--- library_name: safetensors pipeline_tag: text-to-image base_model: Tongyi-MAI/Z-Image-Turbo license: other tags: - z-image - zimage - z-image-turbo - lora - style-lora - comfyui - ai-toolkit - safetensors - holosomnia --- # Holosomnia Z-Image Turbo LoRA  Style LoRA trained for `Tongyi-MAI/Z-Image-Turbo` on original Holosomnia artwork. This upload contains the V10 "content moderate" checkpoint at step 800, which was selected because it showed strong style transfer while preserving more subject/detail structure than later, more overcooked checkpoints. ## Strength Sweep Preview The grid below compares the same five prompts/seeds across LoRA strengths `0.1`, `0.2`, `0.6`, `0.9`, and `1.1`. In this sweep, `0.9` was the strongest general-purpose balance; `1.1` pushes the look harder but can simplify or over-saturate structure.  ## Files - `holosomnia_zimage_turbo_v10_step0800.safetensors` - LoRA checkpoint - `training_config_v10_content_moderate.yaml` - training configuration used for this run - `holosomnia_zimage_turbo_v10_step0800.sha256` - SHA256 checksum - `training_metadata.json` - compact metadata summary - `images/holosomnia_lora_strength_sweep_contact_sheet.png` - LoRA strength comparison sheet ## Usage Use with Z-Image Turbo as a LoRA. Recommended starting points: - LoRA strength: `0.6` to `1.0` - Lower strength preserves the base prompt/content more. - Higher strength pushes the Holosomnia cloud/color/rendering style harder. - Trigger keyword: `zhlxart` Example prompts: ```text zhlxart, a lighthouse on a cliff above the ocean at sunset with dramatic clouds ``` ```text zhlxart, a small white cottage on a rolling green hill beneath towering colorful clouds ``` ```text zhlxart, an ultra detailed portrait of a woman with silver hair wearing embroidered clothing, soft skin texture, intricate jewelry, shallow depth of field ``` The trigger can be omitted for subtler use, but `zhlxart` gives the most direct activation. ## Training Summary | Setting | Value | | --- | --- | | Base model | `Tongyi-MAI/Z-Image-Turbo` | | Training adapter | `ostris/zimage_turbo_training_adapter/zimage_turbo_training_adapter_v2.safetensors` | | Training tool | `ai-toolkit 0.9.6` | | Checkpoint | V10 content moderate, step 800 | | Dataset | 45 curated Holosomnia image/caption pairs | | Captions | `zhlxart` trigger plus content-preserving descriptions | | LoRA rank | 32 | | LoRA alpha | 32 | | Network target | Transformer only | | Text encoder | Not trained | | Optimizer | `adamw8bit` | | Learning rate | `8e-5` | | Batch size | 1 | | Gradient accumulation | 1 | | Training dtype | `bf16` | | Save dtype | `float16` | | Noise scheduler | `flowmatch` | | Timestep type | `sigmoid` | | Differential guidance | Enabled, scale `2.0` | | Resolution buckets | 512, 768, 1024 | | Caption dropout | 0.0 | ## Checksum ```text 08b6aa34bf46a8fce20c02726878a007637b576ca7072f93f9fd7c84f19b57a3 holosomnia_zimage_turbo_v10_step0800.safetensors ``` ## Rights The training images are original artwork by Holosomnia and are not included in this repository. This LoRA is provided for use with Z-Image Turbo. Users are responsible for following the license and terms of the base model and any other components used in their inference workflow. No license grant is made here for the original Holosomnia artwork or training images. ## Limitations This is not a standalone model. It requires a compatible Z-Image Turbo workflow. Like most style LoRAs, high LoRA strength may shift composition, color balance, or facial/detail fidelity; reduce strength when content preservation matters.
