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fix refiner reload/unload

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Vladimir Mandic
2023-08-26 13:35:59 +00:00
parent d48a13a118
commit c4263da0e6
7 changed files with 84 additions and 88 deletions
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modules/processing_diffusers.py
+10 -10
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@@ -27,7 +27,7 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
def hires_resize(latents): # input=latents output=pil
latent_upscaler = shared.latent_upscale_modes.get(p.hr_upscaler, None)
shared.log.info(f'Diffusers Hires: upscaler={p.hr_upscaler} width={p.hr_upscale_to_x} height={p.hr_upscale_to_y} images={latents.shape[0]}')
shared.log.info(f'Hires: upscaler={p.hr_upscaler} width={p.hr_upscale_to_x} height={p.hr_upscale_to_y} images={latents.shape[0]}')
if latent_upscaler is not None:
latents = torch.nn.functional.interpolate(latents, size=(p.hr_upscale_to_y // 8, p.hr_upscale_to_x // 8), mode=latent_upscaler["mode"], antialias=latent_upscaler["antialias"])
first_pass_images = vae_decode(latents=latents, model=shared.sd_model, full_quality=True, output_type='pil')
@@ -54,9 +54,9 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
shared.state.current_latent = latents
def full_vae_decode(latents, model):
shared.log.debug(f'Diffusers VAE decode: name={sd_vae.loaded_vae_file if sd_vae.loaded_vae_file is not None else "baked"} dtype={model.vae.dtype} upcast={model.vae.config.get("force_upcast", None)} images={latents.shape[0]}')
shared.log.debug(f'VAE decode: name={sd_vae.loaded_vae_file if sd_vae.loaded_vae_file is not None else "baked"} dtype={model.vae.dtype} upcast={model.vae.config.get("force_upcast", None)} images={latents.shape[0]}')
if shared.opts.diffusers_move_unet and not model.has_accelerate:
shared.log.debug('Diffusers: Moving UNet to CPU')
shared.log.debug('Moving to CPU: model=UNet')
unet_device = model.unet.device
model.unet.to(devices.cpu)
devices.torch_gc()
@@ -69,7 +69,7 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
return decoded
def taesd_vae_decode(latents):
shared.log.debug(f'Diffusers VAE decode: name=TAESD images={latents.shape[0]}')
shared.log.debug(f'VAE decode: name=TAESD images={latents.shape[0]}')
decoded = torch.zeros((len(latents), 3, p.height, p.width), dtype=devices.dtype_vae, device=devices.device)
for i in range(len(output.images)):
decoded[i] = (sd_vae_taesd.decode(latents[i]) * 2.0) - 1.0
@@ -181,13 +181,13 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
if 'negative_prompt' in clean:
clean['negative_prompt'] = len(clean['negative_prompt'])
if 'prompt_embeds' in clean:
clean['prompt_embeds'] = clean['prompt_embeds'].shape
clean['prompt_embeds'] = clean['prompt_embeds'].shape if torch.is_tensor(clean['prompt_embeds']) else type(clean['prompt_embeds'])
if 'pooled_prompt_embeds' in clean:
clean['pooled_prompt_embeds'] = clean['pooled_prompt_embeds'].shape
clean['pooled_prompt_embeds'] = clean['pooled_prompt_embeds'].shape if torch.is_tensor(clean['pooled_prompt_embeds']) else type(clean['pooled_prompt_embeds'])
if 'negative_prompt_embeds' in clean:
clean['negative_prompt_embeds'] = clean['negative_prompt_embeds'].shape
clean['negative_prompt_embeds'] = clean['negative_prompt_embeds'].shape if torch.is_tensor(clean['negative_prompt_embeds']) else type(clean['negative_prompt_embeds'])
if 'negative_pooled_prompt_embeds' in clean:
clean['negative_pooled_prompt_embeds'] = clean['negative_pooled_prompt_embeds'].shape
clean['negative_pooled_prompt_embeds'] = clean['negative_pooled_prompt_embeds'].shape if torch.is_tensor(clean['negative_pooled_prompt_embeds']) else type(clean['negative_pooled_prompt_embeds'])
clean['generator'] = generator_device
shared.log.debug(f'Diffuser pipeline: {pipeline.__class__.__name__} task={sd_models.get_diffusers_task(model)} set={clean}')
return args
@@ -318,7 +318,7 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
if shared.opts.save and not p.do_not_save_samples and shared.opts.save_images_before_refiner and hasattr(shared.sd_model, 'vae'):
save_intermediate(latents=output.images, suffix="-before-refiner")
if shared.opts.diffusers_move_base and not shared.sd_model.has_accelerate:
shared.log.debug('Diffusers: Moving base model to CPU')
shared.log.debug('Moving to CPU: model=base')
shared.sd_model.to(devices.cpu)
devices.torch_gc()
@@ -363,7 +363,7 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
results.append(refiner_image)
if shared.opts.diffusers_move_refiner and not shared.sd_refiner.has_accelerate:
shared.log.debug('Diffusers: Moving refiner model to CPU')
shared.log.debug('Moving to CPU: model=refiner')
shared.sd_refiner.to(devices.cpu)
devices.torch_gc()