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-52000/assets/step_52000_character_0_512x512.png +3 -0
- checkpoint-52000/assets/step_52000_character_1_1024x1024.png +3 -0
- checkpoint-52000/assets/step_52000_photo_0_512x512.png +3 -0
- checkpoint-52000/assets/step_52000_photo_1_1024x1024.png +3 -0
- checkpoint-52000/assets/step_52000_portrait_0_512x512.png +3 -0
- checkpoint-52000/assets/step_52000_portrait_1_1024x1024.png +3 -0
- checkpoint-52000/assets/step_52000_text_0_512x512.png +3 -0
- checkpoint-52000/assets/step_52000_text_1_1024x1024.png +3 -0
- checkpoint-52000/ema/pytorch_lora_weights.safetensors +3 -0
- checkpoint-52000/pytorch_lora_weights.safetensors +3 -0
- publication.json +13 -1
- validation_images/step_52000_character_0_512x512.png +3 -0
- validation_images/step_52000_character_1_1024x1024.png +3 -0
- validation_images/step_52000_photo_0_512x512.png +3 -0
- validation_images/step_52000_photo_1_1024x1024.png +3 -0
- validation_images/step_52000_portrait_0_512x512.png +3 -0
- validation_images/step_52000_portrait_1_1024x1024.png +3 -0
- validation_images/step_52000_text_0_512x512.png +3 -0
- validation_images/step_52000_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), [18,000](checkpoint-18000), [19,000](checkpoint-19000), [20,000](checkpoint-20000), [21,000](checkpoint-21000), [22,000](checkpoint-22000), [23,000](checkpoint-23000), [24,000](checkpoint-24000), [25,000](checkpoint-25000), [26,000](checkpoint-26000), [27,000](checkpoint-27000), [28,000](checkpoint-28000), [29,000](checkpoint-29000), [30,000](checkpoint-30000), [31,000](checkpoint-31000), [32,000](checkpoint-32000), [33,000](checkpoint-33000), [34,000](checkpoint-34000), [35,000](checkpoint-35000), [36,000](checkpoint-36000), [37,000](checkpoint-37000), [38,000](checkpoint-38000), [39,000](checkpoint-39000), [40,000](checkpoint-40000), [41,000](checkpoint-41000), [42,000](checkpoint-42000), [43,000](checkpoint-43000), [44,000](checkpoint-44000), [45,000](checkpoint-45000), [46,000](checkpoint-46000), [47,000](checkpoint-47000), [48,000](checkpoint-48000), [49,000](checkpoint-49000), [50,000](checkpoint-50000), [51,000](checkpoint-51000)
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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: 52,090 / 100,000.
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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), [19,000](checkpoint-19000), [20,000](checkpoint-20000), [21,000](checkpoint-21000), [22,000](checkpoint-22000), [23,000](checkpoint-23000), [24,000](checkpoint-24000), [25,000](checkpoint-25000), [26,000](checkpoint-26000), [27,000](checkpoint-27000), [28,000](checkpoint-28000), [29,000](checkpoint-29000), [30,000](checkpoint-30000), [31,000](checkpoint-31000), [32,000](checkpoint-32000), [33,000](checkpoint-33000), [34,000](checkpoint-34000), [35,000](checkpoint-35000), [36,000](checkpoint-36000), [37,000](checkpoint-37000), [38,000](checkpoint-38000), [39,000](checkpoint-39000), [40,000](checkpoint-40000), [41,000](checkpoint-41000), [42,000](checkpoint-42000), [43,000](checkpoint-43000), [44,000](checkpoint-44000), [45,000](checkpoint-45000), [46,000](checkpoint-46000), [47,000](checkpoint-47000), [48,000](checkpoint-48000), [49,000](checkpoint-49000), [50,000](checkpoint-50000), [51,000](checkpoint-51000), [52,000](checkpoint-52000)
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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 52,000](checkpoint-52000/pytorch_lora_weights.safetensors).
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## Inference
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publication.json
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"51000": {
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"51000": {
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