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send all three of GFPGAN's and codeformer's models to CPU memory instead of just one for #1283

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AUTOMATIC
2022-10-04 12:32:22 +03:00
parent 556c36b960
commit 6c6ae28bf5
4 changed files with 41 additions and 11 deletions
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16
modules/processing.py
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@@ -1,4 +1,3 @@
import contextlib
import json
import math
import os
@@ -330,9 +329,8 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
infotexts = []
output_images = []
precision_scope = torch.autocast if cmd_opts.precision == "autocast" else contextlib.nullcontext
ema_scope = (contextlib.nullcontext if cmd_opts.lowvram else p.sd_model.ema_scope)
with torch.no_grad(), precision_scope("cuda"), ema_scope():
with torch.no_grad():
p.init(all_prompts, all_seeds, all_subseeds)
if state.job_count == -1:
@@ -351,8 +349,9 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
#uc = p.sd_model.get_learned_conditioning(len(prompts) * [p.negative_prompt])
#c = p.sd_model.get_learned_conditioning(prompts)
uc = prompt_parser.get_learned_conditioning(len(prompts) * [p.negative_prompt], p.steps)
c = prompt_parser.get_learned_conditioning(prompts, p.steps)
with devices.autocast():
uc = prompt_parser.get_learned_conditioning(len(prompts) * [p.negative_prompt], p.steps)
c = prompt_parser.get_learned_conditioning(prompts, p.steps)
if len(model_hijack.comments) > 0:
for comment in model_hijack.comments:
@@ -361,7 +360,9 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
if p.n_iter > 1:
shared.state.job = f"Batch {n+1} out of {p.n_iter}"
samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength)
with devices.autocast():
samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength).to(devices.dtype)
if state.interrupted:
# if we are interruped, sample returns just noise
@@ -386,6 +387,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
devices.torch_gc()
x_sample = modules.face_restoration.restore_faces(x_sample)
devices.torch_gc()
image = Image.fromarray(x_sample)