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modules_forge/forge_loader.py

@@ -0,0 +1,42 @@
+import torch
+
+from ldm_patched.modules import model_management
+from ldm_patched.modules import model_detection
+
+from ldm_patched.modules.sd import VAE
+import ldm_patched.modules.model_patcher
+import ldm_patched.modules.utils
+
+
+def load_unet_and_vae(sd):
+    parameters = ldm_patched.modules.utils.calculate_parameters(sd, "model.diffusion_model.")
+    unet_dtype = model_management.unet_dtype(model_params=parameters)
+    load_device = model_management.get_torch_device()
+    manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device)
+
+    model_config = model_detection.model_config_from_unet(sd, "model.diffusion_model.", unet_dtype)
+    model_config.set_manual_cast(manual_cast_dtype)
+
+    if model_config is None:
+        raise RuntimeError("ERROR: Could not detect model type of")
+
+    initial_load_device = model_management.unet_inital_load_device(parameters, unet_dtype)
+    model = model_config.get_model(sd, "model.diffusion_model.", device=initial_load_device)
+    model.load_model_weights(sd, "model.diffusion_model.")
+
+    model_patcher = ldm_patched.modules.model_patcher.ModelPatcher(model,
+                                                                   load_device=load_device,
+                                                                   offload_device=model_management.unet_offload_device(),
+                                                                   current_device=initial_load_device)
+
+    vae_sd = ldm_patched.modules.utils.state_dict_prefix_replace(sd, {"first_stage_model.": ""}, filter_keys=True)
+    vae_sd = model_config.process_vae_state_dict(vae_sd)
+    vae_patcher = VAE(sd=vae_sd)
+
+    return model_patcher, vae_patcher
+
+
+class FakeObject(torch.nn.Module):
+    def __init__(self, *args, **kwargs):
+        super().__init__()
+        return

modules_forge/initialization.py

@@ -0,0 +1,66 @@
+import argparse
+
+
+def initialize_forge():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--no-vram", action="store_true")
+    parser.add_argument("--normal-vram", action="store_true")
+    parser.add_argument("--high-vram", action="store_true")
+    parser.add_argument("--always-vram", action="store_true")
+    vram_args = vars(parser.parse_known_args()[0])
+
+    parser = argparse.ArgumentParser()
+    attn_group = parser.add_mutually_exclusive_group()
+    attn_group.add_argument("--attention-split", action="store_true")
+    attn_group.add_argument("--attention-quad", action="store_true")
+    attn_group.add_argument("--attention-pytorch", action="store_true")
+    parser.add_argument("--disable-xformers", action="store_true")
+    fpte_group = parser.add_mutually_exclusive_group()
+    fpte_group.add_argument("--clip-in-fp8-e4m3fn", action="store_true")
+    fpte_group.add_argument("--clip-in-fp8-e5m2", action="store_true")
+    fpte_group.add_argument("--clip-in-fp16", action="store_true")
+    fpte_group.add_argument("--clip-in-fp32", action="store_true")
+    fp_group = parser.add_mutually_exclusive_group()
+    fp_group.add_argument("--all-in-fp32", action="store_true")
+    fp_group.add_argument("--all-in-fp16", action="store_true")
+    fpunet_group = parser.add_mutually_exclusive_group()
+    fpunet_group.add_argument("--unet-in-bf16", action="store_true")
+    fpunet_group.add_argument("--unet-in-fp16", action="store_true")
+    fpunet_group.add_argument("--unet-in-fp8-e4m3fn", action="store_true")
+    fpunet_group.add_argument("--unet-in-fp8-e5m2", action="store_true")
+    fpvae_group = parser.add_mutually_exclusive_group()
+    fpvae_group.add_argument("--vae-in-fp16", action="store_true")
+    fpvae_group.add_argument("--vae-in-fp32", action="store_true")
+    fpvae_group.add_argument("--vae-in-bf16", action="store_true")
+    other_args = vars(parser.parse_known_args()[0])
+
+    from ldm_patched.modules.args_parser import args
+
+    args.always_cpu = False
+    args.always_gpu = False
+    args.always_high_vram = False
+    args.always_low_vram = False
+    args.always_no_vram = False
+    args.always_offload_from_vram = True
+    args.async_cuda_allocation = False
+    args.disable_async_cuda_allocation = True
+
+    if vram_args['no_vram']:
+        args.always_cpu = True
+
+    if vram_args['always_vram']:
+        args.always_gpu = True
+
+    if vram_args['high_vram']:
+        args.always_offload_from_vram = False
+
+    for k, v in other_args.items():
+        setattr(args, k, v)
+
+    import ldm_patched.modules.model_management as model_management
+    import torch
+
+    device = model_management.get_torch_device()
+    torch.zeros((1, 1)).to(device, torch.float32)
+    model_management.soft_empty_cache()
+    return

modules_forge/ops.py

@@ -0,0 +1,19 @@
+import torch
+import contextlib
+
+
+@contextlib.contextmanager
+def use_patched_ops(operations):
+    op_names = ['Linear', 'Conv2d', 'Conv3d', 'GroupNorm', 'LayerNorm']
+    backups = {op_name: getattr(torch.nn, op_name) for op_name in op_names}
+
+    try:
+        for op_name in op_names:
+            setattr(torch.nn, op_name, getattr(operations, op_name))
+
+        yield
+
+    finally:
+        for op_name in op_names:
+            setattr(torch.nn, op_name, backups[op_name])
+    return