Add example for Diffusers (#4)

- Add example for Diffusers (8e464fcbfa507e688c84216d26597c4aabf10a1e)
- fix (e9769b0ff7c0c08e4603dd1ef9423756853eaeee)
- finish example (83866b7ebabad73845b577d4c527c6b457c21820)
- final fix (3cbfc6fdea6c4bc5c5851b6e25c3e38ca193fb1d)


Co-authored-by: Patrick von Platen <patrickvonplaten@users.noreply.huggingface.co>

README.md

@@ -14,6 +14,32 @@ SDXL-VAE-FP16-Fix is the [SDXL VAE](https://huggingface.co/stabilityai/sdxl-vae)
 | 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
 
@@ -25,4 +51,4 @@ SDXL-VAE-FP16-Fix was created by finetuning the SDXL-VAE to:
 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.
+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.