5c7ab90a4e
This code (suggested by @abvgdeabvgde2 ) literally does the same thing and it does not break with Python 3.9, making it helpful for Google Colab users (me included). fixes #8927 Also a partial fix for #8902 but it does not resolve the unresponsive UI problem faced by @Archon332
141 lines
5.2 KiB
Python
141 lines
5.2 KiB
Python
import math
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import gradio as gr
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import modules.scripts as scripts
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from modules import deepbooru, images, processing, shared
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from modules.processing import Processed
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from modules.shared import opts, state
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class Script(scripts.Script):
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def title(self):
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return "Loopback"
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def show(self, is_img2img):
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return is_img2img
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def ui(self, is_img2img):
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loops = gr.Slider(minimum=1, maximum=32, step=1, label='Loops', value=4, elem_id=self.elem_id("loops"))
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final_denoising_strength = gr.Slider(minimum=0, maximum=1, step=0.01, label='Final denoising strength', value=0.5, elem_id=self.elem_id("final_denoising_strength"))
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denoising_curve = gr.Dropdown(label="Denoising strength curve", choices=["Aggressive", "Linear", "Lazy"], value="Linear")
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append_interrogation = gr.Dropdown(label="Append interrogated prompt at each iteration", choices=["None", "CLIP", "DeepBooru"], value="None")
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return [loops, final_denoising_strength, denoising_curve, append_interrogation]
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def run(self, p, loops, final_denoising_strength, denoising_curve, append_interrogation):
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processing.fix_seed(p)
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batch_count = p.n_iter
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p.extra_generation_params = {
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"Final denoising strength": final_denoising_strength,
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"Denoising curve": denoising_curve
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}
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p.batch_size = 1
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p.n_iter = 1
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info = None
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initial_seed = None
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initial_info = None
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initial_denoising_strength = p.denoising_strength
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grids = []
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all_images = []
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original_init_image = p.init_images
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original_prompt = p.prompt
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original_inpainting_fill = p.inpainting_fill
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state.job_count = loops * batch_count
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initial_color_corrections = [processing.setup_color_correction(p.init_images[0])]
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def calculate_denoising_strength(loop):
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strength = initial_denoising_strength
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if loops == 1:
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return strength
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progress = loop / (loops - 1)
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if denoising_curve == "Aggressive":
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strength = math.sin((progress) * math.pi * 0.5)
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elif denoising_curve == "Lazy":
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strength = 1 - math.cos((progress) * math.pi * 0.5)
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else:
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strength = progress
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change = (final_denoising_strength - initial_denoising_strength) * strength
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return initial_denoising_strength + change
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history = []
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for n in range(batch_count):
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# Reset to original init image at the start of each batch
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p.init_images = original_init_image
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# Reset to original denoising strength
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p.denoising_strength = initial_denoising_strength
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last_image = None
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for i in range(loops):
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p.n_iter = 1
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p.batch_size = 1
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p.do_not_save_grid = True
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if opts.img2img_color_correction:
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p.color_corrections = initial_color_corrections
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if append_interrogation != "None":
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p.prompt = original_prompt + ", " if original_prompt != "" else ""
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if append_interrogation == "CLIP":
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p.prompt += shared.interrogator.interrogate(p.init_images[0])
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elif append_interrogation == "DeepBooru":
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p.prompt += deepbooru.model.tag(p.init_images[0])
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state.job = f"Iteration {i + 1}/{loops}, batch {n + 1}/{batch_count}"
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processed = processing.process_images(p)
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# Generation cancelled.
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if state.interrupted:
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break
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if initial_seed is None:
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initial_seed = processed.seed
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initial_info = processed.info
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p.seed = processed.seed + 1
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p.denoising_strength = calculate_denoising_strength(i + 1)
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if state.skipped:
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break
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last_image = processed.images[0]
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p.init_images = [last_image]
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p.inpainting_fill = 1 # Set "masked content" to "original" for next loop.
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if batch_count == 1:
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history.append(last_image)
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all_images.append(last_image)
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if batch_count > 1 and not state.skipped and not state.interrupted:
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history.append(last_image)
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all_images.append(last_image)
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p.inpainting_fill = original_inpainting_fill
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if state.interrupted:
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break
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if len(history) > 1:
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grid = images.image_grid(history, rows=1)
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if opts.grid_save:
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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)
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if opts.return_grid:
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grids.append(grid)
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all_images = grids + all_images
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processed = Processed(p, all_images, initial_seed, initial_info)
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return processed
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