Clear root for models subfolder reorganization

.gitattributes

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ace-step/.gitattributes

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ace-step/Qwen3-Embedding-0.6B/added_tokens.json

@@ -1,28 +0,0 @@
-{
-  "</think>": 151668,
-  "</tool_call>": 151658,
-  "</tool_response>": 151666,
-  "<think>": 151667,
-  "<tool_call>": 151657,
-  "<tool_response>": 151665,
-  "<|box_end|>": 151649,
-  "<|box_start|>": 151648,
-  "<|endoftext|>": 151643,
-  "<|file_sep|>": 151664,
-  "<|fim_middle|>": 151660,
-  "<|fim_pad|>": 151662,
-  "<|fim_prefix|>": 151659,
-  "<|fim_suffix|>": 151661,
-  "<|im_end|>": 151645,
-  "<|im_start|>": 151644,
-  "<|image_pad|>": 151655,
-  "<|object_ref_end|>": 151647,
-  "<|object_ref_start|>": 151646,
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-  "<|vision_end|>": 151653,
-  "<|vision_pad|>": 151654,
-  "<|vision_start|>": 151652
-}

ace-step/Qwen3-Embedding-0.6B/chat_template.jinja

@@ -1,85 +0,0 @@
-{%- if tools %}
-    {{- '<|im_start|>system\n' }}
-    {%- if messages[0].role == 'system' %}
-        {{- messages[0].content + '\n\n' }}
-    {%- endif %}
-    {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
-    {%- for tool in tools %}
-        {{- "\n" }}
-        {{- tool | tojson }}
-    {%- endfor %}
-    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
-{%- else %}
-    {%- if messages[0].role == 'system' %}
-        {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
-    {%- endif %}
-{%- endif %}
-{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
-{%- for message in messages[::-1] %}
-    {%- set index = (messages|length - 1) - loop.index0 %}
-    {%- if ns.multi_step_tool and message.role == "user" and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
-        {%- set ns.multi_step_tool = false %}
-        {%- set ns.last_query_index = index %}
-    {%- endif %}
-{%- endfor %}
-{%- for message in messages %}
-    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
-        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
-    {%- elif message.role == "assistant" %}
-        {%- set content = message.content %}
-        {%- set reasoning_content = '' %}
-        {%- if message.reasoning_content is defined and message.reasoning_content is not none %}
-            {%- set reasoning_content = message.reasoning_content %}
-        {%- else %}
-            {%- if '</think>' in message.content %}
-                {%- set content = message.content.split('</think>')[-1].lstrip('\n') %}
-                {%- set reasoning_content = message.content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
-            {%- endif %}
-        {%- endif %}
-        {%- if loop.index0 > ns.last_query_index %}
-            {%- if loop.last or (not loop.last and reasoning_content) %}
-                {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
-            {%- else %}
-                {{- '<|im_start|>' + message.role + '\n' + content }}
-            {%- endif %}
-        {%- else %}
-            {{- '<|im_start|>' + message.role + '\n' + content }}
-        {%- endif %}
-        {%- if message.tool_calls %}
-            {%- for tool_call in message.tool_calls %}
-                {%- if (loop.first and content) or (not loop.first) %}
-                    {{- '\n' }}
-                {%- endif %}
-                {%- if tool_call.function %}
-                    {%- set tool_call = tool_call.function %}
-                {%- endif %}
-                {{- '<tool_call>\n{"name": "' }}
-                {{- tool_call.name }}
-                {{- '", "arguments": ' }}
-                {%- if tool_call.arguments is string %}
-                    {{- tool_call.arguments }}
-                {%- else %}
-                    {{- tool_call.arguments | tojson }}
-                {%- endif %}
-                {{- '}\n</tool_call>' }}
-            {%- endfor %}
-        {%- endif %}
-        {{- '<|im_end|>\n' }}
-    {%- elif message.role == "tool" %}
-        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
-            {{- '<|im_start|>user' }}
-        {%- endif %}
-        {{- '\n<tool_response>\n' }}
-        {{- message.content }}
-        {{- '\n</tool_response>' }}
-        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
-            {{- '<|im_end|>\n' }}
-        {%- endif %}
-    {%- endif %}
-{%- endfor %}
-{%- if add_generation_prompt %}
-    {{- '<|im_start|>assistant\n' }}
-    {%- if enable_thinking is defined and enable_thinking is false %}
-        {{- '<think>\n\n</think>\n\n' }}
-    {%- endif %}
-{%- endif %}

ace-step/Qwen3-Embedding-0.6B/config.json

@@ -1,60 +0,0 @@
-{
-  "architectures": [
-    "Qwen3Model"
-  ],
-  "attention_bias": false,
-  "attention_dropout": 0.0,
-  "bos_token_id": 151643,
-  "dtype": "bfloat16",
-  "eos_token_id": 151643,
-  "head_dim": 128,
-  "hidden_act": "silu",
-  "hidden_size": 1024,
-  "initializer_range": 0.02,
-  "intermediate_size": 3072,
-  "layer_types": [
-    "full_attention",
-    "full_attention",
-    "full_attention",
-    "full_attention",
-    "full_attention",
-    "full_attention",
-    "full_attention",
-    "full_attention",
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-    "full_attention",
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-    "full_attention",
-    "full_attention",
-    "full_attention",
-    "full_attention",
-    "full_attention",
-    "full_attention",
-    "full_attention",
-    "full_attention",
-    "full_attention",
-    "full_attention",
-    "full_attention",
-    "full_attention"
-  ],
-  "max_position_embeddings": 32768,
-  "max_window_layers": 28,
-  "model_type": "qwen3",
-  "num_attention_heads": 16,
-  "num_hidden_layers": 28,
-  "num_key_value_heads": 8,
-  "rms_norm_eps": 1e-06,
-  "rope_scaling": null,
-  "rope_theta": 1000000,
-  "sliding_window": null,
-  "tie_word_embeddings": true,
-  "transformers_version": "4.57.0.dev0",
-  "use_cache": true,
-  "use_sliding_window": false,
-  "vocab_size": 151669
-}

ace-step/Qwen3-Embedding-0.6B/model.safetensors

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ace-step/Qwen3-Embedding-0.6B/special_tokens_map.json

@@ -1,31 +0,0 @@
-{
-  "additional_special_tokens": [
-    "<|im_start|>",
-    "<|im_end|>",
-    "<|object_ref_start|>",
-    "<|object_ref_end|>",
-    "<|box_start|>",
-    "<|box_end|>",
-    "<|quad_start|>",
-    "<|quad_end|>",
-    "<|vision_start|>",
-    "<|vision_end|>",
-    "<|vision_pad|>",
-    "<|image_pad|>",
-    "<|video_pad|>"
-  ],
-  "eos_token": {
-    "content": "<|im_end|>",
-    "lstrip": false,
-    "normalized": false,
-    "rstrip": false,
-    "single_word": false
-  },
-  "pad_token": {
-    "content": "<|endoftext|>",
-    "lstrip": false,
-    "normalized": false,
-    "rstrip": false,
-    "single_word": false
-  }
-}

ace-step/Qwen3-Embedding-0.6B/tokenizer.json

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ace-step/Qwen3-Embedding-0.6B/tokenizer_config.json

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-{
-  "add_bos_token": false,
-  "add_prefix_space": false,
-  "added_tokens_decoder": {
-    "151643": {
-      "content": "<|endoftext|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "151644": {
-      "content": "<|im_start|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "151645": {
-      "content": "<|im_end|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "151646": {
-      "content": "<|object_ref_start|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
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-    "151647": {
-      "content": "<|object_ref_end|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
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-    "151648": {
-      "content": "<|box_start|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "151649": {
-      "content": "<|box_end|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
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-      "content": "<|quad_start|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
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-      "content": "<|quad_end|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "151652": {
-      "content": "<|vision_start|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
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-      "content": "<|vision_end|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
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-      "content": "<|vision_pad|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "151655": {
-      "content": "<|image_pad|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
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-      "content": "<|video_pad|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
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-    "151657": {
-      "content": "<tool_call>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "151658": {
-      "content": "</tool_call>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
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-      "content": "<|fim_prefix|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "151660": {
-      "content": "<|fim_middle|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "151661": {
-      "content": "<|fim_suffix|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "151662": {
-      "content": "<|fim_pad|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "151663": {
-      "content": "<|repo_name|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "151664": {
-      "content": "<|file_sep|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "151665": {
-      "content": "<tool_response>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "151666": {
-      "content": "</tool_response>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
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-      "content": "<think>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
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-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    }
-  },
-  "additional_special_tokens": [
-    "<|im_start|>",
-    "<|im_end|>",
-    "<|object_ref_start|>",
-    "<|object_ref_end|>",
-    "<|box_start|>",
-    "<|box_end|>",
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-    "<|quad_end|>",
-    "<|vision_start|>",
-    "<|vision_end|>",
-    "<|vision_pad|>",
-    "<|image_pad|>",
-    "<|video_pad|>"
-  ],
-  "bos_token": null,
-  "clean_up_tokenization_spaces": false,
-  "eos_token": "<|im_end|>",
-  "errors": "replace",
-  "extra_special_tokens": {},
-  "model_max_length": 131072,
-  "pad_token": "<|endoftext|>",
-  "split_special_tokens": false,
-  "tokenizer_class": "Qwen2Tokenizer",
-  "unk_token": null
-}

ace-step/README.md

@@ -1,99 +0,0 @@
----
-library_name: transformers
-license: mit
-pipeline_tag: text-to-audio
-tags:
-- audio
-- music
-- text2music
----
-
-<h1 align="center">ACE-Step 1.5</h1>
-<h1 align="center">Pushing the Boundaries of Open-Source Music Generation</h1>
-<p align="center">
-    <a href="https://ace-step.github.io/ace-step-v1.5.github.io/">Project</a> |
-    <a href="https://huggingface.co/collections/ACE-Step/ace-step-15">Hugging Face</a> |
-    <a href="https://modelscope.cn/models/ACE-Step/Ace-Step1.5">ModelScope</a> |
-    <a href="https://huggingface.co/spaces/ACE-Step/Ace-Step-v1.5">Space Demo</a> |
-    <a href="https://discord.gg/PeWDxrkdj7">Discord</a>
-    <a href="https://arxiv.org/abs/2602.00744">Tech Report</a>
-</p>
-
-
-![image](https://cdn-uploads.huggingface.co/production/uploads/62dfaf90c42558bcbd0a4f6f/b84r7t0viIw7rKSr_ja9_.png)
-
-## Model Details
-
-🚀 **ACE-Step v1.5** is a highly efficient open-source music foundation model designed to bring commercial-grade music generation to consumer hardware. 
-
-### Key Features
-
-*   **💰 Commercial-Ready:** Unlike many models trained on ambiguous datasets, ACE-Step v1.5 is designed for creators. You can strictly use the generated music for **commercial purposes**.
-*   **📚 Safe & Robust Training Data:** The model is trained on a massive, legally compliant dataset consisting of:
-    *   **Licensed Data:** Professionally licensed music tracks.
-    *   **Royalty-Free / No-Copyright Data:** A vast collection of public domain and royalty-free music.
-    *   **Synthetic Data:** High-quality audio generated via advanced MIDI-to-Audio conversion.
-*   **⚡ Extreme Speed:** Generates a full song in under 2 seconds on an A100 and under 10 seconds on an RTX 3090.
-*   **🖥️ Consumer Hardware Friendly:** Runs locally with less than 4GB of VRAM.
-
-### Technical Capabilities
-
-🌉 At its core lies a novel hybrid architecture where the Language Model (LM) functions as an omni-capable planner: it transforms simple user queries into comprehensive song blueprints—scaling from short loops to 10-minute compositions—while synthesizing metadata, lyrics, and captions via Chain-of-Thought to guide the Diffusion Transformer (DiT). ⚡ Uniquely, this alignment is achieved through intrinsic reinforcement learning relying solely on the model's internal mechanisms, thereby eliminating the biases inherent in external reward models or human preferences. 🎚️
-
-🔮 Beyond standard synthesis, ACE-Step v1.5 unifies precise stylistic control with versatile editing capabilities—such as cover generation, repainting, and vocal-to-BGM conversion—while maintaining strict adherence to prompts across 50+ languages. This paves the way for powerful tools that seamlessly integrate into the creative workflows of music artists, producers, and content creators. 🎸
-
-- **Developed by:** [ACE-STEP]
-- **Model type:** [Text2Music]
-- **Language(s):** [50+ languages]
-- **License:** [MIT]
-
-## Evaluation
-
-![image](https://cdn-uploads.huggingface.co/production/uploads/62dfaf90c42558bcbd0a4f6f/n9aKi_NhSmlMOgmGzahZi.png)
-
-## 🏗️ Architecture
-
-
-![image](https://cdn-uploads.huggingface.co/production/uploads/62dfaf90c42558bcbd0a4f6f/V_d1rTdqkQyoSM8td7OWl.png)
-
-
-## 🦁 Model Zoo
-
-
-![image](https://cdn-uploads.huggingface.co/production/uploads/62dfaf90c42558bcbd0a4f6f/B49V0OTKse_FRefTmTPsQ.png)
-
-### DiT Models
-
-| DiT Model | Pre-Training | SFT | RL | CFG | Step | Refer audio | Text2Music | Cover | Repaint | Extract | Lego | Complete | Quality | Diversity | Fine-Tunability | Hugging Face |
-|-----------|:------------:|:---:|:--:|:---:|:----:|:-----------:|:----------:|:-----:|:-------:|:-------:|:----:|:--------:|:-------:|:---------:|:---------------:|--------------|
-| `acestep-v15-base` | ✅ | ❌ | ❌ | ✅ | 50 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | Medium | High | Easy | [Link](https://huggingface.co/ACE-Step/acestep-v15-base) |
-| `acestep-v15-sft` | ✅ | ✅ | ❌ | ✅ | 50 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | High | Medium | Easy | [Link](https://huggingface.co/ACE-Step/acestep-v15-sft) |
-| `acestep-v15-turbo` | ✅ | ✅ | ❌ | ❌ | 8 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | Very High | Medium | Medium | [Link](https://huggingface.co/ACE-Step/Ace-Step1.5) |
-| `acestep-v15-turbo-rl` | ✅ | ✅ | ✅ | ❌ | 8 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | Very High | Medium | Medium | To be released |
-
-### LM Models
-
-| LM Model | Pretrain from | Pre-Training | SFT | RL | CoT metas | Query rewrite | Audio Understanding | Composition Capability | Copy Melody | Hugging Face |
-|----------|---------------|:------------:|:---:|:--:|:---------:|:-------------:|:-------------------:|:----------------------:|:-----------:|--------------|
-| `acestep-5Hz-lm-0.6B` | Qwen3-0.6B | ✅ | ✅ | ✅ | ✅ | ✅ | Medium | Medium | Weak | ✅ |
-| `acestep-5Hz-lm-1.7B` | Qwen3-1.7B | ✅ | ✅ | ✅ | ✅ | ✅ | Medium | Medium | Medium | ✅ |
-| `acestep-5Hz-lm-4B` | Qwen3-4B | ✅ | ✅ | ✅ | ✅ | ✅ | Strong | Strong | Strong | ✅ |
-
-
-## 🙏 Acknowledgements
-
-This project is co-led by ACE Studio and StepFun.
-
-
-## 📖 Citation
-
-If you find this project useful for your research, please consider citing:
-
-```BibTeX
-@misc{gong2026acestep,
-	title={ACE-Step 1.5: Pushing the Boundaries of Open-Source Music Generation},
-	author={Junmin Gong, Yulin Song, Wenxiao Zhao, Sen Wang, Shengyuan Xu, Jing Guo}, 
-	howpublished={\url{https://github.com/ace-step/ACE-Step-1.5}},
-	year={2026},
-	note={GitHub repository}
-}

ace-step/acestep-5Hz-lm-1.7B/chat_template.jinja

@@ -1,89 +0,0 @@
-{%- if tools %}
-    {{- '<|im_start|>system\n' }}
-    {%- if messages[0].role == 'system' %}
-        {{- messages[0].content + '\n\n' }}
-    {%- endif %}
-    {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
-    {%- for tool in tools %}
-        {{- "\n" }}
-        {{- tool | tojson }}
-    {%- endfor %}
-    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
-{%- else %}
-    {%- if messages[0].role == 'system' %}
-        {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
-    {%- endif %}
-{%- endif %}
-{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
-{%- for message in messages[::-1] %}
-    {%- set index = (messages|length - 1) - loop.index0 %}
-    {%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
-        {%- set ns.multi_step_tool = false %}
-        {%- set ns.last_query_index = index %}
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-{%- endfor %}
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-    {%- if message.content is string %}
-        {%- set content = message.content %}
-    {%- else %}
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-    {%- endif %}
-    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
-        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
-    {%- elif message.role == "assistant" %}
-        {%- set reasoning_content = '' %}
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-        {%- else %}
-            {%- if '</think>' in content %}
-                {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
-                {%- set content = content.split('</think>')[-1].lstrip('\n') %}
-            {%- endif %}
-        {%- endif %}
-        {%- if loop.index0 > ns.last_query_index %}
-            {%- if loop.last or (not loop.last and reasoning_content) %}
-                {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
-            {%- else %}
-                {{- '<|im_start|>' + message.role + '\n' + content }}
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-            {{- '<|im_start|>' + message.role + '\n' + content }}
-        {%- endif %}
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-            {%- for tool_call in message.tool_calls %}
-                {%- if (loop.first and content) or (not loop.first) %}
-                    {{- '\n' }}
-                {%- endif %}
-                {%- if tool_call.function %}
-                    {%- set tool_call = tool_call.function %}
-                {%- endif %}
-                {{- '<tool_call>\n{"name": "' }}
-                {{- tool_call.name }}
-                {{- '", "arguments": ' }}
-                {%- if tool_call.arguments is string %}
-                    {{- tool_call.arguments }}
-                {%- else %}
-                    {{- tool_call.arguments | tojson }}
-                {%- endif %}
-                {{- '}\n</tool_call>' }}
-            {%- endfor %}
-        {%- endif %}
-        {{- '<|im_end|>\n' }}
-    {%- elif message.role == "tool" %}
-        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
-            {{- '<|im_start|>user' }}
-        {%- endif %}
-        {{- '\n<tool_response>\n' }}
-        {{- content }}
-        {{- '\n</tool_response>' }}
-        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
-            {{- '<|im_end|>\n' }}
-        {%- endif %}
-    {%- endif %}
-{%- endfor %}
-{%- if add_generation_prompt %}
-    {{- '<|im_start|>assistant\n' }}
-    {%- if enable_thinking is defined and enable_thinking is false %}
-        {{- '<think>\n\n</think>\n\n' }}
-    {%- endif %}
-{%- endif %}

ace-step/acestep-5Hz-lm-1.7B/config.json

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-{
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-{
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ace-step/acestep-5Hz-lm-4B/tokenizer.json

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-oid sha256:35af56c3f5cb3ea2cc578aa28a8937770981d504f183ac5c8c38baf4bbd4af4d
-size 24321939

ace-step/acestep-5Hz-lm-4B/tokenizer_config.json

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-oid sha256:6cd70cdd89425971794f5235562edcc608b0629a6c4686ae51a8b8c8b8ba5e95
-size 14072925

ace-step/acestep-v15-base/apg_guidance.py

@@ -1,220 +0,0 @@
-import torch
-import torch.nn.functional as F
-
-
-class MomentumBuffer:
-
-    def __init__(self, momentum: float = -0.75):
-        self.momentum = momentum
-        self.running_average = 0
-
-    def update(self, update_value: torch.Tensor):
-        new_average = self.momentum * self.running_average
-        self.running_average = update_value + new_average
-
-
-def project(
-    v0: torch.Tensor,  # [B, C, T]
-    v1: torch.Tensor,  # [B, C, T]
-    dims=[-1],
-):
-    dtype = v0.dtype
-    device_type = v0.device.type
-    if device_type == "mps":
-        v0, v1 = v0.cpu(), v1.cpu()
-
-    v0, v1 = v0.double(), v1.double()
-    v1 = torch.nn.functional.normalize(v1, dim=dims)
-    v0_parallel = (v0 * v1).sum(dim=dims, keepdim=True) * v1
-    v0_orthogonal = v0 - v0_parallel
-    return v0_parallel.to(dtype).to(device_type), v0_orthogonal.to(dtype).to(device_type)
-
-
-def apg_forward(
-    pred_cond: torch.Tensor,  # [B, C, T]
-    pred_uncond: torch.Tensor,  # [B, C, T]
-    guidance_scale: float,
-    momentum_buffer: MomentumBuffer = None,
-    eta: float = 0.0,
-    norm_threshold: float = 2.5,
-    dims=[-1],
-):
-    diff = pred_cond - pred_uncond
-    if momentum_buffer is not None:
-        momentum_buffer.update(diff)
-        diff = momentum_buffer.running_average
-
-    if norm_threshold > 0:
-        ones = torch.ones_like(diff)
-        diff_norm = diff.norm(p=2, dim=dims, keepdim=True)
-        scale_factor = torch.minimum(ones, norm_threshold / diff_norm)
-        diff = diff * scale_factor
-
-    diff_parallel, diff_orthogonal = project(diff, pred_cond, dims)
-    normalized_update = diff_orthogonal + eta * diff_parallel
-    pred_guided = pred_cond + (guidance_scale - 1) * normalized_update
-    return pred_guided
-
-
-def cfg_forward(cond_output, uncond_output, cfg_strength):
-    return uncond_output + cfg_strength * (cond_output - uncond_output)
-
-
-def call_cos_tensor(tensor1, tensor2):
-    """
-    Calculate cosine similarity between two normalized tensors.
-    
-    Args:
-        tensor1: First tensor [B, ...]
-        tensor2: Second tensor [B, ...]
-    
-    Returns:
-        Cosine similarity value [B, 1]
-    """
-    tensor1 = tensor1 / torch.linalg.norm(tensor1, dim=1, keepdim=True)
-    tensor2 = tensor2 / torch.linalg.norm(tensor2, dim=1, keepdim=True)
-    cosvalue = torch.sum(tensor1 * tensor2, dim=1, keepdim=True)
-    return cosvalue
-
-
-def compute_perpendicular_component(latent_diff, latent_hat_uncond):
-    """
-    Decompose latent_diff into parallel and perpendicular components relative to latent_hat_uncond.
-    
-    Args:
-        latent_diff: Difference tensor [B, C, ...]
-        latent_hat_uncond: Unconditional prediction tensor [B, C, ...]
-    
-    Returns:
-        projection: Parallel component
-        perpendicular_component: Perpendicular component
-    """
-    n, t, c = latent_diff.shape
-    latent_diff = latent_diff.view(n * t, c).float()
-    latent_hat_uncond = latent_hat_uncond.view(n * t, c).float()
-
-    if latent_diff.size() != latent_hat_uncond.size():
-        raise ValueError("latent_diff and latent_hat_uncond must have the same shape [n, d].")
-
-    dot_product = torch.sum(latent_diff * latent_hat_uncond, dim=1, keepdim=True)  # [n, 1]
-    norm_square = torch.sum(latent_hat_uncond * latent_hat_uncond, dim=1, keepdim=True)  # [n, 1]
-    projection = (dot_product / (norm_square + 1e-8)) * latent_hat_uncond
-    perpendicular_component = latent_diff - projection
-
-    return projection.view(n, t, c), perpendicular_component.reshape(n, t, c)
-
-
-def adg_forward(
-    latents: torch.Tensor,
-    noise_pred_cond: torch.Tensor,
-    noise_pred_uncond: torch.Tensor,
-    sigma: torch.Tensor,
-    guidance_scale: float,
-    angle_clip: float = 3.14 / 6,  # pi/6 by default
-    apply_norm: bool = False,
-    apply_clip: bool = True,
-):
-    """
-    ADG (Angle-based Dynamic Guidance) forward pass for Flow Matching.
-    
-    In flow matching (including SD3), sigma represents the current timestep t_curr.
-    The predictions are velocity fields v(x_t, t).
-    
-    Args:
-        latents: Current state x_t [N, T, d] where d=64
-        noise_pred_cond: Conditional velocity prediction v_cond [N, T, d]
-        noise_pred_uncond: Unconditional velocity prediction v_uncond [N, T, d]
-        sigma: Current timestep t_curr (not t_prev!)
-        guidance_scale: Guidance strength
-        angle_clip: Maximum angle for clipping (default: pi/6)
-        apply_norm: Whether to normalize the result (ADG_w_norm variant)
-        apply_clip: Whether to clip the angle (ADG_wo_clip when False)
-    
-    Returns:
-        Guided velocity prediction [N, T, d]
-    """
-    # Get batch size
-    n = noise_pred_cond.shape[0]
-    noise_pred_text = noise_pred_cond
-    n, t, c = noise_pred_text.shape
-
-    # Ensure sigma/t has the right shape for broadcasting [N, 1, 1]
-    if isinstance(sigma, (int, float)):
-        sigma = torch.tensor(sigma, device=latents.device, dtype=latents.dtype)
-        sigma = sigma.view(1, 1, 1).expand(n, 1, 1)
-    elif torch.is_tensor(sigma):
-        if sigma.numel() == 1:
-            sigma = sigma.view(1, 1, 1).expand(n, 1, 1)
-        elif sigma.numel() == n:
-            sigma = sigma.view(n, 1, 1)
-        else:
-            raise ValueError(f"sigma has incompatible shape. Expected scalar or size {n}, got {sigma.shape}")
-    else:
-        raise TypeError(f"sigma must be a number or tensor, got {type(sigma)}")
-
-    # Adjust guidance weight
-    weight = guidance_scale - 1
-    weight = weight * (weight > 0) + 1e-3
-
-    latent_hat_text = latents - sigma * noise_pred_text
-    latent_hat_uncond = latents - sigma * noise_pred_uncond
-    latent_diff = latent_hat_text - latent_hat_uncond
-
-    # Calculate angle between conditional and unconditional predicted data
-    latent_theta = torch.acos(
-        call_cos_tensor(latent_hat_text.view(-1, c).to(float),
-                        latent_hat_uncond.reshape(-1, c).contiguous().to(float)))
-    latent_theta_new = torch.clip(weight * latent_theta, -angle_clip, angle_clip) if apply_clip else weight * latent_theta
-    proj, perp = compute_perpendicular_component(latent_diff, latent_hat_uncond)
-    latent_v_new = torch.cos(latent_theta_new) * latent_hat_text
-
-    latent_p_new = perp * torch.sin(latent_theta_new) / torch.sin(latent_theta) * (
-        torch.sin(latent_theta) > 1e-3) + perp * weight * (torch.sin(latent_theta) <= 1e-3)
-    latent_new = latent_v_new + latent_p_new
-    if apply_norm:
-        latent_new = latent_new * torch.linalg.norm(latent_hat_text, dim=1, keepdim=True) / torch.linalg.norm(
-            latent_new, dim=1, keepdim=True)
-
-    noise_pred = (latents - latent_new) / sigma
-    noise_pred = noise_pred.reshape(n, t, c).to(latents.dtype)
-    return noise_pred
-
-
-def adg_w_norm_forward(
-    latents: torch.Tensor,
-    noise_pred_cond: torch.Tensor,
-    noise_pred_uncond: torch.Tensor,
-    sigma: float,
-    guidance_scale: float,
-    angle_clip: float = 3.14 / 3,
-):
-    """
-    ADG with normalization - preserves the magnitude of latent predictions.
-    
-    This variant normalizes the final latent to maintain the same norm as the
-    conditional prediction, which can help preserve image quality.
-    """
-    return adg_forward(latents,
-                       noise_pred_cond,
-                       noise_pred_uncond,
-                       sigma,
-                       guidance_scale,
-                       angle_clip=angle_clip,
-                       apply_norm=True,
-                       apply_clip=True)
-
-
-def adg_wo_clip_forward(
-    latents: torch.Tensor,
-    noise_pred_cond: torch.Tensor,
-    noise_pred_uncond: torch.Tensor,
-    sigma: float,
-    guidance_scale: float,
-):
-    """
-    ADG without angle clipping - allows unbounded angle adjustments.
-    
-    This variant doesn't clip the angle, which may result in more aggressive
-    guidance but could be less stable.
-    """
-    return adg_forward(latents, noise_pred_cond, noise_pred_uncond, sigma, guidance_scale, apply_norm=False, apply_clip=False)

ace-step/acestep-v15-base/config.json

@@ -1,81 +0,0 @@
-{
-  "architectures": [
-    "AceStepConditionGenerationModel"
-  ],
-  "auto_map": {
-    "AutoConfig": "configuration_acestep_v15.AceStepConfig",
-    "AutoModel": "modeling_acestep_v15_base.AceStepConditionGenerationModel"
-  },
-  "attention_bias": false,
-  "attention_dropout": 0.0,
-  "audio_acoustic_hidden_dim": 64,
-  "data_proportion": 0.5,
-  "dtype": "bfloat16",
-  "fsq_dim": 2048,
-  "fsq_input_levels": [
-    8,
-    8,
-    8,
-    5,
-    5,
-    5
-  ],
-  "fsq_input_num_quantizers": 1,
-  "head_dim": 128,
-  "hidden_act": "silu",
-  "hidden_size": 2048,
-  "in_channels": 192,
-  "initializer_range": 0.02,
-  "intermediate_size": 6144,
-  "layer_types": [
-    "sliding_attention",
-    "full_attention",
-    "sliding_attention",
-    "full_attention",
-    "sliding_attention",
-    "full_attention",
-    "sliding_attention",
-    "full_attention",
-    "sliding_attention",
-    "full_attention",
-    "sliding_attention",
-    "full_attention",
-    "sliding_attention",
-    "full_attention",
-    "sliding_attention",
-    "full_attention",
-    "sliding_attention",
-    "full_attention",
-    "sliding_attention",
-    "full_attention",
-    "sliding_attention",
-    "full_attention",
-    "sliding_attention",
-    "full_attention"
-  ],
-  "max_position_embeddings": 32768,
-  "model_type": "acestep",
-  "num_attention_heads": 16,
-  "num_attention_pooler_hidden_layers": 2,
-  "num_audio_decoder_hidden_layers": 24,
-  "num_hidden_layers": 24,
-  "num_key_value_heads": 8,
-  "num_lyric_encoder_hidden_layers": 8,
-  "num_timbre_encoder_hidden_layers": 4,
-  "patch_size": 2,
-  "pool_window_size": 5,
-  "rms_norm_eps": 1e-06,
-  "rope_scaling": null,
-  "rope_theta": 1000000,
-  "sliding_window": 128,
-  "text_hidden_dim": 1024,
-  "timbre_fix_frame": 750,
-  "timbre_hidden_dim": 64,
-  "timestep_mu": -0.4,
-  "timestep_sigma": 1.0,
-  "transformers_version": "4.57.0.dev0",
-  "use_cache": true,
-  "use_sliding_window": true,
-  "vocab_size": 64003,
-  "is_turbo": false
-}

ace-step/acestep-v15-base/configuration_acestep_v15.py

@@ -1,263 +0,0 @@
-# coding=utf-8
-# Copyright 2024 The Qwen team, Alibaba Group and the HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""AceStep model configuration"""
-
-from transformers.configuration_utils import PretrainedConfig, layer_type_validation
-from transformers.modeling_rope_utils import rope_config_validation
-from transformers.utils import logging
-
-
-logger = logging.get_logger(__name__)
-
-
-class AceStepConfig(PretrainedConfig):
-    r"""
-    This is the configuration class to store the configuration of a [`AceStepModel`]. It is used to instantiate an
-    AceStep model according to the specified arguments, defining the model architecture.
-
-    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
-    documentation from [`PretrainedConfig`] for more information.
-
-    Args:
-        vocab_size (`int`, *optional*, defaults to 64003):
-            Vocabulary size of the AceStep model. Defines the number of different tokens that can be represented by the
-            `inputs_ids` passed when calling the model.
-        hidden_size (`int`, *optional*, defaults to 4096):
-            Dimension of the hidden representations.
-        intermediate_size (`int`, *optional*, defaults to 22016):
-            Dimension of the MLP representations.
-        num_hidden_layers (`int`, *optional*, defaults to 32):
-            Number of hidden layers in the Transformer encoder.
-        num_attention_heads (`int`, *optional*, defaults to 32):
-            Number of attention heads for each attention layer in the Transformer encoder.
-        num_key_value_heads (`int`, *optional*, defaults to 32):
-            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
-            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
-            `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
-            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
-            by meanpooling all the original heads within that group. For more details, check out [this
-            paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `32`.
-        head_dim (`int`, *optional*, defaults to 128):
-            The attention head dimension.
-        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
-            The non-linear activation function (function or string) in the decoder.
-        max_position_embeddings (`int`, *optional*, defaults to 32768):
-            The maximum sequence length that this model might ever be used with.
-        initializer_range (`float`, *optional*, defaults to 0.02):
-            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
-        rms_norm_eps (`float`, *optional*, defaults to 1e-06):
-            The epsilon used by the rms normalization layers.
-        use_cache (`bool`, *optional*, defaults to `True`):
-            Whether or not the model should return the last key/values attentions (not used by all models). Only
-            relevant if `config.is_decoder=True`.
-        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
-            Whether the model's input and output word embeddings should be tied.
-        rope_theta (`float`, *optional*, defaults to 10000.0):
-            The base period of the RoPE embeddings.
-        rope_scaling (`Dict`, *optional*):
-            Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
-            and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
-            accordingly.
-            Expected contents:
-                `rope_type` (`str`):
-                    The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
-                    'llama3'], with 'default' being the original RoPE implementation.
-                `factor` (`float`, *optional*):
-                    Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
-                    most scaling types, a `factor` of x will enable the model to handle sequences of length x *
-                    original maximum pre-trained length.
-                `original_max_position_embeddings` (`int`, *optional*):
-                    Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
-                    pretraining.
-                `attention_factor` (`float`, *optional*):
-                    Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
-                    computation. If unspecified, it defaults to value recommended by the implementation, using the
-                    `factor` field to infer the suggested value.
-                `beta_fast` (`float`, *optional*):
-                    Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
-                    ramp function. If unspecified, it defaults to 32.
-                `beta_slow` (`float`, *optional*):
-                    Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
-                    ramp function. If unspecified, it defaults to 1.
-                `short_factor` (`list[float]`, *optional*):
-                    Only used with 'longrope'. The scaling factor to be applied to short contexts (<
-                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
-                    size divided by the number of attention heads divided by 2
-                `long_factor` (`list[float]`, *optional*):
-                    Only used with 'longrope'. The scaling factor to be applied to long contexts (<
-                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
-                    size divided by the number of attention heads divided by 2
-                `low_freq_factor` (`float`, *optional*):
-                    Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
-                `high_freq_factor` (`float`, *optional*):
-                    Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
-        attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
-            Whether to use a bias in the query, key, value and output projection layers during self-attention.
-        use_sliding_window (`bool`, *optional*, defaults to `False`):
-            Whether to use sliding window attention.
-        sliding_window (`int`, *optional*, defaults to 4096):
-            Sliding window attention (SWA) window size. If not specified, will default to `4096`.
-        layer_types (`list`, *optional*):
-            Attention pattern for each layer.
-        attention_dropout (`float`, *optional*, defaults to 0.0):
-            The dropout ratio for the attention probabilities.
-
-    ```python
-    >>> from acestep.models import AceStepConfig
-
-    >>> # Initializing an AceStep configuration
-    >>> configuration = AceStepConfig()
-
-    >>> # Initializing a model from the configuration
-    >>> model = AceStepConditionGenerationModel(configuration)
-
-    >>> # Accessing the model configuration
-    >>> configuration = model.config
-    ```"""
-
-    model_type = "acestep"
-    keys_to_ignore_at_inference = ["past_key_values"]
-    
-    # Default tensor parallel plan for the base model
-    base_model_tp_plan = {
-        "layers.*.self_attn.q_proj": "colwise",
-        "layers.*.self_attn.k_proj": "colwise",
-        "layers.*.self_attn.v_proj": "colwise",
-        "layers.*.self_attn.o_proj": "rowwise",
-        "layers.*.mlp.gate_proj": "colwise",
-        "layers.*.mlp.up_proj": "colwise",
-        "layers.*.mlp.down_proj": "rowwise",
-    }
-    base_model_pp_plan = {
-        "embed_tokens": (["input_ids"], ["inputs_embeds"]),
-        "layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
-        "norm": (["hidden_states"], ["hidden_states"]),
-    }
-    def __init__(
-        self,
-        vocab_size=64003,
-        fsq_dim=2048,
-        fsq_input_levels=[8, 8, 8, 5, 5, 5],
-        fsq_input_num_quantizers=1,
-        hidden_size=2048,
-        intermediate_size=6144,
-        num_hidden_layers=24,
-        num_attention_heads=16,
-        num_key_value_heads=8,
-        head_dim=128,
-        hidden_act="silu",
-        max_position_embeddings=32768,
-        initializer_range=0.02,
-        rms_norm_eps=1e-6,
-        use_cache=True,
-        tie_word_embeddings=True,
-        rope_theta=1000000,
-        rope_scaling=None,
-        attention_bias=False,
-        use_sliding_window=True,
-        sliding_window=128,
-        layer_types=None,
-        attention_dropout=0.0,
-        num_lyric_encoder_hidden_layers=8,
-        audio_acoustic_hidden_dim=64,
-        pool_window_size=5,
-        text_hidden_dim=1024,
-        in_channels=192,
-        data_proportion=0.5,
-        timestep_mu=-0.4,
-        timestep_sigma=1.0,
-        timbre_hidden_dim=64,
-        num_timbre_encoder_hidden_layers=4,
-        timbre_fix_frame=750,
-        patch_size=2,
-        num_attention_pooler_hidden_layers=2,
-        num_audio_decoder_hidden_layers=24,
-        model_version="turbo",
-        **kwargs,
-    ):
-        self.max_position_embeddings = max_position_embeddings
-        self.hidden_size = hidden_size
-        self.intermediate_size = intermediate_size
-        self.num_hidden_layers = num_hidden_layers
-        self.num_attention_heads = num_attention_heads
-        self.use_sliding_window = use_sliding_window
-        self.sliding_window = sliding_window if self.use_sliding_window else None
-        
-        # Text encoder configuration
-        self.text_hidden_dim = text_hidden_dim
-
-        # Lyric encoder configuration
-        self.num_lyric_encoder_hidden_layers = num_lyric_encoder_hidden_layers
-        self.patch_size = patch_size
-
-        # Audio semantic token generation configuration
-        self.audio_acoustic_hidden_dim = audio_acoustic_hidden_dim
-        self.pool_window_size = pool_window_size
-        self.in_channels = in_channels
-        self.data_proportion = data_proportion
-        self.timestep_mu = timestep_mu
-        self.timestep_sigma = timestep_sigma
-        
-        # FSQ (Finite Scalar Quantization) configuration
-        self.fsq_dim = fsq_dim
-        self.fsq_input_levels = fsq_input_levels
-        self.fsq_input_num_quantizers = fsq_input_num_quantizers
-        
-        # Timbre encoder configuration
-        self.timbre_hidden_dim = timbre_hidden_dim
-        self.num_timbre_encoder_hidden_layers = num_timbre_encoder_hidden_layers
-        self.timbre_fix_frame = timbre_fix_frame
-        self.num_attention_pooler_hidden_layers = num_attention_pooler_hidden_layers
-        self.num_audio_decoder_hidden_layers = num_audio_decoder_hidden_layers
-        self.vocab_size = vocab_size
-
-        # Backward compatibility: ensure num_key_value_heads is set
-        if num_key_value_heads is None:
-            num_key_value_heads = num_attention_heads
-
-        self.num_key_value_heads = num_key_value_heads
-        self.head_dim = head_dim
-        self.hidden_act = hidden_act
-        self.initializer_range = initializer_range
-        self.rms_norm_eps = rms_norm_eps
-        self.use_cache = use_cache
-        self.rope_theta = rope_theta
-        self.rope_scaling = rope_scaling
-        self.attention_bias = attention_bias
-        self.attention_dropout = attention_dropout
-        self.model_version = model_version
-        
-        # Validate rotary position embeddings parameters
-        # Backward compatibility: if there is a 'type' field, move it to 'rope_type'
-        if self.rope_scaling is not None and "type" in self.rope_scaling:
-            self.rope_scaling["rope_type"] = self.rope_scaling["type"]
-        rope_config_validation(self)
-
-        self.layer_types = layer_types
-
-        # Set default layer types if not specified
-        if self.layer_types is None:
-            self.layer_types = [
-                "sliding_attention" if bool((i + 1) % 2) else "full_attention" for i in range(self.num_hidden_layers)
-            ]
-        layer_type_validation(self.layer_types)
-
-        super().__init__(
-            tie_word_embeddings=tie_word_embeddings,
-            **kwargs,
-        )
-
-
-__all__ = ["AceStepConfig"]

ace-step/acestep-v15-base/silence_latent.pt

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:a778e9dd942f5e8b2c09c55370782d318834432b03dabbcdf70e6ed49ad6358b
-size 3841215

ace-step/acestep-v15-sft/apg_guidance.py

@@ -1,220 +0,0 @@
-import torch
-import torch.nn.functional as F
-
-
-class MomentumBuffer:
-
-    def __init__(self, momentum: float = -0.75):
-        self.momentum = momentum
-        self.running_average = 0
-
-    def update(self, update_value: torch.Tensor):
-        new_average = self.momentum * self.running_average
-        self.running_average = update_value + new_average
-
-
-def project(
-    v0: torch.Tensor,  # [B, C, T]
-    v1: torch.Tensor,  # [B, C, T]
-    dims=[-1],
-):
-    dtype = v0.dtype
-    device_type = v0.device.type
-    if device_type == "mps":
-        v0, v1 = v0.cpu(), v1.cpu()
-
-    v0, v1 = v0.double(), v1.double()
-    v1 = torch.nn.functional.normalize(v1, dim=dims)
-    v0_parallel = (v0 * v1).sum(dim=dims, keepdim=True) * v1
-    v0_orthogonal = v0 - v0_parallel
-    return v0_parallel.to(dtype).to(device_type), v0_orthogonal.to(dtype).to(device_type)
-
-
-def apg_forward(
-    pred_cond: torch.Tensor,  # [B, C, T]
-    pred_uncond: torch.Tensor,  # [B, C, T]
-    guidance_scale: float,
-    momentum_buffer: MomentumBuffer = None,
-    eta: float = 0.0,
-    norm_threshold: float = 2.5,
-    dims=[-1],
-):
-    diff = pred_cond - pred_uncond
-    if momentum_buffer is not None:
-        momentum_buffer.update(diff)
-        diff = momentum_buffer.running_average
-
-    if norm_threshold > 0:
-        ones = torch.ones_like(diff)
-        diff_norm = diff.norm(p=2, dim=dims, keepdim=True)
-        scale_factor = torch.minimum(ones, norm_threshold / diff_norm)
-        diff = diff * scale_factor
-
-    diff_parallel, diff_orthogonal = project(diff, pred_cond, dims)
-    normalized_update = diff_orthogonal + eta * diff_parallel
-    pred_guided = pred_cond + (guidance_scale - 1) * normalized_update
-    return pred_guided
-
-
-def cfg_forward(cond_output, uncond_output, cfg_strength):
-    return uncond_output + cfg_strength * (cond_output - uncond_output)
-
-
-def call_cos_tensor(tensor1, tensor2):
-    """
-    Calculate cosine similarity between two normalized tensors.
-    
-    Args:
-        tensor1: First tensor [B, ...]
-        tensor2: Second tensor [B, ...]
-    
-    Returns:
-        Cosine similarity value [B, 1]
-    """
-    tensor1 = tensor1 / torch.linalg.norm(tensor1, dim=1, keepdim=True)
-    tensor2 = tensor2 / torch.linalg.norm(tensor2, dim=1, keepdim=True)
-    cosvalue = torch.sum(tensor1 * tensor2, dim=1, keepdim=True)
-    return cosvalue
-
-
-def compute_perpendicular_component(latent_diff, latent_hat_uncond):
-    """
-    Decompose latent_diff into parallel and perpendicular components relative to latent_hat_uncond.
-    
-    Args:
-        latent_diff: Difference tensor [B, C, ...]
-        latent_hat_uncond: Unconditional prediction tensor [B, C, ...]
-    
-    Returns:
-        projection: Parallel component
-        perpendicular_component: Perpendicular component
-    """
-    n, t, c = latent_diff.shape
-    latent_diff = latent_diff.view(n * t, c).float()
-    latent_hat_uncond = latent_hat_uncond.view(n * t, c).float()
-
-    if latent_diff.size() != latent_hat_uncond.size():
-        raise ValueError("latent_diff and latent_hat_uncond must have the same shape [n, d].")
-
-    dot_product = torch.sum(latent_diff * latent_hat_uncond, dim=1, keepdim=True)  # [n, 1]
-    norm_square = torch.sum(latent_hat_uncond * latent_hat_uncond, dim=1, keepdim=True)  # [n, 1]
-    projection = (dot_product / (norm_square + 1e-8)) * latent_hat_uncond
-    perpendicular_component = latent_diff - projection
-
-    return projection.view(n, t, c), perpendicular_component.reshape(n, t, c)
-
-
-def adg_forward(
-    latents: torch.Tensor,
-    noise_pred_cond: torch.Tensor,
-    noise_pred_uncond: torch.Tensor,
-    sigma: torch.Tensor,
-    guidance_scale: float,
-    angle_clip: float = 3.14 / 6,  # pi/6 by default
-    apply_norm: bool = False,
-    apply_clip: bool = True,
-):
-    """
-    ADG (Angle-based Dynamic Guidance) forward pass for Flow Matching.
-    
-    In flow matching (including SD3), sigma represents the current timestep t_curr.
-    The predictions are velocity fields v(x_t, t).
-    
-    Args:
-        latents: Current state x_t [N, T, d] where d=64
-        noise_pred_cond: Conditional velocity prediction v_cond [N, T, d]
-        noise_pred_uncond: Unconditional velocity prediction v_uncond [N, T, d]
-        sigma: Current timestep t_curr (not t_prev!)
-        guidance_scale: Guidance strength
-        angle_clip: Maximum angle for clipping (default: pi/6)
-        apply_norm: Whether to normalize the result (ADG_w_norm variant)
-        apply_clip: Whether to clip the angle (ADG_wo_clip when False)
-    
-    Returns:
-        Guided velocity prediction [N, T, d]
-    """
-    # Get batch size
-    n = noise_pred_cond.shape[0]
-    noise_pred_text = noise_pred_cond
-    n, t, c = noise_pred_text.shape
-
-    # Ensure sigma/t has the right shape for broadcasting [N, 1, 1]
-    if isinstance(sigma, (int, float)):
-        sigma = torch.tensor(sigma, device=latents.device, dtype=latents.dtype)
-        sigma = sigma.view(1, 1, 1).expand(n, 1, 1)
-    elif torch.is_tensor(sigma):
-        if sigma.numel() == 1:
-            sigma = sigma.view(1, 1, 1).expand(n, 1, 1)
-        elif sigma.numel() == n:
-            sigma = sigma.view(n, 1, 1)
-        else:
-            raise ValueError(f"sigma has incompatible shape. Expected scalar or size {n}, got {sigma.shape}")
-    else:
-        raise TypeError(f"sigma must be a number or tensor, got {type(sigma)}")
-
-    # Adjust guidance weight
-    weight = guidance_scale - 1
-    weight = weight * (weight > 0) + 1e-3
-
-    latent_hat_text = latents - sigma * noise_pred_text
-    latent_hat_uncond = latents - sigma * noise_pred_uncond
-    latent_diff = latent_hat_text - latent_hat_uncond
-
-    # Calculate angle between conditional and unconditional predicted data
-    latent_theta = torch.acos(
-        call_cos_tensor(latent_hat_text.view(-1, c).to(float),
-                        latent_hat_uncond.reshape(-1, c).contiguous().to(float)))
-    latent_theta_new = torch.clip(weight * latent_theta, -angle_clip, angle_clip) if apply_clip else weight * latent_theta
-    proj, perp = compute_perpendicular_component(latent_diff, latent_hat_uncond)
-    latent_v_new = torch.cos(latent_theta_new) * latent_hat_text
-
-    latent_p_new = perp * torch.sin(latent_theta_new) / torch.sin(latent_theta) * (
-        torch.sin(latent_theta) > 1e-3) + perp * weight * (torch.sin(latent_theta) <= 1e-3)
-    latent_new = latent_v_new + latent_p_new
-    if apply_norm:
-        latent_new = latent_new * torch.linalg.norm(latent_hat_text, dim=1, keepdim=True) / torch.linalg.norm(
-            latent_new, dim=1, keepdim=True)
-
-    noise_pred = (latents - latent_new) / sigma
-    noise_pred = noise_pred.reshape(n, t, c).to(latents.dtype)
-    return noise_pred
-
-
-def adg_w_norm_forward(
-    latents: torch.Tensor,
-    noise_pred_cond: torch.Tensor,
-    noise_pred_uncond: torch.Tensor,
-    sigma: float,
-    guidance_scale: float,
-    angle_clip: float = 3.14 / 3,
-):
-    """
-    ADG with normalization - preserves the magnitude of latent predictions.
-    
-    This variant normalizes the final latent to maintain the same norm as the
-    conditional prediction, which can help preserve image quality.
-    """
-    return adg_forward(latents,
-                       noise_pred_cond,
-                       noise_pred_uncond,
-                       sigma,
-                       guidance_scale,
-                       angle_clip=angle_clip,
-                       apply_norm=True,
-                       apply_clip=True)
-
-
-def adg_wo_clip_forward(
-    latents: torch.Tensor,
-    noise_pred_cond: torch.Tensor,
-    noise_pred_uncond: torch.Tensor,
-    sigma: float,
-    guidance_scale: float,
-):
-    """
-    ADG without angle clipping - allows unbounded angle adjustments.
-    
-    This variant doesn't clip the angle, which may result in more aggressive
-    guidance but could be less stable.
-    """
-    return adg_forward(latents, noise_pred_cond, noise_pred_uncond, sigma, guidance_scale, apply_norm=False, apply_clip=False)

ace-step/acestep-v15-sft/config.json

@@ -1,81 +0,0 @@
-{
-  "architectures": [
-    "AceStepConditionGenerationModel"
-  ],
-  "auto_map": {
-    "AutoConfig": "configuration_acestep_v15.AceStepConfig",
-    "AutoModel": "modeling_acestep_v15_base.AceStepConditionGenerationModel"
-  },
-  "attention_bias": false,
-  "attention_dropout": 0.0,
-  "audio_acoustic_hidden_dim": 64,
-  "data_proportion": 0.5,
-  "dtype": "bfloat16",
-  "fsq_dim": 2048,
-  "fsq_input_levels": [
-    8,
-    8,
-    8,
-    5,
-    5,
-    5
-  ],
-  "fsq_input_num_quantizers": 1,
-  "head_dim": 128,
-  "hidden_act": "silu",
-  "hidden_size": 2048,
-  "in_channels": 192,
-  "initializer_range": 0.02,
-  "intermediate_size": 6144,
-  "layer_types": [
-    "sliding_attention",
-    "full_attention",
-    "sliding_attention",
-    "full_attention",
-    "sliding_attention",
-    "full_attention",
-    "sliding_attention",
-    "full_attention",
-    "sliding_attention",
-    "full_attention",
-    "sliding_attention",
-    "full_attention",
-    "sliding_attention",
-    "full_attention",
-    "sliding_attention",
-    "full_attention",
-    "sliding_attention",
-    "full_attention",
-    "sliding_attention",
-    "full_attention",
-    "sliding_attention",
-    "full_attention",
-    "sliding_attention",
-    "full_attention"
-  ],
-  "max_position_embeddings": 32768,
-  "model_type": "acestep",
-  "num_attention_heads": 16,
-  "num_attention_pooler_hidden_layers": 2,
-  "num_audio_decoder_hidden_layers": 24,
-  "num_hidden_layers": 24,
-  "num_key_value_heads": 8,
-  "num_lyric_encoder_hidden_layers": 8,
-  "num_timbre_encoder_hidden_layers": 4,
-  "patch_size": 2,
-  "pool_window_size": 5,
-  "rms_norm_eps": 1e-06,
-  "rope_scaling": null,
-  "rope_theta": 1000000,
-  "sliding_window": 128,
-  "text_hidden_dim": 1024,
-  "timbre_fix_frame": 750,
-  "timbre_hidden_dim": 64,
-  "timestep_mu": -0.4,
-  "timestep_sigma": 1.0,
-  "transformers_version": "4.57.0.dev0",
-  "use_cache": true,
-  "use_sliding_window": true,
-  "vocab_size": 64003,
-  "is_turbo": false
-}

ace-step/acestep-v15-sft/configuration_acestep_v15.py

@@ -1,263 +0,0 @@
-# coding=utf-8
-# Copyright 2024 The Qwen team, Alibaba Group and the HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""AceStep model configuration"""
-
-from transformers.configuration_utils import PretrainedConfig, layer_type_validation
-from transformers.modeling_rope_utils import rope_config_validation
-from transformers.utils import logging
-
-
-logger = logging.get_logger(__name__)
-
-
-class AceStepConfig(PretrainedConfig):
-    r"""
-    This is the configuration class to store the configuration of a [`AceStepModel`]. It is used to instantiate an
-    AceStep model according to the specified arguments, defining the model architecture.
-
-    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
-    documentation from [`PretrainedConfig`] for more information.
-
-    Args:
-        vocab_size (`int`, *optional*, defaults to 64003):
-            Vocabulary size of the AceStep model. Defines the number of different tokens that can be represented by the
-            `inputs_ids` passed when calling the model.
-        hidden_size (`int`, *optional*, defaults to 4096):
-            Dimension of the hidden representations.
-        intermediate_size (`int`, *optional*, defaults to 22016):
-            Dimension of the MLP representations.
-        num_hidden_layers (`int`, *optional*, defaults to 32):
-            Number of hidden layers in the Transformer encoder.
-        num_attention_heads (`int`, *optional*, defaults to 32):
-            Number of attention heads for each attention layer in the Transformer encoder.
-        num_key_value_heads (`int`, *optional*, defaults to 32):
-            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
-            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
-            `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
-            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
-            by meanpooling all the original heads within that group. For more details, check out [this
-            paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `32`.
-        head_dim (`int`, *optional*, defaults to 128):
-            The attention head dimension.
-        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
-            The non-linear activation function (function or string) in the decoder.
-        max_position_embeddings (`int`, *optional*, defaults to 32768):
-            The maximum sequence length that this model might ever be used with.
-        initializer_range (`float`, *optional*, defaults to 0.02):
-            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
-        rms_norm_eps (`float`, *optional*, defaults to 1e-06):
-            The epsilon used by the rms normalization layers.
-        use_cache (`bool`, *optional*, defaults to `True`):
-            Whether or not the model should return the last key/values attentions (not used by all models). Only
-            relevant if `config.is_decoder=True`.
-        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
-            Whether the model's input and output word embeddings should be tied.
-        rope_theta (`float`, *optional*, defaults to 10000.0):
-            The base period of the RoPE embeddings.
-        rope_scaling (`Dict`, *optional*):
-            Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
-            and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
-            accordingly.
-            Expected contents:
-                `rope_type` (`str`):
-                    The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
-                    'llama3'], with 'default' being the original RoPE implementation.
-                `factor` (`float`, *optional*):
-                    Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
-                    most scaling types, a `factor` of x will enable the model to handle sequences of length x *
-                    original maximum pre-trained length.
-                `original_max_position_embeddings` (`int`, *optional*):
-                    Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
-                    pretraining.
-                `attention_factor` (`float`, *optional*):
-                    Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
-                    computation. If unspecified, it defaults to value recommended by the implementation, using the
-                    `factor` field to infer the suggested value.
-                `beta_fast` (`float`, *optional*):
-                    Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
-                    ramp function. If unspecified, it defaults to 32.
-                `beta_slow` (`float`, *optional*):
-                    Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
-                    ramp function. If unspecified, it defaults to 1.
-                `short_factor` (`list[float]`, *optional*):
-                    Only used with 'longrope'. The scaling factor to be applied to short contexts (<
-                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
-                    size divided by the number of attention heads divided by 2
-                `long_factor` (`list[float]`, *optional*):
-                    Only used with 'longrope'. The scaling factor to be applied to long contexts (<
-                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
-                    size divided by the number of attention heads divided by 2
-                `low_freq_factor` (`float`, *optional*):
-                    Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
-                `high_freq_factor` (`float`, *optional*):
-                    Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
-        attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
-            Whether to use a bias in the query, key, value and output projection layers during self-attention.
-        use_sliding_window (`bool`, *optional*, defaults to `False`):
-            Whether to use sliding window attention.
-        sliding_window (`int`, *optional*, defaults to 4096):
-            Sliding window attention (SWA) window size. If not specified, will default to `4096`.
-        layer_types (`list`, *optional*):
-            Attention pattern for each layer.
-        attention_dropout (`float`, *optional*, defaults to 0.0):
-            The dropout ratio for the attention probabilities.
-
-    ```python
-    >>> from acestep.models import AceStepConfig
-
-    >>> # Initializing an AceStep configuration
-    >>> configuration = AceStepConfig()
-
-    >>> # Initializing a model from the configuration
-    >>> model = AceStepConditionGenerationModel(configuration)
-
-    >>> # Accessing the model configuration
-    >>> configuration = model.config
-    ```"""
-
-    model_type = "acestep"
-    keys_to_ignore_at_inference = ["past_key_values"]
-    
-    # Default tensor parallel plan for the base model
-    base_model_tp_plan = {
-        "layers.*.self_attn.q_proj": "colwise",
-        "layers.*.self_attn.k_proj": "colwise",
-        "layers.*.self_attn.v_proj": "colwise",
-        "layers.*.self_attn.o_proj": "rowwise",
-        "layers.*.mlp.gate_proj": "colwise",
-        "layers.*.mlp.up_proj": "colwise",
-        "layers.*.mlp.down_proj": "rowwise",
-    }
-    base_model_pp_plan = {
-        "embed_tokens": (["input_ids"], ["inputs_embeds"]),
-        "layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
-        "norm": (["hidden_states"], ["hidden_states"]),
-    }
-    def __init__(
-        self,
-        vocab_size=64003,
-        fsq_dim=2048,
-        fsq_input_levels=[8, 8, 8, 5, 5, 5],
-        fsq_input_num_quantizers=1,
-        hidden_size=2048,
-        intermediate_size=6144,
-        num_hidden_layers=24,
-        num_attention_heads=16,
-        num_key_value_heads=8,
-        head_dim=128,
-        hidden_act="silu",
-        max_position_embeddings=32768,
-        initializer_range=0.02,
-        rms_norm_eps=1e-6,
-        use_cache=True,
-        tie_word_embeddings=True,
-        rope_theta=1000000,
-        rope_scaling=None,
-        attention_bias=False,
-        use_sliding_window=True,
-        sliding_window=128,
-        layer_types=None,
-        attention_dropout=0.0,
-        num_lyric_encoder_hidden_layers=8,
-        audio_acoustic_hidden_dim=64,
-        pool_window_size=5,
-        text_hidden_dim=1024,
-        in_channels=192,
-        data_proportion=0.5,
-        timestep_mu=-0.4,
-        timestep_sigma=1.0,
-        timbre_hidden_dim=64,
-        num_timbre_encoder_hidden_layers=4,
-        timbre_fix_frame=750,
-        patch_size=2,
-        num_attention_pooler_hidden_layers=2,
-        num_audio_decoder_hidden_layers=24,
-        model_version="turbo",
-        **kwargs,
-    ):
-        self.max_position_embeddings = max_position_embeddings
-        self.hidden_size = hidden_size
-        self.intermediate_size = intermediate_size
-        self.num_hidden_layers = num_hidden_layers
-        self.num_attention_heads = num_attention_heads
-        self.use_sliding_window = use_sliding_window
-        self.sliding_window = sliding_window if self.use_sliding_window else None
-        
-        # Text encoder configuration
-        self.text_hidden_dim = text_hidden_dim
-
-        # Lyric encoder configuration
-        self.num_lyric_encoder_hidden_layers = num_lyric_encoder_hidden_layers
-        self.patch_size = patch_size
-
-        # Audio semantic token generation configuration
-        self.audio_acoustic_hidden_dim = audio_acoustic_hidden_dim
-        self.pool_window_size = pool_window_size
-        self.in_channels = in_channels
-        self.data_proportion = data_proportion
-        self.timestep_mu = timestep_mu
-        self.timestep_sigma = timestep_sigma
-        
-        # FSQ (Finite Scalar Quantization) configuration
-        self.fsq_dim = fsq_dim
-        self.fsq_input_levels = fsq_input_levels
-        self.fsq_input_num_quantizers = fsq_input_num_quantizers
-        
-        # Timbre encoder configuration
-        self.timbre_hidden_dim = timbre_hidden_dim
-        self.num_timbre_encoder_hidden_layers = num_timbre_encoder_hidden_layers
-        self.timbre_fix_frame = timbre_fix_frame
-        self.num_attention_pooler_hidden_layers = num_attention_pooler_hidden_layers
-        self.num_audio_decoder_hidden_layers = num_audio_decoder_hidden_layers
-        self.vocab_size = vocab_size
-
-        # Backward compatibility: ensure num_key_value_heads is set
-        if num_key_value_heads is None:
-            num_key_value_heads = num_attention_heads
-
-        self.num_key_value_heads = num_key_value_heads
-        self.head_dim = head_dim
-        self.hidden_act = hidden_act
-        self.initializer_range = initializer_range
-        self.rms_norm_eps = rms_norm_eps
-        self.use_cache = use_cache
-        self.rope_theta = rope_theta
-        self.rope_scaling = rope_scaling
-        self.attention_bias = attention_bias
-        self.attention_dropout = attention_dropout
-        self.model_version = model_version
-        
-        # Validate rotary position embeddings parameters
-        # Backward compatibility: if there is a 'type' field, move it to 'rope_type'
-        if self.rope_scaling is not None and "type" in self.rope_scaling:
-            self.rope_scaling["rope_type"] = self.rope_scaling["type"]
-        rope_config_validation(self)
-
-        self.layer_types = layer_types
-
-        # Set default layer types if not specified
-        if self.layer_types is None:
-            self.layer_types = [
-                "sliding_attention" if bool((i + 1) % 2) else "full_attention" for i in range(self.num_hidden_layers)
-            ]
-        layer_type_validation(self.layer_types)
-
-        super().__init__(
-            tie_word_embeddings=tie_word_embeddings,
-            **kwargs,
-        )
-
-
-__all__ = ["AceStepConfig"]

ace-step/acestep-v15-sft/silence_latent.pt

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:a778e9dd942f5e8b2c09c55370782d318834432b03dabbcdf70e6ed49ad6358b
-size 3841215

ace-step/acestep-v15-turbo/config.json

@@ -1,82 +0,0 @@
-{
-  "architectures": [
-    "AceStepConditionGenerationModel"
-  ],
-  "attention_bias": false,
-  "attention_dropout": 0.0,
-  "audio_acoustic_hidden_dim": 64,
-  "auto_map": {
-    "AutoConfig": "configuration_acestep_v15.AceStepConfig",
-    "AutoModel": "modeling_acestep_v15_turbo.AceStepConditionGenerationModel"
-  },
-  "data_proportion": 0.5,
-  "dtype": "bfloat16",
-  "fsq_dim": 2048,
-  "fsq_input_levels": [
-    8,
-    8,
-    8,
-    5,
-    5,
-    5
-  ],
-  "fsq_input_num_quantizers": 1,
-  "head_dim": 128,
-  "hidden_act": "silu",
-  "hidden_size": 2048,
-  "in_channels": 192,
-  "initializer_range": 0.02,
-  "intermediate_size": 6144,
-  "is_turbo": true,
-  "layer_types": [
-    "sliding_attention",
-    "full_attention",
-    "sliding_attention",
-    "full_attention",
-    "sliding_attention",
-    "full_attention",
-    "sliding_attention",
-    "full_attention",
-    "sliding_attention",
-    "full_attention",
-    "sliding_attention",
-    "full_attention",
-    "sliding_attention",
-    "full_attention",
-    "sliding_attention",
-    "full_attention",
-    "sliding_attention",
-    "full_attention",
-    "sliding_attention",
-    "full_attention",
-    "sliding_attention",
-    "full_attention",
-    "sliding_attention",
-    "full_attention"
-  ],
-  "max_position_embeddings": 32768,
-  "model_type": "acestep",
-  "model_version": "turbo",
-  "num_attention_heads": 16,
-  "num_attention_pooler_hidden_layers": 2,
-  "num_audio_decoder_hidden_layers": 24,
-  "num_hidden_layers": 24,
-  "num_key_value_heads": 8,
-  "num_lyric_encoder_hidden_layers": 8,
-  "num_timbre_encoder_hidden_layers": 4,
-  "patch_size": 2,
-  "pool_window_size": 5,
-  "rms_norm_eps": 1e-06,
-  "rope_scaling": null,
-  "rope_theta": 1000000,
-  "sliding_window": 128,
-  "text_hidden_dim": 1024,
-  "timbre_fix_frame": 750,
-  "timbre_hidden_dim": 64,
-  "timestep_mu": -0.4,
-  "timestep_sigma": 1.0,
-  "transformers_version": "4.57.0.dev0",
-  "use_cache": true,
-  "use_sliding_window": true,
-  "vocab_size": 64003
-}

ace-step/acestep-v15-turbo/configuration_acestep_v15.py

@@ -1,263 +0,0 @@
-# coding=utf-8
-# Copyright 2024 The Qwen team, Alibaba Group and the HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""AceStep model configuration"""
-
-from transformers.configuration_utils import PretrainedConfig, layer_type_validation
-from transformers.modeling_rope_utils import rope_config_validation
-from transformers.utils import logging
-
-
-logger = logging.get_logger(__name__)
-
-
-class AceStepConfig(PretrainedConfig):
-    r"""
-    This is the configuration class to store the configuration of a [`AceStepModel`]. It is used to instantiate an
-    AceStep model according to the specified arguments, defining the model architecture.
-
-    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
-    documentation from [`PretrainedConfig`] for more information.
-
-    Args:
-        vocab_size (`int`, *optional*, defaults to 64003):
-            Vocabulary size of the AceStep model. Defines the number of different tokens that can be represented by the
-            `inputs_ids` passed when calling the model.
-        hidden_size (`int`, *optional*, defaults to 4096):
-            Dimension of the hidden representations.
-        intermediate_size (`int`, *optional*, defaults to 22016):
-            Dimension of the MLP representations.
-        num_hidden_layers (`int`, *optional*, defaults to 32):
-            Number of hidden layers in the Transformer encoder.
-        num_attention_heads (`int`, *optional*, defaults to 32):
-            Number of attention heads for each attention layer in the Transformer encoder.
-        num_key_value_heads (`int`, *optional*, defaults to 32):
-            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
-            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
-            `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
-            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
-            by meanpooling all the original heads within that group. For more details, check out [this
-            paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `32`.
-        head_dim (`int`, *optional*, defaults to 128):
-            The attention head dimension.
-        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
-            The non-linear activation function (function or string) in the decoder.
-        max_position_embeddings (`int`, *optional*, defaults to 32768):
-            The maximum sequence length that this model might ever be used with.
-        initializer_range (`float`, *optional*, defaults to 0.02):
-            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
-        rms_norm_eps (`float`, *optional*, defaults to 1e-06):
-            The epsilon used by the rms normalization layers.
-        use_cache (`bool`, *optional*, defaults to `True`):
-            Whether or not the model should return the last key/values attentions (not used by all models). Only
-            relevant if `config.is_decoder=True`.
-        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
-            Whether the model's input and output word embeddings should be tied.
-        rope_theta (`float`, *optional*, defaults to 10000.0):
-            The base period of the RoPE embeddings.
-        rope_scaling (`Dict`, *optional*):
-            Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
-            and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
-            accordingly.
-            Expected contents:
-                `rope_type` (`str`):
-                    The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
-                    'llama3'], with 'default' being the original RoPE implementation.
-                `factor` (`float`, *optional*):
-                    Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
-                    most scaling types, a `factor` of x will enable the model to handle sequences of length x *
-                    original maximum pre-trained length.
-                `original_max_position_embeddings` (`int`, *optional*):
-                    Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
-                    pretraining.
-                `attention_factor` (`float`, *optional*):
-                    Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
-                    computation. If unspecified, it defaults to value recommended by the implementation, using the
-                    `factor` field to infer the suggested value.
-                `beta_fast` (`float`, *optional*):
-                    Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
-                    ramp function. If unspecified, it defaults to 32.
-                `beta_slow` (`float`, *optional*):
-                    Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
-                    ramp function. If unspecified, it defaults to 1.
-                `short_factor` (`list[float]`, *optional*):
-                    Only used with 'longrope'. The scaling factor to be applied to short contexts (<
-                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
-                    size divided by the number of attention heads divided by 2
-                `long_factor` (`list[float]`, *optional*):
-                    Only used with 'longrope'. The scaling factor to be applied to long contexts (<
-                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
-                    size divided by the number of attention heads divided by 2
-                `low_freq_factor` (`float`, *optional*):
-                    Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
-                `high_freq_factor` (`float`, *optional*):
-                    Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
-        attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
-            Whether to use a bias in the query, key, value and output projection layers during self-attention.
-        use_sliding_window (`bool`, *optional*, defaults to `False`):
-            Whether to use sliding window attention.
-        sliding_window (`int`, *optional*, defaults to 4096):
-            Sliding window attention (SWA) window size. If not specified, will default to `4096`.
-        layer_types (`list`, *optional*):
-            Attention pattern for each layer.
-        attention_dropout (`float`, *optional*, defaults to 0.0):
-            The dropout ratio for the attention probabilities.
-
-    ```python
-    >>> from acestep.models import AceStepConfig
-
-    >>> # Initializing an AceStep configuration
-    >>> configuration = AceStepConfig()
-
-    >>> # Initializing a model from the configuration
-    >>> model = AceStepConditionGenerationModel(configuration)
-
-    >>> # Accessing the model configuration
-    >>> configuration = model.config
-    ```"""
-
-    model_type = "acestep"
-    keys_to_ignore_at_inference = ["past_key_values"]
-    
-    # Default tensor parallel plan for the base model
-    base_model_tp_plan = {
-        "layers.*.self_attn.q_proj": "colwise",
-        "layers.*.self_attn.k_proj": "colwise",
-        "layers.*.self_attn.v_proj": "colwise",
-        "layers.*.self_attn.o_proj": "rowwise",
-        "layers.*.mlp.gate_proj": "colwise",
-        "layers.*.mlp.up_proj": "colwise",
-        "layers.*.mlp.down_proj": "rowwise",
-    }
-    base_model_pp_plan = {
-        "embed_tokens": (["input_ids"], ["inputs_embeds"]),
-        "layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
-        "norm": (["hidden_states"], ["hidden_states"]),
-    }
-    def __init__(
-        self,
-        vocab_size=64003,
-        fsq_dim=2048,
-        fsq_input_levels=[8, 8, 8, 5, 5, 5],
-        fsq_input_num_quantizers=1,
-        hidden_size=2048,
-        intermediate_size=6144,
-        num_hidden_layers=24,
-        num_attention_heads=16,
-        num_key_value_heads=8,
-        head_dim=128,
-        hidden_act="silu",
-        max_position_embeddings=32768,
-        initializer_range=0.02,
-        rms_norm_eps=1e-6,
-        use_cache=True,
-        tie_word_embeddings=True,
-        rope_theta=1000000,
-        rope_scaling=None,
-        attention_bias=False,
-        use_sliding_window=True,
-        sliding_window=128,
-        layer_types=None,
-        attention_dropout=0.0,
-        num_lyric_encoder_hidden_layers=8,
-        audio_acoustic_hidden_dim=64,
-        pool_window_size=5,
-        text_hidden_dim=1024,
-        in_channels=192,
-        data_proportion=0.5,
-        timestep_mu=-0.4,
-        timestep_sigma=1.0,
-        timbre_hidden_dim=64,
-        num_timbre_encoder_hidden_layers=4,
-        timbre_fix_frame=750,
-        patch_size=2,
-        num_attention_pooler_hidden_layers=2,
-        num_audio_decoder_hidden_layers=24,
-        model_version="turbo",
-        **kwargs,
-    ):
-        self.max_position_embeddings = max_position_embeddings
-        self.hidden_size = hidden_size
-        self.intermediate_size = intermediate_size
-        self.num_hidden_layers = num_hidden_layers
-        self.num_attention_heads = num_attention_heads
-        self.use_sliding_window = use_sliding_window
-        self.sliding_window = sliding_window if self.use_sliding_window else None
-        
-        # Text encoder configuration
-        self.text_hidden_dim = text_hidden_dim
-
-        # Lyric encoder configuration
-        self.num_lyric_encoder_hidden_layers = num_lyric_encoder_hidden_layers
-        self.patch_size = patch_size
-
-        # Audio semantic token generation configuration
-        self.audio_acoustic_hidden_dim = audio_acoustic_hidden_dim
-        self.pool_window_size = pool_window_size
-        self.in_channels = in_channels
-        self.data_proportion = data_proportion
-        self.timestep_mu = timestep_mu
-        self.timestep_sigma = timestep_sigma
-        
-        # FSQ (Finite Scalar Quantization) configuration
-        self.fsq_dim = fsq_dim
-        self.fsq_input_levels = fsq_input_levels
-        self.fsq_input_num_quantizers = fsq_input_num_quantizers
-        
-        # Timbre encoder configuration
-        self.timbre_hidden_dim = timbre_hidden_dim
-        self.num_timbre_encoder_hidden_layers = num_timbre_encoder_hidden_layers
-        self.timbre_fix_frame = timbre_fix_frame
-        self.num_attention_pooler_hidden_layers = num_attention_pooler_hidden_layers
-        self.num_audio_decoder_hidden_layers = num_audio_decoder_hidden_layers
-        self.vocab_size = vocab_size
-
-        # Backward compatibility: ensure num_key_value_heads is set
-        if num_key_value_heads is None:
-            num_key_value_heads = num_attention_heads
-
-        self.num_key_value_heads = num_key_value_heads
-        self.head_dim = head_dim
-        self.hidden_act = hidden_act
-        self.initializer_range = initializer_range
-        self.rms_norm_eps = rms_norm_eps
-        self.use_cache = use_cache
-        self.rope_theta = rope_theta
-        self.rope_scaling = rope_scaling
-        self.attention_bias = attention_bias
-        self.attention_dropout = attention_dropout
-        self.model_version = model_version
-        
-        # Validate rotary position embeddings parameters
-        # Backward compatibility: if there is a 'type' field, move it to 'rope_type'
-        if self.rope_scaling is not None and "type" in self.rope_scaling:
-            self.rope_scaling["rope_type"] = self.rope_scaling["type"]
-        rope_config_validation(self)
-
-        self.layer_types = layer_types
-
-        # Set default layer types if not specified
-        if self.layer_types is None:
-            self.layer_types = [
-                "sliding_attention" if bool((i + 1) % 2) else "full_attention" for i in range(self.num_hidden_layers)
-            ]
-        layer_type_validation(self.layer_types)
-
-        super().__init__(
-            tie_word_embeddings=tie_word_embeddings,
-            **kwargs,
-        )
-
-
-__all__ = ["AceStepConfig"]

ace-step/acestep-v15-turbo/silence_latent.pt

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:a778e9dd942f5e8b2c09c55370782d318834432b03dabbcdf70e6ed49ad6358b
-size 3841215

ace-step/config.json

@@ -1,82 +0,0 @@
-{
-  "architectures": [
-    "AceStepConditionGenerationModel"
-  ],
-  "attention_bias": false,
-  "attention_dropout": 0.0,
-  "audio_acoustic_hidden_dim": 64,
-  "auto_map": {
-    "AutoConfig": "configuration_acestep_v15.AceStepConfig",
-    "AutoModel": "modeling_acestep_v15_turbo.AceStepConditionGenerationModel"
-  },
-  "data_proportion": 0.5,
-  "dtype": "bfloat16",
-  "fsq_dim": 2048,
-  "fsq_input_levels": [
-    8,
-    8,
-    8,
-    5,
-    5,
-    5
-  ],
-  "fsq_input_num_quantizers": 1,
-  "head_dim": 128,
-  "hidden_act": "silu",
-  "hidden_size": 2048,
-  "in_channels": 192,
-  "initializer_range": 0.02,
-  "intermediate_size": 6144,
-  "is_turbo": true,
-  "layer_types": [
-    "sliding_attention",
-    "full_attention",
-    "sliding_attention",
-    "full_attention",
-    "sliding_attention",
-    "full_attention",
-    "sliding_attention",
-    "full_attention",
-    "sliding_attention",
-    "full_attention",
-    "sliding_attention",
-    "full_attention",
-    "sliding_attention",
-    "full_attention",
-    "sliding_attention",
-    "full_attention",
-    "sliding_attention",
-    "full_attention",
-    "sliding_attention",
-    "full_attention",
-    "sliding_attention",
-    "full_attention",
-    "sliding_attention",
-    "full_attention"
-  ],
-  "max_position_embeddings": 32768,
-  "model_type": "acestep",
-  "model_version": "turbo",
-  "num_attention_heads": 16,
-  "num_attention_pooler_hidden_layers": 2,
-  "num_audio_decoder_hidden_layers": 24,
-  "num_hidden_layers": 24,
-  "num_key_value_heads": 8,
-  "num_lyric_encoder_hidden_layers": 8,
-  "num_timbre_encoder_hidden_layers": 4,
-  "patch_size": 2,
-  "pool_window_size": 5,
-  "rms_norm_eps": 1e-06,
-  "rope_scaling": null,
-  "rope_theta": 1000000,
-  "sliding_window": 128,
-  "text_hidden_dim": 1024,
-  "timbre_fix_frame": 750,
-  "timbre_hidden_dim": 64,
-  "timestep_mu": -0.4,
-  "timestep_sigma": 1.0,
-  "transformers_version": "4.57.0.dev0",
-  "use_cache": true,
-  "use_sliding_window": true,
-  "vocab_size": 64003
-}

ace-step/vae/config.json

@@ -1,24 +0,0 @@
-{
-  "_class_name": "AutoencoderOobleck",
-  "_diffusers_version": "0.34.0",
-  "_name_or_path": "/root/data/repo/gongjunmin/ACE-Step-1.5/checkpoints/vae/",
-  "audio_channels": 2,
-  "channel_multiples": [
-    1,
-    2,
-    4,
-    8,
-    16
-  ],
-  "decoder_channels": 128,
-  "decoder_input_channels": 64,
-  "downsampling_ratios": [
-    2,
-    4,
-    4,
-    6,
-    10
-  ],
-  "encoder_hidden_size": 128,
-  "sampling_rate": 48000
-}

ace-step/vae/diffusion_pytorch_model.safetensors

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:da17edb604c40deaf09e9b24974e590d1ca83a374070e5d0884cfa4bed9a99b0
-size 337431388

depth/dpt-large/refs/main

@@ -1 +0,0 @@
-bc15f29aa3a80d532f2ed650b5e16ac48d8958f9

depth/dpt-large/snapshots/bc15f29aa3a80d532f2ed650b5e16ac48d8958f9/config.json

@@ -1,47 +0,0 @@
-{
-  "architectures": [
-    "DPTForDepthEstimation"
-  ],
-  "attention_probs_dropout_prob": 0.0,
-  "auxiliary_loss_weight": 0.4,
-  "backbone_out_indices": [
-    5,
-    11,
-    17,
-    23
-  ],
-  "fusion_hidden_size": 256,
-  "head_in_index": -1,
-  "hidden_act": "gelu",
-  "hidden_dropout_prob": 0.0,
-  "hidden_size": 1024,
-  "image_size": 384,
-  "initializer_range": 0.02,
-  "intermediate_size": 4096,
-  "layer_norm_eps": 1e-12,
-  "model_type": "dpt",
-  "neck_hidden_sizes": [
-    256,
-    512,
-    1024,
-    1024
-  ],
-  "num_attention_heads": 16,
-  "num_channels": 3,
-  "num_hidden_layers": 24,
-  "patch_size": 16,
-  "qkv_bias": true,
-  "readout_type": "project",
-  "reassemble_factors": [
-    4,
-    2,
-    1,
-    0.5
-  ],
-  "semantic_classifier_dropout": 0.1,
-  "semantic_loss_ignore_index": 255,
-  "torch_dtype": "float32",
-  "transformers_version": "4.18.0.dev0",
-  "use_auxiliary_head": true,
-  "use_batch_norm_in_fusion_residual": false
-}