sd-webui/stable-diffusion-webui
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Merge branch 'sd-webui:dev' into dev

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ZeroCool 2022-09-11 10:43:01 -07:00 committed by GitHub
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2 changed files with 20 additions and 18 deletions
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2
entrypoint.sh
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@ -7,6 +7,7 @@
SCRIPT_DIR=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd ) SCRIPT_DIR=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )
cd $SCRIPT_DIR cd $SCRIPT_DIR
export PYTHONPATH=$SCRIPT_DIR
MODEL_DIR="${SCRIPT_DIR}/model_cache" MODEL_DIR="${SCRIPT_DIR}/model_cache"
# Array of model files to pre-download # Array of model files to pre-download
@ -31,6 +32,7 @@ ENV_UPDATED=0
ENV_MODIFIED=$(date -r $ENV_FILE "+%s") ENV_MODIFIED=$(date -r $ENV_FILE "+%s")
ENV_MODIFED_FILE="${SCRIPT_DIR}/.env_updated" ENV_MODIFED_FILE="${SCRIPT_DIR}/.env_updated"
if [[ -f $ENV_MODIFED_FILE ]]; then ENV_MODIFIED_CACHED=$(<${ENV_MODIFED_FILE}); else ENV_MODIFIED_CACHED=0; fi if [[ -f $ENV_MODIFED_FILE ]]; then ENV_MODIFIED_CACHED=$(<${ENV_MODIFED_FILE}); else ENV_MODIFIED_CACHED=0; fi
export PIP_EXISTS_ACTION=w
# Create/update conda env if needed # Create/update conda env if needed
if ! conda env list | grep ".*${ENV_NAME}.*" >/dev/null 2>&1; then if ! conda env list | grep ".*${ENV_NAME}.*" >/dev/null 2>&1; then

36
scripts/webui.py
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@ -812,7 +812,7 @@ def oxlamon_matrix(prompt, seed, n_iter, batch_size):
return all_seeds, n_iter, prompt_matrix_parts, all_prompts, needrows return all_seeds, n_iter, prompt_matrix_parts, all_prompts, needrows
def perform_masked_image_restoration(image, init_img, init_mask, mask_blur_strength, mask_restore, use_RealESRGAN, RealESRGAN): def perform_masked_image_restoration(image, init_img, init_mask, mask_blur_strength, mask_restore, use_RealESRGAN, RealESRGAN):
if not mask_restore: if not mask_restore:
return image return image
else: else:
init_mask = init_mask.filter(ImageFilter.GaussianBlur(mask_blur_strength)) init_mask = init_mask.filter(ImageFilter.GaussianBlur(mask_blur_strength))
@ -840,7 +840,7 @@ def perform_color_correction(img_rgb, correction_target_lab, do_color_correction
except: except:
print("Install scikit-image to perform color correction") print("Install scikit-image to perform color correction")
return img_rgb return img_rgb
if not do_color_correction: return img_rgb if not do_color_correction: return img_rgb
if correction_target_lab is None: return img_rgb if correction_target_lab is None: return img_rgb
@ -854,7 +854,7 @@ def perform_color_correction(img_rgb, correction_target_lab, do_color_correction
channel_axis=2 channel_axis=2
), cv2.COLOR_LAB2RGB).astype("uint8") ), cv2.COLOR_LAB2RGB).astype("uint8")
) )
) )
def process_images( def process_images(
outpath, func_init, func_sample, prompt, seed, sampler_name, skip_grid, skip_save, batch_size, outpath, func_init, func_sample, prompt, seed, sampler_name, skip_grid, skip_save, batch_size,
@ -1027,7 +1027,7 @@ def process_images(
while(torch.cuda.memory_allocated()/1e6 >= mem): while(torch.cuda.memory_allocated()/1e6 >= mem):
time.sleep(1) time.sleep(1)
cur_variant_amount = variant_amount cur_variant_amount = variant_amount
if variant_amount == 0.0: if variant_amount == 0.0:
# we manually generate all input noises because each one should have a specific seed # we manually generate all input noises because each one should have a specific seed
x = create_random_tensors(shape, seeds=seeds) x = create_random_tensors(shape, seeds=seeds)
@ -1114,10 +1114,10 @@ def process_images(
gfpgan_image = Image.fromarray(gfpgan_sample) gfpgan_image = Image.fromarray(gfpgan_sample)
gfpgan_image = perform_color_correction(gfpgan_image, correction_target, do_color_correction) gfpgan_image = perform_color_correction(gfpgan_image, correction_target, do_color_correction)
gfpgan_image = perform_masked_image_restoration( gfpgan_image = perform_masked_image_restoration(
gfpgan_image, init_img, init_mask, gfpgan_image, init_img, init_mask,
mask_blur_strength, mask_restore, mask_blur_strength, mask_restore,
use_RealESRGAN = False, RealESRGAN = None use_RealESRGAN = False, RealESRGAN = None
) )
gfpgan_metadata = copy.copy(metadata) gfpgan_metadata = copy.copy(metadata)
gfpgan_metadata.GFPGAN = True gfpgan_metadata.GFPGAN = True
ImageMetadata.set_on_image( gfpgan_image, gfpgan_metadata ) ImageMetadata.set_on_image( gfpgan_image, gfpgan_metadata )
@ -1137,7 +1137,7 @@ skip_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoisin
esrgan_image = Image.fromarray(esrgan_sample) esrgan_image = Image.fromarray(esrgan_sample)
esrgan_image = perform_color_correction(esrgan_image, correction_target, do_color_correction) esrgan_image = perform_color_correction(esrgan_image, correction_target, do_color_correction)
esrgan_image = perform_masked_image_restoration( esrgan_image = perform_masked_image_restoration(
esrgan_image, init_img, init_mask, esrgan_image, init_img, init_mask,
mask_blur_strength, mask_restore, mask_blur_strength, mask_restore,
use_RealESRGAN, RealESRGAN use_RealESRGAN, RealESRGAN
) )
@ -1159,7 +1159,7 @@ skip_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoisin
gfpgan_esrgan_image = Image.fromarray(gfpgan_esrgan_sample) gfpgan_esrgan_image = Image.fromarray(gfpgan_esrgan_sample)
gfpgan_esrgan_image = perform_color_correction(gfpgan_esrgan_image, correction_target, do_color_correction) gfpgan_esrgan_image = perform_color_correction(gfpgan_esrgan_image, correction_target, do_color_correction)
gfpgan_esrgan_image = perform_masked_image_restoration( gfpgan_esrgan_image = perform_masked_image_restoration(
gfpgan_esrgan_image, init_img, init_mask, gfpgan_esrgan_image, init_img, init_mask,
mask_blur_strength, mask_restore, mask_blur_strength, mask_restore,
use_RealESRGAN, RealESRGAN use_RealESRGAN, RealESRGAN
) )
@ -1175,7 +1175,7 @@ skip_save, skip_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size,
output_images.append(image) output_images.append(image)
image = perform_masked_image_restoration( image = perform_masked_image_restoration(
image, init_img, init_mask, image, init_img, init_mask,
mask_blur_strength, mask_restore, mask_blur_strength, mask_restore,
# RealESRGAN image already processed in if-case above. # RealESRGAN image already processed in if-case above.
use_RealESRGAN = False, RealESRGAN = None use_RealESRGAN = False, RealESRGAN = None
@ -1505,7 +1505,7 @@ def img2img(prompt: str, image_editor_mode: str, mask_mode: str, mask_blur_stren
#let's try and find where init_image is 0's #let's try and find where init_image is 0's
#shape is probably (3,width,height)? #shape is probably (3,width,height)?
if image_editor_mode == "Uncrop": if image_editor_mode == "Uncrop":
_image=image.numpy()[0] _image=image.numpy()[0]
_mask=np.ones((_image.shape[1],_image.shape[2])) _mask=np.ones((_image.shape[1],_image.shape[2]))
@ -1526,7 +1526,7 @@ def img2img(prompt: str, image_editor_mode: str, mask_mode: str, mask_blur_stren
boundingbox=np.zeros(shape=(height,width)) boundingbox=np.zeros(shape=(height,width))
boundingbox[colstart+PAD_IMG:colend-PAD_IMG,rowstart+PAD_IMG:rowend-PAD_IMG]=1 boundingbox[colstart+PAD_IMG:colend-PAD_IMG,rowstart+PAD_IMG:rowend-PAD_IMG]=1
boundingbox=blurArr(boundingbox,4) boundingbox=blurArr(boundingbox,4)
#this is the mask for outpainting #this is the mask for outpainting
PAD_MASK=24 PAD_MASK=24
boundingbox2=np.zeros(shape=(height,width)) boundingbox2=np.zeros(shape=(height,width))
@ -1557,7 +1557,7 @@ def img2img(prompt: str, image_editor_mode: str, mask_mode: str, mask_blur_stren
init_image = init_image.to(device) init_image = init_image.to(device)
init_image = repeat(init_image, '1 ... -> b ...', b=batch_size) 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 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: if opt.optimized:
mem = torch.cuda.memory_allocated()/1e6 mem = torch.cuda.memory_allocated()/1e6
modelFS.to("cpu") modelFS.to("cpu")
@ -1617,7 +1617,7 @@ def img2img(prompt: str, image_editor_mode: str, mask_mode: str, mask_blur_stren
# turn on color correction for loopback to prevent known issue of color drift # turn on color correction for loopback to prevent known issue of color drift
do_color_correction = True do_color_correction = True
for i in range(n_iter): for i in range(n_iter):
if do_color_correction and i == 0: if do_color_correction and i == 0:
correction_target = cv2.cvtColor(np.asarray(init_img.copy()), cv2.COLOR_RGB2LAB) correction_target = cv2.cvtColor(np.asarray(init_img.copy()), cv2.COLOR_RGB2LAB)
@ -1658,9 +1658,9 @@ def img2img(prompt: str, image_editor_mode: str, mask_mode: str, mask_blur_stren
write_info_files=write_info_files, write_info_files=write_info_files,
write_sample_info_to_log_file=write_sample_info_to_log_file, write_sample_info_to_log_file=write_sample_info_to_log_file,
jpg_sample=jpg_sample, jpg_sample=jpg_sample,
job_info=job_info, job_info=job_info,
do_color_correction=do_color_correction, do_color_correction=do_color_correction,
correction_target=correction_target correction_target=correction_target
) )
if initial_seed is None: if initial_seed is None:
@ -2074,7 +2074,7 @@ def imgproc(image,image_batch,imgproc_prompt,imgproc_toggles, imgproc_upscale_to
if 1 in imgproc_toggles: if 1 in imgproc_toggles:
if imgproc_upscale_toggles == 0: if imgproc_upscale_toggles == 0:
ModelLoader(['GFPGAN','LDSR'],False,True) # Unload unused models ModelLoader(['GFPGAN','LDSR'],False,True) # Unload unused models
ModelLoader(['RealESGAN'],True,False,imgproc_realesrgan_model_name) # Load used models ModelLoader(['RealESGAN'],True,False,imgproc_realesrgan_model_name) # Load used models
elif imgproc_upscale_toggles == 1: elif imgproc_upscale_toggles == 1:
ModelLoader(['GFPGAN','LDSR'],False,True) # Unload unused models ModelLoader(['GFPGAN','LDSR'],False,True) # Unload unused models
ModelLoader(['RealESGAN','model'],True,False) # Load used models ModelLoader(['RealESGAN','model'],True,False) # Load used models
@ -2306,7 +2306,7 @@ img2img_toggles = [
'Write sample info files', 'Write sample info files',
'Write sample info to one file', 'Write sample info to one file',
'jpg samples', 'jpg samples',
'Color correction (always enabled on loopback mode)' 'Color correction (always enabled on loopback mode)',
'Filter NSFW content', 'Filter NSFW content',
] ]
# removed for now becuase of Image Lab implementation # removed for now becuase of Image Lab implementation
@ -2412,7 +2412,7 @@ class ServerLauncher(threading.Thread):
'inbrowser': opt.inbrowser, 'inbrowser': opt.inbrowser,
'server_name': '0.0.0.0', 'server_name': '0.0.0.0',
'server_port': opt.port, 'server_port': opt.port,
'share': opt.share, 'share': opt.share,
'show_error': True 'show_error': True
} }
if not opt.share: if not opt.share: