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import io
import os
from typing import List
import PIL.Image
import requests
import torch
from diffusers import AutoencoderTiny, StableDiffusionPipeline
from streamdiffusion import StreamDiffusion
from streamdiffusion.image_utils import postprocess_image
def download_image(url: str):
response = requests.get(url)
image = PIL.Image.open(io.BytesIO(response.content))
return image
class StreamDiffusionWrapper:
def __init__(
self,
model_id: str,
lcm_lora_id: str,
vae_id: str,
device: str,
dtype: str,
t_index_list: List[int],
warmup: int,
safety_checker: bool,
):
self.device = device
self.dtype = dtype
self.prompt = ""
self.batch_size = len(t_index_list)
self.stream = self._load_model(
model_id=model_id,
lcm_lora_id=lcm_lora_id,
vae_id=vae_id,
t_index_list=t_index_list,
warmup=warmup,
)
self.safety_checker = None
if safety_checker:
from transformers import CLIPFeatureExtractor
from diffusers.pipelines.stable_diffusion.safety_checker import (
StableDiffusionSafetyChecker,
)
self.safety_checker = StableDiffusionSafetyChecker.from_pretrained(
"CompVis/stable-diffusion-safety-checker"
).to(self.device)
self.feature_extractor = CLIPFeatureExtractor.from_pretrained(
"openai/clip-vit-base-patch32"
)
self.nsfw_fallback_img = PIL.Image.new("RGB", (512, 512), (0, 0, 0))
self.stream.prepare("")
def _load_model(
self,
model_id: str,
lcm_lora_id: str,
vae_id: str,
t_index_list: List[int],
warmup: int,
):
if os.path.exists(model_id):
pipe: StableDiffusionPipeline = StableDiffusionPipeline.from_single_file(
model_id
).to(device=self.device, dtype=self.dtype)
else:
pipe: StableDiffusionPipeline = StableDiffusionPipeline.from_pretrained(
model_id
).to(device=self.device, dtype=self.dtype)
stream = StreamDiffusion(
pipe=pipe,
t_index_list=t_index_list,
torch_dtype=self.dtype,
is_drawing=True,
)
stream.load_lcm_lora(lcm_lora_id)
stream.fuse_lora()
stream.vae = AutoencoderTiny.from_pretrained(vae_id).to(
device=pipe.device, dtype=pipe.dtype
)
try:
from streamdiffusion.acceleration.tensorrt import accelerate_with_tensorrt
stream = accelerate_with_tensorrt(
stream,
"engines",
max_batch_size=self.batch_size,
engine_build_options={"build_static_batch": False},
)
print("TensorRT acceleration enabled.")
except Exception:
print("TensorRT acceleration has failed. Trying to use Stable Fast.")
try:
from streamdiffusion.acceleration.sfast import (
accelerate_with_stable_fast,
)
stream = accelerate_with_stable_fast(stream)
print("StableFast acceleration enabled.")
except Exception:
print("StableFast acceleration has failed. Using normal mode.")
pass
stream.prepare(
"",
num_inference_steps=50,
generator=torch.manual_seed(2),
)
# warmup
for _ in range(warmup):
start = torch.cuda.Event(enable_timing=True)
end = torch.cuda.Event(enable_timing=True)
start.record()
stream.txt2img()
end.record()
torch.cuda.synchronize()
return stream
def __call__(self, prompt: str) -> PIL.Image.Image:
if self.prompt != prompt:
self.stream.update_prompt(prompt)
self.prompt = prompt
for i in range(self.batch_size):
x_output = self.stream.txt2img()
x_output = self.stream.txt2img()
image = postprocess_image(x_output, output_type="pil")[0]
if self.safety_checker:
safety_checker_input = self.feature_extractor(
image, return_tensors="pt"
).to(self.device)
_, has_nsfw_concept = self.safety_checker(
images=x_output,
clip_input=safety_checker_input.pixel_values.to(self.dtype),
)
image = self.nsfw_fallback_img if has_nsfw_concept[0] else image
return image
if __name__ == "__main__":
wrapper = StreamDiffusionWrapper(10, 10)
wrapper()
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