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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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 | #!/usr/bin/env python3
"""v3 matrix: depth-only, handwritten prompts + generic negative, portrait singles. -> outputs_v3."""
import json, os, shutil, sys, time, urllib.request
from run_sweeps import post, HOST, W, H, HW, HH, SEED
OUT_ROOT = "/workspace/outputs_v3"
COMFY_OUT = "/workspace/ComfyUI/output"
R = ["r1.jpg", "r2.jpg", "r3.jpg", "r4.jpg"]
T = ["t1.jpg", "t2.jpg", "t3.jpg", "t4.jpg", "t5.jpg", "t6.jpg"]
POS = ("A photograph of a beautiful woman posing naturally. Her body is anatomically correct, "
"with well-proportioned limbs, natural relaxed hands, and a balanced, graceful posture. "
"The image is sharp and coherent, with realistic skin texture and lighting that matches the scene.")
NEG = ("deformed anatomy, extra limbs, missing limbs, fused or extra fingers, malformed hands, "
"twisted joints, distorted face, mutated body, disfigured, blurry, lowres, jpeg artifacts, "
"watermark, text, logo, oversaturated, plastic skin")
SCENE = ("A candid photograph of a woman walking through a rain-soaked city street at night, "
"neon signs reflecting in the wet pavement, cinematic atmosphere, shallow depth of field, "
"subtle film grain.")
def core2(p, style_img, comp_img, guidance, 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": POS}}
p["guid"] = {"class_type": "FluxGuidance", "inputs": {"conditioning": ["txt", 0], "guidance": guidance}}
p["neg"] = {"class_type": "CLIPTextEncode", "inputs": {"clip": ["c", 0], "text": NEG}}
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 v3_style_comp(style_img, comp_img, tag):
p = {}
core2(p, style_img, comp_img, guidance=3.0)
p["savedepth"] = {"class_type": "SaveImage", "inputs": {"images": ["depth", 0],
"filename_prefix": f"v3/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"v3/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"v3/flux-v2-redux-style-composition/hires/{tag}"}}
return p
def v3_bfl(style_img, comp_img, tag):
p = {}
core2(p, style_img, comp_img, guidance=4.0)
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}}
p["dec"] = {"class_type": "VAEDecode", "inputs": {"samples": ["ks", 0], "vae": ["v", 0]}}
p["save"] = {"class_type": "SaveImage", "inputs": {"images": ["dec", 0],
"filename_prefix": f"v3/flux-v2-redux-style-composition-bfl-lora/{tag}"}}
return p
def single(style_img, tag, 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": POS, "prompt": SCENE, "schnell": ""}[kind]
p["txt"] = {"class_type": "CLIPTextEncode", "inputs": {"clip": ["c", 0], "text": text}}
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"}}
cond = ["txt", 0]
if kind != "schnell":
p["guid"] = {"class_type": "FluxGuidance", "inputs": {"conditioning": ["txt", 0], "guidance": 3.5}}
cond = ["guid", 0]
strength, stype = (0.5, "attn_bias") if kind == "prompt" else (1.0, "multiply")
if kind == "fal":
strength, stype = 0.6, "attn_bias" # prompt must coexist with style
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"v3/{wfname}/{tag}"}}
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 = []
for r, t in combos:
q.append(v3_style_comp(r, t, f"{r.split('.')[0]}x{t.split('.')[0]}"))
for r, t in combos:
q.append(v3_bfl(r, t, f"{r.split('.')[0]}x{t.split('.')[0]}"))
for r in R:
for kind in ["fal", "prompt", "schnell"]:
q.append(single(r, r.split(".")[0], kind))
ids = {}
for i, prompt in enumerate(q):
ids[post(prompt)] = i
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, "v3")
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] + ".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("V3 MATRIX COMPLETE", flush=True)
if __name__ == "__main__":
main()
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