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optimize diffusers memory handling

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Vladimir Mandic
2023-08-18 20:41:34 +00:00
parent 20ba9aa649
commit 5eac99d3f5
11 changed files with 59 additions and 69 deletions
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modules/processing_diffusers.py
+5 -8
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@@ -51,6 +51,7 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
unet_device = model.unet.device
model.unet.to(devices.cpu)
devices.torch_gc()
model.vae.to(devices.device)
latents.to(model.vae.device)
decoded = model.vae.decode(latents / model.vae.config.scaling_factor, return_dict=False)[0]
if shared.opts.diffusers_move_unet and not model.has_accelerate:
@@ -117,13 +118,9 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
negative_pooled = None
prompts, negative_prompts, prompts_2, negative_prompts_2 = fix_prompts(prompts, negative_prompts, prompts_2, negative_prompts_2)
if shared.opts.prompt_attention in {'Compel parser', 'Full parser'}:
prompt_embed, pooled, negative_embed, negative_pooled = prompt_parser_diffusers.compel_encode_prompts(model,
prompts,
negative_prompts,
prompts_2,
negative_prompts_2,
is_refiner,
kwargs.pop("clip_skip", None))
prompt_embed, pooled, negative_embed, negative_pooled = prompt_parser_diffusers.compel_encode_prompts(model, prompts, negative_prompts,
prompts_2, negative_prompts_2,
is_refiner, kwargs.pop("clip_skip", None))
if 'prompt' in possible:
if hasattr(model, 'text_encoder') and 'prompt_embeds' in possible and prompt_embed is not None:
args['prompt_embeds'] = prompt_embed
@@ -261,7 +258,7 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
for i in range(len(decoded)):
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 or shared.cmd_opts.medvram or shared.opts.diffusers_model_cpu_offload) and not (shared.cmd_opts.lowvram or shared.opts.diffusers_seq_cpu_offload):
if shared.opts.diffusers_move_base and not shared.sd_model.has_accelerate:
shared.log.debug('Diffusers: Moving base model to CPU')
shared.sd_model.to(devices.cpu)
devices.torch_gc()