List Diffusers as a runtime engine and trim the Diffusers section

README.md

@@ -493,9 +493,7 @@ Example output generated from `assets/example_first_frame.png`:
 
 ## Usage: Run Inference with Diffusers
 
-`Cosmos3-Super-Image2Video-4Step` is supported by the Hugging Face Diffusers **modular** pipeline, using the same JSON-format upsampled prompt and first-frame image as the vLLM-Omni example above. The distilled checkpoint is not compatible with `Cosmos3OmniPipeline` or `Cosmos3OmniModularPipeline`; it must be loaded with `Cosmos3DistilledModularPipeline`.
-
-This path loads the full 64B model on a single device in BF16, so it needs a B200/GB200-class GPU. On H100/H200-class GPUs, use the multi-GPU vLLM-Omni serving configuration above instead.
+`Cosmos3-Super-Image2Video-4Step` is supported by the Hugging Face Diffusers **modular** pipeline. The distilled checkpoint is not compatible with `Cosmos3OmniPipeline` or `Cosmos3OmniModularPipeline`; it must be loaded with `Cosmos3DistilledModularPipeline`.
 
 ### Install
 
@@ -521,8 +519,6 @@ The safety checker requires access to the gated [nvidia/Cosmos-1.0-Guardrail](ht
 
 ### Example: Image to Video Generation
 
-Run this from the downloaded repo root, using the same `assets/example_prompt.json` and `assets/example_first_frame.png` inputs as the vLLM-Omni request. The example generates at 480p (`832x480` at 16:9), the recommended setting for this distilled I2V checkpoint.
-
 ```python
 import json
 
@@ -530,9 +526,6 @@ import torch
 from diffusers import Cosmos3DistilledModularPipeline
 from diffusers.utils import export_to_video, load_image
 
-# The prompt file holds the same positive and negative prompt fields the vLLM-Omni
-# example uses. Only the positive prompt is passed here: classifier-free guidance is
-# baked into the distilled weights, so the negative prompt has no effect.
 json_prompt = json.load(open("assets/example_prompt.json"))
 image = load_image("assets/example_first_frame.png")