IOPaint/iopaint/model/sd.py

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import PIL.Image
import cv2
import torch
from loguru import logger
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from .base import DiffusionInpaintModel
from .helper.cpu_text_encoder import CPUTextEncoderWrapper
from .utils import handle_from_pretrained_exceptions
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from iopaint.schema import InpaintRequest, ModelType
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class SD(DiffusionInpaintModel):
pad_mod = 8
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min_size = 512
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lcm_lora_id = "latent-consistency/lcm-lora-sdv1-5"
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def init_model(self, device: torch.device, **kwargs):
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from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
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fp16 = not kwargs.get("no_half", False)
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model_kwargs = {}
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if kwargs["disable_nsfw"] or kwargs.get("cpu_offload", False):
logger.info("Disable Stable Diffusion Model NSFW checker")
model_kwargs.update(
dict(
safety_checker=None,
feature_extractor=None,
requires_safety_checker=False,
)
)
use_gpu = device == torch.device("cuda") and torch.cuda.is_available()
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torch_dtype = torch.float16 if use_gpu and fp16 else torch.float32
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if self.model_info.is_single_file_diffusers:
if self.model_info.model_type == ModelType.DIFFUSERS_SD:
model_kwargs["num_in_channels"] = 4
else:
model_kwargs["num_in_channels"] = 9
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self.model = StableDiffusionInpaintPipeline.from_single_file(
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self.model_id_or_path, dtype=torch_dtype, **model_kwargs
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)
else:
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self.model = handle_from_pretrained_exceptions(
StableDiffusionInpaintPipeline.from_pretrained,
pretrained_model_name_or_path=self.model_id_or_path,
variant="fp16",
dtype=torch_dtype,
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**model_kwargs,
)
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if torch.backends.mps.is_available():
# MPS: Recommended RAM < 64 GB https://huggingface.co/docs/diffusers/optimization/mps
# CUDA: Don't enable attention slicing if you're already using `scaled_dot_product_attention` (SDPA) from PyTorch 2.0 or xFormers. https://huggingface.co/docs/diffusers/v0.25.0/en/api/pipelines/stable_diffusion/image_variation#diffusers.StableDiffusionImageVariationPipeline.enable_attention_slicing
self.model.enable_attention_slicing()
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if kwargs.get("cpu_offload", False) and use_gpu:
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logger.info("Enable sequential cpu offload")
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self.model.enable_sequential_cpu_offload(gpu_id=0)
else:
self.model = self.model.to(device)
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if kwargs["sd_cpu_textencoder"]:
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logger.info("Run Stable Diffusion TextEncoder on CPU")
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self.model.text_encoder = CPUTextEncoderWrapper(
self.model.text_encoder, torch_dtype
)
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self.callback = kwargs.pop("callback", None)
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def forward(self, image, mask, config: InpaintRequest):
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"""Input image and output image have same size
image: [H, W, C] RGB
mask: [H, W, 1] 255 means area to repaint
return: BGR IMAGE
"""
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self.set_scheduler(config)
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img_h, img_w = image.shape[:2]
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output = self.model(
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image=PIL.Image.fromarray(image),
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prompt=config.prompt,
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negative_prompt=config.negative_prompt,
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mask_image=PIL.Image.fromarray(mask[:, :, -1], mode="L"),
num_inference_steps=config.sd_steps,
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strength=config.sd_strength,
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guidance_scale=config.sd_guidance_scale,
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output_type="np",
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callback_on_step_end=self.callback,
height=img_h,
width=img_w,
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generator=torch.manual_seed(config.sd_seed),
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).images[0]
output = (output * 255).round().astype("uint8")
output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR)
return output
class SD15(SD):
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name = "runwayml/stable-diffusion-inpainting"
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model_id_or_path = "runwayml/stable-diffusion-inpainting"
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class Anything4(SD):
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name = "Sanster/anything-4.0-inpainting"
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model_id_or_path = "Sanster/anything-4.0-inpainting"
class RealisticVision14(SD):
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name = "Sanster/Realistic_Vision_V1.4-inpainting"
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model_id_or_path = "Sanster/Realistic_Vision_V1.4-inpainting"
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class SD2(SD):
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name = "stabilityai/stable-diffusion-2-inpainting"
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model_id_or_path = "stabilityai/stable-diffusion-2-inpainting"