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fix styles api

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Vladimir Mandic
2023-09-10 18:52:48 -04:00
parent f8fcb6f853
commit 2c06f841fb
2 changed files with 54 additions and 27 deletions
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modules/processing_diffusers.py
+53 -23
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@@ -317,33 +317,63 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
# optional hires pass
if p.is_hr_pass:
p.init_hr()
recompile_model(hires=True)
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")
hires_resize(latents=output.images)
sd_models.set_diffuser_pipe(shared.sd_model, sd_models.DiffusersTaskType.IMAGE_2_IMAGE)
p.ops.append('hires')
hires_args = set_pipeline_args(
model=shared.sd_model,
prompts=prompts,
negative_prompts=negative_prompts,
prompts_2=[p.refiner_prompt] if len(p.refiner_prompt) > 0 else prompts,
negative_prompts_2=[p.refiner_negative] if len(p.refiner_negative) > 0 else negative_prompts,
num_inference_steps=int(p.hr_second_pass_steps // p.denoising_strength + 1),
eta=shared.opts.eta_ddim,
guidance_scale=p.image_cfg_scale if p.image_cfg_scale is not None else p.cfg_scale,
guidance_rescale=p.diffusers_guidance_rescale,
output_type='latent' if hasattr(shared.sd_model, 'vae') else 'np',
clip_skip=p.clip_skip,
image=p.init_images,
strength=p.denoising_strength,
desc='Hires',
)
try:
output = shared.sd_model(**hires_args) # pylint: disable=not-callable
except AssertionError as e:
shared.log.info(e)
if latent_scale_mode is not None:
recompile_model(hires=True)
hires_resize(latents=output.images)
sd_models.set_diffuser_pipe(shared.sd_model, sd_models.DiffusersTaskType.IMAGE_2_IMAGE)
hires_args = set_pipeline_args(
model=shared.sd_model,
prompts=prompts,
negative_prompts=negative_prompts,
prompts_2=[p.refiner_prompt] if len(p.refiner_prompt) > 0 else prompts,
negative_prompts_2=[p.refiner_negative] if len(p.refiner_negative) > 0 else negative_prompts,
num_inference_steps=int(p.hr_second_pass_steps // p.denoising_strength + 1),
eta=shared.opts.eta_ddim,
guidance_scale=p.image_cfg_scale if p.image_cfg_scale is not None else p.cfg_scale,
guidance_rescale=p.diffusers_guidance_rescale,
output_type='latent' if hasattr(shared.sd_model, 'vae') else 'np',
clip_skip=p.clip_skip,
image=p.init_images,
strength=p.denoising_strength,
desc='Hires',
)
try:
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: