mirror of
https://github.com/openvinotoolkit/stable-diffusion-webui.git
synced 2024-12-14 22:53:25 +03:00
loopback moved to scripts, added support for multiple batches, changed to honor save grids and how grids in web setting
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AUTOMATIC
parent
c253d6bdab
commit
c9430e53f6
@ -11,10 +11,9 @@ from modules.ui import plaintext_to_html
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import modules.images as images
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import modules.scripts
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def img2img(prompt: str, negative_prompt: str, prompt_style: str, init_img, init_img_with_mask, init_mask, mask_mode, steps: int, sampler_index: int, mask_blur: int, inpainting_fill: int, restore_faces: bool, tiling: bool, mode: int, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, denoising_strength_change_factor: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, height: int, width: int, resize_mode: int, upscaler_index: str, upscale_overlap: int, inpaint_full_res: bool, inpainting_mask_invert: int, *args):
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def img2img(prompt: str, negative_prompt: str, prompt_style: str, init_img, init_img_with_mask, init_mask, mask_mode, steps: int, sampler_index: int, mask_blur: int, inpainting_fill: int, restore_faces: bool, tiling: bool, mode: int, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, height: int, width: int, resize_mode: int, upscaler_index: str, upscale_overlap: int, inpaint_full_res: bool, inpainting_mask_invert: int, *args):
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is_inpaint = mode == 1
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is_loopback = mode == 2
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is_upscale = mode == 3
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is_upscale = mode == 2
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if is_inpaint:
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if mask_mode == 0:
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@ -61,46 +60,10 @@ def img2img(prompt: str, negative_prompt: str, prompt_style: str, init_img, init
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denoising_strength=denoising_strength,
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inpaint_full_res=inpaint_full_res,
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inpainting_mask_invert=inpainting_mask_invert,
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extra_generation_params={
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"Denoising strength change factor": (denoising_strength_change_factor if is_loopback else None)
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}
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)
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print(f"\nimg2img: {prompt}", file=shared.progress_print_out)
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if is_loopback:
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output_images, info = None, None
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history = []
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initial_seed = None
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initial_info = None
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state.job_count = n_iter
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for i in range(n_iter):
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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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state.job = f"Batch {i + 1} out of {n_iter}"
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processed = process_images(p)
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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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init_img = processed.images[0]
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p.init_images = [init_img]
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p.seed = processed.seed + 1
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p.denoising_strength = min(max(p.denoising_strength * denoising_strength_change_factor, 0.1), 1)
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history.append(processed.images[0])
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grid = images.image_grid(history, batch_size, rows=1)
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images.save_image(grid, p.outpath_grids, "grid", initial_seed, prompt, opts.grid_format, info=info, short_filename=not opts.grid_extended_filename, grid=True, p=p)
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processed = Processed(p, history, initial_seed, initial_info)
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elif is_upscale:
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if is_upscale:
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initial_info = None
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processing.fix_seed(p)
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@ -387,7 +387,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
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with gr.Row().style(equal_height=False):
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with gr.Column(variant='panel'):
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with gr.Group():
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switch_mode = gr.Radio(label='Mode', elem_id="img2img_mode", choices=['Redraw whole image', 'Inpaint a part of image', 'Loopback', 'SD upscale'], value='Redraw whole image', type="index", show_label=False)
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switch_mode = gr.Radio(label='Mode', elem_id="img2img_mode", choices=['Redraw whole image', 'Inpaint a part of image', 'SD upscale'], value='Redraw whole image', type="index", show_label=False)
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init_img = gr.Image(label="Image for img2img", source="upload", interactive=True, type="pil")
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init_img_with_mask = gr.Image(label="Image for inpainting with mask", elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool="sketch", visible=False, image_mode="RGBA")
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init_mask = gr.Image(label="Mask", source="upload", interactive=True, type="pil", visible=False)
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@ -421,7 +421,6 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
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with gr.Group():
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cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0)
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denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.75)
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denoising_strength_change_factor = gr.Slider(minimum=0.9, maximum=1.1, step=0.01, label='Denoising strength change factor', value=1, visible=False)
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with gr.Group():
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width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
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@ -455,8 +454,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
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def apply_mode(mode, uploadmask):
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is_classic = mode == 0
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is_inpaint = mode == 1
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is_loopback = mode == 2
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is_upscale = mode == 3
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is_upscale = mode == 2
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return {
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init_img: gr_show(not is_inpaint or (is_inpaint and uploadmask == 1)),
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@ -466,12 +464,10 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
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mask_mode: gr_show(is_inpaint),
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mask_blur: gr_show(is_inpaint),
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inpainting_fill: gr_show(is_inpaint),
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batch_size: gr_show(not is_loopback),
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sd_upscale_upscaler_name: gr_show(is_upscale),
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sd_upscale_overlap: gr_show(is_upscale),
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inpaint_full_res: gr_show(is_inpaint),
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inpainting_mask_invert: gr_show(is_inpaint),
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denoising_strength_change_factor: gr_show(is_loopback),
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img2img_interrogate: gr_show(not is_inpaint),
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}
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@ -486,12 +482,10 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
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mask_mode,
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mask_blur,
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inpainting_fill,
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batch_size,
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sd_upscale_upscaler_name,
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sd_upscale_overlap,
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inpaint_full_res,
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inpainting_mask_invert,
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denoising_strength_change_factor,
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img2img_interrogate,
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]
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)
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@ -532,7 +526,6 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
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batch_size,
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cfg_scale,
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denoising_strength,
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denoising_strength_change_factor,
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seed,
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subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w,
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height,
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@ -13,7 +13,6 @@ titles = {
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"Seed": "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result",
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"Inpaint a part of image": "Draw a mask over an image, and the script will regenerate the masked area with content according to prompt",
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"Loopback": "Process an image, use it as an input, repeat. Batch count determins number of iterations.",
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"SD upscale": "Upscale image normally, split result into tiles, improve each tile using img2img, merge whole image back",
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"Just resize": "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.",
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@ -58,6 +57,9 @@ titles = {
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"Images filename pattern": "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_spaces], [width], [height], [sampler], [seed], [model_hash], [prompt_words], [date]; leave empty for default.",
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"Directory name pattern": "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_spaces], [width], [height], [sampler], [seed], [model_hash], [prompt_words], [date]; leave empty for default.",
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"Loopback": "Process an image, use it as an input, repeat.",
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"Loops": "How many times to repeat processing an image and using it as input for the next iteration",
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}
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function gradioApp(){
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78
scripts/loopback.py
Normal file
78
scripts/loopback.py
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@ -0,0 +1,78 @@
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import numpy as np
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from tqdm import trange
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import modules.scripts as scripts
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import gradio as gr
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from modules import processing, shared, sd_samplers, images
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from modules.processing import Processed
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from modules.sd_samplers import samplers
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from modules.shared import opts, cmd_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)
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denoising_strength_change_factor = gr.Slider(minimum=0.9, maximum=1.1, step=0.01, label='Denoising strength change factor', value=1)
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return [loops, denoising_strength_change_factor]
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def run(self, p, loops, denoising_strength_change_factor):
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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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"Denoising strength change factor": denoising_strength_change_factor,
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}
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p.batch_size = 1
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p.n_iter = 1
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output_images, info = None, None
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initial_seed = None
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initial_info = None
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grids = []
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all_images = []
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state.job_count = loops * batch_count
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for n in range(batch_count):
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history = []
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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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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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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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init_img = processed.images[0]
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p.init_images = [init_img]
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p.seed = processed.seed + 1
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p.denoising_strength = min(max(p.denoising_strength * denoising_strength_change_factor, 0.1), 1)
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history.append(processed.images[0])
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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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grids.append(grid)
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all_images += history
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if opts.return_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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