Skip to content

Commit

Permalink
Merge pull request AUTOMATIC1111#14 from EyeDeck/master
Browse files Browse the repository at this point in the history 
UI tweaks 👑 🔥
  • Loading branch information
@hlky
hlky committed Aug 25, 2022
2 parents 92661ba + 85a905f commit 07edcd9
Showing 1 changed file with 71 additions and 31 deletions.
102 changes: 71 additions & 31 deletions webui.py
View file
Expand Up @@ -42,6 +42,8 @@

parser = argparse.ArgumentParser()
parser.add_argument("--outdir", type=str, nargs="?", help="dir to write results to", default=None)
parser.add_argument("--outdir_txt2img", type=str, nargs="?", help="dir to write txt2img results to (overrides --outdir)", default=None)
parser.add_argument("--outdir_img2img", type=str, nargs="?", help="dir to write img2img results to (overrides --outdir)", default=None)
parser.add_argument("--skip_grid", action='store_true', help="do not save a grid, only individual samples. Helpful when evaluating lots of samples",)
parser.add_argument("--skip_save", action='store_true', help="do not save indiviual samples. For speed measurements.",)
parser.add_argument("--n_rows", type=int, default=-1, help="rows in the grid; use -1 for autodetect and 0 for n_rows to be same as batch_size (default: -1)",)
Expand Down Expand Up @@ -263,7 +265,7 @@ def image_grid(imgs, batch_size, round_down=False, force_n_rows=None):
return grid

def seed_to_int(s):
if s == 'random':
if s == '':
return random.randint(0,2**32)
n = abs(int(s) if s.isdigit() else hash(s))
while n > 2**32:
Expand Down Expand Up @@ -551,11 +553,18 @@ def process_images(outpath, func_init, func_sample, prompt, seed, sampler_name,
return output_images, seed, info, stats


def txt2img(prompt: str, ddim_steps: int, sampler_name: str, use_GFPGAN: bool, prompt_matrix: bool, skip_grid: bool, skip_save: bool, ddim_eta: float, n_iter: int, batch_size: int, cfg_scale: float, seed: int, height: int, width: int, normalize_prompt_weights: bool, fp):
outpath = opt.outdir or "outputs/txt2img-samples"
def txt2img(prompt: str, ddim_steps: int, sampler_name: str, toggles: list, ddim_eta: float, n_iter: int, batch_size: int, cfg_scale: float, seed: int, height: int, width: int, fp):
outpath = opt.outdir_txt2img or opt.outdir or "outputs/txt2img-samples"
err = False
seed = seed_to_int(seed)

# print('toggles:', toggles)
prompt_matrix = 0 in toggles
normalize_prompt_weights = 1 in toggles
skip_save = 2 not in toggles
skip_grid = 3 not in toggles
use_GFPGAN = 4 in toggles

if sampler_name == 'PLMS':
sampler = PLMSSampler(model)
elif sampler_name == 'DDIM':
Expand Down Expand Up @@ -628,8 +637,8 @@ def flag(self, flag_data, flag_option=None, flag_index=None, username=None):
os.makedirs("log/images", exist_ok=True)

# those must match the "txt2img" function !! + images, seed, comment, stats !! NOTE: changes to UI output must be reflected here too
prompt, ddim_steps, sampler_name, use_GFPGAN, skip_grid, skip_save, prompt_matrix, ddim_eta, n_iter, n_samples, cfg_scale, input_seed, height, width, normalize_prompt_weights, fp, images, seed, comment, stats = flag_data
prompt, ddim_steps, sampler_name, toggles, ddim_eta, n_iter, batch_size, cfg_scale, seed, height, width, fp, images, seed, comment, stats = flag_data

filenames = []

with open("log/log.csv", "a", encoding="utf8", newline='') as file:
Expand All @@ -639,7 +648,7 @@ def flag(self, flag_data, flag_option=None, flag_index=None, username=None):
at_start = file.tell() == 0
writer = csv.writer(file)
if at_start:
writer.writerow(["prompt", "seed", "width", "height", "sampler", "use_GFPGAN", "prompt_matrix", "n_iter", "n_samples", "cfg_scale", "steps", "filename"])
writer.writerow(["prompt", "seed", "width", "height", "sampler", "toggles", "n_iter", "n_samples", "cfg_scale", "steps", "filename"])

filename_base = str(int(time.time() * 1000))
for i, filedata in enumerate(images):
Expand All @@ -653,29 +662,41 @@ def flag(self, flag_data, flag_option=None, flag_index=None, username=None):

filenames.append(filename)

writer.writerow([prompt, seed, width, height, sampler_name, use_GFPGAN, prompt_matrix, n_iter, n_samples, cfg_scale, ddim_steps, filenames[0]])
writer.writerow([prompt, seed, width, height, sampler_name, toggles, n_iter, batch_size, cfg_scale, ddim_steps, filenames[0]])

print("Logged:", filenames[0])


# make sure these indicies line up at the top of txt2img()
txt2img_toggles = [
'Create prompt matrix (separate multiple prompts using |, and get all combinations of them)',
'Normalize Prompt Weights (ensure sum of weights add up to 1.0)',
'Save individual images',
'Save grid',
]
if GFPGAN is not None:
toggle_choices.append('Fix faces using GFPGAN')

txt2img_toggle_defaults = [
'Normalize Prompt Weights (ensure sum of weights add up to 1.0)',
'Save individual images',
'Save grid'
]

txt2img_interface = gr.Interface(
txt2img,
inputs=[
gr.Textbox(label="Prompt", placeholder="A corgi wearing a top hat as an oil painting.", lines=1),
gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=50),
gr.Radio(label='Sampling method (k_lms is default k-diffusion sampler)', choices=["DDIM", "PLMS", 'k_dpm_2_a', 'k_dpm_2', 'k_euler_a', 'k_euler', 'k_heun', 'k_lms'], value="k_lms"),
gr.Checkbox(label='Fix faces using GFPGAN', value=False, visible=GFPGAN is not None),
gr.Checkbox(label='Create prompt matrix (separate multiple prompts using |, and get all combinations of them)', value=False),
gr.Checkbox(label='Skip grid', value=False),
gr.Checkbox(label='Skip save individual images', value=False),
gr.Slider(minimum=1, maximum=250, step=1, label="Sampling Steps", value=50),
gr.Radio(label='Sampling method (k_lms is default k-diffusion sampler)', choices=["DDIM", "PLMS", 'k_dpm_2_a', 'k_dpm_2', 'k_euler_a', 'k_euler', 'k_heun', 'k_lms'], value="k-lms"),
gr.CheckboxGroup(label='', choices=txt2img_toggles, value=txt2img_toggle_defaults, type="index"),
gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label="DDIM ETA", value=0.0, visible=False),
gr.Slider(minimum=1, maximum=250, step=1, label='Batch count (how many batches of images to generate)', value=1),
gr.Slider(minimum=1, maximum=8, step=1, label='Batch size (how many images are in a batch; memory-hungry)', value=1),
gr.Slider(minimum=1.0, maximum=15.0, step=0.5, label='Classifier Free Guidance Scale (how strongly the image should follow the prompt)', value=7.0),
gr.Textbox(label="Seed ('random' to randomize)", lines=1, value="random"),
gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='Classifier Free Guidance Scale (how strongly the image should follow the prompt)', value=7.5),
gr.Textbox(label="Seed (blank to randomize)", lines=1, value=""),
gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512),
gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512),
gr.Checkbox(label="Normalize Prompt Weights (ensure sum of weights add up to 1.0)", value=True),
gr.File(label = "Embeddings file for textual inversion", visible=hasattr(model, "embedding_manager")),
],
outputs=[
Expand All @@ -691,11 +712,18 @@ def flag(self, flag_data, flag_option=None, flag_index=None, username=None):
)


def img2img(prompt: str, init_info, mask_mode, ddim_steps: int, sampler_name: str, use_GFPGAN: bool, prompt_matrix, loopback: bool, skip_grid: bool, skip_save: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, height: int, width: int, resize_mode: int, normalize_prompt_weights: bool, fp):
outpath = opt.outdir or "outputs/img2img-samples"
def img2img(prompt: str, init_info, mask_mode, ddim_steps: int, sampler_name: str, toggles: list, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, height: int, width: int, resize_mode: int, fp):
outpath = opt.outdir_img2img or opt.outdir or "outputs/img2img-samples"
err = False
seed = seed_to_int(seed)

prompt_matrix = 0 in toggles
normalize_prompt_weights = 1
loopback = 2 in toggles
skip_save = 3 not in toggles
skip_grid = 4 not in toggles
use_GFPGAN = 5 in toggles

if sampler_name == 'DDIM':
sampler = DDIMSampler(model)
elif sampler_name == 'k_dpm_2_a':
Expand Down Expand Up @@ -848,28 +876,40 @@ def sample(init_data, x, conditioning, unconditional_conditioning, sampler_name)
sample_img2img = "assets/stable-samples/img2img/sketch-mountains-input.jpg"
sample_img2img = sample_img2img if os.path.exists(sample_img2img) else None

# make sure these indicies line up at the top of img2img()
img2img_toggles = [
'Create prompt matrix (separate multiple prompts using |, and get all combinations of them)',
'Normalize Prompt Weights (ensure sum of weights add up to 1.0)'
'Loopback (use images from previous batch when creating next batch)',
'Save individual images',
'Save grid',
]
if GFPGAN is not None:
toggle_choices.append('Fix faces using GFPGAN')

img2img_toggle_defaults = [
'Normalize Prompt Weights (ensure sum of weights add up to 1.0)',
'Save individual images',
'Save grid',
]

img2img_interface = gr.Interface(
img2img,
inputs=[
gr.Textbox(placeholder="A fantasy landscape, trending on artstation.", lines=1),
gr.Textbox(label="Prompt", placeholder="A fantasy landscape, trending on artstation.", lines=1),
gr.Image(value=sample_img2img, source="upload", interactive=True, type="pil", tool="sketch"),
gr.Radio(choices=["Keep masked area", "Regenerate only masked area"], label="Mask Mode", value="Keep masked area"),
gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=50),
gr.Radio(label='Sampling method (k_lms is default k-diffusion sampler)', choices=["DDIM", 'k_dpm_2_a', 'k_dpm_2', 'k_euler_a', 'k_euler', 'k_heun', 'k_lms'], value="k_lms"),
gr.Checkbox(label='Fix faces using GFPGAN', value=False, visible=GFPGAN is not None),
gr.Checkbox(label='Create prompt matrix (separate multiple prompts using |, and get all combinations of them)', value=False),
gr.Checkbox(label='Loopback (use images from previous batch when creating next batch)', value=False),
gr.Checkbox(label='Skip grid', value=False),
gr.Checkbox(label='Skip save individual images', value=False),
gr.Slider(minimum=1, maximum=16, step=1, label='Batch count (how many batches of images to generate)', value=1),
gr.Slider(minimum=1, maximum=250, step=1, label='Batch size (how many images are in a batch; memory-hungry)', value=1),
gr.Slider(minimum=1.0, maximum=15.0, step=0.5, label='Classifier Free Guidance Scale (how strongly the image should follow the prompt)', value=7.0),
gr.Slider(minimum=1, maximum=250, step=1, label="Sampling Steps", value=50),
gr.Radio(label='Sampling method (k_lms is default k-diffusion sampler)', choices=["DDIM", 'k_dpm_2_a', 'k_dpm_2', 'k_euler_a', 'k_euler', 'k_heun', 'k_lms'], value="k-lms"),
gr.CheckboxGroup(label='', choices=img2img_toggles, value=img2img_toggle_defaults, type="index"),
gr.Slider(minimum=1, maximum=250, step=1, label='Batch count (how many batches of images to generate)', value=1),
gr.Slider(minimum=1, maximum=8, step=1, label='Batch size (how many images are in a batch; memory-hungry)', value=1),
gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='Classifier Free Guidance Scale (how strongly the image should follow the prompt)', value=5.0),
gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising Strength', value=0.75),
gr.Textbox(label="Seed ('random' to randomize)", lines=1, value="random"),
gr.Textbox(label="Seed (blank to randomize)", lines=1, value=""),
gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512),
gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512),
gr.Radio(label="Resize mode", choices=["Just resize", "Crop and resize", "Resize and fill"], type="index", value="Just resize"),
gr.Checkbox(label="Normalize Prompt Weights (ensure sum of weights add up to 1.0)", value=True),
gr.File(label = "Embeddings file for textual inversion", visible=hasattr(model, "embedding_manager")),
],
outputs=[
Expand Down

0 comments on commit 07edcd9

Please sign in to comment.