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[Fooocus 2.0.60] Fooocus Inpaint or Outpaint (Midjourney Left/Right/Top/Bottom) (#402)

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[Fooocus 2.0.60] Fooocus Inpaint or Outpaint (Midjourney Left/Right/Top/Bottom) (#402)
This commit is contained in:
lllyasviel
2023-09-18 01:16:07 -07:00
committed by GitHub
parent 43e59c1676
commit b61642ecba
11 changed files with 572 additions and 53 deletions
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modules/async_worker.py
+58 -3
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@@ -1,4 +1,6 @@
import threading
import numpy as np
import torch
buffer = []
@@ -19,6 +21,7 @@ def worker():
import modules.patch
import modules.virtual_memory as virtual_memory
import comfy.model_management
import modules.inpaint_worker as inpaint_worker
from modules.sdxl_styles import apply_style, aspect_ratios, fooocus_expansion
from modules.private_logger import log
@@ -46,8 +49,10 @@ def worker():
aspect_ratios_selction, image_number, image_seed, sharpness, \
base_model_name, refiner_model_name, \
l1, w1, l2, w2, l3, w3, l4, w4, l5, w5, \
input_image_checkbox, \
uov_method, uov_input_image = task
input_image_checkbox, current_tab, \
uov_method, uov_input_image, outpaint_selections, inpaint_input_image = task
outpaint_selections = [o.lower() for o in outpaint_selections]
loras = [(l1, w1), (l2, w2), (l3, w3), (l4, w4), (l5, w5)]
@@ -63,9 +68,11 @@ def worker():
use_style = len(style_selections) > 0
modules.patch.sharpness = sharpness
modules.patch.negative_adm = True
initial_latent = None
denoising_strength = 1.0
tiled = False
inpaint_worker.current_task = None
if performance_selction == 'Speed':
steps = 30
@@ -80,7 +87,7 @@ def worker():
if input_image_checkbox:
progressbar(0, 'Image processing ...')
if uov_method != flags.disabled and uov_input_image is not None:
if current_tab == 'uov' and uov_method != flags.disabled and uov_input_image is not None:
uov_input_image = HWC3(uov_input_image)
if 'vary' in uov_method:
if not image_is_generated_in_current_ui(uov_input_image, ui_width=width, ui_height=height):
@@ -156,6 +163,49 @@ def worker():
width = W * 8
height = H * 8
print(f'Final resolution is {str((height, width))}.')
if current_tab == 'inpaint' and isinstance(inpaint_input_image, dict):
inpaint_image = inpaint_input_image['image']
inpaint_mask = inpaint_input_image['mask'][:, :, 0]
if isinstance(inpaint_image, np.ndarray) and isinstance(inpaint_mask, np.ndarray) \
and (np.any(inpaint_mask > 127) or len(outpaint_selections) > 0):
if len(outpaint_selections) > 0:
H, W, C = inpaint_image.shape
if 'top' in outpaint_selections:
inpaint_image = np.pad(inpaint_image, [[int(H * 0.3), 0], [0, 0], [0, 0]], mode='edge')
inpaint_mask = np.pad(inpaint_mask, [[int(H * 0.3), 0], [0, 0]], mode='constant', constant_values=255)
if 'bottom' in outpaint_selections:
inpaint_image = np.pad(inpaint_image, [[0, int(H * 0.3)], [0, 0], [0, 0]], mode='edge')
inpaint_mask = np.pad(inpaint_mask, [[0, int(H * 0.3)], [0, 0]], mode='constant', constant_values=255)
H, W, C = inpaint_image.shape
if 'left' in outpaint_selections:
inpaint_image = np.pad(inpaint_image, [[0, 0], [int(H * 0.3), 0], [0, 0]], mode='edge')
inpaint_mask = np.pad(inpaint_mask, [[0, 0], [int(H * 0.3), 0]], mode='constant', constant_values=255)
if 'right' in outpaint_selections:
inpaint_image = np.pad(inpaint_image, [[0, 0], [0, int(H * 0.3)], [0, 0]], mode='edge')
inpaint_mask = np.pad(inpaint_mask, [[0, 0], [0, int(H * 0.3)]], mode='constant', constant_values=255)
inpaint_image = np.ascontiguousarray(inpaint_image.copy())
inpaint_mask = np.ascontiguousarray(inpaint_mask.copy())
inpaint_worker.current_task = inpaint_worker.InpaintWorker(image=inpaint_image, mask=inpaint_mask,
is_outpaint=len(outpaint_selections) > 0)
# print(f'Inpaint task: {str((height, width))}')
# outputs.append(['results', inpaint_worker.current_task.visualize_mask_processing()])
# return
inpaint_pixels = core.numpy_to_pytorch(inpaint_worker.current_task.image_ready)
progressbar(0, 'VAE encoding ...')
initial_latent = core.encode_vae(vae=pipeline.xl_base_patched.vae, pixels=inpaint_pixels)
inpaint_latent = initial_latent['samples']
B, C, H, W = inpaint_latent.shape
inpaint_mask = core.numpy_to_pytorch(inpaint_worker.current_task.mask_ready[None])
inpaint_mask = torch.nn.functional.avg_pool2d(inpaint_mask, (8, 8))
inpaint_mask = torch.nn.functional.interpolate(inpaint_mask, (H, W), mode='bilinear')
width = W * 8
height = H * 8
inpaint_worker.current_task.load_latent(latent=inpaint_latent, mask=inpaint_mask)
progressbar(1, 'Initializing ...')
@@ -262,6 +312,8 @@ def worker():
f'Step {step}/{total_steps} in the {current_task_id + 1}-th Sampling',
y)])
print(f'[ADM] Negative ADM = {modules.patch.negative_adm}')
outputs.append(['preview', (13, 'Starting tasks ...', None)])
for current_task_id, task in enumerate(tasks):
try:
@@ -279,6 +331,9 @@ def worker():
tiled=tiled
)
if inpaint_worker.current_task is not None:
imgs = [inpaint_worker.current_task.post_process(x) for x in imgs]
for x in imgs:
d = [
('Prompt', raw_prompt),