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@ -14,7 +14,7 @@ from lama_cleaner.helper import (
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)
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from lama_cleaner.model.helper.g_diffuser_bot import expand_image
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from lama_cleaner.model.utils import get_scheduler
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from lama_cleaner.schema import Config, HDStrategy, SDSampler
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from lama_cleaner.schema import Config, HDStrategy, SDSampler, ModelInfo
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class InpaintModel:
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@ -266,9 +266,8 @@ class InpaintModel:
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class DiffusionInpaintModel(InpaintModel):
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def __init__(self, device, **kwargs):
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if kwargs.get("model_id_or_path"):
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# 用于自定义 diffusers 模型
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self.model_id_or_path = kwargs["model_id_or_path"]
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self.model_info: ModelInfo = kwargs["model_info"]
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self.model_id_or_path = self.model_info.path
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super().__init__(device, **kwargs)
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@torch.no_grad()
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@ -75,6 +75,11 @@ class ControlNet(DiffusionInpaintModel):
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sd_controlnet_method, torch_dtype=torch_dtype
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)
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if model_info.is_single_file_diffusers:
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if self.model_info.model_type == ModelType.DIFFUSERS_SD:
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model_kwargs["num_in_channels"] = 4
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else:
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model_kwargs["num_in_channels"] = 9
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self.model = PipeClass.from_single_file(
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model_info.path, controlnet=controlnet
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).to(torch_dtype)
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@ -8,7 +8,7 @@ from lama_cleaner.schema import Config
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class InstructPix2Pix(DiffusionInpaintModel):
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name = "instruct_pix2pix"
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name = "timbrooks/instruct-pix2pix"
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pad_mod = 8
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min_size = 512
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@ -73,7 +73,7 @@ class Kandinsky(DiffusionInpaintModel):
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class Kandinsky22(Kandinsky):
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name = "kandinsky2.2"
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name = "kandinsky-community/kandinsky-2-2-decoder-inpaint"
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model_name = "kandinsky-community/kandinsky-2-2-decoder-inpaint"
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@staticmethod
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@ -9,7 +9,7 @@ from lama_cleaner.schema import Config
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class PaintByExample(DiffusionInpaintModel):
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name = "paint_by_example"
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name = "Fantasy-Studio/Paint-by-Example"
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pad_mod = 8
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min_size = 512
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@ -9,7 +9,7 @@ from loguru import logger
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from lama_cleaner.const import DIFFUSERS_MODEL_FP16_REVERSION
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from lama_cleaner.model.base import DiffusionInpaintModel
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from lama_cleaner.model.helper.cpu_text_encoder import CPUTextEncoderWrapper
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from lama_cleaner.schema import Config
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from lama_cleaner.schema import Config, ModelType
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class SD(DiffusionInpaintModel):
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@ -36,7 +36,12 @@ class SD(DiffusionInpaintModel):
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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 os.path.isfile(self.model_id_or_path):
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if self.model_info.is_single_file_diffusers:
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if self.model_info.model_type == ModelType.DIFFUSERS_SD:
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model_kwargs["num_in_channels"] = 4
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else:
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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, torch_dtype=torch_dtype, **model_kwargs
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)
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@ -8,7 +8,7 @@ from diffusers import AutoencoderKL
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from loguru import logger
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from lama_cleaner.model.base import DiffusionInpaintModel
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from lama_cleaner.schema import Config
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from lama_cleaner.schema import Config, ModelType
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class SDXL(DiffusionInpaintModel):
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@ -26,9 +26,16 @@ class SDXL(DiffusionInpaintModel):
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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.model_type == ModelType.DIFFUSERS_SDXL:
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num_in_channels = 4
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else:
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num_in_channels = 9
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if os.path.isfile(self.model_id_or_path):
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self.model = StableDiffusionXLInpaintPipeline.from_single_file(
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self.model_id_or_path, torch_dtype=torch_dtype
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self.model_id_or_path,
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torch_dtype=torch_dtype,
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num_in_channels=num_in_channels,
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)
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else:
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vae = AutoencoderKL.from_pretrained(
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@ -27,25 +27,26 @@ class ModelManager:
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if name not in self.available_models:
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raise NotImplementedError(f"Unsupported model: {name}")
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sd_controlnet_enabled = kwargs.get("sd_controlnet", False)
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model_info = self.available_models[name]
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kwargs = {**kwargs, "model_info": model_info}
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sd_controlnet_enabled = kwargs.get("sd_controlnet", False)
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if model_info.model_type in [ModelType.INPAINT, ModelType.DIFFUSERS_OTHER]:
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return models[name](device, **kwargs)
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if sd_controlnet_enabled:
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return ControlNet(device, **{**kwargs, "model_info": model_info})
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return ControlNet(device, **kwargs)
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else:
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if model_info.model_type in [
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ModelType.DIFFUSERS_SD_INPAINT,
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ModelType.DIFFUSERS_SD,
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]:
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return SD(device, model_id_or_path=model_info.path, **kwargs)
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return SD(device, **kwargs)
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if model_info.model_type in [
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ModelType.DIFFUSERS_SDXL_INPAINT,
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ModelType.DIFFUSERS_SDXL,
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]:
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return SDXL(device, model_id_or_path=model_info.path, **kwargs)
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return SDXL(device, **kwargs)
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raise NotImplementedError(f"Unsupported model: {name}")
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@ -1,6 +1,7 @@
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#!/usr/bin/env python3
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import os
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import hashlib
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import traceback
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os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
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@ -453,7 +454,8 @@ def switch_model():
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try:
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model.switch(new_name)
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except Exception as e:
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error_message = str(e)
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traceback.print_exc()
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error_message = f"{type(e).__name__} - {str(e)}"
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logger.error(error_message)
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return f"Switch model failed: {error_message}", 500
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return f"ok, switch to {new_name}", 200
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