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478cb8f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 | #!/usr/bin/env python3
"""v4 final matrix: per-combo handwritten prompts, multiply 0.35, bfl spec. -> outputs_v4."""
import json, os, shutil, sys, time, urllib.request
from run_sweeps import post, HOST, W, H, HW, HH, SEED
from prompts_v4 import combo_prompt, FAL_PROMPTS, SCENE_PROMPTS
OUT_ROOT = "/workspace/outputs_v4"
COMFY_OUT = "/workspace/ComfyUI/output"
R = ["r1", "r2", "r3", "r4"]
T = ["t1", "t2", "t3", "t4", "t5", "t6"]
IMG = {k: f"{k}.jpg" for k in R + T}
def core4(p, style_key, target_key, guidance):
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": combo_prompt(style_key, target_key)}}
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": IMG[style_key]}}
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": 0.35, "strength_type": "multiply"}}
p["ci"] = {"class_type": "LoadImage", "inputs": {"image": IMG[target_key]}}
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 v4_style_comp(style_key, target_key):
tag = f"{style_key}x{target_key}"
p = {}
core4(p, style_key, target_key, 3.0)
p["savedepth"] = {"class_type": "SaveImage", "inputs": {"images": ["depth", 0],
"filename_prefix": f"v4/flux-v2-redux-style-composition/inputs/{tag}-depth"}}
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": ["sma", 0], "negative": ["neg", 0],
"control_net": ["cnl", 0], "vae": ["v", 0], "image": ["depth", 0],
"strength": 0.7, "start_percent": 0.0, "end_percent": 0.8}}
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": ["cn", 0], "negative": ["cn", 1],
"latent_image": ["lat", 0], "seed": SEED, "steps": 32, "cfg": 1.0, "sampler_name": "euler", "scheduler": "simple", "denoise": 1.0}}
p["dec"] = {"class_type": "VAEDecode", "inputs": {"samples": ["ks", 0], "vae": ["v", 0]}}
p["save"] = {"class_type": "SaveImage", "inputs": {"images": ["dec", 0], "filename_prefix": f"v4/flux-v2-redux-style-composition/{tag}"}}
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": ["sma", 0], "negative": ["neg", 0],
"latent_image": ["up", 0], "seed": SEED, "steps": 32, "cfg": 1.0, "sampler_name": "euler", "scheduler": "simple", "denoise": 0.30}}
p["dec2"] = {"class_type": "VAEDecode", "inputs": {"samples": ["ks2", 0], "vae": ["v", 0]}}
p["save2"] = {"class_type": "SaveImage", "inputs": {"images": ["dec2", 0], "filename_prefix": f"v4/flux-v2-redux-style-composition/hires/{tag}"}}
return p
def v4_bfl(style_key, target_key):
tag = f"{style_key}x{target_key}"
p = {}
core4(p, style_key, target_key, 10.0)
p["lora"] = {"class_type": "LoraLoaderModelOnly", "inputs": {"model": ["u", 0],
"lora_name": "flux1-depth-dev-lora.safetensors", "strength_model": 0.85}}
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}}
p["dec"] = {"class_type": "VAEDecode", "inputs": {"samples": ["ks", 0], "vae": ["v", 0]}}
p["save"] = {"class_type": "SaveImage", "inputs": {"images": ["dec", 0], "filename_prefix": f"v4/flux-v2-redux-style-composition-bfl-lora/{tag}"}}
return p
def single(style_key, kind):
p = {}
unet = "flux1-schnell.safetensors" if kind == "schnell" else "flux1-dev.safetensors"
p["u"] = {"class_type": "UNETLoader", "inputs": {"unet_name": unet, "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"}}
text = {"fal": FAL_PROMPTS[style_key], "prompt": SCENE_PROMPTS[style_key], "schnell": ""}[kind]
p["txt"] = {"class_type": "CLIPTextEncode", "inputs": {"clip": ["c", 0], "text": text}}
p["si"] = {"class_type": "LoadImage", "inputs": {"image": IMG[style_key]}}
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"}}
cond = ["txt", 0]
if kind != "schnell":
p["guid"] = {"class_type": "FluxGuidance", "inputs": {"conditioning": ["txt", 0], "guidance": 3.5}}
cond = ["guid", 0]
strength, stype = {"fal": (0.6, "attn_bias"), "prompt": (0.5, "attn_bias"), "schnell": (1.0, "multiply")}[kind]
p["sma"] = {"class_type": "StyleModelApply", "inputs": {"conditioning": cond,
"style_model": ["sml", 0], "clip_vision_output": ["cve", 0], "strength": strength, "strength_type": stype}}
w, h = (768, 1024) if kind == "fal" else (W, H)
if kind != "schnell":
p["msf"] = {"class_type": "ModelSamplingFlux", "inputs": {"model": ["u", 0], "max_shift": 1.15, "base_shift": 0.5, "width": w, "height": h}}
model = ["msf", 0]
else:
model = ["u", 0]
p["noise"] = {"class_type": "RandomNoise", "inputs": {"noise_seed": SEED}}
p["guider"] = {"class_type": "BasicGuider", "inputs": {"model": model, "conditioning": ["sma", 0]}}
p["samp"] = {"class_type": "KSamplerSelect", "inputs": {"sampler_name": "euler"}}
p["sched"] = {"class_type": "BasicScheduler", "inputs": {"model": model, "scheduler": "simple",
"steps": 4 if kind == "schnell" else 28, "denoise": 1.0}}
p["lat"] = {"class_type": "EmptySD3LatentImage", "inputs": {"width": w, "height": h, "batch_size": 1}}
p["sca"] = {"class_type": "SamplerCustomAdvanced", "inputs": {"noise": ["noise", 0], "guider": ["guider", 0],
"sampler": ["samp", 0], "sigmas": ["sched", 0], "latent_image": ["lat", 0]}}
p["dec"] = {"class_type": "VAEDecode", "inputs": {"samples": ["sca", 0], "vae": ["v", 0]}}
wfname = {"fal": "flux-redux-fal-dev", "prompt": "flux-redux-prompt", "schnell": "flux-redux-schnell"}[kind]
p["save"] = {"class_type": "SaveImage", "inputs": {"images": ["dec", 0], "filename_prefix": f"v4/{wfname}/{style_key}"}}
return p
def main():
combos = [(r, t) for r in R for t in T] + [(a, b) for a in R for b in R if a != b]
q = [v4_style_comp(r, t) for r, t in combos] + [v4_bfl(r, t) for r, t in combos]
q += [single(r, k) for r in R for k in ["fal", "prompt", "schnell"]]
ids = {post(p): i for i, p in enumerate(q)}
print(f"queued {len(q)}", flush=True)
pending, errors = set(ids), []
while pending:
time.sleep(15)
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)
if (len(ids) - len(pending)) % 10 == 0:
print(f"progress {len(ids)-len(pending)}/{len(ids)}", flush=True)
elif st.get("status_str") == "error":
pending.discard(pid); errors.append(pid)
msgs = [m for m in st.get("messages", []) if m[0] == "execution_error"]
print(f"ERROR: {(msgs[-1][1].get('exception_message','?') if msgs else '?')[:300]}", flush=True)
src = os.path.join(COMFY_OUT, "v4")
for root, _, files in os.walk(src):
rel = os.path.relpath(root, src)
dst = os.path.join(OUT_ROOT, rel)
os.makedirs(dst, exist_ok=True)
for f in files:
shutil.copy(os.path.join(root, f), os.path.join(dst, f.split("_0")[0] + ".png"))
print("COLLECTED", flush=True)
for root, dirs, files in os.walk(OUT_ROOT):
if files:
print(f" {os.path.relpath(root, OUT_ROOT)}: {len(files)}", flush=True)
if errors:
print("ERRORS:", len(errors)); sys.exit(1)
print("V4 MATRIX COMPLETE", flush=True)
if __name__ == "__main__":
main()
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