move to new backend - part 2

modules_forge/diffusers_patcher.py

@@ -1,22 +1,21 @@
 import torch
-import ldm_patched.modules.ops as ops
+from backend import operations, memory_management
+from backend.patcher.base import ModelPatcher
 
-from ldm_patched.modules.model_patcher import ModelPatcher
-from ldm_patched.modules import model_management
 from transformers import modeling_utils
 
 
 class DiffusersModelPatcher:
     def __init__(self, pipeline_class, dtype=torch.float16, *args, **kwargs):
-        load_device = model_management.get_torch_device()
+        load_device = memory_management.get_torch_device()
         offload_device = torch.device("cpu")
 
-        if not model_management.should_use_fp16(device=load_device):
+        if not memory_management.should_use_fp16(device=load_device):
             dtype = torch.float32
 
         self.dtype = dtype
 
-        with ops.use_patched_ops(ops.manual_cast):
+        with operations.using_forge_operations():
             with modeling_utils.no_init_weights():
                 self.pipeline = pipeline_class.from_pretrained(*args, **kwargs)
 
@@ -41,7 +40,7 @@ class DiffusersModelPatcher:
     def prepare_memory_before_sampling(self, batchsize, latent_width, latent_height):
         area = 2 * batchsize * latent_width * latent_height
         inference_memory = (((area * 0.6) / 0.9) + 1024) * (1024 * 1024)
-        model_management.load_models_gpu(
+        memory_management.load_models_gpu(
             models=[self.patcher],
             memory_required=inference_memory
         )

modules_forge/forge_alter_samplers.py

@@ -1,22 +1,23 @@
-from modules import sd_samplers_kdiffusion, sd_samplers_common
-from ldm_patched.k_diffusion import sampling as k_diffusion_sampling
-
-
-class AlterSampler(sd_samplers_kdiffusion.KDiffusionSampler):
-    def __init__(self, sd_model, sampler_name):
-        self.sampler_name = sampler_name
-        self.unet = sd_model.forge_objects.unet
-        sampler_function = getattr(k_diffusion_sampling, "sample_{}".format(sampler_name))
-        super().__init__(sampler_function, sd_model, None)
-
-
-def build_constructor(sampler_name):
-    def constructor(m):
-        return AlterSampler(m, sampler_name)
-
-    return constructor
-
+# from modules import sd_samplers_kdiffusion, sd_samplers_common
+# from ldm_patched.k_diffusion import sampling as k_diffusion_sampling
+#
+#
+# class AlterSampler(sd_samplers_kdiffusion.KDiffusionSampler):
+#     def __init__(self, sd_model, sampler_name):
+#         self.sampler_name = sampler_name
+#         self.unet = sd_model.forge_objects.unet
+#         sampler_function = getattr(k_diffusion_sampling, "sample_{}".format(sampler_name))
+#         super().__init__(sampler_function, sd_model, None)
+#
+#
+# def build_constructor(sampler_name):
+#     def constructor(m):
+#         return AlterSampler(m, sampler_name)
+#
+#     return constructor
+#
+#
 
 samplers_data_alter = [
-    sd_samplers_common.SamplerData('DDPM', build_constructor(sampler_name='ddpm'), ['ddpm'], {}),
+    # sd_samplers_common.SamplerData('DDPM', build_constructor(sampler_name='ddpm'), ['ddpm'], {}),
 ]

modules_forge/forge_clip.py

@@ -1,5 +1,5 @@
 from modules.sd_hijack_clip import FrozenCLIPEmbedderWithCustomWords
-from ldm_patched.modules import model_management
+from backend import memory_management
 from modules import sd_models
 from modules.shared import opts
 
@@ -9,7 +9,7 @@ def move_clip_to_gpu():
         print('Error: CLIP called before SD is loaded!')
         return
 
-    model_management.load_model_gpu(sd_models.model_data.sd_model.forge_objects.clip.patcher)
+    memory_management.load_model_gpu(sd_models.model_data.sd_model.forge_objects.clip.patcher)
     return
 
 

modules_forge/forge_loader.py

@@ -1,15 +1,10 @@
 import torch
 import contextlib
 
-from ldm_patched.modules import model_management
-from ldm_patched.modules import model_detection
-
-from ldm_patched.modules.sd import VAE, load_model_weights
+from backend import memory_management, utils
 from backend.patcher.clip import CLIP
 from backend.patcher.vae import VAE
-import ldm_patched.modules.model_patcher
-import ldm_patched.modules.utils
-import ldm_patched.modules.clip_vision
+from backend.patcher.base import ModelPatcher
 import backend.nn.unet
 
 from omegaconf import OmegaConf
@@ -20,7 +15,6 @@ from modules.sd_models_xl import extend_sdxl
 from ldm.util import instantiate_from_config
 from modules_forge import forge_clip
 from modules_forge.unet_patcher import UnetPatcher
-from ldm_patched.modules.model_base import model_sampling, ModelType
 from backend.loader import load_huggingface_components
 from backend.modules.k_model import KModel
 
@@ -85,13 +79,13 @@ def load_checkpoint_guess_config(sd, output_vae=True, output_clip=True, output_c
     model_patcher = None
     clip_target = None
 
-    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)
+    parameters = utils.calculate_parameters(sd, "model.diffusion_model.")
+    unet_dtype = memory_management.unet_dtype(model_params=parameters)
+    load_device = memory_management.get_torch_device()
+    manual_cast_dtype = memory_management.unet_manual_cast(unet_dtype, load_device)
     manual_cast_dtype = unet_dtype if manual_cast_dtype is None else manual_cast_dtype
 
-    initial_load_device = model_management.unet_inital_load_device(parameters, unet_dtype)
+    initial_load_device = memory_management.unet_inital_load_device(parameters, unet_dtype)
     backend.nn.unet.unet_initial_device = initial_load_device
     backend.nn.unet.unet_initial_dtype = unet_dtype
 
@@ -101,7 +95,7 @@ def load_checkpoint_guess_config(sd, output_vae=True, output_clip=True, output_c
         k_model = KModel(huggingface_components, storage_dtype=unet_dtype, computation_dtype=manual_cast_dtype)
         k_model.to(device=initial_load_device, dtype=unet_dtype)
         model_patcher = UnetPatcher(k_model, load_device=load_device,
-                                    offload_device=model_management.unet_offload_device(),
+                                    offload_device=memory_management.unet_offload_device(),
                                     current_device=initial_load_device)
 
     if output_vae:

modules_forge/forge_util.py

@@ -6,17 +6,17 @@ import random
 import string
 import cv2
 
-from ldm_patched.modules import model_management
+from backend import memory_management
 
 
 def prepare_free_memory(aggressive=False):
     if aggressive:
-        model_management.unload_all_models()
+        memory_management.unload_all_models()
         print('Cleanup all memory.')
         return
 
-    model_management.free_memory(memory_required=model_management.minimum_inference_memory(),
-                                 device=model_management.get_torch_device())
+    memory_management.free_memory(memory_required=memory_management.minimum_inference_memory(),
+                                 device=memory_management.get_torch_device())
     print('Cleanup minimal inference memory.')
     return
 
@@ -151,6 +151,6 @@ def resize_image_with_pad(img, resolution):
 
 
 def lazy_memory_management(model):
-    required_memory = model_management.module_size(model) + model_management.minimum_inference_memory()
-    model_management.free_memory(required_memory, device=model_management.get_torch_device())
+    required_memory = memory_management.module_size(model) + memory_management.minimum_inference_memory()
+    memory_management.free_memory(required_memory, device=memory_management.get_torch_device())
     return

modules_forge/initialization.py

@@ -35,15 +35,13 @@ def initialize_forge():
             print(f'In extreme cases, if you want to force previous lowvram/medvram behaviors, '
                   f'please use --always-offload-from-vram')
 
-    from ldm_patched.modules import args_parser
+    from backend.args import args
 
-    args_parser.args, _ = args_parser.parser.parse_known_args()
+    if args.gpu_device_id is not None:
+        os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu_device_id)
+        print("Set device to:", args.gpu_device_id)
 
-    if args_parser.args.gpu_device_id is not None:
-        os.environ['CUDA_VISIBLE_DEVICES'] = str(args_parser.args.gpu_device_id)
-        print("Set device to:", args_parser.args.gpu_device_id)
-
-    if args_parser.args.cuda_malloc:
+    if args.cuda_malloc:
         from modules_forge.cuda_malloc import try_cuda_malloc
         try_cuda_malloc()
 

modules_forge/ops.py

@@ -1,15 +1,15 @@
 import time
 import torch
 import contextlib
-from ldm_patched.modules import model_management
-from ldm_patched.modules.ops import use_patched_ops
+
+from backend import memory_management
 
 
 @contextlib.contextmanager
 def automatic_memory_management():
-    model_management.free_memory(
+    memory_management.free_memory(
         memory_required=3 * 1024 * 1024 * 1024,
-        device=model_management.get_torch_device()
+        device=memory_management.get_torch_device()
     )
 
     module_list = []
@@ -39,7 +39,7 @@ def automatic_memory_management():
     for module in module_list:
         module.cpu()
 
-    model_management.soft_empty_cache()
+    memory_management.soft_empty_cache()
     end = time.perf_counter()
 
     print(f'Automatic Memory Management: {len(module_list)} Modules in {(end - start):.2f} seconds.')

modules_forge/shared.py

@@ -1,7 +1,7 @@
 import os
-import ldm_patched.modules.utils
 import argparse
 
+from backend import utils
 from modules.paths_internal import models_path
 from pathlib import Path
 
@@ -57,7 +57,7 @@ def add_supported_control_model(control_model):
 
 def try_load_supported_control_model(ckpt_path):
     global supported_control_models
-    state_dict = ldm_patched.modules.utils.load_torch_file(ckpt_path, safe_load=True)
+    state_dict = utils.load_torch_file(ckpt_path, safe_load=True)
     for supported_type in supported_control_models:
         state_dict_copy = {k: v for k, v in state_dict.items()}
         model = supported_type.try_build_from_state_dict(state_dict_copy, ckpt_path)

modules_forge/supported_preprocessor.py

@@ -2,10 +2,10 @@ import cv2
 import torch
 
 from modules_forge.shared import add_supported_preprocessor, preprocessor_dir
-from ldm_patched.modules import model_management
-from ldm_patched.modules.model_patcher import ModelPatcher
+from backend import memory_management
+from backend.patcher.base import ModelPatcher
+from backend.patcher import clipvision
 from modules_forge.forge_util import resize_image_with_pad
-import ldm_patched.modules.clip_vision
 from modules.modelloader import load_file_from_url
 from modules_forge.forge_util import numpy_to_pytorch
 
@@ -37,12 +37,12 @@ class Preprocessor:
 
     def setup_model_patcher(self, model, load_device=None, offload_device=None, dtype=torch.float32, **kwargs):
         if load_device is None:
-            load_device = model_management.get_torch_device()
+            load_device = memory_management.get_torch_device()
 
         if offload_device is None:
             offload_device = torch.device('cpu')
 
-        if not model_management.should_use_fp16(load_device):
+        if not memory_management.should_use_fp16(load_device):
             dtype = torch.float32
 
         model.eval()
@@ -53,7 +53,7 @@ class Preprocessor:
         return self.model_patcher
 
     def move_all_model_patchers_to_gpu(self):
-        model_management.load_models_gpu([self.model_patcher])
+        memory_management.load_models_gpu([self.model_patcher])
         return
 
     def send_tensor_to_model_device(self, x):
@@ -127,7 +127,7 @@ class PreprocessorClipVision(Preprocessor):
         if ckpt_path in PreprocessorClipVision.global_cache:
             self.clipvision = PreprocessorClipVision.global_cache[ckpt_path]
         else:
-            self.clipvision = ldm_patched.modules.clip_vision.load(ckpt_path)
+            self.clipvision = clipvision.load(ckpt_path)
             PreprocessorClipVision.global_cache[ckpt_path] = self.clipvision
 
         return self.clipvision