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big improvements to inpainting and outpainting

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AUTOMATIC
2022-09-07 17:00:51 +03:00
parent 2cbda50cdd
commit 700c47a674
3 changed files with 35 additions and 14 deletions
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19
modules/processing.py
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@@ -52,7 +52,7 @@ class StableDiffusionProcessing:
self.overlay_images = overlay_images
self.paste_to = None
def init(self):
def init(self, seed):
pass
def sample(self, x, conditioning, unconditional_conditioning):
@@ -155,7 +155,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
precision_scope = torch.autocast if cmd_opts.precision == "autocast" else contextlib.nullcontext
ema_scope = (contextlib.nullcontext if cmd_opts.lowvram else p.sd_model.ema_scope)
with torch.no_grad(), precision_scope("cuda"), ema_scope():
p.init()
p.init(seed=all_seeds[0])
if state.job_count == -1:
state.job_count = p.n_iter
@@ -240,7 +240,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
sampler = None
def init(self):
def init(self, seed):
self.sampler = samplers[self.sampler_index].constructor(self.sd_model)
def sample(self, x, conditioning, unconditional_conditioning):
@@ -320,7 +320,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
self.mask = None
self.nmask = None
def init(self):
def init(self, seed):
self.sampler = samplers_for_img2img[self.sampler_index].constructor(self.sd_model)
crop_region = None
@@ -347,11 +347,13 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
else:
self.image_mask = images.resize_image(self.resize_mode, self.image_mask, self.width, self.height)
np_mask = np.array(self.image_mask)
np_mask = 255 - np.clip((255 - np_mask.astype(np.float)) * 2, 0, 255).astype(np.uint8)
np_mask = np.clip((np_mask.astype(np.float)) * 2, 0, 255).astype(np.uint8)
self.mask_for_overlay = Image.fromarray(np_mask)
self.overlay_images = []
latent_mask = self.latent_mask if self.latent_mask is not None else self.image_mask
imgs = []
for img in self.init_images:
image = img.convert("RGB")
@@ -361,7 +363,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
if self.image_mask is not None:
if self.inpainting_fill != 1:
image = fill(image, self.mask_for_overlay)
image = fill(image, latent_mask)
image_masked = Image.new('RGBa', (image.width, image.height))
image_masked.paste(image.convert("RGBA").convert("RGBa"), mask=ImageOps.invert(self.mask_for_overlay.convert('L')))
@@ -394,17 +396,18 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
self.init_latent = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image))
if self.image_mask is not None:
init_mask = self.latent_mask if self.latent_mask is not None else self.image_mask
init_mask = latent_mask
latmask = init_mask.convert('RGB').resize((self.init_latent.shape[3], self.init_latent.shape[2]))
latmask = np.moveaxis(np.array(latmask, dtype=np.float64), 2, 0) / 255
latmask = latmask[0]
latmask = np.around(latmask)
latmask = np.tile(latmask[None], (4, 1, 1))
self.mask = torch.asarray(1.0 - latmask).to(shared.device).type(self.sd_model.dtype)
self.nmask = torch.asarray(latmask).to(shared.device).type(self.sd_model.dtype)
if self.inpainting_fill == 2:
self.init_latent = self.init_latent * self.mask + create_random_tensors(self.init_latent.shape[1:], [self.seed + x + 1 for x in range(self.init_latent.shape[0])]) * self.nmask
self.init_latent = self.init_latent * self.mask + create_random_tensors(self.init_latent.shape[1:], [seed + x + 1 for x in range(self.init_latent.shape[0])]) * self.nmask
elif self.inpainting_fill == 3:
self.init_latent = self.init_latent * self.mask