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Fix and enhancements for alternate img2img script for stable diffusion XL #16761

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Fix and enhancements for alternate img2img script for stable diffusion XL #16761

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64a8f9d
Update img2imgalt.py
arrmansa Dec 29, 2024
a63cf10
Update img2imgalt.py
arrmansa Dec 30, 2024
6b6396f
speedup, second order correction, intensity control
arrmansa Dec 31, 2024
0b34349
Update img2imgalt.py
arrmansa Jan 1, 2025
84c4aae
Code cleanup (final commit unless I fix controlnet)
arrmansa Jan 1, 2025
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41 changes: 37 additions & 4 deletions scripts/img2imgalt.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,6 +11,10 @@
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

Expand All @@ -30,15 +34,25 @@ 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]}

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
Expand All @@ -64,6 +78,13 @@ def find_noise_for_image(p, cond, uncond, cfg_scale, steps):

# 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])

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x = p.init_latent

s_in = x.new_ones([x.shape[0]])
Expand All @@ -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]}
Expand All @@ -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
Expand Down
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