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https://github.com/invoke-ai/InvokeAI
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Update HF download logic to work for black-forest-labs/FLUX.1-schnell.
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@ -54,6 +54,7 @@ def filter_files(
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"lora_weights.safetensors",
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"weights.pb",
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"onnx_data",
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"spiece.model", # Added for `black-forest-labs/FLUX.1-schnell`.
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)
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):
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paths.append(file)
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@ -62,7 +63,7 @@ def filter_files(
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# downloading random checkpoints that might also be in the repo. However there is no guarantee
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# that a checkpoint doesn't contain "model" in its name, and no guarantee that future diffusers models
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# will adhere to this naming convention, so this is an area to be careful of.
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elif re.search(r"model(\.[^.]+)?\.(safetensors|bin|onnx|xml|pth|pt|ckpt|msgpack)$", file.name):
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elif re.search(r"model.*\.(safetensors|bin|onnx|xml|pth|pt|ckpt|msgpack)$", file.name):
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paths.append(file)
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# limit search to subfolder if requested
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@ -97,7 +98,9 @@ def _filter_by_variant(files: List[Path], variant: ModelRepoVariant) -> Set[Path
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if variant == ModelRepoVariant.Flax:
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result.add(path)
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elif path.suffix in [".json", ".txt"]:
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# Note: '.model' was added to support:
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# https://huggingface.co/black-forest-labs/FLUX.1-schnell/blob/768d12a373ed5cc9ef9a9dea7504dc09fcc14842/tokenizer_2/spiece.model
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elif path.suffix in [".json", ".txt", ".model"]:
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result.add(path)
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elif variant in [
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@ -140,6 +143,23 @@ def _filter_by_variant(files: List[Path], variant: ModelRepoVariant) -> Set[Path
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continue
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for candidate_list in subfolder_weights.values():
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# Check if at least one of the files has the explicit fp16 variant.
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at_least_one_fp16 = False
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for candidate in candidate_list:
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if len(candidate.path.suffixes) == 2 and candidate.path.suffixes[0] == ".fp16":
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at_least_one_fp16 = True
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break
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if not at_least_one_fp16:
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# If none of the candidates in this candidate_list have the explicit fp16 variant label, then this
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# candidate_list probably doesn't adhere to the variant naming convention that we expected. In this case,
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# we'll simply keep all the candidates. An example of a model that hits this case is
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# `black-forest-labs/FLUX.1-schnell` (as of commit 012d2fd).
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for candidate in candidate_list:
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result.add(candidate.path)
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# The candidate_list seems to have the expected variant naming convention. We'll select the highest scoring
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# candidate.
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highest_score_candidate = max(candidate_list, key=lambda candidate: candidate.score)
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if highest_score_candidate:
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result.add(highest_score_candidate.path)
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@ -326,3 +326,80 @@ def test_select_multiple_weights(
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) -> None:
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filtered_files = filter_files(sd15_test_files, variant)
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assert set(filtered_files) == {Path(f) for f in expected_files}
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@pytest.fixture
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def flux_schnell_test_files() -> list[Path]:
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return [
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Path(f)
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for f in [
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"FLUX.1-schnell/.gitattributes",
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"FLUX.1-schnell/README.md",
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"FLUX.1-schnell/ae.safetensors",
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"FLUX.1-schnell/flux1-schnell.safetensors",
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"FLUX.1-schnell/model_index.json",
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"FLUX.1-schnell/scheduler/scheduler_config.json",
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"FLUX.1-schnell/schnell_grid.jpeg",
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"FLUX.1-schnell/text_encoder/config.json",
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"FLUX.1-schnell/text_encoder/model.safetensors",
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"FLUX.1-schnell/text_encoder_2/config.json",
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"FLUX.1-schnell/text_encoder_2/model-00001-of-00002.safetensors",
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"FLUX.1-schnell/text_encoder_2/model-00002-of-00002.safetensors",
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"FLUX.1-schnell/text_encoder_2/model.safetensors.index.json",
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"FLUX.1-schnell/tokenizer/merges.txt",
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"FLUX.1-schnell/tokenizer/special_tokens_map.json",
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"FLUX.1-schnell/tokenizer/tokenizer_config.json",
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"FLUX.1-schnell/tokenizer/vocab.json",
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"FLUX.1-schnell/tokenizer_2/special_tokens_map.json",
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"FLUX.1-schnell/tokenizer_2/spiece.model",
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"FLUX.1-schnell/tokenizer_2/tokenizer.json",
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"FLUX.1-schnell/tokenizer_2/tokenizer_config.json",
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"FLUX.1-schnell/transformer/config.json",
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"FLUX.1-schnell/transformer/diffusion_pytorch_model-00001-of-00003.safetensors",
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"FLUX.1-schnell/transformer/diffusion_pytorch_model-00002-of-00003.safetensors",
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"FLUX.1-schnell/transformer/diffusion_pytorch_model-00003-of-00003.safetensors",
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"FLUX.1-schnell/transformer/diffusion_pytorch_model.safetensors.index.json",
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"FLUX.1-schnell/vae/config.json",
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"FLUX.1-schnell/vae/diffusion_pytorch_model.safetensors",
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]
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]
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@pytest.mark.parametrize(
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["variant", "expected_files"],
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[
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(
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ModelRepoVariant.Default,
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[
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"FLUX.1-schnell/model_index.json",
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"FLUX.1-schnell/scheduler/scheduler_config.json",
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"FLUX.1-schnell/text_encoder/config.json",
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"FLUX.1-schnell/text_encoder/model.safetensors",
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"FLUX.1-schnell/text_encoder_2/config.json",
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"FLUX.1-schnell/text_encoder_2/model-00001-of-00002.safetensors",
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"FLUX.1-schnell/text_encoder_2/model-00002-of-00002.safetensors",
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"FLUX.1-schnell/text_encoder_2/model.safetensors.index.json",
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"FLUX.1-schnell/tokenizer/merges.txt",
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"FLUX.1-schnell/tokenizer/special_tokens_map.json",
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"FLUX.1-schnell/tokenizer/tokenizer_config.json",
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"FLUX.1-schnell/tokenizer/vocab.json",
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"FLUX.1-schnell/tokenizer_2/special_tokens_map.json",
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"FLUX.1-schnell/tokenizer_2/spiece.model",
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"FLUX.1-schnell/tokenizer_2/tokenizer.json",
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"FLUX.1-schnell/tokenizer_2/tokenizer_config.json",
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"FLUX.1-schnell/transformer/config.json",
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"FLUX.1-schnell/transformer/diffusion_pytorch_model-00001-of-00003.safetensors",
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"FLUX.1-schnell/transformer/diffusion_pytorch_model-00002-of-00003.safetensors",
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"FLUX.1-schnell/transformer/diffusion_pytorch_model-00003-of-00003.safetensors",
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"FLUX.1-schnell/transformer/diffusion_pytorch_model.safetensors.index.json",
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"FLUX.1-schnell/vae/config.json",
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"FLUX.1-schnell/vae/diffusion_pytorch_model.safetensors",
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],
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),
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],
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)
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def test_select_flux_schnell_files(
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flux_schnell_test_files: list[Path], variant: ModelRepoVariant, expected_files: list[str]
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) -> None:
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filtered_files = filter_files(flux_schnell_test_files, variant)
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assert set(filtered_files) == {Path(f) for f in expected_files}
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