sd-webui/stable-diffusion-webui
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#158

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Fixed

At most should produce 2 samples
>original & gfpgan
>original & esrgan
>original & gfpgan -> esrgan

Turn off save individual samples to save only gfpgan, esrgan or gfpgan -> esrgan
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hlky 2022-08-27 23:26:42 +01:00
parent dac090625b
commit f19029aa6b
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GPG Key ID: 55A99F1E80D907D5
1 changed files with 35 additions and 21 deletions
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webui.py
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@ -780,38 +780,52 @@ def process_images(
x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c') x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
x_sample = x_sample.astype(np.uint8) x_sample = x_sample.astype(np.uint8)
if use_GFPGAN and GFPGAN is not None and use_GoBIG is None:
torch_gc()
original_sample = x_sample original_sample = x_sample
original_filename = filename original_filename = filename
if use_GFPGAN and GFPGAN is not None and not use_RealESRGAN:
torch_gc()
cropped_faces, restored_faces, restored_img = GFPGAN.enhance(x_sample[:,:,::-1], has_aligned=False, only_center_face=False, paste_back=True) cropped_faces, restored_faces, restored_img = GFPGAN.enhance(x_sample[:,:,::-1], has_aligned=False, only_center_face=False, paste_back=True)
x_sample = restored_img[:,:,::-1] gfpgan_sample = restored_img[:,:,::-1]
image = Image.fromarray(x_sample) image = Image.fromarray(gfpgan_sample)
filename = filename + '-gfpgan' gfpgan_filename = original_filename + '-gfpgan'
save_sample(image, sample_path_i, filename, jpg_sample, prompts, seeds, width, height, steps, cfg_scale, 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, 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) skip_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoising_strength, resize_mode)
filename = original_filename
x_sample = original_sample x_sample = original_sample
if use_RealESRGAN and RealESRGAN is not None: if use_RealESRGAN and RealESRGAN is not None and not use_GFPGAN:
torch_gc() torch_gc()
original_sample = x_sample
original_filename = filename
if RealESRGAN.model.name != realesrgan_model_name: if RealESRGAN.model.name != realesrgan_model_name:
try_loading_RealESRGAN(realesrgan_model_name) try_loading_RealESRGAN(realesrgan_model_name)
output, img_mode = RealESRGAN.enhance(x_sample[:,:,::-1]) output, img_mode = RealESRGAN.enhance(x_sample[:,:,::-1])
x_sample = output[:,:,::-1] esrgan_filename = original_filename + '-esrgan4x'
image = Image.fromarray(x_sample) esrgan_sample = output[:,:,::-1]
filename = filename + '-esrgan4x' image = Image.fromarray(esrgan_sample)
save_sample(image, sample_path_i, filename, jpg_sample, prompts, seeds, width, height, steps, cfg_scale, 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 = original_sample
if use_RealESRGAN and RealESRGAN is not None and use_GFPGAN and GFPGAN is not None:
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]
if RealESRGAN.model.name != realesrgan_model_name:
try_loading_RealESRGAN(realesrgan_model_name)
output, img_mode = RealESRGAN.enhance(gfpgan_sample[:,:,::-1])
esrgan_filename = original_filename + '-gfpgan-esrgan4x'
esrgan_sample = output[:,:,::-1]
image = Image.fromarray(esrgan_sample)
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, 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) skip_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoising_strength, resize_mode)
filename = original_filename
x_sample = original_sample x_sample = original_sample
if use_GoBIG and RealESRGAN is not None: if use_GoBIG and RealESRGAN is not None:
if use_GFPGAN:
x_sample = gfpgan_sample
original_sample = gfpgan_sample
else:
original_sample = x_sample original_sample = x_sample
original_filename = filename original_filename = filename
def addalpha(im, mask): def addalpha(im, mask):
@ -1129,7 +1143,7 @@ def txt2img(prompt: str, ddim_steps: int, sampler_name: str, toggles: List[int],
jpg_sample = 6 in toggles jpg_sample = 6 in toggles
use_GoBIG = 7 in toggles use_GoBIG = 7 in toggles
use_GFPGAN = 8 in toggles use_GFPGAN = 8 in toggles
use_RealESRGAN = 8 in toggles if GFPGAN is None else 9 in toggles # possible index shift use_RealESRGAN = 9 in toggles # possible index shift
if sampler_name == 'PLMS': if sampler_name == 'PLMS':
sampler = PLMSSampler(model) sampler = PLMSSampler(model)
@ -1259,7 +1273,7 @@ def img2img(prompt: str, image_editor_mode: str, init_info, mask_mode: str, mask
jpg_sample = 8 in toggles jpg_sample = 8 in toggles
use_GoBIG = 9 in toggles use_GoBIG = 9 in toggles
use_GFPGAN = 10 in toggles use_GFPGAN = 10 in toggles
use_RealESRGAN = 11 in toggles if GFPGAN is None else 10 in toggles # possible index shift use_RealESRGAN = 11 in toggles
if sampler_name == 'DDIM': if sampler_name == 'DDIM':
sampler = DDIMSampler(model) sampler = DDIMSampler(model)