mirror of
https://github.com/lllyasviel/Fooocus.git
synced 2026-08-16 13:13:16 +02:00
[Fooocus 2.0.50] Variation/Upscale (Midjourney Toolbar) (#389)
This commit is contained in:
lllyasviel
+121
-43
@@ -1,7 +1,6 @@
|
||||
import threading
|
||||
import torch
|
||||
|
||||
|
||||
buffer = []
|
||||
outputs = []
|
||||
|
||||
@@ -14,14 +13,18 @@ def worker():
|
||||
import random
|
||||
import copy
|
||||
import modules.default_pipeline as pipeline
|
||||
import modules.core as core
|
||||
import modules.flags as flags
|
||||
import modules.path
|
||||
import modules.patch
|
||||
import modules.virtual_memory as virtual_memory
|
||||
import comfy.model_management
|
||||
|
||||
from modules.sdxl_styles import apply_style, aspect_ratios, fooocus_expansion
|
||||
from modules.private_logger import log
|
||||
from modules.expansion import safe_str
|
||||
from modules.util import join_prompts, remove_empty_str
|
||||
from modules.util import join_prompts, remove_empty_str, HWC3, resize_image
|
||||
from modules.upscaler import perform_upscale
|
||||
|
||||
try:
|
||||
async_gradio_app = shared.gradio_root
|
||||
@@ -37,16 +40,21 @@ def worker():
|
||||
outputs.append(['preview', (number, text, None)])
|
||||
|
||||
@torch.no_grad()
|
||||
@torch.inference_mode()
|
||||
def handler(task):
|
||||
prompt, negative_prompt, style_selections, performance_selction, \
|
||||
aspect_ratios_selction, image_number, image_seed, sharpness, \
|
||||
base_model_name, refiner_model_name, \
|
||||
l1, w1, l2, w2, l3, w3, l4, w4, l5, w5 = task
|
||||
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
|
||||
|
||||
loras = [(l1, w1), (l2, w2), (l3, w3), (l4, w4), (l5, w5)]
|
||||
|
||||
raw_style_selections = copy.deepcopy(style_selections)
|
||||
|
||||
uov_method = uov_method.lower()
|
||||
|
||||
if fooocus_expansion in style_selections:
|
||||
use_expansion = True
|
||||
style_selections.remove(fooocus_expansion)
|
||||
@@ -54,8 +62,80 @@ def worker():
|
||||
use_expansion = False
|
||||
|
||||
use_style = len(style_selections) > 0
|
||||
|
||||
modules.patch.sharpness = sharpness
|
||||
initial_latent = None
|
||||
denoising_strength = 1.0
|
||||
tiled = False
|
||||
|
||||
if performance_selction == 'Speed':
|
||||
steps = 30
|
||||
switch = 20
|
||||
else:
|
||||
steps = 60
|
||||
switch = 40
|
||||
|
||||
pipeline.clear_all_caches() # save memory
|
||||
|
||||
width, height = aspect_ratios[aspect_ratios_selction]
|
||||
|
||||
if input_image_checkbox:
|
||||
progressbar(0, 'Image processing ...')
|
||||
if uov_method != flags.disabled and uov_input_image is not None:
|
||||
uov_input_image = HWC3(uov_input_image)
|
||||
H, W, C = uov_input_image.shape
|
||||
if 'vary' in uov_method:
|
||||
if H * W + 8 < width * height or float(abs(H * width - W * height)) > 1.5 * float(max(H, W, width, height)):
|
||||
uov_input_image = resize_image(uov_input_image, width=width, height=height)
|
||||
print(f'Aspect ratio corrected - users are uploading their own images.')
|
||||
if 'subtle' in uov_method:
|
||||
denoising_strength = 0.5
|
||||
if 'strong' in uov_method:
|
||||
denoising_strength = 0.85
|
||||
initial_pixels = core.numpy_to_pytorch(uov_input_image)
|
||||
progressbar(0, 'VAE encoding ...')
|
||||
initial_latent = core.encode_vae(vae=pipeline.xl_base_patched.vae, pixels=initial_pixels)
|
||||
B, C, H, W = initial_latent['samples'].shape
|
||||
width = W * 8
|
||||
height = H * 8
|
||||
print(f'Final resolution is {str((height, width))}.')
|
||||
elif 'upscale' in uov_method:
|
||||
if '1.5x' in uov_method:
|
||||
f = 1.5
|
||||
elif '2x' in uov_method:
|
||||
f = 2.0
|
||||
else:
|
||||
f = 1.0
|
||||
|
||||
width = int(W * f)
|
||||
height = int(H * f)
|
||||
image_is_super_large = width * height > 2800 * 2800
|
||||
progressbar(0, f'Upscaling image from {str((H, W))} to {str((height, width))}...')
|
||||
|
||||
uov_input_image = core.numpy_to_pytorch(uov_input_image)
|
||||
uov_input_image = perform_upscale(uov_input_image)
|
||||
uov_input_image = core.pytorch_to_numpy(uov_input_image)[0]
|
||||
uov_input_image = resize_image(uov_input_image, width=width, height=height)
|
||||
print(f'Image upscaled.')
|
||||
|
||||
if 'fast' in uov_method or image_is_super_large:
|
||||
if 'fast' not in uov_method:
|
||||
print('Image is too large. Directly returned the SR image. '
|
||||
'Usually directly return SR image at 4K resolution '
|
||||
'yields better results than SDXL diffusion.')
|
||||
outputs.append(['results', [uov_input_image]])
|
||||
return
|
||||
|
||||
tiled = True
|
||||
denoising_strength = 1.0 - 0.618
|
||||
steps = int(steps * 0.618)
|
||||
switch = int(steps * 0.67)
|
||||
initial_pixels = core.numpy_to_pytorch(uov_input_image)
|
||||
progressbar(0, 'VAE encoding ...')
|
||||
initial_latent = core.encode_vae(vae=pipeline.xl_base_patched.vae, pixels=initial_pixels, tiled=True)
|
||||
B, C, H, W = initial_latent['samples'].shape
|
||||
width = W * 8
|
||||
height = H * 8
|
||||
print(f'Final resolution is {str((height, width))}.')
|
||||
|
||||
progressbar(1, 'Initializing ...')
|
||||
|
||||
@@ -152,16 +232,6 @@ def worker():
|
||||
|
||||
virtual_memory.try_move_to_virtual_memory(pipeline.xl_refiner.clip.cond_stage_model)
|
||||
|
||||
if performance_selction == 'Speed':
|
||||
steps = 30
|
||||
switch = 20
|
||||
else:
|
||||
steps = 60
|
||||
switch = 40
|
||||
|
||||
pipeline.clear_all_caches() # save memory
|
||||
width, height = aspect_ratios[aspect_ratios_selction]
|
||||
|
||||
results = []
|
||||
all_steps = steps * image_number
|
||||
|
||||
@@ -174,35 +244,43 @@ def worker():
|
||||
|
||||
outputs.append(['preview', (13, 'Starting tasks ...', None)])
|
||||
for current_task_id, task in enumerate(tasks):
|
||||
imgs = pipeline.process_diffusion(
|
||||
positive_cond=task['c'],
|
||||
negative_cond=task['uc'],
|
||||
steps=steps,
|
||||
switch=switch,
|
||||
width=width,
|
||||
height=height,
|
||||
image_seed=task['task_seed'],
|
||||
callback=callback)
|
||||
try:
|
||||
imgs = pipeline.process_diffusion(
|
||||
positive_cond=task['c'],
|
||||
negative_cond=task['uc'],
|
||||
steps=steps,
|
||||
switch=switch,
|
||||
width=width,
|
||||
height=height,
|
||||
image_seed=task['task_seed'],
|
||||
callback=callback,
|
||||
latent=initial_latent,
|
||||
denoise=denoising_strength,
|
||||
tiled=tiled
|
||||
)
|
||||
|
||||
for x in imgs:
|
||||
d = [
|
||||
('Prompt', raw_prompt),
|
||||
('Negative Prompt', raw_negative_prompt),
|
||||
('Fooocus V2 Expansion', task['expansion']),
|
||||
('Styles', str(raw_style_selections)),
|
||||
('Performance', performance_selction),
|
||||
('Resolution', str((width, height))),
|
||||
('Sharpness', sharpness),
|
||||
('Base Model', base_model_name),
|
||||
('Refiner Model', refiner_model_name),
|
||||
('Seed', task['task_seed'])
|
||||
]
|
||||
for n, w in loras:
|
||||
if n != 'None':
|
||||
d.append((f'LoRA [{n}] weight', w))
|
||||
log(x, d, single_line_number=3)
|
||||
for x in imgs:
|
||||
d = [
|
||||
('Prompt', raw_prompt),
|
||||
('Negative Prompt', raw_negative_prompt),
|
||||
('Fooocus V2 Expansion', task['expansion']),
|
||||
('Styles', str(raw_style_selections)),
|
||||
('Performance', performance_selction),
|
||||
('Resolution', str((width, height))),
|
||||
('Sharpness', sharpness),
|
||||
('Base Model', base_model_name),
|
||||
('Refiner Model', refiner_model_name),
|
||||
('Seed', task['task_seed'])
|
||||
]
|
||||
for n, w in loras:
|
||||
if n != 'None':
|
||||
d.append((f'LoRA [{n}] weight', w))
|
||||
log(x, d, single_line_number=3)
|
||||
|
||||
results += imgs
|
||||
results += imgs
|
||||
except comfy.model_management.InterruptProcessingException as e:
|
||||
print('User stopped')
|
||||
break
|
||||
|
||||
outputs.append(['results', results])
|
||||
return
|
||||
|
||||
Reference in New Issue
Block a user