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https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
synced 2024-12-29 19:05:05 +08:00
added poor man's inpainting script
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af133859f0
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7350c71259
@ -39,23 +39,26 @@ def split_grid(image, tile_w=512, tile_h=512, overlap=64):
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w = image.width
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h = image.height
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now = tile_w - overlap # non-overlap width
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noh = tile_h - overlap
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non_overlap_width = tile_w - overlap
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non_overlap_height = tile_h - overlap
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cols = math.ceil((w - overlap) / now)
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rows = math.ceil((h - overlap) / noh)
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cols = math.ceil((w - overlap) / non_overlap_width)
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rows = math.ceil((h - overlap) / non_overlap_height)
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dx = (w - tile_w) // (cols-1) if cols > 1 else 0
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dy = (h - tile_h) // (rows-1) if rows > 1 else 0
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grid = Grid([], tile_w, tile_h, w, h, overlap)
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for row in range(rows):
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row_images = []
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y = row * noh
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y = row * dy
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if y + tile_h >= h:
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y = h - tile_h
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for col in range(cols):
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x = col * now
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x = col * dx
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if x+tile_w >= w:
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x = w - tile_w
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@ -130,7 +130,7 @@ def img2img(prompt: str, init_img, init_img_with_mask, steps: int, sampler_index
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else:
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processed = modules.scripts.run(p, *args)
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processed = modules.scripts.scripts_img2img.run(p, *args)
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if processed is None:
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processed = process_images(p)
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@ -271,7 +271,7 @@ def fill(image, mask):
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image_masked = image_masked.convert('RGBa')
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for radius, repeats in [(64, 1), (16, 2), (4, 4), (2, 2), (0, 1)]:
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for radius, repeats in [(256, 1), (64, 1), (16, 2), (4, 4), (2, 2), (0, 1)]:
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blurred = image_masked.filter(ImageFilter.GaussianBlur(radius)).convert('RGBA')
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for _ in range(repeats):
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image_mod.alpha_composite(blurred)
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@ -290,6 +290,8 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
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self.denoising_strength: float = denoising_strength
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self.init_latent = None
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self.image_mask = mask
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#self.image_unblurred_mask = None
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self.latent_mask = None
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self.mask_for_overlay = None
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self.mask_blur = mask_blur
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self.inpainting_fill = inpainting_fill
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@ -308,6 +310,8 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
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if self.inpainting_mask_invert:
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self.image_mask = ImageOps.invert(self.image_mask)
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#self.image_unblurred_mask = self.image_mask
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if self.mask_blur > 0:
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self.image_mask = self.image_mask.filter(ImageFilter.GaussianBlur(self.mask_blur))
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@ -368,7 +372,8 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
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self.init_latent = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image))
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if self.image_mask is not None:
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latmask = self.image_mask.convert('RGB').resize((self.init_latent.shape[3], self.init_latent.shape[2]))
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init_mask = self.latent_mask if self.latent_mask is not None else self.image_mask
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latmask = init_mask.convert('RGB').resize((self.init_latent.shape[3], self.init_latent.shape[2]))
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latmask = np.moveaxis(np.array(latmask, dtype=np.float64), 2, 0) / 255
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latmask = latmask[0]
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latmask = np.tile(latmask[None], (4, 1, 1))
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@ -18,6 +18,9 @@ class Script:
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def ui(self, is_img2img):
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pass
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def show(self, is_img2img):
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return True
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def run(self, *args):
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raise NotImplementedError()
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@ -25,7 +28,7 @@ class Script:
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return ""
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scripts = []
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scripts_data = []
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def load_scripts(basedir):
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@ -49,10 +52,8 @@ def load_scripts(basedir):
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for key, script_class in module.__dict__.items():
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if type(script_class) == type and issubclass(script_class, Script):
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obj = script_class()
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obj.filename = path
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scripts_data.append((script_class, path))
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scripts.append(obj)
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except Exception:
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print(f"Error loading script: {filename}", file=sys.stderr)
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print(traceback.format_exc(), file=sys.stderr)
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@ -69,52 +70,75 @@ def wrap_call(func, filename, funcname, *args, default=None, **kwargs):
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return default
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def setup_ui(is_img2img):
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titles = [wrap_call(script.title, script.filename, "title") or f"{script.filename} [error]" for script in scripts]
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class ScriptRunner:
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def __init__(self):
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self.scripts = []
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dropdown = gr.Dropdown(label="Script", choices=["None"] + titles, value="None", type="index")
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def setup_ui(self, is_img2img):
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for script_class, path in scripts_data:
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script = script_class()
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script.filename = path
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inputs = [dropdown]
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if not script.show(is_img2img):
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continue
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for script in scripts:
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script.args_from = len(inputs)
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controls = script.ui(is_img2img)
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self.scripts.append(script)
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for control in controls:
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control.visible = False
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titles = [wrap_call(script.title, script.filename, "title") or f"{script.filename} [error]" for script in self.scripts]
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inputs += controls
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script.args_to = len(inputs)
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dropdown = gr.Dropdown(label="Script", choices=["None"] + titles, value="None", type="index")
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inputs = [dropdown]
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def select_script(index):
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if index > 0:
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script = scripts[index-1]
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args_from = script.args_from
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args_to = script.args_to
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else:
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args_from = 0
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args_to = 0
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for script in self.scripts:
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script.args_from = len(inputs)
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return [ui.gr_show(True if i == 0 else args_from <= i < args_to) for i in range(len(inputs))]
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controls = wrap_call(script.ui, script.filename, "ui", is_img2img)
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dropdown.change(
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fn=select_script,
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inputs=[dropdown],
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outputs=inputs
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)
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if controls is None:
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continue
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return inputs
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for control in controls:
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control.visible = False
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inputs += controls
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script.args_to = len(inputs)
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def select_script(script_index):
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if 0 < script_index <= len(self.scripts):
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script = self.scripts[script_index-1]
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args_from = script.args_from
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args_to = script.args_to
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else:
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args_from = 0
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args_to = 0
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return [ui.gr_show(True if i == 0 else args_from <= i < args_to) for i in range(len(inputs))]
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dropdown.change(
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fn=select_script,
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inputs=[dropdown],
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outputs=inputs
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)
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return inputs
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def run(p: StableDiffusionProcessing, *args):
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script_index = args[0] - 1
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def run(self, p: StableDiffusionProcessing, *args):
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script_index = args[0]
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if script_index < 0 or script_index >= len(scripts):
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return None
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if script_index == 0:
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return None
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script = scripts[script_index]
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script = self.scripts[script_index-1]
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script_args = args[script.args_from:script.args_to]
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processed = script.run(p, *script_args)
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if script is None:
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return None
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return processed
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script_args = args[script.args_from:script.args_to]
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processed = script.run(p, *script_args)
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return processed
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scripts_txt2img = ScriptRunner()
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scripts_img2img = ScriptRunner()
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@ -24,7 +24,7 @@ def txt2img(prompt: str, negative_prompt: str, steps: int, sampler_index: int, u
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use_GFPGAN=use_GFPGAN
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)
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processed = modules.scripts.run(p, *args)
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processed = modules.scripts.scripts_txt2img.run(p, *args)
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if processed is not None:
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pass
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@ -162,7 +162,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
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seed = gr.Number(label='Seed', value=-1)
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with gr.Group():
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custom_inputs = modules.scripts.setup_ui(is_img2img=False)
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custom_inputs = modules.scripts.scripts_txt2img.setup_ui(is_img2img=False)
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with gr.Column(variant='panel'):
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with gr.Group():
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@ -244,7 +244,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
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inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='fill', type="index", visible=False)
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with gr.Row():
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inpaint_full_res = gr.Checkbox(label='Inpaint at full resolution', value=True, visible=False)
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inpaint_full_res = gr.Checkbox(label='Inpaint at full resolution', value=False, visible=False)
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inpainting_mask_invert = gr.Radio(label='Masking mode', choices=['Inpaint masked', 'Inpaint not masked'], value='Inpaint masked', type="index", visible=False)
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with gr.Row():
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@ -269,7 +269,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
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seed = gr.Number(label='Seed', value=-1)
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with gr.Group():
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custom_inputs = modules.scripts.setup_ui(is_img2img=True)
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custom_inputs = modules.scripts.scripts_img2img.setup_ui(is_img2img=True)
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with gr.Column(variant='panel'):
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110
scripts/poor_mans_outpainting.py
Normal file
110
scripts/poor_mans_outpainting.py
Normal file
@ -0,0 +1,110 @@
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import math
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import modules.scripts as scripts
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import gradio as gr
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from PIL import Image, ImageDraw
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from modules import images, processing
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from modules.processing import Processed, process_images
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from modules.shared import opts, cmd_opts, state
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class Script(scripts.Script):
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def title(self):
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return "Poor man's outpainting"
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def show(self, is_img2img):
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return is_img2img
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def ui(self, is_img2img):
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if not is_img2img:
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return None
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pixels = gr.Slider(label="Pixels to expand", minimum=8, maximum=128, step=8)
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mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4, visible=False)
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inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='fill', type="index", visible=False)
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return [pixels, mask_blur, inpainting_fill]
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def run(self, p, pixels, mask_blur, inpainting_fill):
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initial_seed = None
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initial_info = None
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p.mask_blur = mask_blur
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p.inpainting_fill = inpainting_fill
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p.inpaint_full_res = False
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init_img = p.init_images[0]
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target_w = math.ceil((init_img.width + pixels * 2) / 64) * 64
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target_h = math.ceil((init_img.height + pixels * 2) / 64) * 64
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border_x = (target_w - init_img.width)//2
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border_y = (target_h - init_img.height)//2
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img = Image.new("RGB", (target_w, target_h))
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img.paste(init_img, (border_x, border_y))
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mask = Image.new("L", (img.width, img.height), "white")
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draw = ImageDraw.Draw(mask)
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draw.rectangle((border_x + mask_blur * 2, border_y + mask_blur * 2, mask.width - border_x - mask_blur * 2, mask.height - border_y - mask_blur * 2), fill="black")
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latent_mask = Image.new("L", (img.width, img.height), "white")
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latent_draw = ImageDraw.Draw(latent_mask)
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latent_draw.rectangle((border_x + 1, border_y + 1, mask.width - border_x - 1, mask.height - border_y - 1), fill="black")
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processing.torch_gc()
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grid = images.split_grid(img, tile_w=p.width, tile_h=p.height, overlap=pixels)
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grid_mask = images.split_grid(mask, tile_w=p.width, tile_h=p.height, overlap=pixels)
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grid_latent_mask = images.split_grid(mask, tile_w=p.width, tile_h=p.height, overlap=pixels)
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p.n_iter = 1
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p.batch_size = 1
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p.do_not_save_grid = True
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p.do_not_save_samples = True
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work = []
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work_mask = []
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work_latent_mask = []
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work_results = []
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for (_, _, row), (_, _, row_mask), (_, _, row_latent_mask) in zip(grid.tiles, grid_mask.tiles, grid_latent_mask.tiles):
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for tiledata, tiledata_mask, tiledata_latent_mask in zip(row, row_mask, row_latent_mask):
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work.append(tiledata[2])
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work_mask.append(tiledata_mask[2])
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work_latent_mask.append(tiledata_latent_mask[2])
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batch_count = len(work)
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print(f"Poor man's outpainting will process a total of {len(work)} images tiled as {len(grid.tiles[0][2])}x{len(grid.tiles)}.")
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for i in range(batch_count):
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p.init_images = [work[i]]
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p.image_mask = work_mask[i]
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p.latent_mask = work_latent_mask[i]
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state.job = f"Batch {i + 1} out of {batch_count}"
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processed = process_images(p)
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if initial_seed is None:
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initial_seed = processed.seed
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initial_info = processed.info
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p.seed = processed.seed + 1
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work_results += processed.images
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image_index = 0
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for y, h, row in grid.tiles:
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for tiledata in row:
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tiledata[2] = work_results[image_index] if image_index < len(work_results) else Image.new("RGB", (p.width, p.height))
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image_index += 1
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combined_image = images.combine_grid(grid)
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if opts.samples_save:
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images.save_image(combined_image, p.outpath_samples, "", initial_seed, p.prompt, opts.grid_format, info=initial_info)
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processed = Processed(p, [combined_image], initial_seed, initial_info)
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return processed
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