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Fix SDXL area composition sometimes not using the right pooled output.

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comfyanonymous 2023-12-18 12:54:23 -05:00
parent d2f322902c
commit 8cf1daa108
2 changed files with 14 additions and 3 deletions
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10
comfy/model_base.py
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@ -126,9 +126,15 @@ class BaseModel(torch.nn.Module):
cond_concat.append(blank_inpaint_image_like(noise)) cond_concat.append(blank_inpaint_image_like(noise))
data = torch.cat(cond_concat, dim=1) data = torch.cat(cond_concat, dim=1)
out['c_concat'] = comfy.conds.CONDNoiseShape(data) out['c_concat'] = comfy.conds.CONDNoiseShape(data)
adm = self.encode_adm(**kwargs) adm = self.encode_adm(**kwargs)
if adm is not None: if adm is not None:
out['y'] = comfy.conds.CONDRegular(adm) out['y'] = comfy.conds.CONDRegular(adm)
cross_attn = kwargs.get("cross_attn", None)
if cross_attn is not None:
out['c_crossattn'] = comfy.conds.CONDCrossAttn(cross_attn)
return out return out
def load_model_weights(self, sd, unet_prefix=""): def load_model_weights(self, sd, unet_prefix=""):
@ -322,6 +328,10 @@ class SVD_img2vid(BaseModel):
out['c_concat'] = comfy.conds.CONDNoiseShape(latent_image) out['c_concat'] = comfy.conds.CONDNoiseShape(latent_image)
cross_attn = kwargs.get("cross_attn", None)
if cross_attn is not None:
out['c_crossattn'] = comfy.conds.CONDCrossAttn(cross_attn)
if "time_conditioning" in kwargs: if "time_conditioning" in kwargs:
out["time_context"] = comfy.conds.CONDCrossAttn(kwargs["time_conditioning"]) out["time_context"] = comfy.conds.CONDCrossAttn(kwargs["time_conditioning"])

7
comfy/samplers.py
View File

@ -599,6 +599,10 @@ def sample(model, noise, positive, negative, cfg, device, sampler, sigmas, model
calculate_start_end_timesteps(model, negative) calculate_start_end_timesteps(model, negative)
calculate_start_end_timesteps(model, positive) calculate_start_end_timesteps(model, positive)
if hasattr(model, 'extra_conds'):
positive = encode_model_conds(model.extra_conds, positive, noise, device, "positive", latent_image=latent_image, denoise_mask=denoise_mask)
negative = encode_model_conds(model.extra_conds, negative, noise, device, "negative", latent_image=latent_image, denoise_mask=denoise_mask)
#make sure each cond area has an opposite one with the same area #make sure each cond area has an opposite one with the same area
for c in positive: for c in positive:
create_cond_with_same_area_if_none(negative, c) create_cond_with_same_area_if_none(negative, c)
@ -613,9 +617,6 @@ def sample(model, noise, positive, negative, cfg, device, sampler, sigmas, model
if latent_image is not None: if latent_image is not None:
latent_image = model.process_latent_in(latent_image) latent_image = model.process_latent_in(latent_image)
if hasattr(model, 'extra_conds'):
positive = encode_model_conds(model.extra_conds, positive, noise, device, "positive", latent_image=latent_image, denoise_mask=denoise_mask)
negative = encode_model_conds(model.extra_conds, negative, noise, device, "negative", latent_image=latent_image, denoise_mask=denoise_mask)
extra_args = {"cond":positive, "uncond":negative, "cond_scale": cfg, "model_options": model_options, "seed":seed} extra_args = {"cond":positive, "uncond":negative, "cond_scale": cfg, "model_options": model_options, "seed":seed}