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from config import MODEL_NAME,ADAPTER_NAME | |
import torch | |
from diffusers import DiffusionPipeline | |
from wandb.integration.diffusers import autolog | |
from scripts.config import PROJECT_NAME | |
autolog(init=dict(project=PROJECT_NAME)) | |
def load_pipeline(model_name, adapter_name): | |
pipe = DiffusionPipeline.from_pretrained(model_name, torch_dtype=torch.float16).to( | |
"cuda" | |
) | |
pipe.load_lora_weights(adapter_name) | |
pipe.unet.to(memory_format=torch.channels_last) | |
pipe.vae.to(memory_format=torch.channels_last) | |
pipe.unet = torch.compile(pipe.unet, mode="max-autotune", fullgraph=True) | |
pipe.vae.decode = torch.compile( | |
pipe.vae.decode, mode="max-autotune", fullgraph=True | |
) | |
pipe.fuse_qkv_projections() | |
return pipe | |
loaded_pipeline = load_pipeline(MODEL_NAME, ADAPTER_NAME) | |
images = loaded_pipeline('toaster', num_inference_steps=30).images[0] | |