Sygil-Dev/sygil-webui
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#287

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hlky 2022-08-29 23:52:49 +01:00
parent 7ddbd6533a
commit dab958377e
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1 changed files with 14 additions and 1 deletions
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webui.py
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@ -793,20 +793,26 @@ def process_images(
x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
x_sample = x_sample.astype(np.uint8)
original_image = Image.fromarray(x_sample)
original_sample = x_sample
original_filename = filename
if use_GFPGAN and GFPGAN is not None and not use_RealESRGAN:
skip_save = True # #287 >_>
torch_gc()
cropped_faces, restored_faces, restored_img = GFPGAN.enhance(x_sample[:,:,::-1], has_aligned=False, only_center_face=False, paste_back=True)
gfpgan_sample = restored_img[:,:,::-1]
image = Image.fromarray(gfpgan_sample)
gfpgan_filename = original_filename + '-gfpgan'
save_sample(original_image, sample_path_i, original_filename, jpg_sample, prompts, seeds, width, height, steps, cfg_scale,
normalize_prompt_weights, use_GFPGAN, write_info_files, prompt_matrix, init_img, uses_loopback, uses_random_seed_loopback, skip_save,
skip_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoising_strength, resize_mode)
save_sample(image, sample_path_i, gfpgan_filename, jpg_sample, prompts, seeds, width, height, steps, cfg_scale,
normalize_prompt_weights, use_GFPGAN, write_info_files, prompt_matrix, init_img, uses_loopback, uses_random_seed_loopback, skip_save,
skip_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoising_strength, resize_mode)
x_sample = gfpgan_sample
if use_RealESRGAN and RealESRGAN is not None and not use_GFPGAN:
skip_save = True # #287 >_>
torch_gc()
if RealESRGAN.model.name != realesrgan_model_name:
try_loading_RealESRGAN(realesrgan_model_name)
@ -814,12 +820,16 @@ skip_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoisin
esrgan_filename = original_filename + '-esrgan4x'
esrgan_sample = output[:,:,::-1]
image = Image.fromarray(esrgan_sample)
save_sample(original_image, sample_path_i, original_filename, jpg_sample, prompts, seeds, width, height, steps, cfg_scale,
normalize_prompt_weights, use_GFPGAN, write_info_files, prompt_matrix, init_img, uses_loopback, uses_random_seed_loopback, skip_save,
skip_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoising_strength, resize_mode)
save_sample(image, sample_path_i, esrgan_filename, jpg_sample, prompts, seeds, width, height, steps, cfg_scale,
normalize_prompt_weights, use_GFPGAN, write_info_files, prompt_matrix, init_img, uses_loopback, uses_random_seed_loopback, skip_save,
skip_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoising_strength, resize_mode)
x_sample = esrgan_sample
if use_RealESRGAN and RealESRGAN is not None and use_GFPGAN and GFPGAN is not None:
skip_save = True # #287 >_>
torch_gc()
cropped_faces, restored_faces, restored_img = GFPGAN.enhance(x_sample[:,:,::-1], has_aligned=False, only_center_face=False, paste_back=True)
gfpgan_sample = restored_img[:,:,::-1]
@ -829,12 +839,15 @@ skip_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoisin
gfpgan_esrgan_filename = original_filename + '-gfpgan-esrgan4x'
gfpgan_esrgan_sample = output[:,:,::-1]
image = Image.fromarray(gfpgan_esrgan_sample)
save_sample(original_image, sample_path_i, original_filename, jpg_sample, prompts, seeds, width, height, steps, cfg_scale,
normalize_prompt_weights, use_GFPGAN, write_info_files, prompt_matrix, init_img, uses_loopback, uses_random_seed_loopback, skip_save,
skip_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoising_strength, resize_mode)
save_sample(image, sample_path_i, gfpgan_esrgan_filename, jpg_sample, prompts, seeds, width, height, steps, cfg_scale,
normalize_prompt_weights, use_GFPGAN, write_info_files, prompt_matrix, init_img, uses_loopback, uses_random_seed_loopback, skip_save,
skip_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoising_strength, resize_mode)
x_sample = gfpgan_esrgan_sample
image = Image.fromarray(x_sample)
if init_mask:
#init_mask = init_mask if keep_mask else ImageOps.invert(init_mask)
init_mask = init_mask.filter(ImageFilter.GaussianBlur(mask_blur_strength))