Instructions to use simulationcartridge/ppl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use simulationcartridge/ppl with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("simulationcartridge/ppl") prompt = "8bit avatar" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Mirza Učanbarlić commited on
Commit ·
c058aa4
1
Parent(s): 71ffd5c
add cuda
Browse files- handler.py +1 -1
handler.py
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@@ -10,7 +10,7 @@ from diffusers.utils import load_image
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class EndpointHandler():
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def __init__(self, path=""):
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repo_id = "runwayml/stable-diffusion-v1-5"
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self.pipeline = StableDiffusionImg2ImgPipeline.from_pretrained(repo_id)
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weight_name = "pixel-portrait-v1.safetensors"
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self.pipeline.load_lora_weights("simulationcartridge/ppl", weight_name=weight_name)
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class EndpointHandler():
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def __init__(self, path=""):
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repo_id = "runwayml/stable-diffusion-v1-5"
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self.pipeline = StableDiffusionImg2ImgPipeline.from_pretrained(repo_id).to("cuda")
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weight_name = "pixel-portrait-v1.safetensors"
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self.pipeline.load_lora_weights("simulationcartridge/ppl", weight_name=weight_name)
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