Now you can load the lora fine-tuning weights for trial. For example, the awesome Hyper-SD from ByteDance can generate an image on a laptop CPU in 1 minute! Paste the following code in your python console to generate a cute cat.
from mindone.diffusers import DiffusionPipeline, DDIMScheduler
pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
pipe.load_lora_weights("ByteDance/Hyper-SD", weight_name="Hyper-SD15-2steps-lora.safetensors")
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
prompt = "a photo of a cat"
image = pipe(prompt=prompt, num_inference_steps=2, guidance_scale=0)[0][0]
image.save("cat.jpg")
Try it out!
Fixes # (issue)
Adds # (feature)
Before submitting
[ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
[ ] Did you make sure to update the documentation with your changes? E.g. record bug fixes or new features in What's New. Here are the
documentation guidelines
[ ] Did you build and run the code without any errors?
[ ] Did you report the running environment (NPU type/MS version) and performance in the doc? (better record it for data loading, model inference, or training tasks)
[ ] Did you write any new necessary tests?
Who can review?
Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.
What does this PR do?
We support LoRA!
Now you can load the lora fine-tuning weights for trial. For example, the awesome Hyper-SD from ByteDance can generate an image on a laptop CPU in 1 minute! Paste the following code in your python console to generate a cute cat.
Try it out!
Fixes # (issue)
Adds # (feature)
Before submitting
What's New. Here are the documentation guidelinesWho can review?
Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR.
@xxx