feat: Stable Diffusion 1.5 model module, so the whole pipeline runs on a Mac (picture held for the clip's length)

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

modules/models/sd15/__init__.py

@@ -0,0 +1,76 @@
+"""Stable Diffusion 1.5 support: a small model the whole pipeline can be run end to end on a Mac.
+
+It is an image model, so its pipeline holds the one picture for the clip's length: the editor gets a
+clip the length it asked for, made the same way every other pipeline makes one. The latent and the
+decode need nothing of their own (a plain 4-channel /8 latent, derived by core from the model).
+
+What this teaches the system is only what the file's header says before it loads: that it is SD1.x
+(a CLIP-L text encoder under `cond_stage_model.transformer`, which SD2's OpenCLIP and SDXL's
+`conditioner` do not have), and the facts core would read off the loaded model anyway, so every
+video-only module hides the moment the file is picked.
+"""
+
+ID = "model_sd15"
+TITLE = "Stable Diffusion 1.5"
+STAGE = "load"
+CATEGORY = "system"
+STATUS = "experimental"
+
+_SIGNATURE_KEYS = ("model.diffusion_model.input_blocks.0.0.weight",
+                   "cond_stage_model.transformer.text_model.embeddings.token_embedding.weight")
+
+
+def detect(keys) -> bool:
+    keyset = set(keys)
+    return all(key in keyset for key in _SIGNATURE_KEYS)
+
+
+def probe_traits(keys) -> list:
+    """What core's `universal()` reads off a loaded SD1.5: a 2-axis latent, noise prediction."""
+    return ["spatial_latent", "predict_eps"] if detect(keys) else []
+
+
+def text_to_image():
+    video = {"at": "project.video", "input": "fps", "label": "FPS", "drives": "fps"}
+    return [
+        {"id": "model", "group": "Loaders", "node": "FunPackCheckpointLoader", "inputs": {"ckpt_name": ""}},
+        {"id": "positive", "group": "Preparation", "node": "CLIPTextEncode",
+         "roles": [{"at": "generation.prompt", "input": "text", "label": "Prompt"}],
+         "inputs": {"clip": ["model", 1], "text": ""}},
+        {"id": "negative", "group": "Preparation", "node": "CLIPTextEncode",
+         "roles": [{"at": "project.negative", "input": "text", "label": "Negative prompt"}],
+         "inputs": {"clip": ["model", 1], "text": ""}},
+        {"id": "latent", "group": "Preparation", "node": "FunPackEmptyLatent",
+         "roles": [{"at": "project.video", "input": "width", "label": "Width"},
+                   {"at": "project.video", "input": "height", "label": "Height"}],
+         "inputs": {"model": ["model", 0], "width": 512, "height": 512, "length": 1, "batch_size": 1}},
+        {"id": "settings", "group": "Preparation", "node": "FunPackModifierSettings", "inputs": {"settings": "{}"}},
+        {"id": "modifiers", "group": "Preparation", "node": "FunPackLoadModifiers",
+         "inputs": {"model": ["model", 0], "settings": ["settings", 0]}},
+        {"id": "sampler", "group": "Sampling", "node": "FunPackSampler",
+         "roles": [{"at": "generation.sampling", "input": "steps", "label": "Steps"},
+                   {"at": "generation.sampling", "input": "sampler_name", "label": "Sampler"},
+                   {"at": "generation.sampling", "input": "scheduler", "label": "Scheduler"},
+                   {"at": "generation.seed", "input": "seed", "label": "Seed"}],
+         "inputs": {"model": ["modifiers", 0], "positive": ["positive", 0], "negative": ["negative", 0],
+                    "latent": ["latent", 0], "settings": ["settings", 0],
+                    "seed": 0, "steps": 20, "cfg": 7.0, "sampler_name": "euler", "scheduler": "normal",
+                    "denoise": 1.0}},
+        {"id": "decode", "group": "Render", "node": "FunPackDecode",
+         "inputs": {"samples": ["sampler", 0], "vae": ["model", 2], "model": ["model", 0]}},
+        {"id": "hold", "group": "Render", "node": "RepeatImageBatch",
+         "roles": [{"at": "project.video", "input": "amount", "label": "Length", "drives": "frames"}],
+         "inputs": {"image": ["decode", 0], "amount": 1}},
+        {"id": "video", "group": "Render", "node": "CreateVideo", "roles": [video],
+         "inputs": {"images": ["hold", 0], "fps": 24.0}},
+        {"id": "save", "group": "Render", "node": "SaveVideo",
+         "inputs": {"video": ["video", 0], "filename_prefix": "FunPack", "format": "auto"}},
+    ]
+
+
+def presets():
+    return [{"id": "sd15_text_to_image", "title": "Stable Diffusion 1.5 · Text to Image (held as a clip)",
+             "slots": text_to_image()}]
+
+
+PROVIDES = {"detect": detect, "probe_traits": probe_traits, "pipeline_presets": presets}

modules/models/sd15/tests/test_sd15.py

@@ -0,0 +1,40 @@
+"""The SD1.5 module: recognising the file, and a pipeline that builds into a real graph."""
+
+import asyncio
+from pathlib import Path
+
+import pytest
+
+UNET = "model.diffusion_model.input_blocks.0.0.weight"
+CLIP_L = "cond_stage_model.transformer.text_model.embeddings.token_embedding.weight"
+
+
+@pytest.fixture(scope="module")
+def registered(comfyui):
+    import nodes as comfy_nodes
+
+    async def load():
+        await comfy_nodes.init_extra_nodes(init_custom_nodes=False)
+        await comfy_nodes.load_custom_node(str(Path(__file__).resolve().parents[4]), module_parent="custom_nodes")
+
+    asyncio.run(load())
+    return comfy_nodes
+
+
+def test_sd1_is_recognised_and_sd2_sdxl_h3_are_not(comfyui):
+    from modules.models import sd15
+    assert sd15.detect({UNET, CLIP_L})
+    assert sd15.probe_traits({UNET, CLIP_L}) == ["spatial_latent", "predict_eps"]
+    assert not sd15.detect({UNET, "cond_stage_model.model.token_embedding.weight"})        # SD2 (OpenCLIP)
+    assert not sd15.detect({UNET, "conditioner.embedders.0.transformer.text_model.embeddings.token_embedding.weight"})  # SDXL
+    assert not sd15.detect({"video_patch_proj.weight", "audio_patch_proj.weight"})       # H3
+    assert sd15.probe_traits({UNET}) == []
+
+
+def test_the_pipeline_builds_a_real_graph_and_is_offered(registered):
+    from core import graph, routes
+    from modules.models.sd15 import text_to_image
+
+    _prompt, problems = graph.build(text_to_image())
+    assert [p for p in problems if "ckpt_name" not in p and "is '', which is not one of" not in p] == []
+    assert "sd15_text_to_image" in {p["id"] for _s, make in routes.modules().providers("pipeline_presets") for p in make()}