ng_deepnegative_v1_75t.pt

0 MB · PickleTensor

ng_deepnegative_v1_75t.safetensors

0 MB · SafeTensor

Vyžaduje členství

<deep-negative>

This embedding will tell you what is REALLY DISGUSTING🤢🤮 So please put it in negative prompt😜 ⚠This model is not trained for SDXL and may bring undesired results when used in SDXL. If you use SDXL, recommended this 👇 another deep-negative: - pony version: - SDXL version: TOP Q&A - how to use TI model? - what is negative prompt? [Special Reminder] If your webui reports the following errors: - CUDA: CUDA error: device-side assert triggered - Assertion -sizes[i] The reason is that many scripts do not handle overly long negative prompt words (greater than 75 tokens) properly, so choosing a smaller token version can improve this situation. [Update:230120] What does it do? These embedding learn what disgusting compositions and color patterns are, including faulty human anatomy, offensive color schemes, upside-down spatial structures, and more. Placing it in the negative can go a long way to avoiding these things. - What is 2T 4T 16T 32T? Number of vectors per token [Update:230120] What is 64T 75T？ 64T: Train over 30,000 steps on mixed datasets. 75T: embedding limit maximum size, training 10,000 steps on a special dataset (generated by many different sd models and special reverse processing) Which one should choose? - 75T: The most ”easy to use“ embedding, which is trained from its accurate dataset created in a special way with almost no side effects. And it contains enough information to cover various usage scenarios. But for some "good-trained-model" may hard to effect and, change about may be subtle and not drastic enough. - 64T: It works for all models, but has side effect. so, some tuning is required to find the best weight. recommend: [( NG_DeepNegative_V1_64T :0.9) :0.1] - 32T: Useful, but too more - 16T: Reduces the chance of drawing bad anatomy, but may draw ugly faces. Suitable for raising architecture level. - 4T: Reduces the chance of drawing bad anatomy, but has a little effect on light and shadow - 2T: ”easy to use“ like T75, but just a little effect Suggestion Because this embedding is learning how to create disgusting concepts, it cannot improve the picture quality accurately, so it is best used with (worst quality, low quality, logo, text, watermark, username) these negative prompts. Of course, it is completely fine to use with other similar negative embeddings. More examples and tests - draw building: - hand fix: - portrait (with ): - fusion body fix: How is it work? I tried to make SD learn what is really disgusting with deepdream algorithm, the dataset is imagenet-mini (1000 images chosen randomly from the dataset again) deepdream is REALLLLLLLLLLLLLLLLLLLLLY disgusting 🤮 and process of training this model really made me experience physical discomfort 😂 Backup

SHA256

54E7E4826D53949A3D0DDE40AEA023B1E456A618C608A7630E3999FD38F93245

ID modelu ModelTrends.ai

#6617

Sampler

Euler a

Kroky

19

Guidance

4

Seed

1737173171

Velikost

832x1216

Clip skip

2

Použitá váha

1

Sampler

DPM++ 2M Karras

Kroky

20

Guidance

7

Seed

2676625826

Velikost

512x768

Sampler

Euler a

Kroky

29

Guidance

4

Seed

4288902158

Velikost

832x1216

Clip skip

2

Použitá váha

1

Sampler

Euler a

Kroky

40

Guidance

4

Seed

338164970

Velikost

832x1216

Clip skip

2

Použitá váha

1

Sampler

DPM++ 2M Karras

Kroky

50

Guidance

3.5

Seed

2031206838

Velikost

832x1216

Clip skip

2

Použitá váha

1

Sampler

DPM++ 2M

Kroky

32

Guidance

4

Seed

149671720

Velikost

832x1216

Clip skip

2

Použitá váha

1