Merge remote-tracking branch 'origin/diffusion-sdxl' into diffusion-lora-ux
This commit is contained in:
Daniel Han
commit
2ac1e77dfc
9 changed files with 492 additions and 98 deletions
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@ -191,10 +191,13 @@ def _snap_to_multiple(img: Any, multiple: int = 16) -> Any:
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# no unsloth-hosted GGUF, so without this its curated catalog entry could not load.
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# Exact-match, lowercased, so it cannot be widened by a typo-squat. Extend
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# deliberately, and never add a repo that carries pickled weights or remote code.
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# The SDXL refiner is intentionally NOT here: it is an img2img-only refiner pipeline
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# (StableDiffusionXLImg2ImgPipeline), but this backend loads every ``sdxl`` repo as the
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# base txt2img StableDiffusionXLPipeline and advertises txt2img, so allowlisting the
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# refiner would surface the wrong workflow and call it without its required input image.
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_TRUSTED_NON_GGUF_REPOS = frozenset(
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{
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"stabilityai/stable-diffusion-xl-base-1.0",
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"stabilityai/stable-diffusion-xl-refiner-1.0",
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"stabilityai/sdxl-turbo",
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}
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)
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@ -208,9 +211,18 @@ def _is_trusted_diffusion_repo(repo_id: str) -> bool:
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non-GGUF paths are gated to the ``unsloth/*`` org (the curated safetensors models),
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a short allowlist of official safetensors-only base repos (``_TRUSTED_NON_GGUF_REPOS``,
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e.g. the SDXL base), and local paths the user explicitly pointed at (already on their
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disk). The GGUF path is unchanged and stays open to any repo, as before."""
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if Path(repo_id).expanduser().exists():
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return True
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disk). The GGUF path is unchanged and stays open to any repo, as before.
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A bare ``owner/name`` HF id is never a real filesystem path, and an id with invalid
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characters makes ``Path.exists()`` raise OSError; treat any such failure as "not a
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local path" so the trust decision falls through to the org/allowlist checks (the
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loader's validate_load_request raises the clear FileNotFoundError for a genuinely
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missing local pick)."""
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try:
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if Path(repo_id).expanduser().exists():
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return True
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except OSError:
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pass
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rid = repo_id.strip().lower()
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return rid.startswith("unsloth/") or rid in _TRUSTED_NON_GGUF_REPOS
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@ -434,6 +446,16 @@ class DiffusionBackend:
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f"pass family_override with that family name. (Video models and image models "
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f"whose diffusers transformer has no single-file loader are not supported.)"
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)
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# A GGUF load builds a transformer-only file via the generic GGUF branch
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# (UNet2DConditionModel.from_single_file(subfolder="transformer", GGUFQuantizationConfig)).
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# Families whose single file IS the whole pipeline (SDXL) have no transformer-only
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# GGUF path, so reject GGUF here -- before the route evicts the current model and
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# the background load fails deep in from_single_file.
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if kind == "gguf" and fam.single_file_is_pipeline:
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raise ValueError(
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f"'{fam.name}' checkpoints are whole-pipeline single files and have no GGUF "
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f"transformer variant; load the .safetensors pipeline instead of a GGUF."
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)
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# Non-GGUF loads (a single-file safetensors transformer, or a full pipeline)
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# are gated to the unsloth org or a local path -- they fetch + deserialise
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# weights, so an arbitrary remote repo is rejected here, before any work.
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@ -568,6 +590,7 @@ class DiffusionBackend:
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base,
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kwargs.get("hf_token"),
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kind = kind,
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single_file_is_pipeline = bool(fam and fam.single_file_is_pipeline),
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)
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with self._lock:
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# Stamp progress only if this load is still current; a superseding
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@ -634,6 +657,7 @@ class DiffusionBackend:
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hf_token: Optional[str],
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*,
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kind: str = "gguf",
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single_file_is_pipeline: bool = False,
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) -> tuple[int, list[str]]:
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"""Total download size for the progress bar, plus the base-repo files to
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fetch (the prefetch reuses this list, so the base is listed only once).
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@ -641,7 +665,9 @@ class DiffusionBackend:
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For a ``pipeline`` load the whole repo IS the pipeline (``base_repo`` is the
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repo itself), so the transformer/ subfolder is INCLUDED -- unlike the GGUF /
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single-file paths, where the transformer is the single file and the base repo
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supplies only the companions."""
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supplies only the companions. For a ``single_file_is_pipeline`` family (SDXL) the
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single file is the WHOLE pipeline, so the base repo supplies only config/tokenizer
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(no weights) and its weight files are skipped."""
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from huggingface_hub import HfApi
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api = HfApi()
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@ -671,9 +697,16 @@ class DiffusionBackend:
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if gguf_filename and not Path(repo_id).expanduser().exists():
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info = api.model_info(repo_id, files_metadata = True, token = hf_token)
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total += sum(s.size or 0 for s in info.siblings if s.rfilename == gguf_filename)
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# A whole-pipeline single file (SDXL) needs only the base repo's config/tokenizer,
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# not its (unused, multi-GB) weight files.
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base_filter = (
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_base_config_file_downloaded
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if (kind == "single_file" and single_file_is_pipeline)
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else _base_file_downloaded
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)
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base_info = api.model_info(base_repo, files_metadata = True, token = hf_token)
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for s in base_info.siblings:
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if _base_file_downloaded(s.rfilename):
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if base_filter(s.rfilename):
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base_files.append(s.rfilename)
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total += s.size or 0
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except Exception as exc: # noqa: BLE001 — estimate is best-effort
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@ -719,6 +752,13 @@ class DiffusionBackend:
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model_kind: Optional[str] = None,
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_load_token: Optional[int] = None,
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) -> dict[str, Any]:
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# A blank / whitespace-only token must degrade to anonymous access, not be passed
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# as an explicit credential (from_single_file / from_pretrained / the Hub client
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# can error on a malformed token instead of falling back). Normalize once here so
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# every load branch and the size estimate below use a real token or None.
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hf_token = hf_token.strip() if isinstance(hf_token, str) else hf_token
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hf_token = hf_token or None
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# Validate first (cheap, no torch/diffusers) so a direct call with a bad
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# family fails with ValueError even in a no-diffusers runtime.
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fam = self.validate_load_request(
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@ -1253,11 +1293,30 @@ class DiffusionBackend:
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if cn_model is None:
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if cancel.is_set():
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raise RuntimeError(DIFFUSION_CANCELLED_MSG)
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cn_model = (
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getattr(diffusers, model_cls_name)
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.from_pretrained(resolved_cn.path, torch_dtype = state.dtype, token = state.hf_token)
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.to(state.device)
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import torch
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# state.dtype is the display string saved at load ("bfloat16"), NOT a
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# torch.dtype; pass the real dtype so diffusers loads the ControlNet at the
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# base compute dtype instead of silently defaulting to float32 (extra VRAM).
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cn_dtype = getattr(torch, str(state.dtype).replace("torch.", ""), None)
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cn_model = getattr(diffusers, model_cls_name).from_pretrained(
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resolved_cn.path,
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torch_dtype = cn_dtype,
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# An empty / malformed token means anonymous access; the HF client can
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# raise on a blank credential instead of falling back, so coerce to None.
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token = state.hf_token or None,
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)
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# Placement must follow the base model's offload policy. A resident base moves
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# the ControlNet resident too; an offloaded (low-VRAM) base streams it through
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# the device with group offloading instead of forcing the whole module onto the
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# GPU, which would defeat the offload and risk an OOM. Best-effort: any failure
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# falls back to the resident placement (the prior behaviour).
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if getattr(state, "offload_policy", OFFLOAD_NONE) != OFFLOAD_NONE and (
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_offload_controlnet_module(cn_model, state.device, logger)
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):
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pass
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else:
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cn_model = cn_model.to(state.device)
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if cancel.is_set():
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# An unload raced the blocking download above and already cleared the
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# ControlNet caches; caching now would pin the module past the unload.
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@ -1288,7 +1347,10 @@ class DiffusionBackend:
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if denoiser is None or vae is None:
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return
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try:
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target_dtype = denoiser.dtype
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# Read the dtype from a parameter (not denoiser.dtype): a plain nn.Module has no
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# .dtype, and a torch.compile'd/ wrapped denoiser can obscure it; this also
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# matches how the VAE dtype is read on the next line.
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target_dtype = next(denoiser.parameters()).dtype
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if next(vae.parameters()).dtype != target_dtype:
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vae.to(dtype = target_dtype)
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except (StopIteration, AttributeError, RuntimeError):
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@ -1332,6 +1394,15 @@ class DiffusionBackend:
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)
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resolved = diffusion_lora.resolve_specs(specs, hf_token = state.hf_token, cancel_event = cancel)
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# The shared catalog scans both .safetensors and .gguf, but diffusers'
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# load_lora_weights only takes safetensors; a .gguf adapter would otherwise fail
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# deep in generation. Reject it here as a clean 400 before touching the pipe.
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bad = [r.id for r in resolved if r.fmt != "safetensors"]
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if bad:
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raise ValueError(
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"GGUF LoRA adapters are not supported on the diffusers engine "
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f"({', '.join(bad)}); use a .safetensors adapter, or the native engine."
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)
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# Unique adapter names (diffusers requires distinct names; sanitized stems can collide).
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uniq: list[tuple[str, str, float]] = []
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seen: set[str] = set()
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@ -1439,8 +1510,24 @@ class DiffusionBackend:
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pipe = state.pipe
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init_pil = mask_pil = None
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control_pil = None
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cn_scale = cn_gstart = cn_gend = None
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cn_scale = cn_gstart = cn_gend = cn_mode = None
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ref_extra: list = []
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# Validate parameter dependencies up front: mask / upscale / reference all
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# need an input image, and reference conditioning needs a family that
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# supports it. Without these guards an unsupported combination would be
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# silently ignored and quietly fall back to txt2img / img2img.
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if init_image is None:
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if mask_image is not None:
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raise ValueError("mask_image requires an input image (init_image).")
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if upscale is not None and upscale > 1.0:
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raise ValueError("upscale requires an input image (init_image).")
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if reference_images:
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raise ValueError("reference_images require an input image (init_image).")
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if reference_images and not getattr(state.family, "reference", False):
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raise ValueError(
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f"Reference images are not supported for the '{state.family.name}' "
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"model family."
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)
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if getattr(state.family, "edit", False):
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# Instruction editing: the loaded pipe is the edit pipeline. It always
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# needs an input image; the prompt is the edit instruction. No mask, no
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@ -1509,41 +1596,55 @@ class DiffusionBackend:
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# and passes a control map. v1 conditions txt2img only (not img2img/inpaint/edit).
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if controlnet is not None:
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from core.inference import diffusion_controlnet
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if workflow != "txt2img":
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raise ValueError(
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"ControlNet currently combines with plain text-to-image only, not the "
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f"{workflow} workflow."
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)
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if not diffusion_controlnet.supports_controlnet(
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engine = "diffusers",
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family = state.family.name,
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has_controlnet_pipeline = bool(
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getattr(state.family, "controlnet_pipeline_class", None)
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),
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model_kind = state.kind,
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transformer_quant = state.transformer_quant,
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):
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raise ValueError(
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"ControlNet is not supported for this model/quantisation on the "
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"diffusers engine (needs a bf16 or bnb-4bit load of a family with a "
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"ControlNet pipeline; not GGUF-via-diffusers or torchao fp8/int8)."
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)
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cn_id, cn_image_b64, cn_type, cn_strength, cn_gs, cn_ge = controlnet
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resolved_cn = diffusion_controlnet.resolve_controlnet(
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cn_id,
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family = state.family.name,
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hf_token = state.hf_token,
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cancel_event = cancel,
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)
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pipe = self._controlnet_pipe(state, resolved_cn, cancel)
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workflow = "controlnet"
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src = _decode_b64_image(cn_image_b64, mode = "RGB")
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# Control map at the OUTPUT size so it aligns with the generated latents.
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control_pil = diffusion_controlnet.preprocess_control(src, cn_type).resize(
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(width, height), Image.LANCZOS
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)
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cn_scale, cn_gstart, cn_gend = cn_strength, cn_gs, cn_ge
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# strength 0 disables ControlNet (documented on the request model, and the
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# frontend slider allows it): skip the whole path so a no-op selection never
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# pays the multi-GB ControlNet download / VRAM cost.
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if cn_strength in (None, 0, 0.0):
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controlnet = None
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else:
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if workflow != "txt2img":
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raise ValueError(
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"ControlNet currently combines with plain text-to-image only, not "
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f"the {workflow} workflow."
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)
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if not diffusion_controlnet.supports_controlnet(
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engine = "diffusers",
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family = state.family.name,
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has_controlnet_pipeline = bool(
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getattr(state.family, "controlnet_pipeline_class", None)
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),
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model_kind = state.kind,
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transformer_quant = state.transformer_quant,
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):
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raise ValueError(
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"ControlNet is not supported for this model/quantisation on the "
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"diffusers engine (needs a bf16 or bnb-4bit load of a family with a "
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"ControlNet pipeline; not GGUF-via-diffusers or torchao fp8/int8)."
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)
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# Decode + preprocess the control image FIRST so a malformed / unsupported
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# image fails as a clean 400 BEFORE any ControlNet download or pipe build,
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# rather than after paying that cost. Control map at the OUTPUT size so it
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# aligns with the generated latents.
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src = _decode_b64_image(cn_image_b64, mode = "RGB")
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control_pil = diffusion_controlnet.preprocess_control(src, cn_type).resize(
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(width, height), Image.LANCZOS
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)
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try:
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resolved_cn = diffusion_controlnet.resolve_controlnet(
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cn_id, family = state.family.name
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)
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except FileNotFoundError as exc:
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# An unknown / missing ControlNet id is a bad selection -> 400, not a
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# generic 500 (the route maps ValueError, not FileNotFoundError).
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raise ValueError(str(exc)) from exc
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pipe = self._controlnet_pipe(state, resolved_cn, cancel)
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workflow = "controlnet"
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cn_scale, cn_gstart, cn_gend = cn_strength, cn_gs, cn_ge
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# Flux Union ControlNet selects the active mode by an integer
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# ``control_mode`` (canny/depth/pose/...); map the chosen control type so
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# the union model applies the right head instead of a default/wrong one.
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cn_mode = diffusion_controlnet.union_control_mode(cn_id, cn_type)
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# Auto-resize odd-sized inputs to a multiple of 16 for the workflows whose
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# OUTPUT size is taken from the input image (img2img / inpaint / extend / edit),
|
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# so an upload like 186px tall no longer fails the pipeline's divisibility check.
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@ -1556,9 +1657,8 @@ class DiffusionBackend:
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mask_pil = mask_pil.resize(init_pil.size, _PILImage.NEAREST)
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if init_pil is not None:
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# Keep the VAE encode dtype consistent with the input image.
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self._align_vae_dtype(
|
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pipe, getattr(state.family, "denoiser_attr", "transformer")
|
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)
|
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# state.family is always a DiffusionFamily, which defines denoiser_attr.
|
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self._align_vae_dtype(pipe, state.family.denoiser_attr)
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||||
# Pipelines vary in which kwargs they accept (img2img derives size from the
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# input image and may reject width/height; a distilled pipe may take no
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|
|
@ -1620,6 +1720,10 @@ class DiffusionBackend:
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kwargs["control_guidance_start"] = cn_gstart
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if "control_guidance_end" in call_params and cn_gend is not None:
|
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kwargs["control_guidance_end"] = cn_gend
|
||||
# Union ControlNet mode index (Flux); only when the pipe accepts it and the
|
||||
# selected control type maps to a known mode.
|
||||
if "control_mode" in call_params and cn_mode is not None:
|
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kwargs["control_mode"] = cn_mode
|
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|
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gen = _GenState(total_steps = steps)
|
||||
|
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|
|
@ -1724,13 +1828,13 @@ class DiffusionBackend:
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|||
if state.eager_patched:
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uninstall_patches()
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uninstall_arch_patches()
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||||
# Drop any LoRA adapters applied to the pipe so a later reference-path load is
|
||||
# bit-identical and the freed transformer carries no adapter layers. Idempotent.
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||||
try:
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if getattr(state.pipe, "_unsloth_loras", ()):
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||||
state.pipe.unload_lora_weights()
|
||||
except Exception: # noqa: BLE001 -- best-effort cleanup on teardown
|
||||
pass
|
||||
# NOTE: we deliberately do NOT call state.pipe.unload_lora_weights() here. unload()
|
||||
# sets the cancel event but does not take _generate_lock, so a LoRA-backed denoise
|
||||
# can still be running on this same pipe for up to one more callback; mutating its
|
||||
# adapter layers now would race that in-flight generation. The whole pipe is dropped
|
||||
# just below (self._state = None; del state; clear_gpu_cache()), so the adapter
|
||||
# tensors are freed with it -- no explicit unload is needed for memory or for a
|
||||
# later load (which builds a fresh pipe).
|
||||
# Drop the workflow pipes built around this load's modules so they don't pin the
|
||||
# freed pipeline (they only re-wire its components, but holding the wrappers
|
||||
# would keep the modules alive past unload).
|
||||
|
|
@ -1861,6 +1965,34 @@ def _hf_base_model(repo_id: str, hf_token: Optional[str]) -> Optional[str]:
|
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return base if isinstance(base, str) and base.strip() else None
|
||||
|
||||
|
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def _offload_controlnet_module(cn_model: Any, device: str, logger: Any) -> bool:
|
||||
"""Stream a ControlNet module through ``device`` via diffusers group offloading.
|
||||
|
||||
Used when the base model was loaded with an offload policy: forcing the ControlNet
|
||||
fully resident with ``.to(device)`` would defeat that low-VRAM placement and can OOM.
|
||||
Group offloading is applied to this single module (it does not touch the base pipe's
|
||||
existing hooks), so it is isolated and reversible. Returns True on success; on any
|
||||
failure the caller falls back to a resident placement, so this never blocks a load."""
|
||||
try:
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||||
import torch
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||||
from diffusers.hooks import apply_group_offloading
|
||||
|
||||
onload = torch.device(device)
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||||
apply_group_offloading(
|
||||
cn_model,
|
||||
onload_device = onload,
|
||||
offload_device = torch.device("cpu"),
|
||||
offload_type = "block_level",
|
||||
num_blocks_per_group = 1,
|
||||
use_stream = onload.type == "cuda",
|
||||
)
|
||||
return True
|
||||
except Exception as exc: # noqa: BLE001 — offload is best-effort; resident is the fallback
|
||||
if logger is not None:
|
||||
logger.warning("diffusion.controlnet: group offload failed (%s); loading resident", exc)
|
||||
return False
|
||||
|
||||
|
||||
def _base_file_downloaded(rfilename: str) -> bool:
|
||||
"""True for base-repo files ``from_pretrained`` actually fetches.
|
||||
|
||||
|
|
@ -1876,6 +2008,33 @@ def _base_file_downloaded(rfilename: str) -> bool:
|
|||
return not rfilename.startswith("assets/")
|
||||
|
||||
|
||||
# Weight file extensions the base repo need NOT supply when the single file is the whole
|
||||
# pipeline (SDXL): from_single_file(config=base) reads only the base repo's structure
|
||||
# (config/tokenizer/scheduler) and takes the weights from the single file.
|
||||
_BASE_WEIGHT_EXTS = (
|
||||
".safetensors",
|
||||
".bin",
|
||||
".ckpt",
|
||||
".pt",
|
||||
".pth",
|
||||
".gguf",
|
||||
".onnx",
|
||||
".onnx_data",
|
||||
".msgpack",
|
||||
".h5",
|
||||
".pb",
|
||||
)
|
||||
|
||||
|
||||
def _base_config_file_downloaded(rfilename: str) -> bool:
|
||||
"""True for base-repo files needed to BUILD a pipeline structure around a whole-pipeline
|
||||
single file WITHOUT its weights: config / tokenizer / scheduler JSON, but no weight
|
||||
tensors (the single file supplies those). Used for ``single_file_is_pipeline`` families."""
|
||||
if not _base_file_downloaded(rfilename):
|
||||
return False
|
||||
return not rfilename.lower().endswith(_BASE_WEIGHT_EXTS)
|
||||
|
||||
|
||||
def _pipeline_file_downloaded(rfilename: str) -> bool:
|
||||
"""True for files a full-pipeline ``from_pretrained`` fetches.
|
||||
|
||||
|
|
|
|||
|
|
@ -19,7 +19,6 @@ backend read an arbitrary location.
|
|||
from __future__ import annotations
|
||||
|
||||
import re
|
||||
import threading
|
||||
from dataclasses import dataclass
|
||||
from pathlib import Path
|
||||
from typing import Any, Optional
|
||||
|
|
@ -138,21 +137,28 @@ def _catalog_by_id() -> dict[str, ControlNetCatalogEntry]:
|
|||
return {e.id: e for e in (list(_CURATED) + _scan_local())}
|
||||
|
||||
|
||||
def resolve_controlnet(
|
||||
spec_id: str,
|
||||
*,
|
||||
family: Optional[str] = None,
|
||||
hf_token: Optional[str] = None,
|
||||
cancel_event: Optional[threading.Event] = None,
|
||||
) -> ResolvedControlNet:
|
||||
def resolve_controlnet(spec_id: str, *, family: Optional[str] = None) -> ResolvedControlNet:
|
||||
"""Resolve a ControlNet id to a loadable repo id / local dir.
|
||||
|
||||
Accepts a catalog/local id, or a bare public HF repo id (``owner/name``). The backend
|
||||
loads the result with ``ControlNetModelClass.from_pretrained(path)`` (download + cache
|
||||
handled there, like the base pipeline). Raises on an unknown id -> the caller maps to 400.
|
||||
|
||||
``family`` (the loaded base family) enforces catalog compatibility: a ControlNet is
|
||||
architecture-specific, so a catalog entry tagged for another family is rejected here
|
||||
with a clear error rather than being loaded through the wrong pipeline class later.
|
||||
"""
|
||||
entry = _catalog_by_id().get(spec_id)
|
||||
if entry is not None:
|
||||
# A curated/local entry may declare the families it is built for. A client that
|
||||
# bypasses the UI filter (direct API call) could send an entry for another family;
|
||||
# reject it before any download so it never reaches the wrong ControlNet pipeline.
|
||||
fam = (family or "").strip().lower()
|
||||
if entry.families and fam and fam not in {f.lower() for f in entry.families}:
|
||||
raise ValueError(
|
||||
f"ControlNet '{spec_id}' is for {', '.join(entry.families)}, not the loaded "
|
||||
f"'{family}' model; pick a ControlNet built for this family."
|
||||
)
|
||||
if entry.source == "local":
|
||||
path = entry.local_path or ""
|
||||
if not path or not Path(path).is_dir():
|
||||
|
|
@ -174,6 +180,33 @@ def resolve_controlnet(
|
|||
)
|
||||
|
||||
|
||||
# Union ControlNet mode indices. A single "union" model covers several control modes and
|
||||
# selects the active one via an integer ``control_mode`` argument; these are the standard
|
||||
# indices used by the FLUX.1 / Qwen-Image union ControlNets. "passthrough" (an already-made
|
||||
# map) carries no intrinsic mode, so it maps to nothing (the caller omits control_mode).
|
||||
_UNION_CONTROL_MODES: dict[str, int] = {
|
||||
"canny": 0,
|
||||
"tile": 1,
|
||||
"depth": 2,
|
||||
"blur": 3,
|
||||
"pose": 4,
|
||||
"gray": 5,
|
||||
"lq": 6,
|
||||
}
|
||||
|
||||
|
||||
def union_control_mode(spec_id: str, control_type: str) -> Optional[int]:
|
||||
"""The integer ``control_mode`` for a union ControlNet, or None.
|
||||
|
||||
Returns a mode only for a curated *union* catalog entry AND a control type that maps to a
|
||||
known index; otherwise None so the caller omits the kwarg (a non-union ControlNet has a
|
||||
single fixed mode, and 'passthrough' does not name one). Pure lookup, no network."""
|
||||
entry = _catalog_by_id().get(spec_id)
|
||||
if entry is None or not entry.is_union:
|
||||
return None
|
||||
return _UNION_CONTROL_MODES.get((control_type or "").strip().lower())
|
||||
|
||||
|
||||
def preprocess_control(image: Any, control_type: str) -> Any:
|
||||
"""Turn a source image into a control map.
|
||||
|
||||
|
|
|
|||
|
|
@ -95,26 +95,31 @@ def sanitize_alias(raw: str) -> str:
|
|||
|
||||
|
||||
def _scan_local() -> list[LoraCatalogEntry]:
|
||||
entries: list[LoraCatalogEntry] = []
|
||||
root = loras_dir()
|
||||
try:
|
||||
children = sorted(root.iterdir())
|
||||
except OSError:
|
||||
return entries
|
||||
for p in children:
|
||||
if not p.is_file():
|
||||
continue
|
||||
return []
|
||||
files = [p for p in children if p.is_file() and p.suffix.lower() in _ALL_EXTS]
|
||||
# Two files that share a stem but differ in extension (foo.safetensors + foo.gguf)
|
||||
# would collide on id (== stem), so the frontend select value and resolve_one's
|
||||
# id->entry lookup could only ever address one of them. Disambiguate a colliding
|
||||
# stem by keeping the full filename as the id; a unique stem stays the clean stem.
|
||||
stem_counts: dict[str, int] = {}
|
||||
for p in files:
|
||||
stem_counts[p.stem] = stem_counts.get(p.stem, 0) + 1
|
||||
entries: list[LoraCatalogEntry] = []
|
||||
for p in files:
|
||||
ext = p.suffix.lower()
|
||||
if ext not in _ALL_EXTS:
|
||||
continue
|
||||
try:
|
||||
size = p.stat().st_size
|
||||
except OSError:
|
||||
size = 0
|
||||
entry_id = p.name if stem_counts.get(p.stem, 0) > 1 else p.stem
|
||||
entries.append(
|
||||
LoraCatalogEntry(
|
||||
id = p.stem,
|
||||
display_name = p.stem,
|
||||
id = entry_id,
|
||||
display_name = entry_id,
|
||||
source = "local",
|
||||
fmt = "gguf" if ext == ".gguf" else "safetensors",
|
||||
local_path = str(p),
|
||||
|
|
@ -157,6 +162,9 @@ def resolve_one(
|
|||
shared xet-fallback helper. Raises FileNotFoundError/ValueError on an unresolvable or
|
||||
unsupported id -- the caller maps that to a clear 400.
|
||||
"""
|
||||
# An empty / whitespace token sent verbatim to HfApi triggers an auth error instead
|
||||
# of falling back to anonymous access; normalise it to None.
|
||||
hf_token = hf_token.strip() if hf_token and hf_token.strip() else None
|
||||
entry = _catalog_by_id().get(spec_id)
|
||||
if entry is not None:
|
||||
if entry.source == "local":
|
||||
|
|
@ -176,6 +184,17 @@ def resolve_one(
|
|||
if "/" in spec_id:
|
||||
repo_id, _, weight_name = spec_id.partition(":")
|
||||
weight_name = weight_name or None
|
||||
if weight_name is not None:
|
||||
# A client-supplied weight file must stay a plain filename inside the repo:
|
||||
# reject traversal / absolute paths so it can never resolve outside the HF
|
||||
# cache dir once handed to the downloader.
|
||||
if (
|
||||
".." in weight_name
|
||||
or weight_name.startswith(("/", "\\", "~"))
|
||||
or "\\" in weight_name
|
||||
or os.path.isabs(weight_name)
|
||||
):
|
||||
raise ValueError(f"invalid LoRA weight file path '{weight_name}'")
|
||||
if weight_name is None:
|
||||
weight_name = _pick_repo_weight_file(repo_id, hf_token)
|
||||
ext = os.path.splitext(weight_name)[1].lower()
|
||||
|
|
@ -218,12 +237,19 @@ def resolve_specs(
|
|||
hf_token: Optional[str] = None,
|
||||
cancel_event: Optional[threading.Event] = None,
|
||||
) -> list[ResolvedLora]:
|
||||
"""Resolve request (id, weight) pairs, dropping zero-weight entries."""
|
||||
"""Resolve request (id, weight) pairs, dropping zero-weight entries.
|
||||
|
||||
A stale / unknown id raises FileNotFoundError inside resolve_one; convert it to
|
||||
ValueError so the route (which maps only ValueError to a 400) reports bad client
|
||||
input instead of a generic 500."""
|
||||
out: list[ResolvedLora] = []
|
||||
for spec_id, weight in specs:
|
||||
if weight == 0:
|
||||
continue
|
||||
out.append(resolve_one(spec_id, weight, hf_token = hf_token, cancel_event = cancel_event))
|
||||
try:
|
||||
for spec_id, weight in specs:
|
||||
if weight == 0:
|
||||
continue
|
||||
out.append(resolve_one(spec_id, weight, hf_token = hf_token, cancel_event = cancel_event))
|
||||
except FileNotFoundError as exc:
|
||||
raise ValueError(str(exc)) from exc
|
||||
return out
|
||||
|
||||
|
||||
|
|
@ -265,20 +291,27 @@ _TAG_RE = re.compile(r"<lora:([^:>]+):([^>]+)>")
|
|||
|
||||
|
||||
def inject_prompt_tags(prompt: str, resolved: list[ResolvedLora]) -> str:
|
||||
"""Append `<lora:ALIAS:WEIGHT>` tags to the prompt, skipping any the user already typed.
|
||||
"""Append `<lora:ALIAS:WEIGHT>` tags for the selected adapters, using the backend-
|
||||
validated weights.
|
||||
|
||||
sd-cli strips these tags before they reach the model, so appending them is safe and
|
||||
deterministic. Duplicate protection: if the prompt already contains a tag for the same
|
||||
alias, we don't add a second one.
|
||||
deterministic. A selected adapter's weight is validated (0-2) and recorded in the
|
||||
request/gallery, so the injected tag must WIN over any `<lora:ALIAS:...>` the user
|
||||
typed for that same alias: strip a user tag whose alias matches a selected adapter,
|
||||
then append the validated one. Tags for aliases the user typed that are NOT selected
|
||||
are left untouched (free-form use).
|
||||
"""
|
||||
existing = {m.group(1) for m in _TAG_RE.finditer(prompt)}
|
||||
tags = [
|
||||
f"<lora:{r.alias}:{_fmt_weight(r.weight)}>" for r in resolved if r.alias not in existing
|
||||
]
|
||||
selected = {r.alias for r in resolved}
|
||||
# Drop any user-typed tag whose alias is one of the selected adapters, so the typed
|
||||
# weight can't override the validated weight (or slip outside the 0-2 bounds).
|
||||
cleaned = _TAG_RE.sub(lambda m: "" if m.group(1) in selected else m.group(0), prompt)
|
||||
# Collapse whitespace left by stripped tags without disturbing the user's text.
|
||||
cleaned = re.sub(r"[ \t]{2,}", " ", cleaned).strip()
|
||||
tags = [f"<lora:{r.alias}:{_fmt_weight(r.weight)}>" for r in resolved]
|
||||
if not tags:
|
||||
return prompt
|
||||
sep = "" if not prompt or prompt.endswith(" ") else " "
|
||||
return f"{prompt}{sep}{' '.join(tags)}"
|
||||
return cleaned
|
||||
sep = "" if not cleaned or cleaned.endswith(" ") else " "
|
||||
return f"{cleaned}{sep}{' '.join(tags)}"
|
||||
|
||||
|
||||
def _fmt_weight(w: float) -> str:
|
||||
|
|
|
|||
|
|
@ -1856,6 +1856,14 @@ class ControlNetSpec(BaseModel):
|
|||
1.0, ge = 0.0, le = 1.0, description = "Fraction of steps at which ControlNet ends"
|
||||
)
|
||||
|
||||
@model_validator(mode = "after")
|
||||
def _check_guidance_range(self) -> "ControlNetSpec":
|
||||
# An inverted range (start > end) means "act over no steps"; reject it as a clean
|
||||
# 422 instead of letting the diffusers pipeline raise a 500 deep in the denoise.
|
||||
if self.guidance_start > self.guidance_end:
|
||||
raise ValueError("guidance_start must be <= guidance_end")
|
||||
return self
|
||||
|
||||
|
||||
class DiffusionGenerateRequest(BaseModel):
|
||||
"""Request to generate one image from the loaded diffusion model."""
|
||||
|
|
|
|||
|
|
@ -483,6 +483,38 @@ def test_generate_img2img_unsupported_family_raises(fake_runtime, tmp_path, monk
|
|||
backend.generate(prompt = "x", steps = 4, init_image = _tiny_png_b64())
|
||||
|
||||
|
||||
def test_generate_rejects_conditioning_without_init_image(fake_runtime, tmp_path):
|
||||
"""mask / upscale / reference all need an input image; without one they must raise a
|
||||
clear ValueError rather than silently degrading to txt2img."""
|
||||
(tmp_path / "model.gguf").write_bytes(b"x")
|
||||
backend = DiffusionBackend()
|
||||
backend.load_pipeline(
|
||||
str(tmp_path), gguf_filename = "model.gguf", base_repo = "base/repo", family_override = "z-image"
|
||||
)
|
||||
with pytest.raises(ValueError, match = "mask_image requires"):
|
||||
backend.generate(prompt = "x", steps = 4, mask_image = _mask_b64(64))
|
||||
with pytest.raises(ValueError, match = "upscale requires"):
|
||||
backend.generate(prompt = "x", steps = 4, upscale = 2.0)
|
||||
with pytest.raises(ValueError, match = "reference_images require"):
|
||||
backend.generate(prompt = "x", steps = 4, reference_images = [_tiny_png_b64()])
|
||||
|
||||
|
||||
def test_generate_rejects_reference_on_unsupported_family(fake_runtime, tmp_path):
|
||||
"""A non-reference family rejects reference_images instead of silently dropping them."""
|
||||
(tmp_path / "model.gguf").write_bytes(b"x")
|
||||
backend = DiffusionBackend()
|
||||
backend.load_pipeline(
|
||||
str(tmp_path), gguf_filename = "model.gguf", base_repo = "base/repo", family_override = "z-image"
|
||||
)
|
||||
with pytest.raises(ValueError, match = "Reference images are not supported"):
|
||||
backend.generate(
|
||||
prompt = "x",
|
||||
steps = 4,
|
||||
init_image = _tiny_png_b64(),
|
||||
reference_images = [_tiny_png_b64()],
|
||||
)
|
||||
|
||||
|
||||
def test_generate_upscale_enlarges_and_low_strength(fake_runtime, tmp_path):
|
||||
"""An init_image + upscale factor routes generate() through the family's img2img
|
||||
pipeline (hires fix): the source is enlarged to size*factor (rounded to /16) before the
|
||||
|
|
|
|||
|
|
@ -45,6 +45,26 @@ def test_resolve_controlnet_rejects_filesystem_like_ids():
|
|||
dc.resolve_controlnet(bad)
|
||||
|
||||
|
||||
def test_resolve_controlnet_enforces_family_match():
|
||||
# A curated entry tagged for another family must be rejected before download so it
|
||||
# never reaches the wrong ControlNet pipeline class.
|
||||
with pytest.raises(ValueError, match = "not the"):
|
||||
dc.resolve_controlnet("qwen-union", family = "flux.1")
|
||||
# The matching family resolves fine, and no family (unfiltered) is permissive.
|
||||
assert dc.resolve_controlnet("qwen-union", family = "qwen-image").path
|
||||
assert dc.resolve_controlnet("qwen-union").path
|
||||
|
||||
|
||||
def test_union_control_mode_maps_only_union_entries():
|
||||
# Union entries map a known control type to its integer mode; passthrough / unknown
|
||||
# types and non-union ids return None so the caller omits control_mode.
|
||||
assert dc.union_control_mode("flux-union-pro", "canny") == 0
|
||||
assert dc.union_control_mode("flux-union-pro", "depth") == 2
|
||||
assert dc.union_control_mode("flux-union-pro", "pose") == 4
|
||||
assert dc.union_control_mode("flux-union-pro", "passthrough") is None
|
||||
assert dc.union_control_mode("some/bare-repo", "canny") is None
|
||||
|
||||
|
||||
def test_resolve_controlnet_local(tmp_path, monkeypatch):
|
||||
d = tmp_path / "controlnets"
|
||||
d.mkdir()
|
||||
|
|
|
|||
|
|
@ -37,10 +37,19 @@ def test_inject_prompt_tags_appends_with_spacing():
|
|||
assert dl.inject_prompt_tags("x", [r1]) == "x <lora:s:1>"
|
||||
|
||||
|
||||
def test_inject_prompt_tags_dedupes_user_typed_tag():
|
||||
def test_inject_prompt_tags_validated_weight_overrides_user_typed():
|
||||
r = dl.ResolvedLora("id", "style", "/p", "safetensors", 0.8)
|
||||
# user already wrote a tag for the same alias -> not duplicated
|
||||
assert dl.inject_prompt_tags("a cat <lora:style:1>", [r]) == "a cat <lora:style:1>"
|
||||
# A user-typed tag for a SELECTED adapter is replaced by the backend-validated weight
|
||||
# (so the recorded/validated 0-2 weight wins over whatever was typed), not duplicated.
|
||||
assert dl.inject_prompt_tags("a cat <lora:style:1>", [r]) == "a cat <lora:style:0.8>"
|
||||
|
||||
|
||||
def test_inject_prompt_tags_keeps_unselected_user_tags():
|
||||
r = dl.ResolvedLora("id", "style", "/p", "safetensors", 0.8)
|
||||
# A user tag for an alias that is NOT one of the selected adapters is left untouched.
|
||||
out = dl.inject_prompt_tags("a cat <lora:other:0.5>", [r])
|
||||
assert "<lora:other:0.5>" in out
|
||||
assert "<lora:style:0.8>" in out
|
||||
|
||||
|
||||
def test_inject_prompt_tags_empty_returns_prompt():
|
||||
|
|
@ -129,6 +138,41 @@ def test_resolve_specs_drops_zero_weight(tmp_path, monkeypatch):
|
|||
assert len(out) == 1 and out[0].weight == 1.0
|
||||
|
||||
|
||||
def test_resolve_specs_maps_unknown_id_to_valueerror(tmp_path, monkeypatch):
|
||||
# An unknown / stale id raises FileNotFoundError in resolve_one; resolve_specs must
|
||||
# surface it as ValueError so the route returns 400, not a generic 500.
|
||||
d = tmp_path / "loras"
|
||||
d.mkdir()
|
||||
monkeypatch.setattr(dl, "loras_dir", lambda: d)
|
||||
with pytest.raises(ValueError):
|
||||
dl.resolve_specs([("nope", 1.0)])
|
||||
|
||||
|
||||
def test_scan_local_disambiguates_identical_stems(tmp_path, monkeypatch):
|
||||
# foo.safetensors and foo.gguf must get distinct ids so each is addressable; a
|
||||
# unique stem keeps its clean stem id.
|
||||
d = tmp_path / "loras"
|
||||
d.mkdir()
|
||||
(d / "foo.safetensors").write_bytes(b"x")
|
||||
(d / "foo.gguf").write_bytes(b"y")
|
||||
(d / "solo.safetensors").write_bytes(b"z")
|
||||
monkeypatch.setattr(dl, "loras_dir", lambda: d)
|
||||
by_id = {e.id: e for e in dl.list_loras()}
|
||||
assert "foo.safetensors" in by_id and "foo.gguf" in by_id
|
||||
assert by_id["foo.safetensors"].fmt == "safetensors"
|
||||
assert by_id["foo.gguf"].fmt == "gguf"
|
||||
assert "solo" in by_id # unique stem is untouched
|
||||
|
||||
|
||||
def test_resolve_one_rejects_traversal_weight_name(tmp_path, monkeypatch):
|
||||
# A client-supplied weight file with traversal / absolute path is rejected before it
|
||||
# can reach the downloader (it must stay a plain filename inside the repo).
|
||||
monkeypatch.setattr(dl, "loras_dir", lambda: tmp_path)
|
||||
for bad in ("owner/name:../secret.safetensors", "owner/name:/etc/x.safetensors"):
|
||||
with pytest.raises(ValueError):
|
||||
dl.resolve_one(bad, 1.0)
|
||||
|
||||
|
||||
# ── Request-model validation ────────────────────────────────────────────────
|
||||
|
||||
|
||||
|
|
@ -264,3 +308,21 @@ def test_diffusers_apply_rejects_unsupported_quant():
|
|||
[("styleA", 1.0)],
|
||||
threading.Event(),
|
||||
)
|
||||
|
||||
|
||||
def test_diffusers_apply_rejects_gguf_adapter(monkeypatch):
|
||||
# A .gguf adapter (discoverable in the shared catalog) cannot load on the diffusers
|
||||
# engine; it must be rejected as a clean 400 before touching the pipe.
|
||||
import threading
|
||||
|
||||
monkeypatch.setattr(
|
||||
dl,
|
||||
"resolve_specs",
|
||||
lambda specs, **_: [
|
||||
dl.ResolvedLora(i, dl.sanitize_alias(i), f"/{i}.gguf", "gguf", w) for i, w in specs
|
||||
],
|
||||
)
|
||||
pipe = _FakePipe()
|
||||
with pytest.raises(ValueError, match = "GGUF LoRA"):
|
||||
_backend()._apply_loras(_fake_state(pipe), [("styleA", 1.0)], threading.Event())
|
||||
assert pipe.loaded == [] # never touched the pipe
|
||||
|
|
|
|||
|
|
@ -15,6 +15,8 @@ from __future__ import annotations
|
|||
|
||||
import types
|
||||
|
||||
import pytest
|
||||
|
||||
from core.inference import diffusion_lora
|
||||
from core.inference.diffusion import (
|
||||
DiffusionBackend,
|
||||
|
|
@ -70,7 +72,8 @@ def test_sdxl_base_repos_are_trusted_non_gguf():
|
|||
# Official safetensors-only base repos are allowlisted so their catalog entries load.
|
||||
assert _is_trusted_diffusion_repo("stabilityai/stable-diffusion-xl-base-1.0")
|
||||
assert _is_trusted_diffusion_repo("stabilityai/sdxl-turbo")
|
||||
assert _is_trusted_diffusion_repo("stabilityai/stable-diffusion-xl-refiner-1.0")
|
||||
# The refiner is img2img-only and is intentionally NOT allowlisted (see
|
||||
# test_sdxl_refiner_not_trusted).
|
||||
# Case-insensitive match.
|
||||
assert _is_trusted_diffusion_repo("StabilityAI/SDXL-Turbo")
|
||||
# A random repo (even one that detects as SDXL) is NOT trusted for a non-GGUF load.
|
||||
|
|
@ -100,9 +103,11 @@ class _FakeVae:
|
|||
|
||||
def test_align_vae_dtype_uses_unet_denoiser():
|
||||
# For SDXL the denoiser lives at pipe.unet; _align_vae_dtype must read it (a pipe
|
||||
# with only .unet and no .transformer) and cast the VAE to the U-Net's dtype.
|
||||
# with only .unet and no .transformer) and cast the VAE to the U-Net's dtype. The
|
||||
# dtype is read from a parameter (denoiser has no .dtype), so use a _FakeVae denoiser.
|
||||
vae = _FakeVae(dtype = "float32")
|
||||
pipe = types.SimpleNamespace(unet = types.SimpleNamespace(dtype = "bfloat16"), vae = vae)
|
||||
unet = _FakeVae(dtype = "bfloat16")
|
||||
pipe = types.SimpleNamespace(unet = unet, vae = vae)
|
||||
DiffusionBackend._align_vae_dtype(pipe, "unet")
|
||||
assert vae.moved_to == "bfloat16"
|
||||
|
||||
|
|
@ -110,7 +115,8 @@ def test_align_vae_dtype_uses_unet_denoiser():
|
|||
def test_align_vae_dtype_transformer_default_unchanged():
|
||||
# DiT default: reads pipe.transformer; a pipe with no transformer is a safe no-op.
|
||||
vae = _FakeVae(dtype = "float32")
|
||||
pipe = types.SimpleNamespace(transformer = types.SimpleNamespace(dtype = "bfloat16"), vae = vae)
|
||||
transformer = _FakeVae(dtype = "bfloat16")
|
||||
pipe = types.SimpleNamespace(transformer = transformer, vae = vae)
|
||||
DiffusionBackend._align_vae_dtype(pipe)
|
||||
assert vae.moved_to == "bfloat16"
|
||||
# No denoiser attribute -> no-op (does not raise, does not move the VAE).
|
||||
|
|
@ -147,3 +153,39 @@ def test_pipeline_prefetch_skips_non_torch_artifacts():
|
|||
assert not keep("unet/flax_model.msgpack")
|
||||
assert not keep("vae_decoder/model.onnx_data")
|
||||
assert not keep("assets/preview.png")
|
||||
|
||||
|
||||
def test_sdxl_refiner_not_trusted():
|
||||
# The refiner is an img2img-only pipeline; the sdxl family loads every repo as the
|
||||
# base txt2img pipeline, so the refiner must NOT be allowlisted for a non-GGUF load.
|
||||
assert not _is_trusted_diffusion_repo("stabilityai/stable-diffusion-xl-refiner-1.0")
|
||||
# The base and turbo remain trusted.
|
||||
assert _is_trusted_diffusion_repo("stabilityai/stable-diffusion-xl-base-1.0")
|
||||
assert _is_trusted_diffusion_repo("stabilityai/sdxl-turbo")
|
||||
|
||||
|
||||
def test_sdxl_gguf_load_rejected_up_front():
|
||||
# SDXL has no transformer-only GGUF variant (its single file is the whole pipeline),
|
||||
# so a GGUF request must fail cheap validation before the GPU handoff.
|
||||
backend = DiffusionBackend()
|
||||
with pytest.raises(ValueError, match = "no GGUF"):
|
||||
backend.validate_load_request(
|
||||
"some-org/my-sdxl.gguf", gguf_filename = "my-sdxl.gguf", family_override = "sdxl"
|
||||
)
|
||||
|
||||
|
||||
def test_base_config_filter_skips_weights():
|
||||
# For a whole-pipeline single file, the base repo supplies only config/tokenizer, not
|
||||
# its (unused) weight tensors.
|
||||
from core.inference.diffusion import _base_config_file_downloaded as keep
|
||||
|
||||
assert keep("model_index.json")
|
||||
assert keep("text_encoder/config.json")
|
||||
assert keep("tokenizer/vocab.json")
|
||||
assert keep("scheduler/scheduler_config.json")
|
||||
assert not keep("unet/diffusion_pytorch_model.safetensors")
|
||||
assert not keep("vae/diffusion_pytorch_model.bin")
|
||||
assert not keep("text_encoder/model.onnx")
|
||||
# transformer/ and assets/ stay excluded (inherited from _base_file_downloaded).
|
||||
assert not keep("transformer/config.json")
|
||||
assert not keep("assets/x.png")
|
||||
|
|
|
|||
|
|
@ -1015,7 +1015,12 @@ export function ImagesPage({ active = true }: { active?: boolean }) {
|
|||
if (!cancelled) setAvailableLoras(list);
|
||||
})
|
||||
.catch(() => {
|
||||
if (!cancelled) setAvailableLoras([]);
|
||||
if (cancelled) return;
|
||||
// Clear the SELECTED adapters too, not just the options: leaving a stale `loras`
|
||||
// selection in state (with the picker now hidden/empty) would still be posted by
|
||||
// handleGenerate and could apply adapters from the previous model, or fail.
|
||||
setAvailableLoras([]);
|
||||
setLoras([]);
|
||||
});
|
||||
return () => {
|
||||
cancelled = true;
|
||||
|
|
@ -2074,7 +2079,7 @@ export function ImagesPage({ active = true }: { active?: boolean }) {
|
|||
)}
|
||||
{loras.map((sel, i) => (
|
||||
<div
|
||||
key={i}
|
||||
key={sel.id || i}
|
||||
className="space-y-1.5 rounded-lg border border-border bg-muted/30 p-2"
|
||||
>
|
||||
<div className="flex items-center gap-2">
|
||||
|
|
@ -2148,7 +2153,7 @@ export function ImagesPage({ active = true }: { active?: boolean }) {
|
|||
hint="Condition the image on a control map (edges / depth / pose). Union models cover many types. Use 'Canny' to trace edges from your image, or 'Passthrough' if it is already a control map."
|
||||
>
|
||||
<div className="space-y-2 rounded-lg border border-border bg-muted/30 p-2">
|
||||
<Select value={controlnetId} onValueChange={setControlnetId}>
|
||||
<Select value={controlnetId || undefined} onValueChange={setControlnetId}>
|
||||
<SelectTrigger className="h-8 w-full text-xs">
|
||||
<SelectValue placeholder="Select a ControlNet" />
|
||||
</SelectTrigger>
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue