---
title: MOSS-Imagine_ComfyUI
canonical_url: "https://www.modelscope.cn/models/realrebelai/MOSS-Imagine_ComfyUI"
md_url: "https://www.modelscope.cn/models/realrebelai/MOSS-Imagine_ComfyUI.md"
repository: realrebelai/MOSS-Imagine_ComfyUI
last_updated: 2026-10-21
license: other
pipeline_tag: text-to-image-synthesis
tasks:
- text-to-image-synthesis
base_model:
- Comfy-Org/MOSS-Image
base_model_relation: finetune
parameters: 5.1B
tensor_type:
- F32
- BF16
library_name:
- diffusers
- lora
- safetensors
- pytorch
frameworks:
- pytorch
downloads: 49
stars: 2
tags:
- diffusers
- comfyui
- moss-image
- moss-imagine
- text-to-image
- image-generation
- adaptation-based-finetune
- grok-imagine
- photorealism
- cinematic
- safetensors
- rebel-ai
---
# MOSS-Imagine_ComfyUI
> MOSS-Imagine_ComfyUI - realrebelai 在 ModelScope 开源的模型。An Adaptation-Based Finetune of MOSS-Image SFT
realrebelai/MOSS-Imagine_ComfyUI 是 ModelScope 魔搭社区上的 5.1B 参数text-to-image-synthesis模型，采用 other 许可，基于 Comfy-Org/MOSS-Image 构建。
- **Repository**: realrebelai/MOSS-Imagine_ComfyUI
- **License**: other
- **Tasks**: text-to-image-synthesis
- **Parameters**: 5.1B
- **Base model**: Comfy-Org/MOSS-Image
- **Tags**: diffusers, comfyui, moss-image, moss-imagine, text-to-image, image-generation, adaptation-based-finetune, grok-imagine, photorealism, cinematic, safetensors, rebel-ai
- **Downloads**: 49
- **Stars**: 2
- **Last updated**: 2026-10-21
Source: https://www.modelscope.cn/models/realrebelai/MOSS-Imagine_ComfyUI
---
# MOSS-Imagine
### An Adaptation-Based Finetune of MOSS-Image SFT
**Developed by [Rebel AI](https://huggingface.co/realrebelai)**
*An exploration of what happens when the visual language of Grok Imagine meets the generative foundation of MOSS-Image.*
---
## A Different Vision for MOSS-Image
**MOSS-Imagine** is an experimental, adaptation-based finetune built upon **MOSS-Image SFT**, using a curated visual dataset generated with **Grok Imagine v0.9 and v1.0**.
The goal is to expand the aesthetic capabilities of MOSS-Image by introducing new visual characteristics, photographic sensibilities, cinematic compositions, and stylistic influences learned through targeted adaptation training.
Rather than replacing the model's underlying knowledge, MOSS-Imagine seeks to build upon it.
The result is an exploration of how an existing image-generation model can absorb the visual characteristics of another generative system while retaining its original architecture and general-purpose capabilities.
## The Philosophy
**Different models. Different visual identities. One adaptation.**
Image-generation models develop recognizable visual tendencies: how they interpret lighting, compose scenes, represent materials, render people, and balance realism with stylization.
MOSS-Imagine explores the transfer of these tendencies through dataset-driven adaptation.
Its objectives include:
- **Photographic realism:** Natural skin textures, convincing materials, and detailed environments.
- **Cinematic presentation:** Atmospheric lighting, depth, contrast, and intentional composition.
- **Creative versatility:** Portraiture, fantasy, architecture, environments, products, and conceptual imagery.
- **Visual coherence:** Consistent subjects, recognizable forms, and structured scenes.
- **Stylistic adaptability:** A broad aesthetic vocabulary without requiring a single rigid visual style.
## Training & Adaptation
| Component | Details |
|---|---|
| Project | MOSS-Imagine |
| Base architecture | MOSS-Image SFT |
| Training approach | LoRA-based adaptation |
| Dataset origin | Grok Imagine v0.9 and v1.0 |
| Dataset focus | Diverse visual styles, subjects, and compositions |
| Adaptation rank | 32 |
| Adaptation alpha | 32 |
| Text encoder training | Disabled |
| Merge target | BF16 diffusion checkpoint |
| Intended formats | BF16, INT8 ConvRot, W4A8 ConvRot |
### What Makes This an Adaptation-Based Finetune?
MOSS-Imagine uses targeted LoRA training to learn visual characteristics from its dataset.
The resulting adapter is intended to be integrated into the original MOSS-Image SFT diffusion weights, producing a standalone adapted checkpoint rather than requiring a separate LoRA during inference.
This approach preserves the original model architecture while modifying its learned parameters to incorporate new visual tendencies.
**MOSS-Imagine is not a model trained from scratch.** It is a derivative adaptation of MOSS-Image SFT.
## Intended Use Cases
MOSS-Imagine is designed for experimentation across a broad range of text-to-image tasks:
- Photorealistic portraits and candid photography
- Cinematic scenes and atmospheric environments
- Fantasy characters, creatures, and worlds
- Product photography and advertising concepts
- Editorial imagery and visual storytelling
- Architecture, interiors, and environmental design
- Surrealism, conceptual art, and stylized compositions
## Model Variants
The project is intended to support several inference configurations.
| Variant | Description |
|---|---|
| **BF16** | Full-precision adapted checkpoint |
| **INT8 ConvRot** | Quantized variant targeting reduced memory requirements |
| **W4A8 ConvRot** | Lower-memory weight-and-activation quantized variant |
Availability and compatibility may vary by release. Quantized versions should be evaluated independently against the BF16 reference.
## Recommended Workflow
MOSS-Imagine is intended for use in compatible MOSS-Image inference workflows, including supported ComfyUI implementations.
For best results:
1. Use the appropriate MOSS-Image text encoder and VAE.
2. Load the MOSS-Imagine diffusion checkpoint.
3. Start with the inference settings recommended for the underlying MOSS-Image SFT model.
4. Experiment with prompts spanning different visual categories.
5. Compare outputs against the original model to evaluate the adaptation's influence.
For a correctly merged checkpoint, the adaptation is already incorporated into the diffusion weights. **The training LoRA should not be loaded a second time.**
## Limitations
MOSS-Imagine is an experimental adaptation and may retain limitations inherited from its base model, including occasional errors in anatomy, typography, spatial relationships, and prompt interpretation.
Dataset-driven adaptation can also introduce aesthetic biases or reduce performance on certain prompts.
Quantized variants may exhibit differences in image fidelity and generation behavior.
Outputs should be evaluated before use in sensitive or consequential applications.
## Credits & Attribution
**Adaptation, dataset curation, training, and project development:** [Rebel AI](https://huggingface.co/realrebelai)
**Base model:** MOSS-Image SFT
**Dataset generation source:** Grok Imagine v0.9 and v1.0
MOSS-Imagine is an independent experimental project. It is not an official release of, or affiliated with, xAI or the original MOSS-Image development team.
All applicable base-model licenses, dataset terms, and usage restrictions remain relevant. Users should review the original model's license and the terms governing the dataset's source material.
---
### Follow the Project
- **Hugging Face:** https://huggingface.co/realrebelai
- **X / Twitter:** https://x.com/realrebelai
- **YouTube:** https://www.youtube.com/@realrebelai
- **Twitch:** https://www.twitch.tv/realrebelai
---
**MOSS-Imagine — A new visual direction, built on an existing foundation.**
*Created by Rebel AI.*
