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from typing import Callable
import PIL.Image
import torch
from controlnet_aux import (
CannyDetector,
LineartDetector,
MidasDetector,
PidiNetDetector,
ZoeDetector,
)
from diffusers import (
AutoencoderKL,
EulerAncestralDiscreteScheduler,
StableDiffusionXLAdapterPipeline,
T2IAdapter,
)
ADAPTER_NAMES = [
"TencentARC/t2i-adapter-canny-sdxl-1.0",
"TencentARC/t2i-adapter-sketch-sdxl-1.0",
"TencentARC/t2i-adapter-lineart-sdxl-1.0",
"TencentARC/t2i-adapter-depth-midas-sdxl-1.0",
"TencentARC/t2i-adapter-depth-zoe-sdxl-1.0",
"TencentARC/t2i-adapter-recolor-sdxl-1.0",
]
class CannyPreprocessor:
def __init__(self):
self.model = CannyDetector()
def __call__(self, image: PIL.Image.Image) -> PIL.Image.Image:
return self.model(image, detect_resolution=384, image_resolution=1024)
class LineartPreprocessor:
def __init__(self):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.model = LineartDetector.from_pretrained("lllyasviel/Annotators").to(device)
def __call__(self, image: PIL.Image.Image) -> PIL.Image.Image:
return self.model(image, detect_resolution=384, image_resolution=1024)
class MidasPreprocessor:
def __init__(self):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.model = MidasDetector.from_pretrained(
"valhalla/t2iadapter-aux-models", filename="dpt_large_384.pt", model_type="dpt_large"
).to(device)
def __call__(self, image: PIL.Image.Image) -> PIL.Image.Image:
return self.model(image, detect_resolution=512, image_resolution=1024)
class PidiNetPreprocessor:
def __init__(self):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.model = PidiNetDetector.from_pretrained("lllyasviel/Annotators").to(device)
def __call__(self, image: PIL.Image.Image) -> PIL.Image.Image:
return self.model(image, detect_resolution=512, image_resolution=1024, apply_filter=True)
class RecolorPreprocessor:
def __call__(self, image: PIL.Image.Image) -> PIL.Image.Image:
return image.convert("L").convert("RGB")
class ZoePreprocessor:
def __init__(self):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.model = ZoeDetector.from_pretrained(
"valhalla/t2iadapter-aux-models", filename="zoed_nk.pth", model_type="zoedepth_nk"
).to(device)
def __call__(self, image: PIL.Image.Image) -> PIL.Image.Image:
return self.model(image, gamma_corrected=True, image_resolution=1024)
def get_preprocessor(adapter_name: str) -> Callable[[PIL.Image.Image], PIL.Image.Image]:
if adapter_name == "TencentARC/t2i-adapter-canny-sdxl-1.0":
return CannyPreprocessor()
elif adapter_name == "TencentARC/t2i-adapter-sketch-sdxl-1.0":
return PidiNetPreprocessor()
elif adapter_name == "TencentARC/t2i-adapter-lineart-sdxl-1.0":
return LineartPreprocessor()
elif adapter_name == "TencentARC/t2i-adapter-depth-midas-sdxl-1.0":
return MidasPreprocessor()
elif adapter_name == "TencentARC/t2i-adapter-depth-zoe-sdxl-1.0":
return ZoePreprocessor()
elif adapter_name == "TencentARC/t2i-adapter-recolor-sdxl-1.0":
return RecolorPreprocessor()
else:
raise ValueError(f"Adapter name must be one of {ADAPTER_NAMES}")
class Model:
MAX_NUM_INFERENCE_STEPS = 50
def __init__(self, adapter_name: str):
if adapter_name not in ADAPTER_NAMES:
raise ValueError(f"Adapter name must be one of {ADAPTER_NAMES}")
self.adapter_name = adapter_name
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if torch.cuda.is_available():
self.preprocessor = get_preprocessor(adapter_name)
model_id = "stabilityai/stable-diffusion-xl-base-1.0"
adapter = T2IAdapter.from_pretrained(
adapter_name,
torch_dtype=torch.float16,
varient="fp16",
).to(self.device)
euler_a = EulerAncestralDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler")
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
self.pipe = StableDiffusionXLAdapterPipeline.from_pretrained(
model_id,
vae=vae,
adapter=adapter,
scheduler=euler_a,
torch_dtype=torch.float16,
variant="fp16",
).to(self.device)
self.pipe.enable_xformers_memory_efficient_attention()
else:
self.pipe = None
def change_adapter(self, adapter_name: str) -> None:
if not torch.cuda.is_available():
raise RuntimeError("This demo does not work on CPU.")
if adapter_name not in ADAPTER_NAMES:
raise ValueError(f"Adapter name must be one of {ADAPTER_NAMES}")
if adapter_name == self.adapter_name:
return
self.preprocessor = None # type: ignore
torch.cuda.empty_cache()
self.preprocessor = get_preprocessor(adapter_name)
self.pipe.adapter = None
torch.cuda.empty_cache()
self.pipe.adapter = T2IAdapter.from_pretrained(
adapter_name,
torch_dtype=torch.float16,
varient="fp16",
).to(self.device)
def resize_image(self, image: PIL.Image.Image) -> PIL.Image.Image:
w, h = image.size
scale = 1024 / max(w, h)
new_w = int(w * scale)
new_h = int(h * scale)
return image.resize((new_w, new_h), PIL.Image.LANCZOS)
def run(
self,
image: PIL.Image.Image,
prompt: str,
negative_prompt: str,
num_inference_steps: int = 30,
guidance_scale: float = 7.5,
adapter_conditioning_scale: float = 0.8,
cond_tau: float = 0.8,
seed: int = 0,
apply_preprocess: bool = True,
) -> list[PIL.Image.Image]:
if num_inference_steps > self.MAX_NUM_INFERENCE_STEPS:
raise ValueError(f"Number of steps must be less than {self.MAX_NUM_INFERENCE_STEPS}")
# Resize image to avoid OOM
image = self.resize_image(image)
if apply_preprocess:
image = self.preprocessor(image)
generator = torch.Generator(device=self.device).manual_seed(seed)
out = self.pipe(
prompt=prompt,
negative_prompt=negative_prompt,
image=image,
num_inference_steps=num_inference_steps,
adapter_conditioning_scale=adapter_conditioning_scale,
cond_tau=cond_tau,
generator=generator,
guidance_scale=guidance_scale,
).images[0]
return [image, out]
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