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Vladimir Mandic
2023-07-24 08:27:13 -04:00
parent d4aa840a77
commit 7bbab3c9a9
3 changed files with 38 additions and 16 deletions
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modules/processing_diffusers.py
+16 -12
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@@ -18,7 +18,7 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
shared.state.current_latent = latents
def vae_decode(latents, model, output_type='np'):
if hasattr(model, 'vae'):
if hasattr(model, 'vae' and isinstance(latents, torch.Tensor)):
shared.log.debug(f'Diffusers VAE decode: name={model.vae.config.get("_name_or_path", "default")} upcast={model.vae.config.get("force_upcast", None)}')
decoded = model.vae.decode(latents / model.vae.config.scaling_factor, return_dict=False)[0]
imgs = model.image_processor.postprocess(decoded, output_type=output_type)
@@ -95,7 +95,12 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
# from modules.prompt_parser import parse_prompt_attention
# parsed_prompt = [parse_prompt_attention(prompt) for prompt in prompts]
shared.sd_model.to(devices.device)
if shared.state.interrupted or shared.state.skipped:
return results
if shared.opts.diffusers_move_base:
shared.sd_model.to(devices.device)
pipe_args = set_pipeline_args(
model=shared.sd_model,
prompt=prompts,
@@ -128,8 +133,9 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
images.save_image(decoded[i], path=p.outpath_samples, basename="", seed=seeds[i], prompt=prompts[i], extension=shared.opts.samples_format, info=info, p=p, suffix="-before-refiner")
if shared.opts.diffusers_move_base:
shared.log.debug('Moving base model to CPU')
shared.log.debug('Diffusers: Moving base model to CPU')
shared.sd_model.to('cpu')
devices.torch_gc()
if (not hasattr(shared.sd_refiner.scheduler, 'name')) or (shared.sd_refiner.scheduler.name != p.latent_sampler) and (p.sampler_name != 'Default'):
sampler = sd_samplers.all_samplers_map.get(p.latent_sampler, None)
@@ -140,8 +146,8 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
if shared.state.interrupted or shared.state.skipped:
return results
shared.sd_refiner.to(devices.device)
devices.torch_gc()
if shared.opts.diffusers_move_refiner:
shared.sd_refiner.to(devices.device)
for i in range(len(output.images)):
pipe_args = set_pipeline_args(
@@ -157,17 +163,15 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
denoising_start=p.refiner_denoise_start,
denoising_end=p.refiner_denoise_end,
image=output.images[i],
output_type='latent' if hasattr(shared.sd_model, 'vae') else 'np',
output_type='latent' if hasattr(shared.sd_refiner, 'vae') else 'np',
)
output = shared.sd_refiner(**pipe_args) # pylint: disable=not-callable
if shared.state.interrupted or shared.state.skipped:
return results
output.images = vae_decode(output.images, shared.sd_model)
results.append(output.images[0])
if not shared.state.interrupted and not shared.state.skipped:
output.images = vae_decode(output.images, shared.sd_refiner)
results.append(output.images[i])
if shared.opts.diffusers_move_refiner:
shared.log.debug('Moving refiner model to CPU')
shared.log.debug('Diffusers: Moving refiner model to CPU')
shared.sd_refiner.to('cpu')
else:
results = output.images