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Update img2imgalt.py

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WIP
This commit is contained in:
arrmansa 2024-12-30 23:33:43 +05:30
parent 64a8f9d1b1
commit a63cf10650
1 changed files with 22 additions and 11 deletions
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scripts/img2imgalt.py
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@ -49,7 +49,12 @@ def find_noise_for_image(p, cond, uncond, cfg_scale, steps):
t = dnw.sigma_to_t(sigma_in)
if shared.sd_model.is_sdxl:
eps = shared.sd_model.model(x_in * c_in, t, {"crossattn": cond_in["c_crossattn"][0]} )
num_classes_hack = shared.sd_model.model.diffusion_model.num_classes
shared.sd_model.model.diffusion_model.num_classes = None
try:
eps = shared.sd_model.model(x_in * c_in, t, {"crossattn": cond_in["c_crossattn"][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)
@ -78,13 +83,6 @@ Cached = namedtuple("Cached", ["noise", "cfg_scale", "steps", "latent", "origina
# 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):
if shared.sd_model.is_sdxl:
cond_tensor = cond['crossattn']
uncond_tensor = uncond['crossattn']
cond_in = torch.cat([uncond_tensor, cond_tensor])
else:
cond_in = torch.cat([uncond, cond])
x = p.init_latent
s_in = x.new_ones([x.shape[0]])
@ -124,7 +122,12 @@ def find_noise_for_image_sigma_adjustment(p, cond, uncond, cfg_scale, steps):
if shared.sd_model.is_sdxl:
eps = shared.sd_model.model(x_in * c_in, t, {"crossattn": cond_in["c_crossattn"][0]} )
num_classes_hack = shared.sd_model.model.diffusion_model.num_classes
shared.sd_model.model.diffusion_model.num_classes = None
try:
eps = shared.sd_model.model(x_in * c_in, t, {"crossattn": cond_in["c_crossattn"][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)
@ -211,9 +214,19 @@ class Script(scripts.Script):
and self.cache.sigma_adjustment == sigma_adjustment
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
else:
# This prevents a crash, because I don't know how to access the underlying .diffusion_model yet when controlnet is enabled. WIP
# 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])
@ -223,8 +236,6 @@ class Script(scripts.Script):
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)
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)
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)