<div data-youtube-video="https://youtu.be/09qEF5oo8Gc"><iframe src="https://www.youtube.com/embed/09qEF5oo8Gc" width="853" height="480" allowfullscreen></iframe></div> 
 
Explore how a color-depth LoRA changes lighting and palette in a MOSS-Image SFT text-to-image workflow. The LoRA is connected in the output path, so this JSON represents an active color-control experiment. 
 
Main features: 
- MOSS-Image SFT INT8 ConvRot model, Qwen3.5 9B BF16 text encoder, and FLUX.2 VAE. 
- moss_image_color_depth_control.safetensors connected at strength 0.8. 
- One output-reachable Euler sampler: 50 steps, CFG 3, simple scheduler, fixed seed 14001. 
- Example neon rain-street prompt with magenta and cyan reflections, plus a separate negative prompt. 
 
Suggested workflow: 
1. Install the SFT model, text encoder, VAE, and color-depth LoRA, then load the JSON. 
2. Generate with the included prompt and LoRA strength 0.8 to establish a baseline. 
3. Change subject, lighting words, and LoRA strength to compare color depth and contrast in SaveImage outputs. 
 
Required model filenames: moss_image_sft_int8_convrot.safetensors; qwen3.5_9b_bf16.safetensors; flux2-vae.safetensors; moss_image_color_depth_control.safetensors. The cover is a related example image; the JSON's current prompt is the neon rain street scene. 
 
RunningHub AI workflow: https://www.runninghub.ai/zh-cn/post/2113906855339978754?inviteCode=rh-v1111 
RunningHub signup: https://www.runninghub.ai/?inviteCode=rh-v1111 
Bilibili video: https://www.bilibili.com/video/BV1ZHHP6FEVZ/ 
Related model files: https://pan.quark.cn/s/20e19307e8ef

MOSS-Image Color Depth LoRA Study

<div data-youtube-video="https://youtu.be/09qEF5oo8Gc"></div> 
 
Explore how a color-depth LoRA changes lighting and palette in a MOSS-Image SFT text-to-image workflow. The LoRA is connected in the output path, so this JSON represents an active color-control experiment. 
 
Main features: 
- MOSS-Image SFT INT8 ConvRot model, Qwen3.5 9B BF16 text encoder, and FLUX.2 VAE. 
- moss_image_color_depth_control.safetensors connected at strength 0.8. 
- One output-reachable Euler sampler: 50 steps, CFG 3, simple scheduler, fixed seed 14001. 
- Example neon rain-street prompt with magenta and cyan reflections, plus a separate negative prompt. 
 
Suggested workflow: 
1. Install the SFT model, text encoder, VAE, and color-depth LoRA, then load the JSON. 
2. Generate with the included prompt and LoRA strength 0.8 to establish a baseline. 
3. Change subject, lighting words, and LoRA strength to compare color depth and contrast in SaveImage outputs. 
 
Required model filenames: moss_image_sft_int8_convrot.safetensors; qwen3.5_9b_bf16.safetensors; flux2-vae.safetensors; moss_image_color_depth_control.safetensors. The cover is a related example image; the JSON's current prompt is the neon rain street scene. 
 
RunningHub AI workflow: https://www.runninghub.ai/zh-cn/post/2113906855339978754?inviteCode=rh-v1111 
RunningHub signup: https://www.runninghub.ai/?inviteCode=rh-v1111 
Bilibili video: https://www.bilibili.com/video/BV1ZHHP6FEVZ/ 
Related model files: https://pan.quark.cn/s/20e19307e8ef

Files with SHA256: df5abfdddb9b9960c72896b3ad0a6bd6fb0dc17e357bce49038e9f7757d2374d