Instructions to use SimpleTuner/moss-image-sft-anyflow-stage1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use SimpleTuner/moss-image-sft-anyflow-stage1 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("OpenMOSS-Team/MOSS-Image-SFT", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("SimpleTuner/moss-image-sft-anyflow-stage1") 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
Publish MOSS-Image SFT AnyFlow recipes, adapters, and validation previews
Browse files- .gitattributes +16 -0
- README.md +3 -3
- checkpoint-18000/assets/step_18000_character_0_512x512.png +3 -0
- checkpoint-18000/assets/step_18000_character_1_1024x1024.png +3 -0
- checkpoint-18000/assets/step_18000_photo_0_512x512.png +3 -0
- checkpoint-18000/assets/step_18000_photo_1_1024x1024.png +3 -0
- checkpoint-18000/assets/step_18000_portrait_0_512x512.png +3 -0
- checkpoint-18000/assets/step_18000_portrait_1_1024x1024.png +3 -0
- checkpoint-18000/assets/step_18000_text_0_512x512.png +3 -0
- checkpoint-18000/assets/step_18000_text_1_1024x1024.png +3 -0
- checkpoint-18000/ema/pytorch_lora_weights.safetensors +3 -0
- checkpoint-18000/pytorch_lora_weights.safetensors +3 -0
- publication.json +13 -1
- validation_images/step_18000_character_0_512x512.png +3 -0
- validation_images/step_18000_character_1_1024x1024.png +3 -0
- validation_images/step_18000_photo_0_512x512.png +3 -0
- validation_images/step_18000_photo_1_1024x1024.png +3 -0
- validation_images/step_18000_portrait_0_512x512.png +3 -0
- validation_images/step_18000_portrait_1_1024x1024.png +3 -0
- validation_images/step_18000_text_0_512x512.png +3 -0
- validation_images/step_18000_text_1_1024x1024.png +3 -0
.gitattributes
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README.md
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Forward MeanFlow pretraining. Stage 2 initializes from the final EMA adapter.
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Status: **training**. Observed updates:
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## Training recipe
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[Pre-correction previews](pre-schedule-fix/validation_images) remain available.
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[1,000](checkpoint-1000), [2,000](checkpoint-2000), [3,000](checkpoint-3000), [4,000](checkpoint-4000), [5,000](checkpoint-5000), [6,000](checkpoint-6000), [7,000](checkpoint-7000), [8,000](checkpoint-8000), [9,000](checkpoint-9000), [10,000](checkpoint-10000), [11,000](checkpoint-11000), [12,000](checkpoint-12000), [13,000](checkpoint-13000), [14,000](checkpoint-14000), [15,000](checkpoint-15000), [16,000](checkpoint-16000), [17,000](checkpoint-17000)
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[All validation previews](validation_images) include live and EMA comparisons at both resolutions.
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Latest live adapter: [update
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## Inference
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Forward MeanFlow pretraining. Stage 2 initializes from the final EMA adapter.
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Status: **training**. Observed updates: 18,130 / 30,000.
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## Training recipe
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[Pre-correction previews](pre-schedule-fix/validation_images) remain available.
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[1,000](checkpoint-1000), [2,000](checkpoint-2000), [3,000](checkpoint-3000), [4,000](checkpoint-4000), [5,000](checkpoint-5000), [6,000](checkpoint-6000), [7,000](checkpoint-7000), [8,000](checkpoint-8000), [9,000](checkpoint-9000), [10,000](checkpoint-10000), [11,000](checkpoint-11000), [12,000](checkpoint-12000), [13,000](checkpoint-13000), [14,000](checkpoint-14000), [15,000](checkpoint-15000), [16,000](checkpoint-16000), [17,000](checkpoint-17000), [18,000](checkpoint-18000)
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[All validation previews](validation_images) include live and EMA comparisons at both resolutions.
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Latest live adapter: [update 18,000](checkpoint-18000/pytorch_lora_weights.safetensors).
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## Inference
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"17000": {
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"state": "training",
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"previews": [
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"17000": {
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"state": "training",
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"step": 18130,
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"previews": [
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