




"""simple docstring"""




import argparse
import math
import os

import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel



def 					a__				(       )		->      List[Any]:
   lowerCamelCase       =      argparse.ArgumentParser()
   parser.add_argument(
       """-m""" ,				"""--pretrained_model_name_or_path""" ,				type=snake_case__ ,				default=snake_case__ ,				required=snake_case__ ,				help="""Path to pretrained model or model identifier from huggingface.co/models.""" ,				)
   parser.add_argument(
       """-c""" ,				"""--caption""" ,				type=snake_case__ ,				default="""robotic cat with wings""" ,				help="""Text used to generate images.""" ,				)
   parser.add_argument(
       """-n""" ,				"""--images_num""" ,				type=snake_case__ ,				default=4 ,				help="""How much images to generate.""" ,				)
   parser.add_argument(
       """-s""" ,				"""--seed""" ,				type=snake_case__ ,				default=42 ,				help="""Seed for random process.""" ,				)
   parser.add_argument(
       """-ci""" ,				"""--cuda_id""" ,				type=snake_case__ ,				default=0 ,				help="""cuda_id.""" ,				)
   lowerCamelCase       =      parser.parse_args()
   return args



def 					a__				(       snake_case__ ,				snake_case__ ,				snake_case__       )		->      Optional[Any]:
   if not len(snake_case__       ) == rows * cols:
      raise ValueError("""The specified number of rows and columns are not correct."""       )

   lowerCamelCase   ,     lowerCamelCase       =      imgs[0].size
   lowerCamelCase       =      Image.new("""RGB""" ,				size=(cols * w, rows * h)       )
   lowerCamelCase   ,     lowerCamelCase       =      grid.size

   for i, img in enumerate(snake_case__       ):
      grid.paste(snake_case__ ,				box=(i % cols * w, i // cols * h)       )
   return grid



def 					a__				(       snake_case__ ,				snake_case__="robotic cat with wings" ,				snake_case__=7.5 ,				snake_case__=50 ,				snake_case__=1 ,				snake_case__=42 ,				)		->      Tuple:
   lowerCamelCase       =      torch.Generator(pipeline.device       ).manual_seed(snake_case__       )
   lowerCamelCase       =      pipeline(
       snake_case__ ,				guidance_scale=snake_case__ ,				num_inference_steps=snake_case__ ,				generator=snake_case__ ,				num_images_per_prompt=snake_case__ ,				).images
   lowerCamelCase       =      int(math.sqrt(snake_case__       )       )
   lowerCamelCase       =      image_grid(snake_case__ ,				rows=_rows ,				cols=num_images_per_prompt // _rows       )
   return grid, images


lowerCAmelCase							:    Optional[Any]  							=							parse_args()
# Load models and create wrapper for stable diffusion
lowerCAmelCase							:    Any  							=							CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="""tokenizer""")
lowerCAmelCase							:    int  							=							CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="""text_encoder""")
lowerCAmelCase							:    Optional[Any]  							=							AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="""vae""")
lowerCAmelCase							:    Optional[Any]  							=							UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="""unet""")

lowerCAmelCase							:    int  							=							StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
lowerCAmelCase							:    Any  							=							lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, """best_model.pt""")):
     lowerCAmelCase							:    str  							=							load(args.pretrained_model_name_or_path, model=unet)
     unet.eval()
     setattr(pipeline, """unet""", unet)
else:
     lowerCAmelCase							:    Dict  							=							unet.to(torch.device("""cuda""", args.cuda_id))
lowerCAmelCase							:    Tuple  							=							pipeline.to(unet.device)
lowerCAmelCase							:    Union[str, Any]  							=							generate_images(pipeline, prompt=args.caption, num_images_per_prompt=args.images_num, seed=args.seed)
grid.save(os.path.join(args.pretrained_model_name_or_path, """{}.png""".format("""_""".join(args.caption.split()))))
lowerCAmelCase							:    List[str]  							=							os.path.join(args.pretrained_model_name_or_path, """_""".join(args.caption.split()))
os.makedirs(dirname, exist_ok=True)
for idx, image in enumerate(images):
     image.save(os.path.join(dirname, """{}.png""".format(idx + 1)))









"""simple docstring"""




import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
					lowerCAmelCase							:    Dict  							=							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.""",
					)
					lowerCAmelCase							:    Dict  							=							parser.parse_args()

					lowerCAmelCase							:    str  							=							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)



