mirror of
https://github.com/NousResearch/hermes-agent.git
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a7780fe05f
The audit of v4.1 surfaced ~70 issues across the five scripts and three
reference docs — most user-visible (silent file overwrites, status-error
misclassified as success, X-API-Key leaked to S3 on /api/view redirect,
Cloud endpoints that 404 because they were renamed). v5.0.0 fixes those
and fills the gaps that previously forced users to write their own glue
(WebSocket monitoring, batch/sweep, img2img upload helper, dep auto-fix,
log fetch, health check, example workflows).
Critical fixes
- run_workflow.py: poll_status now checks status_str==error BEFORE
completed:true, so a failed run no longer reports success
- run_workflow.py: download_output streams to disk via safe_path_join,
preserves server subfolder structure (no silent overwrites), and
retries with exponential backoff
- run_workflow.py: refuses to overwrite a link with a literal in
inject_params (would silently break wiring)
- _common.py: _StripSensitiveOnRedirectSession (subclasses
requests.Session.rebuild_auth) drops X-API-Key/Cookie on cross-host
redirects — fixes a real key-leak path through Cloud's signed-URL
download flow. Tested
- Cloud routing (verified live): /history → /history_v2,
/models/<f> → /experiment/models/<f>, plus folder aliases for the
unet ↔ diffusion_models and clip ↔ text_encoders rename
- check_deps.py: distinguishes 200/empty vs 404 folder_not_found vs
403 free-tier; emits concrete fix_command per missing dep
- extract_schema.py: prompt vs negative_prompt determined by tracing
KSampler.{positive,negative} connections (incl. through Reroute /
Primitive nodes) instead of meta-title heuristic; symmetric
duplicate-name resolution; cycle-safe trace_to_node
- hardware_check.py: multi-GPU pick-best, Apple variant detection,
Rosetta detection, WSL2, ROCm --json, disk-space check, optional
PyTorch probe; powershell preferred over deprecated wmic
- comfyui_setup.sh: prefers pipx → uvx → pip --user (with PEP-668
fallback); idempotent — skips relaunch if server already up;
configurable port/workspace; persistent log; SIGINT trap
New scripts
- run_batch.py — count or sweep (cartesian product), parallel up to
cloud tier limit
- ws_monitor.py — real-time WebSocket viewer; saves preview frames
- auto_fix_deps.py — runs comfy node install / model download for
whatever check_deps reports missing (with --dry-run)
- health_check.py — single command that runs the verification checklist
(comfy-cli + server + checkpoints + optional smoke test that cancels
itself to avoid burning compute)
- fetch_logs.py — pull traceback / status messages for a prompt_id
Coverage expansion
- Param patterns now cover Flux (BasicScheduler, BasicGuider,
RandomNoise, ModelSamplingFlux), SD3, Wan/Hunyuan/LTX video,
IPAdapter, rgthree, easy-use, AnimateDiff
- Embedding refs in CLIPTextEncode strings extracted as model deps
- ckpt_name / vae_name / lora_name / unet_name now controllable so
workflows can be retargeted per run
Examples
- workflows/{sd15,sdxl,flux_dev}_txt2img.json
- workflows/sdxl_{img2img,inpaint}.json
- workflows/upscale_4x.json
- workflows/{animatediff_video,wan_video_t2v}.json + README
Tests
- 117 tests (105 unit + 8 cloud integration + 4 cross-host security)
- Cloud tests auto-skip without COMFY_CLOUD_API_KEY; verified end-to-end
against live cloud API
Backwards compatibility
- All existing CLI flags continue to work; new behavior is opt-in
(--ws, --input-image, --randomize-seed, --flat-output, etc.)
186 lines
7.8 KiB
Python
186 lines
7.8 KiB
Python
"""Tests for extract_schema.py."""
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from __future__ import annotations
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import pytest
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from extract_schema import (
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extract_schema,
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find_negative_prompt_node,
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find_positive_prompt_node,
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trace_to_node,
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)
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# =============================================================================
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# Connection tracing
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# =============================================================================
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class TestConnectionTracing:
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def test_direct_link(self):
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wf = {
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"1": {"class_type": "CLIPTextEncode", "inputs": {"text": "x"}},
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"2": {"class_type": "KSampler",
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"inputs": {"positive": ["1", 0], "negative": ["1", 0]}},
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}
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assert trace_to_node(wf, ["1", 0]) == "1"
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def test_through_reroute(self):
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wf = {
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"1": {"class_type": "CLIPTextEncode", "inputs": {"text": "x"}},
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"2": {"class_type": "Reroute", "inputs": {"input": ["1", 0]}},
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"3": {"class_type": "Reroute", "inputs": {"input": ["2", 0]}},
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}
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assert trace_to_node(wf, ["3", 0]) == "1"
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def test_circular_safe(self):
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wf = {
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"1": {"class_type": "Reroute", "inputs": {"input": ["2", 0]}},
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"2": {"class_type": "Reroute", "inputs": {"input": ["1", 0]}},
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}
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# Should hit max_hops without infinite loop
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result = trace_to_node(wf, ["1", 0], max_hops=5)
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assert result in ("1", "2") # any node, just don't hang
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class TestPositiveNegativeDetection:
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def test_basic(self, sd15_workflow):
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# In sd15_workflow.json node 6 is positive, node 7 is negative
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assert find_positive_prompt_node(sd15_workflow) == "6"
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assert find_negative_prompt_node(sd15_workflow) == "7"
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def test_swapped_order(self):
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wf = {
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"3": {"class_type": "KSampler",
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"inputs": {
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"positive": ["7", 0], "negative": ["6", 0],
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"model": ["4", 0], "latent_image": ["5", 0],
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"seed": 1, "steps": 20, "cfg": 7.5,
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"sampler_name": "euler", "scheduler": "normal", "denoise": 1.0,
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}},
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"4": {"class_type": "CheckpointLoaderSimple", "inputs": {"ckpt_name": "x"}},
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"5": {"class_type": "EmptyLatentImage", "inputs": {"width": 512, "height": 512, "batch_size": 1}},
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"6": {"class_type": "CLIPTextEncode", "inputs": {"text": "ugly", "clip": ["4", 1]}},
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"7": {"class_type": "CLIPTextEncode", "inputs": {"text": "beautiful", "clip": ["4", 1]}},
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}
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# Now 7 is the positive (despite higher node ID)
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assert find_positive_prompt_node(wf) == "7"
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assert find_negative_prompt_node(wf) == "6"
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# =============================================================================
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# Schema extraction
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# =============================================================================
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class TestExtractSchema:
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def test_basic_sd15(self, sd15_workflow):
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schema = extract_schema(sd15_workflow)
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params = schema["parameters"]
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assert "prompt" in params
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assert "negative_prompt" in params
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assert "seed" in params
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assert "steps" in params
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assert "cfg" in params
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assert "width" in params
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assert "height" in params
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def test_prompt_value_correct(self, sd15_workflow):
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schema = extract_schema(sd15_workflow)
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# The positive prompt in the example is the landscape one
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assert "landscape" in schema["parameters"]["prompt"]["value"]
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assert "ugly" in schema["parameters"]["negative_prompt"]["value"]
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def test_model_dependencies(self, sd15_workflow):
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schema = extract_schema(sd15_workflow)
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deps = schema["model_dependencies"]
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ckpts = [d["value"] for d in deps if d["folder"] == "checkpoints"]
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assert "v1-5-pruned-emaonly.safetensors" in ckpts
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def test_output_nodes(self, sd15_workflow):
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schema = extract_schema(sd15_workflow)
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assert "9" in schema["output_nodes"]
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def test_summary(self, sd15_workflow):
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schema = extract_schema(sd15_workflow)
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s = schema["summary"]
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assert s["has_negative_prompt"] is True
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assert s["has_seed"] is True
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assert s["is_video_workflow"] is False
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assert s["parameter_count"] > 5
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def test_flux_workflow(self, flux_workflow):
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schema = extract_schema(flux_workflow)
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# Flux uses RandomNoise for seed
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assert schema["summary"]["has_seed"] is True
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# Flux has only positive prompt (no negative encoder)
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assert schema["summary"]["has_negative_prompt"] is False
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def test_video_detected(self, video_workflow):
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schema = extract_schema(video_workflow)
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assert schema["summary"]["is_video_workflow"] is True
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class TestEmbeddingDeps:
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def test_extract_from_prompt(self):
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wf = {
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"1": {"class_type": "CheckpointLoaderSimple", "inputs": {"ckpt_name": "x"}},
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"5": {"class_type": "EmptyLatentImage",
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"inputs": {"width": 512, "height": 512, "batch_size": 1}},
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"6": {"class_type": "CLIPTextEncode",
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"inputs": {
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"text": "a cat, embedding:goodvibes, embedding:art:1.2",
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"clip": ["1", 1]
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}},
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"7": {"class_type": "CLIPTextEncode",
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"inputs": {
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"text": "ugly, embedding:badhands",
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"clip": ["1", 1]
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}},
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"3": {"class_type": "KSampler",
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"inputs": {
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"positive": ["6", 0], "negative": ["7", 0],
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"model": ["1", 0], "latent_image": ["5", 0],
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"seed": 1, "steps": 20, "cfg": 7.5,
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"sampler_name": "euler", "scheduler": "normal", "denoise": 1.0,
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}},
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"9": {"class_type": "SaveImage", "inputs": {"filename_prefix": "x", "images": ["3", 0]}},
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}
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schema = extract_schema(wf)
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names = [d["embedding_name"] for d in schema["embedding_dependencies"]]
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assert sorted(names) == ["art", "badhands", "goodvibes"]
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class TestDuplicateDeduplication:
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def test_two_ksamplers_get_unique_names(self):
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wf = {
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"1": {"class_type": "CheckpointLoaderSimple", "inputs": {"ckpt_name": "x"}},
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"5": {"class_type": "EmptyLatentImage",
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"inputs": {"width": 512, "height": 512, "batch_size": 1}},
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"6": {"class_type": "CLIPTextEncode", "inputs": {"text": "a", "clip": ["1", 1]}},
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"7": {"class_type": "CLIPTextEncode", "inputs": {"text": "b", "clip": ["1", 1]}},
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"3": {"class_type": "KSampler",
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"inputs": {
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"positive": ["6", 0], "negative": ["7", 0],
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"model": ["1", 0], "latent_image": ["5", 0],
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"seed": 42, "steps": 20, "cfg": 7.5,
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"sampler_name": "euler", "scheduler": "normal", "denoise": 1.0,
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}},
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"4": {"class_type": "KSampler",
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"inputs": {
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"positive": ["6", 0], "negative": ["7", 0],
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"model": ["1", 0], "latent_image": ["5", 0],
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"seed": 99, "steps": 30, "cfg": 8.0,
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"sampler_name": "euler", "scheduler": "normal", "denoise": 0.6,
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}},
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"9": {"class_type": "SaveImage", "inputs": {"filename_prefix": "x", "images": ["3", 0]}},
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}
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schema = extract_schema(wf)
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params = schema["parameters"]
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# Both seeds present with disambiguated names
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seed_keys = [k for k in params if "seed" in k]
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# Symmetric: both renamed (no bare "seed")
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assert "seed" not in params
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assert "seed_3" in params and "seed_4" in params
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assert params["seed_3"]["value"] == 42
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assert params["seed_4"]["value"] == 99
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