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Update img2imgalt.py

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Fix with documentation
This commit is contained in:
arrmansa 2024-12-30 04:14:50 +05:30
parent 82a973c043
commit 64a8f9d1b1
1 changed files with 37 additions and 4 deletions
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scripts/img2imgalt.py
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@ -11,6 +11,10 @@ from modules import processing, shared, sd_samplers, sd_samplers_common
import torch
import k_diffusion as K
# 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
def find_noise_for_image(p, cond, uncond, cfg_scale, steps):
x = p.init_latent
@ -30,7 +34,13 @@ 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])
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])
image_conditioning = torch.cat([p.image_conditioning] * 2)
cond_in = {"c_concat": [image_conditioning], "c_crossattn": [cond_in]}
@ -38,7 +48,11 @@ def find_noise_for_image(p, cond, uncond, cfg_scale, steps):
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)
if shared.sd_model.is_sdxl:
eps = shared.sd_model.model(x_in * c_in, t, {"crossattn": cond_in["c_crossattn"][0]} )
else:
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
@ -64,6 +78,13 @@ 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]])
@ -82,7 +103,14 @@ def find_noise_for_image_sigma_adjustment(p, cond, uncond, cfg_scale, steps):
x_in = torch.cat([x] * 2)
sigma_in = torch.cat([sigmas[i - 1] * s_in] * 2)
cond_in = torch.cat([uncond, cond])
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])
image_conditioning = torch.cat([p.image_conditioning] * 2)
cond_in = {"c_concat": [image_conditioning], "c_crossattn": [cond_in]}
@ -94,7 +122,12 @@ def find_noise_for_image_sigma_adjustment(p, cond, uncond, cfg_scale, steps):
else:
t = dnw.sigma_to_t(sigma_in)
eps = shared.sd_model.apply_model(x_in * c_in, t, cond=cond_in)
if shared.sd_model.is_sdxl:
eps = shared.sd_model.model(x_in * c_in, t, {"crossattn": cond_in["c_crossattn"][0]} )
else:
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