support for running custom code (primarily to generate various labeled grids)
export for 4chan option
This commit is contained in:
AUTOMATIC
parent
d5266c07f9
commit
93e7dbaa71
43
README.md
43
README.md
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@ -205,3 +205,46 @@ image will be upscaled to twice the original width and height, while width and h
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will specify the size of individual tiles. At the moment this method does not support batch size.
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### User scripts
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If the program is launched with `--allow-code` option, an extra text input field for script code
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is available in txt2img interface. It allows you to input python code that will do the work with
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image. If this field is not empty, the processing that would happen normally is skipped.
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In code, access parameters from web UI using the `p` variable, and provide outputs for web UI
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using the `display(images, seed, info)` function. All globals from script are also accessible.
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As an example, here is a script that draws a chart seen below (and also saves it as `test/gnomeplot/gnome5.png`):
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```python
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steps = [4, 8,12,16, 20]
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cfg_scales = [5.0,10.0,15.0]
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def cell(x, y, p=p):
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p.steps = x
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p.cfg_scale = y
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return process_images(p).images[0]
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images = [draw_xy_grid(
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xs = steps,
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ys = cfg_scales,
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x_label = lambda x: f'Steps = {x}',
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y_label = lambda y: f'CFG = {y}',
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cell = cell
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)]
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save_image(images[0], 'test/gnomeplot', 'gnome5')
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display(images)
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```
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A more simple script that would just process the image and output it normally:
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```python
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processed = process_images(p)
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print("Seed was: " + str(processed.seed))
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display(processed.images, processed.seed, processed.info)
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```
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BIN
images/scripting.jpg
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BIN
images/scripting.jpg
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Binary file not shown.
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After Width: | Height: | Size: 1.0 MiB |
133
webui.py
133
webui.py
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@ -50,6 +50,7 @@ parser.add_argument("--no-half", action='store_true', help="do not switch the mo
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parser.add_argument("--no-progressbar-hiding", action='store_true', help="do not hide progressbar in gradio UI (we hide it because it slows down ML if you have hardware accleration in browser)")
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parser.add_argument("--max-batch-count", type=int, default=16, help="maximum batch count value for the UI")
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parser.add_argument("--embeddings-dir", type=str, default='embeddings', help="embeddings dirtectory for textual inversion (default: embeddings)")
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parser.add_argument("--allow-code", action='store_true', help="allow custom script execution from webui")
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cmd_opts = parser.parse_args()
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@ -132,6 +133,7 @@ class Options:
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"grid_extended_filename": OptionInfo(False, "Add extended info (seed, prompt) to filename when saving grid"),
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"n_rows": OptionInfo(-1, "Grid row count; use -1 for autodetect and 0 for it to be same as batch size", gr.Slider, {"minimum": -1, "maximum": 16, "step": 1}),
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"jpeg_quality": OptionInfo(80, "Quality for saved jpeg images", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1}),
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"export_for_4chan": OptionInfo(True, "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG"),
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"enable_pnginfo": OptionInfo(True, "Save text information about generation parameters as chunks to png files"),
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"prompt_matrix_add_to_start": OptionInfo(True, "In prompt matrix, add the variable combination of text to the start of the prompt, rather than the end"),
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"sd_upscale_upscaler_index": OptionInfo("RealESRGAN", "Upscaler to use for SD upscale", gr.Radio, {"choices": list(sd_upscalers.keys())}),
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@ -206,13 +208,12 @@ def torch_gc():
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torch.cuda.ipc_collect()
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def save_image(image, path, basename, seed, prompt, extension, info=None, short_filename=False):
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prompt = sanitize_filename_part(prompt)
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def save_image(image, path, basename, seed=None, prompt=None, extension='png', info=None, short_filename=False):
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if short_filename:
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filename = f"{basename}.{extension}"
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if short_filename or prompt is None or seed is None:
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filename = f"{basename}"
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else:
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filename = f"{basename}-{seed}-{prompt[:128]}.{extension}"
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filename = f"{basename}-{seed}-{sanitize_filename_part(prompt)[:128]}"
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if extension == 'png' and opts.enable_pnginfo and info is not None:
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pnginfo = PngImagePlugin.PngInfo()
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@ -220,7 +221,23 @@ def save_image(image, path, basename, seed, prompt, extension, info=None, short_
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else:
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pnginfo = None
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image.save(os.path.join(path, filename), quality=opts.jpeg_quality, pnginfo=pnginfo)
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os.makedirs(path, exist_ok=True)
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fullfn = os.path.join(path, f"{filename}.{extension}")
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image.save(fullfn, quality=opts.jpeg_quality, pnginfo=pnginfo)
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target_side_length = 4000
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oversize = image.width > target_side_length or image.height > target_side_length
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if opts.export_for_4chan and (oversize or os.stat(fullfn).st_size > 4 * 1024 * 1024):
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ratio = image.width / image.height
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if oversize and ratio > 1:
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image = image.resize((target_side_length, image.height * target_side_length // image.width), LANCZOS)
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elif oversize:
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image = image.resize((image.width * target_side_length // image.height, target_side_length), LANCZOS)
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image.save(os.path.join(path, f"{filename}.jpg"), quality=opts.jpeg_quality, pnginfo=pnginfo)
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def sanitize_filename_part(text):
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@ -244,10 +261,9 @@ def load_gfpgan():
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return GFPGANer(model_path=model_path, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None)
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def image_grid(imgs, batch_size, force_n_rows=None):
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if force_n_rows is not None:
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rows = force_n_rows
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elif opts.n_rows > 0:
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def image_grid(imgs, batch_size=1, rows=None):
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if rows is None:
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if opts.n_rows > 0:
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rows = opts.n_rows
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elif opts.n_rows == 0:
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rows = batch_size
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@ -427,6 +443,22 @@ def draw_prompt_matrix(im, width, height, all_prompts):
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return draw_grid_annotations(im, width, height, hor_texts, ver_texts)
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def draw_xy_grid(xs, ys, x_label, y_label, cell):
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res = []
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ver_texts = [[GridAnnotation(y_label(y))] for y in ys]
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hor_texts = [[GridAnnotation(x_label(x))] for x in xs]
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for y in ys:
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for x in xs:
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res.append(cell(x, y))
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grid = image_grid(res, rows=len(ys))
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grid = draw_grid_annotations(grid, res[0].width, res[0].height, hor_texts, ver_texts)
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return grid
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def resize_image(resize_mode, im, width, height):
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if resize_mode == 0:
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res = im.resize((width, height), resample=LANCZOS)
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@ -742,7 +774,10 @@ class KDiffusionSampler:
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return samples_ddim
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def process_images(p: StableDiffusionProcessing):
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Processed = namedtuple('Processed', ['images','seed', 'info'])
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def process_images(p: StableDiffusionProcessing) -> Processed:
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"""this is the main loop that both txt2img and img2img use; it calls func_init once inside all the scopes and func_sample once per batch"""
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prompt = p.prompt
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@ -753,10 +788,7 @@ def process_images(p: StableDiffusionProcessing):
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seed = int(random.randrange(4294967294) if p.seed == -1 else p.seed)
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os.makedirs(p.outpath, exist_ok=True)
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sample_path = os.path.join(p.outpath, "samples")
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os.makedirs(sample_path, exist_ok=True)
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base_count = len(os.listdir(sample_path))
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grid_count = len(os.listdir(p.outpath)) - 1
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@ -846,7 +878,7 @@ def process_images(p: StableDiffusionProcessing):
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if (p.prompt_matrix or opts.grid_save) and not p.do_not_save_grid:
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if p.prompt_matrix:
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grid = image_grid(output_images, p.batch_size, force_n_rows=1 << ((len(prompt_matrix_parts)-1)//2))
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grid = image_grid(output_images, p.batch_size, rows=1 << ((len(prompt_matrix_parts)-1)//2))
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try:
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grid = draw_prompt_matrix(grid, p.width, p.height, prompt_matrix_parts)
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@ -863,7 +895,7 @@ def process_images(p: StableDiffusionProcessing):
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grid_count += 1
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torch_gc()
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return output_images, seed, infotext()
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return Processed(output_images, seed, infotext())
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class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
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@ -876,8 +908,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
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samples_ddim = self.sampler.sample(self, x, conditioning, unconditional_conditioning)
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return samples_ddim
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def txt2img(prompt: str, ddim_steps: int, sampler_index: int, use_GFPGAN: bool, prompt_matrix: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, height: int, width: int):
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def txt2img(prompt: str, steps: int, sampler_index: int, use_GFPGAN: bool, prompt_matrix: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, height: int, width: int, code: str):
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outpath = opts.outdir or "outputs/txt2img-samples"
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p = StableDiffusionProcessingTxt2Img(
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@ -887,7 +918,7 @@ def txt2img(prompt: str, ddim_steps: int, sampler_index: int, use_GFPGAN: bool,
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sampler_index=sampler_index,
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batch_size=batch_size,
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n_iter=n_iter,
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steps=ddim_steps,
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steps=steps,
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cfg_scale=cfg_scale,
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width=width,
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height=height,
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@ -895,9 +926,29 @@ def txt2img(prompt: str, ddim_steps: int, sampler_index: int, use_GFPGAN: bool,
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use_GFPGAN=use_GFPGAN
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)
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output_images, seed, info = process_images(p)
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if code != '' and cmd_opts.allow_code:
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p.do_not_save_grid = True
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p.do_not_save_samples = True
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return output_images, seed, plaintext_to_html(info)
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display_result_data = [[], -1, ""]
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def display(imgs, s=display_result_data[1], i=display_result_data[2]):
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display_result_data[0] = imgs
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display_result_data[1] = s
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display_result_data[2] = i
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from types import ModuleType
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compiled = compile(code, '', 'exec')
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module = ModuleType("testmodule")
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module.__dict__.update(globals())
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module.p = p
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module.display = display
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exec(compiled, module.__dict__)
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processed = Processed(*display_result_data)
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else:
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processed = process_images(p)
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return processed.images, processed.seed, plaintext_to_html(processed.info)
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class Flagging(gr.FlaggingCallback):
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@ -911,7 +962,7 @@ class Flagging(gr.FlaggingCallback):
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os.makedirs("log/images", exist_ok=True)
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# those must match the "txt2img" function
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prompt, ddim_steps, sampler_name, use_gfpgan, prompt_matrix, ddim_eta, n_iter, n_samples, cfg_scale, request_seed, height, width, images, seed, comment = flag_data
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prompt, ddim_steps, sampler_name, use_gfpgan, prompt_matrix, ddim_eta, n_iter, n_samples, cfg_scale, request_seed, height, width, code, images, seed, comment = flag_data
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filenames = []
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@ -955,6 +1006,7 @@ txt2img_interface = gr.Interface(
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gr.Number(label='Seed', value=-1),
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gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512),
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gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512),
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gr.Textbox(label="Python script", visible=cmd_opts.allow_code, lines=1)
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],
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outputs=[
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gr.Gallery(label="Images"),
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@ -1042,29 +1094,30 @@ def img2img(prompt: str, init_img, ddim_steps: int, sampler_index: int, use_GFPG
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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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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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output_images, seed, info = process_images(p)
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processed = process_images(p)
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if initial_seed is None:
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initial_seed = seed
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initial_seed = processed.seed
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initial_info = processed.info
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p.init_img = output_images[0]
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p.seed = seed + 1
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p.init_img = processed.images[0]
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p.seed = processed.seed + 1
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p.denoising_strength = max(p.denoising_strength * 0.95, 0.1)
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history.append(output_images[0])
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history.append(processed.images[0])
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grid_count = len(os.listdir(outpath)) - 1
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grid = image_grid(history, batch_size, force_n_rows=1)
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grid = image_grid(history, batch_size, rows=1)
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save_image(grid, outpath, f"grid-{grid_count:04}", initial_seed, prompt, opts.grid_format, info=info, short_filename=not opts.grid_extended_filename)
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output_images = history
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seed = initial_seed
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processed = Processed(history, initial_seed, initial_info)
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elif sd_upscale:
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initial_seed = None
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@ -1094,14 +1147,14 @@ def img2img(prompt: str, init_img, ddim_steps: int, sampler_index: int, use_GFPG
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for i in range(batch_count):
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p.init_images = work[i*p.batch_size:(i+1)*p.batch_size]
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output_images, seed, info = process_images(p)
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processed = process_images(p)
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if initial_seed is None:
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initial_seed = seed
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initial_info = info
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initial_seed = processed.seed
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initial_info = processed.info
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p.seed = seed + 1
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work_results += output_images
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p.seed = processed.seed + 1
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work_results += processed.images
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image_index = 0
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for y, h, row in grid.tiles:
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@ -1114,14 +1167,12 @@ def img2img(prompt: str, init_img, ddim_steps: int, sampler_index: int, use_GFPG
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grid_count = len(os.listdir(outpath)) - 1
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save_image(combined_image, outpath, f"grid-{grid_count:04}", initial_seed, prompt, opts.grid_format, info=initial_info, short_filename=not opts.grid_extended_filename)
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output_images = [combined_image]
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seed = initial_seed
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info = initial_info
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processed = Processed([combined_image], initial_seed, initial_info)
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else:
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output_images, seed, info = process_images(p)
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processed = process_images(p)
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return output_images, seed, plaintext_to_html(info)
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return processed.images, processed.seed, plaintext_to_html(processed.info)
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sample_img2img = "assets/stable-samples/img2img/sketch-mountains-input.jpg"
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@ -1192,9 +1243,7 @@ def run_extras(image, GFPGAN_strength, RealESRGAN_upscaling, RealESRGAN_model_in
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if have_realesrgan and RealESRGAN_upscaling != 1.0:
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image = upscale_with_realesrgan(image, RealESRGAN_upscaling, RealESRGAN_model_index)
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os.makedirs(outpath, exist_ok=True)
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base_count = len(os.listdir(outpath))
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save_image(image, outpath, f"{base_count:05}", None, '', opts.samples_format, short_filename=True)
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return image, 0, ''
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