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additional picture for prompt matrix

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proper seeds for img2img
a bit of refactoring
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AUTOMATIC 2022-08-23 14:07:37 +03:00
parent 60e95f1d8c
commit 61bfa6c16b
3 changed files with 63 additions and 60 deletions
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README.md
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@ -83,9 +83,11 @@ For example, if you use `a house in a field of grass|at dawn|illustration` promp
- `a house in a field of grass, at dawn, illustration`
Four images will be produced, in this order, all with same seed and each with corresponding prompt:
![](images/prompt-matrix.png)
Another example, this time with 5 prompts and 16 variations, (text added manually):
![](images/prompt_matrix.jpg)
### Flagging
Click the Flag button under the output section, and generated images will be saved to `log/images` directory, and generation parameters
will be appended to a csv file `log/log.csv` in the `/sd` directory.

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119
webui.py
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@ -106,6 +106,30 @@ class CFGDenoiser(nn.Module):
return uncond + (cond - uncond) * cond_scale
class KDiffusionSampler:
def __init__(self, m):
self.model = m
self.model_wrap = K.external.CompVisDenoiser(m)
def sample(self, S, conditioning, batch_size, shape, verbose, unconditional_guidance_scale, unconditional_conditioning, eta, x_T):
sigmas = self.model_wrap.get_sigmas(S)
x = x_T * sigmas[0]
model_wrap_cfg = CFGDenoiser(self.model_wrap)
samples_ddim = K.sampling.sample_lms(model_wrap_cfg, x, sigmas, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': unconditional_guidance_scale}, disable=False)
return samples_ddim, None
def create_random_tensors(seed, shape, count, same_seed=False):
xs = []
for i in range(count):
current_seed = seed if same_seed else seed + i
torch.manual_seed(current_seed)
xs.append(torch.randn(shape, device=device))
x = torch.stack(xs)
return x
def load_GFPGAN():
model_name = 'GFPGANv1.3'
model_path = os.path.join(GFPGAN_dir, 'experiments/pretrained_models', model_name + '.pth')
@ -166,22 +190,15 @@ def dream(prompt: str, ddim_steps: int, sampler_name: str, use_GFPGAN: bool, pro
seed = int(seed)
keep_same_seed = False
is_PLMS = sampler_name == 'PLMS'
is_DDIM = sampler_name == 'DDIM'
is_Kdif = sampler_name == 'k-diffusion'
sampler = None
if is_PLMS:
if sampler_name == 'PLMS':
sampler = PLMSSampler(model)
elif is_DDIM:
elif sampler_name == 'DDIM':
sampler = DDIMSampler(model)
elif is_Kdif:
pass
elif sampler_name == 'k-diffusion':
sampler = KDiffusionSampler(model)
else:
raise Exception("Unknown sampler: " + sampler_name)
model_wrap = K.external.CompVisDenoiser(model)
os.makedirs(outpath, exist_ok=True)
batch_size = n_samples
@ -238,21 +255,9 @@ def dream(prompt: str, ddim_steps: int, sampler_name: str, use_GFPGAN: bool, pro
batch_seed = seed if keep_same_seed else seed + n * len(prompts)
# we manually generate all input noises because each one should have a specific seed
xs = []
for i in range(len(prompts)):
current_seed = seed if keep_same_seed else batch_seed + i
torch.manual_seed(current_seed)
xs.append(torch.randn(shape, device=device))
x = torch.stack(xs)
x = create_random_tensors(batch_seed, shape, count=len(prompts), same_seed=keep_same_seed)
if is_Kdif:
sigmas = model_wrap.get_sigmas(ddim_steps)
x = x * sigmas[0]
model_wrap_cfg = CFGDenoiser(model_wrap)
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=len(prompts), shape=shape, verbose=False, unconditional_guidance_scale=cfg_scale, unconditional_conditioning=uc, eta=ddim_eta, x_T=x)
samples_ddim, _ = sampler.sample(S=ddim_steps, conditioning=c, batch_size=len(prompts), shape=shape, verbose=False, unconditional_guidance_scale=cfg_scale, unconditional_conditioning=uc, eta=ddim_eta, x_T=x)
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)
@ -274,9 +279,6 @@ def dream(prompt: str, ddim_steps: int, sampler_name: str, use_GFPGAN: bool, pro
output_images.append(image)
base_count += 1
if not opt.skip_grid:
# additionally, save as grid
grid = image_grid(output_images, batch_size, round_down=prompt_matrix)
@ -380,13 +382,11 @@ def translation(prompt: str, init_img, ddim_steps: int, use_GFPGAN: bool, ddim_e
batch_size = n_samples
assert prompt is not None
data = [batch_size * [prompt]]
sample_path = os.path.join(outpath, "samples")
os.makedirs(sample_path, exist_ok=True)
base_count = len(os.listdir(sample_path))
grid_count = len(os.listdir(outpath)) - 1
seedit = 0
image = init_img.convert("RGB")
image = image.resize((width, height), resample=PIL.Image.Resampling.LANCZOS)
@ -407,43 +407,44 @@ def translation(prompt: str, init_img, ddim_steps: int, use_GFPGAN: bool, ddim_e
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)
prompts = batch_size * [prompt]
sigmas = model_wrap.get_sigmas(ddim_steps)
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)
current_seed = seed + n * len(data) + batch_index
torch.manual_seed(current_seed)
batch_seed = seed + n * len(prompts)
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}
sigmas = model_wrap.get_sigmas(ddim_steps)
noise = create_random_tensors(batch_seed, x0.shape[1:], count=len(prompts))
noise = noise * sigmas[ddim_steps - t_enc - 1]
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)
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}
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)
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 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
if not opt.skip_save or not opt.skip_grid:
for i, x_sample in enumerate(x_samples_ddim):
x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
x_sample = x_sample.astype(np.uint8)
image = Image.fromarray(x_sample)
image.save(os.path.join(sample_path, f"{base_count:05}-{current_seed}_{prompt.replace(' ', '_').translate({ord(x): '' for x in invalid_filename_chars})[:128]}.png"))
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
output_images.append(image)
base_count += 1
image = Image.fromarray(x_sample)
image.save(os.path.join(sample_path, f"{base_count:05}-{batch_seed+i}_{prompt.replace(' ', '_').translate({ord(x): '' for x in invalid_filename_chars})[:128]}.png"))
output_images.append(image)
base_count += 1
if not opt.skip_grid:
# additionally, save as grid