diff options
Diffstat (limited to 'modules/processing.py')
| -rw-r--r-- | modules/processing.py | 53 |
1 files changed, 33 insertions, 20 deletions
diff --git a/modules/processing.py b/modules/processing.py index 2f5a2967..e7f9c85e 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -1,4 +1,3 @@ -import contextlib
import json
import math
import os
@@ -85,7 +84,7 @@ class StableDiffusionProcessing: self.s_tmin = opts.s_tmin
self.s_tmax = float('inf') # not representable as a standard ui option
self.s_noise = opts.s_noise
-
+
if not seed_enable_extras:
self.subseed = -1
self.subseed_strength = 0
@@ -249,9 +248,16 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see return x
+def get_fixed_seed(seed):
+ if seed is None or seed == '' or seed == -1:
+ return int(random.randrange(4294967294))
+
+ return seed
+
+
def fix_seed(p):
- p.seed = int(random.randrange(4294967294)) if p.seed is None or p.seed == '' or p.seed == -1 else p.seed
- p.subseed = int(random.randrange(4294967294)) if p.subseed is None or p.subseed == '' or p.subseed == -1 else p.subseed
+ p.seed = get_fixed_seed(p.seed)
+ p.subseed = get_fixed_seed(p.subseed)
def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration=0, position_in_batch=0):
@@ -290,10 +296,11 @@ def process_images(p: StableDiffusionProcessing) -> Processed: assert(len(p.prompt) > 0)
else:
assert p.prompt is not None
-
+
devices.torch_gc()
- fix_seed(p)
+ seed = get_fixed_seed(p.seed)
+ subseed = get_fixed_seed(p.subseed)
if p.outpath_samples is not None:
os.makedirs(p.outpath_samples, exist_ok=True)
@@ -312,15 +319,15 @@ def process_images(p: StableDiffusionProcessing) -> Processed: else:
all_prompts = p.batch_size * p.n_iter * [p.prompt]
- if type(p.seed) == list:
- all_seeds = p.seed
+ if type(seed) == list:
+ all_seeds = seed
else:
- all_seeds = [int(p.seed) + (x if p.subseed_strength == 0 else 0) for x in range(len(all_prompts))]
+ all_seeds = [int(seed) + (x if p.subseed_strength == 0 else 0) for x in range(len(all_prompts))]
- if type(p.subseed) == list:
- all_subseeds = p.subseed
+ if type(subseed) == list:
+ all_subseeds = subseed
else:
- all_subseeds = [int(p.subseed) + x for x in range(len(all_prompts))]
+ all_subseeds = [int(subseed) + x for x in range(len(all_prompts))]
def infotext(iteration=0, position_in_batch=0):
return create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration, position_in_batch)
@@ -330,10 +337,10 @@ 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():
- p.init(all_prompts, all_seeds, all_subseeds)
+
+ with torch.no_grad():
+ with devices.autocast():
+ p.init(all_prompts, all_seeds, all_subseeds)
if state.job_count == -1:
state.job_count = p.n_iter
@@ -352,8 +359,9 @@ 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(shared.sd_model, len(prompts) * [p.negative_prompt], p.steps)
+ c = prompt_parser.get_learned_conditioning(shared.sd_model, prompts, p.steps)
if len(model_hijack.comments) > 0:
for comment in model_hijack.comments:
@@ -362,13 +370,17 @@ 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)
+
if state.interrupted:
# if we are interruped, sample returns just noise
# use the image collected previously in sampler loop
samples_ddim = shared.state.current_latent
+ samples_ddim = samples_ddim.to(devices.dtype)
+
x_samples_ddim = p.sd_model.decode_first_stage(samples_ddim)
x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
@@ -394,6 +406,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed: images.save_image(Image.fromarray(x_sample), p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-face-restoration")
x_sample = modules.face_restoration.restore_faces(x_sample)
+ devices.torch_gc()
devices.torch_gc()
@@ -530,7 +543,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): # GC now before running the next img2img to prevent running out of memory
x = None
devices.torch_gc()
-
+
samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps)
return samples
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