Instructions to use MATLOWAI/MOSS-Image-SFT-DeBand with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusion Single File
How to use MATLOWAI/MOSS-Image-SFT-DeBand with Diffusion Single File:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
Download tools/moss_image_t2i_api.json from MATLOWAI/MOSS-Image-SFT-DeBand: direct link, hf CLI and curl.
- Browser
- Download file 4.96 kB
-
https://huggingface.co/MATLOWAI/MOSS-Image-SFT-DeBand/resolve/main/tools/moss_image_t2i_api.json
- Command line
-
hf download hf://MATLOWAI/MOSS-Image-SFT-DeBand/tools/moss_image_t2i_api.json
-
curl -L -o moss_image_t2i_api.json https://huggingface.co/MATLOWAI/MOSS-Image-SFT-DeBand/resolve/main/tools/moss_image_t2i_api.json
4.96 kB
| { | |
| "1": { | |
| "class_type": "UNETLoader", | |
| "inputs": { | |
| "unet_name": "moss_image_sft_bf16.safetensors", | |
| "weight_dtype": "default" | |
| } | |
| }, | |
| "30": { | |
| "class_type": "LoraLoaderModelOnly", | |
| "inputs": { | |
| "model": [ | |
| "1", | |
| 0 | |
| ], | |
| "lora_name": "moss_image_sft_lora_blocks_21_to_49_rank_512_bf16.safetensors", | |
| "strength_model": -0.5 | |
| } | |
| }, | |
| "16": { | |
| "class_type": "TemporalScoreRescaling", | |
| "inputs": { | |
| "model": [ | |
| "30", | |
| 0 | |
| ], | |
| "tsr_k": 0.95, | |
| "tsr_sigma": 1.0 | |
| } | |
| }, | |
| "2": { | |
| "class_type": "SkipLayerGuidanceDiTSimple", | |
| "inputs": { | |
| "model": [ | |
| "16", | |
| 0 | |
| ], | |
| "double_layers": "", | |
| "single_layers": "22, 24", | |
| "start_percent": 0.1, | |
| "end_percent": 0.7 | |
| } | |
| }, | |
| "3": { | |
| "class_type": "CLIPLoader", | |
| "inputs": { | |
| "clip_name": "qwen3.5_9b_int8_convrot.safetensors", | |
| "type": "moss_image", | |
| "device": "default" | |
| } | |
| }, | |
| "4": { | |
| "class_type": "VAELoader", | |
| "inputs": { | |
| "vae_name": "flux2-vae.safetensors" | |
| } | |
| }, | |
| "18": { | |
| "class_type": "PrimitiveStringMultiline", | |
| "inputs": { | |
| "value": "You turn a user's image idea into one detailed caption for a text-to-image model. The model learned from captions that describe finished images, so write the caption as a description of the image itself, not as instructions.\n\nKeep the request\n- Keep everything the user specified: subjects, counts, actions, relationships, medium, style, mood, colors and anything they excluded. Never contradict or drop it.\n- Fill in what they left open with concrete visual choices: setting, composition and framing, viewpoint, lighting, materials and textures, color palette, and the artistic medium.\n- Describe only what can be seen. Leave out sounds, smells, backstory and intentions.\n- Do not invent real-world facts the user did not give, such as specific dates, prices, names or specifications.\n\nText in the image\n- Quote every piece of visible text exactly, in double quotes. Keep the user's wording, language, spelling, capitalization, punctuation and line breaks; never translate it.\n- If the user asks for a designed image (poster, cover, menu, label, sign, infographic) without giving its text, write short, specific copy that fits instead of placeholders.\n- Describe where each text sits and how it looks: position, reading order, alignment, line breaks, relative size, font style, color and spacing.\n- Do not mention watermarks or artist signatures.\n\nWriting\n- Write in English, in plain prose: one to three paragraphs, usually 80 to 200 words, longer only when there is a lot of text to place.\n- Open with the medium and the main subject, then the setting and composition, then lighting, colors and finer details.\n- Give positions with words such as left, right, top, bottom, foreground and background. No coordinates, lists, headings, JSON or Markdown.\n- No quality keywords such as \"masterpiece\", \"best quality\", \"8k\" or \"highly detailed\", and no negative prompt.\n\nReply with the caption only: no title, introduction, explanation or surrounding quotes.\n" | |
| } | |
| }, | |
| "17": { | |
| "class_type": "TextGenerate", | |
| "inputs": { | |
| "clip": [ | |
| "3", | |
| 0 | |
| ], | |
| "prompt": "", | |
| "max_length": 4096, | |
| "sampling_mode": "on", | |
| "sampling_mode.temperature": 0.7, | |
| "sampling_mode.top_k": 64, | |
| "sampling_mode.top_p": 0.95, | |
| "sampling_mode.min_p": 0.05, | |
| "sampling_mode.repetition_penalty": 1.05, | |
| "sampling_mode.seed": 0, | |
| "thinking": false, | |
| "use_default_template": true, | |
| "mtp": "auto", | |
| "system_prompt": [ | |
| "18", | |
| 0 | |
| ] | |
| } | |
| }, | |
| "19": { | |
| "class_type": "PreviewAny", | |
| "inputs": { | |
| "source": [ | |
| "17", | |
| 0 | |
| ] | |
| } | |
| }, | |
| "5": { | |
| "class_type": "CLIPTextEncode", | |
| "inputs": { | |
| "clip": [ | |
| "3", | |
| 0 | |
| ], | |
| "text": [ | |
| "17", | |
| 0 | |
| ] | |
| } | |
| }, | |
| "6": { | |
| "class_type": "CLIPTextEncode", | |
| "inputs": { | |
| "clip": [ | |
| "3", | |
| 0 | |
| ], | |
| "text": "worst quality, bad anatomy, watermark, jpeg artifact" | |
| } | |
| }, | |
| "7": { | |
| "class_type": "EmptyFlux2LatentImage", | |
| "inputs": { | |
| "width": 1024, | |
| "height": 1024, | |
| "batch_size": 1 | |
| } | |
| }, | |
| "21": { | |
| "class_type": "SamplerER_SDE", | |
| "inputs": { | |
| "solver_type": "ER-SDE", | |
| "max_stage": 3, | |
| "eta": 1.0, | |
| "s_noise": 1.05 | |
| } | |
| }, | |
| "22": { | |
| "class_type": "BasicScheduler", | |
| "inputs": { | |
| "model": [ | |
| "2", | |
| 0 | |
| ], | |
| "scheduler": "simple", | |
| "steps": 25, | |
| "denoise": 1.0 | |
| } | |
| }, | |
| "20": { | |
| "class_type": "SamplerCustom", | |
| "inputs": { | |
| "model": [ | |
| "2", | |
| 0 | |
| ], | |
| "add_noise": true, | |
| "noise_seed": 16, | |
| "cfg": 3.0, | |
| "positive": [ | |
| "5", | |
| 0 | |
| ], | |
| "negative": [ | |
| "6", | |
| 0 | |
| ], | |
| "sampler": [ | |
| "21", | |
| 0 | |
| ], | |
| "sigmas": [ | |
| "22", | |
| 0 | |
| ], | |
| "latent_image": [ | |
| "7", | |
| 0 | |
| ] | |
| } | |
| }, | |
| "9": { | |
| "class_type": "VAEDecode", | |
| "inputs": { | |
| "samples": [ | |
| "20", | |
| 0 | |
| ], | |
| "vae": [ | |
| "4", | |
| 0 | |
| ] | |
| } | |
| }, | |
| "10": { | |
| "class_type": "SaveImage", | |
| "inputs": { | |
| "images": [ | |
| "9", | |
| 0 | |
| ], | |
| "filename_prefix": "moss/moss" | |
| } | |
| } | |
| } |