Architectures de modèles
Image generation & editing
Synthesize or transform pixels, most often through a text or image conditioner, a latent generator, and an image decoder.
Modèle mental
A pipeline, not one network: prompt encoder → generative process → latent/pixel decoder, often with separate control and safety components.
Flux de données
- Text, image, mask, or layout condition
- Condition encoder
- Latent diffusion, DiT, or autoregressive generator
- Image decoder
- Candidate image + checks
Entraînement
Modern systems often learn a denoising or flow objective in pixel or compressed latent space. Alignment to text depends on paired data and conditioning; autoencoders may be trained separately.
Exécution de l’inférence
A sampler starts from noise or a noised source image, iteratively refines it under the condition, then decodes the latent. Seeds improve reproducibility but do not make all runtime paths deterministic.
Atouts
- High-quality open-ended visual synthesis
- Natural support for variation, inpainting, and guided editing
- Latent pipelines make high-resolution generation more practical
Compromis
- Multiple sampling steps add latency
- Text, typography, identity, and exact spatial constraints need explicit evaluation
- Training-data rights, provenance, and misuse controls are product concerns
À utiliser lorsque
- Creative ideation, asset drafts, or controlled image transformation
- The output can be reviewed or constrained
- Visual quality and prompt adherence are evaluated separately
À éviter ou remettre en question lorsque
- Pixel-exact layouts are required
- The output is evidence of a real event or identity
- Rights and provenance requirements are unresolved
Familles publiées à titre d’exemple
- • Latent Diffusion / Stable Diffusion research lineage
- • Diffusion Transformer (DiT) research architecture
Souvent combinée avec
Latent diffusionDiffusion TransformerVision encodersMultimodal fusion