V2.0
Special thanks:
The dataset can be downloaded (26 images). The workflow is similar to V1.0, but I changed the generated model, to get anime style training dataset. With additional prompts you can tweak the poses (looking up/down, smiling, etc.).
I found a thing: when I used realistic training data, the lora can generate realistic photos. However, it's hard to generate anime style ones (though add "anime" prompt). On the other hand, using anime training data is good. You can add "photorealistic" prompt when generating to avoid anime style.
(I added the "anime style" tag in training data, maybe it has influence?)
{
"engine": "kohya",
"unetLR": 1,
"clipSkip": 1,
"loraType": "lora",
"keepTokens": 0,
"networkDim": 2,
"numRepeats": 6,
"resolution": 512,
"lrScheduler": "cosine",
"minSnrGamma": 5,
"noiseOffset": 0.1,
"targetSteps": 1040,
"enableBucket": true,
"networkAlpha": 12,
"optimizerType": "Prodigy",
"textEncoderLR": 0,
"maxTrainEpochs": 20,
"shuffleCaption": false,
"trainBatchSize": 3,
"flipAugmentation": false,
"lrSchedulerNumCycles": 3
}
Finally, I chose the epoch #18 model to publish.
V1.0
Special thanks:
The dataset can be downloaded (27 images), which is generated by the above two models (1024×1024, then reduced to 512×512). Then I tagged them using tools in webui, and used natural language to re-tag them manually based on these auto-generated tags. Finally, using this lora, with additional prompts you can tweak the poses (looking up/down, grin and smiling, close mouth, etc.).
Training parameters:
{
"engine": "kohya",
"unetLR": 1,
"clipSkip": 1,
"loraType": "lora",
"keepTokens": 0,
"networkDim": 2,
"numRepeats": 6,
"resolution": 512,
"lrScheduler": "cosine",
"minSnrGamma": 5,
"noiseOffset": 0.1,
"targetSteps": 1080,
"enableBucket": true,
"networkAlpha": 12,
"optimizerType": "Prodigy",
"textEncoderLR": 0,
"maxTrainEpochs": 20,
"shuffleCaption": false,
"trainBatchSize": 3,
"flipAugmentation": false,
"lrSchedulerNumCycles": 3
}
I think the final epoch is a bit better than epoch #19. So I choose the final one.
描述:
训练词语: A girl is crying with tears in her eyes
名称: 740798_training_data.zip
大小 (KB): 10308
类型: Training Data
Pickle 扫描结果: Success
Pickle 扫描信息: No Pickle imports
病毒扫描结果: Success
名称: crying_with_tears_flux.safetensors
大小 (KB): 18813
类型: Model
Pickle 扫描结果: Success
Pickle 扫描信息: No Pickle imports
病毒扫描结果: Success
