initial commit

.gitignore

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+.idea/
+
+training/
+lightning_logs/
+image_log/
+
+*.pth
+*.ckpt
+*.safetensors
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
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+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
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+.ropeproject
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+/site
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+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+
+*.safetensors
+*.ckpt
+
+checkpoints

README.md

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+# ComfyUI nodes to use Lotus depth/normal prediction
+
+Original repo: https://github.com/EnVision-Research/Lotus

__init__.py

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+from .nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
+
+__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"]

configs/lotus_unet_config.json

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+{
+    "_class_name": "UNet2DConditionModel",
+    "_diffusers_version": "0.28.0.dev0",
+    "_name_or_path": "../Lotus-weights/lotus-depth-d-v1-1/unet",
+    "act_fn": "silu",
+    "addition_embed_type": null,
+    "addition_embed_type_num_heads": 64,
+    "addition_time_embed_dim": null,
+    "attention_head_dim": [
+      5,
+      10,
+      20,
+      20
+    ],
+    "attention_type": "default",
+    "block_out_channels": [
+      320,
+      640,
+      1280,
+      1280
+    ],
+    "center_input_sample": false,
+    "class_embed_type": "projection",
+    "class_embeddings_concat": false,
+    "conv_in_kernel": 3,
+    "conv_out_kernel": 3,
+    "cross_attention_dim": 1024,
+    "cross_attention_norm": null,
+    "down_block_types": [
+      "CrossAttnDownBlock2D",
+      "CrossAttnDownBlock2D",
+      "CrossAttnDownBlock2D",
+      "DownBlock2D"
+    ],
+    "downsample_padding": 1,
+    "dropout": 0.0,
+    "dual_cross_attention": false,
+    "encoder_hid_dim": null,
+    "encoder_hid_dim_type": null,
+    "flip_sin_to_cos": true,
+    "freq_shift": 0,
+    "in_channels": 4,
+    "layers_per_block": 2,
+    "mid_block_only_cross_attention": null,
+    "mid_block_scale_factor": 1,
+    "mid_block_type": "UNetMidBlock2DCrossAttn",
+    "norm_eps": 1e-05,
+    "norm_num_groups": 32,
+    "num_attention_heads": null,
+    "num_class_embeds": null,
+    "only_cross_attention": false,
+    "out_channels": 4,
+    "projection_class_embeddings_input_dim": 4,
+    "resnet_out_scale_factor": 1.0,
+    "resnet_skip_time_act": false,
+    "resnet_time_scale_shift": "default",
+    "reverse_transformer_layers_per_block": null,
+    "sample_size": 64,
+    "time_cond_proj_dim": null,
+    "time_embedding_act_fn": null,
+    "time_embedding_dim": null,
+    "time_embedding_type": "positional",
+    "timestep_post_act": null,
+    "transformer_layers_per_block": 1,
+    "up_block_types": [
+      "UpBlock2D",
+      "CrossAttnUpBlock2D",
+      "CrossAttnUpBlock2D",
+      "CrossAttnUpBlock2D"
+    ],
+    "upcast_attention": false,
+    "use_linear_projection": true
+  }

nodes.py

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+import os
+import torch
+import folder_paths
+import comfy.model_management as mm
+from comfy.utils import load_torch_file, ProgressBar
+
+import logging
+import json
+from diffusers.models import UNet2DConditionModel
+
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+log = logging.getLogger(__name__)
+
+script_directory = os.path.dirname(os.path.abspath(__file__))
+
+class LoadLotusModel:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": (folder_paths.get_filename_list("diffusion_models"), ),
+            },
+            "optional": {
+                "precision": (["fp16", "fp32", "bf16"],
+                    {"default": "fp16"}
+                ),
+            }
+        }
+
+    RETURN_TYPES = ("LOTUSUNET",)
+    RETURN_NAMES = ("lotus_unet", )
+    FUNCTION = "loadmodel"
+    CATEGORY = "ComfyUI-Lotus"
+
+    def loadmodel(self, model, precision):
+        
+        dtype = {"bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}[precision]
+        mm.soft_empty_cache()
+
+        lotus_model_path = folder_paths.get_full_path_or_raise("diffusion_models", model)
+        
+        lotus_sd = load_torch_file(lotus_model_path)
+        in_channels = lotus_sd['conv_in.weight'].shape[1]
+        lotus_config = os.path.join(script_directory, "configs", "lotus_unet_config.json")
+
+        with open(lotus_config, 'r') as config_file:
+           config_data = json.load(config_file)
+        config_data["in_channels"] = in_channels
+        
+        lotus_unet = UNet2DConditionModel.from_config(config_data)
+        
+        lotus_unet.load_state_dict(lotus_sd)
+        lotus_unet.to(dtype)
+
+        lotus_model = {
+            "model": lotus_unet,
+            "dtype": dtype,
+            "in_channels": in_channels,
+        }
+
+        return (lotus_model,)
+    
+class LotusSampler:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "lotus_unet": ("LOTUSUNET",),
+                "samples": ("LATENT",),
+                "seed": ("INT", {"default": 123, "min": 0, "max": 2**32, "step": 1}),
+                "per_batch": ("INT", {"default": 4, "min": 1, "max": 4096, "step": 1}),
+                "keep_model_loaded": ("BOOLEAN", {"default": False}),
+            },
+        }
+
+    RETURN_TYPES = ("LATENT",)
+    RETURN_NAMES = ("samples",)
+    FUNCTION = "loadmodel"
+    CATEGORY = "ComfyUI-Lotus"
+
+    def loadmodel(self, lotus_unet, seed, samples, per_batch, keep_model_loaded):
+        
+        device = mm.get_torch_device()
+        offload_device = mm.unet_offload_device()
+        mm.soft_empty_cache()
+
+        model = lotus_unet["model"]
+        dtype = lotus_unet["dtype"]
+        in_channels = lotus_unet["in_channels"]
+
+        latents = samples["samples"].to(dtype)
+        latents = latents * 0.18215
+
+        torch.manual_seed(seed)
+        torch.cuda.manual_seed(seed)
+
+        if in_channels == 8:  # input for g model is 8 channels
+            single_noise = torch.randn(latents.shape[1:], device=torch.device("cpu"), dtype=dtype, layout=torch.strided)
+            repeated_noise = single_noise.unsqueeze(0).repeat(latents.shape[0], 1, 1, 1)
+            latents = torch.cat([latents, repeated_noise], dim=1)
+
+        timesteps = torch.tensor(999, device=device).long()
+
+        task_emb = torch.tensor([1, 0], device=device, dtype=dtype).unsqueeze(0).repeat(1, 1)
+        task_emb = torch.cat([torch.sin(task_emb), torch.cos(task_emb)], dim=-1).repeat(1, 1)
+
+        prompt_embeds = torch.load(os.path.join(script_directory, "empty_text_embed.pt"), weights_only=True).to(device).to(dtype)
+        extended_prompt_embeds = prompt_embeds.repeat(latents.shape[0], 1, 1)
+
+        model.to(device)
+        pbar = ProgressBar(latents.shape[0])
+
+        results = []
+        for start_idx in range(0, latents.shape[0], per_batch):
+            
+            sub_images = model(
+                latents[start_idx:start_idx+per_batch].to(device),
+                timesteps,
+                encoder_hidden_states=extended_prompt_embeds[start_idx:start_idx+per_batch],
+                cross_attention_kwargs=None,
+                return_dict=False,
+                class_labels=task_emb,
+            )[0]
+
+            results.append(sub_images.cpu())
+            batch_count = sub_images.shape[0]
+            pbar.update(batch_count)
+
+        if not keep_model_loaded:
+            model.to(offload_device)
+            mm.soft_empty_cache()
+
+        results = torch.cat(results, dim=0)
+        results = results / 0.18215
+
+        return {"samples": results},
+    
+
+NODE_CLASS_MAPPINGS = {
+    "LoadLotusModel": LoadLotusModel,
+    "LotusSampler": LotusSampler,
+}
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "LoadLotusModel": "Load Lotus Model",
+    "LotusSampler": "Lotus Sampler",
+    }

requirements.txt

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+diffusers