lllyasviel/stable-diffusion-webui-forge
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lllyasviel
2024-01-25 20:22:04 -08:00
parent 01c422678c
commit ed495d7286
3 changed files with 32 additions and 7 deletions
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4
modules/sd_models_xl.py
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@@ -38,11 +38,11 @@ def get_learned_conditioning(self: sgm.models.diffusion.DiffusionEngine, batch:
return c return c
def apply_model(self: sgm.models.diffusion.DiffusionEngine, x, t, cond): def apply_model(self: sgm.models.diffusion.DiffusionEngine, x, t, cond, *args, **kwargs):
if self.model.diffusion_model.in_channels == 9: if self.model.diffusion_model.in_channels == 9:
x = torch.cat([x] + cond['c_concat'], dim=1) x = torch.cat([x] + cond['c_concat'], dim=1)
return self.model(x, t, cond) return self.model(x, t, cond, *args, **kwargs)
def get_first_stage_encoding(self, x): # SDXL's encode_first_stage does everything so get_first_stage_encoding is just there for compatibility def get_first_stage_encoding(self, x): # SDXL's encode_first_stage does everything so get_first_stage_encoding is just there for compatibility

18
modules/sd_samplers_cfg_denoiser.py
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@@ -77,10 +77,13 @@ class CFGDenoiser(torch.nn.Module):
if "sampler_cfg_function" in model_options or "sampler_post_cfg_function" in model_options: if "sampler_cfg_function" in model_options or "sampler_post_cfg_function" in model_options:
cond_scale = float(cond_scale) cond_scale = float(cond_scale)
model = self.inner_model.inner_model.forge_objects.unet model = self.inner_model.inner_model.forge_objects.unet.model
x = x_in[-uncond.shape[0]:] x = x_in[-uncond.shape[0]:]
uncond_pred = denoised_uncond uncond_pred = denoised_uncond
cond_pred = ((denoised - uncond_pred) / cond_scale) + uncond_pred cond_pred = ((denoised - uncond_pred) / cond_scale) + uncond_pred
timestep = timestep[-uncond.shape[0]:]
from modules_forge.forge_util import cond_from_a1111_to_patched_ldm
if "sampler_cfg_function" in model_options: if "sampler_cfg_function" in model_options:
args = {"cond": x - cond_pred, "uncond": x - uncond_pred, "cond_scale": cond_scale, args = {"cond": x - cond_pred, "uncond": x - uncond_pred, "cond_scale": cond_scale,
@@ -93,10 +96,15 @@ class CFGDenoiser(torch.nn.Module):
# sanity_check = torch.allclose(cfg_result, denoised) # sanity_check = torch.allclose(cfg_result, denoised)
for fn in model_options.get("sampler_post_cfg_function", []): for fn in model_options.get("sampler_post_cfg_function", []):
args = {"denoised": cfg_result, "cond": cond, args = {"denoised": cfg_result,
"uncond": uncond, "model": model, "cond": cond_from_a1111_to_patched_ldm(cond),
"uncond_denoised": uncond_pred, "cond_denoised": cond_pred, "uncond": cond_from_a1111_to_patched_ldm(uncond),
"sigma": timestep, "model_options": model_options, "input": x} "model": model,
"uncond_denoised": uncond_pred,
"cond_denoised": cond_pred,
"sigma": timestep,
"model_options": model_options,
"input": x}
cfg_result = fn(args) cfg_result = fn(args)
else: else:
cfg_result = denoised cfg_result = denoised

17
modules_forge/forge_util.py Normal file
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@@ -0,0 +1,17 @@
from ldm_patched.modules.conds import CONDRegular, CONDCrossAttn
def cond_from_a1111_to_patched_ldm(cond):
cross_attn = cond['crossattn']
pooled_output = cond['vector']
result = dict(
cross_attn=cross_attn,
pooled_output=pooled_output,
model_conds=dict(
c_crossattn=CONDCrossAttn(cross_attn),
y=CONDRegular(pooled_output)
)
)
return [result, ]