mirror of
https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
synced 2024-11-21 03:11:40 +08:00
added interrupt button
added save button --always-batch-cond-uncond as a workaround for performance regression option for low memory users specify gradio version as 3.1.5 because of what looks like a bug
This commit is contained in:
AUTOMATIC
parent
54dc6f9307
commit
a6adc22f07
@ -1,6 +1,6 @@
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basicsr
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gfpgan
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gradio
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gradio==3.1.5
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numpy
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Pillow
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realesrgan
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@ -1,5 +1,3 @@
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console.log("running")
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titles = {
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"Sampling steps": "How many times to imptove the generated image itratively; higher values take longer; very low values can produce bad results",
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"Sampling method": "Which algorithm to use to produce the image",
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@ -29,6 +27,9 @@ titles = {
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"Inpaint at full resolution": "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image",
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"Denoising Strength": "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image.",
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"Interrupt": "Stop processing images and return any results accumulated so far.",
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"Save": "Write image to a directory (default - log/images) and generation parameters into csv file.",
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}
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function gradioApp(){
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@ -36,7 +37,7 @@ function gradioApp(){
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}
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function addTitles(root){
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root.querySelectorAll('span').forEach(function(span){
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root.querySelectorAll('span, button').forEach(function(span){
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tooltip = titles[span.textContent];
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if(tooltip){
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span.title = tooltip;
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244
webui.py
244
webui.py
@ -68,6 +68,7 @@ parser.add_argument("--embeddings-dir", type=str, default='embeddings', help="em
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parser.add_argument("--allow-code", action='store_true', help="allow custom script execution from webui")
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parser.add_argument("--medvram", action='store_true', help="enable stable diffusion model optimizations for sacrficing a little speed for low VRM usage")
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parser.add_argument("--lowvram", action='store_true', help="enable stable diffusion model optimizations for sacrficing a lot of speed for very low VRM usage")
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parser.add_argument("--always-batch-cond-uncond", action='store_true', help="a workaround test; may help with speed in you use --lowvram")
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parser.add_argument("--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast")
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parser.add_argument("--share", action='store_true', help="use share=True for gradio and make the UI accessible through their site (doesn't work for me but you might have better luck)")
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cmd_opts = parser.parse_args()
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@ -75,9 +76,20 @@ cmd_opts = parser.parse_args()
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cpu = torch.device("cpu")
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gpu = torch.device("cuda")
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device = gpu if torch.cuda.is_available() else cpu
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batch_cond_uncond = not (cmd_opts.lowvram or cmd_opts.medvram)
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batch_cond_uncond = cmd_opts.always_batch_cond_uncond or not (cmd_opts.lowvram or cmd_opts.medvram)
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queue_lock = threading.Lock()
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class State:
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interrupted = False
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job = ""
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def interrupt(self):
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self.interrupted = True
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state = State()
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if not cmd_opts.share:
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# fix gradio phoning home
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gradio.utils.version_check = lambda: None
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@ -198,6 +210,7 @@ class Options:
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"outdir_img2img_grids": OptionInfo("outputs/img2img-grids", 'Output dictectory for img2img grids'),
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"save_to_dirs": OptionInfo(False, "When writing images/grids, create a directory with name derived from the prompt"),
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"save_to_dirs_prompt_len": OptionInfo(10, "When using above, how many words from prompt to put into directory name", gr.Slider, {"minimum": 1, "maximum": 32, "step": 1}),
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"outdir_save": OptionInfo("log/images", "Directory for saving images using the Save button"),
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"samples_save": OptionInfo(True, "Save indiviual samples"),
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"samples_format": OptionInfo('png', 'File format for indiviual samples'),
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"grid_save": OptionInfo(True, "Save image grids"),
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@ -400,8 +413,6 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
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image.save(f"{fullfn_without_extension}.jpg", quality=opts.jpeg_quality, pnginfo=pnginfo)
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def sanitize_filename_part(text):
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return text.replace(' ', '_').translate({ord(x): '' for x in invalid_filename_chars})[:128]
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@ -410,6 +421,7 @@ def plaintext_to_html(text):
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text = "".join([f"<p>{html.escape(x)}</p>\n" for x in text.split('\n')])
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return text
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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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@ -652,18 +664,29 @@ def wrap_gradio_gpu_call(func):
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return res
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return f
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return wrap_gradio_call(f)
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def wrap_gradio_call(func):
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def f(*args, **kwargs):
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t = time.perf_counter()
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res = list(func(*args, **kwargs))
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try:
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res = list(func(*args, **kwargs))
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except Exception as e:
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print("Error completing request", file=sys.stderr)
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print("Arguments:", args, kwargs, file=sys.stderr)
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print(traceback.format_exc(), file=sys.stderr)
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res = [None, f"<div class='error'>{plaintext_to_html(type(e).__name__+': '+str(e))}</div>"]
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elapsed = time.perf_counter() - t
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# last item is always HTML
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res[-1] = res[-1] + f"<p class='performance'>Time taken: {elapsed:.2f}s</p>"
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state.interrupted = False
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return tuple(res)
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return f
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@ -883,7 +906,6 @@ class StableDiffusionProcessing:
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self.extra_generation_params: dict = extra_generation_params
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self.overlay_images = overlay_images
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self.paste_to = None
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self.progress_info = ""
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def init(self):
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pass
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@ -959,6 +981,15 @@ class CFGDenoiser(nn.Module):
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return denoised
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def extended_trange(*args, **kwargs):
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for x in tqdm.trange(*args, desc=state.job, **kwargs):
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if state.interrupted:
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break
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yield x
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class KDiffusionSampler:
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def __init__(self, funcname):
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self.model_wrap = k_diffusion.external.CompVisDenoiser(sd_model)
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@ -980,7 +1011,7 @@ class KDiffusionSampler:
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self.model_wrap_cfg.init_latent = p.init_latent
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if hasattr(k_diffusion.sampling, 'trange'):
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k_diffusion.sampling.trange = lambda *args, **kwargs: tqdm.tqdm(range(*args), desc=p.progress_info, **kwargs)
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k_diffusion.sampling.trange = lambda *args, **kwargs: extended_trange(*args, **kwargs)
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return self.func(self.model_wrap_cfg, xi, sigma_sched, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False)
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@ -989,13 +1020,36 @@ class KDiffusionSampler:
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x = x * sigmas[0]
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if hasattr(k_diffusion.sampling, 'trange'):
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k_diffusion.sampling.trange = lambda *args, **kwargs: tqdm.tqdm(range(*args), desc=p.progress_info, **kwargs)
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k_diffusion.sampling.trange = lambda *args, **kwargs: extended_trange(*args, **kwargs)
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samples_ddim = self.func(self.model_wrap_cfg, x, sigmas, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False)
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return samples_ddim
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Processed = namedtuple('Processed', ['images', 'seed', 'info'])
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class Processed:
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def __init__(self, p: StableDiffusionProcessing, images, seed, info):
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self.images = images
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self.prompt = p.prompt
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self.seed = seed
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self.info = info
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self.width = p.width
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self.height = p.height
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self.sampler = samplers[p.sampler_index].name
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self.cfg_scale = p.cfg_scale
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self.steps = p.steps
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def js(self):
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obj = {
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"prompt": self.prompt,
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"seed": int(self.seed),
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"width": self.width,
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"height": self.height,
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"sampler": self.sampler,
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"cfg_scale": self.cfg_scale,
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"steps": self.steps,
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}
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return json.dumps(obj)
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def process_images(p: StableDiffusionProcessing) -> Processed:
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@ -1063,6 +1117,9 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
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p.init()
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for n in range(p.n_iter):
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if state.interrupted:
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break
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prompts = all_prompts[n * p.batch_size:(n + 1) * p.batch_size]
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seeds = all_seeds[n * p.batch_size:(n + 1) * p.batch_size]
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@ -1075,7 +1132,9 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
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# we manually generate all input noises because each one should have a specific seed
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x = create_random_tensors([opt_C, p.height // opt_f, p.width // opt_f], seeds=seeds)
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p.progress_info = f"Batch {n+1} out of {p.n_iter}"
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if p.n_iter > 0:
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state.job = f"Batch {n+1} out of {p.n_iter}"
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samples_ddim = p.sample(x=x, conditioning=c, unconditional_conditioning=uc)
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x_samples_ddim = model.decode_first_stage(samples_ddim)
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@ -1137,7 +1196,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
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save_image(grid, p.outpath_grids, "grid", seed, prompt, opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename)
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torch_gc()
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return Processed(output_images, seed, infotext())
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return Processed(p, output_images, seed, infotext())
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class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
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@ -1188,52 +1247,47 @@ def txt2img(prompt: str, negative_prompt: str, steps: int, sampler_index: int, u
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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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processed = Processed(p, *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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return processed.images, processed.js(), plaintext_to_html(processed.info)
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def save_files(js_data, images):
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import csv
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class Flagging(gr.FlaggingCallback):
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os.makedirs(opts.outdir_save, exist_ok=True)
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def setup(self, components, flagging_dir: str):
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pass
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filenames = []
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def flag(self, flag_data, flag_option=None, flag_index=None, username=None):
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import csv
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data = json.loads(js_data)
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os.makedirs("log/images", exist_ok=True)
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with open("log/log.csv", "a", encoding="utf8", newline='') as file:
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import time
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import base64
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# those must match the "txt2img" function
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prompt, steps, sampler_index, use_gfpgan, prompt_matrix, n_iter, batch_size, cfg_scale, seed, height, width, code, images, seed, comment = flag_data
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at_start = file.tell() == 0
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writer = csv.writer(file)
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if at_start:
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writer.writerow(["prompt", "seed", "width", "height", "sampler", "cfgs", "steps", "filename"])
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filenames = []
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filename_base = str(int(time.time() * 1000))
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for i, filedata in enumerate(images):
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filename = filename_base + ("" if len(images) == 1 else "-" + str(i + 1)) + ".png"
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filepath = os.path.join(opts.outdir_save, filename)
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with open("log/log.csv", "a", encoding="utf8", newline='') as file:
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import time
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import base64
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if filedata.startswith("data:image/png;base64,"):
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filedata = filedata[len("data:image/png;base64,"):]
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at_start = file.tell() == 0
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writer = csv.writer(file)
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if at_start:
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writer.writerow(["prompt", "seed", "width", "height", "cfgs", "steps", "filename"])
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with open(filepath, "wb") as imgfile:
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imgfile.write(base64.decodebytes(filedata.encode('utf-8')))
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filename_base = str(int(time.time() * 1000))
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for i, filedata in enumerate(images):
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filename = "log/images/"+filename_base + ("" if len(images) == 1 else "-"+str(i+1)) + ".png"
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filenames.append(filename)
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if filedata.startswith("data:image/png;base64,"):
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filedata = filedata[len("data:image/png;base64,"):]
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writer.writerow([data["prompt"], data["seed"], data["width"], data["height"], data["sampler"], data["cfg_scale"], data["steps"], filenames[0]])
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with open(filename, "wb") as imgfile:
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imgfile.write(base64.decodebytes(filedata.encode('utf-8')))
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return '', '', plaintext_to_html(f"Saved: {filenames[0]}")
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filenames.append(filename)
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writer.writerow([prompt, seed, width, height, cfg_scale, steps, filenames[0]])
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print("Logged:", filenames[0])
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with gr.Blocks(analytics_enabled=False) as txt2img_interface:
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with gr.Row():
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@ -1267,8 +1321,15 @@ with gr.Blocks(analytics_enabled=False) as txt2img_interface:
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with gr.Column(variant='panel'):
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with gr.Group():
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gallery = gr.Gallery(label='Output')
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output_seed = gr.Number(label='Seed', visible=False)
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with gr.Group():
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with gr.Row():
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interrupt = gr.Button('Interrupt')
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save = gr.Button('Save')
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with gr.Group():
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html_info = gr.HTML()
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generation_info = gr.Textbox(visible=False)
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txt2img_args = dict(
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fn=wrap_gradio_gpu_call(txt2img),
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@ -1289,7 +1350,7 @@ with gr.Blocks(analytics_enabled=False) as txt2img_interface:
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],
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outputs=[
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gallery,
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output_seed,
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generation_info,
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html_info
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]
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)
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@ -1297,6 +1358,25 @@ with gr.Blocks(analytics_enabled=False) as txt2img_interface:
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prompt.submit(**txt2img_args)
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submit.click(**txt2img_args)
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interrupt.click(
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fn=lambda: state.interrupt(),
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inputs=[],
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outputs=[],
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)
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save.click(
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fn=wrap_gradio_call(save_files),
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inputs=[
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generation_info,
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gallery,
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],
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outputs=[
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html_info,
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html_info,
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html_info,
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]
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)
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def get_crop_region(mask, pad=0):
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h, w = mask.shape
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@ -1508,6 +1588,7 @@ def img2img(prompt: str, init_img, init_img_with_mask, steps: int, sampler_index
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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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@ -1523,13 +1604,13 @@ def img2img(prompt: str, init_img, init_img_with_mask, steps: int, sampler_index
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save_image(grid, p.outpath_grids, "grid", initial_seed, prompt, opts.grid_format, info=info, short_filename=not opts.grid_extended_filename)
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processed = Processed(history, initial_seed, initial_info)
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processed = Processed(p, history, initial_seed, initial_info)
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elif is_upscale:
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initial_seed = None
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initial_info = None
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upscaler = sd_upscalers[upscaler_name]
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upscaler = sd_upscalers.get(upscaler_name, next(iter(sd_upscalers.values())))
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img = upscaler(init_img)
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torch_gc()
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@ -1553,6 +1634,7 @@ def img2img(prompt: str, init_img, init_img_with_mask, steps: int, sampler_index
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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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state.job = f"Batch {i + 1} out of {batch_count}"
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processed = process_images(p)
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if initial_seed is None:
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@ -1565,19 +1647,19 @@ def img2img(prompt: str, init_img, init_img_with_mask, steps: int, sampler_index
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image_index = 0
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for y, h, row in grid.tiles:
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for tiledata in row:
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tiledata[2] = work_results[image_index]
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tiledata[2] = work_results[image_index] if image_index<len(work_results) else Image.new("RGB", (p.width, p.height))
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image_index += 1
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combined_image = combine_grid(grid)
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save_image(combined_image, p.outpath_grids, "grid", initial_seed, prompt, opts.grid_format, info=initial_info, short_filename=not opts.grid_extended_filename)
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processed = Processed([combined_image], initial_seed, initial_info)
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processed = Processed(p, [combined_image], initial_seed, initial_info)
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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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return processed.images, processed.js(), plaintext_to_html(processed.info)
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sample_img2img = "assets/stable-samples/img2img/sketch-mountains-input.jpg"
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@ -1609,8 +1691,8 @@ with gr.Blocks(analytics_enabled=False) as img2img_interface:
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inpaint_full_res = gr.Checkbox(label='Inpaint at full resolution', value=True, visible=False)
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with gr.Row():
|
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sd_upscale_upscaler_name = gr.Radio(label='Upscaler', choices=list(sd_upscalers.keys()), value="RealESRGAN")
|
||||
sd_upscale_overlap = gr.Slider(minimum=0, maximum=256, step=16, label='Tile overlap', value=64)
|
||||
sd_upscale_upscaler_name = gr.Radio(label='Upscaler', choices=list(sd_upscalers.keys()), value=list(sd_upscalers.keys())[0], visible=False)
|
||||
sd_upscale_overlap = gr.Slider(minimum=0, maximum=256, step=16, label='Tile overlap', value=64, visible=False)
|
||||
|
||||
with gr.Row():
|
||||
batch_count = gr.Slider(minimum=1, maximum=cmd_opts.max_batch_count, step=1, label='Batch count', value=1)
|
||||
@ -1629,8 +1711,15 @@ with gr.Blocks(analytics_enabled=False) as img2img_interface:
|
||||
with gr.Column(variant='panel'):
|
||||
with gr.Group():
|
||||
gallery = gr.Gallery(label='Output')
|
||||
output_seed = gr.Number(label='Seed', visible=False)
|
||||
|
||||
with gr.Group():
|
||||
with gr.Row():
|
||||
interrupt = gr.Button('Interrupt')
|
||||
save = gr.Button('Save')
|
||||
|
||||
with gr.Group():
|
||||
html_info = gr.HTML()
|
||||
generation_info = gr.Textbox(visible=False)
|
||||
|
||||
def apply_mode(mode):
|
||||
is_classic = mode == 0
|
||||
@ -1647,7 +1736,7 @@ with gr.Blocks(analytics_enabled=False) as img2img_interface:
|
||||
batch_count: gr.update(visible=not is_upscale),
|
||||
batch_size: gr.update(visible=not is_loopback),
|
||||
sd_upscale_upscaler_name: gr.update(visible=is_upscale),
|
||||
sd_upscale_overlap: gr.update(visible=is_upscale),
|
||||
sd_upscale_overlap: gr.Slider.update(visible=is_upscale),
|
||||
inpaint_full_res: gr.update(visible=is_inpaint),
|
||||
}
|
||||
|
||||
@ -1695,7 +1784,7 @@ with gr.Blocks(analytics_enabled=False) as img2img_interface:
|
||||
],
|
||||
outputs=[
|
||||
gallery,
|
||||
output_seed,
|
||||
generation_info,
|
||||
html_info
|
||||
]
|
||||
)
|
||||
@ -1703,6 +1792,25 @@ with gr.Blocks(analytics_enabled=False) as img2img_interface:
|
||||
prompt.submit(**img2img_args)
|
||||
submit.click(**img2img_args)
|
||||
|
||||
interrupt.click(
|
||||
fn=lambda: state.interrupt(),
|
||||
inputs=[],
|
||||
outputs=[],
|
||||
)
|
||||
|
||||
save.click(
|
||||
fn=wrap_gradio_call(save_files),
|
||||
inputs=[
|
||||
generation_info,
|
||||
gallery,
|
||||
],
|
||||
outputs=[
|
||||
html_info,
|
||||
html_info,
|
||||
html_info,
|
||||
]
|
||||
)
|
||||
|
||||
|
||||
def upscale_with_realesrgan(image, RealESRGAN_upscaling, RealESRGAN_model_index):
|
||||
info = realesrgan_models[RealESRGAN_model_index]
|
||||
@ -1744,7 +1852,7 @@ def run_extras(image, GFPGAN_strength, RealESRGAN_upscaling, RealESRGAN_model_in
|
||||
|
||||
save_image(image, outpath, "", None, '', opts.samples_format, short_filename=True)
|
||||
|
||||
return image, 0, ''
|
||||
return image, '', ''
|
||||
|
||||
|
||||
extras_interface = gr.Interface(
|
||||
@ -1757,7 +1865,7 @@ extras_interface = gr.Interface(
|
||||
],
|
||||
outputs=[
|
||||
gr.Image(label="Result"),
|
||||
gr.Number(label='Seed', visible=False),
|
||||
gr.HTML(),
|
||||
gr.HTML(),
|
||||
],
|
||||
allow_flagging="never",
|
||||
@ -1779,7 +1887,7 @@ def run_pnginfo(image):
|
||||
message = "Nothing found in the image."
|
||||
info = f"<div><p>{message}<p></div>"
|
||||
|
||||
return [info]
|
||||
return '', '', info
|
||||
|
||||
|
||||
pnginfo_interface = gr.Interface(
|
||||
@ -1789,6 +1897,8 @@ pnginfo_interface = gr.Interface(
|
||||
],
|
||||
outputs=[
|
||||
gr.HTML(),
|
||||
gr.HTML(),
|
||||
gr.HTML(),
|
||||
],
|
||||
allow_flagging="never",
|
||||
analytics_enabled=False,
|
||||
@ -1809,7 +1919,7 @@ def run_settings(*args):
|
||||
|
||||
opts.save(config_filename)
|
||||
|
||||
return 'Settings saved.', ''
|
||||
return 'Settings saved.', '', ''
|
||||
|
||||
|
||||
def create_setting_component(key):
|
||||
@ -1839,6 +1949,7 @@ settings_interface = gr.Interface(
|
||||
outputs=[
|
||||
gr.Textbox(label='Result'),
|
||||
gr.HTML(),
|
||||
gr.HTML(),
|
||||
],
|
||||
title=None,
|
||||
description=None,
|
||||
@ -1863,17 +1974,18 @@ try:
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
sd_config = OmegaConf.load(cmd_opts.config)
|
||||
sd_model = load_model_from_config(sd_config, cmd_opts.ckpt)
|
||||
sd_model = (sd_model if cmd_opts.no_half else sd_model.half())
|
||||
if False:
|
||||
sd_config = OmegaConf.load(cmd_opts.config)
|
||||
sd_model = load_model_from_config(sd_config, cmd_opts.ckpt)
|
||||
sd_model = (sd_model if cmd_opts.no_half else sd_model.half())
|
||||
|
||||
if cmd_opts.lowvram or cmd_opts.medvram:
|
||||
setup_for_low_vram(sd_model)
|
||||
else:
|
||||
sd_model = sd_model.to(device)
|
||||
if cmd_opts.lowvram or cmd_opts.medvram:
|
||||
setup_for_low_vram(sd_model)
|
||||
else:
|
||||
sd_model = sd_model.to(device)
|
||||
|
||||
model_hijack = StableDiffusionModelHijack()
|
||||
model_hijack.hijack(sd_model)
|
||||
model_hijack = StableDiffusionModelHijack()
|
||||
model_hijack.hijack(sd_model)
|
||||
|
||||
with open(os.path.join(script_path, "style.css"), "r", encoding="utf8") as file:
|
||||
css = file.read()
|
||||
|
||||
Loading…
Reference in New Issue
Block a user