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- import math
- import gradio as gr
- import modules.scripts as scripts
- from modules import deepbooru, images, processing, shared
- from modules.processing import Processed
- from modules.shared import opts, state
- class Script(scripts.Script):
- def title(self):
- return "Loopback"
- def show(self, is_img2img):
- return is_img2img
- def ui(self, is_img2img):
- loops = gr.Slider(minimum=1, maximum=32, step=1, label='Loops', value=4, elem_id=self.elem_id("loops"))
- 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")
- return [loops, final_denoising_strength, denoising_curve, append_interrogation]
- def run(self, p, loops, final_denoising_strength, denoising_curve, append_interrogation):
- processing.fix_seed(p)
- batch_count = p.n_iter
- p.extra_generation_params = {
- "Final denoising strength": final_denoising_strength,
- "Denoising curve": denoising_curve
- }
- p.batch_size = 1
- p.n_iter = 1
- info = None
- initial_seed = None
- initial_info = None
- initial_denoising_strength = p.denoising_strength
- grids = []
- all_images = []
- original_init_image = p.init_images
- original_prompt = p.prompt
- original_inpainting_fill = p.inpainting_fill
- state.job_count = loops * batch_count
- initial_color_corrections = [processing.setup_color_correction(p.init_images[0])]
- def calculate_denoising_strength(loop):
- strength = initial_denoising_strength
- if loops == 1:
- return strength
- progress = loop / (loops - 1)
- if denoising_curve == "Aggressive":
- strength = math.sin((progress) * math.pi * 0.5)
- elif denoising_curve == "Lazy":
- strength = 1 - math.cos((progress) * math.pi * 0.5)
- else:
- strength = progress
- change = (final_denoising_strength - initial_denoising_strength) * strength
- return initial_denoising_strength + change
- history = []
- for n in range(batch_count):
- # Reset to original init image at the start of each batch
- p.init_images = original_init_image
- # Reset to original denoising strength
- p.denoising_strength = initial_denoising_strength
- last_image = None
- for i in range(loops):
- p.n_iter = 1
- p.batch_size = 1
- p.do_not_save_grid = True
- if opts.img2img_color_correction:
- p.color_corrections = initial_color_corrections
- 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)
- # Generation cancelled.
- if state.interrupted:
- 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 # Set "masked content" to "original" for next loop.
- if batch_count == 1:
- history.append(last_image)
- all_images.append(last_image)
- 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:
- 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)
-
- all_images = grids + all_images
- processed = Processed(p, all_images, initial_seed, initial_info)
- return processed
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