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add sdxl and lora-lcm optimization #12444

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merged 2 commits into from
Nov 26, 2024
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add sdxl and lora-lcm optimization #12444

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570d8d1
add sdxl and lora-lcm optimization
JinheTang Nov 26, 2024
7ecb377
fix openjourney speed drop
JinheTang Nov 26, 2024
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8 changes: 5 additions & 3 deletions python/llm/example/GPU/HuggingFace/Multimodal/StableDiffusion/lora-lcm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,16 +17,18 @@

import torch
from diffusers import DiffusionPipeline, LCMScheduler
import ipex_llm
from ipex_llm import optimize_model
import argparse
import time


def main(args):
pipe = DiffusionPipeline.from_pretrained(
args.repo_id_or_model_path,
torch_dtype=torch.bfloat16,
).to("xpu")
torch_dtype=torch.float16,
)
pipe = optimize_model(pipe, low_bit=None)
pipe.to("xpu")

# set scheduler
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
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8 changes: 5 additions & 3 deletions python/llm/example/GPU/HuggingFace/Multimodal/StableDiffusion/sdxl.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,7 +17,7 @@

from diffusers import AutoPipelineForText2Image
import torch
import ipex_llm
from ipex_llm import optimize_model
import numpy as np
from PIL import Image
import argparse
Expand All @@ -27,9 +27,11 @@
def main(args):
pipeline_text2image = AutoPipelineForText2Image.from_pretrained(
args.repo_id_or_model_path,
torch_dtype=torch.bfloat16,
torch_dtype=torch.float16,
use_safetensors=True
).to("xpu")
)
pipeline_text2image = optimize_model(pipeline_text2image, low_bit=None)
pipeline_text2image.to("xpu")

with torch.inference_mode():
# warmup
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2 changes: 1 addition & 1 deletion python/llm/src/ipex_llm/transformers/models/sd.py
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Expand Up @@ -111,7 +111,7 @@ def __call__(
# padding head_dim 40 to 64
query, key, value = padding_qkv_hd(query, key, value, 40, 64)

if use_sdp_non_causal(head_dim, query.device, query.dtype):
if use_sdp_non_causal(query.size(-1), query.device, query.dtype):
import xe_addons
hidden_states = xe_addons.sdp_non_causal(query, key.contiguous(),
value.contiguous(), attention_mask)
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