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add TAESD for i2i and t2i

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Kohaku-Blueleaf
2023-08-04 13:38:52 +08:00
parent 3f9e09a615
commit 75336dfc84
5 changed files with 86 additions and 21 deletions
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38
modules/sd_samplers_common.py
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@@ -23,19 +23,29 @@ def setup_img2img_steps(p, steps=None):
approximation_indexes = {"Full": 0, "Approx NN": 1, "Approx cheap": 2, "TAESD": 3}
def single_sample_to_image(sample, approximation=None):
def samples_to_images_tensor(sample, approximation=None, model=None):
'''latents -> images [-1, 1]'''
if approximation is None:
approximation = approximation_indexes.get(opts.show_progress_type, 0)
if approximation == 2:
x_sample = sd_vae_approx.cheap_approximation(sample) * 0.5 + 0.5
x_sample = sd_vae_approx.cheap_approximation(sample)
elif approximation == 1:
x_sample = sd_vae_approx.model()(sample.to(devices.device, devices.dtype).unsqueeze(0))[0].detach() * 0.5 + 0.5
x_sample = sd_vae_approx.model()(sample.to(devices.device, devices.dtype)).detach()
elif approximation == 3:
x_sample = sample * 1.5
x_sample = sd_vae_taesd.model()(x_sample.to(devices.device, devices.dtype).unsqueeze(0))[0].detach()
x_sample = sd_vae_taesd.decoder_model()(x_sample.to(devices.device, devices.dtype)).detach()
x_sample = x_sample * 2 - 1
else:
x_sample = processing.decode_first_stage(shared.sd_model, sample.unsqueeze(0))[0] * 0.5 + 0.5
if model is None:
model = shared.sd_model
x_sample = model.decode_first_stage(sample)
return x_sample
def single_sample_to_image(sample, approximation=None):
x_sample = samples_to_images_tensor(sample.unsqueeze(0), approximation)[0] * 0.5 + 0.5
x_sample = torch.clamp(x_sample, min=0.0, max=1.0)
x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
@@ -52,6 +62,24 @@ def samples_to_image_grid(samples, approximation=None):
return images.image_grid([single_sample_to_image(sample, approximation) for sample in samples])
def images_tensor_to_samples(image, approximation=None, model=None):
'''image[0, 1] -> latent'''
if approximation is None:
approximation = approximation_indexes.get(opts.sd_vae_encode_method, 0)
if approximation == 3:
image = image.to(devices.device, devices.dtype)
x_latent = sd_vae_taesd.encoder_model()(image) / 1.5
else:
if model is None:
model = shared.sd_model
image = image.to(shared.device, dtype=devices.dtype_vae)
image = image * 2 - 1
x_latent = model.get_first_stage_encoding(model.encode_first_stage(image))
return x_latent
def store_latent(decoded):
state.current_latent = decoded