Update README.md

README.md

@@ -16,27 +16,42 @@ library_name: diffusers
 | ![](example_baseline.png)         | ![](example_finetuned.png)         |
 | ![](example_baseline_closeup.png) | ![](example_finetuned_closeup.png) |
 
+## Explanation
+
+Image generators like Würstchen and Stable Cascade create images via a multi-stage process.
+Stage A is the ultimate stage, responsible for rendering out full-resolution, human-interpretable images (based on the output from prior stages).
+
+The original Stage A tends to render slightly-smoothed-out images with a distinctive noise pattern on top.
+
+`stage-a-ft-hq` was finetuned briefly on a high-quality dataset in order to reduce these artifacts.
+
+## Suggested Settings
+
+To generate highly detailed images, you probably want to use `stage-a-ft-hq` (which improves very fine detail) in combination with a large Stage B step count (which [improves mid-level detail](https://old.reddit.com/r/StableDiffusion/comments/1ar359h/cascade_can_generate_directly_at_1536x1536_and/kqhjtk5/)).
 
 ## 🧨 Diffusers Usage
 
 ⚠️ As of 2024-02-17, Stable Cascade's [PR](https://github.com/huggingface/diffusers/pull/6487) is still under review.
-I've only confirmed Stable Cascade working with this particular version of the PR:
+I've only tested Stable Cascade with this particular version of the PR:
+
 ```bash
 pip install --upgrade --force-reinstall https://github.com/kashif/diffusers/archive/a3dc21385b7386beb3dab3a9845962ede6765887.zip
 ```
 
+TODO: verify this particular sample code works
+
 ```py
 import torch
+device = "cuda"
 
 # Load the Stage-A-ft-HQ model
 from diffusers.pipelines.wuerstchen import PaellaVQModel
-stage_a_ft_hq = PaellaVQModel.from_pretrained("madebyollin/stage_a_ft_hq", torch_dtype=torch.float16)
+stage_a_ft_hq = PaellaVQModel.from_pretrained("madebyollin/stage-a-ft-hq", torch_dtype=torch.float16).to(device)
 
 # Load the normal Stable Cascade pipeline
 from diffusers import StableCascadeDecoderPipeline, StableCascadePriorPipeline
 
-device = "cuda"
-num_images_per_prompt = 2
+num_images_per_prompt = 1
 
 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)
@@ -62,21 +77,8 @@ decoder_output = decoder(
     negative_prompt=negative_prompt,
     guidance_scale=0.0,
     output_type="pil",
-    num_inference_steps=10
+    num_inference_steps=20
 ).images
 
 display(decoder_output[0])
-```
-
-## Explanation
-
-Image generators like Würstchen and Stable Cascade create images via a multi-stage process.
-Stage A is the ultimate stage, responsible for rendering out full-resolution, human-interpretable images (based on the output from prior stages).
-
-The original Stage A tends to render slightly-smoothed-out images with a distinctive noise pattern on top.
-
-`stage-a-ft-hq` was finetuned briefly on a high-quality dataset in order to reduce these artifacts.
-
-## Suggested Settings
-
-To generate highly detailed images, you probably want to use `stage-a-ft-hq` (which improves very fine detail) in combination with a large Stage B step count (which [improves mid-level detail](https://old.reddit.com/r/StableDiffusion/comments/1ar359h/cascade_can_generate_directly_at_1536x1536_and/kqhjtk5/)).
+```