mirror of
https://github.com/Sygil-Dev/sygil-webui.git
synced 2024-12-15 06:21:34 +03:00
Merge pull request #1070 from briansemrau/feature/img2img-masking-fixes
img2img mask fixes and fix image2noise normalization
This commit is contained in:
ZeroCool
GitHub
commit
2f86782acf
@ -53,8 +53,5 @@ def find_noise_for_image(model, device, init_image, prompt, steps=200, cond_scal
|
||||
dt = sigmas[i] - sigmas[i - 1]
|
||||
x = x + d * dt
|
||||
|
||||
if normalize:
|
||||
# multiplying sigmas seems to break things pretty bad...
|
||||
return (x / x.std())# * sigmas[-1]
|
||||
else:
|
||||
return x
|
||||
return x / sigmas[-1]
|
||||
|
||||
|
||||
@ -1149,7 +1149,7 @@ def process_images(
|
||||
while(torch.cuda.memory_allocated()/1e6 >= mem):
|
||||
time.sleep(1)
|
||||
|
||||
if noise_mode == 1:
|
||||
if noise_mode == 1 or noise_mode == 3:
|
||||
# TODO params for find_noise_to_image
|
||||
x = torch.cat(batch_size * [find_noise_for_image.find_noise_for_image(
|
||||
st.session_state["model"], st.session_state["device"],
|
||||
@ -1448,14 +1448,22 @@ def img2img(prompt: str = '', init_info: any = None, init_info_mask: any = None,
|
||||
else:
|
||||
raise Exception("Unknown sampler: " + sampler_name)
|
||||
|
||||
def process_init_mask(init_mask: Image):
|
||||
if init_mask.mode == "RGBA":
|
||||
init_mask = init_mask.convert('RGBA')
|
||||
background = Image.new('RGBA', init_mask.size, (0, 0, 0))
|
||||
init_mask = Image.alpha_composite(background, init_mask)
|
||||
init_mask = init_mask.convert('RGB')
|
||||
return init_mask
|
||||
|
||||
init_img = init_info
|
||||
init_mask = None
|
||||
if mask_mode == 0:
|
||||
if init_info_mask:
|
||||
init_mask = init_info_mask
|
||||
init_mask = process_init_mask(init_info_mask)
|
||||
elif mask_mode == 1:
|
||||
if init_info_mask:
|
||||
init_mask = init_info_mask
|
||||
init_mask = process_init_mask(init_info_mask)
|
||||
init_mask = ImageOps.invert(init_mask)
|
||||
elif mask_mode == 2:
|
||||
init_img_transparency = init_img.split()[-1].convert('L')#.point(lambda x: 255 if x > 0 else 0, mode='1')
|
||||
@ -1467,42 +1475,41 @@ def img2img(prompt: str = '', init_info: any = None, init_info_mask: any = None,
|
||||
assert 0. <= denoising_strength <= 1., 'can only work with strength in [0.0, 1.0]'
|
||||
t_enc = int(denoising_strength * ddim_steps)
|
||||
|
||||
if init_mask is not None and (noise_mode == 2 or noise_mode == 3) and init_img is not None:
|
||||
noise_q = 0.99
|
||||
color_variation = 0.0
|
||||
mask_blend_factor = 1.0
|
||||
|
||||
np_init = (np.asarray(init_img.convert("RGB"))/255.0).astype(np.float64) # annoyingly complex mask fixing
|
||||
np_mask_rgb = 1. - (np.asarray(ImageOps.invert(init_mask).convert("RGB"))/255.0).astype(np.float64)
|
||||
np_mask_rgb -= np.min(np_mask_rgb)
|
||||
np_mask_rgb /= np.max(np_mask_rgb)
|
||||
np_mask_rgb = 1. - np_mask_rgb
|
||||
np_mask_rgb_hardened = 1. - (np_mask_rgb < 0.99).astype(np.float64)
|
||||
blurred = skimage.filters.gaussian(np_mask_rgb_hardened[:], sigma=16., channel_axis=2, truncate=32.)
|
||||
blurred2 = skimage.filters.gaussian(np_mask_rgb_hardened[:], sigma=16., channel_axis=2, truncate=32.)
|
||||
#np_mask_rgb_dilated = np_mask_rgb + blurred # fixup mask todo: derive magic constants
|
||||
#np_mask_rgb = np_mask_rgb + blurred
|
||||
np_mask_rgb_dilated = np.clip((np_mask_rgb + blurred2) * 0.7071, 0., 1.)
|
||||
np_mask_rgb = np.clip((np_mask_rgb + blurred) * 0.7071, 0., 1.)
|
||||
|
||||
noise_rgb = matched_noise.get_matched_noise(np_init, np_mask_rgb, noise_q, color_variation)
|
||||
blend_mask_rgb = np.clip(np_mask_rgb_dilated,0.,1.) ** (mask_blend_factor)
|
||||
noised = noise_rgb[:]
|
||||
blend_mask_rgb **= (2.)
|
||||
noised = np_init[:] * (1. - blend_mask_rgb) + noised * blend_mask_rgb
|
||||
|
||||
np_mask_grey = np.sum(np_mask_rgb, axis=2)/3.
|
||||
ref_mask = np_mask_grey < 1e-3
|
||||
|
||||
all_mask = np.ones((height, width), dtype=bool)
|
||||
noised[all_mask,:] = skimage.exposure.match_histograms(noised[all_mask,:]**1., noised[ref_mask,:], channel_axis=1)
|
||||
|
||||
init_img = Image.fromarray(np.clip(noised * 255., 0., 255.).astype(np.uint8), mode="RGB")
|
||||
st.session_state["editor_image"].image(init_img) # debug
|
||||
|
||||
def init():
|
||||
init_image = init_img
|
||||
if init_mask is not None and noise_mode == 2 and init_image is not None:
|
||||
noise_q = 0.99
|
||||
color_variation = 0.0
|
||||
mask_blend_factor = 1.0
|
||||
|
||||
np_init = (np.asarray(init_image.convert("RGB"))/255.0).astype(np.float64) # annoyingly complex mask fixing
|
||||
np_mask_rgb = 1. - (np.asarray(init_mask.convert("RGB"))/255.0).astype(np.float64)
|
||||
np_mask_rgb -= np.min(np_mask_rgb)
|
||||
np_mask_rgb /= np.max(np_mask_rgb)
|
||||
#np_mask_rgb = 1. - np_mask_rgb
|
||||
np_mask_rgb_hardened = 1. - (np_mask_rgb < 0.99).astype(np.float64)
|
||||
blurred = skimage.filters.gaussian(np_mask_rgb_hardened[:], sigma=16., channel_axis=2, truncate=32.)
|
||||
blurred2 = skimage.filters.gaussian(np_mask_rgb_hardened[:], sigma=16., channel_axis=2, truncate=32.)
|
||||
#np_mask_rgb_dilated = np_mask_rgb + blurred # fixup mask todo: derive magic constants
|
||||
#np_mask_rgb = np_mask_rgb + blurred
|
||||
np_mask_rgb_dilated = np.clip((np_mask_rgb + blurred2) * 0.7071, 0., 1.)
|
||||
np_mask_rgb = np.clip((np_mask_rgb + blurred) * 0.7071, 0., 1.)
|
||||
|
||||
noise_rgb = matched_noise.get_matched_noise(np_init, np_mask_rgb, noise_q, color_variation)
|
||||
blend_mask_rgb = np.clip(np_mask_rgb_dilated,0.,1.) ** (mask_blend_factor)
|
||||
noised = noise_rgb[:]
|
||||
blend_mask_rgb **= (2.)
|
||||
noised = np_init[:] * (1. - blend_mask_rgb) + noised * blend_mask_rgb
|
||||
|
||||
np_mask_grey = np.sum(np_mask_rgb, axis=2)/3.
|
||||
ref_mask = np_mask_grey < 1e-3
|
||||
|
||||
all_mask = np.ones((width, height), dtype=bool)
|
||||
noised[all_mask,:] = skimage.exposure.match_histograms(noised[all_mask,:]**1., noised[ref_mask,:], channel_axis=1)
|
||||
|
||||
init_image = Image.fromarray(np.clip(noised * 255., 0., 255.).astype(np.uint8), mode="RGB")
|
||||
st.session_state["editor_image"].image(init_image)
|
||||
|
||||
image = init_image.convert('RGB')
|
||||
image = init_img.convert('RGB')
|
||||
image = np.array(image).astype(np.float32) / 255.0
|
||||
image = image[None].transpose(0, 3, 1, 2)
|
||||
image = torch.from_numpy(image)
|
||||
@ -1510,7 +1517,7 @@ def img2img(prompt: str = '', init_info: any = None, init_info_mask: any = None,
|
||||
mask_channel = None
|
||||
if init_mask:
|
||||
alpha = resize_image(resize_mode, init_mask, width // 8, height // 8)
|
||||
mask_channel = alpha.split()[1]
|
||||
mask_channel = alpha.split()[-1]
|
||||
|
||||
mask = None
|
||||
if mask_channel is not None:
|
||||
@ -2367,7 +2374,7 @@ def layout():
|
||||
width = st.slider("Width:", min_value=64, max_value=1024, value=defaults.img2img.width, step=64)
|
||||
height = st.slider("Height:", min_value=64, max_value=1024, value=defaults.img2img.height, step=64)
|
||||
seed = st.text_input("Seed:", value=defaults.img2img.seed, help=" The seed to use, if left blank a random seed will be generated.")
|
||||
noise_mode_list = ["Seed", "Find Noise", "Matched Noise"]
|
||||
noise_mode_list = ["Seed", "Find Noise", "Matched Noise", "Find+Matched Noise"]
|
||||
noise_mode = st.selectbox(
|
||||
"Noise Mode", noise_mode_list,
|
||||
help=""
|
||||
@ -2461,14 +2468,18 @@ def layout():
|
||||
help="Upload an mask image which will be used for masking the image to image generation.",
|
||||
)
|
||||
if uploaded_masks:
|
||||
image = Image.open(uploaded_masks).convert('RGB')
|
||||
new_mask = image.resize((width, height))
|
||||
mask_holder.image(new_mask)
|
||||
mask = Image.open(uploaded_masks)
|
||||
if mask.mode == "RGBA":
|
||||
mask = mask.convert('RGBA')
|
||||
background = Image.new('RGBA', mask.size, (0, 0, 0))
|
||||
mask = Image.alpha_composite(background, mask)
|
||||
mask = mask.resize((width, height))
|
||||
mask_holder.image(mask)
|
||||
|
||||
if uploaded_images and uploaded_masks:
|
||||
if mask_mode != 2:
|
||||
final_img = new_img.copy()
|
||||
alpha_layer = new_mask.split()[-1].copy().convert('L')
|
||||
alpha_layer = mask.convert('L')
|
||||
strength = st.session_state["denoising_strength"]
|
||||
if mask_mode == 0:
|
||||
alpha_layer = ImageOps.invert(alpha_layer)
|
||||
@ -2517,7 +2528,7 @@ def layout():
|
||||
#img_array = np.array(image) # if you want to pass it to OpenCV
|
||||
new_mask = None
|
||||
if uploaded_masks:
|
||||
mask = Image.open(uploaded_masks).convert('RGB')
|
||||
mask = Image.open(uploaded_masks).convert('RGBA')
|
||||
new_mask = mask.resize((width, height))
|
||||
|
||||
try:
|
||||
|
||||
Loading…
Reference in New Issue
Block a user