LoRA test

.github/workflows/test.yml

@@ -41,6 +41,10 @@ jobs:
         run: |
           export HSA_OVERRIDE_GFX_VERSION=11.0.0
           pytest -v tests/unit
+      - name: Run lora workflow
+        run: |
+          export HSA_OVERRIDE_GFX_VERSION=11.0.0
+          pytest -v tests/workflows
       - name: Lint for errors
         run: |
           pylint comfy

comfy/client/embedded_comfy_client.py

@@ -130,6 +130,12 @@ class EmbeddedComfyClient:
                 from ..cmd.execution import PromptExecutor, validate_prompt
                 prompt_mut = make_mutable(prompt)
                 validation_tuple = validate_prompt(prompt_mut)
+                if not validation_tuple[0]:
+                    span.set_status(Status(StatusCode.ERROR))
+                    validation_error_dict = validation_tuple[1] or {"message": "Unknown", "details": ""}
+                    error = ValueError("\n".join([validation_error_dict["message"], validation_error_dict["details"]]))
+                    span.record_exception(error)
+                    return {}
 
                 prompt_executor: PromptExecutor = self._prompt_executor
 

comfy/model_downloader.py

@@ -24,7 +24,7 @@ _session = Session()
 
 def get_filename_list_with_downloadable(folder_name: str, known_files: List[Any]) -> List[str]:
     existing = frozenset(folder_paths.get_filename_list(folder_name))
-    downloadable = frozenset() if args.disable_known_models else frozenset(str(f) for f in known_files if not isinstance(f, HuggingFile) or f.show_in_ui)
+    downloadable = frozenset() if args.disable_known_models else frozenset(str(f) for f in known_files)
     return sorted(list(existing | downloadable))
 
 

tests/workflows/lora.py

@@ -0,0 +1,154 @@
+import pytest
+import torch
+
+from comfy import model_management
+from comfy.api.components.schema.prompt import Prompt
+from comfy.model_downloader import add_known_models, KNOWN_LORAS
+from comfy.model_downloader_types import CivitFile
+from comfy.model_management import CPUState
+
+try:
+    has_gpu = torch.device(torch.cuda.current_device()) is not None
+except:
+    has_gpu = False
+
+model_management.cpu_state = CPUState.GPU if has_gpu else CPUState.CPU
+from comfy.client.embedded_comfy_client import EmbeddedComfyClient
+
+
+@pytest.mark.skipif(not has_gpu, reason="requires gpu for performant testing")
+@pytest.mark.asyncio
+async def test_lora_workflow():
+    prompt = Prompt.validate({
+        "3": {
+            "inputs": {
+                "seed": 851616030078638,
+                "steps": 20,
+                "cfg": 8,
+                "sampler_name": "euler",
+                "scheduler": "normal",
+                "denoise": 1,
+                "model": [
+                    "10",
+                    0
+                ],
+                "positive": [
+                    "6",
+                    0
+                ],
+                "negative": [
+                    "7",
+                    0
+                ],
+                "latent_image": [
+                    "5",
+                    0
+                ]
+            },
+            "class_type": "KSampler",
+            "_meta": {
+                "title": "KSampler"
+            }
+        },
+        "4": {
+            "inputs": {
+                "ckpt_name": "v1-5-pruned-emaonly.safetensors"
+            },
+            "class_type": "CheckpointLoaderSimple",
+            "_meta": {
+                "title": "Load Checkpoint"
+            }
+        },
+        "5": {
+            "inputs": {
+                "width": 512,
+                "height": 512,
+                "batch_size": 1
+            },
+            "class_type": "EmptyLatentImage",
+            "_meta": {
+                "title": "Empty Latent Image"
+            }
+        },
+        "6": {
+            "inputs": {
+                "text": "masterpiece best quality girl",
+                "clip": [
+                    "10",
+                    1
+                ]
+            },
+            "class_type": "CLIPTextEncode",
+            "_meta": {
+                "title": "CLIP Text Encode (Prompt)"
+            }
+        },
+        "7": {
+            "inputs": {
+                "text": "bad hands",
+                "clip": [
+                    "10",
+                    1
+                ]
+            },
+            "class_type": "CLIPTextEncode",
+            "_meta": {
+                "title": "CLIP Text Encode (Prompt)"
+            }
+        },
+        "8": {
+            "inputs": {
+                "samples": [
+                    "3",
+                    0
+                ],
+                "vae": [
+                    "4",
+                    2
+                ]
+            },
+            "class_type": "VAEDecode",
+            "_meta": {
+                "title": "VAE Decode"
+            }
+        },
+        "9": {
+            "inputs": {
+                "filename_prefix": "ComfyUI",
+                "images": [
+                    "8",
+                    0
+                ]
+            },
+            "class_type": "SaveImage",
+            "_meta": {
+                "title": "Save Image"
+            }
+        },
+        "10": {
+            "inputs": {
+                "lora_name": "epi_noiseoffset2.safetensors",
+                "strength_model": 1,
+                "strength_clip": 1,
+                "model": [
+                    "4",
+                    0
+                ],
+                "clip": [
+                    "4",
+                    1
+                ]
+            },
+            "class_type": "LoraLoader",
+            "_meta": {
+                "title": "Load LoRA"
+            }
+        }
+    })
+
+    add_known_models("loras", KNOWN_LORAS, CivitFile(13941, 16576, "epi_noiseoffset2.safetensors"))
+    async with EmbeddedComfyClient() as client:
+        outputs = await client.queue_prompt(prompt)
+
+        save_image_node_id = next(key for key in prompt if prompt[key].class_type == "SaveImage")
+        assert outputs[save_image_node_id]["images"][0]["abs_path"] is not None