Datasets:
spinosorus
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html.stablediffusion

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p.denoising_strength = first_denoise
p.control_net_input_image = p.control_net_input_image.resize((initial_width, p.height))
frames.append(p.control_net_input_image)
if append_interrogation != "None":
p.prompt = original_prompt + ", " if original_prompt != "" else ""
if append_interrogation == "CLIP":
p.prompt += shared.interrogator.interrogate(p.init_images[0])
elif append_interrogation == "DeepBooru":
p.prompt += deepbooru.model.tag(p.init_images[0])
state.job = f"Iteration {i + 1}/{loops}, batch {n + 1}/{batch_count}"
processed = processing.process_images(p)
if initial_seed is None:
initial_seed = processed.seed
initial_info = processed.info
init_img = processed.images[0]
if(i > 0):
init_img = init_img.crop((initial_width, 0, initial_width*2, p.height))
if third_frame_image != "None":
if third_frame_image == "FirstGen" and i == 0:
third_image = init_img
third_image_index = 0
elif third_frame_image == "GuideImg" and i == 0:
third_image = original_init_image[0]
third_image_index = 0
elif third_frame_image == "Historical":
third_image = processed.images[0].crop((0, 0, initial_width, p.height))
third_image_index = (i-1)
p.init_images = [init_img]
if(freeze_seed):
p.seed = processed.seed
else:
p.seed = processed.seed + 1
history.append(init_img)
if opts.samples_save:
images.save_image(init_img, p.outpath_samples, "Frame", p.seed, p.prompt, opts.grid_format, info=info, short_filename=not opts.grid_extended_filename, grid=True, p=p)
frames.append(processed.images[0])
grid = images.image_grid(history, rows=1)
if opts.grid_save:
images.save_image(grid, p.outpath_grids, "grid", initial_seed, p.prompt, opts.grid_format, info=info, short_filename=not opts.grid_extended_filename, grid=True, p=p)
grids.append(grid)
# all_images += history + frames
all_images += history
p.seed = p.seed+1
if opts.return_grid:
all_images = grids + all_images
processed = Processed(p, all_images, initial_seed, initial_info)
return processed
Negative prompt:
Steps: 32, Sampler: UniPC, CFG scale: 7.5, Size: 540x960, Model hash: d3976436e9, Denoising strength: 0.5, Hires upscale: 2, Hires steps: 10, Hires upscaler: 4x-UltraSharp, Clip skip: 2
import math
import os
import sys
import traceback
import modules.scripts as scripts
import gradio as gr
from modules.processing import Processed, process_images
class Script(scripts.Script):
def title(self):
return "Run n times"
def ui(self, is_img2img):
n = gr.Textbox(label="n")
return [n]
def run(self, p, n):
for x in range(int(n)):
p.seed = -1
proc = process_images(p)
image = proc.images
return Processed(p, image, p.seed, proc.info)