hoelee/stable-diffusion-webui
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added highres fix feature

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AUTOMATIC
2022-09-19 16:42:56 +03:00
parent 8a32a71ca3
commit 6d7ca54a1a
5 changed files with 121 additions and 38 deletions
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80
modules/processing.py
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@@ -74,11 +74,12 @@ class StableDiffusionProcessing:
self.overlay_images = overlay_images
self.paste_to = None
self.color_corrections = None
self.denoising_strength: float = 0
def init(self, seed):
def init(self, all_prompts, all_seeds, all_subseeds):
pass
def sample(self, x, conditioning, unconditional_conditioning):
def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
raise NotImplementedError()
@@ -303,7 +304,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
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(seed=all_seeds[0])
p.init(all_prompts, all_seeds, all_subseeds)
if state.job_count == -1:
state.job_count = p.n_iter
@@ -328,13 +329,10 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
for comment in model_hijack.comments:
comments[comment] = 1
# we manually generate all input noises because each one should have a specific seed
x = create_random_tensors([opt_C, p.height // opt_f, p.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, seed_resize_from_h=p.seed_resize_from_h, seed_resize_from_w=p.seed_resize_from_w, p=p)
if p.n_iter > 1:
shared.state.job = f"Batch {n+1} out of {p.n_iter}"
samples_ddim = p.sample(x=x, conditioning=c, unconditional_conditioning=uc)
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
@@ -406,13 +404,64 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
sampler = None
firstphase_width = 0
firstphase_height = 0
firstphase_width_truncated = 0
firstphase_height_truncated = 0
def init(self, seed):
def __init__(self, enable_hr=False, scale_latent=True, denoising_strength=0.75, **kwargs):
super().__init__(**kwargs)
self.enable_hr = enable_hr
self.scale_latent = scale_latent
self.denoising_strength = denoising_strength
def init(self, all_prompts, all_seeds, all_subseeds):
if self.enable_hr:
if state.job_count == -1:
state.job_count = self.n_iter * 2
else:
state.job_count = state.job_count * 2
desired_pixel_count = 512 * 512
actual_pixel_count = self.width * self.height
scale = math.sqrt(desired_pixel_count / actual_pixel_count)
self.firstphase_width = math.ceil(scale * self.width / 64) * 64
self.firstphase_height = math.ceil(scale * self.height / 64) * 64
self.firstphase_width_truncated = int(scale * self.width)
self.firstphase_height_truncated = int(scale * self.height)
def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
self.sampler = samplers[self.sampler_index].constructor(self.sd_model)
def sample(self, x, conditioning, unconditional_conditioning):
samples_ddim = self.sampler.sample(self, x, conditioning, unconditional_conditioning)
return samples_ddim
if not self.enable_hr:
x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning)
return samples
x = create_random_tensors([opt_C, self.firstphase_height // opt_f, self.firstphase_width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning)
truncate_x = (self.firstphase_width - self.firstphase_width_truncated) // opt_f
truncate_y = (self.firstphase_height - self.firstphase_height_truncated) // opt_f
samples = samples[:, :, truncate_y//2:samples.shape[2]-truncate_y//2, truncate_x//2:samples.shape[3]-truncate_x//2]
if self.scale_latent:
samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")
else:
decoded_samples = self.sd_model.decode_first_stage(samples)
decoded_samples = torch.nn.functional.interpolate(decoded_samples, size=(self.height, self.width), mode="bilinear")
samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(decoded_samples))
shared.state.nextjob()
self.sampler = samplers[self.sampler_index].constructor(self.sd_model)
noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps)
return samples
class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
sampler = None
@@ -435,7 +484,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
self.mask = None
self.nmask = None
def init(self, seed):
def init(self, all_prompts, all_seeds, all_subseeds):
self.sampler = samplers_for_img2img[self.sampler_index].constructor(self.sd_model)
crop_region = None
@@ -529,12 +578,15 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
self.mask = torch.asarray(1.0 - latmask).to(shared.device).type(self.sd_model.dtype)
self.nmask = torch.asarray(latmask).to(shared.device).type(self.sd_model.dtype)
# this needs to be fixed to be done in sample() using actual seeds for batches
if self.inpainting_fill == 2:
self.init_latent = self.init_latent * self.mask + create_random_tensors(self.init_latent.shape[1:], [seed + x + 1 for x in range(self.init_latent.shape[0])]) * self.nmask
self.init_latent = self.init_latent * self.mask + create_random_tensors(self.init_latent.shape[1:], all_seeds[0:self.init_latent.shape[0]]) * self.nmask
elif self.inpainting_fill == 3:
self.init_latent = self.init_latent * self.mask
def sample(self, x, conditioning, unconditional_conditioning):
def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
samples = self.sampler.sample_img2img(self, self.init_latent, x, conditioning, unconditional_conditioning)
if self.mask is not None: