vram

diffsynth/configs/vram_management_module_maps.py

@@ -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",
+    },
 }

diffsynth/core/vram/disk_map.py

@@ -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:

diffsynth/models/z_image_dit.py

@@ -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
 
 

diffsynth/pipelines/z_image.py

@@ -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([])

examples/flux2/model_inference_low_vram/FLUX.2-dev.py

@@ -0,0 +1,28 @@
+from diffsynth.pipelines.flux2_image import Flux2ImagePipeline, ModelConfig
+import torch
+
+
+vram_config = {
+    "offload_dtype": "disk",
+    "offload_device": "disk",
+    "onload_dtype": torch.float8_e4m3fn,
+    "onload_device": "cpu",
+    "preparing_dtype": torch.float8_e4m3fn,
+    "preparing_device": "cuda",
+    "computation_dtype": torch.bfloat16,
+    "computation_device": "cuda",
+}
+pipe = Flux2ImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="black-forest-labs/FLUX.2-dev", origin_file_pattern="text_encoder/*.safetensors", **vram_config),
+        ModelConfig(model_id="black-forest-labs/FLUX.2-dev", origin_file_pattern="transformer/*.safetensors", **vram_config),
+        ModelConfig(model_id="black-forest-labs/FLUX.2-dev", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+    ],
+    tokenizer_config=ModelConfig(model_id="black-forest-labs/FLUX.2-dev", origin_file_pattern="tokenizer/"),
+    vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5,
+)
+prompt = "Realistic macro photograph of a hermit crab using a soda can as its shell, partially emerging from the can, captured with sharp detail and natural colors, on a sunlit beach with soft shadows and a shallow depth of field, with blurred ocean waves in the background. The can has the text `BFL Diffusers` on it and it has a color gradient that start with #FF5733 at the top and transitions to #33FF57 at the bottom."
+image = pipe(prompt, seed=42, rand_device="cuda", num_inference_steps=50)
+image.save("image_FLUX.2-dev.jpg")

examples/z_image/model_inference/Z-Image-Turbo.py

@@ -14,4 +14,4 @@ pipe = ZImagePipeline.from_pretrained(
 )
 prompt = "Young Chinese woman in red Hanfu, intricate embroidery. Impeccable makeup, red floral forehead pattern. Elaborate high bun, golden phoenix headdress, red flowers, beads. Holds round folding fan with lady, trees, bird. Neon lightning-bolt lamp (⚡️), bright yellow glow, above extended left palm. Soft-lit outdoor night background, silhouetted tiered pagoda (西安大雁塔), blurred colorful distant lights."
 image = pipe(prompt=prompt, seed=42, rand_device="cuda")
-image.save("image.jpg")
+image.save("image_Z-Image-Turbo.jpg")

examples/z_image/model_inference_low_vram/Z-Image-Turbo.py

@@ -0,0 +1,38 @@
+from diffsynth.pipelines.z_image import ZImagePipeline, ModelConfig
+import torch
+
+
+vram_config = {
+    "offload_dtype": "disk",
+    "offload_device": "disk",
+    "onload_dtype": torch.float8_e4m3fn,
+    "onload_device": "cpu",
+    "preparing_dtype": torch.float8_e4m3fn,
+    "preparing_device": "cuda",
+    "computation_dtype": torch.bfloat16,
+    "computation_device": "cuda",
+}
+vram_config_cpu_offload = {
+    "offload_dtype": torch.float8_e4m3fn,
+    "offload_device": "cpu",
+    "onload_dtype": torch.float8_e4m3fn,
+    "onload_device": "cpu",
+    "preparing_dtype": torch.float8_e4m3fn,
+    "preparing_device": "cuda",
+    "computation_dtype": torch.bfloat16,
+    "computation_device": "cuda",
+}
+pipe = ZImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="transformer/*.safetensors", **vram_config),
+        ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="text_encoder/*.safetensors", **vram_config_cpu_offload),
+        ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", **vram_config),
+    ],
+    tokenizer_config=ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="tokenizer/"),
+    vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5,
+)
+prompt = "Young Chinese woman in red Hanfu, intricate embroidery. Impeccable makeup, red floral forehead pattern. Elaborate high bun, golden phoenix headdress, red flowers, beads. Holds round folding fan with lady, trees, bird. Neon lightning-bolt lamp (⚡️), bright yellow glow, above extended left palm. Soft-lit outdoor night background, silhouetted tiered pagoda (西安大雁塔), blurred colorful distant lights."
+image = pipe(prompt=prompt, seed=42, rand_device="cuda")
+image.save("image_Z-Image-Turbo.jpg")