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Make more intermediate values follow the intermediate dtype. (#13051)

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comfyanonymous 2026-03-18 15:14:18 -07:00 committed by GitHub
parent b67ed2a45f
commit dcd659590f
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2 changed files with 6 additions and 6 deletions
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4
comfy/sample.py
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@ -64,10 +64,10 @@ def sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative
sampler = comfy.samplers.KSampler(model, steps=steps, device=model.load_device, sampler=sampler_name, scheduler=scheduler, denoise=denoise, model_options=model.model_options)
samples = sampler.sample(noise, positive, negative, cfg=cfg, latent_image=latent_image, start_step=start_step, last_step=last_step, force_full_denoise=force_full_denoise, denoise_mask=noise_mask, sigmas=sigmas, callback=callback, disable_pbar=disable_pbar, seed=seed)
samples = samples.to(comfy.model_management.intermediate_device())
samples = samples.to(device=comfy.model_management.intermediate_device(), dtype=comfy.model_management.intermediate_dtype())
return samples
def sample_custom(model, noise, cfg, sampler, sigmas, positive, negative, latent_image, noise_mask=None, callback=None, disable_pbar=False, seed=None):
samples = comfy.samplers.sample(model, noise, positive, negative, cfg, model.load_device, sampler, sigmas, model_options=model.model_options, latent_image=latent_image, denoise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed)
samples = samples.to(comfy.model_management.intermediate_device())
samples = samples.to(device=comfy.model_management.intermediate_device(), dtype=comfy.model_management.intermediate_dtype())
return samples

8
comfy/sd1_clip.py
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@ -46,7 +46,7 @@ class ClipTokenWeightEncoder:
out, pooled = o[:2]
if pooled is not None:
first_pooled = pooled[0:1].to(model_management.intermediate_device())
first_pooled = pooled[0:1].to(device=model_management.intermediate_device(), dtype=model_management.intermediate_dtype())
else:
first_pooled = pooled
@ -63,16 +63,16 @@ class ClipTokenWeightEncoder:
output.append(z)
if (len(output) == 0):
r = (out[-1:].to(model_management.intermediate_device()), first_pooled)
r = (out[-1:].to(device=model_management.intermediate_device(), dtype=model_management.intermediate_dtype()), first_pooled)
else:
r = (torch.cat(output, dim=-2).to(model_management.intermediate_device()), first_pooled)
r = (torch.cat(output, dim=-2).to(device=model_management.intermediate_device(), dtype=model_management.intermediate_dtype()), first_pooled)
if len(o) > 2:
extra = {}
for k in o[2]:
v = o[2][k]
if k == "attention_mask":
v = v[:sections].flatten().unsqueeze(dim=0).to(model_management.intermediate_device())
v = v[:sections].flatten().unsqueeze(dim=0).to(device=model_management.intermediate_device(), dtype=model_management.intermediate_dtype())
extra[k] = v
r = r + (extra,)