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fixed bug with images not resizing for img2img

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added GFPGAN as an option for img2img
added GFPGAN as a tab
added autodetection for row counts for grids, enabled by default
removed Fixed Code sampling because no one can figure out what it does; maybe someone will be upset by removal and will tell me
master
AUTOMATIC 3 years ago
parent 3324f31e84
commit b63d0726cd
1 changed files with 109 additions and 91 deletions
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200
webui.py
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@ -13,6 +13,7 @@ from torch import autocast
from contextlib import contextmanager, nullcontext
import mimetypes
import random
import math
import k_diffusion as K
from ldm.util import instantiate_from_config
@ -31,7 +32,7 @@ parser = argparse.ArgumentParser()
parser.add_argument("--outdir", type=str, nargs="?", help="dir to write results to", 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=0, help="rows in the grid (default: n_samples)",)
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)",)
parser.add_argument("--config", type=str, default="configs/stable-diffusion/v1-inference.yaml", help="path to config which constructs model",)
parser.add_argument("--ckpt", type=str, default="models/ldm/stable-diffusion-v1/model.ckpt", help="path to checkpoint of model",)
parser.add_argument("--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast")
@ -118,6 +119,7 @@ if os.path.exists(GFPGAN_dir):
print("Error loading GFPGAN:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
config = OmegaConf.load("configs/stable-diffusion/v1-inference.yaml")
model = load_model_from_config(config, "models/ldm/stable-diffusion-v1/model.ckpt")
@ -125,18 +127,26 @@ device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cp
model = model.half().to(device)
def image_grid(imgs, rows):
cols = len(imgs) // rows
def image_grid(imgs, batch_size):
if opt.n_rows > 0:
rows = opt.n_rows
elif opt.n_rows == 0:
rows = batch_size
else:
rows = round(math.sqrt(len(imgs)))
cols = math.ceil(len(imgs) / rows)
w, h = imgs[0].size
grid = Image.new('RGB', size=(cols * w, rows * h))
grid = Image.new('RGB', size=(cols * w, rows * h), color='black')
for i, img in enumerate(imgs):
grid.paste(img, box=(i % cols * w, i // cols * h))
return grid
def dream(prompt: str, ddim_steps: int, sampler_name: str, fixed_code: bool, use_GFPGAN: bool, ddim_eta: float, n_iter: int, n_samples: int, cfg_scale: float, seed: int, height: int, width: int):
def dream(prompt: str, ddim_steps: int, sampler_name: str, use_GFPGAN: bool, ddim_eta: float, n_iter: int, n_samples: int, cfg_scale: float, seed: int, height: int, width: int):
torch.cuda.empty_cache()
outpath = opt.outdir or "outputs/txt2img-samples"
@ -165,7 +175,6 @@ def dream(prompt: str, ddim_steps: int, sampler_name: str, fixed_code: bool, use
os.makedirs(outpath, exist_ok=True)
batch_size = n_samples
n_rows = opt.n_rows if opt.n_rows > 0 else batch_size
assert prompt is not None
data = [batch_size * [prompt]]
@ -175,15 +184,9 @@ def dream(prompt: str, ddim_steps: int, sampler_name: str, fixed_code: bool, use
base_count = len(os.listdir(sample_path))
grid_count = len(os.listdir(outpath)) - 1
start_code = None
if fixed_code:
start_code = torch.randn([n_samples, opt_C, height // opt_f, width // opt_f], device=device)
precision_scope = autocast if opt.precision == "autocast" else nullcontext
output_images = []
with torch.no_grad(), precision_scope("cuda"), model.ema_scope():
all_samples = []
for n in range(n_iter):
for batch_index, prompts in enumerate(data):
uc = None
@ -204,7 +207,7 @@ def dream(prompt: str, ddim_steps: int, sampler_name: str, fixed_code: bool, use
samples_ddim = K.sampling.sample_lms(model_wrap_cfg, x, sigmas, extra_args={'cond': c, 'uncond': uc, 'cond_scale': cfg_scale}, disable=False)
elif sampler is not None:
samples_ddim, _ = sampler.sample(S=ddim_steps, conditioning=c, batch_size=n_samples, shape=shape, verbose=False, unconditional_guidance_scale=cfg_scale, unconditional_conditioning=uc, eta=ddim_eta, x_T=start_code)
samples_ddim, _ = sampler.sample(S=ddim_steps, conditioning=c, batch_size=n_samples, shape=shape, verbose=False, unconditional_guidance_scale=cfg_scale, unconditional_conditioning=uc, eta=ddim_eta, x_T=None)
x_samples_ddim = model.decode_first_stage(samples_ddim)
x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
@ -224,12 +227,9 @@ def dream(prompt: str, ddim_steps: int, sampler_name: str, fixed_code: bool, use
output_images.append(image)
base_count += 1
if not opt.skip_grid:
all_samples.append(x_sample)
if not opt.skip_grid:
# additionally, save as grid
grid = image_grid(output_images, rows=n_rows)
grid = image_grid(output_images, batch_size)
grid.save(os.path.join(outpath, f'grid-{grid_count:04}.png'))
grid_count += 1
@ -251,7 +251,6 @@ dream_interface = gr.Interface(
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', choices=["DDIM", "PLMS", "k-diffusion"], value="k-diffusion"),
gr.Checkbox(label='Enable Fixed Code sampling', value=False),
gr.Checkbox(label='Fix faces using GFPGAN', value=False, visible=GFPGAN is not None),
gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label="DDIM ETA", value=0.0, visible=False),
gr.Slider(minimum=1, maximum=16, step=1, label='Sampling iterations', value=1),
@ -272,7 +271,7 @@ dream_interface = gr.Interface(
)
def translation(prompt: str, init_img, ddim_steps: int, ddim_eta: float, n_iter: int, n_samples: int, cfg_scale: float, denoising_strength: float, seed: int, height: int, width: int):
def translation(prompt: str, init_img, ddim_steps: int, use_GFPGAN: bool, ddim_eta: float, n_iter: int, n_samples: int, cfg_scale: float, denoising_strength: float, seed: int, height: int, width: int):
torch.cuda.empty_cache()
outpath = opt.outdir or "outputs/img2img-samples"
@ -280,14 +279,11 @@ def translation(prompt: str, init_img, ddim_steps: int, ddim_eta: float, n_iter:
if seed == -1:
seed = random.randrange(4294967294)
sampler = DDIMSampler(model)
model_wrap = K.external.CompVisDenoiser(model)
os.makedirs(outpath, exist_ok=True)
batch_size = n_samples
n_rows = opt.n_rows if opt.n_rows > 0 else batch_size
assert prompt is not None
data = [batch_size * [prompt]]
@ -299,78 +295,68 @@ def translation(prompt: str, init_img, ddim_steps: int, ddim_eta: float, n_iter:
seedit = 0
image = init_img.convert("RGB")
w, h = image.size
image = image.resize((width, height), resample=PIL.Image.Resampling.LANCZOS)
image = np.array(image).astype(np.float32) / 255.0
image = image[None].transpose(0, 3, 1, 2)
image = torch.from_numpy(image)
output_images = []
precision_scope = autocast if opt.precision == "autocast" else nullcontext
with torch.no_grad():
with precision_scope("cuda"):
init_image = 2. * image - 1.
init_image = init_image.to(device)
init_image = repeat(init_image, '1 ... -> b ...', b=batch_size)
init_latent = model.get_first_stage_encoding(model.encode_first_stage(init_image)) # move to latent space
x0 = init_latent
sampler.make_schedule(ddim_num_steps=ddim_steps, ddim_eta=ddim_eta, verbose=False)
assert 0. <= denoising_strength <= 1., 'can only work with strength in [0.0, 1.0]'
t_enc = int(denoising_strength * ddim_steps)
print(f"target t_enc is {t_enc} steps")
with model.ema_scope():
all_samples = list()
for n in range(n_iter):
for batch_index, prompts in enumerate(data):
uc = None
if cfg_scale != 1.0:
uc = model.get_learned_conditioning(batch_size * [""])
if isinstance(prompts, tuple):
prompts = list(prompts)
c = model.get_learned_conditioning(prompts)
sigmas = model_wrap.get_sigmas(ddim_steps)
current_seed = seed + n * len(data) + batch_index
torch.manual_seed(current_seed)
noise = torch.randn_like(x0) * sigmas[ddim_steps - t_enc - 1] # for GPU draw
xi = x0 + noise
sigma_sched = sigmas[ddim_steps - t_enc - 1:]
# x = torch.randn([n_samples, *shape]).to(device) * sigmas[0] # for CPU draw
model_wrap_cfg = CFGDenoiser(model_wrap)
extra_args = {'cond': c, 'uncond': uc, 'cond_scale': cfg_scale}
samples_ddim = K.sampling.sample_lms(model_wrap_cfg, xi, sigma_sched, extra_args=extra_args, disable=False)
x_samples_ddim = model.decode_first_stage(samples_ddim)
x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
if not opt.skip_save:
for x_sample in x_samples_ddim:
x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
image = Image.fromarray(x_sample.astype(np.uint8))
image.save(os.path.join(sample_path, f"{base_count:05}-{current_seed}_{prompt.replace(' ', '_')[:128]}.png"))
output_images.append(image)
base_count += 1
seedit += 1
if not opt.skip_grid:
all_samples.append(x_samples_ddim)
if not opt.skip_grid:
# additionally, save as grid
grid = torch.stack(all_samples, 0)
grid = rearrange(grid, 'n b c h w -> (n b) c h w')
grid = make_grid(grid, nrow=n_rows)
# to image
grid = 255. * rearrange(grid, 'c h w -> h w c').cpu().numpy()
Image.fromarray(grid.astype(np.uint8)).save(os.path.join(outpath, f'grid-{grid_count:04}.png'))
Image.fromarray(grid.astype(np.uint8))
grid_count += 1
del sampler
with torch.no_grad(), precision_scope("cuda"), model.ema_scope():
init_image = 2. * image - 1.
init_image = init_image.to(device)
init_image = repeat(init_image, '1 ... -> b ...', b=batch_size)
init_latent = model.get_first_stage_encoding(model.encode_first_stage(init_image)) # move to latent space
x0 = init_latent
assert 0. <= denoising_strength <= 1., 'can only work with strength in [0.0, 1.0]'
t_enc = int(denoising_strength * ddim_steps)
for n in range(n_iter):
for batch_index, prompts in enumerate(data):
uc = None
if cfg_scale != 1.0:
uc = model.get_learned_conditioning(batch_size * [""])
if isinstance(prompts, tuple):
prompts = list(prompts)
c = model.get_learned_conditioning(prompts)
sigmas = model_wrap.get_sigmas(ddim_steps)
current_seed = seed + n * len(data) + batch_index
torch.manual_seed(current_seed)
noise = torch.randn_like(x0) * sigmas[ddim_steps - t_enc - 1] # for GPU draw
xi = x0 + noise
sigma_sched = sigmas[ddim_steps - t_enc - 1:]
model_wrap_cfg = CFGDenoiser(model_wrap)
extra_args = {'cond': c, 'uncond': uc, 'cond_scale': cfg_scale}
samples_ddim = K.sampling.sample_lms(model_wrap_cfg, xi, sigma_sched, extra_args=extra_args, disable=False)
x_samples_ddim = model.decode_first_stage(samples_ddim)
x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
if not opt.skip_save or not opt.skip_grid:
for x_sample in x_samples_ddim:
x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
x_sample = x_sample.astype(np.uint8)
if use_GFPGAN and GFPGAN is not None:
cropped_faces, restored_faces, restored_img = GFPGAN.enhance(x_sample, has_aligned=False, only_center_face=False, paste_back=True)
x_sample = restored_img
image = Image.fromarray(x_sample)
image.save(os.path.join(sample_path, f"{base_count:05}-{current_seed}_{prompt.replace(' ', '_')[:128]}.png"))
output_images.append(image)
base_count += 1
if not opt.skip_grid:
# additionally, save as grid
grid = image_grid(output_images, batch_size)
grid.save(os.path.join(outpath, f'grid-{grid_count:04}.png'))
grid_count += 1
return output_images, seed
@ -382,9 +368,10 @@ img2img_interface = gr.Interface(
gr.Textbox(placeholder="A fantasy landscape, trending on artstation.", lines=1),
gr.Image(value="https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg", source="upload", interactive=True, type="pil"),
gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=50),
gr.Checkbox(label='Fix faces using GFPGAN', value=False, visible=GFPGAN is not None),
gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label="DDIM ETA", value=0.0, visible=False),
gr.Slider(minimum=1, maximum=50, step=1, label='Sampling iterations', value=2),
gr.Slider(minimum=1, maximum=8, step=1, label='Samples per iteration', value=2),
gr.Slider(minimum=1, maximum=16, step=1, label='Sampling iterations', value=1),
gr.Slider(minimum=1, maximum=4, step=1, label='Samples per iteration', value=1),
gr.Slider(minimum=1.0, maximum=15.0, step=0.5, label='Classifier Free Guidance Scale', value=7.0),
gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising Strength', value=0.75),
gr.Number(label='Seed', value=-1),
@ -399,6 +386,37 @@ img2img_interface = gr.Interface(
description="Generate images from images with Stable Diffusion",
)
demo = gr.TabbedInterface(interface_list=[dream_interface, img2img_interface], tab_names=["Dream", "Image Translation"])
interfaces = [
(dream_interface, "Dream"),
(img2img_interface, "Image Translation")
]
def run_GFPGAN(image, strength):
image = image.convert("RGB")
cropped_faces, restored_faces, restored_img = GFPGAN.enhance(np.array(image, dtype=np.uint8), has_aligned=False, only_center_face=False, paste_back=True)
res = Image.fromarray(restored_img)
if strength < 1.0:
res = PIL.Image.blend(image, res, strength)
return res
if GFPGAN is not None:
interfaces.append((gr.Interface(
run_GFPGAN,
inputs=[
gr.Image(label="Source", source="upload", interactive=True, type="pil"),
gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Effect strength", value=100),
],
outputs=[
gr.Image(label="Result"),
],
title="GFPGAN",
description="Fix faces on images",
), "GFPGAN"))
demo = gr.TabbedInterface(interface_list=[x[0] for x in interfaces], tab_names=[x[1] for x in interfaces])
demo.launch()

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