mirror of
https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
synced 2025-04-04 03:29:00 +08:00
Merge 84c4aaec0df753b91fe5cf476573b806f9244efe into 374bb6cc384d2a19422c0b07d69de0a41d1f3f4d
This commit is contained in:
arrmansa
GitHub
commit
7351aea431
@ -11,8 +11,13 @@ from modules import processing, shared, sd_samplers, sd_samplers_common
|
||||
import torch
|
||||
import k_diffusion as K
|
||||
|
||||
def find_noise_for_image(p, cond, uncond, cfg_scale, steps):
|
||||
x = p.init_latent
|
||||
# Debugging notes - the original method apply_model is being called for sd1.5 is in modules.sd_hijack_utils and is ldm.models.diffusion.ddpm.LatentDiffusion
|
||||
# For sdxl - OpenAIWrapper will be called, which will call the underlying diffusion_model
|
||||
# When controlnet is enabled, the underlying model is not available to use, therefore we skip
|
||||
|
||||
@torch.no_grad()
|
||||
def find_noise_for_image(p, cond, uncond, cfg_scale, steps, skip_sdxl_vector):
|
||||
x = p.init_latent.clone()
|
||||
|
||||
s_in = x.new_ones([x.shape[0]])
|
||||
if shared.sd_model.parameterization == "v":
|
||||
@ -30,41 +35,30 @@ def find_noise_for_image(p, cond, uncond, cfg_scale, steps):
|
||||
|
||||
x_in = torch.cat([x] * 2)
|
||||
sigma_in = torch.cat([sigmas[i] * s_in] * 2)
|
||||
cond_in = torch.cat([uncond, cond])
|
||||
|
||||
image_conditioning = torch.cat([p.image_conditioning] * 2)
|
||||
cond_in = {"c_concat": [image_conditioning], "c_crossattn": [cond_in]}
|
||||
if shared.sd_model.is_sdxl:
|
||||
cond_in = {"crossattn": [torch.cat([uncond['crossattn'], cond['crossattn']])], "vector": [torch.cat([uncond['vector'], cond['vector']])]}
|
||||
else:
|
||||
cond_in = {"c_concat": [torch.cat([p.image_conditioning] * 2)], "c_crossattn": [torch.cat([uncond, cond])]}
|
||||
|
||||
c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)[skip:]]
|
||||
t = dnw.sigma_to_t(sigma_in)
|
||||
|
||||
eps = shared.sd_model.apply_model(x_in * c_in, t, cond=cond_in)
|
||||
denoised_uncond, denoised_cond = (x_in + eps * c_out).chunk(2)
|
||||
|
||||
denoised = denoised_uncond + (denoised_cond - denoised_uncond) * cfg_scale
|
||||
|
||||
d = (x - denoised) / sigmas[i]
|
||||
dt = sigmas[i] - sigmas[i - 1]
|
||||
|
||||
x = x + d * dt
|
||||
x += noise_from_model(x, t, dt, sigma_in, cond_in, cfg_scale, dnw, skip, skip_sdxl_vector)
|
||||
|
||||
sd_samplers_common.store_latent(x)
|
||||
|
||||
# This shouldn't be necessary, but solved some VRAM issues
|
||||
del x_in, sigma_in, cond_in, c_out, c_in, t,
|
||||
del eps, denoised_uncond, denoised_cond, denoised, d, dt
|
||||
del x_in, sigma_in, cond_in, t, dt
|
||||
|
||||
shared.state.nextjob()
|
||||
|
||||
return x / x.std()
|
||||
|
||||
|
||||
Cached = namedtuple("Cached", ["noise", "cfg_scale", "steps", "latent", "original_prompt", "original_negative_prompt", "sigma_adjustment"])
|
||||
return x, sigmas[-1]
|
||||
|
||||
|
||||
# Based on changes suggested by briansemrau in https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/736
|
||||
def find_noise_for_image_sigma_adjustment(p, cond, uncond, cfg_scale, steps):
|
||||
x = p.init_latent
|
||||
@torch.no_grad()
|
||||
def find_noise_for_image_sigma_adjustment(p, cond, uncond, cfg_scale, steps, correction_factor, skip_sdxl_vector):
|
||||
x = p.init_latent.clone()
|
||||
|
||||
s_in = x.new_ones([x.shape[0]])
|
||||
if shared.sd_model.parameterization == "v":
|
||||
@ -79,43 +73,78 @@ def find_noise_for_image_sigma_adjustment(p, cond, uncond, cfg_scale, steps):
|
||||
|
||||
for i in trange(1, len(sigmas)):
|
||||
shared.state.sampling_step += 1
|
||||
|
||||
x_in = torch.cat([x] * 2)
|
||||
sigma_in = torch.cat([sigmas[i - 1] * s_in] * 2)
|
||||
cond_in = torch.cat([uncond, cond])
|
||||
|
||||
image_conditioning = torch.cat([p.image_conditioning] * 2)
|
||||
cond_in = {"c_concat": [image_conditioning], "c_crossattn": [cond_in]}
|
||||
|
||||
c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)[skip:]]
|
||||
if shared.sd_model.is_sdxl:
|
||||
cond_in = {"crossattn": [torch.cat([uncond['crossattn'], cond['crossattn']])], "vector": [torch.cat([uncond['vector'], cond['vector']])]}
|
||||
else:
|
||||
cond_in = {"c_concat": [torch.cat([p.image_conditioning] * 2)], "c_crossattn": [torch.cat([uncond, cond])]}
|
||||
|
||||
if i == 1:
|
||||
t = dnw.sigma_to_t(torch.cat([sigmas[i] * s_in] * 2))
|
||||
dt = (sigmas[i] - sigmas[i - 1]) / (2 * sigmas[i])
|
||||
else:
|
||||
t = dnw.sigma_to_t(sigma_in)
|
||||
dt = (sigmas[i] - sigmas[i - 1]) / sigmas[i - 1]
|
||||
|
||||
eps = shared.sd_model.apply_model(x_in * c_in, t, cond=cond_in)
|
||||
denoised_uncond, denoised_cond = (x_in + eps * c_out).chunk(2)
|
||||
noise = noise_from_model(x, t, dt, sigma_in, cond_in, cfg_scale, dnw, skip, skip_sdxl_vector)
|
||||
|
||||
denoised = denoised_uncond + (denoised_cond - denoised_uncond) * cfg_scale
|
||||
if correction_factor > 0: # runs model with previously calculated noise
|
||||
recalculated_noise = noise_from_model(x + noise, t, dt, sigma_in, cond_in, cfg_scale, dnw, skip, skip_sdxl_vector)
|
||||
noise = recalculated_noise * correction_factor + noise * (1 - correction_factor)
|
||||
|
||||
if i == 1:
|
||||
d = (x - denoised) / (2 * sigmas[i])
|
||||
else:
|
||||
d = (x - denoised) / sigmas[i - 1]
|
||||
|
||||
dt = sigmas[i] - sigmas[i - 1]
|
||||
x = x + d * dt
|
||||
x += noise
|
||||
|
||||
sd_samplers_common.store_latent(x)
|
||||
|
||||
# This shouldn't be necessary, but solved some VRAM issues
|
||||
del x_in, sigma_in, cond_in, c_out, c_in, t,
|
||||
del eps, denoised_uncond, denoised_cond, denoised, d, dt
|
||||
|
||||
shared.state.nextjob()
|
||||
|
||||
return x / sigmas[-1]
|
||||
return x, sigmas[-1]
|
||||
|
||||
@torch.no_grad()
|
||||
def noise_from_model(x, t, dt, sigma_in, cond_in, cfg_scale, dnw, skip, skip_sdxl_vector):
|
||||
|
||||
if cfg_scale == 1: # Case where denoised_uncond should not be calculated - 50% speedup, also good for sdxl in experiments
|
||||
x_in = x
|
||||
sigma_in = sigma_in[1:2]
|
||||
c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)[skip:]]
|
||||
cond_in = {k:[v[0][1:2]] for k, v in cond_in.items()}
|
||||
if shared.sd_model.is_sdxl:
|
||||
num_classes_hack = shared.sd_model.model.diffusion_model.num_classes
|
||||
if skip_sdxl_vector:
|
||||
shared.sd_model.model.diffusion_model.num_classes = None
|
||||
cond_in["vector"][0] = None
|
||||
try:
|
||||
eps = shared.sd_model.model(x_in * c_in, t[1:2], {"crossattn": cond_in["crossattn"][0], "vector": cond_in["vector"][0]})
|
||||
finally:
|
||||
shared.sd_model.model.diffusion_model.num_classes = num_classes_hack
|
||||
else:
|
||||
eps = shared.sd_model.apply_model(x_in * c_in, t[1:2], cond=cond_in)
|
||||
|
||||
return -eps * c_out* dt
|
||||
else :
|
||||
x_in = torch.cat([x] * 2)
|
||||
|
||||
c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)[skip:]]
|
||||
|
||||
if shared.sd_model.is_sdxl:
|
||||
num_classes_hack = shared.sd_model.model.diffusion_model.num_classes
|
||||
if skip_sdxl_vector:
|
||||
shared.sd_model.model.diffusion_model.num_classes = None
|
||||
cond_in["vector"][0] = None
|
||||
try:
|
||||
eps = shared.sd_model.model(x_in * c_in, t, {"crossattn": cond_in["crossattn"][0], "vector": cond_in["vector"][0]} )
|
||||
finally:
|
||||
shared.sd_model.model.diffusion_model.num_classes = num_classes_hack
|
||||
else:
|
||||
eps = shared.sd_model.apply_model(x_in * c_in, t, cond=cond_in)
|
||||
|
||||
denoised_uncond, denoised_cond = (eps * c_out).chunk(2)
|
||||
|
||||
denoised = denoised_uncond + (denoised_cond - denoised_uncond) * cfg_scale
|
||||
|
||||
return -denoised * dt
|
||||
|
||||
Cached = namedtuple("Cached", ["noise", "cfg_scale", "steps", "latent", "original_prompt", "original_negative_prompt", "sigma_adjustment", "second_order_correction", "skip_sdxl_vector"])
|
||||
|
||||
|
||||
class Script(scripts.Script):
|
||||
@ -133,20 +162,25 @@ class Script(scripts.Script):
|
||||
* `CFG Scale` should be 2 or lower.
|
||||
''')
|
||||
|
||||
override_sampler = gr.Checkbox(label="Override `Sampling method` to Euler?(this method is built for it)", value=True, elem_id=self.elem_id("override_sampler"))
|
||||
override_sampler = gr.Checkbox(label="Override `Sampling method` to Euler?(this method is built for it)", value=False, elem_id=self.elem_id("override_sampler"))
|
||||
|
||||
override_prompt = gr.Checkbox(label="Override `prompt` to the same value as `original prompt`?(and `negative prompt`)", value=True, elem_id=self.elem_id("override_prompt"))
|
||||
override_prompt = gr.Checkbox(label="Override `prompt` to the same value as `original prompt`?(and `negative prompt`)", value=False, elem_id=self.elem_id("override_prompt"))
|
||||
original_prompt = gr.Textbox(label="Original prompt", lines=1, elem_id=self.elem_id("original_prompt"))
|
||||
original_negative_prompt = gr.Textbox(label="Original negative prompt", lines=1, elem_id=self.elem_id("original_negative_prompt"))
|
||||
|
||||
override_steps = gr.Checkbox(label="Override `Sampling Steps` to the same value as `Decode steps`?", value=True, elem_id=self.elem_id("override_steps"))
|
||||
st = gr.Slider(label="Decode steps", minimum=1, maximum=150, step=1, value=50, elem_id=self.elem_id("st"))
|
||||
st = gr.Slider(label="Decode steps", minimum=1, maximum=150, step=1, value=20, elem_id=self.elem_id("st"))
|
||||
|
||||
override_strength = gr.Checkbox(label="Override `Denoising strength` to 1?", value=True, elem_id=self.elem_id("override_strength"))
|
||||
|
||||
cfg = gr.Slider(label="Decode CFG scale", minimum=0.0, maximum=15.0, step=0.1, value=1.0, elem_id=self.elem_id("cfg"))
|
||||
randomness = gr.Slider(label="Randomness", minimum=0.0, maximum=1.0, step=0.01, value=0.0, elem_id=self.elem_id("randomness"))
|
||||
sigma_adjustment = gr.Checkbox(label="Sigma adjustment for finding noise for image", value=False, elem_id=self.elem_id("sigma_adjustment"))
|
||||
sigma_adjustment = gr.Checkbox(label="Sigma adjustment for finding noise for image", value=True, elem_id=self.elem_id("sigma_adjustment"))
|
||||
second_order_correction = gr.Slider(label="Correct noise by running model again", minimum=0.0, maximum=1.0, step=0.01, value=0.5, elem_id=self.elem_id("second_order_correction"),
|
||||
info="use 0 (disabled) for original script behaviour, 0.5 reccomended value. Runs the model again to recalculate noise and correct it by given factor. Higher adheres to original image more.")
|
||||
noise_sigma_intensity = gr.Slider(label="Weight scaling std vs sigma based", minimum=-1.0, maximum=2.0, step=0.01, value=0.5, elem_id=self.elem_id("noise_sigma_intensity"),
|
||||
info="use 1 for original script behaviour, 0.5 reccomended value. Decides whether to use fixed sigma value or dynamic standard deviation to scale noise. Lower gives softer images.")
|
||||
skip_sdxl_vector = gr.Checkbox(label="Skip sdxl vectors", info="may cause distortion if false", value=True, elem_id=self.elem_id("skip_sdxl_vector"))
|
||||
|
||||
return [
|
||||
info,
|
||||
@ -154,10 +188,12 @@ class Script(scripts.Script):
|
||||
override_prompt, original_prompt, original_negative_prompt,
|
||||
override_steps, st,
|
||||
override_strength,
|
||||
cfg, randomness, sigma_adjustment,
|
||||
cfg, randomness, sigma_adjustment, second_order_correction,
|
||||
noise_sigma_intensity, skip_sdxl_vector
|
||||
]
|
||||
|
||||
def run(self, p, _, override_sampler, override_prompt, original_prompt, original_negative_prompt, override_steps, st, override_strength, cfg, randomness, sigma_adjustment):
|
||||
@torch.no_grad()
|
||||
def run(self, p, _, override_sampler, override_prompt, original_prompt, original_negative_prompt, override_steps, st, override_strength, cfg, randomness, sigma_adjustment, second_order_correction, noise_sigma_intensity, skip_sdxl_vector):
|
||||
# Override
|
||||
if override_sampler:
|
||||
p.sampler_name = "Euler"
|
||||
@ -175,33 +211,46 @@ class Script(scripts.Script):
|
||||
same_params = self.cache is not None and self.cache.cfg_scale == cfg and self.cache.steps == st \
|
||||
and self.cache.original_prompt == original_prompt \
|
||||
and self.cache.original_negative_prompt == original_negative_prompt \
|
||||
and self.cache.sigma_adjustment == sigma_adjustment
|
||||
and self.cache.sigma_adjustment == sigma_adjustment \
|
||||
and self.cache.second_order_correction == second_order_correction \
|
||||
and self.cache.skip_sdxl_vector == skip_sdxl_vector
|
||||
|
||||
same_everything = same_params and self.cache.latent.shape == lat.shape and np.abs(self.cache.latent-lat).sum() < 100
|
||||
|
||||
rand_noise = processing.create_random_tensors(p.init_latent.shape[1:], 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 same_everything:
|
||||
rec_noise = self.cache.noise
|
||||
rec_noise, sigma_val = self.cache.noise
|
||||
else:
|
||||
# This prevents a crash, because I don't know how to access the underlying .diffusion_model yet when controlnet is enabled.
|
||||
# modules.sd_unet -> we're good
|
||||
# scripts.hook -> we're cooked
|
||||
if "scripts.hook" in str(shared.sd_model.model.diffusion_model.forward.__module__):
|
||||
print("turn off any controlnets, do 1 pass and then turn controlnet back on to cache noise")
|
||||
p.steps = 1
|
||||
return sd_samplers.create_sampler(p.sampler_name, p.sd_model).sample_img2img(p, p.init_latent, rand_noise, conditioning, unconditional_conditioning, image_conditioning=p.image_conditioning)
|
||||
|
||||
shared.state.job_count += 1
|
||||
cond = p.sd_model.get_learned_conditioning(p.batch_size * [original_prompt])
|
||||
uncond = p.sd_model.get_learned_conditioning(p.batch_size * [original_negative_prompt])
|
||||
if sigma_adjustment:
|
||||
rec_noise = find_noise_for_image_sigma_adjustment(p, cond, uncond, cfg, st)
|
||||
rec_noise, sigma_val = find_noise_for_image_sigma_adjustment(p, cond, uncond, cfg, st, second_order_correction, skip_sdxl_vector)
|
||||
else:
|
||||
rec_noise = find_noise_for_image(p, cond, uncond, cfg, st)
|
||||
self.cache = Cached(rec_noise, cfg, st, lat, original_prompt, original_negative_prompt, sigma_adjustment)
|
||||
rec_noise, sigma_val = find_noise_for_image(p, cond, uncond, cfg, st, skip_sdxl_vector)
|
||||
self.cache = Cached((rec_noise, sigma_val), cfg, st, lat, original_prompt, original_negative_prompt, sigma_adjustment, second_order_correction, skip_sdxl_vector)
|
||||
|
||||
rand_noise = processing.create_random_tensors(p.init_latent.shape[1:], 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)
|
||||
rec_noise = rec_noise / (rec_noise.std()*(1 - noise_sigma_intensity) + sigma_val*noise_sigma_intensity)
|
||||
|
||||
combined_noise = ((1 - randomness) * rec_noise + randomness * rand_noise) / ((randomness**2 + (1-randomness)**2) ** 0.5)
|
||||
|
||||
sampler = sd_samplers.create_sampler(p.sampler_name, p.sd_model)
|
||||
|
||||
p.seed = p.seed + 1
|
||||
|
||||
sigmas = sampler.model_wrap.get_sigmas(p.steps)
|
||||
|
||||
noise_dt = combined_noise - (p.init_latent / sigmas[0])
|
||||
|
||||
p.seed = p.seed + 1
|
||||
|
||||
return sampler.sample_img2img(p, p.init_latent, noise_dt, conditioning, unconditional_conditioning, image_conditioning=p.image_conditioning)
|
||||
|
||||
p.sample = sample_extra
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user