899 lines
30 KiB
Python
899 lines
30 KiB
Python
import sys
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import os
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import time
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import json
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from jsonschema import validate
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import numpy as np
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import gradio as gr
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from PIL import Image
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import math
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import json
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from iz_helpers import shrink_and_paste_on_blank, write_video
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from webui import wrap_gradio_gpu_call
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from modules import script_callbacks, scripts
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import modules.shared as shared
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from modules.processing import (
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process_images,
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StableDiffusionProcessingTxt2Img,
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StableDiffusionProcessingImg2Img,
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)
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from scripts import postprocessing_upscale
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from modules.ui import create_output_panel, plaintext_to_html
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import modules.sd_models
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import modules.sd_samplers
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from modules import scripts
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usefulDirs = scripts.basedir().split(os.sep)[
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-2:
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] # contains install and our extension foldername
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jsonprompt_schemafile = (
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usefulDirs[0] + "/" + usefulDirs[1] + "/scripts/promptschema.json"
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)
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available_samplers = [s.name for s in modules.sd_samplers.samplers if "UniPc" not in s.name]
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default_prompt = """
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{
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"prompts":{
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"headers":["outpaint steps","prompt"],
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"data":[
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[0,"Huge spectacular Waterfall in a dense tropical forest,epic perspective,(vegetation overgrowth:1.3)(intricate, ornamentation:1.1),(baroque:1.1), fantasy, (realistic:1) digital painting , (magical,mystical:1.2) , (wide angle shot:1.4), (landscape composed:1.2)(medieval:1.1), divine,cinematic,(tropical forest:1.4),(river:1.3)mythology,india, volumetric lighting, Hindu ,epic, Alex Horley Wenjun Lin greg rutkowski Ruan Jia (Wayne Barlowe:1.2) <lora:epiNoiseoffset_v2:0.6> "],
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]
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},
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"negPrompt":"frames, borderline, text, character, duplicate, error, out of frame, watermark, low quality, ugly, deformed, blur bad-artist"
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}
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"""
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empty_prompt = (
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'{"prompts":{"data":[],"headers":["outpaint steps","prompt"]},"negPrompt":""}'
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)
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# must be python dict
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invalid_prompt = {
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"prompts": {
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"data": [[0, "Your prompt-json is invalid, please check Settings"]],
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"headers": ["outpaint steps", "prompt"],
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},
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"negPrompt": "Invalid prompt-json",
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}
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def closest_upper_divisible_by_eight(num):
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if num % 8 == 0:
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return num
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else:
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return math.ceil(num / 8) * 8
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# example fail: 720 px width * 1.66 upscale => 1195.2 => 1195 crash
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# 512 px * 1.66 = 513.66 = ?
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# assume ffmpeg will CUT to integer
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# 721 /720
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def do_upscaleImg(curImg, upscale_do, upscaler_name, upscale_by):
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if not upscale_do:
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return curImg
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# ensure even width and even height for ffmpeg
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# if odd, switch to scale to mode
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rwidth = round(curImg.width * upscale_by)
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rheight = round(curImg.height * upscale_by)
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ups_mode = 2 # upscale_by
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if ( (rwidth %2) == 1 ):
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ups_mode = 1
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rwidth += 1
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if ( (rheight %2) == 1 ):
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ups_mode = 1
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rheight += 1
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if (1 == ups_mode ):
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print ("Infinite Zoom: aligning output size to even width and height: " + str(rwidth) +" x "+str(rheight), end='\r' )
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pp = postprocessing_upscale.scripts_postprocessing.PostprocessedImage(
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curImg
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)
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ups = postprocessing_upscale.ScriptPostprocessingUpscale()
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ups.process(
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pp,
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upscale_mode=ups_mode,
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upscale_by=upscale_by,
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upscale_to_width=rwidth,
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upscale_to_height=rheight,
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upscale_crop=False,
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upscaler_1_name=upscaler_name,
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upscaler_2_name=None,
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upscaler_2_visibility=0.0,
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)
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return pp.image
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def renderTxt2Img(prompt, negative_prompt, sampler, steps, cfg_scale, seed, width, height):
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processed = None
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p = StableDiffusionProcessingTxt2Img(
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sd_model=shared.sd_model,
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outpath_samples=shared.opts.outdir_txt2img_samples,
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outpath_grids=shared.opts.outdir_txt2img_grids,
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prompt=prompt,
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negative_prompt=negative_prompt,
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seed=seed,
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sampler_name=sampler,
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n_iter=1,
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steps=steps,
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cfg_scale=cfg_scale,
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width=width,
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height=height,
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)
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processed = process_images(p)
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newseed = p.seed
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return processed, newseed
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def renderImg2Img(
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prompt,
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negative_prompt,
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sampler,
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steps,
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cfg_scale,
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seed,
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width,
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height,
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init_image,
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mask_image,
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inpainting_denoising_strength,
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inpainting_mask_blur,
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inpainting_fill_mode,
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inpainting_full_res,
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inpainting_padding,
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):
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processed = None
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p = StableDiffusionProcessingImg2Img(
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sd_model=shared.sd_model,
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outpath_samples=shared.opts.outdir_img2img_samples,
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outpath_grids=shared.opts.outdir_img2img_grids,
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prompt=prompt,
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negative_prompt=negative_prompt,
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seed=seed,
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sampler_name=sampler,
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n_iter=1,
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steps=steps,
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cfg_scale=cfg_scale,
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width=width,
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height=height,
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init_images=[init_image],
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denoising_strength=inpainting_denoising_strength,
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mask_blur=inpainting_mask_blur,
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inpainting_fill=inpainting_fill_mode,
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inpaint_full_res=inpainting_full_res,
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inpaint_full_res_padding=inpainting_padding,
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mask=mask_image,
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)
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# p.latent_mask = Image.new("RGB", (p.width, p.height), "white")
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processed = process_images(p)
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newseed = p.seed
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return processed, newseed
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def fix_env_Path_ffprobe():
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envpath = os.environ["PATH"]
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ffppath = shared.opts.data.get("infzoom_ffprobepath", "")
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if ffppath and not ffppath in envpath:
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path_sep = ";" if os.name == "nt" else ":"
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os.environ["PATH"] = envpath + path_sep + ffppath
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def load_model_from_setting(model_field_name, progress, progress_desc):
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# fix typo in Automatic1111 vs Vlad111
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if hasattr(modules.sd_models, "checkpoint_alisases"):
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checkPList = modules.sd_models.checkpoint_alisases
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elif hasattr(modules.sd_models, "checkpoint_aliases"):
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checkPList = modules.sd_models.checkpoint_aliases
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else:
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raise Exception("This is not a compatible StableDiffusion Platform, can not access checkpoints")
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model_name = shared.opts.data.get(model_field_name)
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if model_name is not None and model_name != "":
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checkinfo = checkPList[model_name]
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if not checkinfo:
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raise NameError(model_field_name + " Does not exist in your models.")
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if progress:
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progress(0, desc=progress_desc + checkinfo.name)
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modules.sd_models.load_model(checkinfo)
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def create_zoom(
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prompts_array,
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negative_prompt,
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num_outpainting_steps,
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guidance_scale,
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num_inference_steps,
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custom_init_image,
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custom_exit_image,
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video_frame_rate,
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video_zoom_mode,
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video_start_frame_dupe_amount,
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video_last_frame_dupe_amount,
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inpainting_denoising_strength,
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inpainting_mask_blur,
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inpainting_fill_mode,
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inpainting_full_res,
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inpainting_padding,
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zoom_speed,
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seed,
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outputsizeW,
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outputsizeH,
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batchcount,
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sampler,
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upscale_do,
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upscaler_name,
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upscale_by,
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progress=None,
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):
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for i in range(batchcount):
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print(f"Batch {i+1}/{batchcount}")
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result = create_zoom_single(
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prompts_array,
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negative_prompt,
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num_outpainting_steps,
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guidance_scale,
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num_inference_steps,
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custom_init_image,
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custom_exit_image,
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video_frame_rate,
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video_zoom_mode,
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video_start_frame_dupe_amount,
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video_last_frame_dupe_amount,
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inpainting_denoising_strength,
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inpainting_mask_blur,
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inpainting_fill_mode,
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inpainting_full_res,
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inpainting_padding,
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zoom_speed,
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seed,
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outputsizeW,
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outputsizeH,
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sampler,
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upscale_do,
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upscaler_name,
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upscale_by,
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progress,
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)
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return result
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def create_zoom_single(
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prompts_array,
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negative_prompt,
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num_outpainting_steps,
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guidance_scale,
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num_inference_steps,
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custom_init_image,
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custom_exit_image,
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video_frame_rate,
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video_zoom_mode,
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video_start_frame_dupe_amount,
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video_last_frame_dupe_amount,
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inpainting_denoising_strength,
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inpainting_mask_blur,
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inpainting_fill_mode,
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inpainting_full_res,
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inpainting_padding,
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zoom_speed,
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seed,
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outputsizeW,
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outputsizeH,
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sampler,
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upscale_do,
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upscaler_name,
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upscale_by,
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progress=None,
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):
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# try:
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# if gr.Progress() is not None:
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# progress = gr.Progress()
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# progress(0, desc="Preparing Initial Image")
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# except Exception:
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# pass
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fix_env_Path_ffprobe()
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prompts = {}
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for x in prompts_array:
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try:
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key = int(x[0])
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value = str(x[1])
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prompts[key] = value
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except ValueError:
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pass
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assert len(prompts_array) > 0, "prompts is empty"
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width = closest_upper_divisible_by_eight(outputsizeW)
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height = closest_upper_divisible_by_eight(outputsizeH)
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current_image = Image.new(mode="RGBA", size=(width, height))
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mask_image = np.array(current_image)[:, :, 3]
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mask_image = Image.fromarray(255 - mask_image).convert("RGB")
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current_image = current_image.convert("RGB")
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current_seed = seed
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if custom_init_image:
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current_image = custom_init_image.resize(
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(width, height), resample=Image.LANCZOS
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)
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print("using Custom Initial Image")
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else:
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load_model_from_setting("infzoom_txt2img_model", progress, "Loading Model for txt2img: ")
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processed, newseed = renderTxt2Img(
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prompts[min(k for k in prompts.keys() if k >= 0)],
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negative_prompt,
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sampler,
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num_inference_steps,
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guidance_scale,
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current_seed,
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width,
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height,
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)
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current_image = processed.images[0]
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current_seed = newseed
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mask_width = math.trunc(width / 4) # was initially 512px => 128px
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mask_height = math.trunc(height / 4) # was initially 512px => 128px
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num_interpol_frames = round(video_frame_rate * zoom_speed)
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all_frames = []
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if upscale_do and progress:
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progress(0, desc="upscaling inital image")
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all_frames.append(
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do_upscaleImg(current_image, upscale_do, upscaler_name, upscale_by)
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if upscale_do
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else current_image
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)
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load_model_from_setting("infzoom_inpainting_model", progress, "Loading Model for inpainting/img2img: " )
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for i in range(num_outpainting_steps):
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print_out = "Outpaint step: " + str(i + 1) + " / " + str(num_outpainting_steps) + " Seed: " + str(current_seed)
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print(print_out)
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if progress:
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progress(((i + 1) / num_outpainting_steps), desc=print_out)
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prev_image_fix = current_image
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prev_image = shrink_and_paste_on_blank(current_image, mask_width, mask_height)
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current_image = prev_image
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# create mask (black image with white mask_width width edges)
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mask_image = np.array(current_image)[:, :, 3]
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mask_image = Image.fromarray(255 - mask_image).convert("RGB")
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# inpainting step
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current_image = current_image.convert("RGB")
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if custom_exit_image and ((i + 1) == num_outpainting_steps):
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current_image = custom_exit_image.resize(
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(width, height), resample=Image.LANCZOS
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)
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print("using Custom Exit Image")
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else:
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processed, newseed = renderImg2Img(
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prompts[max(k for k in prompts.keys() if k <= i)],
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negative_prompt,
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sampler,
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num_inference_steps,
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guidance_scale,
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current_seed,
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width,
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height,
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current_image,
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mask_image,
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inpainting_denoising_strength,
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inpainting_mask_blur,
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inpainting_fill_mode,
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inpainting_full_res,
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inpainting_padding,
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)
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current_image = processed.images[0]
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current_seed = newseed
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current_image.paste(prev_image, mask=prev_image)
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# interpolation steps between 2 inpainted images (=sequential zoom and crop)
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for j in range(num_interpol_frames - 1):
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interpol_image = current_image
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interpol_width = round(
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(
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1
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- (1 - 2 * mask_width / width)
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** (1 - (j + 1) / num_interpol_frames)
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)
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* width
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/ 2
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)
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interpol_height = round(
|
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(
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1
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- (1 - 2 * mask_height / height)
|
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** (1 - (j + 1) / num_interpol_frames)
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)
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* height
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/ 2
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)
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|
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interpol_image = interpol_image.crop(
|
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(
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interpol_width,
|
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interpol_height,
|
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width - interpol_width,
|
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height - interpol_height,
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)
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)
|
|
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interpol_image = interpol_image.resize((width, height))
|
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|
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# paste the higher resolution previous image in the middle to avoid drop in quality caused by zooming
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interpol_width2 = round(
|
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(1 - (width - 2 * mask_width) / (width - 2 * interpol_width))
|
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/ 2
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* width
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)
|
|
|
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interpol_height2 = round(
|
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(1 - (height - 2 * mask_height) / (height - 2 * interpol_height))
|
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/ 2
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* height
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)
|
|
|
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prev_image_fix_crop = shrink_and_paste_on_blank(
|
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prev_image_fix, interpol_width2, interpol_height2
|
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)
|
|
|
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interpol_image.paste(prev_image_fix_crop, mask=prev_image_fix_crop)
|
|
|
|
if upscale_do and progress:
|
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progress(((i + 1) / num_outpainting_steps), desc="upscaling interpol")
|
|
|
|
all_frames.append(
|
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do_upscaleImg(interpol_image, upscale_do, upscaler_name, upscale_by)
|
|
if upscale_do
|
|
else interpol_image
|
|
)
|
|
|
|
if upscale_do and progress:
|
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progress(((i + 1) / num_outpainting_steps), desc="upscaling current")
|
|
|
|
all_frames.append(
|
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do_upscaleImg(current_image, upscale_do, upscaler_name, upscale_by)
|
|
if upscale_do
|
|
else current_image
|
|
)
|
|
|
|
video_file_name = "infinite_zoom_" + str(int(time.time())) + ".mp4"
|
|
output_path = shared.opts.data.get(
|
|
"infzoom_outpath", shared.opts.data.get("outdir_img2img_samples")
|
|
)
|
|
save_path = os.path.join(
|
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output_path, shared.opts.data.get("infzoom_outSUBpath", "infinite-zooms")
|
|
)
|
|
if not os.path.exists(save_path):
|
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os.makedirs(save_path)
|
|
out = os.path.join(save_path, video_file_name)
|
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write_video(
|
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out,
|
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all_frames,
|
|
video_frame_rate,
|
|
video_zoom_mode,
|
|
int(video_start_frame_dupe_amount),
|
|
int(video_last_frame_dupe_amount),
|
|
)
|
|
|
|
return (
|
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out,
|
|
processed.images,
|
|
processed.js(),
|
|
plaintext_to_html(processed.info),
|
|
plaintext_to_html(""),
|
|
)
|
|
|
|
|
|
def validatePromptJson_throws(data):
|
|
with open(jsonprompt_schemafile, "r") as s:
|
|
schema = json.load(s)
|
|
validate(instance=data, schema=schema)
|
|
|
|
|
|
def putPrompts(files):
|
|
try:
|
|
with open(files.name, "r") as f:
|
|
file_contents = f.read()
|
|
data = json.loads(file_contents)
|
|
validatePromptJson_throws(data)
|
|
return [
|
|
gr.DataFrame.update(data["prompts"]),
|
|
gr.Textbox.update(data["negPrompt"]),
|
|
]
|
|
|
|
except Exception:
|
|
gr.Error(
|
|
"loading your prompt failed. It seems to be invalid. Your prompt table is preserved."
|
|
)
|
|
print(
|
|
"[InfiniteZoom:] Loading your prompt failed. It seems to be invalid. Your prompt table is preserved."
|
|
)
|
|
return [gr.DataFrame.update(), gr.Textbox.update()]
|
|
|
|
|
|
def clearPrompts():
|
|
return [
|
|
gr.DataFrame.update(value=[[0, "Infinite Zoom. Start over"]]),
|
|
gr.Textbox.update(""),
|
|
]
|
|
|
|
|
|
def on_ui_tabs():
|
|
with gr.Blocks(analytics_enabled=False) as infinite_zoom_interface:
|
|
gr.HTML(
|
|
"""
|
|
<p style="text-align: center;">
|
|
<a target="_blank" href="https://github.com/v8hid/infinite-zoom-automatic1111-webui"><img src="https://img.shields.io/static/v1?label=github&message=repository&color=blue&style=flat&logo=github&logoColor=white" style="display: inline;" alt="GitHub Repo"/></a>
|
|
<a href="https://discord.gg/v2nHqSrWdW"><img src="https://img.shields.io/discord/1095469311830806630?color=blue&label=discord&logo=discord&logoColor=white" style="display: inline;" alt="Discord server"></a>
|
|
</p>
|
|
|
|
"""
|
|
)
|
|
with gr.Row():
|
|
generate_btn = gr.Button(value="Generate video", variant="primary")
|
|
interrupt = gr.Button(value="Interrupt", elem_id="interrupt_training")
|
|
with gr.Row():
|
|
with gr.Column(scale=1, variant="panel"):
|
|
with gr.Tab("Main"):
|
|
main_outpaint_steps = gr.Slider(
|
|
minimum=2,
|
|
maximum=100,
|
|
step=1,
|
|
value=8,
|
|
label="Total Outpaint Steps",
|
|
info="The more it is, the longer your videos will be",
|
|
)
|
|
|
|
# safe reading json prompt
|
|
pr = shared.opts.data.get("infzoom_defPrompt", default_prompt)
|
|
if not pr:
|
|
pr = empty_prompt
|
|
|
|
try:
|
|
jpr = json.loads(pr)
|
|
validatePromptJson_throws(jpr)
|
|
except Exception:
|
|
jpr = invalid_prompt
|
|
|
|
main_prompts = gr.Dataframe(
|
|
type="array",
|
|
headers=["outpaint step", "prompt"],
|
|
datatype=["number", "str"],
|
|
row_count=1,
|
|
col_count=(2, "fixed"),
|
|
value=jpr["prompts"],
|
|
wrap=True,
|
|
)
|
|
|
|
main_negative_prompt = gr.Textbox(
|
|
value=jpr["negPrompt"], label="Negative Prompt"
|
|
)
|
|
|
|
# these button will be moved using JS unde the dataframe view as small ones
|
|
exportPrompts_button = gr.Button(
|
|
value="Export prompts",
|
|
variant="secondary",
|
|
elem_classes="sm infzoom_tab_butt",
|
|
elem_id="infzoom_exP_butt",
|
|
)
|
|
importPrompts_button = gr.UploadButton(
|
|
label="Import prompts",
|
|
variant="secondary",
|
|
elem_classes="sm infzoom_tab_butt",
|
|
elem_id="infzoom_imP_butt",
|
|
)
|
|
exportPrompts_button.click(
|
|
None,
|
|
_js="exportPrompts",
|
|
inputs=[main_prompts, main_negative_prompt],
|
|
outputs=None,
|
|
)
|
|
importPrompts_button.upload(
|
|
fn=putPrompts,
|
|
outputs=[main_prompts, main_negative_prompt],
|
|
inputs=[importPrompts_button],
|
|
)
|
|
|
|
clearPrompts_button = gr.Button(
|
|
value="Clear prompts",
|
|
variant="secondary",
|
|
elem_classes="sm infzoom_tab_butt",
|
|
elem_id="infzoom_clP_butt",
|
|
)
|
|
clearPrompts_button.click(
|
|
fn=clearPrompts,
|
|
inputs=[],
|
|
outputs=[main_prompts, main_negative_prompt],
|
|
)
|
|
with gr.Row():
|
|
seed = gr.Number(label="Seed", value=-1, precision=0, interactive=True)
|
|
main_sampler = gr.Dropdown(
|
|
label="Sampler",
|
|
choices=available_samplers,
|
|
value="Euler a",
|
|
type="value",
|
|
)
|
|
with gr.Row():
|
|
main_width = gr.Slider(
|
|
minimum=16,
|
|
maximum=2048,
|
|
value=shared.opts.data.get("infzoom_outsizeW", 512),
|
|
step=16,
|
|
label="Output Width",
|
|
)
|
|
main_height = gr.Slider(
|
|
minimum=16,
|
|
maximum=2048,
|
|
value=shared.opts.data.get("infzoom_outsizeH", 512),
|
|
step=16,
|
|
label="Output Height",
|
|
)
|
|
with gr.Row():
|
|
main_guidance_scale = gr.Slider(
|
|
minimum=0.1,
|
|
maximum=15,
|
|
step=0.1,
|
|
value=7,
|
|
label="Guidance Scale",
|
|
)
|
|
sampling_step = gr.Slider(
|
|
minimum=1,
|
|
maximum=100,
|
|
step=1,
|
|
value=50,
|
|
label="Sampling Steps for each outpaint",
|
|
)
|
|
with gr.Row():
|
|
init_image = gr.Image(type="pil", label="custom initial image")
|
|
exit_image = gr.Image(type="pil", label="custom exit image")
|
|
|
|
batchcount_slider = gr.Slider(
|
|
minimum=1,
|
|
maximum=25,
|
|
value=shared.opts.data.get("infzoom_batchcount", 1),
|
|
step=1,
|
|
label="Batch Count",
|
|
)
|
|
with gr.Tab("Video"):
|
|
video_frame_rate = gr.Slider(
|
|
label="Frames per second",
|
|
value=30,
|
|
minimum=1,
|
|
maximum=60,
|
|
)
|
|
video_zoom_mode = gr.Radio(
|
|
label="Zoom mode",
|
|
choices=["Zoom-out", "Zoom-in"],
|
|
value="Zoom-out",
|
|
type="index",
|
|
)
|
|
video_start_frame_dupe_amount = gr.Slider(
|
|
label="number of start frame dupe",
|
|
info="Frames to freeze at the start of the video",
|
|
value=0,
|
|
minimum=1,
|
|
maximum=60,
|
|
)
|
|
video_last_frame_dupe_amount = gr.Slider(
|
|
label="number of last frame dupe",
|
|
info="Frames to freeze at the end of the video",
|
|
value=0,
|
|
minimum=1,
|
|
maximum=60,
|
|
)
|
|
video_zoom_speed = gr.Slider(
|
|
label="Zoom Speed",
|
|
value=1.0,
|
|
minimum=0.1,
|
|
maximum=20.0,
|
|
step=0.1,
|
|
info="Zoom speed in seconds (higher values create slower zoom)",
|
|
)
|
|
|
|
with gr.Tab("Outpaint"):
|
|
inpainting_denoising_strength = gr.Slider(
|
|
label="Denoising Strength", minimum=0.75, maximum=1, value=1
|
|
)
|
|
inpainting_mask_blur = gr.Slider(
|
|
label="Mask Blur", minimum=0, maximum=64, value=0
|
|
)
|
|
inpainting_fill_mode = gr.Radio(
|
|
label="Masked content",
|
|
choices=["fill", "original", "latent noise", "latent nothing"],
|
|
value="latent noise",
|
|
type="index",
|
|
)
|
|
inpainting_full_res = gr.Checkbox(label="Inpaint Full Resolution")
|
|
inpainting_padding = gr.Slider(
|
|
label="masked padding", minimum=0, maximum=256, value=0
|
|
)
|
|
|
|
with gr.Tab("Post proccess"):
|
|
upscale_do = gr.Checkbox(False, label="Enable Upscale")
|
|
upscaler_name = gr.Dropdown(
|
|
label="Upscaler",
|
|
elem_id="infZ_upscaler",
|
|
choices=[x.name for x in shared.sd_upscalers],
|
|
value=shared.sd_upscalers[0].name,
|
|
)
|
|
|
|
upscale_by = gr.Slider(
|
|
label="Upscale by factor", minimum=1, maximum=8, value=1
|
|
)
|
|
with gr.Accordion("Help", open=False):
|
|
gr.Markdown(
|
|
"""# Performance critical
|
|
Depending on amount of frames and which upscaler you choose it might took a long time to render.
|
|
Our best experience and trade-off is the R-ERSGAn4x upscaler.
|
|
"""
|
|
)
|
|
|
|
with gr.Column(scale=1, variant="compact"):
|
|
output_video = gr.Video(label="Output").style(width=512, height=512)
|
|
(
|
|
out_image,
|
|
generation_info,
|
|
html_info,
|
|
html_log,
|
|
) = create_output_panel(
|
|
"infinite-zoom", shared.opts.outdir_img2img_samples
|
|
)
|
|
generate_btn.click(
|
|
fn=wrap_gradio_gpu_call(create_zoom, extra_outputs=[None, "", ""]),
|
|
inputs=[
|
|
main_prompts,
|
|
main_negative_prompt,
|
|
main_outpaint_steps,
|
|
main_guidance_scale,
|
|
sampling_step,
|
|
init_image,
|
|
exit_image,
|
|
video_frame_rate,
|
|
video_zoom_mode,
|
|
video_start_frame_dupe_amount,
|
|
video_last_frame_dupe_amount,
|
|
inpainting_denoising_strength,
|
|
inpainting_mask_blur,
|
|
inpainting_fill_mode,
|
|
inpainting_full_res,
|
|
inpainting_padding,
|
|
video_zoom_speed,
|
|
seed,
|
|
main_width,
|
|
main_height,
|
|
batchcount_slider,
|
|
main_sampler,
|
|
upscale_do,
|
|
upscaler_name,
|
|
upscale_by,
|
|
],
|
|
outputs=[output_video, out_image, generation_info, html_info, html_log],
|
|
)
|
|
interrupt.click(fn=lambda: shared.state.interrupt(), inputs=[], outputs=[])
|
|
infinite_zoom_interface.queue()
|
|
return [(infinite_zoom_interface, "Infinite Zoom", "iz_interface")]
|
|
|
|
|
|
def on_ui_settings():
|
|
section = ("infinite-zoom", "Infinite Zoom")
|
|
|
|
shared.opts.add_option(
|
|
"outputs"
|
|
"infzoom_outpath",
|
|
shared.OptionInfo(
|
|
"",
|
|
"Path where to store your infinite video. Default is Outputs",
|
|
gr.Textbox,
|
|
{"interactive": True},
|
|
section=section,
|
|
),
|
|
)
|
|
|
|
shared.opts.add_option(
|
|
"infzoom_outSUBpath",
|
|
shared.OptionInfo(
|
|
"infinite-zooms",
|
|
"Which subfolder name to be created in the outpath. Default is 'infinite-zooms'",
|
|
gr.Textbox,
|
|
{"interactive": True},
|
|
section=section,
|
|
),
|
|
)
|
|
|
|
shared.opts.add_option(
|
|
"infzoom_outsizeW",
|
|
shared.OptionInfo(
|
|
512,
|
|
"Default width of your video",
|
|
gr.Slider,
|
|
{"minimum": 16, "maximum": 2048, "step": 16},
|
|
section=section,
|
|
),
|
|
)
|
|
|
|
shared.opts.add_option(
|
|
"infzoom_outsizeH",
|
|
shared.OptionInfo(
|
|
512,
|
|
"Default height your video",
|
|
gr.Slider,
|
|
{"minimum": 16, "maximum": 2048, "step": 16},
|
|
section=section,
|
|
),
|
|
)
|
|
|
|
shared.opts.add_option(
|
|
"infzoom_ffprobepath",
|
|
shared.OptionInfo(
|
|
"",
|
|
"Writing videos has dependency to an existing FFPROBE executable on your machine. D/L here (https://github.com/BtbN/FFmpeg-Builds/releases) your OS variant and point to your installation path",
|
|
gr.Textbox,
|
|
{"interactive": True},
|
|
section=section,
|
|
),
|
|
)
|
|
|
|
shared.opts.add_option(
|
|
"infzoom_txt2img_model",
|
|
shared.OptionInfo(
|
|
None,
|
|
"Name of your desired model to render keyframes (txt2img)",
|
|
gr.Dropdown,
|
|
lambda: {"choices": shared.list_checkpoint_tiles()},
|
|
section=section,
|
|
),
|
|
)
|
|
|
|
shared.opts.add_option(
|
|
"infzoom_inpainting_model",
|
|
shared.OptionInfo(
|
|
None,
|
|
"Name of your desired inpaint model (img2img-inpaint). Default is vanilla sd-v1-5-inpainting.ckpt ",
|
|
gr.Dropdown,
|
|
lambda: {"choices": shared.list_checkpoint_tiles()},
|
|
section=section,
|
|
),
|
|
)
|
|
|
|
shared.opts.add_option(
|
|
"infzoom_defPrompt",
|
|
shared.OptionInfo(
|
|
default_prompt,
|
|
"Default prompt-setup to start with'",
|
|
gr.Code,
|
|
{"interactive": True, "language": "json"},
|
|
section=section,
|
|
),
|
|
)
|
|
|
|
|
|
script_callbacks.on_ui_tabs(on_ui_tabs)
|
|
script_callbacks.on_ui_settings(on_ui_settings)
|