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Generative methods · creation and editing workflows · control · evaluation · limitations.
For: Creators · learners · builders.
After this page you can: Choose a generation or editing method and assess its output.
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Part of: Artificial Intelligence › Multimodal AI
Image and video generation turns a description, an input image, or both into visual content. Diffusion is one approach: a model learns a denoising process that can turn a noisy representation into a sample. Other approaches, including flow matching, learn a continuous path between noise and data. These terms describe training and generation methods; they do not tell you by themselves whether a result will suit a particular task.
Editing workflows offer different degrees of control. Image-to-image generation starts from an image, while inpainting uses a mask to select an area to replace or fill. A structural guide such as edges, depth, or pose can further steer an image. Video generation adds a time dimension: keeping text, spatial content, and motion aligned from frame to frame is a challenge. Assess results against the task: check requested details, composition, unwanted changes, and, for video, continuity across frames. Automatic metrics can help compare outputs, but research has found limitations in current image-text alignment evaluations.
A free course that introduces diffusion theory, image generation, conditioning, and guidance. Its later material assumes some Python and deep-learning background.
Start with: Hugging Face Diffusion Models Course
Compare image-to-image generation with mask-based inpainting and structural control.
Start with: Inpainting
Read about generating video from text or images and the challenge of frame-to-frame consistency.
Start with: Video generation
Diffusion models learn to reverse a process that adds noise to data, producing samples through denoising. Some image workflows encode an image into a latent space before adding noise and generating a changed version. Flow matching takes a different training perspective: it fits a vector field along probability paths that transform noise and data. The methods are related but not interchangeable labels for every generative model.
When choosing what to learn first, connect each term to a visible step in the workflow: noise is introduced, a representation is updated, and an image is decoded. Flow matching is useful vocabulary for reading about generative methods beyond the basic diffusion account.
The paper presents diffusion probabilistic models for image synthesis and discusses denoising.
The paper defines flow matching as a training approach for continuous normalizing flows.
Diffusion model
A generative model learning to reverse a process that adds noise.
Denoising
Removing or reducing noise in a representation.
Latent space
A space of learned representations rather than raw observations.
Flow matching
Training a model to transform distributions through a learned continuous flow.
Inpainting
Generating or editing content within selected image regions.
In one sentence, describe how a diffusion model uses noise and denoising to generate a sample.
A diffusion model learns a denoising process that reverses noise addition to generate a sample.
A text-to-image workflow starts from a prompt. Image-to-image adds an initial image as a starting point; in a latent diffusion workflow, the image is encoded, noise is added, and the model generates a changed image from the prompt and noisy representation. Inpainting is for targeted edits: a mask marks the area to fill or replace, while the unmasked area is kept. A structural control image, such as edges, depth, or pose, can guide composition while a prompt describes the content.
Choose the input that matches the constraint you want to preserve. Use an initial image when you want a starting point, a mask when the change should be localized, and a structural guide when layout or pose matters. Inspect whether the output respected the intended boundary and whether changes outside it are acceptable.
The guide explains how an initial image is used as a starting point for image generation.
The guide describes structural control images for guiding generated images.
You want to change only the sky in an existing picture. Which workflow would you choose, and what input marks the area to change?
Choose inpainting. A mask marks the area to fill or replace.
For an image, separate general visual quality from compositional quality: does it look coherent, and does it place the requested elements and relationships correctly? Text-image alignment metrics aim to assess how well an image reflects a prompt, but a metric score is only one view of the result. Research on automatic alignment evaluation reports that current evaluation frameworks do not fully satisfy the robustness and significance criteria discussed by the authors.
For video, inspect the clip across time as well as frame by frame. Check whether the requested subject, scene, and action remain aligned, and whether motion and details stay coherent between frames. Video-generation documentation identifies frame-to-frame consistency as a challenge. For a practical review, compare the output with the request and note both successful details and visible mismatches.
The paper examines robustness and significance in automatic image-text alignment evaluation.
The guide identifies frame-to-frame consistency as a challenge for video generation.
A generated image contains the requested objects but puts them in the wrong relationship. Which evaluation dimension should you inspect?
Compositional quality: whether the requested elements and their relationships are represented correctly.
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David Foster · 2023
An accessible introduction to generative mechanics with examples including Midjourney, DALL.E 2, Stable Diffusion, and ControlNet. Its named systems reflect the 2023 edition’s time-bound examples.
Jakub M. Tomczak · 2024
A mathematically oriented treatment of generative model families, with examples and code snippets. The 2024 second edition covers score-based models, useful background for understanding diffusion.