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HuggingFace Diffusers library for diffusion models: text-to-image, image-to-image, inpainting, super-resolution. Supports Stable Diffusion, Flux, SDXL, and custom pipelines.
Passed 19 of 21 test cases with the skill, and 15 of 21 without it.
and 1 more in the full report
4 fixed16 passed either way2 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 3 test cases the skill fixed. Same model, same prompt, with and without the skill.
When loading a model pipeline via from_pretrained, what method call should be chained at the end to move execution onto an NVIDIA graphics card?
| Checked | Without skill | With skill |
|---|---|---|
| Calls .to("cuda") on the initialized pipeline object | Missed | Passed |
The instructions your model reads when the skill loads.
HuggingFace Diffusers provides diffusion models for text-to-image, image-to-image, inpainting, and super-resolution. Supports Stable Diffusion, Flux, and SDXL with full pipeline customization.
uv pip install diffusers transformers accelerate
from diffusers import StableDiffusionPipeline
import torch
pipe = StableDiffusionPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5",
torch_dtype=torch.float16,
).to("cuda")
image = pipe("a photo of a cat wearing a space suit").images[0]
image.save("cat_astronaut.png")
from diffusers import DiffusionPipeline
pipe = DiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
torch_dtype=torch.float16,
).to("cuda")
image = pipe(prompt="a cinematic shot of a mountain", num_inference_steps=30).images[0]
test cases passed, vs 15 / 21 without
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