mirror of
https://github.com/Sygil-Dev/sygil-webui.git
synced 2024-12-15 06:21:34 +03:00
Added abilty to log prompts and info to one file instead of several (#476)
* Added abilty to log prompts and info to one file instead of several
This is my first pull request be gentle I made changes so instead of having several files with prompts with one for each file you can just have one file with the prompt info I also made sure the prompts would work with params in prompts
* Add files via upload
* Fixed Image to image not working
* Add files via upload
* Revert "Add files via upload"
This reverts commit bb10b632bf.
* updated log
Co-authored-by: hlky <106811348+hlky@users.noreply.github.com>
This commit is contained in:
TingTingin
GitHub
parent
4af33ccda9
commit
e35fcb911f
165
webui.py
165
webui.py
@ -44,7 +44,7 @@ opt = parser.parse_args()
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#if opt.extra_models_gpu:
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# gpus = set([opt.gpu, opt.esrgan_gpu, opt.gfpgan_gpu])
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# os.environ["CUDA_VISIBLE_DEVICES"] = ','.join(str(g) for g in set(gpus))
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#else:
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#else:
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# os.environ["CUDA_VISIBLE_DEVICES"] = str(opt.gpu)
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import gradio as gr
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@ -294,7 +294,7 @@ def load_GFPGAN():
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sys.path.append(os.path.abspath(GFPGAN_dir))
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from gfpgan import GFPGANer
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if opt.gfpgan_cpu or opt.extra_models_cpu:
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instance = GFPGANer(model_path=model_path, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None, device=torch.device('cpu'))
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elif opt.extra_models_gpu:
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@ -399,7 +399,7 @@ def load_SD_model():
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_, _ = modelCS.load_state_dict(sd, strict=False)
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modelCS.cond_stage_model.device = device
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modelCS.eval()
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modelFS = instantiate_from_config(config.modelFirstStage)
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_, _ = modelFS.load_state_dict(sd, strict=False)
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modelFS.eval()
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@ -564,8 +564,8 @@ def check_prompt_length(prompt, comments):
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comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n")
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def save_sample(image, sample_path_i, filename, jpg_sample, prompts, seeds, width, height, steps, cfg_scale,
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normalize_prompt_weights, use_GFPGAN, write_info_files, prompt_matrix, init_img, uses_loopback, uses_random_seed_loopback, skip_save,
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def save_sample(image, sample_path_i, filename, jpg_sample, prompts, seeds, width, height, steps, cfg_scale,
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normalize_prompt_weights, use_GFPGAN, write_info_files, write_sample_info_to_log_file, prompt_matrix, init_img, uses_loopback, uses_random_seed_loopback, skip_save,
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skip_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoising_strength, resize_mode, skip_metadata):
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filename_i = os.path.join(sample_path_i, filename)
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if not jpg_sample:
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@ -586,7 +586,7 @@ skip_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoisin
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image.save(f"{filename_i}.png")
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else:
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image.save(f"{filename_i}.jpg", 'jpeg', quality=100, optimize=True)
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if write_info_files:
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if write_info_files or write_sample_info_to_log_file:
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# toggles differ for txt2img vs. img2img:
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offset = 0 if init_img is None else 2
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toggles = []
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@ -607,8 +607,11 @@ skip_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoisin
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toggles.append(4 + offset)
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if write_info_files:
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toggles.append(5 + offset)
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if write_sample_info_to_log_file:
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toggles.append(6+offset)
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if use_GFPGAN:
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toggles.append(6 + offset)
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toggles.append(7 + offset)
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info_dict = dict(
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target="txt2img" if init_img is None else "img2img",
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prompt=prompts[i], ddim_steps=steps, toggles=toggles, sampler_name=sampler_name,
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@ -621,8 +624,31 @@ skip_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoisin
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#info_dict["init_mask"] = init_mask
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info_dict["denoising_strength"] = denoising_strength
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info_dict["resize_mode"] = resize_mode
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with open(f"{filename_i}.yaml", "w", encoding="utf8") as f:
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yaml.dump(info_dict, f, allow_unicode=True, width=10000)
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if write_info_files:
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with open(f"{filename_i}.yaml", "w", encoding="utf8") as f:
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yaml.dump(info_dict, f, allow_unicode=True, width=10000)
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if write_sample_info_to_log_file:
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ignore_list = ["prompt", "target", "toggles", "ddim_eta", "batch_size"]
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rename_dict = {"ddim_steps": "steps", "n_iter": "number", "sampler_name": "sampler"} #changes the name of parameters to match with dynamic parameters
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sample_log_path = os.path.join(sample_path_i, "log.yaml")
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log_dump = info_dict.get("prompt") # making sure the first item that is listed in the txt is the prompt text
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for key, value in info_dict.items():
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if key in ignore_list:
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continue
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found_key = rename_dict.get(key)
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if key == "cfg_scale": #adds zeros to to cfg_scale necessary for dynamic params
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value = str(value).zfill(2)
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if found_key:
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key = found_key
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log_dump += f" {key} {value}"
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log_dump = log_dump + " \n" #space at the end for dynamic params to accept the last param
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with open(sample_log_path, "a", encoding="utf8") as log_file:
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log_file.write(log_dump)
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def get_next_sequence_number(path, prefix=''):
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@ -702,7 +728,7 @@ def oxlamon_matrix(prompt, seed, n_iter, batch_size):
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texts.append( item.text )
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parts.append( f"Seed: {itemseed}\n" + "\n".join(item.parts) )
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seeds.append( itemseed )
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itemseed += 1
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itemseed += 1
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return seeds, texts, parts
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@ -727,7 +753,7 @@ def process_images(
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n_iter, steps, cfg_scale, width, height, prompt_matrix, use_GFPGAN, use_RealESRGAN, realesrgan_model_name,
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fp, ddim_eta=0.0, do_not_save_grid=False, normalize_prompt_weights=True, init_img=None, init_mask=None,
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keep_mask=False, mask_blur_strength=3, denoising_strength=0.75, resize_mode=None, uses_loopback=False,
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uses_random_seed_loopback=False, sort_samples=True, write_info_files=True, jpg_sample=False,
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uses_random_seed_loopback=False, sort_samples=True, write_info_files=True, write_sample_info_to_log_file=False, jpg_sample=False,
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variant_amount=0.0, variant_seed=None,imgProcessorTask=True):
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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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assert prompt is not None
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@ -804,9 +830,9 @@ def process_images(
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target_seed_randomizer = seed_to_int('') # random seed
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torch.manual_seed(seed) # this has to be the single starting seed (not per-iteration)
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base_x = create_random_tensors([opt_C, height // opt_f, width // opt_f], seeds=[seed])
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# we don't want all_seeds to be sequential from starting seed with variants,
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# since that makes the same variants each time,
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# so we add target_seed_randomizer as a random offset
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# we don't want all_seeds to be sequential from starting seed with variants,
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# since that makes the same variants each time,
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# so we add target_seed_randomizer as a random offset
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for si in range(len(all_seeds)):
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all_seeds[si] += target_seed_randomizer
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@ -847,7 +873,7 @@ def process_images(
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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(shape, seeds=seeds)
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else: # we are making variants
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# using variant_seed as sneaky toggle,
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# using variant_seed as sneaky toggle,
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# when not None or '' use the variant_seed
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# otherwise use seeds
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if variant_seed != None and variant_seed != '':
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@ -857,13 +883,13 @@ def process_images(
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target_x = create_random_tensors(shape, seeds=seeds)
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# finally, slerp base_x noise to target_x noise for creating a variant
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x = slerp(device, max(0.0, min(1.0, variant_amount)), base_x, target_x)
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samples_ddim = func_sample(init_data=init_data, x=x, conditioning=c, unconditional_conditioning=uc, sampler_name=sampler_name)
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if opt.optimized:
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modelFS.to(device)
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x_samples_ddim = (model if not opt.optimized else modelFS).decode_first_stage(samples_ddim)
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x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
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@ -893,8 +919,8 @@ def process_images(
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gfpgan_sample = restored_img[:,:,::-1]
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gfpgan_image = Image.fromarray(gfpgan_sample)
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gfpgan_filename = original_filename + '-gfpgan'
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save_sample(gfpgan_image, sample_path_i, gfpgan_filename, jpg_sample, prompts, seeds, width, height, steps, cfg_scale,
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normalize_prompt_weights, use_GFPGAN, write_info_files, prompt_matrix, init_img, uses_loopback, uses_random_seed_loopback, skip_save,
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save_sample(gfpgan_image, sample_path_i, gfpgan_filename, jpg_sample, prompts, seeds, width, height, steps, cfg_scale,
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normalize_prompt_weights, use_GFPGAN, write_info_files, write_sample_info_to_log_file, prompt_matrix, init_img, uses_loopback, uses_random_seed_loopback, skip_save,
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skip_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoising_strength, resize_mode, False)
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#output_images.append(gfpgan_image) #287
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#if simple_templating:
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@ -909,8 +935,8 @@ skip_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoisin
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esrgan_filename = original_filename + '-esrgan4x'
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esrgan_sample = output[:,:,::-1]
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esrgan_image = Image.fromarray(esrgan_sample)
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save_sample(esrgan_image, sample_path_i, esrgan_filename, jpg_sample, prompts, seeds, width, height, steps, cfg_scale,
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normalize_prompt_weights, use_GFPGAN, write_info_files, prompt_matrix, init_img, uses_loopback, uses_random_seed_loopback, skip_save,
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save_sample(esrgan_image, sample_path_i, esrgan_filename, jpg_sample, prompts, seeds, width, height, steps, cfg_scale,
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normalize_prompt_weights, use_GFPGAN,write_info_files, write_sample_info_to_log_file, prompt_matrix, init_img, uses_loopback, uses_random_seed_loopback, skip_save,
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skip_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoising_strength, resize_mode, False)
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#output_images.append(esrgan_image) #287
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#if simple_templating:
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@ -927,8 +953,8 @@ skip_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoisin
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gfpgan_esrgan_filename = original_filename + '-gfpgan-esrgan4x'
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gfpgan_esrgan_sample = output[:,:,::-1]
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gfpgan_esrgan_image = Image.fromarray(gfpgan_esrgan_sample)
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save_sample(gfpgan_esrgan_image, sample_path_i, gfpgan_esrgan_filename, jpg_sample, prompts, seeds, width, height, steps, cfg_scale,
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normalize_prompt_weights, use_GFPGAN, write_info_files, prompt_matrix, init_img, uses_loopback, uses_random_seed_loopback, skip_save,
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save_sample(gfpgan_esrgan_image, sample_path_i, gfpgan_esrgan_filename, jpg_sample, prompts, seeds, width, height, steps, cfg_scale,
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normalize_prompt_weights, use_GFPGAN, write_info_files, write_sample_info_to_log_file, prompt_matrix, init_img, uses_loopback, uses_random_seed_loopback, skip_save,
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skip_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoising_strength, resize_mode, False)
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#output_images.append(gfpgan_esrgan_image) #287
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#if simple_templating:
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@ -938,8 +964,8 @@ skip_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoisin
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# output_images.append(image)
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if not skip_save:
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save_sample(image, sample_path_i, filename, jpg_sample, prompts, seeds, width, height, steps, cfg_scale,
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normalize_prompt_weights, use_GFPGAN, write_info_files, prompt_matrix, init_img, uses_loopback, uses_random_seed_loopback, skip_save,
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save_sample(image, sample_path_i, filename, jpg_sample, prompts, seeds, width, height, steps, cfg_scale,
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normalize_prompt_weights, use_GFPGAN, write_info_files, write_sample_info_to_log_file, prompt_matrix, init_img, uses_loopback, uses_random_seed_loopback, skip_save,
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skip_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoising_strength, resize_mode, False)
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if add_original_image or not simple_templating:
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output_images.append(image)
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@ -984,7 +1010,7 @@ skip_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoisin
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"cfg_scale": cfg_scale,
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"sampler": sampler_name,
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}
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full_string = f"{prompt}\n"+ " ".join([f"{k}:" for k,v in args_and_names.items()])
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info = {
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'text': full_string,
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@ -1019,9 +1045,10 @@ def txt2img(prompt: str, ddim_steps: int, sampler_name: str, toggles: List[int],
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skip_grid = 3 not in toggles
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sort_samples = 4 in toggles
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write_info_files = 5 in toggles
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jpg_sample = 6 in toggles
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use_GFPGAN = 7 in toggles
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use_RealESRGAN = 8 in toggles
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write_to_one_file = 6 in toggles
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jpg_sample = 7 in toggles
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use_GFPGAN = 8 in toggles
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use_RealESRGAN = 9 in toggles
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if sampler_name == 'PLMS':
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sampler = PLMSSampler(model)
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@ -1074,6 +1101,7 @@ def txt2img(prompt: str, ddim_steps: int, sampler_name: str, toggles: List[int],
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normalize_prompt_weights=normalize_prompt_weights,
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sort_samples=sort_samples,
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write_info_files=write_info_files,
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write_sample_info_to_log_file=write_to_one_file,
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jpg_sample=jpg_sample,
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variant_amount=variant_amount,
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variant_seed=variant_seed,
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@ -1135,8 +1163,8 @@ class Flagging(gr.FlaggingCallback):
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def img2img(prompt: str, image_editor_mode: str, init_info, mask_mode: str, mask_blur_strength: int, ddim_steps: int, sampler_name: str,
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toggles: List[int], realesrgan_model_name: str, n_iter: int, cfg_scale: float, denoising_strength: float,
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seed: int, height: int, width: int, resize_mode: int, fp = None):
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toggles: List[int], realesrgan_model_name: str, n_iter: int, cfg_scale: float, denoising_strength: float,
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seed: int, height: int, width: int, resize_mode: int, fp=None):
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outpath = opt.outdir_img2img or opt.outdir or "outputs/img2img-samples"
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err = False
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seed = seed_to_int(seed)
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@ -1151,9 +1179,10 @@ def img2img(prompt: str, image_editor_mode: str, init_info, mask_mode: str, mask
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skip_grid = 5 not in toggles
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sort_samples = 6 in toggles
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write_info_files = 7 in toggles
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jpg_sample = 8 in toggles
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use_GFPGAN = 9 in toggles
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use_RealESRGAN = 10 in toggles
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write_sample_info_to_log_file = 8 in toggles
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jpg_sample = 9 in toggles
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use_GFPGAN = 10 in toggles
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use_RealESRGAN = 11 in toggles
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if sampler_name == 'DDIM':
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sampler = DDIMSampler(model)
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@ -1177,6 +1206,7 @@ def img2img(prompt: str, image_editor_mode: str, init_info, mask_mode: str, mask
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init_img = init_img.convert("RGB")
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init_img = resize_image(resize_mode, init_img, width, height)
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init_mask = init_info["mask"]
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print(init_mask)
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init_mask = resize_image(resize_mode, init_mask, width, height)
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keep_mask = mask_mode == 0
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init_mask = init_mask.convert("RGB")
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@ -1226,7 +1256,7 @@ def img2img(prompt: str, image_editor_mode: str, init_info, mask_mode: str, mask
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init_image = init_image.to(device)
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init_image = repeat(init_image, '1 ... -> b ...', b=batch_size)
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init_latent = (model if not opt.optimized else modelFS).get_first_stage_encoding((model if not opt.optimized else modelFS).encode_first_stage(init_image)) # move to latent space
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if opt.optimized:
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mem = torch.cuda.memory_allocated()/1e6
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modelFS.to("cpu")
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@ -1317,6 +1347,7 @@ def img2img(prompt: str, image_editor_mode: str, init_info, mask_mode: str, mask
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uses_random_seed_loopback=random_seed_loopback,
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sort_samples=sort_samples,
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write_info_files=write_info_files,
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write_sample_info_to_log_file=write_sample_info_to_log_file,
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jpg_sample=jpg_sample,
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)
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@ -1372,6 +1403,7 @@ def img2img(prompt: str, image_editor_mode: str, init_info, mask_mode: str, mask
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uses_loopback=loopback,
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sort_samples=sort_samples,
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write_info_files=write_info_files,
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write_sample_info_to_log_file=write_sample_info_to_log_file,
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jpg_sample=jpg_sample,
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)
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@ -1413,7 +1445,7 @@ def split_weighted_subprompts(input_string, normalize=True):
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def slerp(device, t, v0:torch.Tensor, v1:torch.Tensor, DOT_THRESHOLD=0.9995):
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v0 = v0.detach().cpu().numpy()
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v1 = v1.detach().cpu().numpy()
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dot = np.sum(v0 * v1 / (np.linalg.norm(v0) * np.linalg.norm(v1)))
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if np.abs(dot) > DOT_THRESHOLD:
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v2 = (1 - t) * v0 + t * v1
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@ -1458,15 +1490,15 @@ def imgproc(image,image_batch,imgproc_prompt,imgproc_toggles, imgproc_upscale_to
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if 'x2' in imgproc_realesrgan_model_name:
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# downscale to 1/2 size
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result = result.resize((result.width//2, result.height//2), LANCZOS)
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return result
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def processGoBig(image):
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result = processRealESRGAN(image,)
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if 'x4' in imgproc_realesrgan_model_name:
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#downscale to 1/2 size
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result = result.resize((result.width//2, result.height//2), LANCZOS)
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#make sense of parameters
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n_iter = 1
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@ -1483,7 +1515,7 @@ def imgproc(image,image_batch,imgproc_prompt,imgproc_toggles, imgproc_upscale_to
|
||||
prompt = imgproc_prompt
|
||||
t_enc = int(denoising_strength * ddim_steps)
|
||||
sampler_name = imgproc_sampling
|
||||
|
||||
|
||||
|
||||
if sampler_name == 'DDIM':
|
||||
sampler = DDIMSampler(model)
|
||||
@ -1501,12 +1533,12 @@ def imgproc(image,image_batch,imgproc_prompt,imgproc_toggles, imgproc_upscale_to
|
||||
sampler = KDiffusionSampler(model,'lms')
|
||||
else:
|
||||
raise Exception("Unknown sampler: " + sampler_name)
|
||||
pass
|
||||
pass
|
||||
init_img = result
|
||||
init_mask = None
|
||||
keep_mask = False
|
||||
assert 0. <= denoising_strength <= 1., 'can only work with strength in [0.0, 1.0]'
|
||||
|
||||
|
||||
def init():
|
||||
image = init_img.convert("RGB")
|
||||
image = resize_image(resize_mode, image, width, height)
|
||||
@ -1521,7 +1553,7 @@ def imgproc(image,image_batch,imgproc_prompt,imgproc_toggles, imgproc_upscale_to
|
||||
init_image = init_image.to(device)
|
||||
init_image = repeat(init_image, '1 ... -> b ...', b=batch_size)
|
||||
init_latent = (model if not opt.optimized else modelFS).get_first_stage_encoding((model if not opt.optimized else modelFS).encode_first_stage(init_image)) # move to latent space
|
||||
|
||||
|
||||
if opt.optimized:
|
||||
mem = torch.cuda.memory_allocated()/1e6
|
||||
modelFS.to("cpu")
|
||||
@ -1613,7 +1645,7 @@ def imgproc(image,image_batch,imgproc_prompt,imgproc_toggles, imgproc_upscale_to
|
||||
combined_image.paste(combined_row.crop((0, grid.overlap, combined_row.width, h)), (0, y + grid.overlap))
|
||||
|
||||
return combined_image
|
||||
|
||||
|
||||
grid = split_grid(result, tile_w=width, tile_h=height, overlap=64)
|
||||
work = []
|
||||
work_results = []
|
||||
@ -1655,6 +1687,7 @@ def imgproc(image,image_batch,imgproc_prompt,imgproc_toggles, imgproc_upscale_to
|
||||
uses_loopback=False,
|
||||
sort_samples=True,
|
||||
write_info_files=True,
|
||||
write_sample_info_to_log_file=False,
|
||||
jpg_sample=False,
|
||||
imgProcessorTask=True
|
||||
)
|
||||
@ -1672,13 +1705,13 @@ def imgproc(image,image_batch,imgproc_prompt,imgproc_toggles, imgproc_upscale_to
|
||||
combined_image = combine_grid(grid)
|
||||
grid_count = len(os.listdir(outpath)) - 1
|
||||
del sampler
|
||||
|
||||
|
||||
torch.cuda.empty_cache()
|
||||
return combined_image
|
||||
def processLDSR(image):
|
||||
result = LDSR.superResolution(image)
|
||||
return result
|
||||
|
||||
return result
|
||||
|
||||
if image_batch != None:
|
||||
if image != None:
|
||||
print("Batch detected and single image detected, please only use one of the two. Aborting.")
|
||||
@ -1694,7 +1727,7 @@ def imgproc(image,image_batch,imgproc_prompt,imgproc_toggles, imgproc_upscale_to
|
||||
return None
|
||||
else:
|
||||
images.append(image)
|
||||
|
||||
|
||||
if len(images) > 0:
|
||||
print("Processing images...")
|
||||
for image in images:
|
||||
@ -1706,16 +1739,16 @@ def imgproc(image,image_batch,imgproc_prompt,imgproc_toggles, imgproc_upscale_to
|
||||
os.makedirs(outpathDir, exist_ok=True)
|
||||
batchNumber = get_next_sequence_number(outpathDir)
|
||||
outFilename = str(batchNumber)+'-'+'result'
|
||||
|
||||
|
||||
if 1 not in imgproc_toggles:
|
||||
output.append(image)
|
||||
save_sample(image, outpathDir, outFilename, False, None, None, None, None, None, None, None, None, None, None, None, None, None, False, None, None, None, None, None, None, None, None, None, True)
|
||||
|
||||
|
||||
if 1 in imgproc_toggles:
|
||||
if imgproc_upscale_toggles == 0:
|
||||
|
||||
|
||||
ModelLoader(['model','GFPGAN','LDSR'],False,True) # Unload unused models
|
||||
ModelLoader(['RealESGAN'],True,False) # Load used models
|
||||
ModelLoader(['RealESGAN'],True,False) # Load used models
|
||||
image = processRealESRGAN(image)
|
||||
outpathDir = os.path.join(outpath,'RealESRGAN')
|
||||
os.makedirs(outpathDir, exist_ok=True)
|
||||
@ -1723,7 +1756,7 @@ def imgproc(image,image_batch,imgproc_prompt,imgproc_toggles, imgproc_upscale_to
|
||||
outFilename = str(batchNumber)+'-'+'result'
|
||||
output.append(image)
|
||||
save_sample(image, outpathDir, outFilename, False, None, None, None, None, None, None, None, None, None, None, None, None, None, False, None, None, None, None, None, None, None, None, None, True)
|
||||
|
||||
|
||||
elif imgproc_upscale_toggles == 1:
|
||||
|
||||
ModelLoader(['GFPGAN','LDSR'],False,True) # Unload unused models
|
||||
@ -1734,8 +1767,8 @@ def imgproc(image,image_batch,imgproc_prompt,imgproc_toggles, imgproc_upscale_to
|
||||
batchNumber = get_next_sequence_number(outpathDir)
|
||||
outFilename = str(batchNumber)+'-'+'result'
|
||||
output.append(image)
|
||||
save_sample(image, outpathDir, outFilename, False, None, None, None, None, None, None, None, None, None, None, None, None, None, False, None, None, None, None, None, None, None, None, None, True)
|
||||
|
||||
save_sample(image, outpathDir, outFilename, False, None, None, None, None, None, None, None, None, None, None, None, None, None, False, None, None, None, None, None, None, None, None, None, True)
|
||||
|
||||
elif imgproc_upscale_toggles == 2:
|
||||
|
||||
ModelLoader(['model','GFPGAN','RealESGAN'],False,True) # Unload unused models
|
||||
@ -1747,7 +1780,7 @@ def imgproc(image,image_batch,imgproc_prompt,imgproc_toggles, imgproc_upscale_to
|
||||
outFilename = str(batchNumber)+'-'+'result'
|
||||
output.append(image)
|
||||
save_sample(image, outpathDir, outFilename, False, None, None, None, None, None, None, None, None, None, None, None, None, None, False, None, None, None, None, None, None, None, None, None, True)
|
||||
|
||||
|
||||
elif imgproc_upscale_toggles == 3:
|
||||
|
||||
ModelLoader(['GFPGAN','LDSR'],False,True) # Unload unused models
|
||||
@ -1761,9 +1794,9 @@ def imgproc(image,image_batch,imgproc_prompt,imgproc_toggles, imgproc_upscale_to
|
||||
batchNumber = get_next_sequence_number(outpathDir)
|
||||
outFilename = str(batchNumber)+'-'+'result'
|
||||
output.append(image)
|
||||
|
||||
|
||||
save_sample(image, outpathDir, outFilename, None, None, None, None, None, None, None, None, None, None, None, None, None, None, False, None, None, None, None, None, None, None, None, None, True)
|
||||
|
||||
|
||||
#LDSR is always unloaded to avoid memory issues
|
||||
ModelLoader(['LDSR'],False,True)
|
||||
print("Reloading default models...")
|
||||
@ -1806,7 +1839,7 @@ def ModelLoader(models,load=False,unload=False):
|
||||
if m =='model':
|
||||
m='Stable Diffusion'
|
||||
print('Loaded ' + m)
|
||||
|
||||
|
||||
torch_gc()
|
||||
|
||||
|
||||
@ -1852,6 +1885,7 @@ txt2img_toggles = [
|
||||
'Save grid',
|
||||
'Sort samples by prompt',
|
||||
'Write sample info files',
|
||||
'write sample info to log file',
|
||||
'jpg samples',
|
||||
]
|
||||
|
||||
@ -1911,6 +1945,7 @@ img2img_toggles = [
|
||||
'Save grid',
|
||||
'Sort samples by prompt',
|
||||
'Write sample info files',
|
||||
'Write sample info to one file',
|
||||
'jpg samples',
|
||||
]
|
||||
"""
|
||||
@ -2026,7 +2061,7 @@ demo = draw_gradio_ui(opt,
|
||||
GFPGAN=GFPGAN,
|
||||
LDSR=LDSR,
|
||||
run_GFPGAN=run_GFPGAN,
|
||||
run_RealESRGAN=run_RealESRGAN
|
||||
run_RealESRGAN=run_RealESRGAN
|
||||
)
|
||||
|
||||
class ServerLauncher(threading.Thread):
|
||||
@ -2039,17 +2074,17 @@ class ServerLauncher(threading.Thread):
|
||||
loop = asyncio.new_event_loop()
|
||||
asyncio.set_event_loop(loop)
|
||||
gradio_params = {
|
||||
'show_error': True,
|
||||
'show_error': True,
|
||||
'server_name': '0.0.0.0',
|
||||
'server_port': opt.port,
|
||||
'server_port': opt.port,
|
||||
'share': opt.share
|
||||
}
|
||||
if not opt.share:
|
||||
demo.queue(concurrency_count=1)
|
||||
if opt.share and opt.share_password:
|
||||
gradio_params['auth'] = ('webui', opt.share_password)
|
||||
|
||||
# Check to see if Port 7860 is open
|
||||
gradio_params['auth'] = ('webui', opt.share_password)
|
||||
|
||||
# Check to see if Port 7860 is open
|
||||
port_status = 1
|
||||
while port_status != 0:
|
||||
try:
|
||||
|
||||
Loading…
Reference in New Issue
Block a user