, CLIPModel

# Load models
sd_pipeline = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2")
clip_model = CLIPModel.from_pretrained("openai/clip-vit-large-patch14")
clip_processor = CLIPProcessor.from_pretrained("openai/clip-vit-large-patch14")

# Encode multiple images
image_latents = []
for img_path in ["img1.jpg", "img2.jpg", "img3.jpg", "img4.jpg", "img5.jpg"]:
    image = clip_processor(images=img_path, return_tensors="pt")["pixel_values"]
    image_latents.append(clip_model.encode_image(image))

# Average latent representations
combined_latent = torch.mean(torch.stack(image_latents), dim=0)

# Generate final image
prompt = "A fusion of multiple artistic styles and subjects."
output = sd_pipeline(prompt=prompt, latent=combined_latent)
output.save("final_generated_image.jpg")

Alternative Approaches

  • If you need precise fusion (like controlling exact features from each image), consider using StyleGAN or ControlNet.
  • If you want text-driven blending, explore Prompt-to-Prompt (P2P) editing in Stable Diffusion.

Would you like help refining the implementation further? :rocket:

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