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enable non-latent hires upscalers

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Vladimir Mandic
2023-09-10 19:48:22 -04:00
parent 2c06f841fb
commit cbed61732f
3 changed files with 22 additions and 31 deletions
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modules/processing_diffusers.py
+18 -30
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@@ -2,6 +2,8 @@ import time
import inspect
import typing
import torch
import numpy as np
from PIL import Image
import modules.devices as devices
import modules.shared as shared
import modules.sd_samplers as sd_samplers
@@ -315,15 +317,14 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
return results
# optional hires pass
latent_scale_mode = shared.latent_upscale_modes.get(p.hr_upscaler, None) if p.hr_upscaler is not None else shared.latent_upscale_modes.get(shared.latent_upscale_default_mode, "None")
if p.is_hr_pass:
p.init_hr()
latent_scale_mode = shared.latent_upscale_modes.get(p.hr_upscaler, None) if p.hr_upscaler is not None else shared.latent_upscale_modes.get(shared.latent_upscale_default_mode, "None")
print('HERE1', latent_scale_mode)
if p.width != p.hr_upscale_to_x or p.height != p.hr_upscale_to_y:
if shared.opts.save and not p.do_not_save_samples and shared.opts.save_images_before_highres_fix and hasattr(shared.sd_model, 'vae'):
save_intermediate(latents=output.images, suffix="-before-hires")
p.ops.append('hires')
if latent_scale_mode is not None:
p.ops.append('hires')
recompile_model(hires=True)
hires_resize(latents=output.images)
sd_models.set_diffuser_pipe(shared.sd_model, sd_models.DiffusersTaskType.IMAGE_2_IMAGE)
@@ -347,33 +348,6 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
output = shared.sd_model(**hires_args) # pylint: disable=not-callable
except AssertionError as e:
shared.log.info(e)
else:
"""
decoded_samples = decode_first_stage(self.sd_model, samples.to(dtype=devices.dtype_vae))
lowres_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0)
batch_images = []
for i, x_sample in enumerate(lowres_samples):
x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
x_sample = validate_sample(x_sample)
image = Image.fromarray(x_sample)
save_intermediate(image, i)
image = images.resize_image(1, image, target_width, target_height, upscaler_name=self.hr_upscaler)
image = np.array(image).astype(np.float32) / 255.0
image = np.moveaxis(image, 2, 0)
batch_images.append(image)
decoded_samples = torch.from_numpy(np.array(batch_images))
decoded_samples = decoded_samples.to(device=shared.device, dtype=devices.dtype_vae)
decoded_samples = 2. * decoded_samples - 1.
if shared.opts.sd_vae_sliced_encode and len(decoded_samples) > 1:
samples = torch.stack([
self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(torch.unsqueeze(decoded_sample, 0)))[0]
for decoded_sample
in decoded_samples
])
else:
samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(decoded_samples))
image_conditioning = self.img2img_image_conditioning(decoded_samples, samples)
"""
# optional refiner pass or decode
if is_refiner_enabled:
@@ -429,6 +403,20 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
shared.sd_refiner.to(devices.cpu)
devices.torch_gc()
if p.is_hr_pass and latent_scale_mode is None:
if p.width != p.hr_upscale_to_x or p.height != p.hr_upscale_to_y:
p.ops.append('upscale')
if not is_refiner_enabled:
results = vae_decode(latents=output.images, model=shared.sd_model, full_quality=p.full_quality)
upscaled = []
for image in results:
image = (image * 255.0).astype(np.uint8)
image = Image.fromarray(image)
image = images.resize_image(1, image, p.hr_upscale_to_x, p.hr_upscale_to_y, upscaler_name=p.hr_upscaler)
image = np.array(image).astype(np.float32) / 255.0
upscaled.append(image)
return upscaled
# final decode since there is no refiner
if not is_refiner_enabled:
results = vae_decode(latents=output.images, model=shared.sd_model, full_quality=p.full_quality)