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add remote vae

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Signed-off-by: Vladimir Mandic <mandic00@live.com>
This commit is contained in:
Vladimir Mandic
2025-02-22 12:50:18 -05:00
parent f8f987fed6
commit 1b2d4286b5
21 changed files with 133 additions and 70 deletions
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modules/processing_diffusers.py
+5 -5
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@@ -197,10 +197,10 @@ def process_hires(p: processing.StableDiffusionProcessing, output):
if p.hr_force:
shared.sd_model = sd_models.set_diffuser_pipe(shared.sd_model, sd_models.DiffusersTaskType.IMAGE_2_IMAGE)
if 'Upscale' in shared.sd_model.__class__.__name__ or 'Flux' in shared.sd_model.__class__.__name__ or 'Kandinsky' in shared.sd_model.__class__.__name__:
output.images = processing_vae.vae_decode(latents=output.images, model=shared.sd_model, full_quality=p.full_quality, output_type='pil', width=p.width, height=p.height)
output.images = processing_vae.vae_decode(latents=output.images, model=shared.sd_model, vae_type=p.vae_type, output_type='pil', width=p.width, height=p.height)
if p.is_control and hasattr(p, 'task_args') and p.task_args.get('image', None) is not None:
if hasattr(shared.sd_model, "vae") and output.images is not None and len(output.images) > 0:
output.images = processing_vae.vae_decode(latents=output.images, model=shared.sd_model, full_quality=p.full_quality, output_type='pil', width=p.hr_upscale_to_x, height=p.hr_upscale_to_y) # controlnet cannnot deal with latent input
output.images = processing_vae.vae_decode(latents=output.images, model=shared.sd_model, vae_type=p.vae_type, output_type='pil', width=p.hr_upscale_to_x, height=p.hr_upscale_to_y) # controlnet cannnot deal with latent input
update_sampler(p, shared.sd_model, second_pass=True)
orig_denoise = p.denoising_strength
p.denoising_strength = strength
@@ -289,7 +289,7 @@ def process_refine(p: processing.StableDiffusionProcessing, output):
noise_level = round(350 * p.denoising_strength)
output_type='latent'
if 'Upscale' in shared.sd_refiner.__class__.__name__ or 'Flux' in shared.sd_refiner.__class__.__name__ or 'Kandinsky' 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)
image = processing_vae.vae_decode(latents=image, model=shared.sd_model, vae_type=p.vae_type, output_type='pil', width=p.width, height=p.height)
p.extra_generation_params['Noise level'] = noise_level
output_type = 'np'
update_sampler(p, shared.sd_refiner, second_pass=True)
@@ -370,7 +370,7 @@ def process_decode(p: processing.StableDiffusionProcessing, output):
result_batch = processing_vae.vae_decode(
latents = output.images[i],
model = model,
full_quality = p.full_quality,
vae_type = p.vae_type,
width = width,
height = height,
frames = frames,
@@ -381,7 +381,7 @@ def process_decode(p: processing.StableDiffusionProcessing, output):
results = processing_vae.vae_decode(
latents = output.images,
model = model,
full_quality = p.full_quality,
vae_type = p.vae_type,
width = width,
height = height,
frames = frames,