| import math
|
|
|
| import gradio as gr
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| import modules.scripts as scripts
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| from modules import deepbooru, images, processing, shared
|
| 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"))
|
| denoising_curve = gr.Dropdown(label="Denoising strength curve", choices=["Aggressive", "Linear", "Lazy"], value="Linear")
|
| 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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| }
|
|
|
| p.batch_size = 1
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| p.n_iter = 1
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|
|
| info = None
|
| 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
|
|
|
| progress = loop / (loops - 1)
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| if denoising_curve == "Aggressive":
|
| strength = math.sin((progress) * math.pi * 0.5)
|
| 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
|
| return initial_denoising_strength + change
|
|
|
| history = []
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|
|
| for n in range(batch_count):
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|
|
| p.init_images = original_init_image
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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):
|
| 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
|
|
|
| if append_interrogation != "None":
|
| p.prompt = f"{original_prompt}, " if original_prompt else ""
|
| if append_interrogation == "CLIP":
|
| p.prompt += shared.interrogator.interrogate(p.init_images[0])
|
| 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}"
|
|
|
| processed = processing.process_images(p)
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|
|
|
|
| if state.interrupted:
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| break
|
|
|
| if initial_seed is None:
|
| initial_seed = processed.seed
|
| initial_info = processed.info
|
|
|
| p.seed = processed.seed + 1
|
| p.denoising_strength = calculate_denoising_strength(i + 1)
|
|
|
| if state.skipped:
|
| break
|
|
|
| last_image = processed.images[0]
|
| p.init_images = [last_image]
|
| p.inpainting_fill = 1
|
|
|
| if batch_count == 1:
|
| history.append(last_image)
|
| all_images.append(last_image)
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|
|
| if batch_count > 1 and not state.skipped and not state.interrupted:
|
| history.append(last_image)
|
| all_images.append(last_image)
|
|
|
| p.inpainting_fill = original_inpainting_fill
|
|
|
| if state.interrupted:
|
| break
|
|
|
| if len(history) > 1:
|
| grid = images.image_grid(history, rows=1)
|
| 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)
|
|
|
| if opts.return_grid:
|
| grids.append(grid)
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|
|
| all_images = grids + all_images
|
|
|
| processed = Processed(p, all_images, initial_seed, initial_info)
|
|
|
| return processed
|
|
|
