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Do You Want to fine-tune a SD model? +Strategy Talk +Certains Certains Certains
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Problems +and +Proposed +Solutions +When a model has learned to focus too much on the specific +characteristics of the training data, rather than generalizing to new +situations. +2.Overfitting +Solution 1 Dreambooth used prior-preservation loss, and the +ratio of prior-preservation is never easy to determine. +Solution 2 It is training data that caused the overfitting. Thus we use a subset of +the training data to train an overfitted model, select the previous checkpoint which +and use it to generate images by prompt for a single word. These images can be +placed in the regular training data according to the word frequency ratio, and the +subset of the which that caused the overfitting can be removed, and then retrain +the model from the very beginning.
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Problems +and +Proposed +Solutions +Some merged models have good performance, such as AnythingV3. +Should I continue to merge? +This is not scientifically sound and will ultimately result in a model +that is overfitted in some cases and normal in others. This model +looks good at first, but you will find that it is not faithful to the +input prompt and suffers from the problem of language drift +mentioned above. +3.Merged Models? +Solution We can use the method mentioned above to train two models together using a +word frequency list with Dreambooth. We can add or replace the training data with the +images generated by the model we want to merge, according to the calculated ratio, and +maintain a dynamic training dataset during training to prevent overfitting, as mentioned +above. We get a balanced model that does not overfit in certain directions. Then choose a +checkpoint that is about to be overfitted but not yet as the final version. This type of +model is popular in the community because it has good output even under poorly written +prompt inputs, such as the CertainThing.
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Future Outlook +Certains Certains Certains +To be honest, I have already achieved my satisfactory model, and +the remaining work will only be labor-intensive if no new transfor- +mative methods is introduced. +Such work as adding all the data from Danbooru2022 to the +dataset and continuing to train, I think this is something anyone +with GPU power and spare time can do. I will further improve it +when I have more leisure time.
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Unsolved +Issues & Advice +There are still problems with the portrayal of details such as eyes, hands, and feet, which is +largely due to the fact that the LAION dataset used by Stable Diffusion contains a lot of +inappropriate data, such as Tom and Jerry, who are not five-fingered creatures. +In the three models I released this time, I tried to eliminate some of the negative effects +brought by the "laion-aesthetics v2 5+" used by Stable Diffusion 1.4. Can you believe that in +the "laion-aesthetics v2 5+" data, if you search for Apple iPod, you will get many meme +images of apples that can be eaten with headphones inserted, with a high aesthetics score. +This is obviously wrong; on the other hand, Stable Diffusion 1.2 is a better version. +If you want to continue training a Stable Diffusion model with anime style, please avoid +Stable Diffusion 1.4+ version as possible. When you see through its releasing history, you will +find that the subsequent versions mostly involve struggles and interests among the founding +teams, and there is no good quality assurance. It is not that the higher the version, the better. +My personal opinion is that Stable Diffusion 1.2 is the best version. +Certains Certains Certains
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Get In Touch +https://huggingface.co/JosephusCheung +Huggingface +@RealJosephus +Twitter +Certains Certains Certains
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Summary on Certains Certains Certains +CertainModel +Main inference model, +prompt input has good +accuracy. +CertainThing +Anythingv3 style (improved +for poorly written prompts, +but less freedom of creation) +Certainty +Balanced model better +for further Dreambooth +and finetuning
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Workflow +Certains Certains Certains +* without prior +preservation +pretrained +SD model +first time +overfitted +Certain +Base +community +models +dreambooth +dreambooth +*booru +auto-genrated +single prompt +Certain +Model +Certainty +Certain +Thing +LoRA +& native fine-tuning* +LoRA +dreambooth +Anythingv3 +dream booth +auto-genrated +single prompt
+ + + +
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Comparable Analysis +Certains Certains Certains +NovelAI AnyV3 Model Thing Certainty +masterpiece, best quality, 1girl, brown hair, green eyes, colorful, autumn, cumulonimbus clouds, lighting, blue sky, falling leaves, garden +Negative prompt: lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low +quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, artist name +Steps: 28, Sampler: Euler a, CFG scale: 11, Seed: 114514, Size: 512x768, ENSD: 31337
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Comparable Analysis +NovelAI AnyV3 Model Thing Certainty +masterpiece, best quality, 1boy, brown hair, green eyes, colorful, autumn, cumulonimbus clouds, lighting, blue sky, falling leaves, garden +Negative prompt: lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low +quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, artist name +Steps: 28, Sampler: Euler a, CFG scale: 11, Seed: 114514, Size: 512x768, ENSD: 31337 +Certains Certains Certains
+ + + +
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Comparable Analysis +CertainThing CertainThing CertainThing +((((masterpiece, best quality, ultra-detailed)))), (official_art, +thick_eyebrows, laugh), kawaii, cleavage, (((two side up, white and +orangeish and medium streaked hair))), ((tsurime)), Thigh-high socks, +Clear vinyl jacket, skindentation, multicolored black bikini, +dappled_sunlight, Santorini, geometrical pattern, sport fashion, chaos +Courtesy of +@hcanadli12345 +masterpiece emphasized +Note: the "masterpiece" tag is already +fine-tuned and it is not recommended +to emphasize it again.
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Comparable Analysis +CertainThing CertainThing CertainThing +Courtesy of +@hcanadli12345 +((((best quality, ultra-detailed)))), (official_art, thick_eyebrows, laugh), +kawaii, cleavage, (((two side up, white and orangeish and medium +streaked hair))), ((tsurime)), Thigh-high socks, Clear vinyl jacket, +skindentation, multicolored black bikini, dappled_sunlight, Santorini, +geometrical pattern, sport fashion, chaos +no masterpiece +Note: the "masterpiece" tag is already +fine-tuned and it is not recommended +to emphasize it again.
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Relation +with other +SD models +Facts speak louder than words +Calculate the cosine similarity to compare +the similarity with other models +SimilarityCalculatior made by +Nyanko Lepsoni and RcINS. +Danke schön! +SimilarityCalculatior SimilarityCalculatior SimilarityCalculatior +100% +Base NovelAI +71.5% 83.8% +SD1.4 SD1.2 +83.8% 67.9% 100% +80.9% 74.7% 90.3% +80.5% 74.7% 89.9% +71.5% 100% 67.9% +80.9% 74.7% 90.3% +84.1% 67.7% 98.3% +87.8% 68.9% 95.5% +SD1.4 +SD1.2 +NovelAI +CModel +CThing +AnythingV3 +83.9% 68% 99.9% +WD1.3.5 +Emis1.0 +Certinty
+ + + +
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Problems +and +Proposed +Solutions +Fine-tuning a diffusion model on a small set of subject images +causes it to lose the ability to generate generic images of the same +class and forget the class-specific prior. +1.Language Drift +Solution 1 Dreambooth use the model's own generated samples +by adding a relative weight of the prior-preservation loss. +However the ratio of prior-preservation is not easy to determine. +Solution 2 This is a method that requires a lot of GPU time - during the regular +training process, we add auto-generated images from the current model with +prompt of a single word, with words chosen from a pre-estimated word frequency +list randomly according to a certain ratio (we chose our word list from Danbooru +Tags). To avoid overfitting, each auto-generated image is used only once.
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