





import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
					UpperCamelCase__           =			argparse.ArgumentParser()

					parser.add_argument(
					    "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
					)
					# !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
					parser.add_argument(
					    "--original_config_file",
					    default=None,
					    type=str,
					    help="The YAML config file corresponding to the original architecture.",
					)
					parser.add_argument(
					    "--num_in_channels",
					    default=None,
					    type=int,
					    help="The number of input channels. If `None` number of input channels will be automatically inferred.",
					)
					parser.add_argument(
					    "--scheduler_type",
					    default="pndm",
					    type=str,
					    help="Type of scheduler to use. Should be one of ['pndm', 'lms', 'ddim', 'euler', 'euler-ancestral', 'dpm']",
					)
					parser.add_argument(
					    "--pipeline_type",
					    default=None,
					    type=str,
					    help=(
					        "The pipeline type. One of 'FrozenOpenCLIPEmbedder', 'FrozenCLIPEmbedder', 'PaintByExample'"
					        ". If `None` pipeline will be automatically inferred."
					    ),
					)
					parser.add_argument(
					    "--image_size",
					    default=None,
					    type=int,
					    help=(
					        "The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2"
					        " Base. Use 768 for Stable Diffusion v2."
					    ),
					)
					parser.add_argument(
					    "--prediction_type",
					    default=None,
					    type=str,
					    help=(
					        "The prediction type that the model was trained on. Use 'epsilon' for Stable Diffusion v1.X and Stable"
					        " Diffusion v2 Base. Use 'v_prediction' for Stable Diffusion v2."
					    ),
					)
					parser.add_argument(
					    "--extract_ema",
					    action="store_true",
					    help=(
					        "Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights"
					        " or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield"
					        " higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning."
					    ),
					)
					parser.add_argument(
					    "--upcast_attention",
					    action="store_true",
					    help=(
					        "Whether the attention computation should always be upcasted. This is necessary when running stable"
					        " diffusion 2.1."
					    ),
					)
					parser.add_argument(
					    "--from_safetensors",
					    action="store_true",
					    help="If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.",
					)
					parser.add_argument(
					    "--to_safetensors",
					    action="store_true",
					    help="Whether to store pipeline in safetensors format or not.",
					)
					parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
					parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)")
					parser.add_argument(
					    "--stable_unclip",
					    type=str,
					    default=None,
					    required=False,
					    help="Set if this is a stable unCLIP model. One of 'txt2img' or 'img2img'.",
					)
					parser.add_argument(
					    "--stable_unclip_prior",
					    type=str,
					    default=None,
					    required=False,
					    help="Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.",
					)
					parser.add_argument(
					    "--clip_stats_path",
					    type=str,
					    help="Path to the clip stats file. Only required if the stable unclip model's config specifies `model.params.noise_aug_config.params.clip_stats_path`.",
					    required=False,
					)
					parser.add_argument(
					    "--controlnet", action="store_true", default=None, help="Set flag if this is a controlnet checkpoint."
					)
					parser.add_argument("--half", action="store_true", help="Save weights in half precision.")
					parser.add_argument(
					    "--vae_path",
					    type=str,
					    default=None,
					    required=False,
					    help="Set to a path, hub id to an already converted vae to not convert it again.",
					)
					UpperCamelCase__           =			parser.parse_args()

					UpperCamelCase__           =			download_from_original_stable_diffusion_ckpt(
					    checkpoint_path=args.checkpoint_path,
					    original_config_file=args.original_config_file,
					    image_size=args.image_size,
					    prediction_type=args.prediction_type,
					    model_type=args.pipeline_type,
					    extract_ema=args.extract_ema,
					    scheduler_type=args.scheduler_type,
					    num_in_channels=args.num_in_channels,
					    upcast_attention=args.upcast_attention,
					    from_safetensors=args.from_safetensors,
					    device=args.device,
					    stable_unclip=args.stable_unclip,
					    stable_unclip_prior=args.stable_unclip_prior,
					    clip_stats_path=args.clip_stats_path,
					    controlnet=args.controlnet,
					    vae_path=args.vae_path,
					)

					if args.half:
										pipe.to(torch_dtype=torch.floataa)

					if args.controlnet:
										# only save the controlnet model
										pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
					else:
										pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)











from manim import *

class 							__SCREAMING_SNAKE_CASE       (  _a							):






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      Rectangle(height=0.5	,			width=0.5	)
														UpperCamelCase__					  =      Rectangle(height=0.25	,			width=0.25	)
														UpperCamelCase__					  =      Rectangle(height=0.46	,			width=0.46	).set_stroke(width=0	)

														UpperCamelCase__					  =      [mem.copy() for i in range(6	)]
														UpperCamelCase__					  =      [mem.copy() for i in range(6	)]
														UpperCamelCase__					  =      VGroup(*__lowerCAmelCase	).arrange(__lowerCAmelCase	,			buff=0	)
														UpperCamelCase__					  =      VGroup(*__lowerCAmelCase	).arrange(__lowerCAmelCase	,			buff=0	)
														UpperCamelCase__					  =      VGroup(__lowerCAmelCase	,			__lowerCAmelCase	).arrange(__lowerCAmelCase	,			buff=0	)
														UpperCamelCase__					  =      Text("""CPU"""	,			font_size=24	)
														UpperCamelCase__					  =      Group(__lowerCAmelCase	,			__lowerCAmelCase	).arrange(__lowerCAmelCase	,			buff=0.5	,			aligned_edge=__lowerCAmelCase	)
														cpu.move_to([-2.5, -0.5, 0]	)
														self.add(__lowerCAmelCase	)

														UpperCamelCase__					  =      [mem.copy() for i in range(4	)]
														UpperCamelCase__					  =      VGroup(*__lowerCAmelCase	).arrange(__lowerCAmelCase	,			buff=0	)
														UpperCamelCase__					  =      Text("""GPU"""	,			font_size=24	)
														UpperCamelCase__					  =      Group(__lowerCAmelCase	,			__lowerCAmelCase	).arrange(__lowerCAmelCase	,			buff=0.5	,			aligned_edge=__lowerCAmelCase	)
														gpu.move_to([-1, -1, 0]	)
														self.add(__lowerCAmelCase	)

														UpperCamelCase__					  =      [mem.copy() for i in range(6	)]
														UpperCamelCase__					  =      VGroup(*__lowerCAmelCase	).arrange(__lowerCAmelCase	,			buff=0	)

														UpperCamelCase__					  =      Text("""Model"""	,			font_size=24	)
														UpperCamelCase__					  =      Group(__lowerCAmelCase	,			__lowerCAmelCase	).arrange(__lowerCAmelCase	,			buff=0.5	,			aligned_edge=__lowerCAmelCase	)
														model.move_to([3, -1.0, 0]	)
														self.add(__lowerCAmelCase	)

														UpperCamelCase__					  =      []
														UpperCamelCase__					  =      []
														UpperCamelCase__					  =      []

														for i, rect in enumerate(__lowerCAmelCase	):
																					rect.set_stroke(__lowerCAmelCase	)

																					UpperCamelCase__					  =      Rectangle(height=0.46 / 4	,			width=0.46 / 3	).set_stroke(width=0.0	).set_fill(__lowerCAmelCase	,			opacity=0.7	)

																					if i == 0:
																												cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT	)	,			buff=0.02	,			direction=__lowerCAmelCase	)
																												cpu_target.set_x(cpu_target.get_x() + 0.1	)
																					elif i == 3:
																												cpu_target.next_to(model_cpu_arr[0]	,			direction=__lowerCAmelCase	,			buff=0.0	)
																					else:
																												cpu_target.next_to(model_cpu_arr[i - 1]	,			direction=__lowerCAmelCase	,			buff=0.0	)
																					self.add(__lowerCAmelCase	)
																					model_cpu_arr.append(__lowerCAmelCase	)

														self.add(*__lowerCAmelCase	,			*__lowerCAmelCase	,			*__lowerCAmelCase	)

														UpperCamelCase__					  =      [mem.copy() for i in range(6	)]
														UpperCamelCase__					  =      VGroup(*__lowerCAmelCase	).arrange(__lowerCAmelCase	,			buff=0	)

														UpperCamelCase__					  =      Text("""Loaded Checkpoint"""	,			font_size=24	)
														UpperCamelCase__					  =      Group(__lowerCAmelCase	,			__lowerCAmelCase	).arrange(__lowerCAmelCase	,			buff=0.5	,			aligned_edge=__lowerCAmelCase	)
														checkpoint.move_to([3, 0.5, 0]	)

														self.add(__lowerCAmelCase	)

														UpperCamelCase__					  =      []
														UpperCamelCase__					  =      []

														for i, rect in enumerate(__lowerCAmelCase	):
																					UpperCamelCase__					  =      fill.copy().set_fill(__lowerCAmelCase	,			opacity=0.7	)
																					target.move_to(__lowerCAmelCase	)
																					ckpt_arr.append(__lowerCAmelCase	)

																					UpperCamelCase__					  =      target.copy()
																					if i < 5:
																												cpu_target.move_to(cpu_left_col_base[i + 1]	)
																					else:
																												cpu_target.move_to(cpu_right_col_base[i - 5]	)
																					ckpt_cpu_arr.append(__lowerCAmelCase	)
														self.add(*__lowerCAmelCase	,			*__lowerCAmelCase	)

														UpperCamelCase__					  =      Square(side_length=2.2	)
														key.move_to([-5, 2, 0]	)

														UpperCamelCase__					  =      MarkupText(
														    f"""<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model"""	,			font_size=18	,			)

														key_text.move_to([-5, 2.4, 0]	)

														self.add(__lowerCAmelCase	,			__lowerCAmelCase	)

														UpperCamelCase__					  =      MarkupText(
														    f"""<span fgcolor='{BLUE}'>●</span> Checkpoint"""	,			font_size=18	,			)

														blue_text.next_to(__lowerCAmelCase	,			DOWN * 2.4	,			aligned_edge=key_text.get_left()	)
														self.add(__lowerCAmelCase	)

														UpperCamelCase__					  =      MarkupText(
														    f"""Based on the passed in configuration, weights are stored in\na variety of np.memmaps on disk or to a particular device."""	,			font_size=24	,			)
														step_a.move_to([2, 2, 0]	)

														UpperCamelCase__					  =      [meta_mem.copy() for i in range(6	)]
														UpperCamelCase__					  =      [meta_mem.copy() for i in range(6	)]
														UpperCamelCase__					  =      VGroup(*__lowerCAmelCase	).arrange(__lowerCAmelCase	,			buff=0	)
														UpperCamelCase__					  =      VGroup(*__lowerCAmelCase	).arrange(__lowerCAmelCase	,			buff=0	)
														UpperCamelCase__					  =      VGroup(__lowerCAmelCase	,			__lowerCAmelCase	).arrange(__lowerCAmelCase	,			buff=0	)
														UpperCamelCase__					  =      Text("""Disk"""	,			font_size=24	)
														UpperCamelCase__					  =      Group(__lowerCAmelCase	,			__lowerCAmelCase	).arrange(__lowerCAmelCase	,			buff=0.5	,			aligned_edge=__lowerCAmelCase	)
														disk.move_to([-4.0, -1.25, 0]	)
														self.play(Write(__lowerCAmelCase	,			run_time=3	)	,			Write(__lowerCAmelCase	,			run_time=1	)	,			Create(__lowerCAmelCase	,			run_time=1	)	)

														UpperCamelCase__					  =      []
														for i, rect in enumerate(__lowerCAmelCase	):
																					UpperCamelCase__					  =      rect.copy()
																					target.generate_target()
																					target.target.move_to(disk_left_col_base[i]	).scale(0.5	)
																					animations.append(MoveToTarget(__lowerCAmelCase	,			run_time=1.5	)	)
														self.play(*__lowerCAmelCase	)

														self.play(FadeOut(__lowerCAmelCase	)	)

														UpperCamelCase__					  =      MarkupText(f"""Then, the checkpoint is removed from memory\nthrough garbage collection."""	,			font_size=24	)
														step_a.move_to([2, 2, 0]	)

														self.play(Write(__lowerCAmelCase	,			run_time=3	)	)

														self.play(
														    FadeOut(__lowerCAmelCase	,			__lowerCAmelCase	,			*__lowerCAmelCase	,			*__lowerCAmelCase	)	,			)

														self.wait()




