Delete MODELS/sdxl-vae-fp16-fix

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MODELS/sdxl-vae-fp16-fix/README.md

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----
-license: mit
-tags:
-- stable-diffusion
-- stable-diffusion-diffusers
-inference: false
----
-# SDXL-VAE-FP16-Fix
-
-SDXL-VAE-FP16-Fix is the [SDXL VAE](https://huggingface.co/stabilityai/sdxl-vae)*, but modified to run in fp16 precision without generating NaNs.
-
-| VAE                   | Decoding in `float32` / `bfloat16` precision | Decoding in `float16` precision |
-| --------------------- | -------------------------------------------- | ------------------------------- |
-| SDXL-VAE              | ✅ ![](./images/orig-fp32.png)              | ⚠️ ![](./images/orig-fp16.png)  |
-| SDXL-VAE-FP16-Fix     | ✅ ![](./images/fix-fp32.png)               | ✅ ![](./images/fix-fp16.png)   |
-
-## 🧨 Diffusers Usage
-
-Just load this checkpoint via `AutoencoderKL`:
-
-```py
-import torch
-from diffusers import DiffusionPipeline, AutoencoderKL
-
-vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
-pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-0.9", vae=vae, torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
-pipe.to("cuda")
-
-refiner = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-0.9", vae=vae, torch_dtype=torch.float16, use_safetensors=True, variant="fp16")
-refiner.to("cuda")
-
-n_steps = 40
-high_noise_frac = 0.7
-
-prompt = "A majestic lion jumping from a big stone at night"
-
-image = pipe(prompt=prompt, num_inference_steps=n_steps, denoising_end=high_noise_frac, output_type="latent").images
-image = refiner(prompt=prompt, num_inference_steps=n_steps, denoising_start=high_noise_frac, image=image).images[0]
-image
-```
-
-![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/lion_refined.png)
-
-## Details
-
-SDXL-VAE generates NaNs in fp16 because the internal activation values are too big:
-![](./images/activation-magnitudes.jpg)
-
-SDXL-VAE-FP16-Fix was created by finetuning the SDXL-VAE to:
-1. keep the final output the same, but
-2. make the internal activation values smaller, by
-3. scaling down weights and biases within the network
-
-There are slight discrepancies between the output of SDXL-VAE-FP16-Fix and SDXL-VAE, but the decoded images should be [close enough for most purposes](https://huggingface.co/madebyollin/sdxl-vae-fp16-fix/discussions/7#64c5c0f8e2e5c94bd04eaa80).
-
----
-
-\* `sdxl-vae-fp16-fix` is specifically based on [SDXL-VAE (0.9)](https://huggingface.co/stabilityai/sdxl-vae/discussions/6#64acea3f7ac35b7de0554490), but it works with SDXL 1.0 too

MODELS/sdxl-vae-fp16-fix/config.json

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-{
-  "_class_name": "AutoencoderKL",
-  "_diffusers_version": "0.18.0.dev0",
-  "_name_or_path": ".",
-  "act_fn": "silu",
-  "block_out_channels": [
-    128,
-    256,
-    512,
-    512
-  ],
-  "down_block_types": [
-    "DownEncoderBlock2D",
-    "DownEncoderBlock2D",
-    "DownEncoderBlock2D",
-    "DownEncoderBlock2D"
-  ],
-  "in_channels": 3,
-  "latent_channels": 4,
-  "layers_per_block": 2,
-  "norm_num_groups": 32,
-  "out_channels": 3,
-  "sample_size": 512,
-  "scaling_factor": 0.13025,
-  "up_block_types": [
-    "UpDecoderBlock2D",
-    "UpDecoderBlock2D",
-    "UpDecoderBlock2D",
-    "UpDecoderBlock2D"
-  ],
-  "force_upcast": false
-}

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