#!/usr/bin/env python3 """Phase A calibration sweeps on inputs_v2, seed 777. Outputs under sweeps/ prefixes.""" import json, os, shutil, sys, time, urllib.request HOST = "http://127.0.0.1:7865" SEED = 777 W, H = 896, 1152 HW, HH = 1344, 1728 OUT_ROOT = "/workspace/outputs_v2/sweeps" COMFY_OUT = "/workspace/ComfyUI/output" PAIRS = [("r1.jpg", "t2.jpg"), ("r2.jpg", "t5.jpg"), ("r4.jpg", "t3.jpg")] # variety subset def post(prompt): req = urllib.request.Request(f"{HOST}/prompt", data=json.dumps({"prompt": prompt}).encode(), headers={"Content-Type": "application/json"}) with urllib.request.urlopen(req) as r: d = json.loads(r.read()) if "prompt_id" not in d: raise RuntimeError(d) return d["prompt_id"] def core(p, style_img, comp_img, guidance=3.5, redux=0.5): p["u"] = {"class_type": "UNETLoader", "inputs": {"unet_name": "flux1-dev.safetensors", "weight_dtype": "default"}} p["c"] = {"class_type": "DualCLIPLoader", "inputs": {"clip_name1": "t5xxl_fp16.safetensors", "clip_name2": "clip_l.safetensors", "type": "flux", "device": "default"}} p["v"] = {"class_type": "VAELoader", "inputs": {"vae_name": "ae.safetensors"}} p["txt"] = {"class_type": "CLIPTextEncode", "inputs": {"clip": ["c", 0], "text": ""}} p["guid"] = {"class_type": "FluxGuidance", "inputs": {"conditioning": ["txt", 0], "guidance": guidance}} p["neg"] = {"class_type": "ConditioningZeroOut", "inputs": {"conditioning": ["guid", 0]}} p["si"] = {"class_type": "LoadImage", "inputs": {"image": style_img}} p["cvl"] = {"class_type": "CLIPVisionLoader", "inputs": {"clip_name": "sigclip_vision_patch14_384.safetensors"}} p["cve"] = {"class_type": "CLIPVisionEncode", "inputs": {"clip_vision": ["cvl", 0], "image": ["si", 0], "crop": "center"}} p["sml"] = {"class_type": "StyleModelLoader", "inputs": {"style_model_name": "flux1-redux-dev.safetensors"}} p["sma"] = {"class_type": "StyleModelApply", "inputs": {"conditioning": ["guid", 0], "style_model": ["sml", 0], "clip_vision_output": ["cve", 0], "strength": redux, "strength_type": "attn_bias"}} p["ci"] = {"class_type": "LoadImage", "inputs": {"image": comp_img}} p["rs"] = {"class_type": "ImageResize+", "inputs": {"image": ["ci", 0], "width": W, "height": H, "interpolation": "lanczos", "method": "fill / crop", "condition": "always", "multiple_of": 0}} p["depth"] = {"class_type": "DepthAnythingV2Preprocessor", "inputs": { "image": ["rs", 0], "ckpt_name": "depth_anything_v2_vitl.pth", "resolution": W}} def save(p, src, prefix): p["dec"] = {"class_type": "VAEDecode", "inputs": {"samples": src, "vae": ["v", 0]}} p["save"] = {"class_type": "SaveImage", "inputs": {"images": ["dec", 0], "filename_prefix": prefix}} def cnet_wf(style_img, comp_img, tag, *, guidance=3.5, scheduler="simple", redux=0.5, depth_str=0.7, canny=False, radv=False, hires=False): p = {} core(p, style_img, comp_img, guidance=guidance, redux=redux) pos = ["sma", 0] if radv: del p["sma"], p["cve"] p["radv"] = {"class_type": "ReduxAdvanced", "inputs": {"conditioning": ["guid", 0], "style_model": ["sml", 0], "clip_vision": ["cvl", 0], "image": ["si", 0], "downsampling_factor": 3, "downsampling_function": "area", "mode": "center crop (square)", "weight": 1.0, "autocrop_margin": 0.1}} pos = ["radv", 0] p["cnl"] = {"class_type": "ControlNetLoader", "inputs": {"control_net_name": "FLUX.1-dev-ControlNet-Union-Pro-2.0.safetensors"}} p["cn"] = {"class_type": "ControlNetApplySD3", "inputs": {"positive": pos, "negative": ["neg", 0], "control_net": ["cnl", 0], "vae": ["v", 0], "image": ["depth", 0], "strength": depth_str, "start_percent": 0.0, "end_percent": 0.8}} last = "cn" if canny: p["cne"] = {"class_type": "Canny", "inputs": {"image": ["rs", 0], "low_threshold": 0.2, "high_threshold": 0.5}} p["cn2"] = {"class_type": "ControlNetApplySD3", "inputs": {"positive": ["cn", 0], "negative": ["cn", 1], "control_net": ["cnl", 0], "vae": ["v", 0], "image": ["cne", 0], "strength": 0.35, "start_percent": 0.0, "end_percent": 0.6}} last = "cn2" p["msf"] = {"class_type": "ModelSamplingFlux", "inputs": {"model": ["u", 0], "max_shift": 1.15, "base_shift": 0.5, "width": W, "height": H}} p["lat"] = {"class_type": "EmptySD3LatentImage", "inputs": {"width": W, "height": H, "batch_size": 1}} p["ks"] = {"class_type": "KSampler", "inputs": {"model": ["msf", 0], "positive": [last, 0], "negative": [last, 1], "latent_image": ["lat", 0], "seed": SEED, "steps": 32, "cfg": 1.0, "sampler_name": "euler", "scheduler": scheduler, "denoise": 1.0}} final = ["ks", 0] if hires: p["up"] = {"class_type": "LatentUpscaleBy", "inputs": {"samples": ["ks", 0], "upscale_method": "bislerp", "scale_by": 1.5}} p["msf2"] = {"class_type": "ModelSamplingFlux", "inputs": {"model": ["u", 0], "max_shift": 1.15, "base_shift": 0.5, "width": HW, "height": HH}} p["ks2"] = {"class_type": "KSampler", "inputs": {"model": ["msf2", 0], "positive": pos, "negative": ["neg", 0], "latent_image": ["up", 0], "seed": SEED, "steps": 32, "cfg": 1.0, "sampler_name": "euler", "scheduler": scheduler, "denoise": 0.30}} final = ["ks2", 0] save(p, final, f"sweeps/{tag}") return p def bfl_wf(style_img, comp_img, tag, guidance): p = {} core(p, style_img, comp_img, guidance=guidance) p["lora"] = {"class_type": "LoraLoaderModelOnly", "inputs": {"model": ["u", 0], "lora_name": "flux1-depth-dev-lora.safetensors", "strength_model": 1.0}} p["ip2p"] = {"class_type": "InstructPixToPixConditioning", "inputs": {"positive": ["sma", 0], "negative": ["neg", 0], "vae": ["v", 0], "pixels": ["depth", 0]}} p["msf"] = {"class_type": "ModelSamplingFlux", "inputs": {"model": ["lora", 0], "max_shift": 1.15, "base_shift": 0.5, "width": W, "height": H}} p["ks"] = {"class_type": "KSampler", "inputs": {"model": ["msf", 0], "positive": ["ip2p", 0], "negative": ["ip2p", 1], "latent_image": ["ip2p", 2], "seed": SEED, "steps": 32, "cfg": 1.0, "sampler_name": "euler", "scheduler": "simple", "denoise": 1.0}} save(p, ["ks", 0], f"sweeps/{tag}") return p def main(): q = [] def tagname(r, t): return f"{r.split('.')[0]}x{t.split('.')[0]}" for r, t in PAIRS: # A1: bfl guidance for gv in [4.0, 7.0, 10.0]: q.append((f"A1-bfl-g{gv:g}-{tagname(r,t)}", bfl_wf(r, t, f"A1-bfl-g{gv:g}-{tagname(r,t)}", gv))) for r, t in PAIRS: # A2: guidance x scheduler for gv in [2.5, 3.0, 3.5]: for sch in ["simple", "beta"]: tag = f"A2-g{gv:g}-{sch}-{tagname(r,t)}" q.append((tag, cnet_wf(r, t, tag, guidance=gv, scheduler=sch))) for r, t in PAIRS[:2]: # A3: redux x depth strength for rx in [0.4, 0.5, 0.7]: for ds in [0.55, 0.7, 0.85]: tag = f"A3-rx{rx:g}-ds{ds:g}-{tagname(r,t)}" q.append((tag, cnet_wf(r, t, tag, redux=rx, depth_str=ds))) for r, t in PAIRS: # A4: hires tag = f"A4-hires-{tagname(r,t)}" q.append((tag, cnet_wf(r, t, tag, hires=True))) for r, t in PAIRS: # A5: ReduxAdvanced tag = f"A5-radv-{tagname(r,t)}" q.append((tag, cnet_wf(r, t, tag, radv=True))) for r, t in PAIRS[:2]: # A6: canny stack tag = f"A6-canny-{tagname(r,t)}" q.append((tag, cnet_wf(r, t, tag, canny=True))) ids = {} for tag, prompt in q: ids[post(prompt)] = tag print("queued", tag, flush=True) pending, errors = set(ids), [] while pending: time.sleep(10) for pid in list(pending): try: with urllib.request.urlopen(f"{HOST}/history/{pid}") as r: h = json.loads(r.read()) except Exception: continue if pid not in h: continue st = h[pid].get("status", {}) if st.get("completed"): pending.discard(pid); print(f"done {ids[pid]} ({len(ids)-len(pending)}/{len(ids)})", flush=True) elif st.get("status_str") == "error": pending.discard(pid); errors.append(ids[pid]) msgs = [m for m in st.get("messages", []) if m[0] == "execution_error"] print(f"ERROR {ids[pid]}: {(msgs[-1][1].get('exception_message','?') if msgs else '?')[:300]}", flush=True) os.makedirs(OUT_ROOT, exist_ok=True) src = os.path.join(COMFY_OUT, "sweeps") for f in sorted(os.listdir(src)): shutil.copy(os.path.join(src, f), os.path.join(OUT_ROOT, f.split("_")[0] + ".png")) print(f"COLLECTED {len(os.listdir(OUT_ROOT))} sweep outputs", flush=True) if errors: print("ERRORS:", errors); sys.exit(1) print("SWEEPS COMPLETE", flush=True) if __name__ == "__main__": main()