Robot Jung
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README.md
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license: creativeml-openrail-m
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---
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---
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license: creativeml-openrail-m
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language:
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- en
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tags:
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- stable-diffusion
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- diffusers
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- text-to-image
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---
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# SemiRealMix
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The result of many merges aimed at making semi-realistic human images.
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I use the following options to get good generation results:
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#### Prompt:
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delicate, masterpiece, best shadow, (1 girl:1.3), (korean girl:1.2), (from side:1.2), (from below:0.5), (photorealistic:1.5), extremely detailed skin, studio, beige background, warm soft light, low contrast, head tilt
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#### Negative prompt:
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(worst quality, low quality:1.4), nsfw, nude, (loli, child, infant, baby:1.5), jewely, (hard light:1.5), back light, spot light, hight contrast, (eyelid:1.3), outdoor, monochrome
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Sampler: DPM++ SDE Karras
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CFG Scale: 7
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Steps: 20
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Size: 512x768
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Denoising strength: 0.5, Hires upscale: 2, Hires upscaler: R-ESRGAN 4x+ Anime6B, Eta: 0.2
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Clip skip: 2
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Base Model : SD 1.5
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VAE: vae-ft-mse-840000-ema-pruned
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Use xformers : True
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## 🧨 Diffusers
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This model can be used just like any other Stable Diffusion model. For more information,
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please have a look at the [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion).
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You can also export the model to [ONNX](https://huggingface.co/docs/diffusers/optimization/onnx), [MPS](https://huggingface.co/docs/diffusers/optimization/mps) and/or [FLAX/JAX]().
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```python
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from diffusers import StableDiffusionPipeline
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import torch
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model_id = "andite/anything-v4.0"
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pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
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pipe = pipe.to("cuda")
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prompt = "1girl"
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image = pipe(prompt).images[0]
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image.save("./output.png")
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```
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## Examples:
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Here are some examples of images generated using this model:
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