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from datetime import time |
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import io |
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import os |
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import numpy as np |
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from pydantic import BaseModel, Field |
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from typing import List, Literal, Optional, Union |
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import requests |
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import torch |
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from helpers.save_images import get_output_folder |
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from PIL import Image |
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from PIL.GifImagePlugin import GifImageFile |
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from PIL.JpegImagePlugin import JpegImageFile |
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from PIL.PngImagePlugin import PngImageFile |
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from PIL.TiffImagePlugin import TiffImageFile |
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from pydantic import ( |
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BaseConfig, |
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BaseModel, |
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Field, |
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validator, |
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) |
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import validators |
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from devtools import debug |
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ImageType = Union[JpegImageFile, PngImageFile, GifImageFile, TiffImageFile, Image.Image] |
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def is_image(v): |
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return ( |
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isinstance(v, PngImageFile) |
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or isinstance(v, JpegImageFile) |
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or isinstance(v, GifImageFile) |
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or isinstance(v, TiffImageFile) |
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or isinstance(v, Image.Image) |
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) |
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def validate_image(v, throw=True): |
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if v is None: |
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return v |
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elif ( |
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isinstance(v, PngImageFile) |
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or isinstance(v, JpegImageFile) |
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or isinstance(v, GifImageFile) |
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or isinstance(v, TiffImageFile) |
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or isinstance(v, Image.Image) |
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): |
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return v |
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elif isinstance(v, bytes) or isinstance(v, io.BytesIO): |
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return v |
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elif isinstance(v, str): |
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if validators.url(v): |
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try: |
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v = Image.open(requests.get(v, stream=True).raw).convert("RGB") |
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return v |
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except Exception as err: |
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if throw: |
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raise ValueError( |
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"Invalid remote url, failed to parse image" |
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) from err |
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else: |
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return False |
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elif os.path.isfile(v): |
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try: |
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with Image.open(v) as fd: |
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return fd.convert("RGB") |
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except Exception as err: |
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if throw: |
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raise ValueError( |
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"Invalid path, failed to parse image from local path" |
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) from err |
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else: |
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return False |
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else: |
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if throw: |
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raise ValueError("Invalid string, no image or remote url") |
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else: |
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return False |
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else: |
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if throw: |
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raise ValueError( |
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f"Bad image type. Expected: bytes, Image, or Image url. Got: {debug.format(v)}" |
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) |
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else: |
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return False |
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def output_folder_factory(output_path="outputs", batch_folder="deforum"): |
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prefix = os.path.abspath(os.path.dirname(__file__)) |
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return get_output_folder(f"{prefix}/{output_path}".replace("//", "/"), batch_folder) |
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class DeforumArgs(BaseModel): |
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W: Optional[int] = 512 |
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H: Optional[int] = 512 |
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seed: Optional[int] = -1 |
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sampler: Optional[ |
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Literal[ |
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"klms", |
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"dpm2", |
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"dpm2_ancestral", |
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"heun", |
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"euler", |
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"euler_ancestral", |
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"plms", |
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"ddim", |
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] |
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] = "euler_ancestral" |
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steps: Optional[int] = 80 |
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scale: Optional[int] = 7 |
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ddim_eta: Optional[float] = 0.0 |
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dynamic_threshold: Optional[float] = None |
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static_threshold: Optional[float] = None |
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save_samples: Optional[bool] = True |
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save_settings: Optional[bool] = True |
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display_samples: Optional[bool] = True |
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save_sample_per_step: Optional[bool] = False |
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show_sample_per_step: Optional[bool] = False |
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prompt_weighting: Optional[bool] = False |
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normalize_prompt_weights: Optional[bool] = False |
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log_weighted_subprompts: Optional[bool] = False |
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n_batch: Optional[int] = 1 |
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batch_name: Optional[str] = "StableFun" |
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filename_format: Optional[ |
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Literal["{timestring}_{index}_{seed}.png", "{timestring}_{index}_{prompt}.png"] |
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] = "{timestring}_{index}_{prompt}.png" |
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seed_behavior: Optional[Literal["iter", "constant", "random"]] = "iter" |
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make_grid: Optional[bool] = False |
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grid_rows: Optional[int] = 2 |
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outdir: Optional[str] = Field(default_factory=output_folder_factory) |
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use_init: Optional[bool] = False |
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strength: Optional[float] = 0.0 |
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strength_0_no_init: Optional[bool] = True |
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init_image: Optional[ |
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ImageType |
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] = "https://cdn.pixabay.com/photo/2022/07/30/13/10/green-longhorn-beetle-7353749_1280.jpg" |
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use_mask: Optional[bool] = False |
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use_alpha_as_mask: Optional[bool] = False |
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mask_file: Optional[ |
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ImageType |
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] = "https://www.filterforge.com/wiki/images/archive/b/b7/20080927223728%21Polygonal_gradient_thumb.jpg" |
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invert_mask: Optional[bool] = False |
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mask_brightness_adjust: Optional[float] = 1.0 |
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mask_contrast_adjust: Optional[float] = 1.0 |
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overlay_mask: Optional[bool] = True |
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mask_overlay_blur: Optional[float] = 5 |
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mean_loss_scale: Optional[float] = 0 |
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var_loss_scale: Optional[float] = 0 |
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exposure_loss_scale: Optional[float] = 0 |
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exposure_target: Optional[float] = 0.5 |
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colormatch_loss_scale: Optional[float] = 0 |
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colormatch_image: Optional[ |
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ImageType |
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] = "https://www.saasdesign.io/wp-content/uploads/2021/02/palette-3-min-980x588.png" |
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colormatch_n_colors: Optional[int] = 4 |
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ignore_sat_scale: Optional[float] = 0 |
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clip_name: Optional[ |
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Literal["ViT-L/14", "ViT-L/14@336px", "ViT-B/16", "ViT-B/32"] |
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] = "ViT-L/14" |
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clip_loss_scale: Optional[float] = 0 |
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aesthetics_loss_scale: Optional[float] = 0 |
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cutn: Optional[int] = 1 |
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cut_pow: Optional[float] = 0.0001 |
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init_mse_scale: Optional[float] = 0 |
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blue_loss_scale: Optional[float] = 0 |
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gradient_wrt: Optional[Literal["x", "x0_pred"]] = "x0_pred" |
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gradient_add_to: Optional[Literal["cond", "uncond", "both"]] = "both" |
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decode_method: Optional[Literal["autoencoder", "linear"]] = "linear" |
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grad_threshold_type: Optional[ |
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Literal["dynamic", "static", "mean", "schedule"] |
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] = "dynamic" |
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clamp_grad_threshold: Optional[float] = 0.2 |
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clamp_start: Optional[float] = 0.2 |
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clamp_stop: Optional[float] = 0.01 |
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cond_uncond_sync: Optional[bool] = True |
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n_samples: Optional[int] = 1 |
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precision: Optional[Literal["fp16", "autocast", "fp32"]] = "autocast" |
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C: Optional[int] = 4 |
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f: Optional[int] = 8 |
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prompt: Optional[str] = "" |
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timestring: Optional[str] = Field( |
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default_factory=lambda: time.strftime("%Y%m%d%H%M%S") |
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) |
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init_latent: Optional[Union[float, torch.Tensor, np.ndarray]] = None |
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init_sample: Optional[Union[float, torch.Tensor, np.ndarray]] = None |
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init_c: Optional[Union[float, torch.Tensor, np.ndarray]] = None |
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class Config(BaseConfig): |
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arbitrary_types_allowed: Optional[bool] = True |
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@validator("init_image", pre=True) |
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def validate_image_init(cls, v): |
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return validate_image(v) |
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@validator("colormatch_image", pre=True) |
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def validate_image_colormatch(cls, v): |
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return validate_image(v) |
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@validator("mask_file", pre=True) |
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def validate_image_mask(cls, v): |
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return validate_image(v) |
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class DeforumAnimArgs(BaseModel): |
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animation_mode: Optional[ |
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Literal["None", "2D", "3D", "Video Input", "Interpolation"] |
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] = "None" |
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max_frames: Optional[int] = 1000 |
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border: Optional[Literal["wrap", "replicate"]] = "replicate" |
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angle: Optional[str] = "0:(0)" |
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zoom: Optional[str] = "0:(1.04)" |
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translation_x: Optional[str] = "0:(10*sin(2*3.14*t/10))" |
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translation_y: Optional[str] = "0:(0)" |
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translation_z: Optional[str] = "0:(10)" |
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rotation_3d_x: Optional[str] = "0:(0)" |
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rotation_3d_y: Optional[str] = "0:(0)" |
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rotation_3d_z: Optional[str] = "0:(0)" |
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flip_2d_perspective: Optional[bool] = False |
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perspective_flip_theta: Optional[str] = "0:(0)" |
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perspective_flip_phi: Optional[str] = "0:(t%15)" |
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perspective_flip_gamma: Optional[str] = "0:(0)" |
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perspective_flip_fv: Optional[str] = "0:(53)" |
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noise_schedule: Optional[str] = "0: (0.02)" |
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strength_schedule: Optional[str] = "0: (0.65)" |
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contrast_schedule: Optional[str] = "0: (1.0)" |
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color_coherence: Optional[str] = "Match Frame 0 LAB" |
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diffusion_cadence: Optional[Literal["1", "2", "3", "4", "5", "6", "7", "8"]] = "1" |
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use_depth_warping: Optional[bool] = True |
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midas_weight: Optional[float] = 0.3 |
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near_plane: Optional[int] = 200 |
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far_plane: Optional[int] = 10000 |
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fov: Optional[float] = 40 |
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padding_mode: Optional[Literal["border", "reflection", "zeros"]] = "border" |
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sampling_mode: Optional[Literal["bicubic", "bilinear", "nearest"]] = "bicubic" |
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save_depth_maps: Optional[bool] = False |
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video_init_path: Optional[str] = "/content/video_in.mp4" |
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extract_nth_frame: Optional[int] = 1 |
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overwrite_extracted_frames: Optional[bool] = True |
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use_mask_video: Optional[bool] = False |
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video_mask_path: Optional[str] = "/content/video_in.mp4" |
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interpolate_key_frames: Optional[bool] = False |
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interpolate_x_frames: Optional[int] = 4 |
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resume_from_timestring: Optional[bool] = False |
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resume_timestring: Optional[str] = "20220829210106" |
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class Config(BaseConfig): |
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arbitrary_types_allowed: Optional[bool] = True |
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