update
This commit is contained in:
Qing
parent
0e5e16ba20
commit
371db2d771
@ -10,11 +10,8 @@ from lama_cleaner.parse_args import parse_args
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def entry_point():
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args = parse_args()
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if args is None:
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return
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# To make os.environ["XDG_CACHE_HOME"] = args.model_cache_dir works for diffusers
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# https://github.com/huggingface/diffusers/blob/be99201a567c1ccd841dc16fb24e88f7f239c187/src/diffusers/utils/constants.py#L18
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from lama_cleaner.server import main
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from lama_cleaner.server import typer_app
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main(args)
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typer_app()
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@ -103,6 +103,5 @@ if __name__ == "__main__":
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device=device,
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disable_nsfw=True,
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sd_cpu_textencoder=True,
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hf_access_token="123"
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)
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benchmark(model, args.times, args.empty_cache)
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@ -21,19 +21,17 @@ AVAILABLE_MODELS = [
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"zits",
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"mat",
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"fcf",
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"manga",
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"cv2",
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"sd1.5",
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"sdxl",
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"anything4",
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"realisticVision1.4",
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"cv2",
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"manga",
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"sd2",
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"sdxl",
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"paint_by_example",
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"instruct_pix2pix",
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"kandinsky2.2",
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"sdxl",
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]
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SD15_MODELS = ["sd1.5", "anything4", "realisticVision1.4"]
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DIFFUSERS_MODEL_FP16_REVERSION = [
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"runwayml/stable-diffusion-inpainting",
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"Sanster/anything-4.0-inpainting",
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@ -46,26 +44,22 @@ AVAILABLE_DEVICES = ["cuda", "cpu", "mps"]
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DEFAULT_DEVICE = "cuda"
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NO_HALF_HELP = """
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Using full precision model.
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If your generate result is always black or green, use this argument. (sd/paint_by_exmaple)
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Using full precision(fp32) model.
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If your diffusion model generate result is always black or green, use this argument.
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"""
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CPU_OFFLOAD_HELP = """
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Offloads all models to CPU, significantly reducing vRAM usage. (sd/paint_by_example)
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Offloads diffusion model's weight to CPU RAM, significantly reducing vRAM usage.
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"""
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DISABLE_NSFW_HELP = """
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Disable NSFW checker. (sd/paint_by_example)
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Disable NSFW checker for diffusion model.
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"""
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SD_CPU_TEXTENCODER_HELP = """
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Run Stable Diffusion text encoder model on CPU to save GPU memory.
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CPU_TEXTENCODER_HELP = """
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Run diffusion models text encoder on CPU to reduce vRAM usage.
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"""
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SD_CONTROLNET_HELP = """
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Run Stable Diffusion normal or inpainting model with ControlNet.
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"""
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DEFAULT_SD_CONTROLNET_METHOD = "lllyasviel/control_v11p_sd15_canny"
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SD_CONTROLNET_CHOICES = [
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"lllyasviel/control_v11p_sd15_canny",
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# "lllyasviel/control_v11p_sd15_seg",
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@ -74,46 +68,36 @@ SD_CONTROLNET_CHOICES = [
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"lllyasviel/control_v11f1p_sd15_depth",
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]
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DEFAULT_SD2_CONTROLNET_METHOD = "thibaud/controlnet-sd21-canny-diffusers"
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SD2_CONTROLNET_CHOICES = [
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"thibaud/controlnet-sd21-canny-diffusers",
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"thibaud/controlnet-sd21-depth-diffusers",
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"thibaud/controlnet-sd21-openpose-diffusers",
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]
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DEFAULT_SDXL_CONTROLNET_METHOD = "diffusers/controlnet-canny-sdxl-1.0"
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SDXL_CONTROLNET_CHOICES = [
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"thibaud/controlnet-openpose-sdxl-1.0",
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"destitech/controlnet-inpaint-dreamer-sdxl"
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"destitech/controlnet-inpaint-dreamer-sdxl",
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"diffusers/controlnet-canny-sdxl-1.0",
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"diffusers/controlnet-canny-sdxl-1.0-mid",
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"diffusers/controlnet-canny-sdxl-1.0-small"
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"diffusers/controlnet-canny-sdxl-1.0-small",
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"diffusers/controlnet-depth-sdxl-1.0",
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"diffusers/controlnet-depth-sdxl-1.0-mid",
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"diffusers/controlnet-depth-sdxl-1.0-small",
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]
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SD_LOCAL_MODEL_HELP = """
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Load Stable Diffusion 1.5 model(ckpt/safetensors) from local path.
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"""
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LOCAL_FILES_ONLY_HELP = """
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Use local files only, not connect to Hugging Face server. (sd/paint_by_example)
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"""
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ENABLE_XFORMERS_HELP = """
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Enable xFormers optimizations. Requires xformers package has been installed. See: https://github.com/facebookresearch/xformers (sd/paint_by_example)
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When loading diffusion models, using local files only, not connect to HuggingFace server.
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"""
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DEFAULT_MODEL_DIR = os.getenv(
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"XDG_CACHE_HOME", os.path.join(os.path.expanduser("~"), ".cache")
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)
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MODEL_DIR_HELP = """
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Model download directory (by setting XDG_CACHE_HOME environment variable), by default model downloaded to ~/.cache
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MODEL_DIR_HELP = f"""
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Model download directory (by setting XDG_CACHE_HOME environment variable), by default model download to {DEFAULT_MODEL_DIR}
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"""
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OUTPUT_DIR_HELP = """
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Result images will be saved to output directory automatically without confirmation.
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Result images will be saved to output directory automatically.
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"""
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INPUT_HELP = """
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@ -125,37 +109,45 @@ GUI_HELP = """
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Launch Lama Cleaner as desktop app
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"""
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NO_GUI_AUTO_CLOSE_HELP = """
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Prevent backend auto close after the GUI window closed.
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"""
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QUALITY_HELP = """
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Quality of image encoding, 0-100. Default is 95, higher quality will generate larger file size.
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"""
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class RealESRGANModelName(str, Enum):
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class Choices(str, Enum):
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@classmethod
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def values(cls):
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return [member.value for member in cls]
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class RealESRGANModel(Choices):
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realesr_general_x4v3 = "realesr-general-x4v3"
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RealESRGAN_x4plus = "RealESRGAN_x4plus"
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RealESRGAN_x4plus_anime_6B = "RealESRGAN_x4plus_anime_6B"
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RealESRGANModelNameList = [e.value for e in RealESRGANModelName]
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class Device(Choices):
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cpu = "cpu"
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cuda = "cuda"
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mps = "mps"
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class InteractiveSegModel(Choices):
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vit_b = "vit_b"
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vit_l = "vit_l"
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vit_h = "vit_h"
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mobile_sam = "mobile_sam"
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INTERACTIVE_SEG_HELP = "Enable interactive segmentation using Segment Anything."
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INTERACTIVE_SEG_MODEL_HELP = "Model size: vit_b < vit_l < vit_h. Bigger model size means better segmentation but slower speed."
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AVAILABLE_INTERACTIVE_SEG_MODELS = ["vit_b", "vit_l", "vit_h", "vit_t"]
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AVAILABLE_INTERACTIVE_SEG_DEVICES = ["cuda", "cpu", "mps"]
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REMOVE_BG_HELP = "Enable remove background. Always run on CPU"
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ANIMESEG_HELP = "Enable anime segmentation. Always run on CPU"
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REALESRGAN_HELP = "Enable realesrgan super resolution"
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REALESRGAN_AVAILABLE_DEVICES = ["cpu", "cuda", "mps"]
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GFPGAN_HELP = (
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"Enable GFPGAN face restore. To enhance background, use with --enable-realesrgan"
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)
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GFPGAN_AVAILABLE_DEVICES = ["cpu", "cuda", "mps"]
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RESTOREFORMER_HELP = "Enable RestoreFormer face restore. To enhance background, use with --enable-realesrgan"
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RESTOREFORMER_AVAILABLE_DEVICES = ["cpu", "cuda", "mps"]
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GIF_HELP = "Enable GIF plugin. Make GIF to compare original and cleaned image"
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@ -164,8 +156,6 @@ class Config(BaseModel):
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port: int = 8080
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model: str = DEFAULT_MODEL
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sd_local_model_path: str = None
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sd_controlnet: bool = False
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sd_controlnet_method: str = DEFAULT_SD_CONTROLNET_METHOD
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device: str = DEFAULT_DEVICE
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gui: bool = False
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no_gui_auto_close: bool = False
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@ -173,7 +163,6 @@ class Config(BaseModel):
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cpu_offload: bool = False
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disable_nsfw: bool = False
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sd_cpu_textencoder: bool = False
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enable_xformers: bool = False
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local_files_only: bool = False
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model_dir: str = DEFAULT_MODEL_DIR
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input: str = None
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@ -186,7 +175,7 @@ class Config(BaseModel):
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enable_anime_seg: bool = False
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enable_realesrgan: bool = False
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realesrgan_device: str = "cpu"
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realesrgan_model: str = RealESRGANModelName.realesr_general_x4v3.value
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realesrgan_model: str = RealESRGANModel.realesr_general_x4v3.value
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realesrgan_no_half: bool = False
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enable_gfpgan: bool = False
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gfpgan_device: str = "cpu"
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@ -6,6 +6,7 @@ from loguru import logger
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from pathlib import Path
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from lama_cleaner.const import DIFFUSERS_MODEL_FP16_REVERSION, DEFAULT_MODEL_DIR
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from lama_cleaner.runtime import setup_model_dir
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from lama_cleaner.schema import (
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ModelInfo,
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ModelType,
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@ -16,16 +17,8 @@ from lama_cleaner.schema import (
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)
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def cli_download_model(model: str, model_dir: str):
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if os.path.isfile(model_dir):
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raise ValueError(f"invalid --model-dir: {model_dir} is a file")
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if not os.path.exists(model_dir):
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logger.info(f"Create model cache directory: {model_dir}")
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Path(model_dir).mkdir(exist_ok=True, parents=True)
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os.environ["XDG_CACHE_HOME"] = model_dir
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def cli_download_model(model: str, model_dir: Path):
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setup_model_dir(model_dir)
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from lama_cleaner.model import models
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if model in models:
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@ -38,7 +31,7 @@ def cli_download_model(model: str, model_dir: str):
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downloaded_path = DiffusionPipeline.download(
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pretrained_model_name=model,
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revision="fp16" if model in DIFFUSERS_MODEL_FP16_REVERSION else "main",
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variant="fp16" if model in DIFFUSERS_MODEL_FP16_REVERSION else "main",
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resume_download=True,
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)
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logger.info(f"Done. Downloaded to {downloaded_path}")
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@ -101,7 +94,7 @@ def scan_inpaint_models() -> List[ModelInfo]:
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from lama_cleaner.model import models
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for name, m in models.items():
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if m.is_erase_model:
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if m.is_erase_model and m.is_downloaded():
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res.append(
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ModelInfo(
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name=name,
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@ -41,7 +41,7 @@ class InpaintModel:
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@staticmethod
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@abc.abstractmethod
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def is_downloaded() -> bool:
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...
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return False
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@abc.abstractmethod
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def forward(self, image, mask, config: Config):
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@ -67,6 +67,8 @@ class InpaintModel:
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logger.info(f"final forward pad size: {pad_image.shape}")
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image, mask = self.forward_pre_process(image, mask, config)
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result = self.forward(pad_image, pad_mask, config)
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result = result[0:origin_height, 0:origin_width, :]
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@ -77,6 +79,9 @@ class InpaintModel:
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result = result * (mask / 255) + image[:, :, ::-1] * (1 - (mask / 255))
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return result
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def forward_pre_process(self, image, mask, config):
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return image, mask
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def forward_post_process(self, result, image, mask, config):
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return result, image, mask
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@ -400,6 +405,13 @@ class DiffusionInpaintModel(InpaintModel):
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scheduler = get_scheduler(sd_sampler, scheduler_config)
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self.model.scheduler = scheduler
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def forward_pre_process(self, image, mask, config):
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if config.sd_mask_blur != 0:
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k = 2 * config.sd_mask_blur + 1
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mask = cv2.GaussianBlur(mask, (k, k), 0)[:, :, np.newaxis]
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return image, mask
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def forward_post_process(self, result, image, mask, config):
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if config.sd_match_histograms:
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result = self._match_histograms(result, image[:, :, ::-1], mask)
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@ -17,14 +17,6 @@ from lama_cleaner.model.helper.cpu_text_encoder import CPUTextEncoderWrapper
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from lama_cleaner.model.utils import get_scheduler
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from lama_cleaner.schema import Config, ModelInfo, ModelType
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# 为了兼容性
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controlnet_name_map = {
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"control_v11p_sd15_canny": "lllyasviel/control_v11p_sd15_canny",
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"control_v11p_sd15_openpose": "lllyasviel/control_v11p_sd15_openpose",
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"control_v11p_sd15_inpaint": "lllyasviel/control_v11p_sd15_inpaint",
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"control_v11f1p_sd15_depth": "lllyasviel/control_v11f1p_sd15_depth",
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}
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class ControlNet(DiffusionInpaintModel):
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name = "controlnet"
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@ -49,9 +41,6 @@ class ControlNet(DiffusionInpaintModel):
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fp16 = not kwargs.get("no_half", False)
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model_info: ModelInfo = kwargs["model_info"]
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sd_controlnet_method = kwargs["sd_controlnet_method"]
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sd_controlnet_method = controlnet_name_map.get(
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sd_controlnet_method, sd_controlnet_method
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)
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self.model_info = model_info
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self.sd_controlnet_method = sd_controlnet_method
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@ -113,12 +102,6 @@ class ControlNet(DiffusionInpaintModel):
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**model_kwargs,
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)
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# https://huggingface.co/docs/diffusers/v0.7.0/en/api/pipelines/stable_diffusion#diffusers.StableDiffusionInpaintPipeline.enable_attention_slicing
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self.model.enable_attention_slicing()
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# https://huggingface.co/docs/diffusers/v0.7.0/en/optimization/fp16#memory-efficient-attention
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if kwargs.get("enable_xformers", False):
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self.model.enable_xformers_memory_efficient_attention()
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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)
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@ -162,10 +145,6 @@ class ControlNet(DiffusionInpaintModel):
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scheduler = get_scheduler(config.sd_sampler, scheduler_config)
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self.model.scheduler = scheduler
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if config.sd_mask_blur != 0:
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k = 2 * config.sd_mask_blur + 1
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mask = cv2.GaussianBlur(mask, (k, k), 0)[:, :, np.newaxis]
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img_h, img_w = image.shape[:2]
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control_image = self._get_control_image(image, mask)
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mask_image = PIL.Image.fromarray(mask[:, :, -1], mode="L")
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@ -190,8 +169,3 @@ class ControlNet(DiffusionInpaintModel):
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output = (output * 255).round().astype("uint8")
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output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR)
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return output
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@staticmethod
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def is_downloaded() -> bool:
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# model will be downloaded when app start, and can't switch in frontend settings
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return True
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@ -31,30 +31,15 @@ class InstructPix2Pix(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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self.model = StableDiffusionInstructPix2PixPipeline.from_pretrained(
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"timbrooks/instruct-pix2pix",
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revision="fp16" if use_gpu and fp16 else "main",
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torch_dtype=torch_dtype,
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**model_kwargs
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self.name, variant="fp16", torch_dtype=torch_dtype, **model_kwargs
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)
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self.model.enable_attention_slicing()
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if kwargs.get("enable_xformers", False):
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self.model.enable_xformers_memory_efficient_attention()
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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)
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else:
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self.model = self.model.to(device)
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@staticmethod
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def download():
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from diffusers import StableDiffusionInstructPix2PixPipeline
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StableDiffusionInstructPix2PixPipeline.from_pretrained(
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"timbrooks/instruct-pix2pix", revision="fp16"
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)
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def forward(self, image, mask, config: Config):
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"""Input image and output image have same size
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image: [H, W, C] RGB
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@ -76,8 +61,3 @@ class InstructPix2Pix(DiffusionInpaintModel):
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output = (output * 255).round().astype("uint8")
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output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR)
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return output
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@staticmethod
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def is_downloaded() -> bool:
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# model will be downloaded when app start, and can't switch in frontend settings
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return True
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@ -24,7 +24,7 @@ class Kandinsky(DiffusionInpaintModel):
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}
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self.model = AutoPipelineForInpainting.from_pretrained(
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self.model_name, **model_kwargs
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self.model_id_or_path, **model_kwargs
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).to(device)
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self.callback = kwargs.pop("callback", None)
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@ -40,9 +40,6 @@ class Kandinsky(DiffusionInpaintModel):
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self.model.scheduler = scheduler
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generator = torch.manual_seed(config.sd_seed)
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if config.sd_mask_blur != 0:
|
||||
k = 2 * config.sd_mask_blur + 1
|
||||
mask = cv2.GaussianBlur(mask, (k, k), 0)[:, :, np.newaxis]
|
||||
mask = mask.astype(np.float32) / 255
|
||||
img_h, img_w = image.shape[:2]
|
||||
|
||||
@ -66,20 +63,7 @@ class Kandinsky(DiffusionInpaintModel):
|
||||
output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR)
|
||||
return output
|
||||
|
||||
@staticmethod
|
||||
def is_downloaded() -> bool:
|
||||
# model will be downloaded when app start, and can't switch in frontend settings
|
||||
return True
|
||||
|
||||
|
||||
class Kandinsky22(Kandinsky):
|
||||
name = "kandinsky-community/kandinsky-2-2-decoder-inpaint"
|
||||
model_name = "kandinsky-community/kandinsky-2-2-decoder-inpaint"
|
||||
|
||||
@staticmethod
|
||||
def download():
|
||||
from diffusers import AutoPipelineForInpainting
|
||||
|
||||
AutoPipelineForInpainting.from_pretrained(
|
||||
"kandinsky-community/kandinsky-2-2-decoder-inpaint"
|
||||
)
|
||||
name = "kandinsky2.2"
|
||||
model_id_or_path = "kandinsky-community/kandinsky-2-2-decoder-inpaint"
|
||||
|
||||
@ -31,10 +31,6 @@ class PaintByExample(DiffusionInpaintModel):
|
||||
"Fantasy-Studio/Paint-by-Example", torch_dtype=torch_dtype, **model_kwargs
|
||||
)
|
||||
|
||||
self.model.enable_attention_slicing()
|
||||
if kwargs.get("enable_xformers", False):
|
||||
self.model.enable_xformers_memory_efficient_attention()
|
||||
|
||||
# TODO: gpu_id
|
||||
if kwargs.get("cpu_offload", False) and use_gpu:
|
||||
self.model.image_encoder = self.model.image_encoder.to(device)
|
||||
@ -68,8 +64,3 @@ class PaintByExample(DiffusionInpaintModel):
|
||||
output = (output * 255).round().astype("uint8")
|
||||
output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR)
|
||||
return output
|
||||
|
||||
@staticmethod
|
||||
def is_downloaded() -> bool:
|
||||
# model will be downloaded when app start, and can't switch in frontend settings
|
||||
return True
|
||||
|
||||
@ -1,8 +1,5 @@
|
||||
import os
|
||||
|
||||
import PIL.Image
|
||||
import cv2
|
||||
import numpy as np
|
||||
import torch
|
||||
from loguru import logger
|
||||
|
||||
@ -49,23 +46,12 @@ class SD(DiffusionInpaintModel):
|
||||
self.model = StableDiffusionInpaintPipeline.from_pretrained(
|
||||
self.model_id_or_path,
|
||||
revision="fp16"
|
||||
if (
|
||||
self.model_id_or_path in DIFFUSERS_MODEL_FP16_REVERSION
|
||||
and use_gpu
|
||||
and fp16
|
||||
)
|
||||
if self.model_id_or_path in DIFFUSERS_MODEL_FP16_REVERSION
|
||||
else "main",
|
||||
torch_dtype=torch_dtype,
|
||||
use_auth_token=kwargs["hf_access_token"],
|
||||
**model_kwargs,
|
||||
)
|
||||
|
||||
# https://huggingface.co/docs/diffusers/v0.7.0/en/api/pipelines/stable_diffusion#diffusers.StableDiffusionInpaintPipeline.enable_attention_slicing
|
||||
self.model.enable_attention_slicing()
|
||||
# https://huggingface.co/docs/diffusers/v0.7.0/en/optimization/fp16#memory-efficient-attention
|
||||
if kwargs.get("enable_xformers", False):
|
||||
self.model.enable_xformers_memory_efficient_attention()
|
||||
|
||||
if kwargs.get("cpu_offload", False) and use_gpu:
|
||||
# TODO: gpu_id
|
||||
logger.info("Enable sequential cpu offload")
|
||||
@ -88,10 +74,6 @@ class SD(DiffusionInpaintModel):
|
||||
"""
|
||||
self.set_scheduler(config)
|
||||
|
||||
if config.sd_mask_blur != 0:
|
||||
k = 2 * config.sd_mask_blur + 1
|
||||
mask = cv2.GaussianBlur(mask, (k, k), 0)[:, :, np.newaxis]
|
||||
|
||||
img_h, img_w = image.shape[:2]
|
||||
|
||||
output = self.model(
|
||||
@ -114,17 +96,6 @@ class SD(DiffusionInpaintModel):
|
||||
output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR)
|
||||
return output
|
||||
|
||||
@staticmethod
|
||||
def is_downloaded() -> bool:
|
||||
# model will be downloaded when app start, and can't switch in frontend settings
|
||||
return True
|
||||
|
||||
@classmethod
|
||||
def download(cls):
|
||||
from diffusers import StableDiffusionInpaintPipeline
|
||||
|
||||
StableDiffusionInpaintPipeline.from_pretrained(cls.model_id_or_path)
|
||||
|
||||
|
||||
class SD15(SD):
|
||||
name = "sd1.5"
|
||||
|
||||
@ -45,16 +45,9 @@ class SDXL(DiffusionInpaintModel):
|
||||
self.model_id_or_path,
|
||||
revision="main",
|
||||
torch_dtype=torch_dtype,
|
||||
use_auth_token=kwargs["hf_access_token"],
|
||||
vae=vae,
|
||||
)
|
||||
|
||||
# https://huggingface.co/docs/diffusers/v0.7.0/en/api/pipelines/stable_diffusion#diffusers.StableDiffusionInpaintPipeline.enable_attention_slicing
|
||||
self.model.enable_attention_slicing()
|
||||
# https://huggingface.co/docs/diffusers/v0.7.0/en/optimization/fp16#memory-efficient-attention
|
||||
if kwargs.get("enable_xformers", False):
|
||||
self.model.enable_xformers_memory_efficient_attention()
|
||||
|
||||
if kwargs.get("cpu_offload", False) and use_gpu:
|
||||
logger.info("Enable sequential cpu offload")
|
||||
self.model.enable_sequential_cpu_offload(gpu_id=0)
|
||||
@ -65,14 +58,6 @@ class SDXL(DiffusionInpaintModel):
|
||||
|
||||
self.callback = kwargs.pop("callback", None)
|
||||
|
||||
@staticmethod
|
||||
def download():
|
||||
from diffusers import AutoPipelineForInpainting
|
||||
|
||||
AutoPipelineForInpainting.from_pretrained(
|
||||
"diffusers/stable-diffusion-xl-1.0-inpainting-0.1"
|
||||
)
|
||||
|
||||
def forward(self, image, mask, config: Config):
|
||||
"""Input image and output image have same size
|
||||
image: [H, W, C] RGB
|
||||
@ -81,10 +66,6 @@ class SDXL(DiffusionInpaintModel):
|
||||
"""
|
||||
self.set_scheduler(config)
|
||||
|
||||
if config.sd_mask_blur != 0:
|
||||
k = 2 * config.sd_mask_blur + 1
|
||||
mask = cv2.GaussianBlur(mask, (k, k), 0)[:, :, np.newaxis]
|
||||
|
||||
img_h, img_w = image.shape[:2]
|
||||
|
||||
output = self.model(
|
||||
@ -106,8 +87,3 @@ class SDXL(DiffusionInpaintModel):
|
||||
output = (output * 255).round().astype("uint8")
|
||||
output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR)
|
||||
return output
|
||||
|
||||
@staticmethod
|
||||
def is_downloaded() -> bool:
|
||||
# model will be downloaded when app start, and can't switch in frontend settings
|
||||
return True
|
||||
|
||||
@ -3,7 +3,6 @@ from typing import List, Dict
|
||||
import torch
|
||||
from loguru import logger
|
||||
|
||||
from lama_cleaner.const import DEFAULT_SD_CONTROLNET_METHOD
|
||||
from lama_cleaner.download import scan_models
|
||||
from lama_cleaner.helper import switch_mps_device
|
||||
from lama_cleaner.model import models, ControlNet, SD, SDXL
|
||||
@ -19,16 +18,25 @@ class ModelManager:
|
||||
self.available_models: Dict[str, ModelInfo] = {}
|
||||
self.scan_models()
|
||||
|
||||
self.sd_controlnet = kwargs.get("sd_controlnet", False)
|
||||
self.sd_controlnet_method = kwargs.get(
|
||||
"sd_controlnet_method", DEFAULT_SD_CONTROLNET_METHOD
|
||||
)
|
||||
self.sd_controlnet = False
|
||||
self.sd_controlnet_method = ""
|
||||
self.model = self.init_model(name, device, **kwargs)
|
||||
|
||||
def init_model(self, name: str, device, **kwargs):
|
||||
def _map_old_name(self, name: str) -> str:
|
||||
for old_name, model_cls in models.items():
|
||||
if name == old_name and hasattr(model_cls, "model_id_or_path"):
|
||||
name = model_cls.model_id_or_path
|
||||
break
|
||||
return name
|
||||
|
||||
@property
|
||||
def current_model(self) -> Dict:
|
||||
name = self._map_old_name(self.name)
|
||||
return self.available_models[name].model_dump()
|
||||
|
||||
def init_model(self, name: str, device, **kwargs):
|
||||
name = self._map_old_name(name)
|
||||
logger.info(f"Loading model: {name}")
|
||||
if name not in self.available_models:
|
||||
raise NotImplementedError(f"Unsupported model: {name}")
|
||||
|
||||
@ -86,6 +94,7 @@ class ModelManager:
|
||||
):
|
||||
self.sd_controlnet_method = self.available_models[new_name].controlnets[0]
|
||||
try:
|
||||
# TODO: enable/disable controlnet without reload model
|
||||
del self.model
|
||||
torch_gc()
|
||||
|
||||
|
||||
@ -55,7 +55,7 @@ def parse_args():
|
||||
parser.add_argument("--cpu-offload", action="store_true", help=CPU_OFFLOAD_HELP)
|
||||
parser.add_argument("--disable-nsfw", action="store_true", help=DISABLE_NSFW_HELP)
|
||||
parser.add_argument(
|
||||
"--sd-cpu-textencoder", action="store_true", help=SD_CPU_TEXTENCODER_HELP
|
||||
"--sd-cpu-textencoder", action="store_true", help=CPU_TEXTENCODER_HELP
|
||||
)
|
||||
parser.add_argument("--sd-controlnet", action="store_true", help=SD_CONTROLNET_HELP)
|
||||
parser.add_argument(
|
||||
@ -66,16 +66,10 @@ def parse_args():
|
||||
parser.add_argument(
|
||||
"--local-files-only", action="store_true", help=LOCAL_FILES_ONLY_HELP
|
||||
)
|
||||
parser.add_argument(
|
||||
"--enable-xformers", action="store_true", help=ENABLE_XFORMERS_HELP
|
||||
)
|
||||
parser.add_argument(
|
||||
"--device", default=DEFAULT_DEVICE, type=str, choices=AVAILABLE_DEVICES
|
||||
)
|
||||
parser.add_argument("--gui", action="store_true", help=GUI_HELP)
|
||||
parser.add_argument(
|
||||
"--no-gui-auto-close", action="store_true", help=NO_GUI_AUTO_CLOSE_HELP
|
||||
)
|
||||
parser.add_argument(
|
||||
"--gui-size",
|
||||
default=[1600, 1000],
|
||||
|
||||
@ -22,7 +22,7 @@ SEGMENT_ANYTHING_MODELS = {
|
||||
"url": "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth",
|
||||
"md5": "4b8939a88964f0f4ff5f5b2642c598a6",
|
||||
},
|
||||
"vit_t": {
|
||||
"mobile_sam": {
|
||||
"url": "https://github.com/Sanster/models/releases/download/MobileSAM/mobile_sam.pt",
|
||||
"md5": "f3c0d8cda613564d499310dab6c812cd",
|
||||
},
|
||||
|
||||
@ -3,7 +3,7 @@ from enum import Enum
|
||||
import cv2
|
||||
from loguru import logger
|
||||
|
||||
from lama_cleaner.const import RealESRGANModelName
|
||||
from lama_cleaner.const import RealESRGANModel
|
||||
from lama_cleaner.helper import download_model
|
||||
from lama_cleaner.plugins.base_plugin import BasePlugin
|
||||
|
||||
@ -18,7 +18,7 @@ class RealESRGANUpscaler(BasePlugin):
|
||||
from realesrgan.archs.srvgg_arch import SRVGGNetCompact
|
||||
|
||||
REAL_ESRGAN_MODELS = {
|
||||
RealESRGANModelName.realesr_general_x4v3: {
|
||||
RealESRGANModel.realesr_general_x4v3: {
|
||||
"url": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth",
|
||||
"scale": 4,
|
||||
"model": lambda: SRVGGNetCompact(
|
||||
@ -31,7 +31,7 @@ class RealESRGANUpscaler(BasePlugin):
|
||||
),
|
||||
"model_md5": "91a7644643c884ee00737db24e478156",
|
||||
},
|
||||
RealESRGANModelName.RealESRGAN_x4plus: {
|
||||
RealESRGANModel.RealESRGAN_x4plus: {
|
||||
"url": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth",
|
||||
"scale": 4,
|
||||
"model": lambda: RRDBNet(
|
||||
@ -44,7 +44,7 @@ class RealESRGANUpscaler(BasePlugin):
|
||||
),
|
||||
"model_md5": "99ec365d4afad750833258a1a24f44ca",
|
||||
},
|
||||
RealESRGANModelName.RealESRGAN_x4plus_anime_6B: {
|
||||
RealESRGANModel.RealESRGAN_x4plus_anime_6B: {
|
||||
"url": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth",
|
||||
"scale": 4,
|
||||
"model": lambda: RRDBNet(
|
||||
|
||||
@ -109,7 +109,7 @@ sam_model_registry = {
|
||||
"vit_h": build_sam,
|
||||
"vit_l": build_sam_vit_l,
|
||||
"vit_b": build_sam_vit_b,
|
||||
"vit_t": build_sam_vit_t,
|
||||
"mobile_sam": build_sam_vit_t,
|
||||
}
|
||||
|
||||
|
||||
|
||||
@ -1,10 +1,16 @@
|
||||
# https://github.com/huggingface/huggingface_hub/blob/5a12851f54bf614be39614034ed3a9031922d297/src/huggingface_hub/utils/_runtime.py
|
||||
import os
|
||||
import platform
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
import packaging.version
|
||||
from loguru import logger
|
||||
from rich import print
|
||||
from typing import Dict, Any
|
||||
|
||||
from lama_cleaner.const import Device
|
||||
|
||||
_PY_VERSION: str = sys.version.split()[0].rstrip("+")
|
||||
|
||||
if packaging.version.Version(_PY_VERSION) < packaging.version.Version("3.8.0"):
|
||||
@ -21,7 +27,6 @@ _CANDIDATES = [
|
||||
"diffusers",
|
||||
"transformers",
|
||||
"opencv-python",
|
||||
"xformers",
|
||||
"accelerate",
|
||||
"lama-cleaner",
|
||||
"rembg",
|
||||
@ -38,7 +43,7 @@ for name in _CANDIDATES:
|
||||
|
||||
|
||||
def dump_environment_info() -> Dict[str, str]:
|
||||
"""Dump information about the machine to help debugging issues. """
|
||||
"""Dump information about the machine to help debugging issues."""
|
||||
|
||||
# Generic machine info
|
||||
info: Dict[str, Any] = {
|
||||
@ -48,3 +53,34 @@ def dump_environment_info() -> Dict[str, str]:
|
||||
info.update(_package_versions)
|
||||
print("\n".join([f"- {prop}: {val}" for prop, val in info.items()]) + "\n")
|
||||
return info
|
||||
|
||||
|
||||
def check_device(device: Device) -> Device:
|
||||
if device == Device.cuda:
|
||||
import platform
|
||||
|
||||
if platform.system() == "Darwin":
|
||||
logger.warning("MacOS does not support cuda, use cpu instead")
|
||||
return Device.cpu
|
||||
else:
|
||||
import torch
|
||||
|
||||
if not torch.cuda.is_available():
|
||||
logger.warning("CUDA is not available, use cpu instead")
|
||||
return Device.cpu
|
||||
elif device == Device.mps:
|
||||
import torch
|
||||
|
||||
if not torch.backends.mps.is_available():
|
||||
logger.warning("mps is not available, use cpu instead")
|
||||
return Device.cpu
|
||||
return device
|
||||
|
||||
|
||||
def setup_model_dir(model_dir: Path):
|
||||
model_dir = model_dir.expanduser().absolute()
|
||||
os.environ["U2NET_HOME"] = str(model_dir)
|
||||
os.environ["XDG_CACHE_HOME"] = str(model_dir)
|
||||
if not model_dir.exists():
|
||||
logger.info(f"Create model directory: {model_dir}")
|
||||
model_dir.mkdir(exist_ok=True, parents=True)
|
||||
|
||||
@ -1,10 +1,18 @@
|
||||
#!/usr/bin/env python3
|
||||
import json
|
||||
import os
|
||||
import hashlib
|
||||
import traceback
|
||||
|
||||
import typer
|
||||
from typer import Option
|
||||
|
||||
from lama_cleaner.download import cli_download_model, scan_models
|
||||
from lama_cleaner.runtime import setup_model_dir, dump_environment_info, check_device
|
||||
|
||||
os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
|
||||
import hashlib
|
||||
import traceback
|
||||
from dataclasses import dataclass
|
||||
|
||||
|
||||
import imghdr
|
||||
import io
|
||||
@ -20,12 +28,7 @@ import torch
|
||||
from PIL import Image
|
||||
from loguru import logger
|
||||
|
||||
from lama_cleaner.const import (
|
||||
SD15_MODELS,
|
||||
SD_CONTROLNET_CHOICES,
|
||||
SDXL_CONTROLNET_CHOICES,
|
||||
SD2_CONTROLNET_CHOICES,
|
||||
)
|
||||
from lama_cleaner.const import *
|
||||
from lama_cleaner.file_manager import FileManager
|
||||
from lama_cleaner.model.utils import torch_gc
|
||||
from lama_cleaner.model_manager import ModelManager
|
||||
@ -39,6 +42,8 @@ from lama_cleaner.plugins import (
|
||||
)
|
||||
from lama_cleaner.schema import Config
|
||||
|
||||
typer_app = typer.Typer(pretty_exceptions_show_locals=False, add_completion=False)
|
||||
|
||||
try:
|
||||
torch._C._jit_override_can_fuse_on_cpu(False)
|
||||
torch._C._jit_override_can_fuse_on_gpu(False)
|
||||
@ -103,23 +108,34 @@ logging.getLogger("werkzeug").addFilter(NoFlaskwebgui())
|
||||
|
||||
app = Flask(__name__, static_folder=os.path.join(BUILD_DIR, "static"))
|
||||
app.config["JSON_AS_ASCII"] = False
|
||||
CORS(app, expose_headers=["Content-Disposition", "X-seed"])
|
||||
CORS(app, expose_headers=["Content-Disposition", "X-seed", "X-Height", "X-Width"])
|
||||
|
||||
sio_logger = logging.getLogger("sio-logger")
|
||||
sio_logger.setLevel(logging.ERROR)
|
||||
socketio = SocketIO(app, cors_allowed_origins="*", async_mode="threading")
|
||||
|
||||
model: ModelManager = None
|
||||
thumb: FileManager = None
|
||||
output_dir: str = None
|
||||
device = None
|
||||
input_image_path: str = None
|
||||
is_disable_model_switch: bool = False
|
||||
enable_file_manager: bool = False
|
||||
enable_auto_saving: bool = False
|
||||
is_desktop: bool = False
|
||||
image_quality: int = 95
|
||||
plugins = {}
|
||||
|
||||
@dataclass
|
||||
class GlobalConfig:
|
||||
model_manager: ModelManager = None
|
||||
file_manager: FileManager = None
|
||||
output_dir: Path = None
|
||||
input_image_path: Path = None
|
||||
disable_model_switch: bool = False
|
||||
is_desktop: bool = False
|
||||
image_quality: int = 95
|
||||
plugins = {}
|
||||
|
||||
@property
|
||||
def enable_auto_saving(self) -> bool:
|
||||
return self.output_dir is not None
|
||||
|
||||
@property
|
||||
def enable_file_manager(self) -> bool:
|
||||
return self.file_manager is not None
|
||||
|
||||
|
||||
global_config = GlobalConfig()
|
||||
|
||||
|
||||
def get_image_ext(img_bytes):
|
||||
@ -135,7 +151,7 @@ def diffuser_callback(i, t, latents):
|
||||
|
||||
@app.route("/save_image", methods=["POST"])
|
||||
def save_image():
|
||||
if output_dir is None:
|
||||
if global_config.output_dir is None:
|
||||
return "--output-dir is None", 500
|
||||
|
||||
input = request.files
|
||||
@ -143,7 +159,7 @@ def save_image():
|
||||
origin_image_bytes = input["image"].read() # RGB
|
||||
ext = get_image_ext(origin_image_bytes)
|
||||
image, alpha_channel, exif_infos = load_img(origin_image_bytes, return_exif=True)
|
||||
save_path = os.path.join(output_dir, filename)
|
||||
save_path = str(global_config.output_dir / filename)
|
||||
|
||||
if alpha_channel is not None:
|
||||
if alpha_channel.shape[:2] != image.shape[:2]:
|
||||
@ -157,7 +173,7 @@ def save_image():
|
||||
img_bytes = pil_to_bytes(
|
||||
pil_image,
|
||||
ext,
|
||||
quality=image_quality,
|
||||
quality=global_config.image_quality,
|
||||
exif_infos=exif_infos,
|
||||
)
|
||||
with open(save_path, "wb") as fw:
|
||||
@ -169,9 +185,11 @@ def save_image():
|
||||
@app.route("/medias/<tab>")
|
||||
def medias(tab):
|
||||
if tab == "image":
|
||||
response = make_response(jsonify(thumb.media_names), 200)
|
||||
response = make_response(jsonify(global_config.file_manager.media_names), 200)
|
||||
else:
|
||||
response = make_response(jsonify(thumb.output_media_names), 200)
|
||||
response = make_response(
|
||||
jsonify(global_config.file_manager.output_media_names), 200
|
||||
)
|
||||
# response.last_modified = thumb.modified_time[tab]
|
||||
# response.cache_control.no_cache = True
|
||||
# response.cache_control.max_age = 0
|
||||
@ -182,8 +200,8 @@ def medias(tab):
|
||||
@app.route("/media/<tab>/<filename>")
|
||||
def media_file(tab, filename):
|
||||
if tab == "image":
|
||||
return send_from_directory(thumb.root_directory, filename)
|
||||
return send_from_directory(thumb.output_dir, filename)
|
||||
return send_from_directory(global_config.file_manager.root_directory, filename)
|
||||
return send_from_directory(global_config.file_manager.output_dir, filename)
|
||||
|
||||
|
||||
@app.route("/media_thumbnail/<tab>/<filename>")
|
||||
@ -198,10 +216,10 @@ def media_thumbnail_file(tab, filename):
|
||||
if height:
|
||||
height = int(float(height))
|
||||
|
||||
directory = thumb.root_directory
|
||||
directory = global_config.file_manager.root_directory
|
||||
if tab == "output":
|
||||
directory = thumb.output_dir
|
||||
thumb_filename, (width, height) = thumb.get_thumbnail(
|
||||
directory = global_config.file_manager.output_dir
|
||||
thumb_filename, (width, height) = global_config.file_manager.get_thumbnail(
|
||||
directory, filename, width, height
|
||||
)
|
||||
thumb_filepath = f"{app.config['THUMBNAIL_MEDIA_THUMBNAIL_ROOT']}{thumb_filename}"
|
||||
@ -257,13 +275,11 @@ def process():
|
||||
croper_y=form["croperY"],
|
||||
croper_height=form["croperHeight"],
|
||||
croper_width=form["croperWidth"],
|
||||
|
||||
use_extender=form["useExtender"],
|
||||
extender_x=form["extenderX"],
|
||||
extender_y=form["extenderY"],
|
||||
extender_height=form["extenderHeight"],
|
||||
extender_width=form["extenderWidth"],
|
||||
|
||||
sd_scale=form["sdScale"],
|
||||
sd_mask_blur=form["sdMaskBlur"],
|
||||
sd_strength=form["sdStrength"],
|
||||
@ -294,7 +310,7 @@ def process():
|
||||
|
||||
start = time.time()
|
||||
try:
|
||||
res_np_img = model(image, mask, config)
|
||||
res_np_img = global_config.model_manager(image, mask, config)
|
||||
except RuntimeError as e:
|
||||
if "CUDA out of memory. " in str(e):
|
||||
# NOTE: the string may change?
|
||||
@ -322,7 +338,7 @@ def process():
|
||||
pil_to_bytes(
|
||||
Image.fromarray(res_np_img),
|
||||
ext,
|
||||
quality=image_quality,
|
||||
quality=global_config.image_quality,
|
||||
exif_infos=exif_infos,
|
||||
)
|
||||
)
|
||||
@ -345,7 +361,7 @@ def run_plugin():
|
||||
form = request.form
|
||||
files = request.files
|
||||
name = form["name"]
|
||||
if name not in plugins:
|
||||
if name not in global_config.plugins:
|
||||
return "Plugin not found", 500
|
||||
|
||||
origin_image_bytes = files["image"].read() # RGB
|
||||
@ -359,7 +375,7 @@ def run_plugin():
|
||||
if name == InteractiveSeg.name:
|
||||
img_md5 = hashlib.md5(origin_image_bytes).hexdigest()
|
||||
form["img_md5"] = img_md5
|
||||
bgr_res = plugins[name](rgb_np_img, files, form)
|
||||
bgr_res = global_config.plugins[name](rgb_np_img, files, form)
|
||||
except RuntimeError as e:
|
||||
torch.cuda.empty_cache()
|
||||
if "CUDA out of memory. " in str(e):
|
||||
@ -401,7 +417,7 @@ def run_plugin():
|
||||
pil_to_bytes(
|
||||
Image.fromarray(rgb_res),
|
||||
ext,
|
||||
quality=image_quality,
|
||||
quality=global_config.image_quality,
|
||||
exif_infos=exif_infos,
|
||||
)
|
||||
),
|
||||
@ -414,41 +430,40 @@ def run_plugin():
|
||||
@app.route("/server_config", methods=["GET"])
|
||||
def get_server_config():
|
||||
return {
|
||||
"plugins": list(plugins.keys()),
|
||||
"enableFileManager": enable_file_manager,
|
||||
"enableAutoSaving": enable_auto_saving,
|
||||
"enableControlnet": model.sd_controlnet,
|
||||
"controlnetMethod": model.sd_controlnet_method,
|
||||
"disableModelSwitch": is_disable_model_switch,
|
||||
"plugins": list(global_config.plugins.keys()),
|
||||
"enableFileManager": global_config.enable_file_manager,
|
||||
"enableAutoSaving": global_config.enable_auto_saving,
|
||||
"enableControlnet": global_config.model_manager.sd_controlnet,
|
||||
"controlnetMethod": global_config.model_manager.sd_controlnet_method,
|
||||
"disableModelSwitch": global_config.disable_model_switch,
|
||||
"isDesktop": global_config.is_desktop,
|
||||
}, 200
|
||||
|
||||
|
||||
@app.route("/models", methods=["GET"])
|
||||
def get_models():
|
||||
return [it.model_dump() for it in model.scan_models()]
|
||||
return [it.model_dump() for it in global_config.model_manager.scan_models()]
|
||||
|
||||
|
||||
@app.route("/model")
|
||||
def current_model():
|
||||
return model.available_models[model.name].model_dump(), 200
|
||||
|
||||
|
||||
@app.route("/is_desktop")
|
||||
def get_is_desktop():
|
||||
return str(is_desktop), 200
|
||||
return (
|
||||
global_config.model_manager.current_model,
|
||||
200,
|
||||
)
|
||||
|
||||
|
||||
@app.route("/model", methods=["POST"])
|
||||
def switch_model():
|
||||
if is_disable_model_switch:
|
||||
if global_config.disable_model_switch:
|
||||
return "Switch model is disabled", 400
|
||||
|
||||
new_name = request.form.get("name")
|
||||
if new_name == model.name:
|
||||
if new_name == global_config.model_manager.name:
|
||||
return "Same model", 200
|
||||
|
||||
try:
|
||||
model.switch(new_name)
|
||||
global_config.model_manager.switch(new_name)
|
||||
except Exception as e:
|
||||
traceback.print_exc()
|
||||
error_message = f"{type(e).__name__} - {str(e)}"
|
||||
@ -464,160 +479,230 @@ def index():
|
||||
|
||||
@app.route("/inputimage")
|
||||
def get_cli_input_image():
|
||||
if input_image_path:
|
||||
with open(input_image_path, "rb") as f:
|
||||
if global_config.input_image_path:
|
||||
with open(global_config.input_image_path, "rb") as f:
|
||||
image_in_bytes = f.read()
|
||||
return send_file(
|
||||
input_image_path,
|
||||
global_config.input_image_path,
|
||||
as_attachment=True,
|
||||
download_name=Path(input_image_path).name,
|
||||
download_name=Path(global_config.input_image_path).name,
|
||||
mimetype=f"image/{get_image_ext(image_in_bytes)}",
|
||||
)
|
||||
else:
|
||||
return "No Input Image"
|
||||
|
||||
|
||||
def build_plugins(args):
|
||||
global plugins
|
||||
if args.enable_interactive_seg:
|
||||
def build_plugins(
|
||||
enable_interactive_seg: bool,
|
||||
interactive_seg_model: InteractiveSegModel,
|
||||
interactive_seg_device: Device,
|
||||
enable_remove_bg: bool,
|
||||
enable_anime_seg: bool,
|
||||
enable_realesrgan: bool,
|
||||
realesrgan_device: Device,
|
||||
realesrgan_model: str,
|
||||
enable_gfpgan: bool,
|
||||
gfpgan_device: Device,
|
||||
enable_restoreformer: bool,
|
||||
restoreformer_device: Device,
|
||||
no_half: bool,
|
||||
):
|
||||
if enable_interactive_seg:
|
||||
logger.info(f"Initialize {InteractiveSeg.name} plugin")
|
||||
plugins[InteractiveSeg.name] = InteractiveSeg(
|
||||
args.interactive_seg_model, args.interactive_seg_device
|
||||
global_config.plugins[InteractiveSeg.name] = InteractiveSeg(
|
||||
interactive_seg_model, interactive_seg_device
|
||||
)
|
||||
|
||||
if args.enable_remove_bg:
|
||||
if enable_remove_bg:
|
||||
logger.info(f"Initialize {RemoveBG.name} plugin")
|
||||
plugins[RemoveBG.name] = RemoveBG()
|
||||
global_config.plugins[RemoveBG.name] = RemoveBG()
|
||||
|
||||
if args.enable_anime_seg:
|
||||
if enable_anime_seg:
|
||||
logger.info(f"Initialize {AnimeSeg.name} plugin")
|
||||
plugins[AnimeSeg.name] = AnimeSeg()
|
||||
global_config.plugins[AnimeSeg.name] = AnimeSeg()
|
||||
|
||||
if args.enable_realesrgan:
|
||||
if enable_realesrgan:
|
||||
logger.info(
|
||||
f"Initialize {RealESRGANUpscaler.name} plugin: {args.realesrgan_model}, {args.realesrgan_device}"
|
||||
f"Initialize {RealESRGANUpscaler.name} plugin: {realesrgan_model}, {realesrgan_device}"
|
||||
)
|
||||
plugins[RealESRGANUpscaler.name] = RealESRGANUpscaler(
|
||||
args.realesrgan_model,
|
||||
args.realesrgan_device,
|
||||
no_half=args.realesrgan_no_half,
|
||||
global_config.plugins[RealESRGANUpscaler.name] = RealESRGANUpscaler(
|
||||
realesrgan_model,
|
||||
realesrgan_device,
|
||||
no_half=no_half,
|
||||
)
|
||||
|
||||
if args.enable_gfpgan:
|
||||
if enable_gfpgan:
|
||||
logger.info(f"Initialize {GFPGANPlugin.name} plugin")
|
||||
if args.enable_realesrgan:
|
||||
if enable_realesrgan:
|
||||
logger.info("Use realesrgan as GFPGAN background upscaler")
|
||||
else:
|
||||
logger.info(
|
||||
f"GFPGAN no background upscaler, use --enable-realesrgan to enable it"
|
||||
)
|
||||
plugins[GFPGANPlugin.name] = GFPGANPlugin(
|
||||
args.gfpgan_device, upscaler=plugins.get(RealESRGANUpscaler.name, None)
|
||||
global_config.plugins[GFPGANPlugin.name] = GFPGANPlugin(
|
||||
gfpgan_device,
|
||||
upscaler=global_config.plugins.get(RealESRGANUpscaler.name, None),
|
||||
)
|
||||
|
||||
if args.enable_restoreformer:
|
||||
if enable_restoreformer:
|
||||
logger.info(f"Initialize {RestoreFormerPlugin.name} plugin")
|
||||
plugins[RestoreFormerPlugin.name] = RestoreFormerPlugin(
|
||||
args.restoreformer_device,
|
||||
upscaler=plugins.get(RealESRGANUpscaler.name, None),
|
||||
global_config.plugins[RestoreFormerPlugin.name] = RestoreFormerPlugin(
|
||||
restoreformer_device,
|
||||
upscaler=global_config.plugins.get(RealESRGANUpscaler.name, None),
|
||||
)
|
||||
|
||||
def main(args):
|
||||
global model
|
||||
global device
|
||||
global input_image_path
|
||||
global is_disable_model_switch
|
||||
global enable_file_manager
|
||||
global is_desktop
|
||||
global thumb
|
||||
global output_dir
|
||||
global image_quality
|
||||
global enable_auto_saving
|
||||
|
||||
build_plugins(args)
|
||||
@typer_app.command(help="Install all plugins dependencies")
|
||||
def install_plugins_packages():
|
||||
from lama_cleaner.installer import install_plugins_package
|
||||
|
||||
image_quality = args.quality
|
||||
output_dir = args.output_dir
|
||||
install_plugins_package()
|
||||
|
||||
|
||||
@typer_app.command(help="Download SD/SDXL normal/inpainting model from HuggingFace")
|
||||
def download(
|
||||
model: str = Option(
|
||||
..., help="Model id on HuggingFace e.g: runwayml/stable-diffusion-inpainting"
|
||||
),
|
||||
model_dir: Path = Option(DEFAULT_MODEL_DIR, help=MODEL_DIR_HELP, file_okay=False),
|
||||
):
|
||||
cli_download_model(model, model_dir)
|
||||
|
||||
|
||||
@typer_app.command(help="List downloaded models")
|
||||
def list_model(
|
||||
model_dir: Path = Option(DEFAULT_MODEL_DIR, help=MODEL_DIR_HELP, file_okay=False),
|
||||
):
|
||||
setup_model_dir(model_dir)
|
||||
scanned_models = scan_models()
|
||||
for it in scanned_models:
|
||||
print(it.name)
|
||||
|
||||
|
||||
@typer_app.command(help="Start lama cleaner server")
|
||||
def start(
|
||||
host: str = Option("127.0.0.1"),
|
||||
port: int = Option(8080),
|
||||
model: str = Option(
|
||||
DEFAULT_MODEL,
|
||||
help=f"Available models: [{', '.join(AVAILABLE_MODELS)}]. "
|
||||
f"You can use download command to download other SD/SDXL normal/inpainting models on huggingface",
|
||||
),
|
||||
model_dir: Path = Option(
|
||||
DEFAULT_MODEL_DIR, help=MODEL_DIR_HELP, dir_okay=True, file_okay=False
|
||||
),
|
||||
no_half: bool = Option(False, help=NO_HALF_HELP),
|
||||
cpu_offload: bool = Option(False, help=CPU_OFFLOAD_HELP),
|
||||
disable_nsfw_checker: bool = Option(False, help=DISABLE_NSFW_HELP),
|
||||
cpu_textencoder: bool = Option(False, help=CPU_TEXTENCODER_HELP),
|
||||
local_files_only: bool = Option(False, help=LOCAL_FILES_ONLY_HELP),
|
||||
device: Device = Option(Device.cpu),
|
||||
gui: bool = Option(False, help=GUI_HELP),
|
||||
disable_model_switch: bool = Option(False),
|
||||
input: Path = Option(None, help=INPUT_HELP),
|
||||
output_dir: Path = Option(
|
||||
None, help=OUTPUT_DIR_HELP, dir_okay=True, file_okay=False
|
||||
),
|
||||
quality: int = Option(95, help=QUALITY_HELP),
|
||||
enable_interactive_seg: bool = Option(False, help=INTERACTIVE_SEG_HELP),
|
||||
interactive_seg_model: InteractiveSegModel = Option(
|
||||
InteractiveSegModel.vit_b, help=INTERACTIVE_SEG_MODEL_HELP
|
||||
),
|
||||
interactive_seg_device: Device = Option(Device.cpu),
|
||||
enable_remove_bg: bool = Option(False, help=REMOVE_BG_HELP),
|
||||
enable_anime_seg: bool = Option(False, help=ANIMESEG_HELP),
|
||||
enable_realesrgan: bool = Option(False),
|
||||
realesrgan_device: Device = Option(Device.cpu),
|
||||
realesrgan_model: str = Option(RealESRGANModel.realesr_general_x4v3),
|
||||
enable_gfpgan: bool = Option(False),
|
||||
gfpgan_device: Device = Option(Device.cpu),
|
||||
enable_restoreformer: bool = Option(False),
|
||||
restoreformer_device: Device = Option(Device.cpu),
|
||||
):
|
||||
global global_config
|
||||
dump_environment_info()
|
||||
|
||||
if input:
|
||||
if not input.exists():
|
||||
logger.error(f"invalid --input: {input} not exists")
|
||||
exit()
|
||||
if input.is_dir():
|
||||
logger.info(f"Initialize file manager")
|
||||
file_manager = FileManager(app)
|
||||
app.config["THUMBNAIL_MEDIA_ROOT"] = input
|
||||
app.config["THUMBNAIL_MEDIA_THUMBNAIL_ROOT"] = os.path.join(
|
||||
output_dir, "lama_cleaner_thumbnails"
|
||||
)
|
||||
file_manager.output_dir = output_dir
|
||||
else:
|
||||
global_config.input_image_path = input
|
||||
|
||||
device = check_device(device)
|
||||
setup_model_dir(model_dir)
|
||||
|
||||
if local_files_only:
|
||||
os.environ["TRANSFORMERS_OFFLINE"] = "1"
|
||||
os.environ["HF_HUB_OFFLINE"] = "1"
|
||||
|
||||
if model not in AVAILABLE_MODELS:
|
||||
scanned_models = scan_models()
|
||||
if model not in [it.name for it in scanned_models]:
|
||||
logger.error(
|
||||
f"invalid --model: {model} not exists. Available models: {AVAILABLE_MODELS} or {[it.name for it in scanned_models]}"
|
||||
)
|
||||
exit()
|
||||
|
||||
global_config.image_quality = quality
|
||||
global_config.disable_model_switch = disable_model_switch
|
||||
global_config.is_desktop = gui
|
||||
build_plugins(
|
||||
enable_interactive_seg,
|
||||
interactive_seg_model,
|
||||
interactive_seg_device,
|
||||
enable_remove_bg,
|
||||
enable_anime_seg,
|
||||
enable_realesrgan,
|
||||
realesrgan_device,
|
||||
realesrgan_model,
|
||||
enable_gfpgan,
|
||||
gfpgan_device,
|
||||
enable_restoreformer,
|
||||
restoreformer_device,
|
||||
no_half,
|
||||
)
|
||||
if output_dir:
|
||||
output_dir = os.path.abspath(output_dir)
|
||||
logger.info(f"Output dir: {output_dir}")
|
||||
enable_auto_saving = True
|
||||
output_dir = output_dir.expanduser().absolute()
|
||||
logger.info(f"Image will auto save to output dir: {output_dir}")
|
||||
global_config.output_dir = output_dir
|
||||
|
||||
device = torch.device(args.device)
|
||||
is_disable_model_switch = args.disable_model_switch
|
||||
is_desktop = args.gui
|
||||
if is_disable_model_switch:
|
||||
logger.info(
|
||||
f"Start with --disable-model-switch, model switch on frontend is disable"
|
||||
)
|
||||
|
||||
if args.input and os.path.isdir(args.input):
|
||||
logger.info(f"Initialize file manager")
|
||||
thumb = FileManager(app)
|
||||
enable_file_manager = True
|
||||
app.config["THUMBNAIL_MEDIA_ROOT"] = args.input
|
||||
app.config["THUMBNAIL_MEDIA_THUMBNAIL_ROOT"] = os.path.join(
|
||||
output_dir, "lama_cleaner_thumbnails"
|
||||
)
|
||||
thumb.output_dir = Path(output_dir)
|
||||
# thumb.start()
|
||||
# try:
|
||||
# while True:
|
||||
# time.sleep(1)
|
||||
# finally:
|
||||
# thumb.image_dir_observer.stop()
|
||||
# thumb.image_dir_observer.join()
|
||||
# thumb.output_dir_observer.stop()
|
||||
# thumb.output_dir_observer.join()
|
||||
|
||||
else:
|
||||
input_image_path = args.input
|
||||
|
||||
# 为了兼容性
|
||||
model_name_map = {
|
||||
"sd1.5": "runwayml/stable-diffusion-inpainting",
|
||||
"anything4": "Sanster/anything-4.0-inpainting",
|
||||
"realisticVision1.4": "Sanster/Realistic_Vision_V1.4-inpainting",
|
||||
"sd2": "stabilityai/stable-diffusion-2-inpainting",
|
||||
"sdxl": "diffusers/stable-diffusion-xl-1.0-inpainting-0.1",
|
||||
"kandinsky2.2": "kandinsky-community/kandinsky-2-2-decoder-inpaint",
|
||||
"paint_by_example": "Fantasy-Studio/Paint-by-Example",
|
||||
"instruct_pix2pix": "timbrooks/instruct-pix2pix",
|
||||
}
|
||||
|
||||
model = ModelManager(
|
||||
name=model_name_map.get(args.model, args.model),
|
||||
sd_controlnet=args.sd_controlnet,
|
||||
sd_controlnet_method=args.sd_controlnet_method,
|
||||
device=device,
|
||||
no_half=args.no_half,
|
||||
hf_access_token=args.hf_access_token,
|
||||
disable_nsfw=args.sd_disable_nsfw or args.disable_nsfw,
|
||||
sd_cpu_textencoder=args.sd_cpu_textencoder,
|
||||
cpu_offload=args.cpu_offload,
|
||||
enable_xformers=args.sd_enable_xformers or args.enable_xformers,
|
||||
global_config.model_manager = ModelManager(
|
||||
name=model,
|
||||
device=torch.device(device),
|
||||
no_half=no_half,
|
||||
disable_nsfw=disable_nsfw_checker,
|
||||
sd_cpu_textencoder=cpu_textencoder,
|
||||
cpu_offload=cpu_offload,
|
||||
callback=diffuser_callback,
|
||||
)
|
||||
|
||||
if args.gui:
|
||||
app_width, app_height = args.gui_size
|
||||
if gui:
|
||||
from flaskwebgui import FlaskUI
|
||||
|
||||
ui = FlaskUI(
|
||||
app,
|
||||
socketio=socketio,
|
||||
width=app_width,
|
||||
height=app_height,
|
||||
host=args.host,
|
||||
port=args.port,
|
||||
close_server_on_exit=not args.no_gui_auto_close,
|
||||
width=1200,
|
||||
height=800,
|
||||
host=host,
|
||||
port=port,
|
||||
close_server_on_exit=True,
|
||||
idle_interval=60,
|
||||
)
|
||||
ui.run()
|
||||
else:
|
||||
socketio.run(
|
||||
app,
|
||||
host=args.host,
|
||||
port=args.port,
|
||||
debug=args.debug,
|
||||
host=host,
|
||||
port=port,
|
||||
allow_unsafe_werkzeug=True,
|
||||
)
|
||||
|
||||
@ -39,7 +39,6 @@ def test_runway_sd_1_5(
|
||||
name=model_name,
|
||||
sd_controlnet=True,
|
||||
device=torch.device(sd_device),
|
||||
hf_access_token="",
|
||||
disable_nsfw=disable_nsfw,
|
||||
sd_cpu_textencoder=cpu_textencoder,
|
||||
sd_controlnet_method=sd_controlnet_method,
|
||||
@ -87,7 +86,6 @@ def test_local_file_path(sd_device, sampler):
|
||||
name=model_name,
|
||||
sd_controlnet=True,
|
||||
device=torch.device(sd_device),
|
||||
hf_access_token="",
|
||||
disable_nsfw=True,
|
||||
sd_cpu_textencoder=False,
|
||||
cpu_offload=True,
|
||||
@ -125,7 +123,6 @@ def test_local_file_path_controlnet_native_inpainting(sd_device, sampler):
|
||||
name=model_name,
|
||||
sd_controlnet=True,
|
||||
device=torch.device(sd_device),
|
||||
hf_access_token="",
|
||||
disable_nsfw=True,
|
||||
sd_cpu_textencoder=False,
|
||||
cpu_offload=True,
|
||||
@ -166,7 +163,6 @@ def test_controlnet_switch(sd_device, sampler):
|
||||
name=model_name,
|
||||
sd_controlnet=True,
|
||||
device=torch.device(sd_device),
|
||||
hf_access_token="",
|
||||
disable_nsfw=True,
|
||||
sd_cpu_textencoder=False,
|
||||
cpu_offload=True,
|
||||
|
||||
@ -21,7 +21,6 @@ def test_instruct_pix2pix(disable_nsfw, cpu_offload):
|
||||
model = ModelManager(
|
||||
name=model_name,
|
||||
device=torch.device(device),
|
||||
hf_access_token="",
|
||||
disable_nsfw=disable_nsfw,
|
||||
sd_cpu_textencoder=False,
|
||||
cpu_offload=cpu_offload,
|
||||
@ -52,7 +51,6 @@ def test_instruct_pix2pix_snow(disable_nsfw, cpu_offload):
|
||||
model = ModelManager(
|
||||
name=model_name,
|
||||
device=torch.device(device),
|
||||
hf_access_token="",
|
||||
disable_nsfw=disable_nsfw,
|
||||
sd_cpu_textencoder=False,
|
||||
cpu_offload=cpu_offload,
|
||||
|
||||
@ -17,11 +17,9 @@ def test_load_model():
|
||||
name=m,
|
||||
device="cpu",
|
||||
no_half=False,
|
||||
hf_access_token="",
|
||||
disable_nsfw=False,
|
||||
sd_cpu_textencoder=True,
|
||||
cpu_offload=True,
|
||||
enable_xformers=False,
|
||||
)
|
||||
|
||||
|
||||
|
||||
@ -16,11 +16,9 @@ def test_model_switch():
|
||||
sd_controlnet=True,
|
||||
sd_controlnet_method="lllyasviel/control_v11p_sd15_canny",
|
||||
device=torch.device("mps"),
|
||||
hf_access_token="",
|
||||
disable_nsfw=True,
|
||||
sd_cpu_textencoder=True,
|
||||
cpu_offload=False,
|
||||
enable_xformers=False,
|
||||
callback=None,
|
||||
)
|
||||
|
||||
@ -34,11 +32,9 @@ def test_controlnet_switch_onoff(caplog):
|
||||
sd_controlnet=True,
|
||||
sd_controlnet_method="lllyasviel/control_v11p_sd15_canny",
|
||||
device=torch.device("mps"),
|
||||
hf_access_token="",
|
||||
disable_nsfw=True,
|
||||
sd_cpu_textencoder=True,
|
||||
cpu_offload=False,
|
||||
enable_xformers=False,
|
||||
callback=None,
|
||||
)
|
||||
|
||||
@ -61,11 +57,9 @@ def test_controlnet_switch_method(caplog):
|
||||
sd_controlnet=True,
|
||||
sd_controlnet_method=old_method,
|
||||
device=torch.device("mps"),
|
||||
hf_access_token="",
|
||||
disable_nsfw=True,
|
||||
sd_cpu_textencoder=True,
|
||||
cpu_offload=False,
|
||||
enable_xformers=False,
|
||||
callback=None,
|
||||
)
|
||||
|
||||
|
||||
@ -41,7 +41,6 @@ def test_outpainting(name, sd_device, rect):
|
||||
model = ModelManager(
|
||||
name=name,
|
||||
device=torch.device(sd_device),
|
||||
hf_access_token="",
|
||||
disable_nsfw=True,
|
||||
sd_cpu_textencoder=False,
|
||||
callback=callback,
|
||||
@ -86,7 +85,6 @@ def test_kandinsky_outpainting(name, sd_device, rect):
|
||||
model = ModelManager(
|
||||
name=name,
|
||||
device=torch.device(sd_device),
|
||||
hf_access_token="",
|
||||
disable_nsfw=True,
|
||||
sd_cpu_textencoder=False,
|
||||
callback=callback,
|
||||
|
||||
@ -38,7 +38,6 @@ def test_runway_sd_1_5_all_samplers(
|
||||
model = ModelManager(
|
||||
name="runwayml/stable-diffusion-inpainting",
|
||||
device=torch.device(sd_device),
|
||||
hf_access_token="",
|
||||
disable_nsfw=True,
|
||||
sd_cpu_textencoder=False,
|
||||
)
|
||||
@ -69,7 +68,6 @@ def test_runway_sd_lcm_lora(sd_device, strategy, sampler):
|
||||
model = ModelManager(
|
||||
name="runwayml/stable-diffusion-inpainting",
|
||||
device=torch.device(sd_device),
|
||||
hf_access_token="",
|
||||
disable_nsfw=True,
|
||||
sd_cpu_textencoder=False,
|
||||
)
|
||||
@ -102,7 +100,6 @@ def test_runway_sd_freeu(sd_device, strategy, sampler):
|
||||
model = ModelManager(
|
||||
name="runwayml/stable-diffusion-inpainting",
|
||||
device=torch.device(sd_device),
|
||||
hf_access_token="",
|
||||
disable_nsfw=True,
|
||||
sd_cpu_textencoder=False,
|
||||
)
|
||||
@ -136,7 +133,6 @@ def test_runway_sd_sd_strength(sd_device, strategy, sampler):
|
||||
model = ModelManager(
|
||||
name="runwayml/stable-diffusion-inpainting",
|
||||
device=torch.device(sd_device),
|
||||
hf_access_token="",
|
||||
disable_nsfw=True,
|
||||
sd_cpu_textencoder=False,
|
||||
)
|
||||
@ -165,7 +161,6 @@ def test_runway_norm_sd_model(sd_device, strategy, sampler):
|
||||
model = ModelManager(
|
||||
name="runwayml/stable-diffusion-v1-5",
|
||||
device=torch.device(sd_device),
|
||||
hf_access_token="",
|
||||
disable_nsfw=True,
|
||||
sd_cpu_textencoder=False,
|
||||
)
|
||||
@ -192,7 +187,6 @@ def test_runway_sd_1_5_cpu_offload(sd_device, strategy, sampler):
|
||||
model = ModelManager(
|
||||
name="runwayml/stable-diffusion-inpainting",
|
||||
device=torch.device(sd_device),
|
||||
hf_access_token="",
|
||||
disable_nsfw=True,
|
||||
sd_cpu_textencoder=False,
|
||||
cpu_offload=True,
|
||||
@ -229,7 +223,6 @@ def test_local_file_path(sd_device, sampler, name):
|
||||
model = ModelManager(
|
||||
name=name,
|
||||
device=torch.device(sd_device),
|
||||
hf_access_token="",
|
||||
disable_nsfw=True,
|
||||
sd_cpu_textencoder=False,
|
||||
cpu_offload=False,
|
||||
|
||||
@ -29,7 +29,6 @@ def test_sdxl(sd_device, strategy, sampler):
|
||||
model = ModelManager(
|
||||
name="diffusers/stable-diffusion-xl-1.0-inpainting-0.1",
|
||||
device=torch.device(sd_device),
|
||||
hf_access_token="",
|
||||
disable_nsfw=True,
|
||||
sd_cpu_textencoder=False,
|
||||
callback=callback,
|
||||
@ -70,7 +69,6 @@ def test_sdxl_lcm_lora_and_freeu(sd_device, strategy, sampler):
|
||||
model = ModelManager(
|
||||
name="diffusers/stable-diffusion-xl-1.0-inpainting-0.1",
|
||||
device=torch.device(sd_device),
|
||||
hf_access_token="",
|
||||
disable_nsfw=True,
|
||||
sd_cpu_textencoder=False,
|
||||
callback=callback,
|
||||
@ -131,7 +129,6 @@ def test_sdxl_outpainting(sd_device, rect):
|
||||
model = ModelManager(
|
||||
name="diffusers/stable-diffusion-xl-1.0-inpainting-0.1",
|
||||
device=torch.device(sd_device),
|
||||
hf_access_token="",
|
||||
disable_nsfw=True,
|
||||
sd_cpu_textencoder=False,
|
||||
)
|
||||
|
||||
@ -24,7 +24,6 @@ def save_config(
|
||||
cpu_offload,
|
||||
disable_nsfw,
|
||||
sd_cpu_textencoder,
|
||||
enable_xformers,
|
||||
local_files_only,
|
||||
model_dir,
|
||||
input,
|
||||
@ -102,9 +101,6 @@ def main(config_file: str):
|
||||
|
||||
with gr.Column():
|
||||
gui = gr.Checkbox(init_config.gui, label=f"{GUI_HELP}")
|
||||
no_gui_auto_close = gr.Checkbox(
|
||||
init_config.no_gui_auto_close, label=f"{NO_GUI_AUTO_CLOSE_HELP}"
|
||||
)
|
||||
|
||||
with gr.Column():
|
||||
model_dir = gr.Textbox(
|
||||
@ -193,14 +189,11 @@ def main(config_file: str):
|
||||
init_config.cpu_offload, label=f"{CPU_OFFLOAD_HELP}"
|
||||
)
|
||||
sd_cpu_textencoder = gr.Checkbox(
|
||||
init_config.sd_cpu_textencoder, label=f"{SD_CPU_TEXTENCODER_HELP}"
|
||||
init_config.sd_cpu_textencoder, label=f"{CPU_TEXTENCODER_HELP}"
|
||||
)
|
||||
disable_nsfw = gr.Checkbox(
|
||||
init_config.disable_nsfw, label=f"{DISABLE_NSFW_HELP}"
|
||||
)
|
||||
enable_xformers = gr.Checkbox(
|
||||
init_config.enable_xformers, label=f"{ENABLE_XFORMERS_HELP}"
|
||||
)
|
||||
local_files_only = gr.Checkbox(
|
||||
init_config.local_files_only, label=f"{LOCAL_FILES_ONLY_HELP}"
|
||||
)
|
||||
@ -221,7 +214,6 @@ def main(config_file: str):
|
||||
cpu_offload,
|
||||
disable_nsfw,
|
||||
sd_cpu_textencoder,
|
||||
enable_xformers,
|
||||
local_files_only,
|
||||
model_dir,
|
||||
input,
|
||||
|
||||
@ -71,6 +71,8 @@ const Cropper = (props: Props) => {
|
||||
setY,
|
||||
setWidth,
|
||||
setHeight,
|
||||
isResizing,
|
||||
setIsResizing,
|
||||
] = useStore((state) => [
|
||||
state.imageWidth,
|
||||
state.imageHeight,
|
||||
@ -80,9 +82,11 @@ const Cropper = (props: Props) => {
|
||||
state.setCropperY,
|
||||
state.setCropperWidth,
|
||||
state.setCropperHeight,
|
||||
state.isCropperExtenderResizing,
|
||||
state.setIsCropperExtenderResizing,
|
||||
])
|
||||
|
||||
const [isResizing, setIsResizing] = useState(false)
|
||||
// const [isResizing, setIsResizing] = useState(false)
|
||||
const [isMoving, setIsMoving] = useState(false)
|
||||
|
||||
useEffect(() => {
|
||||
|
||||
@ -65,6 +65,7 @@ export default function Editor(props: EditorProps) {
|
||||
updateAppState,
|
||||
runMannually,
|
||||
runInpainting,
|
||||
isCropperExtenderResizing,
|
||||
] = useStore((state) => [
|
||||
state.disableShortCuts,
|
||||
state.windowSize,
|
||||
@ -87,6 +88,7 @@ export default function Editor(props: EditorProps) {
|
||||
state.updateAppState,
|
||||
state.runMannually(),
|
||||
state.runInpainting,
|
||||
state.isCropperExtenderResizing,
|
||||
])
|
||||
const baseBrushSize = useStore((state) => state.editorState.baseBrushSize)
|
||||
const brushSize = useStore((state) => state.getBrushSize())
|
||||
@ -537,7 +539,7 @@ export default function Editor(props: EditorProps) {
|
||||
}
|
||||
|
||||
const toggleShowBrush = (newState: boolean) => {
|
||||
if (newState !== showBrush && !isPanning) {
|
||||
if (newState !== showBrush && !isPanning && !isCropperExtenderResizing) {
|
||||
setShowBrush(newState)
|
||||
}
|
||||
}
|
||||
@ -693,7 +695,7 @@ export default function Editor(props: EditorProps) {
|
||||
limitToBounds={false}
|
||||
doubleClick={{ disabled: true }}
|
||||
initialScale={minScale}
|
||||
minScale={minScale * 0.6}
|
||||
minScale={minScale * 0.3}
|
||||
onPanning={(ref) => {
|
||||
if (!panned) {
|
||||
setPanned(true)
|
||||
|
||||
@ -54,6 +54,8 @@ const Extender = (props: Props) => {
|
||||
setWidth,
|
||||
setHeight,
|
||||
extenderDirection,
|
||||
isResizing,
|
||||
setIsResizing,
|
||||
] = useStore((state) => [
|
||||
state.isInpainting,
|
||||
state.imageHeight,
|
||||
@ -64,10 +66,10 @@ const Extender = (props: Props) => {
|
||||
state.setExtenderWidth,
|
||||
state.setExtenderHeight,
|
||||
state.settings.extenderDirection,
|
||||
state.isCropperExtenderResizing,
|
||||
state.setIsCropperExtenderResizing,
|
||||
])
|
||||
|
||||
const [isResizing, setIsResizing] = useState(false)
|
||||
|
||||
const [evData, setEVData] = useState<EVData>({
|
||||
initX: 0,
|
||||
initY: 0,
|
||||
@ -122,10 +124,9 @@ const Extender = (props: Props) => {
|
||||
const moveBottom = () => {
|
||||
const newHeight = evData.initHeight + offsetY
|
||||
let [clampedY, clampedHeight] = clampTopBottom(evData.initY, newHeight)
|
||||
|
||||
if (extenderDirection === EXTENDER_ALL) {
|
||||
if (clampedY + clampedHeight < imageHeight) {
|
||||
clampedHeight = imageHeight
|
||||
if (clampedHeight < Math.abs(clampedY) + imageHeight) {
|
||||
clampedHeight = Math.abs(clampedY) + imageHeight
|
||||
}
|
||||
}
|
||||
setHeight(clampedHeight)
|
||||
@ -155,8 +156,8 @@ const Extender = (props: Props) => {
|
||||
const newWidth = evData.initWidth + offsetX
|
||||
let [clampedX, clampedWidth] = clampLeftRight(evData.initX, newWidth)
|
||||
if (extenderDirection === EXTENDER_ALL) {
|
||||
if (clampedX + clampedWidth < imageWdith) {
|
||||
clampedWidth = imageWdith
|
||||
if (clampedWidth < Math.abs(clampedX) + imageWdith) {
|
||||
clampedWidth = Math.abs(clampedX) + imageWdith
|
||||
}
|
||||
}
|
||||
setWidth(clampedWidth)
|
||||
|
||||
@ -105,18 +105,22 @@ const LabelTitle = ({
|
||||
{text}
|
||||
</Label>
|
||||
</TooltipTrigger>
|
||||
<TooltipContent className="flex flex-col max-w-xs text-sm" side="left">
|
||||
<p>{toolTip}</p>
|
||||
{url ? (
|
||||
<Button variant="link" className="justify-end">
|
||||
<a href={url} target="_blank">
|
||||
More info
|
||||
</a>
|
||||
</Button>
|
||||
) : (
|
||||
<></>
|
||||
)}
|
||||
</TooltipContent>
|
||||
{toolTip ? (
|
||||
<TooltipContent className="flex flex-col max-w-xs text-sm" side="left">
|
||||
<p>{toolTip}</p>
|
||||
{url ? (
|
||||
<Button variant="link" className="justify-end">
|
||||
<a href={url} target="_blank">
|
||||
More info
|
||||
</a>
|
||||
</Button>
|
||||
) : (
|
||||
<></>
|
||||
)}
|
||||
</TooltipContent>
|
||||
) : (
|
||||
<></>
|
||||
)}
|
||||
</Tooltip>
|
||||
)
|
||||
}
|
||||
@ -172,7 +176,11 @@ const SidePanel = () => {
|
||||
<div className="flex flex-col gap-4">
|
||||
<div className="flex flex-col gap-4">
|
||||
<div className="flex justify-between items-center pr-2">
|
||||
<LabelTitle text="Controlnet" />
|
||||
<LabelTitle
|
||||
text="ControlNet"
|
||||
toolTip="Using an additional conditioning image to control how an image is generated"
|
||||
url="https://huggingface.co/docs/diffusers/main/en/using-diffusers/inpaint#controlnet"
|
||||
/>
|
||||
<Switch
|
||||
id="controlnet"
|
||||
checked={settings.enableControlnet}
|
||||
@ -271,7 +279,11 @@ const SidePanel = () => {
|
||||
return (
|
||||
<div className="flex flex-col gap-4">
|
||||
<div className="flex justify-between items-center pr-2">
|
||||
<LabelTitle text="Freeu" />
|
||||
<LabelTitle
|
||||
text="FreeU"
|
||||
toolTip="FreeU is a technique for improving image quality. Different models may require different FreeU-specific hyperparameters, which can be viewed in the more info section."
|
||||
url="https://huggingface.co/docs/diffusers/main/en/using-diffusers/freeu"
|
||||
/>
|
||||
<Switch
|
||||
id="freeu"
|
||||
checked={settings.enableFreeu}
|
||||
@ -408,7 +420,10 @@ const SidePanel = () => {
|
||||
return (
|
||||
<div>
|
||||
<RowContainer>
|
||||
<div>Example Image</div>
|
||||
<LabelTitle
|
||||
text="Example Image"
|
||||
toolTip="An example image to guide image generation."
|
||||
/>
|
||||
<ImageUploadButton
|
||||
tooltip="Upload example image"
|
||||
onFileUpload={(file) => {
|
||||
@ -450,8 +465,9 @@ const SidePanel = () => {
|
||||
return (
|
||||
<div className="flex flex-col gap-1">
|
||||
<LabelTitle
|
||||
htmlFor="image-guidance-scale"
|
||||
text="Image guidance scale"
|
||||
toolTip="Push the generated image towards the inital image. Higher image guidance scale encourages generated images that are closely linked to the source image, usually at the expense of lower image quality."
|
||||
url="https://huggingface.co/docs/diffusers/main/en/api/pipelines/pix2pix"
|
||||
/>
|
||||
<RowContainer>
|
||||
<Slider
|
||||
@ -518,11 +534,17 @@ const SidePanel = () => {
|
||||
}
|
||||
|
||||
const renderExtender = () => {
|
||||
if (!settings.model.support_outpainting) {
|
||||
return null
|
||||
}
|
||||
return (
|
||||
<>
|
||||
<div className="flex flex-col gap-4">
|
||||
<RowContainer>
|
||||
<LabelTitle text="Extender" />
|
||||
<LabelTitle
|
||||
text="Extender"
|
||||
toolTip="Perform outpainting on images to expand it's content."
|
||||
/>
|
||||
<Switch
|
||||
id="extender"
|
||||
checked={settings.showExtender}
|
||||
@ -709,7 +731,10 @@ const SidePanel = () => {
|
||||
>
|
||||
<div className="flex flex-col gap-4 mt-4">
|
||||
<RowContainer>
|
||||
<LabelTitle text="Cropper" />
|
||||
<LabelTitle
|
||||
text="Cropper"
|
||||
toolTip="Inpainting on part of image, improve inference speed and reduce memory usage."
|
||||
/>
|
||||
<Switch
|
||||
id="cropper"
|
||||
checked={settings.showCropper}
|
||||
@ -725,7 +750,11 @@ const SidePanel = () => {
|
||||
{renderExtender()}
|
||||
|
||||
<div className="flex flex-col gap-1">
|
||||
<LabelTitle htmlFor="steps" text="Steps" />
|
||||
<LabelTitle
|
||||
htmlFor="steps"
|
||||
text="Steps"
|
||||
toolTip="The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference."
|
||||
/>
|
||||
<RowContainer>
|
||||
<Slider
|
||||
className="w-[180px]"
|
||||
|
||||
@ -150,8 +150,11 @@ type AppState = {
|
||||
|
||||
interactiveSegState: InteractiveSegState
|
||||
fileManagerState: FileManagerState
|
||||
|
||||
cropperState: CropperState
|
||||
extenderState: CropperState
|
||||
isCropperExtenderResizing: bool
|
||||
|
||||
serverConfig: ServerConfig
|
||||
|
||||
settings: Settings
|
||||
@ -177,6 +180,7 @@ type AppAction = {
|
||||
setExtenderY: (newValue: number) => void
|
||||
setExtenderWidth: (newValue: number) => void
|
||||
setExtenderHeight: (newValue: number) => void
|
||||
setIsCropperExtenderResizing: (newValue: boolean) => void
|
||||
updateExtenderDirection: (newValue: string) => void
|
||||
resetExtender: (width: number, height: number) => void
|
||||
updateExtenderByBuiltIn: (direction: string, scale: number) => void
|
||||
@ -261,6 +265,7 @@ const defaultValues: AppState = {
|
||||
width: 512,
|
||||
height: 512,
|
||||
},
|
||||
isCropperExtenderResizing: false,
|
||||
|
||||
fileManagerState: {
|
||||
sortBy: SortBy.CTIME,
|
||||
@ -889,6 +894,11 @@ export const useStore = createWithEqualityFn<AppState & AppAction>()(
|
||||
state.extenderState.height = newValue
|
||||
}),
|
||||
|
||||
setIsCropperExtenderResizing: (newValue: boolean) =>
|
||||
set((state) => {
|
||||
state.isCropperExtenderResizing = newValue
|
||||
}),
|
||||
|
||||
updateExtenderDirection: (newValue: string) => {
|
||||
console.log(
|
||||
`updateExtenderDirection: ${JSON.stringify(get().extenderState)}`
|
||||
|
||||
Loading…
Reference in New Issue
Block a user