mirror of
https://github.com/vladmandic/sdnext.git
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761ea1c327
Change "Images folder" and "Grids folder" settings to act as base paths that combine with specific folder settings, rather than replacing them. - Add resolve_output_path() helper function to modules/paths.py - Update all output path usages to use combined base + specific paths - Update gallery API to return resolved paths with display labels - Update gallery UI to show short labels with full path on hover Example: If base is "C:\Database\" and specific is "outputs/text", the resolved path becomes "C:\Database\outputs\text" Edge cases handled: - Empty base path: uses specific path directly (backward compatible) - Absolute specific path: ignores base path - Empty specific path: uses base path only
336 lines
15 KiB
Python
336 lines
15 KiB
Python
import os
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import itertools # SBM Batch frames
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import numpy as np
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import filetype
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from PIL import Image, ImageOps, ImageFilter, ImageEnhance, ImageChops, UnidentifiedImageError
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from modules import scripts_manager, shared, processing, images, errors
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from modules.generation_parameters_copypaste import create_override_settings_dict
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from modules.ui_common import plaintext_to_html
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from modules.memstats import memory_stats
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from modules.paths import resolve_output_path
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debug = shared.log.trace if os.environ.get('SD_PROCESS_DEBUG', None) is not None else lambda *args, **kwargs: None
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debug('Trace: PROCESS')
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def validate_inputs(inputs):
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outputs = []
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for image in inputs:
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if filetype.is_image(image):
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outputs.append(image)
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else:
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shared.log.warning(f'Input skip: file="{image}" filetype={filetype.guess(image)}')
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return outputs
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def process_batch(p, input_files, input_dir, output_dir, inpaint_mask_dir, args):
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# shared.log.debug(f'batch: {input_files}|{input_dir}|{output_dir}|{inpaint_mask_dir}')
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processing.fix_seed(p)
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image_files = []
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if input_files is not None and len(input_files) > 0:
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image_files = [f.name for f in input_files]
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image_files = validate_inputs(image_files)
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shared.log.info(f'Process batch: input images={len(image_files)}')
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elif os.path.isdir(input_dir):
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image_files = [os.path.join(input_dir, f) for f in os.listdir(input_dir)]
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image_files = validate_inputs(image_files)
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shared.log.info(f'Process batch: input folder="{input_dir}" images={len(image_files)}')
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is_inpaint_batch = False
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if inpaint_mask_dir and os.path.isdir(inpaint_mask_dir):
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inpaint_masks = [os.path.join(inpaint_mask_dir, f) for f in os.listdir(inpaint_mask_dir)]
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inpaint_masks = validate_inputs(inpaint_masks)
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is_inpaint_batch = len(inpaint_masks) > 0
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shared.log.info(f'Process batch: mask folder="{input_dir}" images={len(inpaint_masks)}')
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p.do_not_save_grid = True
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p.do_not_save_samples = True
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p.default_prompt = p.prompt
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if p.n_iter > 1:
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p.n_iter = 1
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shared.log.warning(f'Process batch: batch_count={p.n_iter} forced to 1')
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shared.state.job_count = len(image_files) * p.n_iter
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if shared.opts.batch_frame_mode: # SBM Frame mode is on, process each image in batch with same seed
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window_size = p.batch_size
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btcrept = 1
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p.seed = [p.seed] * window_size # SBM MONKEYPATCH: Need to change processing to support a fixed seed value.
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p.subseed = [p.subseed] * window_size # SBM MONKEYPATCH
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shared.log.info(f"Process batch: inputs={len(image_files)} outputs={p.n_iter}x{len(image_files)} parallel={window_size}")
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else: # SBM Frame mode is off, standard operation of repeating same images with sequential seed.
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window_size = 1
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btcrept = p.batch_size
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shared.log.info(f"Process batch: inputs={len(image_files)} outputs={p.n_iter*p.batch_size}x{len(image_files)}")
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for i in range(0, len(image_files), window_size):
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if shared.state.skipped:
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shared.state.skipped = False
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if shared.state.interrupted:
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break
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batch_image_files = image_files[i:i+window_size]
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batch_images = []
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for image_file in batch_image_files:
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try:
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img = Image.open(image_file)
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img = ImageOps.exif_transpose(img)
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batch_images.append(img)
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# p.init()
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p.width = int(img.width * p.scale_by)
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p.height = int(img.height * p.scale_by)
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caption_file = os.path.splitext(image_file)[0] + '.txt'
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prompt_type='default'
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if os.path.exists(caption_file):
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with open(caption_file, 'r', encoding='utf8') as f:
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p.prompt = f.read()
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prompt_type='file'
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else:
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p.prompt = p.default_prompt
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p.all_prompts = None
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p.all_negative_prompts = None
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p.all_seeds = None
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p.all_subseeds = None
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shared.log.debug(f'Process batch: image="{image_file}" prompt={prompt_type} i={i+1}/{len(image_files)}')
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except UnidentifiedImageError as e:
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shared.log.error(f'Process batch: image="{image_file}" {e}')
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if len(batch_images) == 0:
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shared.log.warning("Process batch: no images found in batch")
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continue
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batch_images = batch_images * btcrept # Standard mode sends the same image per batchsize.
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p.init_images = batch_images
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if is_inpaint_batch:
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# try to find corresponding mask for an image using simple filename matching
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batch_mask_images = []
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for image_file in batch_image_files:
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mask_image_path = os.path.join(inpaint_mask_dir, os.path.basename(image_file))
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# if not found use first one ("same mask for all images" use-case)
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if mask_image_path not in inpaint_masks:
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mask_image_path = inpaint_masks[0]
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mask_image = Image.open(mask_image_path)
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batch_mask_images.append(mask_image)
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batch_mask_images = batch_mask_images * btcrept
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p.image_mask = batch_mask_images
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batch_image_files = batch_image_files * btcrept # List used for naming later.
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try:
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processed = scripts_manager.scripts_img2img.run(p, *args)
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if processed is None:
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processed = processing.process_images(p)
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except Exception as e:
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shared.log.error(f'Process batch: {e}')
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errors.display(e, 'batch')
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processed = None
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if processed is None or len(processed.images) == 0:
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shared.log.warning(f'Process batch: i={i+1}/{len(image_files)} no images processed')
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continue
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for n, (image, image_file) in enumerate(itertools.zip_longest(processed.images, batch_image_files)):
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if image is None:
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continue
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basename = ''
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if shared.opts.use_original_name_batch:
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forced_filename, ext = os.path.splitext(os.path.basename(image_file))
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else:
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forced_filename = None
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ext = shared.opts.samples_format
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if len(processed.images) > 1:
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basename = f'{n + i}' if shared.opts.batch_frame_mode else f'{n}'
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else:
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basename = ''
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if output_dir == '':
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output_dir = shared.opts.outdir_img2img_samples
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os.makedirs(output_dir, exist_ok=True)
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info, items = images.read_info_from_image(image)
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for k, v in items.items():
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image.info[k] = v
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images.save_image(image, path=output_dir, basename=basename, seed=None, prompt=None, extension=ext, info=info, grid=False, pnginfo_section_name="extras", existing_info=image.info, forced_filename=forced_filename)
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processed = scripts_manager.scripts_img2img.after(p, processed, *args)
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shared.log.debug(f'Processed: images={len(batch_image_files)} memory={memory_stats()} batch')
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def img2img(id_task: str, state: str, mode: int,
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prompt, negative_prompt, prompt_styles,
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init_img,
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sketch,
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init_img_with_mask,
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inpaint_color_sketch,
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inpaint_color_sketch_orig,
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init_img_inpaint,
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init_mask_inpaint,
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steps,
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sampler_index,
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mask_blur, mask_alpha,
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vae_type, tiling, hidiffusion,
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detailer_enabled, detailer_prompt, detailer_negative, detailer_steps, detailer_strength, detailer_resolution,
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n_iter, batch_size,
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guidance_name, guidance_scale, guidance_rescale, guidance_start, guidance_stop,
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cfg_scale, image_cfg_scale, diffusers_guidance_rescale, pag_scale, pag_adaptive, cfg_end,
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refiner_start,
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clip_skip,
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denoising_strength,
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seed, subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w,
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selected_scale_tab,
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height, width,
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scale_by,
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resize_mode, resize_name, resize_context,
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inpaint_full_res, inpaint_full_res_padding, inpainting_mask_invert,
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img2img_batch_files, img2img_batch_input_dir, img2img_batch_output_dir, img2img_batch_inpaint_mask_dir,
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hdr_mode, hdr_brightness, hdr_color, hdr_sharpen, hdr_clamp, hdr_boundary, hdr_threshold, hdr_maximize, hdr_max_center, hdr_max_boundary, hdr_color_picker, hdr_tint_ratio,
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enable_hr, hr_sampler_index, hr_denoising_strength, hr_resize_mode, hr_resize_context, hr_upscaler, hr_force, hr_second_pass_steps, hr_scale, hr_resize_x, hr_resize_y, refiner_steps, hr_refiner_start, refiner_prompt, refiner_negative,
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override_settings_texts,
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*args):
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debug(f'img2img: {id_task}')
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if shared.sd_model is None:
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shared.log.warning('Aborted: op=img model not loaded')
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return [], '', '', 'Error: model not loaded'
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if sampler_index is None:
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shared.log.warning('Sampler: invalid')
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sampler_index = 0
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mode = int(mode)
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image = None
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mask = None
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override_settings = create_override_settings_dict(override_settings_texts)
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if mode == 0: # img2img
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if init_img is None:
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return [], '', '', 'Error: init image not provided'
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image = init_img.convert("RGB")
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elif mode == 1: # inpaint
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if init_img_with_mask is None:
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return [], '', '', 'Error: init image with mask not provided'
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image = init_img_with_mask["image"]
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mask = init_img_with_mask["mask"]
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alpha_mask = ImageOps.invert(image.split()[-1]).convert('L').point(lambda x: 255 if x > 0 else 0, mode='1')
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mask = ImageChops.lighter(alpha_mask, mask.convert('L')).convert('L')
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image = image.convert("RGB")
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elif mode == 2: # sketch
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if sketch is None:
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return [], '', '', 'Error: sketch image not provided'
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image = sketch.convert("RGB")
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elif mode == 3: # composite
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if inpaint_color_sketch is None:
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return [], '', '', 'Error: color sketch image not provided'
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image = inpaint_color_sketch
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orig = inpaint_color_sketch_orig or inpaint_color_sketch
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pred = np.any(np.array(image) != np.array(orig), axis=-1)
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mask = Image.fromarray((255.0 * pred).astype(np.uint8), "L")
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mask = ImageEnhance.Brightness(mask).enhance(mask_alpha)
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blur = ImageFilter.GaussianBlur(mask_blur)
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image = Image.composite(image.filter(blur), orig, mask.filter(blur))
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image = image.convert("RGB")
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elif mode == 4: # inpaint upload mask
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if init_img_inpaint is None:
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return [], '', '', 'Error: inpaint image not provided'
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image = init_img_inpaint
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mask = init_mask_inpaint
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elif mode == 5: # process batch
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pass # handled later
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else:
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shared.log.error(f'Image processing unknown mode: {mode}')
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if image is not None:
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image = ImageOps.exif_transpose(image)
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if selected_scale_tab == 1 and resize_mode != 0:
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width = int(image.width * scale_by)
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height = int(image.height * scale_by)
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p = processing.StableDiffusionProcessingImg2Img(
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sd_model=shared.sd_model,
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outpath_samples=resolve_output_path(shared.opts.outdir_samples, shared.opts.outdir_img2img_samples),
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outpath_grids=resolve_output_path(shared.opts.outdir_grids, shared.opts.outdir_img2img_grids),
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prompt=prompt,
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negative_prompt=negative_prompt,
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styles=prompt_styles,
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seed=seed,
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subseed=subseed,
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subseed_strength=subseed_strength,
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seed_resize_from_h=seed_resize_from_h,
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seed_resize_from_w=seed_resize_from_w,
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sampler_name = processing.get_sampler_name(sampler_index, img=True),
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batch_size=batch_size,
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n_iter=n_iter,
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steps=steps,
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guidance_name=guidance_name,
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guidance_scale=guidance_scale,
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guidance_rescale=guidance_rescale,
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guidance_start=guidance_start,
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guidance_stop=guidance_stop,
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cfg_scale=cfg_scale,
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cfg_end=cfg_end,
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clip_skip=clip_skip,
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width=width,
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height=height,
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vae_type=vae_type,
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tiling=tiling,
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hidiffusion=hidiffusion,
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detailer_enabled=detailer_enabled,
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detailer_prompt=detailer_prompt,
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detailer_negative=detailer_negative,
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detailer_steps=detailer_steps,
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detailer_strength=detailer_strength,
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detailer_resolution=detailer_resolution,
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init_images=[image],
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mask=mask,
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mask_blur=mask_blur,
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resize_mode=resize_mode,
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resize_name=resize_name,
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resize_context=resize_context,
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scale_by=scale_by,
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denoising_strength=denoising_strength,
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image_cfg_scale=image_cfg_scale,
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diffusers_guidance_rescale=diffusers_guidance_rescale,
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pag_scale=pag_scale,
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pag_adaptive=pag_adaptive,
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refiner_start=refiner_start,
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inpaint_full_res=inpaint_full_res != 0,
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inpaint_full_res_padding=inpaint_full_res_padding,
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inpainting_mask_invert=inpainting_mask_invert,
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hdr_mode=hdr_mode, hdr_brightness=hdr_brightness, hdr_color=hdr_color, hdr_sharpen=hdr_sharpen, hdr_clamp=hdr_clamp,
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hdr_boundary=hdr_boundary, hdr_threshold=hdr_threshold, hdr_maximize=hdr_maximize, hdr_max_center=hdr_max_center, hdr_max_boundary=hdr_max_boundary, hdr_color_picker=hdr_color_picker, hdr_tint_ratio=hdr_tint_ratio,
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# refiner
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enable_hr=enable_hr,
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hr_denoising_strength=hr_denoising_strength,
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hr_scale=hr_scale,
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hr_resize_mode=hr_resize_mode,
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hr_resize_context=hr_resize_context,
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hr_upscaler=hr_upscaler,
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hr_force=hr_force,
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hr_second_pass_steps=hr_second_pass_steps,
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hr_resize_x=hr_resize_x,
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hr_resize_y=hr_resize_y,
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hr_sampler_name = processing.get_sampler_name(hr_sampler_index),
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refiner_steps=refiner_steps,
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hr_refiner_start=hr_refiner_start,
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refiner_prompt=refiner_prompt,
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refiner_negative=refiner_negative,
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# override
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override_settings=override_settings,
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)
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p.scripts = scripts_manager.scripts_img2img
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p.script_args = args
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p.state = state
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if mask:
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p.extra_generation_params["Mask blur"] = mask_blur
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p.extra_generation_params["Mask alpha"] = mask_alpha
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p.extra_generation_params["Mask padding"] = inpaint_full_res_padding
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p.extra_generation_params["Mask invert"] = ['masked', 'invert'][inpainting_mask_invert]
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p.extra_generation_params["Mask area"] = ["full", "masked"][inpaint_full_res]
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p.is_batch = mode == 5
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if p.is_batch:
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process_batch(p, img2img_batch_files, img2img_batch_input_dir, img2img_batch_output_dir, img2img_batch_inpaint_mask_dir, args)
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processed = processing.get_processed(p, [], p.seed, "")
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else:
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processed = scripts_manager.scripts_img2img.run(p, *args)
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if processed is None:
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processed = processing.process_images(p)
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processed = scripts_manager.scripts_img2img.after(p, processed, *args)
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p.close()
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generation_info_js = processed.js() if processed is not None else ''
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if processed is None:
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return [], generation_info_js, '', 'Error: no images'
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return processed.images, generation_info_js, processed.info, plaintext_to_html(processed.comments)
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