feat(mcp-image-gen): add test suite (19 tests) and Lumen profile pictures

FLUX.1-schnell is a gated model — bare wget returns HTTP 401.

- Replace bare wget with huggingface-cli login + download (Option A)
- Add wget with Authorization header as Option B
- Add license acceptance prerequisite (huggingface.co gated repo)
- Add token creation link (huggingface.co/settings/tokens)
- Add fp8 quantized variant as alternative (~8.1GB, faster inference)
- Add download size note (~8GB, 10-30min)

mcp/mcp-image-gen/USAGE.md

@@ -27,11 +27,11 @@ The MCP server connects to ComfyUI's REST API at `http://localhost:8188`. If Com
 
 ### Install ComfyUI
 
-```bash
-# Option A — pip install (simplest)
-pip install comfyui
+> ⚠️ **ComfyUI is NOT on PyPI** — `pip install comfyui` will fail with "No matching distribution found".
+> It must be installed from source via `git clone`.
 
-# Option B — git clone (more control)
+```bash
+# Clone from source (the only correct installation method)
 git clone https://github.com/comfyanonymous/ComfyUI.git
 cd ComfyUI
 pip install -r requirements.txt
@@ -53,17 +53,48 @@ pip install torch torchvision --index-url https://download.pytorch.org/whl/rocm6
 
 FLUX.1-schnell is the recommended model — fast (4 steps), Apache 2.0 licensed, excellent quality.
 
-```bash
-# Download (~8GB) — place in ComfyUI/models/checkpoints/
-wget https://huggingface.co/black-forest-labs/FLUX.1-schnell/resolve/main/flux1-schnell.safetensors \
-     -O ~/ComfyUI/models/checkpoints/flux1-schnell.safetensors
+> ⚠️ **FLUX.1-schnell is a gated model on HuggingFace.**
+> A bare `wget` on the URL returns HTTP 401. You must:
+> 1. Accept the license at https://huggingface.co/black-forest-labs/FLUX.1-schnell (click **"Agree and access repository"** — one-time)
+> 2. Create a HuggingFace access token with **Read** permissions at https://huggingface.co/settings/tokens
 
-# Or use huggingface_hub:
+#### Option A — `huggingface-cli` (recommended)
+
+```bash
+# Install the HuggingFace Hub CLI
+pip install huggingface_hub
+
+# Log in — paste your Read token when prompted
+huggingface-cli login
+
+# Download (~8GB) directly into ComfyUI checkpoints
 huggingface-cli download black-forest-labs/FLUX.1-schnell \
     flux1-schnell.safetensors \
     --local-dir ~/ComfyUI/models/checkpoints/
 ```
 
+#### Option B — `wget` with Authorization header
+
+```bash
+wget --header="Authorization: Bearer hf_YOUR_TOKEN_HERE" \
+  https://huggingface.co/black-forest-labs/FLUX.1-schnell/resolve/main/flux1-schnell.safetensors \
+  -O ~/ComfyUI/models/checkpoints/flux1-schnell.safetensors
+```
+
+> Replace `hf_YOUR_TOKEN_HERE` with your actual HuggingFace token from https://huggingface.co/settings/tokens
+
+#### Alternative: fp8 quantized variant (~8.1GB, faster inference)
+
+If you want slightly faster inference with near-identical quality, the fp8 quantized version is also available:
+
+```bash
+huggingface-cli download black-forest-labs/FLUX.1-schnell-fp8 \
+    flux1-schnell-fp8.safetensors \
+    --local-dir ~/ComfyUI/models/checkpoints/
+```
+
+> **Download note:** Both variants are ~8GB — expect 10–30 minutes depending on connection speed.
+
 You'll also need the CLIP and VAE models — see the [ComfyUI FLUX guide](https://github.com/comfyanonymous/ComfyUI/blob/master/README.md) for full model list.
 
 ### Start ComfyUI (AMD ROCm)

mcp/mcp-image-gen/src/server.py

@@ -40,7 +40,9 @@ class ComfyUIClient:
 
     async def queue_prompt(self, workflow: dict) -> str:
         """Submit a workflow to ComfyUI and return the prompt_id."""
-        payload = {"prompt": workflow}
+        # Strip internal metadata keys (e.g. "_meta") — they are not ComfyUI nodes
+        clean_workflow = {k: v for k, v in workflow.items() if not k.startswith("_")}
+        payload = {"prompt": clean_workflow}
         async with httpx.AsyncClient(timeout=30.0) as client:
             resp = await client.post(f"{self.base_url}/api/prompt", json=payload)
             resp.raise_for_status()
@@ -115,7 +117,8 @@ def build_flux_workflow(
     wf["27"]["inputs"]["height"] = height
     wf["13"]["inputs"]["steps"] = steps
     wf["13"]["inputs"]["seed"] = actual_seed
-    wf["30"]["inputs"]["ckpt_name"] = model
+    # Node 32 = UNETLoader (flux1-schnell.safetensors is UNet-only, not all-in-one checkpoint)
+    wf["32"]["inputs"]["unet_name"] = model
 
     # Attach the actual seed as metadata so callers can retrieve it
     wf["_meta"] = {"actual_seed": actual_seed}

mcp/mcp-image-gen/src/workflows/flux_schnell.json

@@ -2,7 +2,7 @@
   "6": {
     "class_type": "CLIPTextEncode",
     "inputs": {
-      "clip": ["30", 1],
+      "clip": ["30", 0],
       "text": "PROMPT_PLACEHOLDER"
     }
   },
@@ -10,7 +10,7 @@
     "class_type": "VAEDecode",
     "inputs": {
       "samples": ["13", 0],
-      "vae": ["30", 2]
+      "vae": ["31", 0]
     }
   },
   "9": {
@@ -26,7 +26,7 @@
       "cfg": 1.0,
       "denoise": 1.0,
       "latent_image": ["27", 0],
-      "model": ["30", 0],
+      "model": ["32", 0],
       "negative": ["33", 0],
       "positive": ["6", 0],
       "sampler_name": "euler",
@@ -44,15 +44,31 @@
     }
   },
   "30": {
-    "class_type": "CheckpointLoaderSimple",
+    "class_type": "DualCLIPLoader",
     "inputs": {
-      "ckpt_name": "flux1-schnell.safetensors"
+      "clip_name1": "t5xxl_fp8_e4m3fn.safetensors",
+      "clip_name2": "clip_l.safetensors",
+      "type": "flux",
+      "device": "default"
+    }
+  },
+  "31": {
+    "class_type": "VAELoader",
+    "inputs": {
+      "vae_name": "ae.safetensors"
+    }
+  },
+  "32": {
+    "class_type": "UNETLoader",
+    "inputs": {
+      "unet_name": "flux1-schnell.safetensors",
+      "weight_dtype": "fp8_e4m3fn"
     }
   },
   "33": {
     "class_type": "CLIPTextEncode",
     "inputs": {
-      "clip": ["30", 1],
+      "clip": ["30", 0],
       "text": "NEGATIVE_PLACEHOLDER"
     }
   }

mcp/mcp-image-gen/tests/test_server.py

@@ -44,7 +44,9 @@ def test_build_flux_workflow_structure():
     assert wf["9"]["class_type"] == "SaveImage"
     assert wf["13"]["class_type"] == "KSampler"
     assert wf["27"]["class_type"] == "EmptySD3LatentImage"
-    assert wf["30"]["class_type"] == "CheckpointLoaderSimple"
+    assert wf["30"]["class_type"] == "DualCLIPLoader"
+    assert wf["31"]["class_type"] == "VAELoader"
+    assert wf["32"]["class_type"] == "UNETLoader"
     assert wf["33"]["class_type"] == "CLIPTextEncode"
 
 
@@ -65,7 +67,7 @@ def test_build_flux_workflow_params_injected():
     assert wf["27"]["inputs"]["height"] == 768
     assert wf["13"]["inputs"]["steps"] == 8
     assert wf["13"]["inputs"]["seed"] == 12345
-    assert wf["30"]["inputs"]["ckpt_name"] == "sdxl.safetensors"
+    assert wf["32"]["inputs"]["unet_name"] == "sdxl.safetensors"
 
 
 def test_negative_prompt_included():