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Fooocus
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This commit is contained in:
lvmin 2023-08-10 21:00:55 -07:00
parent 80ce0abf9a
commit 8bdb8f6889
2 changed files with 30 additions and 13 deletions
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12
modules/default_pipeline.py
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@ -18,14 +18,14 @@ xl_refiner = core.load_model(xl_refiner_filename)
@torch.no_grad() @torch.no_grad()
def process(positive_prompt, negative_prompt, width=1280, height=960, batch_size=1): def process(positive_prompt, negative_prompt, steps, switch, width, height, image_seed):
positive_conditions = core.encode_prompt_condition(clip=xl_base.clip, prompt=positive_prompt) positive_conditions = core.encode_prompt_condition(clip=xl_base.clip, prompt=positive_prompt)
negative_conditions = core.encode_prompt_condition(clip=xl_base.clip, prompt=negative_prompt) negative_conditions = core.encode_prompt_condition(clip=xl_base.clip, prompt=negative_prompt)
positive_conditions_refiner = core.encode_prompt_condition(clip=xl_refiner.clip, prompt=positive_prompt) positive_conditions_refiner = core.encode_prompt_condition(clip=xl_refiner.clip, prompt=positive_prompt)
negative_conditions_refiner = core.encode_prompt_condition(clip=xl_refiner.clip, prompt=negative_prompt) negative_conditions_refiner = core.encode_prompt_condition(clip=xl_refiner.clip, prompt=negative_prompt)
empty_latent = core.generate_empty_latent(width=width, height=height, batch_size=batch_size) empty_latent = core.generate_empty_latent(width=width, height=height, batch_size=1)
sampled_latent = core.ksampler_with_refiner( sampled_latent = core.ksampler_with_refiner(
model=xl_base.unet, model=xl_base.unet,
@ -34,10 +34,10 @@ def process(positive_prompt, negative_prompt, width=1280, height=960, batch_size
refiner=xl_refiner.unet, refiner=xl_refiner.unet,
refiner_positive=positive_conditions_refiner, refiner_positive=positive_conditions_refiner,
refiner_negative=negative_conditions_refiner, refiner_negative=negative_conditions_refiner,
refiner_switch_step=20, refiner_switch_step=switch,
latent=empty_latent, latent=empty_latent,
steps=30, start_step=0, last_step=30, disable_noise=False, force_full_denoise=True, steps=steps, start_step=0, last_step=steps, disable_noise=False, force_full_denoise=True,
seed=123456 seed=image_seed
) )
decoded_latent = core.decode_vae(vae=xl_refiner.vae, latent_image=sampled_latent) decoded_latent = core.decode_vae(vae=xl_refiner.vae, latent_image=sampled_latent)
@ -46,4 +46,4 @@ def process(positive_prompt, negative_prompt, width=1280, height=960, batch_size
close_all_preview() close_all_preview()
return images * 2 return images

31
webui.py
View File

@ -1,18 +1,35 @@
import gradio as gr import gradio as gr
import random
from modules.sdxl_styles import apply_style, style_keys, aspect_ratios from modules.sdxl_styles import apply_style, style_keys, aspect_ratios
# from modules.default_pipeline import process from modules.default_pipeline import process
def generate_clicked(positive_prompt): def generate_clicked(prompt, negative_prompt, style_selction, performance_selction,
aspect_ratios_selction, image_number, image_seed):
p, n = apply_style('cinematic-default', positive_prompt, '') p_txt, n_txt = apply_style(style_selction, prompt, negative_prompt)
print(p) if performance_selction == 'Speed':
print(n) steps = 30
switch = 20
else:
steps = 60
switch = 40
return process(positive_prompt=p, width, height = aspect_ratios[aspect_ratios_selction]
negative_prompt=n)
results = []
seed = image_seed
if not isinstance(seed, int) or seed < 0 or seed > 65535:
seed = random.randint(1, 65535)
for i in range(image_number):
imgs = process(p_txt, n_txt, steps, switch, width, height, seed)
seed += 1
results += imgs
return results
block = gr.Blocks() block = gr.Blocks()