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LICENSE.md

@@ -0,0 +1,55 @@
+Ideogram Non-Commercial Model Agreement
+
+Last Updated: June 3, 2026
+
+This Ideogram Non-Commercial Model Agreement (“Agreement”) is a legal agreement between you and Ideogram, Inc. (“Company”, “we” or “us”). This Agreement governs your access to and use of the Models (as defined below) that we make available to you under this Agreement. You expressly agree that if you accept the terms of this Agreement or if you otherwise download, access, use or Distribute any portion of the Model or Model Derivatives, then you agree to be bound by this Agreement. Company may designate certain materials that are designed to be used with the Models (such as related inference code) under a separate license or agreement, and nothing in this Agreement will be deemed to restrict or limit any other rights or licenses granted by Company in such other materials.
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README.md

@@ -0,0 +1,329 @@
+---
+license: other
+license_name: ideogram-4-non-commercial
+license_link: https://huggingface.co/ideogram-ai/ideogram-4-fp8/blob/main/LICENSE.md
+pipeline_tag: text-to-image
+tags:
+  - text-to-image
+  - image-generation
+  - diffusion
+  - flow-matching
+  - dit
+  - ideogram
+---
+
+<p align="center"><a href="https://ideogram.ai/" target="_blank" rel="noopener noreferrer"><img src="assets/ideogram_logo.svg" alt="Ideogram" width="500"></a></p>
+
+<p align="center"><em>Ideogram 4: Open image model at the forefront of design</em></p>
+
+<p align="center">
+  <a href="https://ideogram.ai/blog/ideogram-4.0/" target="_blank" rel="noopener noreferrer"><img src="https://img.shields.io/badge/Blog-Post-orange" alt="Blog Post"></a>
+  <a href="https://github.com/ideogram-oss/ideogram4" target="_blank" rel="noopener noreferrer"><img src="https://img.shields.io/badge/Code-GitHub-181717?logo=github" alt="Code"></a>
+  <a href="https://huggingface.co/collections/ideogram-ai/ideogram-4" target="_blank" rel="noopener noreferrer"><img src="https://img.shields.io/badge/Model-HuggingFace-blue?logo=huggingface" alt="Model"></a>
+  <a href="https://developer.ideogram.ai/" target="_blank" rel="noopener noreferrer"><img src="https://img.shields.io/badge/API-developer.ideogram.ai-purple" alt="API"></a>
+  <a href="https://ideogram.ai/" target="_blank" rel="noopener noreferrer"><img src="https://img.shields.io/badge/Official%20Site-ideogram.ai-ff69b4" alt="Official Site"></a>
+</p>
+
+<p align="center">
+  <img src="assets/samples/collage_landscape.jpg" alt="A collage of Ideogram 4 samples spanning photorealism, illustration, typography, and poster design">
+</p>
+
+
+Ideogram 4 is **[Ideogram](https://ideogram.ai)'s first open-source text-to-image model**. It is a **state-of-the-art foundation model trained from scratch** — not a fine-tune of any existing model. It introduces a new structured JSON prompting interface, with best-in-class multilingual text rendering, deep language understanding, explicit bounding-box layout and color-palette controls, and native 2k resolution images. The easiest way to try the model is online at **[ideogram.ai](https://ideogram.ai/)**.
+
+We believe openness drives innovation, and we invite the research community to innovate with us on the forefront of visual intelligence.
+
+## Table of Contents
+
+1. [News](#news)
+2. [Model Zoo](#model-zoo)
+3. [Performance](#performance)
+4. [Quick Start](#quick-start)
+5. [Model Summary](#model-summary)
+6. [Prompting Guide](#prompting-guide)
+7. [Documentation](#documentation)
+8. [Citation](#citation)
+
+## News
+
+* **[2026-06-03]** **Ideogram 4 released!** Inference code and weights
+  are now public, and our [technical blog post](https://ideogram.ai/blog/ideogram-4.0/) is live. See the
+  [Quick Start](#quick-start) section to generate your first image, or try the
+  model online at [ideogram.ai](https://ideogram.ai/).
+
+## Model Zoo
+
+| Model | Params | Weight Quantization | Supported Hardware | Diffusers Support | License |
+| :---  | :---:  | :---:        | :---:   | :---:   | :---:   |
+| **[Ideogram 4 (nf4)](https://huggingface.co/ideogram-ai/ideogram-4-nf4)** | 9.3B | nf4 | CUDA | Yes | [Ideogram 4 Non-Commercial](https://huggingface.co/ideogram-ai/ideogram-4-nf4/blob/main/LICENSE.md) |
+| **[Ideogram 4 (fp8)](https://huggingface.co/ideogram-ai/ideogram-4-fp8)** | 9.3B | fp8 | All | No | [Ideogram 4 Non-Commercial](https://huggingface.co/ideogram-ai/ideogram-4-fp8/blob/main/LICENSE.md) |
+
+We plan to support more quantizations in the future.
+
+
+## Performance
+
+We evaluate Ideogram 4 across third-party arenas and benchmarks, standard
+open-source benchmarks, and our own internal human-preference benchmark. Across
+all of them, **Ideogram 4 is the best open-weight image model by far, and sits
+at the frontier of design.**
+
+### Design Arena
+
+[Design Arena](https://www.designarena.ai/) is a third-party image Elo
+leaderboard focused specifically on design-oriented generation. On the overall
+board, Ideogram 4 is the top-ranked open-weight model, trailing only proprietary
+GPT and Gemini models:
+
+<p align="center">
+  <img src="assets/benchmarks/design_arena.png" alt="Design Arena overall image Elo leaderboard with Ideogram 4.0 as the top open-weight model">
+</p>
+
+Filtered to open-weight models only, Ideogram 4 leads by a commanding margin,
+well ahead of the next-best open model:
+
+<p align="center">
+  <img src="assets/benchmarks/design_arena2.png" alt="Design Arena open-weight image Elo leaderboard, with Ideogram 4.0 well ahead of all other open models">
+</p>
+
+### ContraLabs
+
+[ContraLabs](https://contralabs.com/research) ran a blind typography evaluation judged by
+ten professional designers from Contra's top-earning talent. Ideogram 4 leads on
+first-place win rate, picked as the best of four models 47.9% of the time
+overall — well ahead of Gemini 3.1 Flash Image Preview (Nano Banana 2) at 30.0%,
+FLUX.2 [max] (15.5%), and Grok Imagine 1.0 (15.0%):
+
+<p align="center">
+  <img src="assets/benchmarks/contralabs_typography.png" alt="ContraLabs typography first-place win rate, with Ideogram v4 leading">
+</p>
+
+It also wins on practical usability: asked "Would you use this in real client
+work?", the same designers rated Ideogram 4 highest at 3.55 / 5 — significantly
+above Nano Banana 2 (2.84), Grok Imagine 1.0 (2.61), and FLUX.2 [max] (2.49):
+
+<p align="center">
+  <img src="assets/benchmarks/contralabs_typography2.png" alt="ContraLabs 'would you use this in real client work?' rating, with Ideogram v4 leading">
+</p>
+
+### LMArena
+
+On [LMArena](https://lmarena.ai/), a third-party text-to-image leaderboard that
+measures general-purpose text-to-image use cases, Ideogram is the top-ranked
+open-weight lab and a top-5 image generation lab overall — beaten only by giant
+companies with vastly larger budgets and resources:
+
+<p align="center">
+  <img src="assets/benchmarks/lmarena_benchmark.png" alt="LMArena text-to-image lab leaderboard with Ideogram">
+</p>
+
+### Ideogram internal eval
+
+For our internal human-preference benchmark, focused on graphic design and
+photography, we had graphic designers deeply familiar with professional design
+work do the rating blind. Bradley-Terry scores rank Ideogram 4 #2 overall —
+behind only GPT Image 2 medium — and the top open-weight model:
+
+<p align="center">
+  <img src="assets/benchmarks/ideogram_benchmark.png" alt="Ideogram internal design leaderboard with Ideogram 4.0">
+</p>
+
+### Open-source benchmarks
+
+On standard open-source benchmarks measuring core capabilities — layout control
+(7Bench), spatial reasoning and object fidelity (SpatialGenEval), text rendering
+(X-Omni OCR), and prompt alignment (Prism) — Ideogram 4 closes the gap to the
+leading closed-source models across every axis. On layout control (7Bench), it
+is significantly better than all closed-source models:
+
+<p align="center">
+  <img src="assets/benchmarks/opensource.png" alt="Five-axis capability radar comparing Ideogram 4.0 to leading closed-source models on layout control, spatial reasoning, object fidelity, prompt alignment, and text rendering">
+</p>
+
+At 9.3B parameters, Ideogram 4 delivers the best text rendering of any open-weight
+release we benchmarked — ahead of much larger models like Qwen-Image (20B),
+FLUX.2 [dev] (32B), and HunyuanImage 3.0 (80B MoE):
+
+<p align="center">
+  <img src="assets/benchmarks/opensource2.png" alt="Parameter-efficiency scatter plot showing Ideogram 4.0 at 9.3B parameters leading all other open-weight models on text rendering">
+</p>
+
+
+## Quick Start
+
+### Install
+
+The inference code lives in the [`ideogram4`](https://github.com/ideogram-oss/ideogram4) GitHub repo. Clone it, then from the repo root:
+
+```bash
+pip install .
+```
+
+If you plan to modify the code, install in editable mode instead so changes
+under `src/ideogram4/` take effect without reinstalling:
+
+```bash
+pip install -e .
+```
+
+### CLI
+
+The plain `--prompt` is rewritten into the structured JSON caption the model
+expects by a "magic prompt" LLM. By default this uses Ideogram's hosted
+magic-prompt API, which is **free** and does the expansion server-side (no local
+model or system prompt needed). It reads `IDEOGRAM_API_KEY` — get a key at
+[developer.ideogram.ai](https://developer.ideogram.ai/):
+
+```bash
+python run_inference.py \
+  --prompt "a ginger cat wearing a tiny wizard hat reading a spellbook" \
+  --output out.png \
+  --quantization "nf4" \
+  --magic-prompt-key "$IDEOGRAM_API_KEY"
+```
+
+You can also run the expansion through your own LLM provider — one of our magic-prompt
+system prompt is **open source**. See the
+[Prompting Guide](https://github.com/ideogram-oss/ideogram4/blob/main/docs/prompting.md#magic-prompt) for details.
+
+For the highest-quality images, set `--height 2048 --width 2048` and
+`--sampler-preset V4_QUALITY_48`.
+
+#### Safety screening with Hive
+
+Prompt and output safety screening is performed via [Hive](https://thehive.ai/).
+Sign up and create a Text Moderation key and a Visual Content Moderation key,
+then export them as `HIVE_TEXT_MODERATION_KEY` and `HIVE_VISUAL_MODERATION_KEY`
+(or pass them via `--hive-text-key` / `--hive-visual-key`).
+
+```bash
+python run_inference.py \
+  --prompt "an isometric illustration of a tiny city floating in the clouds" \
+  --output out.png \
+  --quantization "nf4" \
+  --magic-prompt-key "$MAGIC_PROMPT_API_KEY" \
+  --hive-text-key "$HIVE_TEXT_MODERATION_KEY" \
+  --hive-visual-key "$HIVE_VISUAL_MODERATION_KEY"
+```
+
+For sampler presets, parameter reference, and optimization tips, see
+[docs/inference.md](https://github.com/ideogram-oss/ideogram4/blob/main/docs/inference.md).
+
+## Model Summary
+
+Ideogram 4 is a **foundation model trained entirely from scratch**, not a
+fine-tune or distillation of any existing checkpoint. It is a flow-matching
+text-to-image model built on a **fully single-stream** Diffusion Transformer
+(DiT) architecture.
+
+**Architecture:**
+- **Fully single-stream DiT.** Text and image tokens are concatenated into one
+  unified sequence and processed through the same 34-layer transformer, with no
+  separate text or image branches. This enables deep cross-modal interaction at
+  every layer.
+- **Vision-language model as text encoder.** Instead of a text-only encoder
+  like CLIP or T5, Ideogram 4 uses
+  [Qwen3-VL-8B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct),
+  a full vision-language model that provides far richer understanding of visual
+  concepts. Hidden states are extracted from **13 intermediate layers** and
+  concatenated, giving the model multi-scale semantic features ranging from
+  surface-level token information to deep compositional understanding.
+- **Dual-branch classifier-free guidance.** The conditional (positive) and
+  unconditional (negative) branches can be independently refined, enabling
+  separate control over prompt adherence and image quality.
+- **Flexible resolution.** Native support for any resolution from 256 to 2048
+  (multiples of 16), with aspect ratios up to 6:1. A single model handles
+  everything from square thumbnails to ultrawide banners, with the noise
+  schedule auto-adjusting per resolution.
+
+**Key Capabilities:**
+- **Extreme controllability.** Ideogram 4 is trained on structured JSON
+  captions, giving users unprecedented control over composition, style,
+  lighting, color palette, typography, and spatial layout, all from a single
+  prompt.
+- **State-of-the-art text rendering.** Ideogram 4 delivers best-in-class
+  in-image text generation (signage, logos, captions, watermarks, multi-line
+  text) with high fidelity directly from the prompt.
+- **Spatial layout control.** Bounding-box coordinates in the prompt allow
+  explicit placement of subjects, text elements, and background regions.
+- **Color palette conditioning.** Specify hex colors in the prompt to steer the
+  image's dominant color scheme.
+
+For full architecture details, see
+[docs/model_architecture.md](https://github.com/ideogram-oss/ideogram4/blob/main/docs/model_architecture.md). For a walkthrough of
+how the pipeline components fit together, see
+[docs/pipeline.md](https://github.com/ideogram-oss/ideogram4/blob/main/docs/pipeline.md).
+
+## Prompting Guide
+
+Ideogram 4 is trained exclusively on **structured JSON captions**. While
+plain-text prompts work, you will get the best results by providing a JSON
+object that follows our caption schema.
+
+
+Key points:
+
+- **Use JSON prompts** for maximum controllability — the model was trained on
+  them and understands the structure natively.
+- **Color palette conditioning** — specify a `colour_palette` array of hex
+  colors in the style description to steer the image's color scheme.
+- **Aspect ratio flexibility** — Ideogram 4 supports a wide range of aspect
+  ratios (any multiple-of-16 resolution from 256 to 2048 on each side). This
+  is a key advantage for practical use: portraits, landscapes, banners,
+  phone wallpapers, social media formats, etc.
+- **Bounding-box layout** — specify `bbox` coordinates in the prompt to
+  explicitly place subjects, text elements, and background regions.
+- **Compositional control** — use `compositional_deconstruction` with bounding
+  boxes and per-element descriptions for precise spatial layout.
+
+
+**Why JSON-only training?** We train exclusively on JSON so that training
+and inference share a single, common prompt format. The training captions themselves are deliberately
+**extremely descriptive**: each JSON exhaustively describes everything in
+the image to maximize training efficiency. The more
+text-to-image relationships each caption pins down, the more grounded
+supervision the model extracts from a single training pair, rather than
+having to infer those relationships across many sparsely-captioned samples.
+
+**Why JSON at inference time?** Because the model was trained on captions
+that name every object explicitly, the most reliable way to get every
+requested object rendered is to mirror that pattern. Plain-text prompts still work, but
+won't perform as well since the model was only trained on structured JSON captions.
+
+**Don't want to write JSON by hand?** That's what *magic prompt* is for: it uses
+an LLM to expand a plain-text prompt into a full structured caption before
+generation, so you get JSON-quality results from a casual prompt. It runs by
+default in `run_inference.py` (see the [CLI](#cli) section).
+
+See [docs/prompting.md](https://github.com/ideogram-oss/ideogram4/blob/main/docs/prompting.md) for a full guide.
+
+## Documentation
+
+| Document | Description |
+| :------- | :---------- |
+| [docs/prompting.md](https://github.com/ideogram-oss/ideogram4/blob/main/docs/prompting.md) | How to write JSON prompts, color palette conditioning, aspect ratios |
+| [docs/inference.md](https://github.com/ideogram-oss/ideogram4/blob/main/docs/inference.md) | Sampler presets, parameter reference, resolutions, optimization tips |
+| [docs/model_architecture.md](https://github.com/ideogram-oss/ideogram4/blob/main/docs/model_architecture.md) | Architecture diagram, DiT spec, component details |
+| [docs/pipeline.md](https://github.com/ideogram-oss/ideogram4/blob/main/docs/pipeline.md) | Conceptual pipeline walkthrough — how all components fit together |
+| [docs/development.md](https://github.com/ideogram-oss/ideogram4/blob/main/docs/development.md) | Dev setup, pre-commit hooks, contributing |
+| [docs/safety.md](https://github.com/ideogram-oss/ideogram4/blob/main/docs/safety.md) | Pre-training, post-training, and inference-time safety mitigations; how to report violations |
+
+## Citation
+
+If you find the provided code or models useful for your research, consider citing them as:
+
+
+```bibtex
+@misc{ideogram-4-2026,
+    author={Ideogram AI},
+    title={{Ideogram 4}},
+    year={2026},
+    howpublished={\url{https://ideogram.ai/blog/ideogram-4.0/}},
+}
+```
+
+## We're Hiring!
+
+We're looking for **Research Scientists** and **Research Engineers** to
+work on next-generation generative models and the products built on top of
+them. Interested candidates please apply https://jobs.ashbyhq.com/ideogram

model_index.json

@@ -0,0 +1,29 @@
+{
+  "_class_name": "Ideogram4Pipeline",
+  "_diffusers_version": "0.39.0.dev0",
+  "_name_or_path": "ideogram-ai/debug-ideogram-v4",
+  "scheduler": [
+    "diffusers",
+    "FlowMatchEulerDiscreteScheduler"
+  ],
+  "text_encoder": [
+    "transformers",
+    "Qwen3VLModel"
+  ],
+  "tokenizer": [
+    "transformers",
+    "Qwen2Tokenizer"
+  ],
+  "transformer": [
+    "diffusers",
+    "Ideogram4Transformer2DModel"
+  ],
+  "unconditional_transformer": [
+    "diffusers",
+    "Ideogram4Transformer2DModel"
+  ],
+  "vae": [
+    "diffusers",
+    "AutoencoderKLFlux2"
+  ]
+}

scheduler/scheduler_config.json

@@ -0,0 +1,18 @@
+{
+  "_class_name": "FlowMatchEulerDiscreteScheduler",
+  "_diffusers_version": "0.39.0.dev0",
+  "base_image_seq_len": 256,
+  "base_shift": 0.5,
+  "invert_sigmas": false,
+  "max_image_seq_len": 4096,
+  "max_shift": 1.15,
+  "num_train_timesteps": 1000,
+  "shift": 1.0,
+  "shift_terminal": null,
+  "stochastic_sampling": false,
+  "time_shift_type": "exponential",
+  "use_beta_sigmas": false,
+  "use_dynamic_shifting": false,
+  "use_exponential_sigmas": false,
+  "use_karras_sigmas": false
+}

text_encoder/config.json

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+{
+  "architectures": [
+    "Qwen3VLModel"
+  ],
+  "dtype": "bfloat16",
+  "image_token_id": 151655,
+  "model_type": "qwen3_vl",
+  "text_config": {
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "bos_token_id": 151643,
+    "dtype": "bfloat16",
+    "eos_token_id": 151645,
+    "head_dim": 128,
+    "hidden_act": "silu",
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+    "initializer_range": 0.02,
+    "intermediate_size": 12288,
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+    "model_type": "qwen3_vl_text",
+    "num_attention_heads": 32,
+    "num_hidden_layers": 36,
+    "num_key_value_heads": 8,
+    "pad_token_id": null,
+    "rms_norm_eps": 1e-06,
+    "rope_parameters": {
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+        20
+      ],
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+      "rope_type": "default"
+    },
+    "use_cache": true,
+    "vocab_size": 151936
+  },
+  "tie_word_embeddings": false,
+  "transformers_version": "5.8.0",
+  "video_token_id": 151656,
+  "vision_config": {
+    "deepstack_visual_indexes": [
+      8,
+      16,
+      24
+    ],
+    "depth": 27,
+    "dtype": "bfloat16",
+    "hidden_act": "gelu_pytorch_tanh",
+    "hidden_size": 1152,
+    "in_channels": 3,
+    "initializer_range": 0.02,
+    "intermediate_size": 4304,
+    "model_type": "qwen3_vl_vision",
+    "num_heads": 16,
+    "num_position_embeddings": 2304,
+    "out_hidden_size": 4096,
+    "patch_size": 16,
+    "spatial_merge_size": 2,
+    "temporal_patch_size": 2
+  },
+  "vision_end_token_id": 151653,
+  "vision_start_token_id": 151652,
+  "ideogram_fp8_weight_only": true
+}

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tokenizer/chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {%- if messages[0].content is string %}
+            {{- messages[0].content }}
+        {%- else %}
+            {%- for content in messages[0].content %}
+                {%- if 'text' in content %}
+                    {{- content.text }}
+                {%- endif %}
+            {%- endfor %}
+        {%- endif %}
+        {{- '\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' }}
+        {%- if messages[0].content is string %}
+            {{- messages[0].content }}
+        {%- else %}
+            {%- for content in messages[0].content %}
+                {%- if 'text' in content %}
+                    {{- content.text }}
+                {%- endif %}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- set image_count = namespace(value=0) %}
+{%- set video_count = namespace(value=0) %}
+{%- for message in messages %}
+    {%- if message.role == "user" %}
+        {{- '<|im_start|>' + message.role + '\n' }}
+        {%- if message.content is string %}
+            {{- message.content }}
+        {%- else %}
+            {%- for content in message.content %}
+                {%- if content.type == 'image' or 'image' in content or 'image_url' in content %}
+                    {%- set image_count.value = image_count.value + 1 %}
+                    {%- if add_vision_id %}Picture {{ image_count.value }}: {% endif -%}
+                    <|vision_start|><|image_pad|><|vision_end|>
+                {%- elif content.type == 'video' or 'video' in content %}
+                    {%- set video_count.value = video_count.value + 1 %}
+                    {%- if add_vision_id %}Video {{ video_count.value }}: {% endif -%}
+                    <|vision_start|><|video_pad|><|vision_end|>
+                {%- elif 'text' in content %}
+                    {{- content.text }}
+                {%- endif %}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role + '\n' }}
+        {%- if message.content is string %}
+            {{- message.content }}
+        {%- else %}
+            {%- for content_item in message.content %}
+                {%- if 'text' in content_item %}
+                    {{- content_item.text }}
+                {%- endif %}
+            {%- endfor %}
+        {%- endif %}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and message.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' }}
+        {%- if message.content is string %}
+            {{- message.content }}
+        {%- else %}
+            {%- for content in message.content %}
+                {%- if content.type == 'image' or 'image' in content or 'image_url' in content %}
+                    {%- set image_count.value = image_count.value + 1 %}
+                    {%- if add_vision_id %}Picture {{ image_count.value }}: {% endif -%}
+                    <|vision_start|><|image_pad|><|vision_end|>
+                {%- elif content.type == 'video' or 'video' in content %}
+                    {%- set video_count.value = video_count.value + 1 %}
+                    {%- if add_vision_id %}Video {{ video_count.value }}: {% endif -%}
+                    <|vision_start|><|video_pad|><|vision_end|>
+                {%- elif 'text' in content %}
+                    {{- content.text }}
+                {%- endif %}
+            {%- endfor %}
+        {%- endif %}
+        {{- '\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' }}
+{%- endif %}

tokenizer/tokenizer.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:be75606093db2094d7cd20f3c2f385c212750648bd6ea4fb2bf507a6a4c55506
+size 11422650

tokenizer/tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_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|>"
+  ],
+  "is_local": true,
+  "local_files_only": false,
+  "model_max_length": 262144,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}

transformer/config.json

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+{
+  "_class_name": "Ideogram4Transformer2DModel",
+  "_diffusers_version": "0.39.0.dev0",
+  "_name_or_path": "/home/jinli/.cache/huggingface/hub/models--ideogram-ai--debug-ideogram-v4/snapshots/41af6183c9fd9b6254864b0720319ef984535bfc/transformer",
+  "adaln_dim": 512,
+  "attention_head_dim": 256,
+  "in_channels": 128,
+  "intermediate_size": 12288,
+  "llm_features_dim": 53248,
+  "mrope_section": [
+    24,
+    20,
+    20
+  ],
+  "norm_eps": 1e-05,
+  "num_attention_heads": 18,
+  "num_layers": 34,
+  "rope_theta": 5000000
+}

transformer/diffusion_pytorch_model.safetensors

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+size 9289792888

transformer/diffusion_pytorch_model.safetensors.index.json

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vae/config.json

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+{
+  "_class_name": "AutoencoderKLFlux2",
+  "_diffusers_version": "0.39.0.dev0",
+  "_name_or_path": "/home/jinli/.cache/huggingface/hub/models--ideogram-ai--debug-ideogram-v4/snapshots/41af6183c9fd9b6254864b0720319ef984535bfc/vae",
+  "act_fn": "silu",
+  "batch_norm_eps": 0.0001,
+  "batch_norm_momentum": 0.1,
+  "block_out_channels": [
+    128,
+    256,
+    512,
+    512
+  ],
+  "decoder_block_out_channels": null,
+  "down_block_types": [
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D"
+  ],
+  "force_upcast": true,
+  "in_channels": 3,
+  "latent_channels": 32,
+  "layers_per_block": 2,
+  "mid_block_add_attention": true,
+  "norm_num_groups": 32,
+  "out_channels": 3,
+  "patch_size": [
+    2,
+    2
+  ],
+  "sample_size": 1024,
+  "up_block_types": [
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D"
+  ],
+  "use_post_quant_conv": true,
+  "use_quant_conv": true
+}

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