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vram

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Artiprocher
2025-11-30 20:03:14 +08:00
parent 20cf2317e0
commit 9048d2e9d4
7 changed files with 91 additions and 3 deletions
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20
diffsynth/configs/vram_management_module_maps.py
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@@ -155,4 +155,24 @@ VRAM_MANAGEMENT_MODULE_MAPS = {
"torch.nn.LayerNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
"torch.nn.RMSNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
},
"diffsynth.models.flux2_text_encoder.Flux2TextEncoder": {
"torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
"torch.nn.Conv2d": "diffsynth.core.vram.layers.AutoWrappedModule",
"torch.nn.Embedding": "diffsynth.core.vram.layers.AutoWrappedModule",
"transformers.models.mistral.modeling_mistral.MistralRMSNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
},
"diffsynth.models.flux2_vae.Flux2VAE": {
"torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
"torch.nn.Conv2d": "diffsynth.core.vram.layers.AutoWrappedModule",
"torch.nn.GroupNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
},
"diffsynth.models.z_image_text_encoder.ZImageTextEncoder": {
"torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
"transformers.models.qwen3.modeling_qwen3.Qwen3RMSNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
"torch.nn.Embedding": "diffsynth.core.vram.layers.AutoWrappedModule",
},
"diffsynth.models.z_image_dit.ZImageDiT": {
"torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
"diffsynth.models.z_image_dit.RMSNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
},
}

2
diffsynth/core/vram/disk_map.py
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@@ -62,6 +62,8 @@ class DiskMap:
param = self.files[file_id].get_tensor(name)
if self.torch_dtype is not None and isinstance(param, torch.Tensor):
param = param.to(self.torch_dtype)
if param.device == "cpu":
param = param.clone()
if isinstance(param, torch.Tensor):
self.num_params += param.numel()
if self.num_params > self.buffer_size:

2
diffsynth/models/z_image_dit.py
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@@ -51,7 +51,7 @@ class TimestepEmbedder(nn.Module):
def forward(self, t):
t_freq = self.timestep_embedding(t, self.frequency_embedding_size)
t_emb = self.mlp(t_freq.to(self.mlp[0].weight.dtype))
t_emb = self.mlp(t_freq.to(torch.bfloat16))
return t_emb

2
diffsynth/pipelines/z_image.py
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@@ -119,7 +119,7 @@ class ZImagePipeline(BasePipeline):
inputs_shared["latents"] = self.step(self.scheduler, progress_id=progress_id, noise_pred=noise_pred, **inputs_shared)
# Decode
self.load_models_to_device(['vae'])
self.load_models_to_device(['vae_decoder'])
image = self.vae_decoder(inputs_shared["latents"])
image = self.vae_output_to_image(image)
self.load_models_to_device([])