Wording in the code example

README.md

@@ -65,7 +65,9 @@ aesthetic prompts. Specifically, Stable Cascade (30 inference steps) was compare
 steps), SDXL (50 inference steps), SDXL Turbo (1 inference step) and Würstchen v2 (30 inference steps).
 
 ## Code Example
-Iinstall `diffusers` from this branch while the PR is WIP.
+
+**⚠️ Important**: For the code below to work, you have to install `diffusers` from this branch while the PR is WIP.
+
 ```shell
 pip install git+https://github.com/kashif/diffusers.git@wuerstchen-v3
 ```
@@ -75,31 +77,33 @@ import torch
 from diffusers import StableCascadeDecoderPipeline, StableCascadePriorPipeline
 
 device = "cuda"
-dtype = torch.bfloat16
 num_images_per_prompt = 2
 
-prior = StableCascadePriorPipeline.from_pretrained("stabilityai/stable-cascade-prior", torch_dtype=dtype).to(device)
-decoder = StableCascadeDecoderPipeline.from_pretrained("stabilityai/stable-cascade",  torch_dtype=dtype).to(device)
+prior = StableCascadePriorPipeline.from_pretrained("stabilityai/stable-cascade-prior", torch_dtype=torch.bfloat16).to(device)
+decoder = StableCascadeDecoderPipeline.from_pretrained("stabilityai/stable-cascade",  torch_dtype=torch.float16).to(device)
 
 prompt = "Anthropomorphic cat dressed as a pilot"
 negative_prompt = ""
 
-with torch.cuda.amp.autocast(dtype=dtype):
-    prior_output = prior(
-        prompt=prompt,
-        height=1024,
-        width=1024,
-        negative_prompt=negative_prompt,
-        guidance_scale=4.0,
-        num_images_per_prompt=num_images_per_prompt,
-    )
-    decoder_output = decoder(
-        image_embeddings=prior_output.image_embeddings,
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        guidance_scale=0.0,
-        output_type="pil",
-    ).images
+prior_output = prior(
+    prompt=prompt,
+    height=1024,
+    width=1024,
+    negative_prompt=negative_prompt,
+    guidance_scale=4.0,
+    num_images_per_prompt=num_images_per_prompt,
+    num_inference_steps=20
+)
+decoder_output = decoder(
+    image_embeddings=prior_output.image_embeddings.half(),
+    prompt=prompt,
+    negative_prompt=negative_prompt,
+    guidance_scale=0.0,
+    output_type="pil",
+    num_inference_steps=10
+).images
+
+#Now decoder_output is a list with your PIL images
 ```
 
 ## Uses