mirror of
https://github.com/vladmandic/automatic
synced 2026-08-25 22:20:46 +02:00
resonantsky
207 lines
9.0 KiB
Python
207 lines
9.0 KiB
Python
import os
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import glob
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import copy
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import gradio as gr
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from PIL import Image
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from modules import processing, scripts_manager, sd_samplers, images
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from modules import shared
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from modules.processing import Processed, get_processed
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from modules.shared import state, log
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class Script(scripts_manager.Script):
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def title(self):
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return "CeeTeeDee's I2I folder batch inference"
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def show(self, is_img2img): # pylint: disable=unused-argument
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return True
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def ui(self, is_img2img): # pylint: disable=unused-argument
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with gr.Row():
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gr.HTML('<p><h2>Image folder batch inference by CeeTeeDee</h2><p><br>')
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with gr.Row():
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gr.HTML('<p><b>The purpose of this script is to assist in Img2Img of folders containing incrementally named images such as one would use when extracting frames from video.</p><br><p>It can use any model though is best done with high consistency models such as Flux.</p><br><p>Note the full path of your folder containing incrementally numbered images such as Frame000.png to Frame900.png and the script will run Inference on each image in order saving the images with identical names in an output subfolder.</p>')
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with gr.Row():
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folder = gr.Textbox(
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label="Input folder",
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placeholder="Path to folder containing PNG images",
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elem_id=self.elem_id("folder"),
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)
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with gr.Row():
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output_dir = gr.Textbox(
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label="Output folder",
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placeholder="Leave empty to save alongside inputs in <input folder>/output/",
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elem_id=self.elem_id("output_dir"),
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)
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with gr.Row():
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prompt_override = gr.Textbox(
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label="Prompt override",
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placeholder="Leave empty to use the prompt from the main panel",
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elem_id=self.elem_id("prompt_override"),
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)
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with gr.Row():
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negative_override = gr.Textbox(
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label="Negative prompt override",
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placeholder="Leave empty to use the negative prompt from the main panel",
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elem_id=self.elem_id("negative_override"),
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)
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with gr.Row():
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seed_override = gr.Textbox(
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label="Seed override (-1 = use panel seed)",
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value="-1",
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elem_id=self.elem_id("seed_override"),
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)
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with gr.Row():
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steps_override = gr.Slider(
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minimum=0, maximum=150, step=1, value=0,
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label="Steps override (0 = use panel steps)",
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elem_id=self.elem_id("steps_override"),
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)
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with gr.Row():
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cfg_scale_override = gr.Slider(
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minimum=0.0, maximum=30.0, step=0.5, value=0.0,
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label="Guidance scale override (0.0 = use panel value)",
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elem_id=self.elem_id("cfg_scale_override"),
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)
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with gr.Row():
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sampler_override = gr.Dropdown(
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label="Sampler override (empty = use panel sampler)",
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choices=[""] + [s.name for s in sd_samplers.samplers_for_img2img],
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value="",
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elem_id=self.elem_id("sampler_override"),
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)
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with gr.Row():
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strength_override = gr.Slider(
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minimum=0.0, maximum=1.0, step=0.01, value=0.0,
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label="Denoising strength override (0.0 = use panel value)",
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elem_id=self.elem_id("strength_override"),
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)
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with gr.Row():
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gr.HTML('<b>Post-inference resize</b>')
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with gr.Row():
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resize_enabled = gr.Checkbox(
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label="Enable post-inference resize",
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value=False,
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elem_id=self.elem_id("resize_enabled"),
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)
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with gr.Row():
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_upscaler_choices = [x.name for x in shared.sd_upscalers] or ["None"]
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resize_mode = gr.Dropdown(
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label="Resize mode",
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choices=shared.resize_modes,
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type="index",
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value="None",
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elem_id=self.elem_id("resize_mode"),
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)
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resize_name = gr.Dropdown(
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label="Resize method",
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choices=_upscaler_choices,
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value=_upscaler_choices[0],
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elem_id=self.elem_id("resize_name"),
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)
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with gr.Row():
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resize_scale = gr.Slider(
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minimum=1.0, maximum=8.0, step=0.05, value=2.0,
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label="Scale factor",
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elem_id=self.elem_id("resize_scale"),
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)
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return [folder, output_dir, prompt_override, negative_override, seed_override, steps_override, cfg_scale_override, sampler_override, strength_override, resize_enabled, resize_mode, resize_name, resize_scale]
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def run(self, p, folder, output_dir, prompt_override, negative_override, seed_override, steps_override, cfg_scale_override, sampler_override, strength_override, resize_enabled, resize_mode, resize_name, resize_scale): # pylint: disable=arguments-differ
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folder = (folder or "").strip()
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if not folder or not os.path.isdir(folder):
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log.error(f"Image folder batch: invalid or missing folder: {folder!r}")
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return Processed(p, [], p.seed, "Invalid or missing folder")
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files = sorted(glob.glob(os.path.join(folder, "*.png")))
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if not files:
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log.error(f"Image folder batch: no PNG files found in: {folder!r}")
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return Processed(p, [], p.seed, "No PNG files found")
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out_dir = (output_dir or "").strip() or os.path.join(folder, "output")
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os.makedirs(out_dir, exist_ok=True)
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resize_out_dir = os.path.join(os.path.dirname(out_dir), "output-resized") if resize_enabled else None
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if resize_out_dir:
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os.makedirs(resize_out_dir, exist_ok=True)
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log.info(f"Image folder batch: folder={folder!r} images={len(files)} output={out_dir!r}")
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processing.fix_seed(p)
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try:
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seed_val = int(str(seed_override).strip())
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except (ValueError, TypeError):
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seed_val = -1
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if seed_val >= 0:
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p.seed = seed_val
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if int(steps_override) > 0:
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p.steps = int(steps_override)
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if float(cfg_scale_override) > 0.0:
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p.cfg_scale = float(cfg_scale_override)
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if str(sampler_override).strip():
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p.sampler_name = str(sampler_override).strip()
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if float(strength_override) > 0.0:
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p.denoising_strength = float(strength_override)
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if prompt_override.strip():
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p.prompt = prompt_override.strip()
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if negative_override.strip():
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p.negative_prompt = negative_override.strip()
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state.job_count = len(files)
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all_images = []
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all_prompts = []
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all_seeds = []
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all_negative = []
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infotexts = []
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for i, filepath in enumerate(files):
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if state.interrupted:
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break
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state.job = f"{i + 1}/{len(files)}"
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state.job_no = i
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img = Image.open(filepath)
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if img.mode not in ('RGB', 'L'):
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img = img.convert('RGB')
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cp = copy.copy(p)
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cp.init_images = [img]
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cp.width = img.width
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cp.height = img.height
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cp.batch_size = 1
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cp.n_iter = 1
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cp.do_not_save_samples = True
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cp.do_not_save_grid = True
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log.info(f"Image folder batch: [{i + 1}/{len(files)}] file={os.path.basename(filepath)} size={img.size} seed={cp.seed}")
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proc = processing.process_images(cp)
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img.close()
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if proc is None or not proc.images:
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log.warning(f"Image folder batch: no output for {filepath!r}")
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continue
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out_img = proc.images[0]
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if resize_enabled and resize_mode != 0 and resize_name != 'None':
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target_w = int(out_img.width * resize_scale)
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target_h = int(out_img.height * resize_scale)
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resized_img = images.resize_image(resize_mode, out_img, target_w, target_h, resize_name)
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log.info(f"Image folder batch: resized to {resized_img.size} mode={shared.resize_modes[resize_mode]!r} method={resize_name!r}")
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res_name = os.path.splitext(os.path.basename(filepath))[0] + ".png"
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resized_img.save(os.path.join(resize_out_dir, res_name))
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out_name = os.path.splitext(os.path.basename(filepath))[0] + ".png"
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out_path = os.path.join(out_dir, out_name)
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out_img.save(out_path)
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log.info(f"Image folder batch: saved {out_path!r}")
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all_images.append(out_img)
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all_prompts += proc.all_prompts
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all_seeds += proc.all_seeds
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all_negative += proc.all_negative_prompts
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infotexts += proc.infotexts
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return get_processed(p, all_images, p.seed, "", all_prompts=all_prompts, all_seeds=all_seeds, all_negative_prompts=all_negative, infotexts=infotexts)
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