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A10G
import gc | |
import os | |
from abc import ABC, abstractmethod | |
import PIL.Image | |
import torch | |
from controlnet_aux import ( | |
CannyDetector, | |
LineartDetector, | |
MidasDetector, | |
PidiNetDetector, | |
ZoeDetector, | |
) | |
from diffusers import ( | |
AutoencoderKL, | |
EulerAncestralDiscreteScheduler, | |
StableDiffusionXLAdapterPipeline, | |
T2IAdapter, | |
) | |
SD_XL_BASE_RATIOS = { | |
"0.5": (704, 1408), | |
"0.52": (704, 1344), | |
"0.57": (768, 1344), | |
"0.6": (768, 1280), | |
"0.68": (832, 1216), | |
"0.72": (832, 1152), | |
"0.78": (896, 1152), | |
"0.82": (896, 1088), | |
"0.88": (960, 1088), | |
"0.94": (960, 1024), | |
"1.0": (1024, 1024), | |
"1.07": (1024, 960), | |
"1.13": (1088, 960), | |
"1.21": (1088, 896), | |
"1.29": (1152, 896), | |
"1.38": (1152, 832), | |
"1.46": (1216, 832), | |
"1.67": (1280, 768), | |
"1.75": (1344, 768), | |
"1.91": (1344, 704), | |
"2.0": (1408, 704), | |
"2.09": (1472, 704), | |
"2.4": (1536, 640), | |
"2.5": (1600, 640), | |
"2.89": (1664, 576), | |
"3.0": (1728, 576), | |
} | |
def find_closest_aspect_ratio(target_width: int, target_height: int) -> str: | |
target_ratio = target_width / target_height | |
closest_ratio = "" | |
min_difference = float("inf") | |
for ratio_str, (width, height) in SD_XL_BASE_RATIOS.items(): | |
ratio = width / height | |
difference = abs(target_ratio - ratio) | |
if difference < min_difference: | |
min_difference = difference | |
closest_ratio = ratio_str | |
return closest_ratio | |
def resize_to_closest_aspect_ratio(image: PIL.Image.Image) -> PIL.Image.Image: | |
target_width, target_height = image.size | |
closest_ratio = find_closest_aspect_ratio(target_width, target_height) | |
# Get the dimensions from the closest aspect ratio in the dictionary | |
new_width, new_height = SD_XL_BASE_RATIOS[closest_ratio] | |
# Resize the image to the new dimensions while preserving the aspect ratio | |
resized_image = image.resize((new_width, new_height), PIL.Image.LANCZOS) | |
return resized_image | |
ADAPTER_REPO_IDS = { | |
"canny": "TencentARC/t2i-adapter-canny-sdxl-1.0", | |
"sketch": "TencentARC/t2i-adapter-sketch-sdxl-1.0", | |
"lineart": "TencentARC/t2i-adapter-lineart-sdxl-1.0", | |
"depth-midas": "TencentARC/t2i-adapter-depth-midas-sdxl-1.0", | |
"depth-zoe": "TencentARC/t2i-adapter-depth-zoe-sdxl-1.0", | |
# "recolor": "TencentARC/t2i-adapter-recolor-sdxl-1.0", | |
} | |
ADAPTER_NAMES = list(ADAPTER_REPO_IDS.keys()) | |
class Preprocessor(ABC): | |
def to(self, device: torch.device | str) -> "Preprocessor": | |
pass | |
def __call__(self, image: PIL.Image.Image) -> PIL.Image.Image: | |
pass | |
class CannyPreprocessor(Preprocessor): | |
def __init__(self): | |
self.model = CannyDetector() | |
def to(self, device: torch.device | str) -> Preprocessor: | |
return self | |
def __call__(self, image: PIL.Image.Image) -> PIL.Image.Image: | |
return self.model(image, detect_resolution=384, image_resolution=1024) | |
class LineartPreprocessor(Preprocessor): | |
def __init__(self): | |
self.model = LineartDetector.from_pretrained("lllyasviel/Annotators") | |
def to(self, device: torch.device | str) -> Preprocessor: | |
return self.model.to(device) | |
def __call__(self, image: PIL.Image.Image) -> PIL.Image.Image: | |
return self.model(image, detect_resolution=512, image_resolution=1024) | |
class MidasPreprocessor(Preprocessor): | |
def __init__(self): | |
self.model = MidasDetector.from_pretrained( | |
"valhalla/t2iadapter-aux-models", filename="dpt_large_384.pt", model_type="dpt_large" | |
) | |
def to(self, device: torch.device | str) -> Preprocessor: | |
return self.model.to(device) | |
def __call__(self, image: PIL.Image.Image) -> PIL.Image.Image: | |
return self.model(image, detect_resolution=512, image_resolution=1024) | |
class PidiNetPreprocessor(Preprocessor): | |
def __init__(self): | |
self.model = PidiNetDetector.from_pretrained("lllyasviel/Annotators") | |
def to(self, device: torch.device | str) -> Preprocessor: | |
return self.model.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(Preprocessor): | |
def to(self, device: torch.device | str) -> Preprocessor: | |
return self | |
def __call__(self, image: PIL.Image.Image) -> PIL.Image.Image: | |
return image.convert("L").convert("RGB") | |
class ZoePreprocessor(Preprocessor): | |
def __init__(self): | |
self.model = ZoeDetector.from_pretrained( | |
"valhalla/t2iadapter-aux-models", filename="zoed_nk.pth", model_type="zoedepth_nk" | |
) | |
def to(self, device: torch.device | str) -> Preprocessor: | |
return self.model.to(device) | |
def __call__(self, image: PIL.Image.Image) -> PIL.Image.Image: | |
return self.model(image, gamma_corrected=True, image_resolution=1024) | |
PRELOAD_PREPROCESSORS_IN_GPU_MEMORY = os.getenv("PRELOAD_PREPROCESSORS_IN_GPU_MEMORY", "1") == "1" | |
PRELOAD_PREPROCESSORS_IN_CPU_MEMORY = os.getenv("PRELOAD_PREPROCESSORS_IN_CPU_MEMORY", "0") == "1" | |
if PRELOAD_PREPROCESSORS_IN_GPU_MEMORY: | |
device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
preprocessors_gpu: dict[str, Preprocessor] = { | |
"canny": CannyPreprocessor().to(device), | |
"sketch": PidiNetPreprocessor().to(device), | |
"lineart": LineartPreprocessor().to(device), | |
"depth-midas": MidasPreprocessor().to(device), | |
"depth-zoe": ZoePreprocessor().to(device), | |
"recolor": RecolorPreprocessor().to(device), | |
} | |
def get_preprocessor(adapter_name: str) -> Preprocessor: | |
return preprocessors_gpu[adapter_name] | |
elif PRELOAD_PREPROCESSORS_IN_CPU_MEMORY: | |
preprocessors_cpu: dict[str, Preprocessor] = { | |
"canny": CannyPreprocessor(), | |
"sketch": PidiNetPreprocessor(), | |
"lineart": LineartPreprocessor(), | |
"depth-midas": MidasPreprocessor(), | |
"depth-zoe": ZoePreprocessor(), | |
"recolor": RecolorPreprocessor(), | |
} | |
def get_preprocessor(adapter_name: str) -> Preprocessor: | |
return preprocessors_cpu[adapter_name] | |
else: | |
def get_preprocessor(adapter_name: str) -> Preprocessor: | |
if adapter_name == "canny": | |
return CannyPreprocessor() | |
elif adapter_name == "sketch": | |
return PidiNetPreprocessor() | |
elif adapter_name == "lineart": | |
return LineartPreprocessor() | |
elif adapter_name == "depth-midas": | |
return MidasPreprocessor() | |
elif adapter_name == "depth-zoe": | |
return ZoePreprocessor() | |
elif adapter_name == "recolor": | |
return RecolorPreprocessor() | |
else: | |
raise ValueError(f"Adapter name must be one of {ADAPTER_NAMES}") | |
def download_all_preprocessors(): | |
for adapter_name in ADAPTER_NAMES: | |
get_preprocessor(adapter_name) | |
gc.collect() | |
download_all_preprocessors() | |
def download_all_adapters(): | |
for adapter_name in ADAPTER_NAMES: | |
T2IAdapter.from_pretrained( | |
ADAPTER_REPO_IDS[adapter_name], | |
torch_dtype=torch.float16, | |
varient="fp16", | |
) | |
gc.collect() | |
download_all_adapters() | |
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.preprocessor_name = adapter_name | |
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).to(self.device) | |
model_id = "stabilityai/stable-diffusion-xl-base-1.0" | |
adapter = T2IAdapter.from_pretrained( | |
ADAPTER_REPO_IDS[adapter_name], | |
torch_dtype=torch.float16, | |
varient="fp16", | |
).to(self.device) | |
self.pipe = StableDiffusionXLAdapterPipeline.from_pretrained( | |
model_id, | |
vae=AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16), | |
adapter=adapter, | |
scheduler=EulerAncestralDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler"), | |
torch_dtype=torch.float16, | |
variant="fp16", | |
).to(self.device) | |
self.pipe.enable_xformers_memory_efficient_attention() | |
self.pipe.load_lora_weights( | |
"stabilityai/stable-diffusion-xl-base-1.0", weight_name="sd_xl_offset_example-lora_1.0.safetensors" | |
) | |
self.pipe.fuse_lora(lora_scale=0.4) | |
else: | |
self.preprocessor = None # type: ignore | |
self.pipe = None | |
def change_preprocessor(self, adapter_name: str) -> None: | |
if adapter_name not in ADAPTER_NAMES: | |
raise ValueError(f"Adapter name must be one of {ADAPTER_NAMES}") | |
if adapter_name == self.preprocessor_name: | |
return | |
if PRELOAD_PREPROCESSORS_IN_GPU_MEMORY: | |
pass | |
elif PRELOAD_PREPROCESSORS_IN_CPU_MEMORY: | |
self.preprocessor.to("cpu") | |
else: | |
del self.preprocessor | |
self.preprocessor = get_preprocessor(adapter_name).to(self.device) | |
self.preprocessor_name = adapter_name | |
gc.collect() | |
torch.cuda.empty_cache() | |
def change_adapter(self, adapter_name: str) -> None: | |
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.pipe.adapter = T2IAdapter.from_pretrained( | |
ADAPTER_REPO_IDS[adapter_name], | |
torch_dtype=torch.float16, | |
varient="fp16", | |
).to(self.device) | |
self.adapter_name = adapter_name | |
gc.collect() | |
torch.cuda.empty_cache() | |
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, | |
adapter_name: str, | |
num_inference_steps: int = 30, | |
guidance_scale: float = 5.0, | |
adapter_conditioning_scale: float = 1.0, | |
adapter_conditioning_factor: float = 1.0, | |
seed: int = 0, | |
apply_preprocess: bool = True, | |
) -> list[PIL.Image.Image]: | |
if not torch.cuda.is_available(): | |
raise RuntimeError("This demo does not work on CPU.") | |
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) | |
self.change_preprocessor(adapter_name) | |
self.change_adapter(adapter_name) | |
if apply_preprocess: | |
image = self.preprocessor(image) | |
image = resize_to_closest_aspect_ratio(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, | |
adapter_conditioning_factor=adapter_conditioning_factor, | |
generator=generator, | |
guidance_scale=guidance_scale, | |
).images[0] | |
return [image, out] | |