diffusers/pr_13860/en/_app/immutable/nodes/161.dd1dc798.js

import{s as ze,o as Ye,n as De}from"../chunks/scheduler.53228c21.js";import{S as Oe,i as Ae,e as i,s as a,c as u,h as Qe,a as l,d as n,b as o,f as F,g,j as b,k as B,l as r,m as s,n as f,t as h,o as _,p as v}from"../chunks/index.cac5d66a.js";import{C as Xe}from"../chunks/CopyLLMTxtMenu.94f1b9f3.js";import{D as oe}from"../chunks/Docstring.ad6457df.js";import{C as Be}from"../chunks/CodeBlock.606cbaf4.js";import{E as Ke}from"../chunks/ExampleCodeBlock.1a02521d.js";import{H as ye,E as et}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.fff5b5d9.js";function tt(se){let p,k="Examples:",$,y,x;return y=new Be({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> Ideogram4Pipeline

<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = Ideogram4Pipeline.from_pretrained(<span class="hljs-string">&quot;ideogram-ai/ideogram-v4&quot;</span>, torch_dtype=torch.bfloat16)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe.to(<span class="hljs-string">&quot;cuda&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>prompt = <span class="hljs-string">&quot;A photo of a cat holding a sign that says hello world&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># The defaults are the recommended settings for best quality.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>image = pipe(prompt, height=<span class="hljs-number">2048</span>, width=<span class="hljs-number">2048</span>, generator=torch.Generator(<span class="hljs-string">&quot;cuda&quot;</span>).manual_seed(<span class="hljs-number">0</span>)).images[<span class="hljs-number">0</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>image.save(<span class="hljs-string">&quot;ideogram4.png&quot;</span>)`,lang:"py",wrap:!1}}),{c(){p=i("p"),p.textContent=k,$=a(),u(y.$$.fragment)},l(d){p=l(d,"P",{"data-svelte-h":!0}),b(p)!=="svelte-kvfsh7"&&(p.textContent=k),$=o(d),g(y.$$.fragment,d)},m(d,w){s(d,p,w),s(d,$,w),f(y,d,w),x=!0},p:De,i(d){x||(h(y.$$.fragment,d),x=!0)},o(d){_(y.$$.fragment,d),x=!1},d(d){d&&(n(p),n($)),v(y,d)}}}function nt(se){let p,k,$,y,x,d,w,re,J,Ge=`Ideogram 4 is a flow-matching text-to-image model that uses a multimodal text encoder and an asymmetric
classifier-free guidance scheme: a dedicated <code>unconditional_transformer</code> produces the negative branch with zeroed text
features, while the main <code>transformer</code> consumes the full packed text + image sequence.`,ie,C,We=`The pipeline defaults are the recommended settings for best quality, so a plain <code>pipe(prompt)</code> call produces
best-quality results out of the box: 48 flow-matching steps on a logit-normal schedule (<code>mu=0.0</code>, <code>std=1.5</code>) with
classifier-free guidance held at 7.0 for the main steps and dropped to 3.0 for the final 3 “polish” steps.`,le,U,Ee="Key inference-time knobs are exposed via the pipeline call:",de,G,qe="<li><code>num_inference_steps</code>, <code>mu</code>, and <code>std</code> control the resolution-aware logit-normal flow-matching schedule.</li> <li><code>guidance_scale</code> (or a full per-step <code>guidance_schedule</code>) blends the conditional and unconditional velocities.</li>",pe,W,ce,E,me,q,ue,c,Z,xe,z,Ze="Text-to-image pipeline for Ideogram4.",we,Y,Ne=`Ideogram4 is a flow-matching model trained with asymmetric classifier-free guidance: a <code>transformer</code> consumes
text-conditioned features alongside the image latents, while a separate <code>unconditional_transformer</code> denoises with
zeroed text features. The two velocity predictions are linearly blended each step.`,$e,I,N,Ie,D,Le="Run text-to-image generation.",Me,P,Te,M,L,je,O,Re="Prepare the conditioning for the packed text+image sequence (one entry per prompt).",Pe,A,Ve=`Returns a flat tuple <code>(prompt_embeds, position_ids, segment_ids, indicator)</code>. The unconditional branch carries
no text, so the pipeline builds its (zeroed) inputs directly rather than encoding a negative prompt.`,ke,T,R,Je,Q,He="Rewrite each prompt into Ideogram4’s native structured JSON caption.",Ce,X,Se=`Requires the optional <code>prompt_enhancer_head</code> component, which is grafted onto the shared <code>text_encoder</code> body to
make it generative. Generation is schema-constrained when <code>outlines</code> is installed, otherwise it runs
unconstrained. Pass <code>generator</code> (the same one accepted by <code>__call__</code>) to make sampling reproducible.`,ge,V,fe,j,H,Ue,K,Fe="Output class for the Ideogram 4 pipeline.",he,S,_e,ae,ve;return x=new Xe({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),w=new ye({props:{title:"Ideogram 4",local:"ideogram-4",headingTag:"h1"}}),W=new ye({props:{title:"Text-to-image",local:"text-to-image",headingTag:"h2"}}),E=new Be({props:{code:"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",highlighted:`<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> Ideogram4Pipeline

pipe = Ideogram4Pipeline.from_pretrained(<span class="hljs-string">&quot;ideogram-ai/ideogram-v4&quot;</span>, torch_dtype=torch.bfloat16)
pipe.to(<span class="hljs-string">&quot;cuda&quot;</span>)

prompt = <span class="hljs-string">&quot;A photo of a cat holding a sign that says hello world&quot;</span>
<span class="hljs-comment"># The defaults are the recommended settings for best quality.</span>
image = pipe(prompt, height=<span class="hljs-number">1024</span>, width=<span class="hljs-number">1024</span>, generator=torch.Generator(<span class="hljs-string">&quot;cuda&quot;</span>).manual_seed(<span class="hljs-number">0</span>)).images[<span class="hljs-number">0</span>]
image.save(<span class="hljs-string">&quot;ideogram4.png&quot;</span>)`,lang:"python",wrap:!1}}),q=new ye({props:{title:"Ideogram4Pipeline",local:"diffusers.Ideogram4Pipeline",headingTag:"h2"}}),Z=new oe({props:{name:"class diffusers.Ideogram4Pipeline",anchor:"diffusers.Ideogram4Pipeline",parameters:[{name:"scheduler",val:": FlowMatchEulerDiscreteScheduler"},{name:"vae",val:": AutoencoderKLFlux2"},{name:"text_encoder",val:": PreTrainedModel"},{name:"tokenizer",val:": AutoTokenizer"},{name:"transformer",val:": Ideogram4Transformer2DModel"},{name:"unconditional_transformer",val:": Ideogram4Transformer2DModel"},{name:"prompt_enhancer_head",val:": diffusers.pipelines.ideogram4.prompt_enhancer.Ideogram4PromptEnhancerHead | None = None"}],parametersDescription:[{anchor:"diffusers.Ideogram4Pipeline.scheduler",description:`<strong>scheduler</strong> (<a href="/docs/diffusers/pr_13860/en/api/schedulers/flow_match_euler_discrete#diffusers.FlowMatchEulerDiscreteScheduler">FlowMatchEulerDiscreteScheduler</a>) &#x2014;
Flow-matching scheduler. The pipeline overrides the default sigma schedule with a resolution-aware
logit-normal schedule.`,name:"scheduler"},{anchor:"diffusers.Ideogram4Pipeline.vae",description:`<strong>vae</strong> (<code>AutoencoderKLFlux2</code>) &#x2014;
Variational auto-encoder used to decode latents back into images.`,name:"vae"},{anchor:"diffusers.Ideogram4Pipeline.text_encoder",description:`<strong>text_encoder</strong> (<code>PreTrainedModel</code>) &#x2014;
Multimodal text encoder. The pipeline consumes hidden states from a fixed set of intermediate decoder
layers (see <code>QWEN3_VL_ACTIVATION_LAYERS</code>).`,name:"text_encoder"},{anchor:"diffusers.Ideogram4Pipeline.tokenizer",description:`<strong>tokenizer</strong> (<code>AutoTokenizer</code>) &#x2014;
Tokenizer paired with <code>text_encoder</code>.`,name:"tokenizer"},{anchor:"diffusers.Ideogram4Pipeline.transformer",description:`<strong>transformer</strong> (<a href="/docs/diffusers/pr_13860/en/api/models/ideogram4_transformer2d#diffusers.Ideogram4Transformer2DModel">Ideogram4Transformer2DModel</a>) &#x2014;
Conditional flow-matching transformer.`,name:"transformer"},{anchor:"diffusers.Ideogram4Pipeline.unconditional_transformer",description:`<strong>unconditional_transformer</strong> (<a href="/docs/diffusers/pr_13860/en/api/models/ideogram4_transformer2d#diffusers.Ideogram4Transformer2DModel">Ideogram4Transformer2DModel</a>) &#x2014;
Unconditional (asymmetric-CFG) flow-matching transformer.`,name:"unconditional_transformer"}],source:"https://github.com/huggingface/diffusers/blob/vr_13860/src/diffusers/pipelines/ideogram4/pipeline_ideogram4.py#L140"}}),N=new oe({props:{name:"__call__",anchor:"diffusers.Ideogram4Pipeline.__call__",parameters:[{name:"prompt",val:": str | list[str] | None = None"},{name:"height",val:": int = 2048"},{name:"width",val:": int = 2048"},{name:"num_inference_steps",val:": int = 48"},{name:"guidance_scale",val:": float | None = None"},{name:"guidance_schedule",val:": list[float] | torch.Tensor | None = (7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 3.0, 3.0, 3.0)"},{name:"mu",val:": float = 0.0"},{name:"std",val:": float = 1.5"},{name:"prompt_upsampling",val:": bool = False"},{name:"prompt_upsampling_temperature",val:": float = 1.0"},{name:"max_sequence_length",val:": int = 2048"},{name:"num_images_per_prompt",val:": int = 1"},{name:"generator",val:": torch._C.Generator | list[torch._C.Generator] | None = None"},{name:"latents",val:": torch.Tensor | None = None"},{name:"output_type",val:": str = 'pil'"},{name:"return_dict",val:": bool = True"},{name:"callback_on_step_end",val:": typing.Optional[typing.Callable[[ForwardRef('Ideogram4Pipeline'), int, int, dict[str, typing.Any]], dict[str, typing.Any]]] = None"},{name:"callback_on_step_end_tensor_inputs",val:": list = ['latents']"}],parametersDescription:[{anchor:"diffusers.Ideogram4Pipeline.__call__.prompt",description:`<strong>prompt</strong> (<code>str</code> or <code>list[str]</code>) &#x2014;
Prompt(s) to guide image generation.`,name:"prompt"},{anchor:"diffusers.Ideogram4Pipeline.__call__.height",description:`<strong>height</strong> (<code>int</code>, <em>optional</em>, defaults to 2048) &#x2014;
Output image height in pixels; must be a multiple of <code>vae_scale_factor * patch_size</code>.`,name:"height"},{anchor:"diffusers.Ideogram4Pipeline.__call__.width",description:`<strong>width</strong> (<code>int</code>, <em>optional</em>, defaults to 2048) &#x2014;
Output image width in pixels; must be a multiple of <code>vae_scale_factor * patch_size</code>.`,name:"width"},{anchor:"diffusers.Ideogram4Pipeline.__call__.num_inference_steps",description:`<strong>num_inference_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 48) &#x2014;
Number of flow-matching steps. The default is the recommended setting for best quality.`,name:"num_inference_steps"},{anchor:"diffusers.Ideogram4Pipeline.__call__.guidance_scale",description:`<strong>guidance_scale</strong> (<code>float</code>, <em>optional</em>) &#x2014;
Constant classifier-free guidance scale applied at every step. The conditional and unconditional
velocity predictions are blended as <code>v = guidance_scale * v_pos + (1 - guidance_scale) * v_neg</code>.
Mutually exclusive with <code>guidance_schedule</code> (setting both raises). Defaults to <code>None</code>.`,name:"guidance_scale"},{anchor:"diffusers.Ideogram4Pipeline.__call__.guidance_schedule",description:`<strong>guidance_schedule</strong> (<code>list[float]</code> or <code>torch.Tensor</code>, <em>optional</em>) &#x2014;
Per-step guidance scale schedule; must have length <code>num_inference_steps</code>. The first entry corresponds
to the first step (largest noise level). Mutually exclusive with <code>guidance_scale</code>; exactly one must be
set. Defaults to the recommended schedule (7.0 for the main steps, dropping to 3.0 for the final 3
&#x201C;polish&#x201D; steps). To use a constant scale instead, pass <code>guidance_scale</code> and <code>guidance_schedule=None</code>.`,name:"guidance_schedule"},{anchor:"diffusers.Ideogram4Pipeline.__call__.mu",description:`<strong>mu</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
Base mean of the logit-normal flow-matching schedule. The schedule mean is shifted by half the log of
the resolution ratio relative to 512x512.`,name:"mu"},{anchor:"diffusers.Ideogram4Pipeline.__call__.std",description:`<strong>std</strong> (<code>float</code>, <em>optional</em>, defaults to 1.5) &#x2014;
Standard deviation of the logit-normal flow-matching schedule.`,name:"std"},{anchor:"diffusers.Ideogram4Pipeline.__call__.prompt_upsampling",description:`<strong>prompt_upsampling</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
If <code>True</code>, rewrite <code>prompt</code> into Ideogram4&#x2019;s native structured JSON caption via
<a href="/docs/diffusers/pr_13860/en/api/pipelines/ideogram4#diffusers.Ideogram4Pipeline.upsample_prompt">upsample_prompt()</a> before encoding. Requires the optional <code>prompt_enhancer_head</code>
component; install <code>outlines</code> for schema-constrained captions. <code>generator</code> is reused to make the
upsampling reproducible.`,name:"prompt_upsampling"},{anchor:"diffusers.Ideogram4Pipeline.__call__.prompt_upsampling_temperature",description:`<strong>prompt_upsampling_temperature</strong> (<code>float</code>, <em>optional</em>, defaults to 1.0) &#x2014;
Sampling temperature for prompt upsampling when <code>prompt_upsampling=True</code>.`,name:"prompt_upsampling_temperature"},{anchor:"diffusers.Ideogram4Pipeline.__call__.max_sequence_length",description:`<strong>max_sequence_length</strong> (<code>int</code>, <em>optional</em>, defaults to 2048) &#x2014;
Maximum number of text tokens per prompt.`,name:"max_sequence_length"},{anchor:"diffusers.Ideogram4Pipeline.__call__.num_images_per_prompt",description:`<strong>num_images_per_prompt</strong> (<code>int</code>, <em>optional</em>, defaults to 1) &#x2014;
Number of images to generate per prompt.`,name:"num_images_per_prompt"},{anchor:"diffusers.Ideogram4Pipeline.__call__.generator",description:`<strong>generator</strong> (<code>torch.Generator</code> or <code>list[torch.Generator]</code>, <em>optional</em>) &#x2014;
Generator(s) used to make sampling deterministic.`,name:"generator"},{anchor:"diffusers.Ideogram4Pipeline.__call__.latents",description:`<strong>latents</strong> (<code>torch.Tensor</code>, <em>optional</em>) &#x2014;
Pre-generated noise of shape <code>(batch_size, num_image_tokens, latent_dim)</code>.`,name:"latents"},{anchor:"diffusers.Ideogram4Pipeline.__call__.output_type",description:`<strong>output_type</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;pil&quot;</code>) &#x2014;
One of <code>&quot;pil&quot;</code>, <code>&quot;np&quot;</code>, <code>&quot;pt&quot;</code>, or <code>&quot;latent&quot;</code>.`,name:"output_type"},{anchor:"diffusers.Ideogram4Pipeline.__call__.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to return an <a href="/docs/diffusers/pr_13860/en/api/pipelines/ideogram4#diffusers.pipelines.ideogram4.Ideogram4PipelineOutput">Ideogram4PipelineOutput</a>.`,name:"return_dict"},{anchor:"diffusers.Ideogram4Pipeline.__call__.callback_on_step_end",description:`<strong>callback_on_step_end</strong> (<code>Callable</code>, <em>optional</em>) &#x2014;
Callback invoked at the end of every denoising step.`,name:"callback_on_step_end"},{anchor:"diffusers.Ideogram4Pipeline.__call__.callback_on_step_end_tensor_inputs",description:`<strong>callback_on_step_end_tensor_inputs</strong> (<code>list[str]</code>, <em>optional</em>) &#x2014;
Names of tensors to expose to the callback via <code>callback_kwargs</code>.`,name:"callback_on_step_end_tensor_inputs"}],source:"https://github.com/huggingface/diffusers/blob/vr_13860/src/diffusers/pipelines/ideogram4/pipeline_ideogram4.py#L468",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/diffusers/pr_13860/en/api/pipelines/ideogram4#diffusers.pipelines.ideogram4.Ideogram4PipelineOutput"
>Ideogram4PipelineOutput</a> or <code>tuple</code>.</p>
`}}),P=new Ke({props:{anchor:"diffusers.Ideogram4Pipeline.__call__.example",$$slots:{default:[tt]},$$scope:{ctx:se}}}),L=new oe({props:{name:"encode_prompt",anchor:"diffusers.Ideogram4Pipeline.encode_prompt",parameters:[{name:"prompt",val:": str | list[str]"},{name:"grid_h",val:": int"},{name:"grid_w",val:": int"},{name:"max_sequence_length",val:": int"},{name:"device",val:": device"}],source:"https://github.com/huggingface/diffusers/blob/vr_13860/src/diffusers/pipelines/ideogram4/pipeline_ideogram4.py#L334"}}),R=new oe({props:{name:"upsample_prompt",anchor:"diffusers.Ideogram4Pipeline.upsample_prompt",parameters:[{name:"prompt",val:": str | list[str]"},{name:"height",val:": int = 2048"},{name:"width",val:": int = 2048"},{name:"temperature",val:": float = 1.0"},{name:"max_new_tokens",val:": int = 1024"},{name:"generator",val:": torch._C.Generator | list[torch._C.Generator] | None = None"},{name:"device",val:": torch.device | None = None"}],source:"https://github.com/huggingface/diffusers/blob/vr_13860/src/diffusers/pipelines/ideogram4/pipeline_ideogram4.py#L203"}}),V=new ye({props:{title:"Ideogram4PipelineOutput",local:"diffusers.pipelines.ideogram4.Ideogram4PipelineOutput",headingTag:"h2"}}),H=new oe({props:{name:"class diffusers.pipelines.ideogram4.Ideogram4PipelineOutput",anchor:"diffusers.pipelines.ideogram4.Ideogram4PipelineOutput",parameters:[{name:"images",val:": list[PIL.Image.Image] | numpy.ndarray"}],parametersDescription:[{anchor:"diffusers.pipelines.ideogram4.Ideogram4PipelineOutput.images",description:`<strong>images</strong> (<code>list[PIL.Image.Image]</code> or <code>np.ndarray</code>) &#x2014;
List of denoised PIL images of length <code>batch_size</code>, or numpy array of shape <code>(batch_size, height, width, num_channels)</code>.`,name:"images"}],source:"https://github.com/huggingface/diffusers/blob/vr_13860/src/diffusers/pipelines/ideogram4/pipeline_output.py#L24"}}),S=new 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