Sygil-Dev/sygil-webui
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Merge pull request #14 from EyeDeck/master

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UI tweaks 👑 🔥
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hlky 2022-08-25 04:48:46 +01:00 committed by GitHub
commit 07edcd9a92
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1 changed files with 71 additions and 31 deletions
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webui.py
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@ -42,6 +42,8 @@ invalid_filename_chars = '<>:"/\|?*\n'
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)",)
@ -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:
@ -551,11 +553,18 @@ Peak memory usage: { -(mem_max_used // -1_048_576) } MiB / { -(mem_total // -1_0
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':
@ -628,8 +637,8 @@ class Flagging(gr.FlaggingCallback):
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:
@ -639,7 +648,7 @@ class Flagging(gr.FlaggingCallback):
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):
@ -653,29 +662,41 @@ class Flagging(gr.FlaggingCallback):
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=[
@ -691,11 +712,18 @@ txt2img_interface = gr.Interface(
)
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':
@ -848,28 +876,40 @@ def img2img(prompt: str, init_info, mask_mode, ddim_steps: int, sampler_name: st
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=[