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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer/tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,96 @@
+---
+license: other
+library_name: diffusers
+tags:
+- text-to-image
+- z-image
+- diffusers
+- quantized
+- int8
+- sdnq
+- safetensors
+pipeline_tag: text-to-image
+---
+
+# Tongyi-MAI/Z-Image-Turbo - Quantized (8-bit)
+
+## Overview
+This is a **quantized version** of [Tongyi-MAI/Z-Image-Turbo](https://huggingface.co/Tongyi-MAI/Z-Image-Turbo).
+
+All components have been quantized to 8-bit using SDNQ, while preserving the original folder structure for seamless integration.
+
+## Architecture
+- **Pipeline**: ZImagePipeline
+- **Main component**: ZImageTransformer2DModel
+- **Quantization**: 8-bit
+
+## Usage
+
+```python
+import torch
+from diffusers import ZImagePipeline, AutoencoderKL, FlowMatchEulerDiscreteScheduler
+from transformers import Qwen3Model, AutoTokenizer
+from sdnq import load_sdnq_model
+
+model_path = "Tongyi-MAI_Z-Image-Turbo-int8"
+
+# Load transformer with SDNQ (quantized to 8-bit)
+transformer = load_sdnq_model(
+    f"{model_path}/transformer",
+    model_cls=ZImageTransformer2DModel,
+    device="cpu"
+)
+
+# Load other components from this model (all included!)
+vae = AutoencoderKL.from_pretrained(f"{model_path}/vae", torch_dtype=torch.float16)
+text_encoder = Qwen3Model.from_pretrained(f"{model_path}/text_encoder", torch_dtype=torch.float16)
+tokenizer = AutoTokenizer.from_pretrained(f"{model_path}/tokenizer")
+scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(f"{model_path}/scheduler")
+
+# Construct pipeline
+pipe = ZImagePipeline(
+    transformer=transformer,
+    vae=vae,
+    text_encoder=text_encoder,
+    tokenizer=tokenizer,
+    scheduler=scheduler,
+)
+
+pipe.to("cuda")
+
+# Generate an image
+image = pipe(
+    prompt="A serene mountain landscape at sunrise",
+    num_inference_steps=20,
+).images[0]
+image.save("output.png")
+```
+
+## Components
+
+- ✅ **transformer** (ZImageTransformer2DModel) - Quantized to 8-bit
+- ✅ **vae** (AutoencoderKL) - Quantized to 8-bit
+
+**Note**: Some components are included unquantized due to SDNQ library limitations:
+- 📦 **text_encoder** - Included unquantized (SDNQ bug workaround)
+
+
+## Quantization Details
+- **Original model**: [Tongyi-MAI/Z-Image-Turbo](https://huggingface.co/Tongyi-MAI/Z-Image-Turbo)
+- **Quantization**: 8-bit
+- **Quantizer**: SDNQ
+- **Date**: 2026-01-18 13:41:50
+
+## Size Reduction
+- Original: ~30GB (estimated)
+- Quantized: See individual component sizes
+
+## Notes
+- This is a complete drop-in replacement - all components included
+- SDNQ quantization provides excellent quality at reduced size
+- Requires `sdnq` library to be installed: `pip install sdnq`
+- Quality loss is minimal with 8-bit quantization
+- Some components may be included unquantized due to library limitations
+
+---
+Quantized with [BugQuant](https://github.com/yourusername/BugQuant)

model_index.json

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+{
+  "_class_name": "ZImagePipeline",
+  "_diffusers_version": "0.36.0",
+  "transformer": [
+    "diffusers",
+    "ZImageTransformer2DModel"
+  ],
+  "vae": [
+    "diffusers",
+    "AutoencoderKL"
+  ],
+  "scheduler": [
+    "diffusers",
+    "FlowMatchEulerDiscreteScheduler"
+  ],
+  "tokenizer": [
+    "transformers",
+    "CLIPTokenizer"
+  ]
+}

scheduler/scheduler_config.json

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+{
+  "_class_name": "FlowMatchEulerDiscreteScheduler",
+  "_diffusers_version": "0.36.0.dev0",
+  "num_train_timesteps": 1000,
+  "use_dynamic_shifting": false,
+  "shift": 3.0
+}

text_encoder/bugquant_metadata.json

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+{
+  "source_model": "Tongyi-MAI/Z-Image-Turbo",
+  "subfolder": "text_encoder",
+  "architecture": "Qwen3ForCausalLM",
+  "quantization": "int8",
+  "quantizer": "SDNQ",
+  "group_size": 0
+}

text_encoder/config.json

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+{
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 2560,
+  "initializer_range": 0.02,
+  "intermediate_size": 9728,
+  "max_position_embeddings": 40960,
+  "max_window_layers": 36,
+  "model_type": "qwen3",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000,
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.51.0",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

text_encoder/generation_config.json

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+    "do_sample": true,
+    "eos_token_id": [
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+    ],
+    "pad_token_id": 151643,
+    "temperature": 0.6,
+    "top_k": 20,
+    "top_p": 0.95,
+    "transformers_version": "4.51.0"
+}

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+}

text_encoder/quantization_config.json

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+{
+  "add_skip_keys": false,
+  "dequantize_fp32": false,
+  "dynamic_loss_threshold": 0.01,
+  "group_size": 0,
+  "is_integer": true,
+  "is_training": false,
+  "modules_dtype_dict": {},
+  "modules_to_not_convert": [
+    ".norm_out",
+    ".t_embedder",
+    ".context_embedder",
+    ".vid_out",
+    ".img_in",
+    ".txt_out",
+    "patch_embed",
+    "prediction_coefs",
+    "patch_emb",
+    "multi_modal_projector",
+    "lm_head",
+    "time_text_embed",
+    ".img_out",
+    "model.embed_tokens",
+    "norm_out",
+    ".proj_out",
+    ".emb_in",
+    "patch_embedding",
+    ".time_embed",
+    "embedding_projection",
+    "lm_head.weight",
+    "correction_coefs",
+    ".x_embedder",
+    "embed_tokens",
+    ".y_embedder",
+    ".condition_embedder",
+    "proj_out",
+    "model.norm",
+    ".vid_in",
+    ".txt_in",
+    "wte",
+    ".final_layer",
+    ".emb_out"
+  ],
+  "non_blocking": false,
+  "quant_conv": false,
+  "quant_method": "sdnq",
+  "quantization_device": null,
+  "quantized_matmul_dtype": null,
+  "return_device": null,
+  "sdnq_version": "0.1.3",
+  "svd_rank": 32,
+  "svd_steps": 8,
+  "use_dynamic_quantization": false,
+  "use_grad_ckpt": true,
+  "use_quantized_matmul": true,
+  "use_quantized_matmul_conv": false,
+  "use_static_quantization": true,
+  "use_stochastic_rounding": false,
+  "use_svd": false,
+  "weights_dtype": "int8"
+}

tokenizer/tokenizer.json

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+size 11422654

tokenizer/tokenizer_config.json

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+{
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+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "151643": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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+      "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
+    },
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+      "single_word": false,
+      "special": true
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+      "content": "<|object_ref_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151648": {
+      "content": "<|box_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151649": {
+      "content": "<|box_end|>",
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+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+    "151650": {
+      "content": "<|quad_start|>",
+      "lstrip": false,
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+      "rstrip": false,
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+      "special": true
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+      "content": "<|quad_end|>",
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+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "content": "<|vision_start|>",
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+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "content": "<|vision_end|>",
+      "lstrip": false,
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+      "content": "<|vision_pad|>",
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+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151655": {
+      "content": "<|image_pad|>",
+      "lstrip": false,
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+      "rstrip": false,
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+      "special": true
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+      "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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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151660": {
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+      "lstrip": false,
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+      "special": false
+    },
+    "151661": {
+      "content": "<|fim_suffix|>",
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+      "single_word": false,
+      "special": false
+    },
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+      "normalized": false,
+      "rstrip": false,
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+      "content": "<|repo_name|>",
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+      "special": false
+    },
+    "151664": {
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+      "lstrip": false,
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+      "rstrip": false,
+      "single_word": false,
+      "special": false
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+    "151665": {
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+      "content": "</tool_response>",
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+    "151668": {
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+      "special": false
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+  },
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+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
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+    "<|quad_end|>",
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+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "bos_token": null,
+  "chat_template": "{%- if tools %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0].role == 'system' %}\n        {{- messages[0].content + '\\n\\n' }}\n    {%- endif %}\n    {{- \"# 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>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\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\" }}\n{%- else %}\n    {%- if messages[0].role == 'system' %}\n        {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n    {%- set index = (messages|length - 1) - loop.index0 %}\n    {%- 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>')) %}\n        {%- set ns.multi_step_tool = false %}\n        {%- set ns.last_query_index = index %}\n    {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n    {%- if message.content is string %}\n        {%- set content = message.content %}\n    {%- else %}\n        {%- set content = '' %}\n    {%- endif %}\n    {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n        {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {%- set reasoning_content = '' %}\n        {%- if message.reasoning_content is string %}\n            {%- set reasoning_content = message.reasoning_content %}\n        {%- else %}\n            {%- if '</think>' in content %}\n                {%- set reasoning_content = content.split('</think>')[0].rstrip('\\n').split('<think>')[-1].lstrip('\\n') %}\n                {%- set content = content.split('</think>')[-1].lstrip('\\n') %}\n            {%- endif %}\n        {%- endif %}\n        {%- if loop.index0 > ns.last_query_index %}\n            {%- if loop.last or (not loop.last and reasoning_content) %}\n                {{- '<|im_start|>' + message.role + '\\n<think>\\n' + reasoning_content.strip('\\n') + '\\n</think>\\n\\n' + content.lstrip('\\n') }}\n            {%- else %}\n                {{- '<|im_start|>' + message.role + '\\n' + content }}\n            {%- endif %}\n        {%- else %}\n            {{- '<|im_start|>' + message.role + '\\n' + content }}\n        {%- endif %}\n        {%- if message.tool_calls %}\n            {%- for tool_call in message.tool_calls %}\n                {%- if (loop.first and content) or (not loop.first) %}\n                    {{- '\\n' }}\n                {%- endif %}\n                {%- if tool_call.function %}\n                    {%- set tool_call = tool_call.function %}\n                {%- endif %}\n                {{- '<tool_call>\\n{\"name\": \"' }}\n                {{- tool_call.name }}\n                {{- '\", \"arguments\": ' }}\n                {%- if tool_call.arguments is string %}\n                    {{- tool_call.arguments }}\n                {%- else %}\n                    {{- tool_call.arguments | tojson }}\n                {%- endif %}\n                {{- '}\\n</tool_call>' }}\n            {%- endfor %}\n        {%- endif %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- content }}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n    {%- if enable_thinking is defined and enable_thinking is false %}\n        {{- '<think>\\n\\n</think>\\n\\n' }}\n    {%- endif %}\n{%- endif %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}

transformer/bugquant_metadata.json

@@ -0,0 +1,8 @@
+{
+  "source_model": "Tongyi-MAI/Z-Image-Turbo",
+  "subfolder": "transformer",
+  "architecture": "ZImageTransformer2DModel",
+  "quantization": "int8",
+  "quantizer": "SDNQ",
+  "group_size": 0
+}

transformer/config.json

@@ -0,0 +1,73 @@
+{
+  "_class_name": "ZImageTransformer2DModel",
+  "_diffusers_version": "0.36.0",
+  "_name_or_path": "Tongyi-MAI/Z-Image-Turbo",
+  "all_f_patch_size": [
+    1
+  ],
+  "all_patch_size": [
+    2
+  ],
+  "axes_dims": [
+    32,
+    48,
+    48
+  ],
+  "axes_lens": [
+    1536,
+    512,
+    512
+  ],
+  "cap_feat_dim": 2560,
+  "dim": 3840,
+  "in_channels": 16,
+  "n_heads": 30,
+  "n_kv_heads": 30,
+  "n_layers": 30,
+  "n_refiner_layers": 2,
+  "norm_eps": 1e-05,
+  "qk_norm": true,
+  "quantization_config": {
+    "add_skip_keys": true,
+    "dequantize_fp32": false,
+    "dynamic_loss_threshold": 0.01,
+    "group_size": 0,
+    "is_integer": true,
+    "is_training": false,
+    "modules_dtype_dict": {},
+    "modules_to_not_convert": [
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+      "siglip_embedder",
+      "cap_embedder",
+      "norm_out",
+      "correction_coefs",
+      "lm_head",
+      "embed_tokens",
+      "layers.0.adaLN_modulation.0.weight",
+      "all_final_layer",
+      "embedding_projection",
+      "t_embedder",
+      "proj_out",
+      "all_x_embedder"
+    ],
+    "non_blocking": false,
+    "quant_conv": false,
+    "quant_method": "sdnq",
+    "quantization_device": "cuda",
+    "quantized_matmul_dtype": null,
+    "return_device": "cpu",
+    "sdnq_version": "0.1.3",
+    "svd_rank": 32,
+    "svd_steps": 8,
+    "use_dynamic_quantization": false,
+    "use_grad_ckpt": true,
+    "use_quantized_matmul": true,
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+    "use_stochastic_rounding": false,
+    "use_svd": false,
+    "weights_dtype": "int8"
+  },
+  "rope_theta": 256.0,
+  "t_scale": 1000.0
+}

transformer/diffusion_pytorch_model.safetensors

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transformer/quantization_config.json

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+  "is_training": false,
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+    "all_final_layer",
+    "embedding_projection",
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+    "all_x_embedder"
+  ],
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+  "svd_rank": 32,
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+  "use_dynamic_quantization": false,
+  "use_grad_ckpt": true,
+  "use_quantized_matmul": true,
+  "use_quantized_matmul_conv": false,
+  "use_static_quantization": true,
+  "use_stochastic_rounding": false,
+  "use_svd": false,
+  "weights_dtype": "int8"
+}

vae/bugquant_metadata.json

@@ -0,0 +1,8 @@
+{
+  "source_model": "Tongyi-MAI/Z-Image-Turbo",
+  "subfolder": "vae",
+  "architecture": "AutoencoderKL",
+  "quantization": "int8",
+  "quantizer": "SDNQ",
+  "group_size": 0
+}

vae/config.json

@@ -0,0 +1,38 @@
+{
+  "_class_name": "AutoencoderKL",
+  "_diffusers_version": "0.36.0",
+  "_name_or_path": "Tongyi-MAI/Z-Image-Turbo",
+  "act_fn": "silu",
+  "block_out_channels": [
+    128,
+    256,
+    512,
+    512
+  ],
+  "down_block_types": [
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D"
+  ],
+  "force_upcast": true,
+  "in_channels": 3,
+  "latent_channels": 16,
+  "latents_mean": null,
+  "latents_std": null,
+  "layers_per_block": 2,
+  "mid_block_add_attention": true,
+  "norm_num_groups": 32,
+  "out_channels": 3,
+  "sample_size": 1024,
+  "scaling_factor": 0.3611,
+  "shift_factor": 0.1159,
+  "up_block_types": [
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D"
+  ],
+  "use_post_quant_conv": false,
+  "use_quant_conv": false
+}

vae/diffusion_pytorch_model.safetensors

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