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This is an embedding of a woman that I've generated consistent faces of thru SD and decided to make into an embedding for easier access in the future. It uses a mere 5 tokens.
This model is a fine-tuned version of MCG-NJU/videomae-base-finetuned-kinetics on an unknown dataset. It achieves the following results on the evaluation set: Loss: 0.5065 Accuracy: 0.7952
Split Realities, Material Transformations, and Fractured Worlds. Krea2 can do that (sometimes) already , but I wanted to change/enhance it in my way.
Model Type: Multilabel Image classification / feature backbone Model Stats: Params: 692.6M FLOPs / MACs: 1.2T / 600.4G Image size: train = 512 x 512, test = 512 x 512 Dataset:...
Works best at 0.95 weight.
another merger using i2i adetailer and reactor. used for DENlilslilsyourfav and DENapexSG the look
Experimental model of Dan Mumford Style. v1.0g : This is an experiment to reach more coherence, its more tricky to use but it may get more truthful to the style. v1.0 : This is a regressed Recommended version of the model which has lower and softer weights. You can really go as 0.4 and 2.5 to get interesting results . v0.9-rc-LoHa : More iterations and training compared to v0.4 It can handle more weights and has much more style influence in it. Also I used refiner with same prompts in some of the examples. You may crank up the weights like 1.5+ to get similar styling compared to v0.4, since LoHa is more flexible with weights and that way it can work much nicely with other styles. v0.4-LoCon : Added more training, it will handle shapes better but style might get extreme depends on the prompt and mix of other style tokens, so you can adjust the weight +/- around 0.5 to get more desired result. Also SDXL + eularA and DPM++ samplers gives better generation with more steps like 600+ unlike SD1.5 (where the sampler stops impacting quality after ~60). So maybe it takes 6-10 minutes to generate an image but end results will be more defined, especially for background elements. Support me here if you want: https://ko-fi.com/sensai3 Thank you.
Lance: Unified Multimodal Modeling by Multi-Task Synergy
Trained on Krea2 . Best result with weight between : 0.8-1
This model is a 14.75% smaller version of google/siglip2-giant-opt-patch16-384 optimized for Azerbaijani language via vocabulary size reduction using the trimming method. This trimmed model should perform similarly...
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