cqyan
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hybrid-sd-tinyvae-xl

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 library_name: diffusers
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-# Model Card for Model ID
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-## Model Details
-### Model Description
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-This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated.
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 ---
 library_name: diffusers
+base_model:
+- madebyollin/taesdxl
 ---
+# 🍰 Hybrid-sd-tinyvae-xl for Stable Diffusion (XL)
+[Hybrid-sd-tinyvae-xl](https://huggingface.co/cqyan/hybrid-sd-tinyvae-xl) is very tiny autoencoder which uses the same "latent API" as [SDXL-VAE](https://huggingface.co/stabilityai/sdxl-vae).
+Hybrid-sd-tinyvae-xl is a finetuned model based on the excellent work on [TAESDXL](https://github.com/madebyollin/taesdxl). In general, we mainly fix the low-saturation problem encountering in SDXL base model, by which we strengthening the saturation and contrast of images to deliver more clarity and colorfulness.
+The model is useful for real-time previewing of the SDXL generation process. It saves 13x decoder inference time (61.2ms,fp16,V100) compared to using the SDXL decoder (802.7ms,fp16,V100), and you are very welcome to try it !!!!!!
+T2I Comparison using one A100 GPU, The image order from left to right :  [SDXL-VAE](https://huggingface.co/stabilityai/sdxl-vae)  ->  [TAESDXL](https://github.com/madebyollin/taesdxl)  ->  [Hybrid-sd-tinyvae-xl](https://huggingface.co/cqyan/hybrid-sd-tinyvae-xl)
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/664afcc45fdb7108205a15c3/Ckn1JBG9GkILsPytJgnyY.png)
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/664afcc45fdb7108205a15c3/Hkz8WEq0sFmn4jo3HsRkv.png)
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/664afcc45fdb7108205a15c3/0ql_vOAU_LPNJsKYonJKi.png)
+This repo contains `.safetensors` versions of the Hybrid-sd-tinyvae-xl weights.
+For SD1.x / SD2.x, use [Hybrid-sd-tinyvae](https://huggingface.co/cqyan/hybrid-sd-tinyvae) instead (the SD and SDXL VAEs are incompatible).
+## Using in 🧨 diffusers
+```python
+import torch
+from diffusers.models import AutoencoderTiny
+from diffusers import DiffusionPipeline
+pipe = DiffusionPipeline.from_pretrained(
+    "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
+)
+vae = AutoencoderTiny.from_config('cqyan/hybrid-sd-tinyvae-xl/config.json')
+state_dict = safetensors.torch.load_file("cqyan/hybrid-sd-tinyvae-xl/diffusion_pytorch_model.safetensors")
+vae.load_state_dict(state_dict, strict=True)
+pipe.vae = vae
+pipe = pipe.to("cuda")
+prompt = "A warm and loving family portrait, highly detailed, hyper-realistic, 8k resolution, photorealistic, soft and natural lighting"
+image = pipe(prompt, num_inference_steps=25).images[0]
+image.save("family.png")
+```