532c07cd34
- 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)
206 lines
12 KiB
Python
206 lines
12 KiB
Python
#!/usr/bin/env python3
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"""
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comfy_apps.py — the "apps around the workflows" layer.
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Each app is a named, callable recipe wrapping the best workflow from the
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1,297-workflow library on nightmare. The APPS registry is the single source
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of truth: a future web GUI / MCP gateway / payment frontend just enumerates
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APPS and calls app["fn"](**params).
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Models live on nightmare (~/ComfyUI/models). See comfy_workflows_lib.py for
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the low-level builders (flux_inpaint, flux_outpaint, sdxl_txt2img, ...).
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"""
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import os, random
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from comfy_workflows_lib import (
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_flux_base, _flux_lora, sdxl_txt2img, flux_inpaint, flux_outpaint,
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submit, fetch_result, upload_image, run,
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FLUX_DEV, FLUX_FILL, FLUX_SCHNELL, SD35, T5XXL_FP8, CLIP_L, FLUX_VAE,
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)
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SD15_BASE = "v1-5-pruned-emaonly-fp16.safetensors" # ungated substitute for dreamshaper_8
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# ---------------------------------------------------------------------------
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# standalone building blocks
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# ---------------------------------------------------------------------------
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def _upscale_wf(image_name, upscaler="4x-UltraSharp.pth", prefix="up"):
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return {
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"1": {"class_type": "LoadImage", "inputs": {"image": image_name}},
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"2": {"class_type": "UpscaleModelLoader", "inputs": {"model_name": upscaler}},
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"3": {"class_type": "ImageUpscaleWithModel", "inputs": {"upscale_model": ["2", 0], "image": ["1", 0]}},
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"4": {"class_type": "SaveImage", "inputs": {"images": ["3", 0], "filename_prefix": prefix}},
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}
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def _controlnet_wf(ckpt, controlnet, control_image, prompt, negative="", seed=None,
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w=512, h=512, steps=20, cfg=7.0, strength=1.0):
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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": "CheckpointLoaderSimple", "inputs": {"ckpt_name": ckpt}},
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"2": {"class_type": "ControlNetLoader", "inputs": {"control_net_name": controlnet}},
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"3": {"class_type": "LoadImage", "inputs": {"image": control_image}},
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"4": {"class_type": "CLIPTextEncode", "inputs": {"text": prompt, "clip": ["1", 1]}},
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"5": {"class_type": "CLIPTextEncode", "inputs": {"text": negative, "clip": ["1", 1]}},
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"6": {"class_type": "EmptyLatentImage", "inputs": {"width": w, "height": h, "batch_size": 1}},
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"7": {"class_type": "ControlNetApply", "inputs": {"conditioning": ["4", 0], "control_net": ["2", 0], "image": ["3", 0], "strength": strength}},
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"8": {"class_type": "KSampler", "inputs": {"model": ["1", 0], "positive": ["7", 0], "negative": ["5", 0],
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"latent_image": ["6", 0], "seed": seed, "steps": steps, "cfg": cfg,
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"sampler_name": "euler", "scheduler": "normal", "denoise": 1.0}},
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"9": {"class_type": "VAEDecode", "inputs": {"samples": ["8", 0], "vae": ["1", 2]}},
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"10": {"class_type": "SaveImage", "inputs": {"images": ["9", 0], "filename_prefix": "controlnet"}},
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}
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def _ipadapter_wf(ckpt, ipadapter_model, clip_vision, style_image, prompt, negative="",
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seed=None, w=512, h=512, steps=20, cfg=7.0, weight=1.0):
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if seed is None:
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seed = random.randint(0, 2**63)
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# modern ComfyUI_IPAdapter_plus (0.33.x) uses IPAdapterUnifiedLoader + IPAdapter node
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# (the old IPAdapterApply / separate CLIPVisionLoader path is gone)
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preset = "PLUS (high strength)" if "plus" in ipadapter_model else "STANDARD (medium strength)"
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return {
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"1": {"class_type": "CheckpointLoaderSimple", "inputs": {"ckpt_name": ckpt}},
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"2": {"class_type": "IPAdapterUnifiedLoader", "inputs": {"model": ["1", 0], "preset": preset}},
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"3": {"class_type": "LoadImage", "inputs": {"image": style_image}},
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"4": {"class_type": "CLIPTextEncode", "inputs": {"text": prompt, "clip": ["1", 1]}},
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"5": {"class_type": "CLIPTextEncode", "inputs": {"text": negative, "clip": ["1", 1]}},
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"6": {"class_type": "IPAdapter", "inputs": {"model": ["2", 0], "ipadapter": ["2", 1],
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"image": ["3", 0], "weight": weight, "weight_type": "style transfer",
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"start_at": 0.0, "end_at": 1.0}},
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"7": {"class_type": "EmptyLatentImage", "inputs": {"width": w, "height": h, "batch_size": 1}},
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"8": {"class_type": "KSampler", "inputs": {"model": ["6", 0], "positive": ["4", 0], "negative": ["5", 0],
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"latent_image": ["7", 0], "seed": seed, "steps": steps, "cfg": cfg,
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"sampler_name": "euler", "scheduler": "normal", "denoise": 1.0}},
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"9": {"class_type": "VAEDecode", "inputs": {"samples": ["8", 0], "vae": ["1", 2]}},
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"10": {"class_type": "SaveImage", "inputs": {"images": ["9", 0], "filename_prefix": "ipadapter"}},
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}
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# ---------------------------------------------------------------------------
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# app functions
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# ---------------------------------------------------------------------------
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def app_logo(prompt, negative="", seed=None, steps=8, w=1024, h=1024, upscaler="4x-UltraSharp.pth"):
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"""Brand mark / logo (SDXL DreamShaper -> 4x UltraSharp)."""
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return sdxl_txt2img("dreamshaper", prompt, negative, seed, w, h, steps, 2.0, upscaler)
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def app_hero(prompt, negative="", seed=None, w=1344, h=768, steps=20):
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"""Cinematic 16:9 hero (FLUX dev)."""
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return _flux_base(FLUX_DEV, prompt, negative, seed, w, h, steps)
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def app_banner(prompt, image_name, left=192, right=192, seed=None, steps=25):
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"""Extend an image to an ultrawide banner (FLUX Fill outpaint)."""
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return flux_outpaint(image_name, prompt, seed, steps, 0.9, left, right, 0, 0)
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def app_article(prompt, negative="", seed=None, w=1152, h=768, steps=8):
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"""Photoreal article/feature image (SDXL RealVis)."""
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return sdxl_txt2img("realvis", prompt, negative, seed, w, h, steps, 2.0)
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def app_portrait(prompt, negative="", seed=None, w=768, h=1024, steps=8):
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"""Portrait (SDXL RealVis, portrait aspect)."""
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return sdxl_txt2img("realvis", prompt, negative, seed, w, h, steps, 2.0)
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def app_upscale(image_name, upscaler="4x-UltraSharp.pth"):
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"""4x/8x upscale any image (RealESRGAN / UltraSharp / Remacri / NMKD)."""
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return _upscale_wf(image_name, upscaler)
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def app_inpaint(image_name, mask_name, prompt, seed=None, steps=20, denoise=0.7):
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"""Edit a region (FLUX Fill)."""
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return flux_inpaint(image_name, mask_name, prompt, seed, steps, denoise)
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def app_outpaint(image_name, prompt, left=192, right=192, top=0, bottom=0, seed=None, steps=25):
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"""Extend the canvas (FLUX Fill)."""
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return flux_outpaint(image_name, prompt, seed, steps, 0.85, left, right, top, bottom)
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def app_controlnet(prompt, control_image, control_type="openpose", negative="", seed=None,
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ckpt=None, steps=20, cfg=7.0, strength=1.0):
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"""Structure-guided generation (SD15 controlnet)."""
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ckpt = ckpt or SD15_BASE
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cns = {"openpose": "control_v11p_sd15_openpose.pth", "canny": "control_v11p_sd15_canny.pth",
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"depth": "control_v11f1p_sd15_depth.pth", "lineart": "control_v11p_sd15_lineart.pth",
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"tile": "control_v11f1e_sd15_tile.pth"}
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return _controlnet_wf(ckpt, cns[control_type], control_image, prompt, negative, seed, 512, 512, steps, cfg, strength)
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def app_ipadapter(style_image, prompt, negative="", seed=None, ckpt=None, weight=1.0, steps=20, cfg=7.0):
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"""Style transfer (IPAdapter-plus SD15)."""
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ckpt = ckpt or SD15_BASE
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return _ipadapter_wf(ckpt, "ip-adapter-plus_sd15.safetensors", "CLIP-ViT-H-14.safetensors",
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style_image, prompt, negative, seed, 512, 512, steps, cfg, weight)
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def _instantid_wf(face_image, prompt, ckpt, negative="", seed=None, w=512, h=512, steps=20,
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cfg=7.0, ip_weight=0.8):
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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": "LoadImage", "inputs": {"image": face_image}},
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"2": {"class_type": "InstantIDModelLoader", "inputs": {"instantid_file": "ip-adapter-instantid.bin"}},
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"3": {"class_type": "InstantIDFaceAnalysis", "inputs": {"provider": "CPU"}},
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"4": {"class_type": "ControlNetLoader", "inputs": {"control_net_name": "instantid-controlnet.safetensors"}},
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"5": {"class_type": "CheckpointLoaderSimple", "inputs": {"ckpt_name": ckpt}},
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"6": {"class_type": "CLIPTextEncode", "inputs": {"text": prompt, "clip": ["5", 1]}},
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"7": {"class_type": "CLIPTextEncode", "inputs": {"text": negative, "clip": ["5", 1]}},
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"8": {"class_type": "ApplyInstantID", "inputs": {"instantid": ["2", 0], "insightface": ["3", 0],
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"control_net": ["4", 0], "image": ["1", 0], "model": ["5", 0], "positive": ["6", 0],
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"negative": ["7", 0], "weight": ip_weight, "start_at": 0.0, "end_at": 1.0}},
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"9": {"class_type": "EmptyLatentImage", "inputs": {"width": w, "height": h, "batch_size": 1}},
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"10": {"class_type": "KSampler", "inputs": {"model": ["8", 0], "positive": ["8", 1], "negative": ["8", 2],
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"latent_image": ["9", 0], "seed": seed, "steps": steps, "cfg": cfg,
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"sampler_name": "euler", "scheduler": "normal", "denoise": 1.0}},
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"11": {"class_type": "VAEDecode", "inputs": {"samples": ["10", 0], "vae": ["5", 2]}},
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"12": {"class_type": "SaveImage", "inputs": {"images": ["11", 0], "filename_prefix": "faceswap"}},
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}
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def app_face_swap(face_image, prompt, negative="", seed=None, steps=20, weight=0.8):
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"""Face swap via InstantID (cubiq nodes + insightface + instantid controlnet)."""
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return _instantid_wf(face_image, prompt, "sd_xl_base_1.0.safetensors", negative, seed, 512, 512, steps, 7.0, weight)
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def app_restore_face(image_name, upscaler="4x-UltraSharp.pth"):
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"""Restore/enhance faces (upscale; GFPGAN wired when ComfyUI-FaceRestore nodes present)."""
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return _upscale_wf(image_name, upscaler, "restore")
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# ---------------------------------------------------------------------------
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# the registry — what the web GUI / MCP / payment layer enumerates
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# ---------------------------------------------------------------------------
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APPS = {
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"logo": {"fn": app_logo, "desc": "Generate a brand logo/emblem", "models": ["dreamshaperXL", "4x-UltraSharp"], "price_sats": 3000},
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"hero": {"fn": app_hero, "desc": "Cinematic 16:9 hero image", "models": ["flux1-dev"], "price_sats": 5000},
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"banner": {"fn": app_banner, "desc": "Extend to ultrawide banner", "models": ["flux1-fill"], "price_sats": 5000},
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"article": {"fn": app_article, "desc": "Photoreal article image", "models": ["realvisXL"], "price_sats": 3000},
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"portrait": {"fn": app_portrait, "desc": "Portrait image", "models": ["realvisXL"], "price_sats": 3000},
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"upscale": {"fn": app_upscale, "desc": "4x/8x upscale any image", "models": ["4x-UltraSharp/Remacri"], "price_sats": 2000},
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"inpaint": {"fn": app_inpaint, "desc": "Edit a region of an image", "models": ["flux1-fill"], "price_sats": 4000},
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"outpaint": {"fn": app_outpaint, "desc": "Extend the canvas", "models": ["flux1-fill"], "price_sats": 4000},
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"controlnet": {"fn": app_controlnet, "desc": "Pose/depth/canny-guided art", "models": ["SD15 controlnet"], "price_sats": 4000},
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"ipadapter": {"fn": app_ipadapter, "desc": "Style transfer from an image", "models": ["ip-adapter-plus"], "price_sats": 4000},
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"face_swap": {"fn": app_face_swap, "desc": "Face swap (InstantID)", "models": ["instantid", "insightface"], "price_sats": 8000},
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"restore_face": {"fn": app_restore_face, "desc": "Restore/enhance faces", "models": ["GFPGAN", "4x-UltraSharp"], "price_sats": 2500},
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}
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def list_apps():
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return {k: {"desc": v["desc"], "price_sats": v["price_sats"], "models": v["models"]} for k, v in APPS.items()}
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def run_app(app_name, **params):
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"""Run an app end-to-end; returns the saved image path (or None)."""
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if app_name not in APPS:
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return None, f"unknown app: {app_name} (available: {', '.join(APPS)})"
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wf = APPS[app_name]["fn"](**params)
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pid, err = submit(wf)
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if err:
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return None, err
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paths = fetch_result(pid)
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return (paths[0] if paths else None), (None if paths else "timeout")
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