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Merge branch 'comfyanonymous:master' into master

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patientx 2024-09-28 22:28:25 +03:00 committed by GitHub
commit 9a0f99ba71
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2 changed files with 7 additions and 3 deletions
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4
comfy/ldm/flux/model.py
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@ -151,8 +151,8 @@ class Flux(nn.Module):
h_len = ((h + (patch_size // 2)) // patch_size) h_len = ((h + (patch_size // 2)) // patch_size)
w_len = ((w + (patch_size // 2)) // patch_size) w_len = ((w + (patch_size // 2)) // patch_size)
img_ids = torch.zeros((h_len, w_len, 3), device=x.device, dtype=x.dtype) img_ids = torch.zeros((h_len, w_len, 3), device=x.device, dtype=x.dtype)
img_ids[..., 1] = img_ids[..., 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype)[:, None] img_ids[:, :, 1] = torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype).unsqueeze(1)
img_ids[..., 2] = img_ids[..., 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype)[None, :] img_ids[:, :, 2] = torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype).unsqueeze(0)
img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs) img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs)
txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype) txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype)

6
comfy/model_patcher.py
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@ -88,8 +88,12 @@ class LowVramPatch:
self.key = key self.key = key
self.patches = patches self.patches = patches
def __call__(self, weight): def __call__(self, weight):
return comfy.lora.calculate_weight(self.patches[self.key], weight, self.key, intermediate_dtype=weight.dtype) intermediate_dtype = weight.dtype
if intermediate_dtype not in [torch.float32, torch.float16, torch.bfloat16]: #intermediate_dtype has to be one that is supported in math ops
intermediate_dtype = torch.float32
return comfy.float.stochastic_rounding(comfy.lora.calculate_weight(self.patches[self.key], weight.to(intermediate_dtype), self.key, intermediate_dtype=intermediate_dtype), weight.dtype, seed=string_to_seed(self.key))
return comfy.lora.calculate_weight(self.patches[self.key], weight, self.key, intermediate_dtype=intermediate_dtype)
class ModelPatcher: class ModelPatcher:
def __init__(self, model, load_device, offload_device, size=0, weight_inplace_update=False): def __init__(self, model, load_device, offload_device, size=0, weight_inplace_update=False):
self.size = size self.size = size