mirror of
https://github.com/comfyanonymous/ComfyUI.git
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126 lines
5.2 KiB
Python
126 lines
5.2 KiB
Python
import torch
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import pytest
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from unittest.mock import patch
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from comfy.model_detection import detect_unet_config, model_config_from_unet_config
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import comfy.supported_models
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def _make_longcat_diffusers_sd():
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"""Minimal Diffusers-format state dict that triggers the LongCat-Image detection path."""
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sd = {}
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H = 3072 # hidden_size (matches real LongCat-Image)
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C_IN = 16
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C_CTX = 3584 # context_in_dim that distinguishes LongCat from standard Flux (4096)
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sd["x_embedder.weight"] = torch.empty(H, C_IN * 4)
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sd["x_embedder.bias"] = torch.empty(H)
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sd["context_embedder.weight"] = torch.empty(H, C_CTX)
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sd["context_embedder.bias"] = torch.empty(H)
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sd["time_embed.timestep_embedder.linear_1.weight"] = torch.empty(H, 256)
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sd["time_embed.timestep_embedder.linear_1.bias"] = torch.empty(H)
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sd["time_embed.timestep_embedder.linear_2.weight"] = torch.empty(H, H)
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sd["time_embed.timestep_embedder.linear_2.bias"] = torch.empty(H)
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sd["norm_out.linear.weight"] = torch.empty(2 * H, H)
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sd["norm_out.linear.bias"] = torch.empty(2 * H)
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sd["proj_out.weight"] = torch.empty(C_IN * 4, H)
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sd["proj_out.bias"] = torch.empty(C_IN * 4)
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# Need enough transformer_blocks and single_transformer_blocks for count_blocks
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# and for the required_keys check (single_transformer_blocks.10.*)
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for i in range(19):
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sd[f"transformer_blocks.{i}.attn.to_q.weight"] = torch.empty(H, H)
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sd[f"transformer_blocks.{i}.norm1.linear.weight"] = torch.empty(H)
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for i in range(38):
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sd[f"single_transformer_blocks.{i}.attn.to_q.weight"] = torch.empty(H, H)
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sd[f"single_transformer_blocks.{i}.norm.linear.weight"] = torch.empty(H)
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return sd
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def _make_flux_schnell_comfyui_sd():
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"""Minimal ComfyUI-format state dict that triggers the standard Flux detection path."""
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sd = {}
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H = 3072
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C_IN = 16
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sd["img_in.weight"] = torch.empty(H, C_IN * 4)
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sd["img_in.bias"] = torch.empty(H)
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sd["txt_in.weight"] = torch.empty(H, 4096)
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sd["txt_in.bias"] = torch.empty(H)
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sd["double_blocks.0.img_attn.norm.key_norm.weight"] = torch.empty(128)
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sd["double_blocks.0.img_attn.qkv.weight"] = torch.empty(3 * H, H)
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sd["double_blocks.0.img_mod.lin.weight"] = torch.empty(H, H)
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for i in range(19):
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sd[f"double_blocks.{i}.img_attn.norm.key_norm.weight"] = torch.empty(128)
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for i in range(38):
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sd[f"single_blocks.{i}.modulation.lin.weight"] = torch.empty(H, H)
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return sd
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class TestModelDetectionSpecificity:
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"""Verify that model_config_from_unet_config picks the most specific match."""
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def test_longcat_wins_regardless_of_list_order(self):
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"""Specificity logic must pick LongCatImage even when FluxSchnell appears first."""
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sd = _make_longcat_diffusers_sd()
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unet_config = detect_unet_config(sd, "")
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original_models = comfy.supported_models.models
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longcat_cls = comfy.supported_models.LongCatImage
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schnell_cls = comfy.supported_models.FluxSchnell
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# Order A: FluxSchnell before LongCatImage
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order_a = [schnell_cls, longcat_cls]
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# Order B: LongCatImage before FluxSchnell
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order_b = [longcat_cls, schnell_cls]
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for label, order in [("schnell-first", order_a), ("longcat-first", order_b)]:
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with patch.object(comfy.supported_models, "models", order):
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result = model_config_from_unet_config(unet_config, sd)
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assert result is not None, f"No match with order {label}"
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assert type(result).__name__ == "LongCatImage", (
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f"Expected LongCatImage with order {label}, got {type(result).__name__}"
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)
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def test_longcat_diffusers_detected_as_longcat(self):
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sd = _make_longcat_diffusers_sd()
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unet_config = detect_unet_config(sd, "")
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assert unet_config is not None
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assert unet_config["image_model"] == "flux"
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assert unet_config["context_in_dim"] == 3584
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assert unet_config["txt_ids_dims"] == [1, 2]
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model_config = model_config_from_unet_config(unet_config, sd)
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assert model_config is not None
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assert type(model_config).__name__ == "LongCatImage"
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def test_longcat_process_unet_state_dict_converts_keys(self):
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sd = _make_longcat_diffusers_sd()
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unet_config = detect_unet_config(sd, "")
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model_config = model_config_from_unet_config(unet_config, sd)
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converted = model_config.process_unet_state_dict(dict(sd))
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assert "img_in.weight" in converted
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assert "img_in.bias" in converted
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assert "txt_in.weight" in converted
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assert "x_embedder.weight" not in converted
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assert "context_embedder.weight" not in converted
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def test_flux_schnell_comfyui_detected_as_flux_schnell(self):
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sd = _make_flux_schnell_comfyui_sd()
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unet_config = detect_unet_config(sd, "")
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assert unet_config is not None
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assert unet_config["image_model"] == "flux"
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assert unet_config["context_in_dim"] == 4096
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assert unet_config["txt_ids_dims"] == []
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model_config = model_config_from_unet_config(unet_config, sd)
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assert model_config is not None
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assert type(model_config).__name__ == "FluxSchnell"
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