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pulid optimizations: dtype, vae, offload

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Signed-off-by: Vladimir Mandic <mandic00@live.com>
This commit is contained in:
Vladimir Mandic
2024-11-09 20:03:41 -05:00
parent 79f96fb509
commit 6510a13904
7 changed files with 103 additions and 92 deletions
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modules/processing_diffusers.py
+9 -5
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@@ -71,7 +71,7 @@ def process_base(p: processing.StableDiffusionProcessing):
guidance_rescale=p.diffusers_guidance_rescale,
denoising_start=0 if use_refiner_start else p.refiner_start if use_denoise_start else None,
denoising_end=p.refiner_start if use_refiner_start else 1 if use_denoise_start else None,
output_type='latent' if hasattr(shared.sd_model, 'vae') else 'np',
output_type='latent',
clip_skip=p.clip_skip,
desc='Base',
)
@@ -217,7 +217,7 @@ def process_hires(p: processing.StableDiffusionProcessing, output):
eta=shared.opts.scheduler_eta,
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',
output_type='latent',
clip_skip=p.clip_skip,
image=output.images,
strength=p.denoising_strength,
@@ -278,7 +278,7 @@ def process_refine(p: processing.StableDiffusionProcessing, output):
for i in range(len(output.images)):
image = output.images[i]
noise_level = round(350 * p.denoising_strength)
output_type='latent' if hasattr(shared.sd_refiner, 'vae') else 'np'
output_type='latent'
if 'Upscale' in shared.sd_refiner.__class__.__name__ or 'Flux' in shared.sd_refiner.__class__.__name__:
image = processing_vae.vae_decode(latents=image, model=shared.sd_model, full_quality=p.full_quality, output_type='pil', width=p.width, height=p.height)
p.extra_generation_params['Noise level'] = noise_level
@@ -346,7 +346,11 @@ def process_decode(p: processing.StableDiffusionProcessing, output):
if not hasattr(output, 'images') and hasattr(output, 'frames'):
shared.log.debug(f'Generated: frames={len(output.frames[0])}')
output.images = output.frames[0]
if hasattr(shared.sd_model, "vae") and output.images is not None and len(output.images) > 0:
model = shared.sd_model if not is_refiner_enabled(p) else shared.sd_refiner
if not hasattr(model, 'vae'):
if hasattr(model, 'pipe') and hasattr(model.pipe, 'vae'):
model = model.pipe
if hasattr(model, "vae") and output.images is not None and len(output.images) > 0:
if p.hr_resize_mode > 0 and (p.hr_upscaler != 'None' or p.hr_resize_mode == 5):
width = max(getattr(p, 'width', 0), getattr(p, 'hr_upscale_to_x', 0))
height = max(getattr(p, 'height', 0), getattr(p, 'hr_upscale_to_y', 0))
@@ -355,7 +359,7 @@ def process_decode(p: processing.StableDiffusionProcessing, output):
height = getattr(p, 'height', 0)
results = processing_vae.vae_decode(
latents = output.images,
model = shared.sd_model if not is_refiner_enabled(p) else shared.sd_refiner,
model = model,
full_quality = p.full_quality,
width = width,
height = height,