digital-garbageandClaude Opus 5.5
99f00f2950
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
79 lines
4.4 KiB
Python
79 lines
4.4 KiB
Python
"""Stable Diffusion 1.5 support: a small model the whole pipeline can be run end to end on a Mac.
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It is an image model, so its pipeline holds the one picture for the clip's length: the editor gets a
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clip the length it asked for, made the same way every other pipeline makes one. Each held frame is a
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full picture in memory (about 3 MB at 512x512): a minute at 25 fps wants ~4.5 GB, and ComfyUI refuses
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more than 4096 frames. The latent and the decode need nothing of their own (a plain 4-channel /8
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latent, derived by core from the model).
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What this teaches the system is only what the file's header says before it loads: that it is SD1.x
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(a CLIP-L text encoder under `cond_stage_model.transformer`, which SD2's OpenCLIP and SDXL's
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`conditioner` do not have), and the facts core would read off the loaded model anyway, so every
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video-only module hides the moment the file is picked.
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"""
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ID = "model_sd15"
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TITLE = "Stable Diffusion 1.5"
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STAGE = "load"
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CATEGORY = "system"
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STATUS = "experimental"
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_UNET = "model.diffusion_model.input_blocks.0.0.weight"
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# Older SD1.x files name the CLIP-L embedding without `text_model.`; ComfyUI renames it on load.
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_CLIP_L = ("cond_stage_model.transformer.text_model.embeddings.token_embedding.weight",
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"cond_stage_model.transformer.embeddings.token_embedding.weight")
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def detect(keys) -> bool:
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keyset = set(keys)
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return _UNET in keyset and any(key in keyset for key in _CLIP_L)
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def probe_traits(keys) -> list:
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"""What core's `universal()` reads off a loaded SD1.5: a 2-axis latent, noise prediction."""
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return ["spatial_latent", "predict_eps"] if detect(keys) else []
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def text_to_image():
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video = {"at": "project.video", "input": "fps", "label": "FPS", "drives": "fps"}
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return [
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{"id": "model", "group": "Loaders", "node": "FunPackCheckpointLoader", "inputs": {"ckpt_name": ""}},
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{"id": "positive", "group": "Preparation", "node": "CLIPTextEncode",
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"roles": [{"at": "generation.prompt", "input": "text", "label": "Prompt"}],
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"inputs": {"clip": ["model", 1], "text": ""}},
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{"id": "negative", "group": "Preparation", "node": "CLIPTextEncode",
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"roles": [{"at": "project.negative", "input": "text", "label": "Negative prompt"}],
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"inputs": {"clip": ["model", 1], "text": ""}},
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{"id": "latent", "group": "Preparation", "node": "FunPackEmptyLatent",
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"roles": [{"at": "project.video", "input": "width", "label": "Width"},
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{"at": "project.video", "input": "height", "label": "Height"}],
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"inputs": {"model": ["model", 0], "width": 512, "height": 512, "length": 1, "batch_size": 1}},
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{"id": "settings", "group": "Preparation", "node": "FunPackModifierSettings", "inputs": {"settings": "{}"}},
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{"id": "modifiers", "group": "Preparation", "node": "FunPackLoadModifiers",
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"inputs": {"model": ["model", 0], "settings": ["settings", 0]}},
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{"id": "sampler", "group": "Sampling", "node": "FunPackSampler",
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"roles": [{"at": "generation.sampling", "input": "steps", "label": "Steps"},
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{"at": "generation.sampling", "input": "sampler_name", "label": "Sampler"},
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{"at": "generation.sampling", "input": "scheduler", "label": "Scheduler"},
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{"at": "generation.seed", "input": "seed", "label": "Seed"}],
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"inputs": {"model": ["modifiers", 0], "positive": ["positive", 0], "negative": ["negative", 0],
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"latent": ["latent", 0], "settings": ["settings", 0],
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"seed": 0, "steps": 20, "cfg": 7.0, "sampler_name": "euler", "scheduler": "normal",
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"denoise": 1.0}},
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{"id": "decode", "group": "Render", "node": "FunPackDecode",
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"inputs": {"samples": ["sampler", 0], "vae": ["model", 2], "model": ["model", 0]}},
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{"id": "hold", "group": "Render", "node": "RepeatImageBatch",
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"roles": [{"at": "project.video", "input": "amount", "label": "Length", "drives": "frames"}],
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"inputs": {"image": ["decode", 0], "amount": 1}},
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{"id": "save", "group": "Render", "node": "FunPackSaveVideo", "roles": [video],
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"inputs": {"images": ["hold", 0], "fps": 24.0, "filename_prefix": "FunPack"}},
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]
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def presets():
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return [{"id": "sd15_text_to_image", "title": "Stable Diffusion 1.5 · Text to Image (held as a clip)",
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"slots": text_to_image()}]
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PROVIDES = {"detect": detect, "probe_traits": probe_traits, "pipeline_presets": presets}
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