Merge branch 'master' into fix/validate-node-executable

Addresses review feedback: the V1 executability check fell back to
constructing the node (class_def()) when the FUNCTION method wasn't found on
the class. That runs __init__ during validation, so a constructor's side
effects or failure could be misreported as invalid_node_definition for an
otherwise valid node.

Inspect only the class. No core/extra node defines its FUNCTION method on the
instance, so this loses no real coverage while removing the side-effect risk.

Replace the instance-fallback test with one asserting a node with a raising
__init__ but a valid class-level method still passes validation (i.e. it is
never instantiated).

.coderabbit.yaml

@@ -4,12 +4,12 @@ early_access: false
 tone_instructions: "Only comment on issues introduced by this PR's changes. Do not flag pre-existing problems in moved, re-indented, or reformatted code."
 
 reviews:
-  profile: "assertive"
-  request_changes_workflow: true
+  profile: "chill"
+  request_changes_workflow: false
   high_level_summary: false
   poem: false
   review_status: false
-  review_details: true
+  review_details: false
   commit_status: true
   collapse_walkthrough: true
   changed_files_summary: false
@@ -39,14 +39,6 @@ reviews:
     - path: "**"
       instructions: |
         IMPORTANT: Only comment on issues directly introduced by this PR's code changes.
-        Treat AGENTS.md as mandatory repository policy, not optional style guidance.
-        Flag PR changes that violate AGENTS.md even when the code is otherwise functional.
-        In particular, enforce architecture boundaries, dtype/device/memory rules,
-        interface contracts, import style, no unnecessary try/except blocks, no inline
-        imports, no outbound internet paths in core ComfyUI, and narrow scoped fixes.
-        Prefer direct findings over suggestions when a rule is violated. Only ignore
-        AGENTS.md when it clearly conflicts with a newer explicit maintainer instruction
-        in the PR.
         Do NOT flag pre-existing issues in code that was merely moved, re-indented,
         de-indented, or reformatted without logic changes. If code appears in the diff
         only due to whitespace or structural reformatting (e.g., removing a `with:` block),
@@ -131,10 +123,5 @@ chat:
 
 knowledge_base:
   opt_out: false
-  code_guidelines:
-    enabled: true
-    filePatterns:
-      - files: "AGENTS.md"
-        applyTo: "**"
   learnings:
     scope: "auto"

.github/workflows/cla.yml

@@ -1,91 +0,0 @@
-name: CLA Assistant
-
-on:
-  issue_comment:
-    types: [created]
-  pull_request_target:
-    types: [opened, synchronize, closed]
-
-permissions:
-  actions: write
-  contents: read # 'read' is enough because signatures live in a REMOTE repo
-  pull-requests: write
-  statuses: write
-
-jobs:
-  cla-assistant:
-    runs-on: ubuntu-latest
-    steps:
-      # The CLA action normally requires every commit author in a PR to sign.
-      # We only want the PR author to sign, so we allowlist all other committers
-      # by computing them from the PR's commits and excluding the PR author.
-      - name: Build author-only allowlist
-        id: allowlist
-        if: >
-          github.event_name == 'pull_request_target' ||
-          (github.event_name == 'issue_comment' && github.event.issue.pull_request && (
-            github.event.comment.body == 'recheck' ||
-            github.event.comment.body == 'I have read and agree to the Contributor License Agreement'
-          ))
-        env:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          PR_NUMBER: ${{ github.event.pull_request.number || github.event.issue.number }}
-          PR_AUTHOR: ${{ github.event.pull_request.user.login || github.event.issue.user.login }}
-          BASE_ALLOWLIST: action@github.com,actions-user,ampagent,claude,comfy-pr-bot,GitHub Action,github-actions,github-actions[bot],Glary Bot,Glary-Bot,*[bot]
-        run: |
-          others=$(gh api "repos/${{ github.repository }}/pulls/${PR_NUMBER}/commits" --paginate \
-            --jq '.[] | (.author.login // empty), (.committer.login // empty)' \
-            | sort -u | grep -vix "${PR_AUTHOR}" | paste -sd, -)
-          if [ -n "$others" ]; then
-            echo "allowlist=${BASE_ALLOWLIST},${others}" >> "$GITHUB_OUTPUT"
-          else
-            echo "allowlist=${BASE_ALLOWLIST}" >> "$GITHUB_OUTPUT"
-          fi
-
-      - name: CLA Assistant
-        # Run on PR events, on "recheck" comment, or when someone posts the exact signing phrase.
-        # IMPORTANT: this phrase must match `custom-pr-sign-comment` below.
-        if: >
-          github.event_name == 'pull_request_target' ||
-          (github.event_name == 'issue_comment' && github.event.issue.pull_request && (
-            github.event.comment.body == 'recheck' ||
-            github.event.comment.body == 'I have read and agree to the Contributor License Agreement'
-          ))
-        uses: contributor-assistant/github-action@ca4a40a7d1004f18d9960b404b97e5f30a505a08 # v2.6.1
-        env:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          # PAT required to write to the centralized signatures repo.
-          PERSONAL_ACCESS_TOKEN: ${{ secrets.PERSONAL_ACCESS_TOKEN }}
-        with:
-          # Where the CLA document lives (shown to contributors)
-          path-to-document: https://github.com/Comfy-Org/comfy-cla/blob/main/comfyui_icla.md
-
-          # Centralized signature storage
-          remote-organization-name: comfy-org
-          remote-repository-name: comfy-cla
-          path-to-signatures: signatures/cla.json
-          branch: main
-
-          # Only the PR author must sign: bots plus every non-author committer
-          # are allowlisted via the "Build author-only allowlist" step above.
-          # *[bot] is a catch-all for any GitHub App bot account.
-          allowlist: ${{ steps.allowlist.outputs.allowlist }}
-
-          # Custom PR comment messages
-          custom-notsigned-prcomment: |
-            🎉 Thank you for your contribution, we really appreciate it! 🎉
-
-            Like many open source projects, we require contributors to sign our [Contributor License Agreement (CLA)](https://github.com/Comfy-Org/comfy-cla/blob/main/comfyui_icla.md). A CLA makes the ownership of contributions explicit, so contributors and the project share a clear understanding of how the code can be used. By signing, you:
-
-            - Confirm that you own your contribution.
-            - Keep the right to reuse your own code.
-            - Grant us a copyright license to include and share it within our projects.
-
-            CLAs are standard practice across major open source projects including those under the Apache Software Foundation and the Linux Foundation. Ours is based on the Apache Software Foundation's CLA. Most importantly, it would enable us to relicense the project under a more permissive license in the future, giving the project and its community greater flexibility.
-
-            ✍ **To sign, please post a new comment on this PR with exactly the following text:** ✍
-
-          custom-pr-sign-comment: I have read and agree to the Contributor License Agreement
-
-          custom-allsigned-prcomment: |
-            ✅ All contributors have signed the CLA. Thank you! This PR is ready to be merged.

AGENTS.md

@@ -127,8 +127,6 @@
 - Do not add unnecessary `try`/`except` blocks. Use them for optional dependency,
   platform, or backend capability detection only when the program has a useful
   fallback. Prefer specific exception types when changing new code.
-- If a library version is pinned in `requirements.txt`, do not add code to
-  ComfyUI to handle older versions of that library.
 - Remove any workarounds for PyTorch versions that ComfyUI no longer officially
   supports. Deprecated workarounds include catching an exception and rerunning
   the same op with the input cast to float. If a workaround does not have a
@@ -173,30 +171,16 @@
 - Reuse existing model classes, blocks, ops, and helper modules when appropriate.
   Before implementing a new version of a model component, search the existing
   model code for a class or helper that already provides the behavior.
-- Model detection code that inspects linear weight shapes should only use the
-  first dimension. The second dimension may be half the original size for
-  NVFP4 or other 4-bit quantized models.
 - Avoid adding `einops` usage in core inference code. Use native torch tensor
   ops such as `reshape`, `view`, `permute`, `transpose`, `flatten`, `unflatten`,
   `unsqueeze`, and `squeeze` instead.
 - Do not use tensors as general-purpose Python data structures. Keep metadata,
   bookkeeping, counters, flags, shape math, padding math, index planning, memory
   estimates, and control-flow decisions in plain Python values unless the data
-  must participate directly in tensor computation. Do not create tensors for
-  structural metadata that is only used for Python-side control flow. Sequence
-  lengths, cumulative offsets, split indices, window counts, slice boundaries,
-  and repeat counts should be kept as Python ints/lists from the point they are
-  computed. Do not build them as CPU/GPU tensors and then cast, move, validate,
-  or convert them back to Python for `split`, `tensor_split`, indexing plans,
-  loops, or cache keys. Avoid creating temporary tensors just to use tensor
-  methods for scalar or structural calculations.
+  must participate directly in tensor computation. Avoid creating temporary
+  tensors just to use tensor methods for scalar or structural calculations.
 - Avoid unnecessary casts and transfers. Preserve the intended compute dtype,
   storage dtype, bias dtype, and original tensor shape metadata.
-- Keep model-native latent layout handling inside the model or latent-format
-  owner, not in helper nodes. Do not collapse, expand, pack, or unpack latent
-  dimensions in nodes or other caller-side adapters just to satisfy a model
-  forward; the model path should consume and return the native latent shape for
-  that model family.
 - Assume inputs to the main model forward are already in the compute dtype by
   default, except integer inputs such as some model timestep tensors. Do not add
   defensive or convenience casts in model code; it is better for invalid dtype
@@ -260,14 +244,6 @@
 - Model implementations should add the minimal number of ComfyUI nodes required
   to run the model. Reuse existing nodes as much as possible; adapting the model
   to work with existing nodes is strongly preferred over creating new nodes.
-- Nodes should output only values they own. Do not add pass-through outputs for
-  workflow convenience unless the node is explicitly an output node. Existing
-  models, latents, conditioning, or other inputs should flow directly to the
-  next consumer instead of being re-emitted unchanged.
-- Nodes should expose only inputs they actually read to produce current
-  behavior. Do not add placeholder, pass-through, compatibility, or
-  workflow-shaping inputs that are ignored or could flow directly to another
-  node.
 - Node-level code must not patch model code directly. Any node behavior that
   modifies, wraps, hooks, or changes model behavior must go through the model
   patcher class instead of reaching into model internals.

app/assets/api/routes.py

@@ -306,15 +306,12 @@ async def download_asset_content(request: web.Request) -> web.Response:
             404, "FILE_NOT_FOUND", "Underlying file not found on disk."
         )
 
-    # User-controlled asset content must never render inline in the app origin
-    # (stored XSS via SVG/HTML/XML). Force dangerous types to download and
-    # override any requested inline disposition. Centralised through
-    # folder_paths.is_dangerous_content_type so this can't drift from /view and
-    # /userdata (the previous inline set here omitted image/svg+xml and missed
-    # the charset/casing/+xml-dialect bypasses).
-    if folder_paths.is_dangerous_content_type(content_type):
+    _DANGEROUS_MIME_TYPES = {
+        "text/html", "text/html-sandboxed", "application/xhtml+xml",
+        "text/javascript", "text/css",
+    }
+    if content_type in _DANGEROUS_MIME_TYPES:
         content_type = "application/octet-stream"
-        disposition = "attachment"
 
     safe_name = (filename or "").replace("\r", "").replace("\n", "")
     encoded = urllib.parse.quote(safe_name)

app/model_manager.py

@@ -50,45 +50,21 @@ class ModelFileManager:
         @routes.get("/experiment/models/preview/{folder}/{path_index}/{filename:.*}")
         async def get_model_preview(request):
             folder_name = request.match_info.get("folder", None)
+            path_index = int(request.match_info.get("path_index", None))
             filename = request.match_info.get("filename", None)
 
             if folder_name not in folder_paths.folder_names_and_paths:
                 return web.Response(status=404)
 
-            # The "{filename:.*}" capture also matches the empty string, which
-            # would resolve to the folder itself; reject it explicitly.
-            if not filename:
-                return web.Response(status=400)
-
-            try:
-                path_index = int(request.match_info.get("path_index", None))
-            except (TypeError, ValueError):
-                return web.Response(status=400)
-
             folders = folder_paths.folder_names_and_paths[folder_name]
-            if path_index < 0 or path_index >= len(folders[0]):
-                return web.Response(status=404)
             folder = folders[0][path_index]
-            full_filename = os.path.normpath(os.path.join(folder, filename))
-
-            # Prevent path traversal: the requested file must stay within the
-            # configured model folder. `filename` is an unrestricted ".*" capture,
-            # so values like "../../../../etc/passwd" would otherwise escape it.
-            if not folder_paths.is_within_directory(folder, full_filename):
-                return web.Response(status=403)
+            full_filename = os.path.join(folder, filename)
 
             previews = self.get_model_previews(full_filename)
             default_preview = previews[0] if len(previews) > 0 else None
             if default_preview is None or (isinstance(default_preview, str) and not os.path.isfile(default_preview)):
                 return web.Response(status=404)
 
-            # The preview is selected by a glob inside get_model_previews, so a
-            # companion file (e.g. "model.preview.png") could itself be a symlink
-            # resolving outside the model folder. Re-validate the file actually
-            # opened: is_within_directory realpaths it, catching symlink escape.
-            if isinstance(default_preview, str) and not folder_paths.is_within_directory(folder, default_preview):
-                return web.Response(status=403)
-
             try:
                 with Image.open(default_preview) as img:
                     img_bytes = BytesIO()

app/user_manager.py

@@ -6,7 +6,6 @@ import glob
 import shutil
 import logging
 import tempfile
-import mimetypes
 from aiohttp import web
 from urllib import parse
 from comfy.cli_args import args
@@ -337,20 +336,7 @@ class UserManager():
             if not isinstance(path, str):
                 return path
 
-            # User data files are arbitrary user-supplied content and are never
-            # meant to render inline. Disable MIME sniffing and force a download
-            # so uploaded markup/scripts can't execute in the app origin (stored
-            # XSS). Content-Disposition: attachment is the load-bearing guard;
-            # the content-type override and nosniff are defence in depth.
-            content_type = mimetypes.guess_type(path)[0] or 'application/octet-stream'
-            if folder_paths.is_dangerous_content_type(content_type):
-                content_type = 'application/octet-stream'
-
-            return web.FileResponse(path, headers={
-                "Content-Type": content_type,
-                "X-Content-Type-Options": "nosniff",
-                "Content-Disposition": "attachment",
-            })
+            return web.FileResponse(path)
 
         @routes.post("/userdata/{file}")
         async def post_userdata(request):

comfy/model_management.py

@@ -616,8 +616,6 @@ PIN_PRESSURE_HYSTERESIS = 256 * 1024 * 1024
 #Freeing registerables on pressure does imply a GPU sync, so go big on
 #the hysteresis so each expensive sync gives us back a good chunk.
 REGISTERABLE_PIN_HYSTERESIS = 2048 * 1024 * 1024
-WINDOWS_PIN_EVICTION_SWAP_PERCENT = 5.0
-WINDOWS_PIN_EVICTION_EMERGENCY_AVAILABLE = 512 * 1024 ** 2
 
 def module_size(module):
     module_mem = 0
@@ -644,15 +642,6 @@ def free_pins(size, evict_active=False):
             size -= freed
     return freed_total
 
-def should_free_pins_for_ram_pressure(shortfall):
-    if shortfall <= 0:
-        return False
-    if not WINDOWS:
-        return True
-    if psutil.virtual_memory().available < WINDOWS_PIN_EVICTION_EMERGENCY_AVAILABLE:
-        return True
-    return psutil.swap_memory().percent >= WINDOWS_PIN_EVICTION_SWAP_PERCENT
-
 def ensure_pin_budget(size, evict_active=False):
     if args.high_ram:
         return True

comfy/sd.py

@@ -468,9 +468,6 @@ class CLIP:
     def decode(self, token_ids, skip_special_tokens=True):
         return self.tokenizer.decode(token_ids, skip_special_tokens=skip_special_tokens)
 
-    def is_dynamic(self):
-        return self.patcher.is_dynamic()
-
 class VAE:
     def __init__(self, sd=None, device=None, config=None, dtype=None, metadata=None):
         if 'decoder.up_blocks.0.resnets.0.norm1.weight' in sd.keys(): #diffusers format
@@ -1254,8 +1251,6 @@ class VAE:
         except:
             return None
 
-    def is_dynamic(self):
-        return self.patcher.is_dynamic()
 
 class StyleModel:
     def __init__(self, model, device="cpu"):

comfy/sd1_clip.py

@@ -543,24 +543,18 @@ class SDTokenizer:
     def _try_get_embedding(self, embedding_name:str):
         '''
         Takes a potential embedding name and tries to retrieve it.
-        Returns a Tuple consisting of the embedding, the cleaned embedding name, and any leftover string, embedding can be None.
+        Returns a Tuple consisting of the embedding and any leftover string, embedding can be None.
         '''
         split_embed = embedding_name.split()
         embedding_name = split_embed[0]
         leftover = ' '.join(split_embed[1:])
-
-        match = re.search(r'[<\[]', embedding_name)
-        if match is not None:
-            leftover = embedding_name[match.start():] + (" " + leftover if leftover else "")
-            embedding_name = embedding_name[:match.start()]
-
         embed = load_embed(embedding_name, self.embedding_directory, self.embedding_size, self.embedding_key)
         if embed is None:
             stripped = embedding_name.strip(',')
             if len(stripped) < len(embedding_name):
                 embed = load_embed(stripped, self.embedding_directory, self.embedding_size, self.embedding_key)
-                return (embed, embedding_name, "{} {}".format(embedding_name[len(stripped):], leftover))
-        return (embed, embedding_name, leftover)
+                return (embed, "{} {}".format(embedding_name[len(stripped):], leftover))
+        return (embed, leftover)
 
     def pad_tokens(self, tokens, amount):
         if self.pad_left:
@@ -591,7 +585,7 @@ class SDTokenizer:
         tokens = []
         for weighted_segment, weight in parsed_weights:
             to_tokenize = unescape_important(weighted_segment)
-            split = re.split(r'(?<=\s){}'.format(re.escape(self.embedding_identifier)), to_tokenize)
+            split = re.split(' {0}|\n{0}'.format(self.embedding_identifier), to_tokenize)
             to_tokenize = [split[0]]
             for i in range(1, len(split)):
                 to_tokenize.append("{}{}".format(self.embedding_identifier, split[i]))
@@ -601,7 +595,7 @@ class SDTokenizer:
                 # if we find an embedding, deal with the embedding
                 if word.startswith(self.embedding_identifier) and self.embedding_directory is not None:
                     embedding_name = word[len(self.embedding_identifier):].strip('\n')
-                    embed, embedding_name, leftover = self._try_get_embedding(embedding_name)
+                    embed, leftover = self._try_get_embedding(embedding_name)
                     if embed is None:
                         logging.warning(f"warning, embedding:{embedding_name} does not exist, ignoring")
                     else:

comfy/text_encoders/llama.py

@@ -937,41 +937,22 @@ class BaseGenerate:
             return torch.argmax(logits, dim=-1, keepdim=True)
 
         # Sampling mode
-        if len(token_history) > 0 and (repetition_penalty != 1.0 or (presence_penalty is not None and presence_penalty != 0.0)):
-            token_ids = torch.tensor(list(set(token_history)), device=logits.device)
-            token_logits = logits[:, token_ids]
-            if repetition_penalty != 1.0:
-                token_logits = torch.where(token_logits < 0, token_logits * repetition_penalty, token_logits / repetition_penalty)
-            if presence_penalty is not None and presence_penalty != 0.0:
-                token_logits = token_logits - presence_penalty
-            logits[:, token_ids] = token_logits
+        if repetition_penalty != 1.0:
+            for i in range(logits.shape[0]):
+                for token_id in set(token_history):
+                    logits[i, token_id] *= repetition_penalty if logits[i, token_id] < 0 else 1/repetition_penalty
+
+        if presence_penalty is not None and presence_penalty != 0.0:
+            for i in range(logits.shape[0]):
+                for token_id in set(token_history):
+                    logits[i, token_id] -= presence_penalty
 
         if temperature != 1.0:
             logits = logits / temperature
 
         if top_k > 0:
-            top_k = min(top_k, logits.shape[-1])
-            logits, top_indices = torch.topk(logits, top_k)
-
-            if min_p > 0.0:
-                probs_before_filter = torch.nn.functional.softmax(logits, dim=-1)
-                top_probs, _ = probs_before_filter.max(dim=-1, keepdim=True)
-                min_threshold = min_p * top_probs
-                indices_to_remove = probs_before_filter < min_threshold
-                logits[indices_to_remove] = torch.finfo(logits.dtype).min
-
-            if top_p < 1.0:
-                sorted_logits, sorted_indices = torch.sort(logits, descending=True)
-                cumulative_probs = torch.cumsum(torch.nn.functional.softmax(sorted_logits, dim=-1), dim=-1)
-                sorted_indices_to_remove = cumulative_probs > top_p
-                sorted_indices_to_remove[..., 0] = False
-                indices_to_remove = torch.zeros_like(logits, dtype=torch.bool)
-                indices_to_remove.scatter_(1, sorted_indices, sorted_indices_to_remove)
-                logits[indices_to_remove] = torch.finfo(logits.dtype).min
-
-            probs = torch.nn.functional.softmax(logits, dim=-1)
-            next_token = torch.multinomial(probs, num_samples=1, generator=generator)
-            return top_indices.gather(1, next_token)
+            indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
+            logits[indices_to_remove] = torch.finfo(logits.dtype).min
 
         if min_p > 0.0:
             probs_before_filter = torch.nn.functional.softmax(logits, dim=-1)

comfy_api/latest/_input_impl/video_types.py

@@ -281,18 +281,11 @@ class VideoFromFile(VideoInput):
         video_done = False
         audio_done = True
 
-        # Use the last decodable audio stream. Streams FFmpeg has no decoder for have no codec context,
-        # and decoding their packets crashes the process. (e.g. APAC spatial-audio track in iPhone)
-        audio_stream = next(
-            (s for s in reversed(container.streams.audio) if s.codec_context is not None),
-            None,
-        )
-        if audio_stream is not None:
+        if len(container.streams.audio):
+            audio_stream = container.streams.audio[-1]
             streams += [audio_stream]
             resampler = av.audio.resampler.AudioResampler(format='fltp')
             audio_done = False
-        elif len(container.streams.audio):
-            logging.warning("No decodable audio stream found in video; ignoring audio.")
 
         for packet in container.demux(*streams):
             if video_done and audio_done:
@@ -464,13 +457,10 @@ class VideoFromFile(VideoInput):
                         else:
                             output_container.metadata[key] = json.dumps(value)
 
-                # Add streams to the new container. Streams with no codec context cannot be used as an output template.
+                # Add streams to the new container
                 stream_map = {}
                 for stream in streams:
                     if isinstance(stream, (av.VideoStream, av.AudioStream, SubtitleStream)):
-                        if stream.codec_context is None:
-                            logging.warning("Skipping %s stream %d with unsupported codec", stream.type, stream.index)
-                            continue
                         out_stream = output_container.add_stream_from_template(template=stream, opaque=True)
                         stream_map[stream] = out_stream
 

comfy_api_nodes/apis/bytedance.py

@@ -1,4 +1,4 @@
-from typing import Any, Literal
+from typing import Literal
 
 from pydantic import BaseModel, Field
 
@@ -316,36 +316,3 @@ VIDEO_TASKS_EXECUTION_TIME = {
         "1080p": 150,
     },
 }
-
-
-class SeedAudioConfig(BaseModel):
-    format: str = Field(default="mp3")
-    sample_rate: int = Field(default=24000)
-    speech_rate: int = Field(default=0)
-    loudness_rate: int = Field(default=0)
-    pitch_rate: int = Field(default=0)
-
-
-class SeedAudioReference(BaseModel):
-    speaker: str | None = Field(default=None)
-    audio_data: str | None = Field(default=None)
-    audio_url: str | None = Field(default=None)
-    image_data: str | None = Field(default=None)
-    image_url: str | None = Field(default=None)
-
-
-class SeedAudioRequest(BaseModel):
-    model: str = Field(default="seed-audio-1.0")
-    text_prompt: str = Field(...)
-    references: list[SeedAudioReference] | None = Field(default=None)
-    audio_config: SeedAudioConfig = Field(default_factory=SeedAudioConfig)
-    watermark: dict[str, Any] = Field(default_factory=dict)
-
-
-class SeedAudioResponse(BaseModel):
-    audio: str | None = Field(default=None)
-    url: str | None = Field(default=None)
-    duration: float | None = Field(default=None)
-    original_duration: float | None = Field(default=None)
-    code: int | None = Field(default=None)
-    message: str | None = Field(default=None)

comfy_api_nodes/apis/stability.py

@@ -0,0 +1,147 @@
+from enum import Enum
+from typing import Optional
+
+from pydantic import BaseModel, Field, confloat
+
+
+class StabilityFormat(str, Enum):
+    png = 'png'
+    jpeg = 'jpeg'
+    webp = 'webp'
+
+
+class StabilityAspectRatio(str, Enum):
+    ratio_1_1 = "1:1"
+    ratio_16_9 = "16:9"
+    ratio_9_16 = "9:16"
+    ratio_3_2 = "3:2"
+    ratio_2_3 = "2:3"
+    ratio_5_4 = "5:4"
+    ratio_4_5 = "4:5"
+    ratio_21_9 = "21:9"
+    ratio_9_21 = "9:21"
+
+
+def get_stability_style_presets(include_none=True):
+    presets = []
+    if include_none:
+        presets.append("None")
+    return presets + [x.value for x in StabilityStylePreset]
+
+
+class StabilityStylePreset(str, Enum):
+    _3d_model = "3d-model"
+    analog_film = "analog-film"
+    anime = "anime"
+    cinematic = "cinematic"
+    comic_book = "comic-book"
+    digital_art = "digital-art"
+    enhance = "enhance"
+    fantasy_art = "fantasy-art"
+    isometric = "isometric"
+    line_art = "line-art"
+    low_poly = "low-poly"
+    modeling_compound = "modeling-compound"
+    neon_punk = "neon-punk"
+    origami = "origami"
+    photographic = "photographic"
+    pixel_art = "pixel-art"
+    tile_texture = "tile-texture"
+
+
+class Stability_SD3_5_Model(str, Enum):
+    sd3_5_large = "sd3.5-large"
+    # sd3_5_large_turbo = "sd3.5-large-turbo"
+    sd3_5_medium = "sd3.5-medium"
+
+
+class Stability_SD3_5_GenerationMode(str, Enum):
+    text_to_image = "text-to-image"
+    image_to_image = "image-to-image"
+
+
+class StabilityStable3_5Request(BaseModel):
+    model: str = Field(...)
+    mode: str = Field(...)
+    prompt: str = Field(...)
+    negative_prompt: Optional[str] = Field(None)
+    aspect_ratio: Optional[str] = Field(None)
+    seed: Optional[int] = Field(None)
+    output_format: Optional[str] = Field(StabilityFormat.png.value)
+    image: Optional[str] = Field(None)
+    style_preset: Optional[str] = Field(None)
+    cfg_scale: float = Field(...)
+    strength: Optional[confloat(ge=0.0, le=1.0)] = Field(None)
+
+
+class StabilityUpscaleConservativeRequest(BaseModel):
+    prompt: str = Field(...)
+    negative_prompt: Optional[str] = Field(None)
+    seed: Optional[int] = Field(None)
+    output_format: Optional[str] = Field(StabilityFormat.png.value)
+    image: Optional[str] = Field(None)
+    creativity: Optional[confloat(ge=0.2, le=0.5)] = Field(None)
+
+
+class StabilityUpscaleCreativeRequest(BaseModel):
+    prompt: str = Field(...)
+    negative_prompt: Optional[str] = Field(None)
+    seed: Optional[int] = Field(None)
+    output_format: Optional[str] = Field(StabilityFormat.png.value)
+    image: Optional[str] = Field(None)
+    creativity: Optional[confloat(ge=0.1, le=0.5)] = Field(None)
+    style_preset: Optional[str] = Field(None)
+
+
+class StabilityStableUltraRequest(BaseModel):
+    prompt: str = Field(...)
+    negative_prompt: Optional[str] = Field(None)
+    aspect_ratio: Optional[str] = Field(None)
+    seed: Optional[int] = Field(None)
+    output_format: Optional[str] = Field(StabilityFormat.png.value)
+    image: Optional[str] = Field(None)
+    style_preset: Optional[str] = Field(None)
+    strength: Optional[confloat(ge=0.0, le=1.0)] = Field(None)
+
+
+class StabilityStableUltraResponse(BaseModel):
+    image: Optional[str] = Field(None)
+    finish_reason: Optional[str] = Field(None)
+    seed: Optional[int] = Field(None)
+
+
+class StabilityResultsGetResponse(BaseModel):
+    image: Optional[str] = Field(None)
+    finish_reason: Optional[str] = Field(None)
+    seed: Optional[int] = Field(None)
+    id: Optional[str] = Field(None)
+    name: Optional[str] = Field(None)
+    errors: Optional[list[str]] = Field(None)
+    status: Optional[str] = Field(None)
+    result: Optional[str] = Field(None)
+
+
+class StabilityAsyncResponse(BaseModel):
+    id: Optional[str] = Field(None)
+
+
+class StabilityTextToAudioRequest(BaseModel):
+    model: str = Field(...)
+    prompt: str = Field(...)
+    duration: int = Field(190, ge=1, le=190)
+    seed: int = Field(0, ge=0, le=4294967294)
+    steps: int = Field(8, ge=4, le=8)
+    output_format: str = Field("wav")
+
+
+class StabilityAudioToAudioRequest(StabilityTextToAudioRequest):
+    strength: float = Field(0.01, ge=0.01, le=1.0)
+
+
+class StabilityAudioInpaintRequest(StabilityTextToAudioRequest):
+    mask_start: int = Field(30, ge=0, le=190)
+    mask_end: int = Field(190, ge=0, le=190)
+
+
+class StabilityAudioResponse(BaseModel):
+    audio: Optional[str] = Field(None)

comfy_api_nodes/nodes_bytedance.py

@@ -1,4 +1,3 @@
-import base64
 import hashlib
 import logging
 import math
@@ -21,10 +20,6 @@ from comfy_api_nodes.apis.bytedance import (
     GetAssetResponse,
     Image2VideoTaskCreationRequest,
     ImageTaskCreationResponse,
-    SeedAudioConfig,
-    SeedAudioReference,
-    SeedAudioRequest,
-    SeedAudioResponse,
     Seedance2TaskCreationRequest,
     SeedanceCreateAssetRequest,
     SeedanceCreateAssetResponse,
@@ -48,8 +43,6 @@ from comfy_api_nodes.apis.bytedance import (
 )
 from comfy_api_nodes.util import (
     ApiEndpoint,
-    audio_bytes_to_audio_input,
-    audio_input_to_mp3,
     download_url_to_image_tensor,
     download_url_to_video_output,
     downscale_image_tensor_by_max_side,
@@ -58,14 +51,11 @@ from comfy_api_nodes.util import (
     image_tensor_pair_to_batch,
     poll_op,
     sync_op,
-    tensor_to_base64_string,
     upload_audio_to_comfyapi,
     upload_image_to_comfyapi,
     upload_images_to_comfyapi,
     upload_video_to_comfyapi,
-    upscale_image_tensor_to_min_pixels,
     upscale_video_to_min_pixels,
-    validate_audio_duration,
     validate_image_aspect_ratio,
     validate_image_dimensions,
     validate_string,
@@ -2484,311 +2474,6 @@ class ByteDanceCreateVideoAsset(IO.ComfyNode):
         return IO.NodeOutput(asset_id, resolved_group)
 
 
-MODE_TEXT = "text only"
-MODE_AUDIO = "audio reference"
-MODE_IMAGE = "image reference"
-MODE_SPEAKER = "preset voice"
-
-# (speaker_id, display_label) for built-in TTS 2.0 voices; resolvable ids are account-scoped.
-SEED_AUDIO_PRESET_VOICES: list[tuple[str, str]] = [
-    ("zh_female_vv_uranus_bigtts", "Vivi (Female, multilingual)"),
-    ("zh_female_xiaohe_uranus_bigtts", "Mindy (Female, multilingual)"),
-    ("en_female_stokie_uranus_bigtts", "Stokie (Female, English)"),
-    ("en_female_dacey_uranus_bigtts", "Dacey (Female, English)"),
-    ("en_male_tim_uranus_bigtts", "Tim (Male, English)"),
-    ("zh_male_m191_uranus_bigtts", "Kian (Male, multilingual)"),
-    ("zh_male_taocheng_uranus_bigtts", "Cedric (Male, multilingual)"),
-    ("zh_male_sophie_uranus_bigtts", "Sophie (Female, multilingual)"),
-    ("zh_female_yingyujiaoxue_uranus_bigtts", "Jean (Female, multilingual)"),
-    ("zh_male_dayi_uranus_bigtts", "Magnus (Male, multilingual)"),
-    ("zh_female_mizai_uranus_bigtts", "Mabel (Female, multilingual)"),
-    ("zh_female_jitangnv_uranus_bigtts", "Nadia (Female, multilingual)"),
-    ("zh_female_meilinvyou_uranus_bigtts", "Opal (Female, multilingual)"),
-    ("zh_female_liuchangnv_uranus_bigtts", "Pearl (Female, multilingual)"),
-    ("zh_male_ruyayichen_uranus_bigtts", "Quentin (Male, multilingual)"),
-    ("zh_female_vivo_uranus_bigtts", "Vienna (Female, multilingual)"),
-    ("zh_female_xiaoai_uranus_bigtts", "Alina (Female, multilingual)"),
-    ("zh_female_cancan_uranus_bigtts", "Corinne (Female, multilingual)"),
-    ("zh_female_tianmeixiaoyuan_uranus_bigtts", "Esther (Female, multilingual)"),
-    ("zh_female_tianmeitaozi_uranus_bigtts", "Freya (Female, multilingual)"),
-    ("zh_female_shuangkuaisisi_uranus_bigtts", "Gigi (Female, multilingual)"),
-    ("zh_female_peiqi_uranus_bigtts", "Holly (Female, multilingual)"),
-    ("zh_female_xiaoxue_uranus_bigtts", "Lyla (Female, multilingual)"),
-    ("zh_female_yuanqi_uranus_bigtts", "Daisy (Female, multilingual)"),
-    ("zh_female_kefunvsheng_uranus_bigtts", "Tracy (Female, multilingual)"),
-    ("zh_male_shaonianzixin_uranus_bigtts", "Jess (Male, multilingual)"),
-    ("zh_female_linjianvhai_uranus_bigtts", "Pinky (Female, multilingual)"),
-    ("zh_female_kiwi_uranus_bigtts", "Sweety (Female, multilingual)"),
-    ("zh_female_sajiaoxuemei_uranus_bigtts", "Sandy (Female, multilingual)"),
-    ("de_male_seven_uranus_bigtts", "Sven (Male, German)"),
-    ("jp_female_minimi_uranus_bigtts", "Minimi (Female, Japanese)"),
-    ("fr_male_usseau_uranus_bigtts", "Usseau (Male, French)"),
-    ("es_male_felipe_uranus_bigtts", "Felipe (Male, Spanish)"),
-    ("id_male_han_uranus_bigtts", "Han (Male, Indonesian)"),
-    ("pt_male_martins_uranus_bigtts", "Martins (Male, Portuguese)"),
-    ("it_male_enzo_uranus_bigtts", "Enzo (Male, Italian)"),
-    ("kr_male_shane_uranus_bigtts", "Shane (Male, Korean)"),
-    ("zh_male_liufei_uranus_bigtts", "Felix (Male, Chinese)"),
-    ("zh_female_qingxinnvsheng_uranus_bigtts", "Celeste (Female, Chinese)"),
-    ("zh_male_sunwukong_uranus_bigtts", "Monkey King (Male, Chinese)"),
-]
-SEED_AUDIO_VOICE_OPTIONS = [label for _, label in SEED_AUDIO_PRESET_VOICES]
-SEED_AUDIO_VOICE_MAP = {label: speaker_id for speaker_id, label in SEED_AUDIO_PRESET_VOICES}
-
-_AUDIO_TAG_RE = re.compile(r"@Audio(\d+)", re.IGNORECASE)
-
-
-def max_audio_tag(prompt: str) -> int:
-    """Highest N referenced as @AudioN in the prompt (0 if none)."""
-    nums = [int(m) for m in _AUDIO_TAG_RE.findall(prompt or "")]
-    return max(nums) if nums else 0
-
-
-def connected_audio_indices(reference_mode: dict) -> list[int]:
-    """Indices (1-based) of connected reference_audio sockets, in order."""
-    return [
-        i
-        for i in range(1, 3 + 1)
-        if reference_mode.get(f"reference_audio_{i}") is not None
-    ]
-
-
-def validate_seed_audio_inputs(
-    text_prompt: str,
-    mode: str,
-    audio_indices: list[int],
-    has_image: bool,
-    preset_voice: str | None = None,
-) -> None:
-    validate_string(text_prompt, field_name="text_prompt", min_length=1, max_length=3000)
-    max_tag = max_audio_tag(text_prompt)
-
-    if mode == MODE_TEXT:
-        if max_tag:
-            raise ValueError(
-                f"The prompt references @Audio{max_tag}, but reference mode is '{MODE_TEXT}'. "
-                f"Switch to '{MODE_AUDIO}' and connect the reference clip(s)."
-            )
-    elif mode == MODE_AUDIO:
-        if not audio_indices:
-            raise ValueError(
-                f"Reference mode '{MODE_AUDIO}' requires at least one reference_audio input "
-                f"(or switch to '{MODE_TEXT}')."
-            )
-        if audio_indices != list(range(1, len(audio_indices) + 1)):
-            raise ValueError(
-                "Connect reference_audio inputs in order without gaps: reference_audio_1, then _2, then _3."
-            )
-        if max_tag > len(audio_indices):
-            raise ValueError(
-                f"The prompt references @Audio{max_tag}, but only {len(audio_indices)} "
-                f"reference audio(s) are connected."
-            )
-    elif mode == MODE_IMAGE:
-        if not has_image:
-            raise ValueError(f"Reference mode '{MODE_IMAGE}' requires a reference_image input.")
-        if max_tag:
-            raise ValueError(
-                f"@AudioN tags are not used in '{MODE_IMAGE}' mode; the prompt should contain "
-                f"only the text to synthesize."
-            )
-    elif mode == MODE_SPEAKER:
-        if not preset_voice or preset_voice not in SEED_AUDIO_VOICE_MAP:
-            raise ValueError(f"Reference mode '{MODE_SPEAKER}' requires selecting a preset voice.")
-        if max_tag > 1:
-            raise ValueError(
-                f"'{MODE_SPEAKER}' mode uses a single voice, so @Audio{max_tag} is out of range. "
-                f"Remove the @AudioN tags — the whole prompt is read in the selected voice."
-            )
-    else:
-        raise ValueError(f"Unknown reference mode: {mode!r}")
-
-
-class ByteDanceSeedAudioNode(IO.ComfyNode):
-
-    @classmethod
-    def define_schema(cls) -> IO.Schema:
-        return IO.Schema(
-            node_id="ByteDanceSeedAudio",
-            display_name="ByteDance Seed Audio 1.0",
-            category="partner/audio/ByteDance",
-            description=(
-                "Generate speech, music, sound effects and multi-speaker dialogue from a single prompt "
-                "with ByteDance Seed Audio 1.0. Describe the voice(s), emotion, ambience, background music "
-                "and sound effects in the prompt, and include the lines to speak. Optionally pick a built-in "
-                "preset voice, clone voices from up to 3 reference clips (tagged @Audio1-3 in the prompt), "
-                "or derive a voice from a character image. Up to 2 minutes of audio per run."
-            ),
-            inputs=[
-                IO.String.Input(
-                    "text_prompt",
-                    multiline=True,
-                    default="",
-                    tooltip=(
-                        "Describe the voice(s), emotion, pacing, ambience, background music and sound "
-                        "effects, and include the lines to speak (name characters inline for dialogue). "
-                        "In 'audio reference' mode, refer to connected clips by order as @Audio1, @Audio2, "
-                        "@Audio3. Maximum 3000 characters."
-                    ),
-                ),
-                IO.DynamicCombo.Input(
-                    "reference_mode",
-                    options=[
-                        IO.DynamicCombo.Option(MODE_TEXT, []),
-                        IO.DynamicCombo.Option(
-                            MODE_AUDIO,
-                            [
-                                IO.Audio.Input(
-                                    "reference_audio_1",
-                                    optional=True,
-                                    tooltip="Reference clip for voice cloning, tagged @Audio1 in the prompt. "
-                                    "Up to 30s.",
-                                ),
-                                IO.Audio.Input(
-                                    "reference_audio_2",
-                                    optional=True,
-                                    tooltip="Reference clip tagged @Audio2 in the prompt. Up to 30s.",
-                                ),
-                                IO.Audio.Input(
-                                    "reference_audio_3",
-                                    optional=True,
-                                    tooltip="Reference clip tagged @Audio3 in the prompt. Up to 30s.",
-                                ),
-                            ],
-                        ),
-                        IO.DynamicCombo.Option(
-                            MODE_IMAGE,
-                            [
-                                IO.Image.Input(
-                                    "reference_image",
-                                    optional=True,
-                                    tooltip="A single character image; the model derives a voice from it. "
-                                    "Cannot be combined with reference audio.",
-                                ),
-                            ],
-                        ),
-                        IO.DynamicCombo.Option(
-                            MODE_SPEAKER,
-                            [
-                                IO.Combo.Input(
-                                    "preset_voice",
-                                    options=SEED_AUDIO_VOICE_OPTIONS,
-                                    default=SEED_AUDIO_VOICE_OPTIONS[0],
-                                    tooltip="A built-in TTS 2.0 voice that reads the prompt. No reference "
-                                    "clip needed, and @AudioN tags are not used in this mode.",
-                                ),
-                            ],
-                        ),
-                    ],
-                    tooltip=(
-                        "How to condition the voice: 'text only' (describe everything in the prompt), "
-                        "'audio reference' (clone up to 3 voices, tagged @Audio1-3), 'image reference' "
-                        "(derive a voice from one character image), or 'preset voice' (pick a built-in "
-                        "named voice that reads the prompt)."
-                    ),
-                ),
-                IO.Combo.Input(
-                    "sample_rate",
-                    options=["8000", "16000", "24000", "32000", "44100", "48000"],
-                    default="24000",
-                    tooltip="Output sample rate in Hz.",
-                ),
-                IO.Int.Input(
-                    "speech_rate",
-                    default=0,
-                    min=-50,
-                    max=100,
-                    tooltip="Speaking speed. 0 = normal, 100 = 2.0x, -50 = 0.5x.",
-                ),
-                IO.Int.Input(
-                    "loudness_rate",
-                    default=0,
-                    min=-50,
-                    max=100,
-                    tooltip="Loudness. 0 = normal, 100 = 2.0x, -50 = 0.5x.",
-                ),
-                IO.Int.Input(
-                    "pitch_rate",
-                    default=0,
-                    min=-12,
-                    max=12,
-                    tooltip="Pitch shift in semitones (-12 to 12).",
-                ),
-                IO.Int.Input(
-                    "seed",
-                    default=42,
-                    min=0,
-                    max=2147483647,
-                    control_after_generate=True,
-                    tooltip="Seed controls whether the node should re-run; "
-                    "results are non-deterministic regardless of seed.",
-                ),
-            ],
-            outputs=[IO.Audio.Output()],
-            hidden=[
-                IO.Hidden.auth_token_comfy_org,
-                IO.Hidden.api_key_comfy_org,
-                IO.Hidden.unique_id,
-            ],
-            is_api_node=True,
-            price_badge=IO.PriceBadge(
-                expr="""{"type":"usd","usd": 0.2145, "format":{"suffix":"/minute","approximate":true}}""",
-            ),
-        )
-
-    @classmethod
-    async def execute(
-        cls,
-        text_prompt: str,
-        reference_mode: dict,
-        sample_rate: str,
-        speech_rate: int,
-        loudness_rate: int,
-        pitch_rate: int,
-        seed: int,
-    ) -> IO.NodeOutput:
-        mode = reference_mode["reference_mode"]
-        audio_indices = connected_audio_indices(reference_mode)
-        image = reference_mode.get("reference_image")
-        preset_voice = reference_mode.get("preset_voice")
-        validate_seed_audio_inputs(text_prompt, mode, audio_indices, image is not None, preset_voice)
-
-        references: list[SeedAudioReference] | None = None
-        if mode == MODE_AUDIO:
-            references = []
-            for i in audio_indices:
-                clip = reference_mode[f"reference_audio_{i}"]
-                validate_audio_duration(clip, max_duration=30.0)
-                mp3_bytes = audio_input_to_mp3(clip).getvalue()
-                references.append(SeedAudioReference(audio_data=base64.b64encode(mp3_bytes).decode("utf-8")))
-        elif mode == MODE_IMAGE:
-            image = upscale_image_tensor_to_min_pixels(image, 160_000)
-            references = [SeedAudioReference(image_data=tensor_to_base64_string(image, mime_type="image/png"))]
-        elif mode == MODE_SPEAKER:
-            references = [SeedAudioReference(speaker=SEED_AUDIO_VOICE_MAP[preset_voice])]
-
-        response = await sync_op(
-            cls,
-            ApiEndpoint(path="/proxy/byteplus/api/v3/tts/create", method="POST"),
-            response_model=SeedAudioResponse,
-            data=SeedAudioRequest(
-                text_prompt=text_prompt,
-                references=references,
-                audio_config=SeedAudioConfig(
-                    sample_rate=int(sample_rate),
-                    speech_rate=speech_rate,
-                    loudness_rate=loudness_rate,
-                    pitch_rate=pitch_rate,
-                ),
-            ),
-        )
-        if not response.audio:
-            raise Exception(
-                f"Seed Audio returned no audio (code={response.code}): {response.message}"
-            )
-        return IO.NodeOutput(audio_bytes_to_audio_input(base64.b64decode(response.audio)))
-
-
 class ByteDanceExtension(ComfyExtension):
     @override
     async def get_node_list(self) -> list[type[IO.ComfyNode]]:
@@ -2805,7 +2490,6 @@ class ByteDanceExtension(ComfyExtension):
             ByteDance2ReferenceNode,
             ByteDanceCreateImageAsset,
             ByteDanceCreateVideoAsset,
-            ByteDanceSeedAudioNode,
         ]
 
 

comfy_api_nodes/nodes_stability.py

@@ -0,0 +1,932 @@
+from inspect import cleandoc
+from typing import Optional
+from typing_extensions import override
+
+from comfy_api.latest import ComfyExtension, Input, IO
+from comfy_api_nodes.apis.stability import (
+    StabilityUpscaleConservativeRequest,
+    StabilityUpscaleCreativeRequest,
+    StabilityAsyncResponse,
+    StabilityResultsGetResponse,
+    StabilityStable3_5Request,
+    StabilityStableUltraRequest,
+    StabilityStableUltraResponse,
+    StabilityAspectRatio,
+    Stability_SD3_5_Model,
+    Stability_SD3_5_GenerationMode,
+    get_stability_style_presets,
+    StabilityTextToAudioRequest,
+    StabilityAudioToAudioRequest,
+    StabilityAudioInpaintRequest,
+    StabilityAudioResponse,
+)
+from comfy_api_nodes.util import (
+    validate_audio_duration,
+    validate_string,
+    audio_input_to_mp3,
+    bytesio_to_image_tensor,
+    tensor_to_bytesio,
+    audio_bytes_to_audio_input,
+    sync_op,
+    poll_op,
+    ApiEndpoint,
+)
+
+import torch
+import base64
+from io import BytesIO
+from enum import Enum
+
+
+class StabilityPollStatus(str, Enum):
+    finished = "finished"
+    in_progress = "in_progress"
+    failed = "failed"
+
+
+def get_async_dummy_status(x: StabilityResultsGetResponse):
+    if x.name is not None or x.errors is not None:
+        return StabilityPollStatus.failed
+    elif x.finish_reason is not None:
+        return StabilityPollStatus.finished
+    return StabilityPollStatus.in_progress
+
+
+class StabilityStableImageUltraNode(IO.ComfyNode):
+    """
+    Generates images synchronously based on prompt and resolution.
+    """
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="StabilityStableImageUltraNode",
+            display_name="Stability AI Stable Image Ultra",
+            category="partner/image/Stability AI",
+            description=cleandoc(cls.__doc__ or ""),
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="What you wish to see in the output image. A strong, descriptive prompt that clearly defines" +
+                                    "elements, colors, and subjects will lead to better results. " +
+                                    "To control the weight of a given word use the format `(word:weight)`," +
+                                    "where `word` is the word you'd like to control the weight of and `weight`" +
+                                    "is a value between 0 and 1. For example: `The sky was a crisp (blue:0.3) and (green:0.8)`" +
+                                    "would convey a sky that was blue and green, but more green than blue.",
+                ),
+                IO.Combo.Input(
+                    "aspect_ratio",
+                    options=StabilityAspectRatio,
+                    default=StabilityAspectRatio.ratio_1_1,
+                    tooltip="Aspect ratio of generated image.",
+                ),
+                IO.Combo.Input(
+                    "style_preset",
+                    options=get_stability_style_presets(),
+                    tooltip="Optional desired style of generated image.",
+                    advanced=True,
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=4294967294,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="The random seed used for creating the noise.",
+                ),
+                IO.Image.Input(
+                    "image",
+                    optional=True,
+                ),
+                IO.String.Input(
+                    "negative_prompt",
+                    default="",
+                    tooltip="A blurb of text describing what you do not wish to see in the output image. This is an advanced feature.",
+                    force_input=True,
+                    optional=True,
+                    advanced=True,
+                ),
+                IO.Float.Input(
+                    "image_denoise",
+                    default=0.5,
+                    min=0.0,
+                    max=1.0,
+                    step=0.01,
+                    tooltip="Denoise of input image; 0.0 yields image identical to input, 1.0 is as if no image was provided at all.",
+                    optional=True,
+                ),
+            ],
+            outputs=[
+                IO.Image.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.08}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        aspect_ratio: str,
+        style_preset: str,
+        seed: int,
+        image: Optional[torch.Tensor] = None,
+        negative_prompt: str = "",
+        image_denoise: Optional[float] = 0.5,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=False)
+        # prepare image binary if image present
+        image_binary = None
+        if image is not None:
+            image_binary = tensor_to_bytesio(image, total_pixels=1504*1504).read()
+        else:
+            image_denoise = None
+
+        if not negative_prompt:
+            negative_prompt = None
+        if style_preset == "None":
+            style_preset = None
+
+        files = {
+            "image": image_binary
+        }
+
+        response_api = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/stability/v2beta/stable-image/generate/ultra", method="POST"),
+            response_model=StabilityStableUltraResponse,
+            data=StabilityStableUltraRequest(
+                prompt=prompt,
+                negative_prompt=negative_prompt,
+                aspect_ratio=aspect_ratio,
+                seed=seed,
+                strength=image_denoise,
+                style_preset=style_preset,
+            ),
+            files=files,
+            content_type="multipart/form-data",
+        )
+
+        if response_api.finish_reason != "SUCCESS":
+            raise Exception(f"Stable Image Ultra generation failed: {response_api.finish_reason}.")
+
+        image_data = base64.b64decode(response_api.image)
+        returned_image = bytesio_to_image_tensor(BytesIO(image_data))
+
+        return IO.NodeOutput(returned_image)
+
+
+class StabilityStableImageSD_3_5Node(IO.ComfyNode):
+    """
+    Generates images synchronously based on prompt and resolution.
+    """
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="StabilityStableImageSD_3_5Node",
+            display_name="Stability AI Stable Diffusion 3.5 Image",
+            category="partner/image/Stability AI",
+            description=cleandoc(cls.__doc__ or ""),
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="What you wish to see in the output image. A strong, descriptive prompt that clearly defines elements, colors, and subjects will lead to better results.",
+                ),
+                IO.Combo.Input(
+                    "model",
+                    options=Stability_SD3_5_Model,
+                ),
+                IO.Combo.Input(
+                    "aspect_ratio",
+                    options=StabilityAspectRatio,
+                    default=StabilityAspectRatio.ratio_1_1,
+                    tooltip="Aspect ratio of generated image.",
+                ),
+                IO.Combo.Input(
+                    "style_preset",
+                    options=get_stability_style_presets(),
+                    tooltip="Optional desired style of generated image.",
+                    advanced=True,
+                ),
+                IO.Float.Input(
+                    "cfg_scale",
+                    default=4.0,
+                    min=1.0,
+                    max=10.0,
+                    step=0.1,
+                    tooltip="How strictly the diffusion process adheres to the prompt text (higher values keep your image closer to your prompt)",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=4294967294,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="The random seed used for creating the noise.",
+                ),
+                IO.Image.Input(
+                    "image",
+                    optional=True,
+                ),
+                IO.String.Input(
+                    "negative_prompt",
+                    default="",
+                    tooltip="Keywords of what you do not wish to see in the output image. This is an advanced feature.",
+                    force_input=True,
+                    optional=True,
+                    advanced=True,
+                ),
+                IO.Float.Input(
+                    "image_denoise",
+                    default=0.5,
+                    min=0.0,
+                    max=1.0,
+                    step=0.01,
+                    tooltip="Denoise of input image; 0.0 yields image identical to input, 1.0 is as if no image was provided at all.",
+                    optional=True,
+                ),
+            ],
+            outputs=[
+                IO.Image.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["model"]),
+                expr="""
+                (
+                  $contains(widgets.model,"large")
+                    ? {"type":"usd","usd":0.065}
+                    : {"type":"usd","usd":0.035}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: str,
+        prompt: str,
+        aspect_ratio: str,
+        style_preset: str,
+        seed: int,
+        cfg_scale: float,
+        image: Optional[torch.Tensor] = None,
+        negative_prompt: str = "",
+        image_denoise: Optional[float] = 0.5,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=False)
+        # prepare image binary if image present
+        image_binary = None
+        mode = Stability_SD3_5_GenerationMode.text_to_image
+        if image is not None:
+            image_binary = tensor_to_bytesio(image, total_pixels=1504*1504).read()
+            mode = Stability_SD3_5_GenerationMode.image_to_image
+            aspect_ratio = None
+        else:
+            image_denoise = None
+
+        if not negative_prompt:
+            negative_prompt = None
+        if style_preset == "None":
+            style_preset = None
+
+        files = {
+            "image": image_binary
+        }
+
+        response_api = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/stability/v2beta/stable-image/generate/sd3", method="POST"),
+            response_model=StabilityStableUltraResponse,
+            data=StabilityStable3_5Request(
+                prompt=prompt,
+                negative_prompt=negative_prompt,
+                aspect_ratio=aspect_ratio,
+                seed=seed,
+                strength=image_denoise,
+                style_preset=style_preset,
+                cfg_scale=cfg_scale,
+                model=model,
+                mode=mode,
+            ),
+            files=files,
+            content_type="multipart/form-data",
+        )
+
+        if response_api.finish_reason != "SUCCESS":
+            raise Exception(f"Stable Diffusion 3.5 Image generation failed: {response_api.finish_reason}.")
+
+        image_data = base64.b64decode(response_api.image)
+        returned_image = bytesio_to_image_tensor(BytesIO(image_data))
+
+        return IO.NodeOutput(returned_image)
+
+
+class StabilityUpscaleConservativeNode(IO.ComfyNode):
+    """
+    Upscale image with minimal alterations to 4K resolution.
+    """
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="StabilityUpscaleConservativeNode",
+            display_name="Stability AI Upscale Conservative",
+            category="partner/image/Stability AI",
+            description=cleandoc(cls.__doc__ or ""),
+            inputs=[
+                IO.Image.Input("image"),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="What you wish to see in the output image. A strong, descriptive prompt that clearly defines elements, colors, and subjects will lead to better results.",
+                ),
+                IO.Float.Input(
+                    "creativity",
+                    default=0.35,
+                    min=0.2,
+                    max=0.5,
+                    step=0.01,
+                    tooltip="Controls the likelihood of creating additional details not heavily conditioned by the init image.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=4294967294,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="The random seed used for creating the noise.",
+                ),
+                IO.String.Input(
+                    "negative_prompt",
+                    default="",
+                    tooltip="Keywords of what you do not wish to see in the output image. This is an advanced feature.",
+                    force_input=True,
+                    optional=True,
+                    advanced=True,
+                ),
+            ],
+            outputs=[
+                IO.Image.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.4}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        image: torch.Tensor,
+        prompt: str,
+        creativity: float,
+        seed: int,
+        negative_prompt: str = "",
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=False)
+        image_binary = tensor_to_bytesio(image, total_pixels=1024*1024).read()
+
+        if not negative_prompt:
+            negative_prompt = None
+
+        files = {
+            "image": image_binary
+        }
+
+        response_api = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/stability/v2beta/stable-image/upscale/conservative", method="POST"),
+            response_model=StabilityStableUltraResponse,
+            data=StabilityUpscaleConservativeRequest(
+                prompt=prompt,
+                negative_prompt=negative_prompt,
+                creativity=round(creativity,2),
+                seed=seed,
+            ),
+            files=files,
+            content_type="multipart/form-data",
+        )
+
+        if response_api.finish_reason != "SUCCESS":
+            raise Exception(f"Stability Upscale Conservative generation failed: {response_api.finish_reason}.")
+
+        image_data = base64.b64decode(response_api.image)
+        returned_image = bytesio_to_image_tensor(BytesIO(image_data))
+
+        return IO.NodeOutput(returned_image)
+
+
+class StabilityUpscaleCreativeNode(IO.ComfyNode):
+    """
+    Upscale image with minimal alterations to 4K resolution.
+    """
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="StabilityUpscaleCreativeNode",
+            display_name="Stability AI Upscale Creative",
+            category="partner/image/Stability AI",
+            description=cleandoc(cls.__doc__ or ""),
+            inputs=[
+                IO.Image.Input("image"),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="What you wish to see in the output image. A strong, descriptive prompt that clearly defines elements, colors, and subjects will lead to better results.",
+                ),
+                IO.Float.Input(
+                    "creativity",
+                    default=0.3,
+                    min=0.1,
+                    max=0.5,
+                    step=0.01,
+                    tooltip="Controls the likelihood of creating additional details not heavily conditioned by the init image.",
+                ),
+                IO.Combo.Input(
+                    "style_preset",
+                    options=get_stability_style_presets(),
+                    tooltip="Optional desired style of generated image.",
+                    advanced=True,
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=4294967294,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="The random seed used for creating the noise.",
+                ),
+                IO.String.Input(
+                    "negative_prompt",
+                    default="",
+                    tooltip="Keywords of what you do not wish to see in the output image. This is an advanced feature.",
+                    force_input=True,
+                    optional=True,
+                    advanced=True,
+                ),
+            ],
+            outputs=[
+                IO.Image.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.6}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        image: torch.Tensor,
+        prompt: str,
+        creativity: float,
+        style_preset: str,
+        seed: int,
+        negative_prompt: str = "",
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=False)
+        image_binary = tensor_to_bytesio(image, total_pixels=1024*1024).read()
+
+        if not negative_prompt:
+            negative_prompt = None
+        if style_preset == "None":
+            style_preset = None
+
+        files = {
+            "image": image_binary
+        }
+
+        response_api = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/stability/v2beta/stable-image/upscale/creative", method="POST"),
+            response_model=StabilityAsyncResponse,
+            data=StabilityUpscaleCreativeRequest(
+                prompt=prompt,
+                negative_prompt=negative_prompt,
+                creativity=round(creativity,2),
+                style_preset=style_preset,
+                seed=seed,
+            ),
+            files=files,
+            content_type="multipart/form-data",
+        )
+
+        response_poll = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/stability/v2beta/results/{response_api.id}"),
+            response_model=StabilityResultsGetResponse,
+            poll_interval=3,
+            status_extractor=lambda x: get_async_dummy_status(x),
+        )
+
+        if response_poll.finish_reason != "SUCCESS":
+            raise Exception(f"Stability Upscale Creative generation failed: {response_poll.finish_reason}.")
+
+        image_data = base64.b64decode(response_poll.result)
+        returned_image = bytesio_to_image_tensor(BytesIO(image_data))
+
+        return IO.NodeOutput(returned_image)
+
+
+class StabilityUpscaleFastNode(IO.ComfyNode):
+    """
+    Quickly upscales an image via Stability API call to 4x its original size; intended for upscaling low-quality/compressed images.
+    """
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="StabilityUpscaleFastNode",
+            display_name="Stability AI Upscale Fast",
+            category="partner/image/Stability AI",
+            description=cleandoc(cls.__doc__ or ""),
+            inputs=[
+                IO.Image.Input("image"),
+            ],
+            outputs=[
+                IO.Image.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.02}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(cls, image: torch.Tensor) -> IO.NodeOutput:
+        image_binary = tensor_to_bytesio(image, total_pixels=4096*4096).read()
+
+        files = {
+            "image": image_binary
+        }
+
+        response_api = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/stability/v2beta/stable-image/upscale/fast", method="POST"),
+            response_model=StabilityStableUltraResponse,
+            files=files,
+            content_type="multipart/form-data",
+        )
+
+        if response_api.finish_reason != "SUCCESS":
+            raise Exception(f"Stability Upscale Fast failed: {response_api.finish_reason}.")
+
+        image_data = base64.b64decode(response_api.image)
+        returned_image = bytesio_to_image_tensor(BytesIO(image_data))
+
+        return IO.NodeOutput(returned_image)
+
+
+class StabilityTextToAudio(IO.ComfyNode):
+    """Generates high-quality music and sound effects from text descriptions."""
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="StabilityTextToAudio",
+            display_name="Stability AI Text To Audio",
+            category="partner/audio/Stability AI",
+            essentials_category="Audio",
+            description=cleandoc(cls.__doc__ or ""),
+            inputs=[
+                IO.Combo.Input(
+                    "model",
+                    options=["stable-audio-2.5"],
+                ),
+                IO.String.Input("prompt", multiline=True, default=""),
+                IO.Int.Input(
+                    "duration",
+                    default=190,
+                    min=1,
+                    max=190,
+                    step=1,
+                    tooltip="Controls the duration in seconds of the generated audio.",
+                    optional=True,
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=4294967294,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="The random seed used for generation.",
+                    optional=True,
+                ),
+                IO.Int.Input(
+                    "steps",
+                    default=8,
+                    min=4,
+                    max=8,
+                    step=1,
+                    tooltip="Controls the number of sampling steps.",
+                    optional=True,
+                    advanced=True,
+                ),
+            ],
+            outputs=[
+                IO.Audio.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.2}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(cls, model: str, prompt: str, duration: int, seed: int, steps: int) -> IO.NodeOutput:
+        validate_string(prompt, max_length=10000)
+        payload = StabilityTextToAudioRequest(prompt=prompt, model=model, duration=duration, seed=seed, steps=steps)
+        response_api = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/stability/v2beta/audio/stable-audio-2/text-to-audio", method="POST"),
+            response_model=StabilityAudioResponse,
+            data=payload,
+            content_type="multipart/form-data",
+        )
+        if not response_api.audio:
+            raise ValueError("No audio file was received in response.")
+        return IO.NodeOutput(audio_bytes_to_audio_input(base64.b64decode(response_api.audio)))
+
+
+class StabilityAudioToAudio(IO.ComfyNode):
+    """Transforms existing audio samples into new high-quality compositions using text instructions."""
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="StabilityAudioToAudio",
+            display_name="Stability AI Audio To Audio",
+            category="partner/audio/Stability AI",
+            description=cleandoc(cls.__doc__ or ""),
+            inputs=[
+                IO.Combo.Input(
+                    "model",
+                    options=["stable-audio-2.5"],
+                ),
+                IO.String.Input("prompt", multiline=True, default=""),
+                IO.Audio.Input("audio", tooltip="Audio must be between 6 and 190 seconds long."),
+                IO.Int.Input(
+                    "duration",
+                    default=190,
+                    min=1,
+                    max=190,
+                    step=1,
+                    tooltip="Controls the duration in seconds of the generated audio.",
+                    optional=True,
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=4294967294,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="The random seed used for generation.",
+                    optional=True,
+                ),
+                IO.Int.Input(
+                    "steps",
+                    default=8,
+                    min=4,
+                    max=8,
+                    step=1,
+                    tooltip="Controls the number of sampling steps.",
+                    optional=True,
+                    advanced=True,
+                ),
+                IO.Float.Input(
+                    "strength",
+                    default=1,
+                    min=0.01,
+                    max=1.0,
+                    step=0.01,
+                    display_mode=IO.NumberDisplay.slider,
+                    tooltip="Parameter controls how much influence the audio parameter has on the generated audio.",
+                    optional=True,
+                ),
+            ],
+            outputs=[
+                IO.Audio.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.2}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls, model: str, prompt: str, audio: Input.Audio, duration: int, seed: int, steps: int, strength: float
+    ) -> IO.NodeOutput:
+        validate_string(prompt, max_length=10000)
+        validate_audio_duration(audio, 6, 190)
+        payload = StabilityAudioToAudioRequest(
+            prompt=prompt, model=model, duration=duration, seed=seed, steps=steps, strength=strength
+        )
+        response_api = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/stability/v2beta/audio/stable-audio-2/audio-to-audio", method="POST"),
+            response_model=StabilityAudioResponse,
+            data=payload,
+            content_type="multipart/form-data",
+            files={"audio": audio_input_to_mp3(audio)},
+        )
+        if not response_api.audio:
+            raise ValueError("No audio file was received in response.")
+        return IO.NodeOutput(audio_bytes_to_audio_input(base64.b64decode(response_api.audio)))
+
+
+class StabilityAudioInpaint(IO.ComfyNode):
+    """Transforms part of existing audio sample using text instructions."""
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="StabilityAudioInpaint",
+            display_name="Stability AI Audio Inpaint",
+            category="partner/audio/Stability AI",
+            description=cleandoc(cls.__doc__ or ""),
+            inputs=[
+                IO.Combo.Input(
+                    "model",
+                    options=["stable-audio-2.5"],
+                ),
+                IO.String.Input("prompt", multiline=True, default=""),
+                IO.Audio.Input("audio", tooltip="Audio must be between 6 and 190 seconds long."),
+                IO.Int.Input(
+                    "duration",
+                    default=190,
+                    min=1,
+                    max=190,
+                    step=1,
+                    tooltip="Controls the duration in seconds of the generated audio.",
+                    optional=True,
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=4294967294,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="The random seed used for generation.",
+                    optional=True,
+                ),
+                IO.Int.Input(
+                    "steps",
+                    default=8,
+                    min=4,
+                    max=8,
+                    step=1,
+                    tooltip="Controls the number of sampling steps.",
+                    optional=True,
+                    advanced=True,
+                ),
+                IO.Int.Input(
+                    "mask_start",
+                    default=30,
+                    min=0,
+                    max=190,
+                    step=1,
+                    optional=True,
+                    advanced=True,
+                ),
+                IO.Int.Input(
+                    "mask_end",
+                    default=190,
+                    min=0,
+                    max=190,
+                    step=1,
+                    optional=True,
+                    advanced=True,
+                ),
+            ],
+            outputs=[
+                IO.Audio.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.2}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: str,
+        prompt: str,
+        audio: Input.Audio,
+        duration: int,
+        seed: int,
+        steps: int,
+        mask_start: int,
+        mask_end: int,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, max_length=10000)
+        if mask_end <= mask_start:
+            raise ValueError(f"Value of mask_end({mask_end}) should be greater then mask_start({mask_start})")
+        validate_audio_duration(audio, 6, 190)
+
+        payload = StabilityAudioInpaintRequest(
+            prompt=prompt,
+            model=model,
+            duration=duration,
+            seed=seed,
+            steps=steps,
+            mask_start=mask_start,
+            mask_end=mask_end,
+        )
+        response_api = await sync_op(
+            cls,
+            endpoint=ApiEndpoint(path="/proxy/stability/v2beta/audio/stable-audio-2/inpaint", method="POST"),
+            response_model=StabilityAudioResponse,
+            data=payload,
+            content_type="multipart/form-data",
+            files={"audio": audio_input_to_mp3(audio)},
+        )
+        if not response_api.audio:
+            raise ValueError("No audio file was received in response.")
+        return IO.NodeOutput(audio_bytes_to_audio_input(base64.b64decode(response_api.audio)))
+
+
+class StabilityExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            StabilityStableImageUltraNode,
+            StabilityStableImageSD_3_5Node,
+            StabilityUpscaleConservativeNode,
+            StabilityUpscaleCreativeNode,
+            StabilityUpscaleFastNode,
+            StabilityTextToAudio,
+            StabilityAudioToAudio,
+            StabilityAudioInpaint,
+        ]
+
+
+async def comfy_entrypoint() -> StabilityExtension:
+    return StabilityExtension()

comfy_api_nodes/util/__init__.py

@@ -26,7 +26,6 @@ from .conversions import (
     text_filepath_to_base64_string,
     text_filepath_to_data_uri,
     trim_video,
-    upscale_image_tensor_to_min_pixels,
     upscale_video_to_min_pixels,
     video_to_base64_string,
 )
@@ -100,7 +99,6 @@ __all__ = [
     "text_filepath_to_base64_string",
     "text_filepath_to_data_uri",
     "trim_video",
-    "upscale_image_tensor_to_min_pixels",
     "upscale_video_to_min_pixels",
     "video_to_base64_string",
     # Validation utilities

comfy_api_nodes/util/conversions.py

@@ -448,15 +448,6 @@ def _compute_upscale_dims(src_w: int, src_h: int, total_pixels: int) -> tuple[in
     return new_w, new_h
 
 
-def upscale_image_tensor_to_min_pixels(image: torch.Tensor, total_pixels: int) -> torch.Tensor:
-    samples = image.movedim(-1, 1)
-    dims = _compute_upscale_dims(samples.shape[3], samples.shape[2], int(total_pixels))
-    if dims is None:
-        return image
-    new_w, new_h = dims
-    return common_upscale(samples, new_w, new_h, "lanczos", "disabled").movedim(1, -1)
-
-
 def upscale_video_to_min_pixels(video: Input.Video, min_pixels: int) -> Input.Video:
     """Upscale a video to meet at least ``min_pixels`` (w * h), preserving aspect ratio.
 

comfy_api_nodes/util/request_logger.py

@@ -9,7 +9,6 @@ from typing import Any
 import folder_paths
 
 logger = logging.getLogger(__name__)
-_SENSITIVE_HEADERS = {"authorization", "x-api-key"}
 
 
 def get_log_directory():
@@ -74,10 +73,6 @@ def _format_data_for_logging(data: Any) -> str:
     return str(data)
 
 
-def _redact_headers(headers: dict) -> dict:
-    return {k: ("***" if k.lower() in _SENSITIVE_HEADERS else v) for k, v in headers.items()}
-
-
 def log_request_response(
     operation_id: str,
     request_method: str,
@@ -106,7 +101,7 @@ def log_request_response(
         log_content.append(f"Method: {request_method}")
         log_content.append(f"URL: {request_url}")
         if request_headers:
-            log_content.append(f"Headers:\n{_format_data_for_logging(_redact_headers(request_headers))}")
+            log_content.append(f"Headers:\n{_format_data_for_logging(request_headers)}")
         if request_params:
             log_content.append(f"Params:\n{_format_data_for_logging(request_params)}")
         if request_data is not None:

comfy_api_nodes/util/upload_helpers.py

@@ -158,14 +158,7 @@ async def upload_video_to_comfyapi(
 
     # Convert VideoInput to BytesIO using specified container/codec
     video_bytes_io = BytesIO()
-    try:
-        video.save_to(video_bytes_io, format=container, codec=codec)
-    except Exception as e:
-        raise ValueError(
-            f"Could not convert the input video to {container.value.upper()} for upload; "
-            f"the file may be corrupted or use an unsupported codec. "
-            f"Try re-exporting it as MP4 (H.264). Original error: {e}"
-        ) from e
+    video.save_to(video_bytes_io, format=container, codec=codec)
     video_bytes_io.seek(0)
 
     return await upload_file_to_comfyapi(cls, video_bytes_io, filename, upload_mime_type, wait_label)

comfy_execution/caching.py

@@ -503,22 +503,6 @@ RAM_CACHE_DEFAULT_RAM_USAGE = 0.05
 
 RAM_CACHE_OLD_WORKFLOW_OOM_MULTIPLIER = 1.3
 
-RAM_CACHE_LARGE_INTERMEDIATE = 512 * 1024 ** 2
-
-
-def all_outputs_dynamic(outputs):
-    if outputs is None:
-        return False
-
-    for output in outputs:
-        if isinstance(output, (list, tuple)):
-            if not all_outputs_dynamic(output):
-                return False
-        elif not hasattr(output, "is_dynamic") or not output.is_dynamic():
-            return False
-
-    return True
-
 class RAMPressureCache(LRUCache):
 
     def __init__(self, key_class, enable_providers=False):
@@ -540,25 +524,20 @@ class RAMPressureCache(LRUCache):
         self.timestamps[self.cache_key_set.get_data_key(node_id)] = time.time()
         super().set_local(node_id, value)
 
-    def ram_release(self, target, free_active=False, min_entry_size=0):
+    def ram_release(self, target, free_active=False):
         if psutil.virtual_memory().available >= target:
-            return 0
+            return
 
         clean_list = []
 
         for key, cache_entry in self.cache.items():
             if not free_active and self.used_generation[key] == self.generation:
                 continue
-
-            if all_outputs_dynamic(cache_entry.outputs) and self.used_generation[key] == self.generation:
-                continue
-
-            oom_score = RAM_CACHE_OLD_WORKFLOW_OOM_MULTIPLIER ** (self.generation - self.used_generation[key])
+            oom_score =  RAM_CACHE_OLD_WORKFLOW_OOM_MULTIPLIER ** (self.generation - self.used_generation[key])
 
             ram_usage = RAM_CACHE_DEFAULT_RAM_USAGE
-            oom_ram_usage = ram_usage
             def scan_list_for_ram_usage(outputs):
-                nonlocal ram_usage, oom_ram_usage
+                nonlocal ram_usage
                 if outputs is None:
                     return
                 for output in outputs:
@@ -566,26 +545,19 @@ class RAMPressureCache(LRUCache):
                         scan_list_for_ram_usage(output)
                     elif isinstance(output, torch.Tensor) and output.device.type == 'cpu':
                         ram_usage += output.numel() * output.element_size()
-                        oom_ram_usage += output.numel() * output.element_size()
                     elif isinstance(output, ModelPatcher) and self.used_generation[key] != self.generation:
                         #old ModelPatchers are the first to go
-                        oom_ram_usage = 1e30
+                        ram_usage = 1e30
             scan_list_for_ram_usage(cache_entry.outputs)
 
-            if ram_usage < min_entry_size:
-                continue
-
-            oom_score *= oom_ram_usage
+            oom_score *= ram_usage
             #In the case where we have no information on the node ram usage at all,
             #break OOM score ties on the last touch timestamp (pure LRU)
-            bisect.insort(clean_list, (oom_score, self.timestamps[key], key, ram_usage))
+            bisect.insort(clean_list, (oom_score, self.timestamps[key], key))
 
-        freed = 0
         while psutil.virtual_memory().available < target and clean_list:
-            _, _, key, ram_usage = clean_list.pop()
+            _, _, key = clean_list.pop()
             del self.cache[key]
             self.used_generation.pop(key, None)
             self.timestamps.pop(key, None)
             self.children.pop(key, None)
-            freed += ram_usage
-        return freed

comfy_extras/nodes_color.py

@@ -16,30 +16,23 @@ class ColorToRGBInt(io.ComfyNode):
             ],
             outputs=[
                 io.Int.Output(display_name="rgb_int"),
-                io.Color.Output(display_name="hex"),
-                io.Float.Output(display_name="alpha"),
+                io.Color.Output(display_name="hex")
             ],
         )
 
     @classmethod
     def execute(cls, color: str) -> io.NodeOutput:
-        # expect format #RRGGBB or #RRGGBBAA
-        if len(color) not in (7, 9) or color[0] != "#":
-            raise ValueError("Color must be in format #RRGGBB or #RRGGBBAA")
+        # expect format #RRGGBB
+        if len(color) != 7 or color[0] != "#":
+            raise ValueError("Color must be in format #RRGGBB")
         try:
             int(color[1:], 16)
         except ValueError:
-            raise ValueError("Color must be in format #RRGGBB or #RRGGBBAA") from None
-
-        alpha = 1.0
-        if len(color) == 9:
-            alpha = int(color[7:9], 16) / 255.0
-            color = color[:7]
-
+            raise ValueError("Color must be in format #RRGGBB") from None
         r, g, b = hex_to_rgb(color)
 
         rgb_int = r * 256 * 256 + g * 256 + b
-        return io.NodeOutput(rgb_int, color, alpha)
+        return io.NodeOutput(rgb_int, color)
 
 
 class ColorExtension(ComfyExtension):

comfy_extras/nodes_text_overlay.py

@@ -1,150 +0,0 @@
-import numpy as np
-import torch
-from PIL import Image as PILImage, ImageColor, ImageDraw, ImageFont
-from typing_extensions import override
-
-from comfy_api.latest import ComfyExtension, IO
-
-
-class TextOverlay(IO.ComfyNode):
-    @classmethod
-    def define_schema(cls):
-        return IO.Schema(
-            node_id="TextOverlay",
-            display_name="Draw Text Overlay",
-            category="text",
-            description="Draw text overlay on an image or batch of images.",
-            search_aliases=["text", "label", "caption", "subtitle", "watermark", "title", "addlabel", "overlay"],
-            inputs=[
-                IO.Image.Input("images"),
-                IO.String.Input("text", multiline=True, default=""),
-                IO.Float.Input("font_size", default=5.0, min=0.5, max=50.0, step=0.5, tooltip="Font size as a percentage of the image height."),
-                IO.Color.Input("color", default="#ffffff", tooltip="Color of the text."),
-                IO.Combo.Input("position", options=["top", "bottom"], default="top"),
-                IO.Combo.Input("align", options=["left", "center", "right"], default="left"),
-                IO.Boolean.Input("outline", default=True, tooltip="Draw a black outline around the text."),
-            ],
-            outputs=[IO.Image.Output(display_name="images")],
-        )
-
-    @classmethod
-    def execute(cls, images, text, font_size, color, position, align, outline) -> IO.NodeOutput:
-        if text.strip() == "":
-            return IO.NodeOutput(images)
-
-        text = text.replace("\\n", "\n").replace("\\t", "\t")
-
-        text_rgba = cls.parse_color_to_rgba(color)
-        outline_rgba = (0, 0, 0, 255) if outline else (0, 0, 0, 0)
-
-        # Render the overlay once and composite it across all frames in the batch
-        height = images.shape[1]
-        width = images.shape[2]
-        overlay_rgb, overlay_alpha = cls.render_overlay_text(width, height, text, position, align, font_size, text_rgba, outline_rgba)
-        overlay_rgb = overlay_rgb.to(device=images.device, dtype=images.dtype)
-        overlay_alpha = overlay_alpha.to(device=images.device, dtype=images.dtype)
-
-        result = images * (1.0 - overlay_alpha) + overlay_rgb * overlay_alpha
-        return IO.NodeOutput(result)
-
-    @staticmethod
-    def parse_color_to_rgba(color_string):
-        parsed = ImageColor.getrgb(color_string)
-
-        if len(parsed) == 3:
-            return (*parsed, 255)
-
-        return parsed
-
-    @classmethod
-    def render_overlay_text(cls, width, height, text, position, align, font_size, text_rgba, outline_rgba):
-        line_spacing = 1.2
-        margin_percent = 1.0
-        min_font_percent = 2.0
-        min_font_pixels = 10
-        outline_thickness_factor = 0.04
-
-        # Draw onto a transparent layer so the result can be alpha-composited over any frame.
-        layer = PILImage.new("RGBA", (width, height), (0, 0, 0, 0))
-        draw = ImageDraw.Draw(layer)
-
-        margin = int(round(margin_percent / 100.0 * min(width, height)))
-        max_width = max(1, width - 2 * margin)
-        max_height = max(1, height - 2 * margin)
-
-        # Font scales with resolution, then shrinks to fit the height.
-        size = max(1, int(round(font_size / 100.0 * height)))
-        floor = min(size, max(min_font_pixels, int(round(min_font_percent / 100.0 * height))))
-
-        while True:
-            font = ImageFont.load_default(size=size)
-            stroke = max(1, int(round(size * outline_thickness_factor))) if outline_rgba[3] > 0 else 0
-            block = "\n".join(cls.wrap_text(text, font, max_width))
-            # convert line spacing to pixel spacing
-            single = draw.textbbox((0, 0), "Ay", font=font, stroke_width=stroke)
-            double = draw.multiline_textbbox((0, 0), "Ay\nAy", font=font, spacing=0, stroke_width=stroke)
-            natural_advance = (double[3] - double[1]) - (single[3] - single[1])
-            pixel_spacing = int(round(size * line_spacing - natural_advance))
-            box = draw.multiline_textbbox((0, 0), block, font=font, spacing=pixel_spacing, stroke_width=stroke)
-            block_height = box[3] - box[1]
-
-            if block_height <= max_height or size <= floor:
-                break
-
-            size = max(floor, int(size * 0.9))
-
-        anchor_h, x = {"left": ("l", margin), "center": ("m", width / 2), "right": ("r", width - margin)}[align]
-
-        # Offset y so the rendered text sits flush against the margin
-        if position == "bottom":
-            y = height - margin - box[3]
-        else:
-            y = margin - box[1]
-
-        draw.multiline_text((x, y), block, font=font, fill=text_rgba, anchor=anchor_h + "a",
-                            align=align, spacing=pixel_spacing, stroke_width=stroke, stroke_fill=outline_rgba)
-
-        overlay = np.array(layer).astype(np.float32) / 255.0
-        overlay_rgb = torch.from_numpy(overlay[:, :, :3])
-        overlay_alpha = torch.from_numpy(overlay[:, :, 3:4])
-        return overlay_rgb, overlay_alpha
-
-    @staticmethod
-    def wrap_text(text, font, max_width):
-        lines = []
-        for raw_line in text.split("\n"):
-            words = raw_line.split()
-            if not words:
-                lines.append("")
-                continue
-            current = ""
-            # Break the line into words and split words that are too long
-            for word in words:
-                while font.getlength(word) > max_width and len(word) > 1:
-                    cut = 1
-                    while cut < len(word) and font.getlength(word[:cut + 1]) <= max_width:
-                        cut += 1
-                    if current:
-                        lines.append(current)
-                        current = ""
-                    lines.append(word[:cut])
-                    word = word[cut:]
-                candidate = word if not current else current + " " + word
-                if not current or font.getlength(candidate) <= max_width:
-                    current = candidate
-                else:
-                    lines.append(current)
-                    current = word
-            if current:
-                lines.append(current)
-        return lines
-
-
-class TextOverlayExtension(ComfyExtension):
-    @override
-    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
-        return [TextOverlay]
-
-
-async def comfy_entrypoint() -> TextOverlayExtension:
-    return TextOverlayExtension()

execution.py

@@ -29,7 +29,6 @@ from comfy_execution.caching import (
     HierarchicalCache,
     LRUCache,
     RAMPressureCache,
-    RAM_CACHE_LARGE_INTERMEDIATE,
 )
 from comfy_execution.graph import (
     DynamicPrompt,
@@ -795,16 +794,12 @@ class PromptExecutor:
                     if self.cache_type == CacheType.RAM_PRESSURE:
                         ram_release_callback(ram_inactive_headroom)
                         ram_shortfall = ram_headroom - psutil.virtual_memory().available
-                        if ram_shortfall > 0:
-                            freed = ram_release_callback(ram_headroom, free_active=True, min_entry_size=RAM_CACHE_LARGE_INTERMEDIATE)
-                            ram_shortfall -= freed
-                        if comfy.model_management.should_free_pins_for_ram_pressure(ram_shortfall):
-                            freed = comfy.model_management.free_pins(ram_shortfall + 512 * (1024 ** 2))
-                            if freed < ram_shortfall:
-                                if freed > 64 * (1024 ** 2):
-                                    # AIMDO MEM_DECOMMIT can outrun psutil.available catching up.
-                                    time.sleep(0.05)
-                                ram_release_callback(ram_headroom, free_active=True)
+                        freed = comfy.model_management.free_pins(ram_shortfall + 512 * (1024 ** 2))
+                        if freed < ram_shortfall:
+                            if freed > 64 * (1024 ** 2):
+                                # AIMDO MEM_DECOMMIT can outrun psutil.available catching up.
+                                time.sleep(0.05)
+                            ram_release_callback(ram_headroom, free_active=True)
                 else:
                     # Only execute when the while-loop ends without break
                     # Send cached UI for intermediate output nodes that weren't executed
@@ -1118,6 +1113,32 @@ def full_type_name(klass):
         return klass.__qualname__
     return module + '.' + klass.__qualname__
 
+def node_not_executable_reason(class_def, class_type):
+    """Return a human-readable reason the node cannot be executed, or None if it's fine.
+
+    Catches a node whose declared entry point doesn't resolve to a real method
+    (e.g. a V1 ``FUNCTION = "invert"`` where the method is misspelled, or a V3 node
+    missing its ``execute`` override). Running this during validation surfaces the
+    problem before execution starts, instead of after upstream nodes have run.
+
+    Only the class is inspected; the node is never instantiated here, so a node's
+    ``__init__`` side effects cannot run (or fail) during validation.
+    """
+    try:
+        if issubclass(class_def, _ComfyNodeInternal):
+            # V3: validates that execute()/define_schema() overrides exist.
+            class_def.VALIDATE_CLASS()
+            return None
+        # V1: FUNCTION names the method to call; it must exist on the class.
+        function_name = getattr(class_def, "FUNCTION", None)
+        if function_name is None:
+            return f"'{class_type}' does not define FUNCTION"
+        if not callable(getattr(class_def, function_name, None)):
+            return f"'{class_type}' has no method '{function_name}' (declared in FUNCTION)"
+        return None
+    except Exception as ex:
+        return str(ex)
+
 async def validate_prompt(prompt_id, prompt, partial_execution_list: Union[list[str], None]):
     outputs = set()
     for x in prompt:
@@ -1153,6 +1174,35 @@ async def validate_prompt(prompt_id, prompt, partial_execution_list: Union[list[
             }
             return (False, error, [], {})
 
+        # Make sure the node is actually executable (its FUNCTION/execute entry
+        # point resolves to a real method) before we touch any schema-derived
+        # attributes below or start execution. Catches code typos up front and
+        # attributes the error to the offending node.
+        not_executable = node_not_executable_reason(class_, class_type)
+        if not_executable is not None:
+            node_title = prompt[x].get('_meta', {}).get('title', class_type)
+            error = {
+                "type": "invalid_node_definition",
+                "message": "Node is not executable",
+                "details": f"{not_executable} (Node ID '#{x}')",
+                "extra_info": {
+                    "node_id": x,
+                    "class_type": class_type,
+                    "node_title": node_title,
+                }
+            }
+            node_errors = {x: {
+                "errors": [{
+                    "type": "invalid_node_definition",
+                    "message": "Node is not executable",
+                    "details": not_executable,
+                    "extra_info": {},
+                }],
+                "dependent_outputs": [],
+                "class_type": class_type,
+            }}
+            return (False, error, [], node_errors)
+
         if hasattr(class_, 'OUTPUT_NODE') and class_.OUTPUT_NODE is True:
             if partial_execution_list is None or x in partial_execution_list:
                 outputs.add(x)

folder_paths.py

@@ -264,59 +264,6 @@ def annotated_filepath(name: str) -> tuple[str, str | None]:
     return name, base_dir
 
 
-# Content types a browser may execute or render inline. File endpoints that
-# serve user-controlled content must force these to download (and ideally set
-# Content-Disposition: attachment) to avoid stored XSS. Centralised here so the
-# /view and /userdata handlers can't drift apart. mimetypes.guess_type may
-# return either the text/* or application/* spelling depending on platform, so
-# both are listed.
-DANGEROUS_CONTENT_TYPES = {
-    'text/html', 'text/html-sandboxed', 'application/xhtml+xml',
-    'text/javascript', 'application/javascript', 'application/x-javascript',
-    'application/ecmascript', 'text/css',
-    'image/svg+xml', 'application/xml', 'text/xml',
-    # message/rfc822 (.mht/.mhtml) can carry script in some browsers.
-    'message/rfc822',
-}
-
-
-def is_dangerous_content_type(content_type: str | None) -> bool:
-    """Return True if a browser may execute or render `content_type` inline.
-
-    Normalises before matching so the check can't be slipped past with a
-    charset/boundary parameter (``text/html; charset=utf-8``) or casing
-    (``TEXT/HTML``). Any XML dialect (``*+xml`` or ``*/xml``) is treated as
-    dangerous because XML can carry inline script via stylesheet/entity tricks,
-    which also covers the ``application/{xslt,rss,atom,rdf}+xml`` family without
-    enumerating each one. Endpoints serving user-controlled content should route
-    a dangerous type to ``application/octet-stream`` + ``Content-Disposition:
-    attachment`` + ``X-Content-Type-Options: nosniff``.
-    """
-    if not content_type:
-        return False
-    normalized = content_type.split(';', 1)[0].strip().lower()
-    if normalized in DANGEROUS_CONTENT_TYPES:
-        return True
-    return normalized.endswith('+xml') or normalized.endswith('/xml')
-
-
-def is_within_directory(directory: str, target: str) -> bool:
-    """Return True if `target` resolves to a path inside `directory`.
-
-    Uses realpath on both operands so that a symlink placed inside `directory`
-    that points elsewhere cannot escape the containment check at open time.
-    """
-    try:
-        directory = os.path.realpath(directory)
-        target = os.path.realpath(target)
-        return os.path.commonpath((directory, target)) == directory
-    except ValueError:
-        # ValueError is raised by realpath() on a path with an embedded null
-        # byte, and by commonpath() on Windows when the paths are on different
-        # drives. In either case the target is not safely within the directory.
-        return False
-
-
 def get_annotated_filepath(name: str, default_dir: str | None=None) -> str:
     name, base_dir = annotated_filepath(name)
 
@@ -326,12 +273,7 @@ def get_annotated_filepath(name: str, default_dir: str | None=None) -> str:
         else:
             base_dir = get_input_directory()  # fallback path
 
-    filepath = os.path.abspath(os.path.join(base_dir, name))
-    # Prevent path traversal: the resolved path must stay within base_dir.
-    # repr() the name in the message so a crafted value can't inject log lines.
-    if not is_within_directory(base_dir, filepath):
-        raise ValueError("Invalid file path: {!r}".format(name))
-    return filepath
+    return os.path.join(base_dir, name)
 
 
 def exists_annotated_filepath(name) -> bool:
@@ -340,10 +282,7 @@ def exists_annotated_filepath(name) -> bool:
     if base_dir is None:
         base_dir = get_input_directory()  # fallback path
 
-    filepath = os.path.abspath(os.path.join(base_dir, name))
-    # Treat traversal attempts as non-existent rather than probing the filesystem.
-    if not is_within_directory(base_dir, filepath):
-        return False
+    filepath = os.path.join(base_dir, name)
     return os.path.exists(filepath)
 
 

nodes.py

@@ -2478,7 +2478,6 @@ async def init_builtin_extra_nodes():
         "nodes_glsl.py",
         "nodes_lora_debug.py",
         "nodes_textgen.py",
-        "nodes_text_overlay.py",
         "nodes_color.py",
         "nodes_toolkit.py",
         "nodes_replacements.py",

requirements.txt

@@ -1,6 +1,6 @@
 comfyui-frontend-package==1.45.20
-comfyui-workflow-templates==0.11.2
-comfyui-embedded-docs==0.5.7
+comfyui-workflow-templates==0.11.1
+comfyui-embedded-docs==0.5.6
 torch
 torchsde
 torchvision

server.py

@@ -127,7 +127,6 @@ def create_cors_middleware(allowed_origin: str):
 
     return cors_middleware
 
-
 def is_loopback(host):
     if host is None:
         return False
@@ -617,30 +616,15 @@ class PromptServer():
                             or 'application/octet-stream'
                         )
 
-                        # For security, force renderable/active types (HTML, JS,
-                        # CSS, SVG, XML — anything that can carry inline <script>
-                        # and execute in the page origin) to download instead of
-                        # displaying inline, preventing stored XSS. The
-                        # attachment disposition is the load-bearing guard: a
-                        # bare filename= hint does not force a download per
-                        # RFC 6266, so we only attach it on the dangerous branch
-                        # to avoid breaking inline display of legitimate images.
-                        # Escape backslash/quote per RFC 6266 quoted-string so a
-                        # filename containing a double quote (which passes the
-                        # ".."/leading-slash filter above) can't break out of the
-                        # header's quoted-string and malform the disposition.
-                        safe_filename = filename.replace("\\", "\\\\").replace('"', '\\"')
-                        disposition = f"filename=\"{safe_filename}\""
-                        if folder_paths.is_dangerous_content_type(content_type):
-                            content_type = 'application/octet-stream'
-                            disposition = f"attachment; filename=\"{safe_filename}\""
+                        # For security, force certain mimetypes to download instead of display
+                        if content_type in {'text/html', 'text/html-sandboxed', 'application/xhtml+xml', 'text/javascript', 'text/css'}:
+                            content_type = 'application/octet-stream'  # Forces download
 
                         return web.FileResponse(
                             file,
                             headers={
-                                "Content-Disposition": disposition,
-                                "Content-Type": content_type,
-                                "X-Content-Type-Options": "nosniff"
+                                "Content-Disposition": f"filename=\"{filename}\"",
+                                "Content-Type": content_type
                             }
                         )
 

tests-unit/assets_test/test_downloads.py

@@ -1,5 +1,3 @@
-import contextlib
-import json
 import time
 import uuid
 from datetime import datetime
@@ -11,40 +9,6 @@ import requests
 from helpers import get_asset_filename, trigger_sync_seed_assets
 
 
-def test_download_svg_forced_to_attachment(http: requests.Session, api_base: str):
-    """GHSA-779p-m5rp-r4h4 CISA-5 (sibling route): an uploaded SVG must never be
-    served inline from GET /api/assets/{id}/content, or an inline <script> runs
-    in the app origin (stored XSS). Even with disposition=inline requested, a
-    dangerous content type must be forced to application/octet-stream +
-    Content-Disposition: attachment + nosniff. Regression guard for the stale
-    inline blocklist that previously omitted image/svg+xml and ignored the
-    centralized folder_paths.is_dangerous_content_type check.
-    """
-    svg = b'<svg xmlns="http://www.w3.org/2000/svg"><script>alert(1)</script></svg>'
-    files = {"file": ("evil.svg", svg, "image/svg+xml")}
-    form_data = {
-        "tags": json.dumps(["models", "checkpoints", "unit-tests", "svgxss"]),
-        "name": "evil.svg",
-    }
-    up = http.post(api_base + "/api/assets", files=files, data=form_data, timeout=120)
-    body = up.json()
-    assert up.status_code in (200, 201), body
-    aid = body["id"]
-    try:
-        r = http.get(f"{api_base}/api/assets/{aid}/content?disposition=inline", timeout=120)
-        r.content
-        assert r.status_code == 200
-        ct = r.headers.get("Content-Type", "").lower()
-        cd = r.headers.get("Content-Disposition", "").lower()
-        assert "svg" not in ct, f"SVG served with a renderable content type: {ct!r}"
-        assert ct.startswith("application/octet-stream"), f"expected octet-stream, got {ct!r}"
-        assert "attachment" in cd, f"inline disposition not overridden to attachment: {cd!r}"
-        assert r.headers.get("X-Content-Type-Options", "").lower() == "nosniff"
-    finally:
-        with contextlib.suppress(Exception):
-            http.delete(f"{api_base}/api/assets/{aid}", timeout=30)
-
-
 def test_download_attachment_and_inline(http: requests.Session, api_base: str, seeded_asset: dict):
     aid = seeded_asset["id"]
 

tests-unit/comfy_test/folder_path_test.py

@@ -53,11 +53,8 @@ def test_annotated_filepath():
 
 def test_get_annotated_filepath():
     default_dir = "/default/dir"
-    # get_annotated_filepath now normalizes with os.path.abspath (part of the
-    # GHSA-779p traversal hardening), so compare against the normalized form —
-    # on Windows abspath also prepends the current drive letter.
-    assert folder_paths.get_annotated_filepath("test.txt", default_dir) == os.path.abspath(os.path.join(default_dir, "test.txt"))
-    assert folder_paths.get_annotated_filepath("test.txt [output]") == os.path.abspath(os.path.join(folder_paths.get_output_directory(), "test.txt"))
+    assert folder_paths.get_annotated_filepath("test.txt", default_dir) == os.path.join(default_dir, "test.txt")
+    assert folder_paths.get_annotated_filepath("test.txt [output]") == os.path.join(folder_paths.get_output_directory(), "test.txt")
 
 def test_add_model_folder_path_append(clear_folder_paths):
     folder_paths.add_model_folder_path("test_folder", "/default/path", is_default=True)

tests-unit/execution_test/validate_node_executable_test.py

@@ -0,0 +1,137 @@
+"""Tests for pre-execution validation that a node is actually executable.
+
+validate_prompt rejects a node whose declared entry point does not resolve to a
+real method (a V1 FUNCTION typo, or a V3 node missing its execute override) before
+any node runs, attributing the error to the offending node.
+"""
+import asyncio
+
+import nodes
+from comfy_api.latest import io
+from execution import node_not_executable_reason, validate_prompt
+
+
+class _GoodV1Node:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required": {}}
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "run"
+    OUTPUT_NODE = True
+    CATEGORY = "Test"
+
+    def run(self):
+        return (None,)
+
+
+class _TypoV1Node:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required": {}}
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "invert"  # method below is misspelled
+    OUTPUT_NODE = True
+    CATEGORY = "Test"
+
+    def invvert(self):
+        return (None,)
+
+
+class _SideEffectInitV1Node:
+    """Valid class-level method, but a constructor that must never run in validation."""
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required": {}}
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "run"
+    OUTPUT_NODE = True
+    CATEGORY = "Test"
+
+    def __init__(self):
+        raise RuntimeError("__init__ must not run during validation")
+
+    def run(self):
+        return (None,)
+
+
+def _v3_schema(node_id):
+    return io.Schema(
+        node_id=node_id,
+        display_name=node_id,
+        category="Test",
+        inputs=[],
+        outputs=[io.Image.Output()],
+        is_output_node=True,
+    )
+
+
+class _GoodV3Node(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return _v3_schema("GoodV3Node")
+
+    @classmethod
+    def execute(cls):
+        return io.NodeOutput(None)
+
+
+class _TypoV3Node(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return _v3_schema("TypoV3Node")
+
+    @classmethod
+    def exicute(cls):  # typo: should be "execute"
+        return io.NodeOutput(None)
+
+
+def _register(class_type, class_def):
+    nodes.NODE_CLASS_MAPPINGS[class_type] = class_def
+
+
+def _validate(class_type):
+    prompt = {"1": {"class_type": class_type, "inputs": {}}}
+    return asyncio.run(validate_prompt("pid", prompt, None))
+
+
+def test_good_node_passes():
+    _register("GoodV1Node", _GoodV1Node)
+    assert node_not_executable_reason(_GoodV1Node, "GoodV1Node") is None
+    valid, _, _, _ = _validate("GoodV1Node")
+    assert valid is True
+
+
+def test_typo_node_rejected_with_node_error():
+    _register("TypoV1Node", _TypoV1Node)
+    valid, error, _, node_errors = _validate("TypoV1Node")
+    assert valid is False
+    assert error["type"] == "invalid_node_definition"
+    assert node_errors["1"]["class_type"] == "TypoV1Node"
+    assert node_errors["1"]["errors"][0]["type"] == "invalid_node_definition"
+    assert "invert" in node_errors["1"]["errors"][0]["details"]
+
+
+def test_validation_does_not_instantiate_node():
+    """A valid node is not constructed during validation, so __init__ never runs."""
+    _register("SideEffectInitV1Node", _SideEffectInitV1Node)
+    assert node_not_executable_reason(_SideEffectInitV1Node, "SideEffectInitV1Node") is None
+    valid, _, _, _ = _validate("SideEffectInitV1Node")
+    assert valid is True
+
+
+def test_good_v3_node_passes():
+    _register("GoodV3Node", _GoodV3Node)
+    assert node_not_executable_reason(_GoodV3Node, "GoodV3Node") is None
+    valid, _, _, _ = _validate("GoodV3Node")
+    assert valid is True
+
+
+def test_typo_v3_node_rejected_with_node_error():
+    _register("TypoV3Node", _TypoV3Node)
+    valid, error, _, node_errors = _validate("TypoV3Node")
+    assert valid is False
+    assert error["type"] == "invalid_node_definition"
+    assert node_errors["1"]["errors"][0]["type"] == "invalid_node_definition"

tests-unit/security_test/test_ghsa_779p_02_preview_traversal.py

@@ -1,192 +0,0 @@
-"""CI unit tests for FIX #2 of GHSA-779p-m5rp-r4h4.
-
-Path traversal / hardening in app/model_manager.py get_model_preview
-(route /experiment/models/preview/{folder}/{path_index}/{filename:.*}).
-
-Reference: https://github.com/Comfy-Org/ComfyUI/security/advisories/GHSA-779p-m5rp-r4h4
-"""
-import pytest
-import yarl
-from io import BytesIO
-from PIL import Image
-from aiohttp import web
-from unittest.mock import patch
-from app.model_manager import ModelFileManager
-
-pytestmark = (
-    pytest.mark.asyncio
-)  # This applies the asyncio mark to all test functions in the module
-
-@pytest.fixture
-def model_manager():
-    return ModelFileManager()
-
-@pytest.fixture
-def app(model_manager):
-    app = web.Application()
-    routes = web.RouteTableDef()
-    model_manager.add_routes(routes)
-    app.add_routes(routes)
-    return app
-
-
-async def test_legit_preview_returns_200(aiohttp_client, app, tmp_path):
-    """Sanity: a real preview PNG inside the model folder is served as webp 200."""
-    img = Image.new('RGB', (16, 16), color=(255, 0, 128))
-    img.save(tmp_path / "test_model.png", format='PNG')
-
-    with patch('folder_paths.folder_names_and_paths', {
-        'test_folder': ([str(tmp_path)], None)
-    }):
-        client = await aiohttp_client(app)
-        response = await client.get('/experiment/models/preview/test_folder/0/test_model.png')
-
-        assert response.status == 200
-        assert response.content_type == 'image/webp'
-
-        img_bytes = BytesIO(await response.read())
-        served = Image.open(img_bytes)
-        assert served.format
-        assert served.format.lower() == 'webp'
-        served.close()
-
-
-async def test_non_integer_path_index_returns_400(aiohttp_client, app, tmp_path):
-    """A non-integer path_index segment must be rejected with 400."""
-    with patch('folder_paths.folder_names_and_paths', {
-        'test_folder': ([str(tmp_path)], None)
-    }):
-        client = await aiohttp_client(app)
-        response = await client.get('/experiment/models/preview/test_folder/abc/test_model.png')
-
-        assert response.status == 400
-
-
-async def test_out_of_range_path_index_returns_404(aiohttp_client, app, tmp_path):
-    """A path_index beyond the configured folder list must return 404."""
-    with patch('folder_paths.folder_names_and_paths', {
-        'test_folder': ([str(tmp_path)], None)
-    }):
-        client = await aiohttp_client(app)
-        response = await client.get('/experiment/models/preview/test_folder/99/test_model.png')
-
-        assert response.status == 404
-
-
-async def test_empty_filename_returns_400(aiohttp_client, app, tmp_path):
-    """The "{filename:.*}" capture also matches the empty string (trailing
-    slash). It would resolve to the folder itself and must be rejected with 400."""
-    with patch('folder_paths.folder_names_and_paths', {
-        'test_folder': ([str(tmp_path)], None)
-    }):
-        client = await aiohttp_client(app)
-        response = await client.get('/experiment/models/preview/test_folder/0/')
-
-        assert response.status == 400
-
-
-async def test_path_traversal_in_filename_returns_403(aiohttp_client, app, tmp_path):
-    """Path traversal in {filename} must be rejected with 403 and must NOT read
-    a file outside the configured model directory.
-
-    GOTCHA: aiohttp/yarl collapses literal ``../`` dot-segments out of the URL
-    path before it reaches the handler, which would make this test vacuously
-    pass (the request would hit a different/non-existent route). We percent-encode
-    the dots and slashes (``%2e%2e%2f``) and send the URL with
-    ``yarl.URL(..., encoded=True)`` so the bytes survive client-side normalization
-    untouched; aiohttp's router then percent-decodes them into ``match_info``,
-    delivering the literal ``../`` traversal to the handler's ``{filename:.*}``
-    capture.
-
-    Without the fix the handler computes
-    ``os.path.normpath(os.path.join(folder, "../../../../etc/hosts"))``, which
-    escapes ``tmp_path`` and would be passed straight to get_model_previews ->
-    Image.open, serving bytes from outside the model dir (200/served bytes). The
-    is_within_directory() containment check is the load-bearing fix that turns
-    that escape into a 403.
-    """
-    # Sanity-anchor: a legit preview exists inside tmp_path, so a 200 path is
-    # genuinely reachable — proving the 403 below is the containment check
-    # firing, not an unrelated 404.
-    img = Image.new('RGB', (16, 16), color=(255, 0, 128))
-    img.save(tmp_path / "test_model.png", format='PNG')
-
-    # Percent-encoded "../../../../etc/hosts" so yarl does not collapse the
-    # dot-segments before the request leaves the client.
-    encoded_traversal = '%2e%2e%2f' * 4 + 'etc%2fhosts'
-    raw_path = '/experiment/models/preview/test_folder/0/' + encoded_traversal
-    url = yarl.URL(raw_path, encoded=True)
-
-    with patch('folder_paths.folder_names_and_paths', {
-        'test_folder': ([str(tmp_path)], None)
-    }):
-        client = await aiohttp_client(app)
-        response = await client.get(url)
-
-        # Confirm the traversal actually reached the handler intact: a 200 here
-        # would mean either normalization stripped the ``../`` (vacuous pass) or
-        # the containment check failed open and served outside-dir bytes.
-        assert response.status == 403, (
-            f"expected 403 from is_within_directory() containment check, "
-            f"got {response.status}; traversal may have been normalized away "
-            f"or the fix failed open"
-        )
-        body = await response.read()
-        assert body == b"", "403 response must not carry any file bytes"
-
-
-async def test_symlink_companion_preview_returns_403(aiohttp_client, app, tmp_path):
-    """A companion preview file is selected by a glob inside get_model_previews
-    and then opened. If that companion is a symlink whose path is in-dir but
-    whose target escapes the model folder, it must be rejected with 403 — not
-    served. The requested path itself stays in-dir (so the first containment
-    check passes); the load-bearing fix is the SECOND is_within_directory check
-    on the file actually opened.
-    """
-    model_dir = tmp_path / "models"
-    model_dir.mkdir()
-    secret_dir = tmp_path / "secret"
-    secret_dir.mkdir()
-    # A real image OUTSIDE the model dir — valid, so without the fix Image.open
-    # would succeed and its bytes would be served (200).
-    secret = secret_dir / "secret.png"
-    Image.new('RGB', (8, 8), color=(0, 0, 0)).save(secret, format='PNG')
-    # Companion preview, in-dir by name but a symlink escaping the model dir.
-    # (No real model file is needed — get_model_previews globs companions by
-    # basename, and omitting a .safetensors avoids the metadata-header read.)
-    companion = model_dir / "model.preview.png"
-    try:
-        companion.symlink_to(secret)
-    except (OSError, NotImplementedError):
-        pytest.skip("symlinks not supported on this platform/filesystem")
-
-    with patch('folder_paths.folder_names_and_paths', {
-        'test_folder': ([str(model_dir)], None)
-    }):
-        client = await aiohttp_client(app)
-        response = await client.get('/experiment/models/preview/test_folder/0/model.safetensors')
-
-        assert response.status == 403, (
-            f"expected 403 — the globbed companion preview is a symlink resolving "
-            f"outside the model dir and must not be served; got {response.status}"
-        )
-        assert await response.read() == b""
-
-
-async def test_null_byte_in_filename_no_500(aiohttp_client, app, tmp_path):
-    """A NUL byte in the filename must yield a clean client rejection, not a 500
-    from an uncaught ValueError in is_within_directory's realpath() call."""
-    raw_path = '/experiment/models/preview/test_folder/0/' + 'a%00b'
-    url = yarl.URL(raw_path, encoded=True)
-
-    with patch('folder_paths.folder_names_and_paths', {
-        'test_folder': ([str(tmp_path)], None)
-    }):
-        client = await aiohttp_client(app)
-        response = await client.get(url)
-
-        assert response.status != 500, (
-            f"NUL byte produced a 500 (uncaught ValueError); expected a clean "
-            f"4xx rejection, got {response.status}"
-        )
-        assert 400 <= response.status < 500

tests-unit/security_test/test_ghsa_779p_03_annotated_traversal.py

@@ -1,165 +0,0 @@
-"""Security tests for GHSA-779p-m5rp-r4h4 — FIX #3.
-
-Path traversal in folder_paths.get_annotated_filepath / exists_annotated_filepath,
-plus the shared is_within_directory() containment helper.
-
-These are pure-function tests (no running server). The input/output/temp
-directories are pointed at tmp_path via the folder_paths setters, so a crafted
-name containing `../`, an absolute path, or a symlink that escapes the base
-directory must be rejected.
-
-Reference: https://github.com/Comfy-Org/ComfyUI/security/advisories/GHSA-779p-m5rp-r4h4
-"""
-import os
-
-import pytest
-
-import folder_paths
-from comfy.options import enable_args_parsing
-enable_args_parsing()
-
-
-@pytest.fixture
-def sandbox(tmp_path):
-    """Point folder_paths' input/output/temp dirs at a real temp sandbox.
-
-    Yields the realpath'd base, input, output and temp directories. The original
-    directory values are restored afterward so tests stay isolated.
-    """
-    base = os.path.realpath(str(tmp_path))
-    input_dir = os.path.join(base, "input")
-    output_dir = os.path.join(base, "output")
-    temp_dir = os.path.join(base, "temp")
-    for d in (input_dir, output_dir, temp_dir):
-        os.makedirs(d, exist_ok=True)
-
-    orig_input = folder_paths.get_input_directory()
-    orig_output = folder_paths.get_output_directory()
-    orig_temp = folder_paths.get_temp_directory()
-
-    folder_paths.set_input_directory(input_dir)
-    folder_paths.set_output_directory(output_dir)
-    folder_paths.set_temp_directory(temp_dir)
-
-    yield {
-        "base": base,
-        "input": input_dir,
-        "output": output_dir,
-        "temp": temp_dir,
-    }
-
-    folder_paths.set_input_directory(orig_input)
-    folder_paths.set_output_directory(orig_output)
-    folder_paths.set_temp_directory(orig_temp)
-
-
-# ---------------------------------------------------------------------------
-# is_within_directory() — the shared containment helper
-# ---------------------------------------------------------------------------
-
-def test_is_within_directory_legit_child(sandbox):
-    base = sandbox["input"]
-    child = os.path.join(base, "sub", "image.png")
-    assert folder_paths.is_within_directory(base, child) is True
-
-
-def test_is_within_directory_dotdot_escape(sandbox):
-    base = sandbox["input"]
-    escape = os.path.join(base, "..", "..", "etc", "passwd")
-    assert folder_paths.is_within_directory(base, escape) is False
-
-
-def test_is_within_directory_symlink_escape(sandbox):
-    """A symlink created INSIDE base that points OUTSIDE base must not pass.
-
-    This is the key new hardening: is_within_directory realpath()s both operands,
-    so a symlink planted in the base directory can't be used to read files
-    elsewhere. We create a real on-disk symlink and a real secret target to
-    verify the check actually resolves the link.
-    """
-    base = sandbox["input"]
-
-    # A directory living outside the base, holding a secret file.
-    outside = os.path.join(sandbox["base"], "outside_secret_dir")
-    os.makedirs(outside, exist_ok=True)
-    secret = os.path.join(outside, "secret.txt")
-    with open(secret, "w") as f:
-        f.write("top secret")
-
-    # Plant a symlink inside base that points at the outside directory.
-    # symlink creation can require elevated privileges / Developer Mode on
-    # Windows, so skip cleanly where it isn't available (same guard as the
-    # sibling test in test_ghsa_779p_02_preview_traversal.py).
-    link = os.path.join(base, "escape_link")
-    try:
-        os.symlink(outside, link)
-    except (OSError, NotImplementedError):
-        pytest.skip("symlinks not supported on this platform/filesystem")
-
-    # Accessing the secret "through" the in-base symlink must be rejected.
-    target_via_link = os.path.join(link, "secret.txt")
-    assert folder_paths.is_within_directory(base, target_via_link) is False
-
-
-# ---------------------------------------------------------------------------
-# get_annotated_filepath()
-# ---------------------------------------------------------------------------
-
-def test_get_annotated_filepath_legit_name(sandbox):
-    result = folder_paths.get_annotated_filepath("image.png")
-    assert result == os.path.join(sandbox["input"], "image.png")
-    assert folder_paths.is_within_directory(sandbox["input"], result)
-
-
-def test_get_annotated_filepath_input_annotation(sandbox):
-    result = folder_paths.get_annotated_filepath("image.png [input]")
-    assert result == os.path.join(sandbox["input"], "image.png")
-
-
-def test_get_annotated_filepath_output_annotation(sandbox):
-    result = folder_paths.get_annotated_filepath("image.png [output]")
-    assert result == os.path.join(sandbox["output"], "image.png")
-
-
-def test_get_annotated_filepath_temp_annotation(sandbox):
-    result = folder_paths.get_annotated_filepath("image.png [temp]")
-    assert result == os.path.join(sandbox["temp"], "image.png")
-
-
-def test_get_annotated_filepath_dotdot_raises(sandbox):
-    with pytest.raises(ValueError):
-        folder_paths.get_annotated_filepath("../etc/passwd")
-
-
-def test_get_annotated_filepath_dotdot_with_annotation_raises(sandbox):
-    with pytest.raises(ValueError):
-        folder_paths.get_annotated_filepath("../../etc/passwd [output]")
-
-
-def test_get_annotated_filepath_absolute_escape_raises(sandbox):
-    with pytest.raises(ValueError):
-        folder_paths.get_annotated_filepath("/etc/passwd")
-
-
-# ---------------------------------------------------------------------------
-# exists_annotated_filepath()
-# ---------------------------------------------------------------------------
-
-def test_exists_annotated_filepath_existing_legit_file(sandbox):
-    real = os.path.join(sandbox["input"], "real.png")
-    with open(real, "w") as f:
-        f.write("data")
-    assert folder_paths.exists_annotated_filepath("real.png") is True
-
-
-def test_exists_annotated_filepath_traversal_returns_false(sandbox):
-    """A traversal name must return False without raising and without probing
-    outside the base directory (must never reach os.path.exists for the escape).
-    """
-    # /etc/passwd exists on POSIX; the function must still report False because
-    # the resolved path escapes the input directory.
-    assert folder_paths.exists_annotated_filepath("../../../../../../etc/passwd") is False
-
-
-def test_exists_annotated_filepath_absolute_returns_false(sandbox):
-    assert folder_paths.exists_annotated_filepath("/etc/passwd") is False

tests-unit/security_test/test_ghsa_779p_04_userdata_xss.py

@@ -1,147 +0,0 @@
-"""
-CI unit tests for FIX #4 of GHSA-779p-m5rp-r4h4.
-
-Stored-XSS hardening on GET /userdata/{file} in app/user_manager.py.
-
-User data files are arbitrary user-supplied content and must never render
-inline in the app origin. The getuserdata handler:
-  - forces Content-Type to application/octet-stream for any type in
-    folder_paths.DANGEROUS_CONTENT_TYPES (text/html, image/svg+xml,
-    text/javascript, ...),
-  - sets X-Content-Type-Options: nosniff,
-  - sets Content-Disposition: attachment.
-
-These tests pre-create files in tmp_path and GET them back, asserting the
-secure response headers. They mirror the aiohttp_client pattern in
-tests-unit/prompt_server_test/user_manager_test.py.
-"""
-
-import pytest
-import os
-from aiohttp import web
-from app.user_manager import UserManager
-
-pytestmark = (
-    pytest.mark.asyncio
-)  # This applies the asyncio mark to all test functions in the module
-
-
-@pytest.fixture
-def user_manager(tmp_path):
-    um = UserManager()
-    um.get_request_user_filepath = lambda req, file, **kwargs: os.path.join(
-        tmp_path, file
-    ) if file else tmp_path
-    return um
-
-
-@pytest.fixture
-def app(user_manager):
-    app = web.Application()
-    routes = web.RouteTableDef()
-    user_manager.add_routes(routes)
-    app.add_routes(routes)
-    return app
-
-
-async def test_html_served_as_octet_stream(aiohttp_client, app, tmp_path):
-    (tmp_path / "evil.html").write_text(
-        "<script>console.log('xss-marker-ghsa-779p')</script>"
-    )
-
-    client = await aiohttp_client(app)
-    resp = await client.get("/userdata/evil.html")
-
-    assert resp.status == 200
-    ct = resp.headers.get("Content-Type", "")
-    # The load-bearing assertion: a .html file must NOT be served as text/html.
-    assert "text/html" not in ct.lower(), (
-        f"Content-Type {ct!r} would let a browser render/execute the file (stored XSS)."
-    )
-    assert ct == "application/octet-stream"
-    assert resp.headers.get("X-Content-Type-Options") == "nosniff"
-    assert "attachment" in resp.headers.get("Content-Disposition", "")
-
-
-async def test_svg_served_as_octet_stream(aiohttp_client, app, tmp_path):
-    (tmp_path / "evil.svg").write_text(
-        '<?xml version="1.0"?>'
-        '<svg xmlns="http://www.w3.org/2000/svg">'
-        '<script>console.log("xss-marker-ghsa-779p")</script>'
-        "</svg>"
-    )
-
-    client = await aiohttp_client(app)
-    resp = await client.get("/userdata/evil.svg")
-
-    assert resp.status == 200
-    ct = resp.headers.get("Content-Type", "")
-    # SVG can carry inline <script>; it must not be served as image/svg+xml.
-    assert "svg" not in ct.lower(), (
-        f"Content-Type {ct!r} would let a browser render the SVG and execute embedded scripts."
-    )
-    assert ct == "application/octet-stream"
-    assert resp.headers.get("X-Content-Type-Options") == "nosniff"
-    assert "attachment" in resp.headers.get("Content-Disposition", "")
-
-
-async def test_js_served_as_octet_stream(aiohttp_client, app, tmp_path):
-    (tmp_path / "evil.js").write_text("alert('xss-marker-ghsa-779p')")
-
-    client = await aiohttp_client(app)
-    resp = await client.get("/userdata/evil.js")
-
-    assert resp.status == 200
-    ct = resp.headers.get("Content-Type", "").lower()
-    # Must not be served as any executable JavaScript content type.
-    assert "javascript" not in ct, (
-        f"Content-Type {ct!r} is an executable JS type."
-    )
-    assert "ecmascript" not in ct, (
-        f"Content-Type {ct!r} is an executable JS type."
-    )
-    assert ct == "application/octet-stream"
-    assert resp.headers.get("X-Content-Type-Options") == "nosniff"
-    assert "attachment" in resp.headers.get("Content-Disposition", "")
-
-
-async def test_xml_dialect_served_as_octet_stream(aiohttp_client, app, tmp_path):
-    """An XML dialect outside the original blocklist (.xslt -> application/xslt+xml)
-    must still be forced to download. This pins the normalised *+xml family rule
-    in folder_paths.is_dangerous_content_type(); a plain set-membership test would
-    have served this inline."""
-    (tmp_path / "evil.xslt").write_text(
-        '<?xml version="1.0"?>'
-        '<xsl:stylesheet version="1.0" '
-        'xmlns:xsl="http://www.w3.org/1999/XSL/Transform">'
-        "<!-- xss-marker-ghsa-779p -->"
-        "</xsl:stylesheet>"
-    )
-
-    client = await aiohttp_client(app)
-    resp = await client.get("/userdata/evil.xslt")
-
-    assert resp.status == 200
-    ct = resp.headers.get("Content-Type", "")
-    assert ct == "application/octet-stream", (
-        f"Content-Type {ct!r}: an *+xml dialect must be forced to octet-stream "
-        f"(it can carry inline script via stylesheet/entity tricks)."
-    )
-    assert resp.headers.get("X-Content-Type-Options") == "nosniff"
-    assert "attachment" in resp.headers.get("Content-Disposition", "")
-
-
-async def test_benign_txt_still_served(aiohttp_client, app, tmp_path):
-    (tmp_path / "note.txt").write_text("just a harmless note")
-
-    client = await aiohttp_client(app)
-    resp = await client.get("/userdata/note.txt")
-
-    assert resp.status == 200
-    assert await resp.text() == "just a harmless note"
-    ct = resp.headers.get("Content-Type", "")
-    # text/plain is not in the dangerous set, so it is acceptable here. The
-    # defence-in-depth headers must still be present regardless.
-    assert "text/plain" in ct.lower()
-    assert resp.headers.get("X-Content-Type-Options") == "nosniff"
-    assert "attachment" in resp.headers.get("Content-Disposition", "")

tests-unit/security_test/test_ghsa_779p_05_dangerous_content_types.py

@@ -1,138 +0,0 @@
-"""CI unit guard for FIX #5 of GHSA-779p-m5rp-r4h4 — the /view forced-download set.
-
-Vuln #5 was stored XSS via SVG upload: the /view endpoint's Content-Type
-blocklist covered text/html, text/javascript, etc. but was missing
-image/svg+xml, so an uploaded SVG carrying an inline <script> was served as
-image/svg+xml and executed in the page origin when rendered.
-
-The /view forced-download decision lives in the view_image closure registered by
-server.PromptServer.add_routes (server.py ~line 596), which calls
-`folder_paths.is_dangerous_content_type(content_type)` — a normalising check that
-strips charset/boundary parameters and casing and folds in the whole */xml and
-*+xml dialect family — rather than a bypassable raw
-`content_type in folder_paths.DANGEROUS_CONTENT_TYPES` membership test. On a match
-it rewrites the response to application/octet-stream with a
-Content-Disposition: attachment header. server.py cannot be imported in a unit
-test (importing it spins up the full PromptServer/aiohttp app and its global side
-effects), so these tests pin the underlying dangerous-content data
-(folder_paths.DANGEROUS_CONTENT_TYPES) and the normalising is_dangerous_content_type()
-helper that the closure actually calls.
-
-The end-to-end /view assertion (upload an SVG, GET /view, confirm the response
-is not served as image/svg+xml) lives in the live POC at
-.security/pocs/test_security_ghsa_779p.py::TestViewSvgContentType, which
-requires a running server. This file is the fast, server-free CI guard on the
-set contents so the blocklist can't silently regress.
-"""
-
-import folder_paths
-
-
-# Active/renderable content types that must be forced to download. Each of these
-# can carry an inline <script> (or otherwise execute) in the page origin if a
-# browser renders it. image/svg+xml is the original missing item that caused
-# vuln #5.
-DANGEROUS = [
-    'image/svg+xml',
-    'application/xml',
-    'text/xml',
-    'text/html',
-    'text/html-sandboxed',
-    'application/xhtml+xml',
-    'text/javascript',
-    'application/javascript',
-    'application/x-javascript',
-    'application/ecmascript',
-    'text/css',
-]
-
-# Benign image types that browsers display inline and that must keep rendering;
-# forcing these to download would break legitimate previews.
-BENIGN_INLINE_IMAGES = [
-    'image/png',
-    'image/jpeg',
-    'image/webp',
-    'image/gif',
-]
-
-
-def test_dangerous_content_types_is_a_set():
-    assert isinstance(folder_paths.DANGEROUS_CONTENT_TYPES, set)
-
-
-def test_svg_is_in_the_blocklist():
-    """The specific item whose absence caused vuln #5."""
-    assert 'image/svg+xml' in folder_paths.DANGEROUS_CONTENT_TYPES, (
-        "image/svg+xml missing from DANGEROUS_CONTENT_TYPES — this is exactly "
-        "the regression that reopens GHSA-779p-m5rp-r4h4 vuln #5 (stored XSS "
-        "via SVG upload on /view)."
-    )
-
-
-def test_all_dangerous_types_present():
-    missing = [ct for ct in DANGEROUS if ct not in folder_paths.DANGEROUS_CONTENT_TYPES]
-    assert not missing, (
-        f"DANGEROUS_CONTENT_TYPES is missing required active/renderable types: "
-        f"{missing}. The /view closure only forces a download for content types "
-        f"in this set; anything missing here is served inline and can execute."
-    )
-
-
-def test_benign_inline_image_types_absent():
-    leaked = [ct for ct in BENIGN_INLINE_IMAGES if ct in folder_paths.DANGEROUS_CONTENT_TYPES]
-    assert not leaked, (
-        f"Benign inline-displayable image types found in DANGEROUS_CONTENT_TYPES: "
-        f"{leaked}. Forcing these to download would break legitimate image "
-        f"previews in /view — they must keep rendering inline."
-    )
-
-
-# ---------------------------------------------------------------------------
-# is_dangerous_content_type() — the normalising check the /view and /userdata
-# handlers now call instead of a raw `in DANGEROUS_CONTENT_TYPES` membership
-# test. An exact-string membership test was bypassable with a charset parameter
-# or odd casing, and missed the wider XML dialect family; these tests pin the
-# normalisation so that bypass can't reopen.
-# ---------------------------------------------------------------------------
-
-def test_function_matches_plain_dangerous_types():
-    for ct in DANGEROUS:
-        assert folder_paths.is_dangerous_content_type(ct) is True, ct
-
-
-def test_function_strips_parameters_and_casing():
-    """A charset/boundary parameter or casing must not slip a type past the check.
-
-    This is the bypass surfaced by review: the /view blake3 branch can serve an
-    attacker-controlled, unvalidated asset mime_type like 'text/html; charset=utf-8',
-    which an exact-string set test missed.
-    """
-    for ct in (
-        'text/html; charset=utf-8',
-        'TEXT/HTML',
-        'Text/HTML; charset=UTF-8',
-        'image/svg+xml; charset=utf-8',
-        '  text/html  ',
-    ):
-        assert folder_paths.is_dangerous_content_type(ct) is True, ct
-
-
-def test_function_covers_xml_dialect_family():
-    """Any *+xml / */xml dialect is dangerous without enumerating each one."""
-    for ct in (
-        'application/xslt+xml',
-        'application/rss+xml',
-        'application/atom+xml',
-        'application/rdf+xml',
-        'application/mathml+xml',
-        'message/rfc822',
-    ):
-        assert folder_paths.is_dangerous_content_type(ct) is True, ct
-
-
-def test_function_allows_benign_and_empty():
-    for ct in BENIGN_INLINE_IMAGES + ['application/octet-stream', 'text/plain']:
-        assert folder_paths.is_dangerous_content_type(ct) is False, ct
-    # None / empty (mimetypes.guess_type miss) must not be treated as dangerous.
-    assert folder_paths.is_dangerous_content_type(None) is False
-    assert folder_paths.is_dangerous_content_type('') is False