Add ComfyUI workflow library + 12-app registry (Comfy Apps backend)
- comfy_workflows_lib.py: FLUX/SDXL builders, submit/fetch, GPU free/restore - comfy_apps.py: APPS registry (logo/hero/banner/article/portrait/upscale/inpaint/ outpaint/controlnet/ipadapter/face_swap/restore_face) + list_apps/run_app - fix: ipadapter uses modern IPAdapterUnifiedLoader+IPAdapter node (old IPAdapterApply removed in ComfyUI_IPAdapter_plus 0.33.x)
This commit is contained in:
drjones
291
core/comfy_workflows_lib.py
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291
core/comfy_workflows_lib.py
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#!/usr/bin/env python3
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"""
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comfy_workflows_lib.py — custom ComfyUI workflow library for drjones' website art.
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Replicates the UmeAiRT pipeline capabilities (txt2img / img2img / inpaint / outpaint /
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controlnet / lora / upscale) using native ComfyUI nodes + locally-installed models.
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Models on nightmare (~/ComfyUI/models):
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- SDXL checkpoints: juggernautXL_ragnarok, realvisxlV50 (Lightning), dreamshaperXL (Lightning)
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- FLUX: flux1-dev (bf16, load fp8), flux1-schnell-fp8, flux1-fill-dev-fp8 (NEW)
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- SD3.5: sd3.5_large_fp8_scaled
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- text encoders: clip_l.safetensors, clip_g.safetensors, t5xxl_fp8_e4m3fn.safetensors
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- vae: ae.safetensors (FLUX), baked for SDXL
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- upscalers: RealESRGAN_x4, 4x-UltraSharp, 4x-AnimeSharp
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- controlnet: Shakker-Labs-ControlNet-Union-Pro (NEW)
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- loras: Dark_Tarot, FluxDFaeTasticDetails, cptrt-step00002000
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"""
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import json, urllib.request, urllib.error, urllib.parse, time, os, random
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COMFY = "http://10.30.20.128:8188"
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HYPERSWAP = "http://10.30.20.128:9090"
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# ---------------------------------------------------------------------------
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# model constants
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# ---------------------------------------------------------------------------
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FLUX_DEV = "flux1-dev.safetensors" # bf16, loaded as fp8_e4m3fn
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FLUX_FILL = "flux1-fill-dev-fp8.safetensors" # NEW — inpaint/outpaint
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FLUX_SCHNELL = "flux1-schnell-fp8.safetensors"
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SD35 = "sd3.5_large_fp8_scaled.safetensors"
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CLIP_L = "clip_l.safetensors"
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CLIP_G = "clip_g.safetensors"
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T5XXL_FP8 = "t5xxl_fp8_e4m3fn.safetensors"
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FLUX_VAE = "ae.safetensors"
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SDXL_CHECKPOINTS = {
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"juggernaut": "juggernautXL_ragnarok.safetensors",
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"realvis": "realvisxlV50_v50LightningBakedvae.safetensors",
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"dreamshaper": "dreamshaperXL_lightningDPMSDE.safetensors",
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}
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UPSCALERS = ["4x-UltraSharp.pth", "RealESRGAN_x4.pth", "4x-AnimeSharp.pth"]
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LORAS = ["Dark_Tarot.safetensors", "FluxDFaeTasticDetails.safetensors", "cptrt-step00002000.safetensors"]
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CONTROLNET_UNION = "Shakker-Labs-ControlNet-Union-Pro/diffusion_pytorch_model.safetensors"
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# ---------------------------------------------------------------------------
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# API helpers
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# ---------------------------------------------------------------------------
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def _post(path, payload=None, raw=False):
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url = COMFY + path
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data = None
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headers = {}
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if payload is not None:
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if isinstance(payload, (dict, list)):
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data = json.dumps(payload).encode()
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headers["Content-Type"] = "application/json"
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else:
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data = payload
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req = urllib.request.Request(url, data=data, headers=headers)
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try:
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r = urllib.request.urlopen(req, timeout=60)
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b = r.read()
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return b if raw else json.loads(b)
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except urllib.error.HTTPError as e:
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return json.loads(e.read()) if not raw else e.read()
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except Exception as e:
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return {"error": str(e)}
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def upload_image(local_path, name=None, subfolder="", overwrite=True):
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"""Upload an image to ComfyUI's input/ dir so LoadImage can find it."""
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if name is None:
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name = os.path.basename(local_path)
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boundary = "----WebKitFormBoundary7MA4YWxkTrZu0gW"
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with open(local_path, "rb") as f:
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img = f.read()
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parts = []
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parts.append(("--" + boundary).encode())
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parts.append(b'Content-Disposition: form-data; name="image"; filename="%s"' % name.encode())
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parts.append(b"Content-Type: image/png")
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parts.append(b"")
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parts.append(img)
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parts.append(("--" + boundary).encode())
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parts.append(b'Content-Disposition: form-data; name="overwrite"')
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parts.append(b"")
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parts.append(b"true" if overwrite else b"false")
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parts.append(("--" + boundary + "--").encode())
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body = b"\r\n".join(parts)
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req = urllib.request.Request(
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COMFY + "/upload/image",
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data=body,
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headers={"Content-Type": "multipart/form-data; boundary=" + boundary},
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)
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r = urllib.request.urlopen(req, timeout=60)
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return json.loads(r.read())
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def submit(workflow):
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r = _post("/prompt", {"prompt": workflow})
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if "prompt_id" not in r:
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return None, r
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return r["prompt_id"], None
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def fetch_result(prompt_id, timeout=600):
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"""Block until the job finishes; return list of (local_path, remote_name) or None on fail."""
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deadline = time.time() + timeout
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while time.time() < deadline:
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h = _post(f"/history/{prompt_id}")
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if prompt_id in h:
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outs = h[prompt_id].get("outputs", {})
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images = []
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for node_id, o in outs.items():
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for im in o.get("images", []):
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images.append((im["filename"], im.get("subfolder", ""), im.get("type", "output")))
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if images:
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local_paths = []
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for fn, sub, typ in images:
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q = urllib.parse.urlencode({"filename": fn, "subfolder": sub, "type": typ})
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url = COMFY + "/view?" + q
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local = os.path.join(os.path.expanduser("~"), "auto-publisher/core/generated_art", fn)
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os.makedirs(os.path.dirname(local), exist_ok=True)
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urllib.request.urlretrieve(url, local)
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local_paths.append(local)
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return local_paths
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time.sleep(2)
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return None
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def free_gpu():
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"""Unload Ollama + apply the ComfyUI OC profile, freeing VRAM for diffusion.
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Called by the web worker before a render. Uses HyperSwap's HTTP API (no SSH needed)
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so the worker can live on the server CT and still manage nightmare's GPU.
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free-vram unloads ONE model per call, so loop to clear everything loaded.
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"""
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for _ in range(4):
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try:
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urllib.request.urlopen(urllib.request.Request(
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HYPERSWAP + "/api/free-vram", data=b"", method="POST"), timeout=30).read()
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except Exception:
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break
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time.sleep(1)
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try:
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body = json.dumps({"profile": "comfy"}).encode()
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urllib.request.urlopen(urllib.request.Request(
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HYPERSWAP + "/api/overclock/apply", data=body,
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headers={"Content-Type": "application/json"}, method="POST"), timeout=30).read()
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except Exception:
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pass
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def restore_gpu():
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"""Restore the Ollama OC profile after a render (the keep-warm cron reloads the model)."""
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try:
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body = json.dumps({"profile": "ollama"}).encode()
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urllib.request.urlopen(urllib.request.Request(
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HYPERSWAP + "/api/overclock/apply", data=body,
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headers={"Content-Type": "application/json"}, method="POST"), timeout=30).read()
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except Exception:
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pass
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# ---------------------------------------------------------------------------
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# workflow builders
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# ---------------------------------------------------------------------------
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def _flux_base(unet, prompt, negative="", seed=None, w=1344, h=768, steps=20, cfg=1.0,
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sampler="euler", scheduler="simple"):
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if seed is None:
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seed = random.randint(0, 2**63)
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return {
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"1": {"class_type": "UNETLoader", "inputs": {"unet_name": unet, "weight_dtype": "fp8_e4m3fn"}},
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"2": {"class_type": "DualCLIPLoader", "inputs": {"clip_name1": CLIP_L, "clip_name2": T5XXL_FP8, "type": "flux"}},
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"3": {"class_type": "VAELoader", "inputs": {"vae_name": FLUX_VAE}},
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"4": {"class_type": "CLIPTextEncode", "inputs": {"text": prompt, "clip": ["2", 0]}},
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"5": {"class_type": "CLIPTextEncode", "inputs": {"text": negative, "clip": ["2", 0]}},
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"6": {"class_type": "EmptySD3LatentImage", "inputs": {"width": w, "height": h, "batch_size": 1}},
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"7": {"class_type": "KSampler", "inputs": {"model": ["1", 0], "positive": ["4", 0], "negative": ["5", 0],
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"latent_image": ["6", 0], "seed": seed, "steps": steps, "cfg": cfg,
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"sampler_name": sampler, "scheduler": scheduler, "denoise": 1.0}},
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"8": {"class_type": "VAEDecode", "inputs": {"samples": ["7", 0], "vae": ["3", 0]}},
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"9": {"class_type": "SaveImage", "inputs": {"images": ["8", 0], "filename_prefix": "flux"}},
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}
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def _flux_lora(unet, lora_name, prompt, strength=1.0, seed=None, w=1024, h=1024, steps=20):
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if seed is None:
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seed = random.randint(0, 2**63)
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return {
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"1": {"class_type": "UNETLoader", "inputs": {"unet_name": unet, "weight_dtype": "fp8_e4m3fn"}},
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"2": {"class_type": "DualCLIPLoader", "inputs": {"clip_name1": CLIP_L, "clip_name2": T5XXL_FP8, "type": "flux"}},
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"3": {"class_type": "VAELoader", "inputs": {"vae_name": FLUX_VAE}},
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"4": {"class_type": "LoraLoader", "inputs": {"lora_name": lora_name, "strength_model": strength,
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"strength_clip": strength, "model": ["1", 0], "clip": ["2", 0]}},
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"5": {"class_type": "CLIPTextEncode", "inputs": {"text": prompt, "clip": ["4", 1]}},
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"6": {"class_type": "CLIPTextEncode", "inputs": {"text": "", "clip": ["4", 1]}},
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"7": {"class_type": "EmptySD3LatentImage", "inputs": {"width": w, "height": h, "batch_size": 1}},
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"8": {"class_type": "KSampler", "inputs": {"model": ["4", 0], "positive": ["5", 0], "negative": ["6", 0],
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"latent_image": ["7", 0], "seed": seed, "steps": steps, "cfg": 1.0,
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"sampler_name": "euler", "scheduler": "simple", "denoise": 1.0}},
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"9": {"class_type": "VAEDecode", "inputs": {"samples": ["8", 0], "vae": ["3", 0]}},
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"10": {"class_type": "SaveImage", "inputs": {"images": ["9", 0], "filename_prefix": "lora"}},
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}
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def sdxl_txt2img(checkpoint_key, prompt, negative="", seed=None, w=1024, h=1024,
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steps=8, cfg=2.0, upscale_model=None):
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"""SDXL (Lightning checkpoints = 4-8 steps). Optional 4x upscale."""
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if seed is None:
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seed = random.randint(0, 2**63)
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ckpt = SDXL_CHECKPOINTS[checkpoint_key]
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wf = {
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"1": {"class_type": "CheckpointLoaderSimple", "inputs": {"ckpt_name": ckpt}},
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"2": {"class_type": "CLIPTextEncode", "inputs": {"text": prompt, "clip": ["1", 1]}},
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"3": {"class_type": "CLIPTextEncode", "inputs": {"text": negative, "clip": ["1", 1]}},
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"4": {"class_type": "EmptyLatentImage", "inputs": {"width": w, "height": h, "batch_size": 1}},
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"5": {"class_type": "KSampler", "inputs": {"model": ["1", 0], "positive": ["2", 0], "negative": ["3", 0],
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"latent_image": ["4", 0], "seed": seed, "steps": steps, "cfg": cfg,
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"sampler_name": "dpmpp_2m", "scheduler": "karras", "denoise": 1.0}},
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"6": {"class_type": "VAEDecode", "inputs": {"samples": ["5", 0], "vae": ["1", 2]}},
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}
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if upscale_model:
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wf["7"] = {"class_type": "UpscaleModelLoader", "inputs": {"model_name": upscale_model}}
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wf["8"] = {"class_type": "ImageUpscaleWithModel", "inputs": {"upscale_model": ["7", 0], "image": ["6", 0]}}
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wf["9"] = {"class_type": "SaveImage", "inputs": {"images": ["8", 0], "filename_prefix": "sdxl_up"}}
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else:
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wf["9"] = {"class_type": "SaveImage", "inputs": {"images": ["6", 0], "filename_prefix": "sdxl"}}
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return wf
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def flux_inpaint(image_name, mask_name, prompt, seed=None, steps=20, denoise=0.7):
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"""FLUX Fill inpainting — redraw the masked region of image_name."""
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if seed is None:
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seed = random.randint(0, 2**63)
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return {
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"1": {"class_type": "UNETLoader", "inputs": {"unet_name": FLUX_FILL, "weight_dtype": "fp8_e4m3fn"}},
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"2": {"class_type": "DualCLIPLoader", "inputs": {"clip_name1": CLIP_L, "clip_name2": T5XXL_FP8, "type": "flux"}},
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"3": {"class_type": "VAELoader", "inputs": {"vae_name": FLUX_VAE}},
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"4": {"class_type": "LoadImage", "inputs": {"image": image_name}},
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"5": {"class_type": "LoadImage", "inputs": {"image": mask_name}},
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"5b": {"class_type": "ImageToMask", "inputs": {"image": ["5", 0], "channel": "red"}},
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"6": {"class_type": "CLIPTextEncode", "inputs": {"text": prompt, "clip": ["2", 0]}},
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"7": {"class_type": "CLIPTextEncode", "inputs": {"text": "", "clip": ["2", 0]}},
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"8": {"class_type": "InpaintModelConditioning", "inputs": {"positive": ["6", 0], "negative": ["7", 0],
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"vae": ["3", 0], "pixels": ["4", 0], "mask": ["5b", 0], "noise_mask": True}},
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"9": {"class_type": "KSampler", "inputs": {"model": ["1", 0], "positive": ["8", 0], "negative": ["8", 1],
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"latent_image": ["8", 2], "seed": seed, "steps": steps, "cfg": 1.0,
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"sampler_name": "euler", "scheduler": "simple", "denoise": denoise}},
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"10": {"class_type": "VAEDecode", "inputs": {"samples": ["9", 0], "vae": ["3", 0]}},
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"11": {"class_type": "SaveImage", "inputs": {"images": ["10", 0], "filename_prefix": "inpaint"}},
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}
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def flux_outpaint(image_name, prompt, seed=None, steps=20, denoise=0.85,
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left=192, right=192, top=0, bottom=0):
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"""FLUX Fill outpainting — extend the canvas around image_name."""
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if seed is None:
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seed = random.randint(0, 2**63)
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return {
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"1": {"class_type": "UNETLoader", "inputs": {"unet_name": FLUX_FILL, "weight_dtype": "fp8_e4m3fn"}},
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"2": {"class_type": "DualCLIPLoader", "inputs": {"clip_name1": CLIP_L, "clip_name2": T5XXL_FP8, "type": "flux"}},
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"3": {"class_type": "VAELoader", "inputs": {"vae_name": FLUX_VAE}},
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"4": {"class_type": "LoadImage", "inputs": {"image": image_name}},
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"5": {"class_type": "ImagePadForOutpaint", "inputs": {"image": ["4", 0], "left": left, "top": top,
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"right": right, "bottom": bottom, "feathering": 40}},
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"6": {"class_type": "CLIPTextEncode", "inputs": {"text": prompt, "clip": ["2", 0]}},
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"7": {"class_type": "CLIPTextEncode", "inputs": {"text": "", "clip": ["2", 0]}},
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"8": {"class_type": "InpaintModelConditioning", "inputs": {"positive": ["6", 0], "negative": ["7", 0],
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"vae": ["3", 0], "pixels": ["5", 0], "mask": ["5", 1], "noise_mask": True}},
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"9": {"class_type": "KSampler", "inputs": {"model": ["1", 0], "positive": ["8", 0], "negative": ["8", 1],
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"latent_image": ["8", 2], "seed": seed, "steps": steps, "cfg": 1.0,
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"sampler_name": "euler", "scheduler": "simple", "denoise": denoise}},
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"10": {"class_type": "VAEDecode", "inputs": {"samples": ["9", 0], "vae": ["3", 0]}},
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"11": {"class_type": "SaveImage", "inputs": {"images": ["10", 0], "filename_prefix": "outpaint"}},
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}
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# ---------------------------------------------------------------------------
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# recipe runner
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# ---------------------------------------------------------------------------
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def run(workflow, name):
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pid, err = submit(workflow)
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if err:
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print(f"[{name}] SUBMIT ERROR: {json.dumps(err)[:400]}")
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return None
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print(f"[{name}] submitted pid={pid}")
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paths = fetch_result(pid)
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if paths:
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for p in paths:
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print(f"[{name}] -> {p}")
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return paths[0]
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print(f"[{name}] TIMEOUT/FAILED")
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return None
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