mirror of
https://github.com/invoke-ai/InvokeAI
synced 2026-03-01 20:49:10 +01:00
eb3f1c9a61
Add comprehensive support for Z-Image-Turbo (S3-DiT) models including: Backend: - New BaseModelType.ZImage in taxonomy - Z-Image model config classes (ZImageTransformerConfig, Qwen3TextEncoderConfig) - Model loader for Z-Image transformer and Qwen3 text encoder - Z-Image conditioning data structures - Step callback support for Z-Image with FLUX latent RGB factors Invocations: - z_image_model_loader: Load Z-Image transformer and Qwen3 encoder - z_image_text_encoder: Encode prompts using Qwen3 with chat template - z_image_denoise: Flow matching denoising with time-shifted sigmas - z_image_image_to_latents: Encode images to 16-channel latents - z_image_latents_to_image: Decode latents using FLUX VAE Frontend: - Z-Image graph builder for text-to-image generation - Model picker and validation updates for z-image base type - CFG scale now allows 0 (required for Z-Image-Turbo) - Clip skip disabled for Z-Image (uses Qwen3, not CLIP) - Optimal dimension settings for Z-Image (1024x1024) Technical details: - Uses Qwen3 text encoder (not CLIP/T5) - 16 latent channels with FLUX-compatible VAE - Flow matching scheduler with dynamic time shift - 8 inference steps recommended for Turbo variant - bfloat16 inference dtype |
||
|---|---|---|
| .. | ||
| diffusion | ||
| extensions | ||
| schedulers | ||
| __init__.py | ||
| denoise_context.py | ||
| diffusers_pipeline.py | ||
| diffusion_backend.py | ||
| extension_callback_type.py | ||
| extensions_manager.py | ||
| multi_diffusion_pipeline.py | ||
| vae_tiling.py | ||