Text-to-Image
Diffusers
Safetensors
English
MOSSImagePipeline
moss-image
16gb
structured-pruning
alpha
Instructions to use AiArtLab/mossling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use AiArtLab/mossling with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("AiArtLab/mossling", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Commit History
9 days ago
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weights: final epoch (4947 steps, batch 8)
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card: samples on the final weights — anime rows from the dataset
Four prompts, seed 7, 50 steps, CFG 3, dataset resolution. Two random rows from the fine-tune set replace the photo prompts; the armoured figure and the mouse knight were re-shot. Status now describes the final one-epoch build instead of the 25k-step one.24750fb -
upd
46e62d2
10 days ago
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src
3467e0f
11 days ago
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src: dataset builder as used for the 39k-row set
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src: block-wise training loop used for the 25k-step run
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weights: step_25000 fine-tune
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drop samples from the step_02000 weights
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card: dataset row sample
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card: dataset row sample
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card: dataset row sample
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card: cafe re-shot at step_25000
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card: new hero strip
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card: step_25000 samples + honest status
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12 days ago
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Remove notebook/cache leftovers from the release
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Mossling: MOSS-Image pruned to a 16 GB card (alpha)
Verified signature30-block DiT (3.723 B) + Qwen3.5-0.8B with a 158M adapter; 12.4 GiB peak at 1024 px. Assembled in two days on one RTX 4080, undertrained.2e26e31 -
initial commit
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