diffusers/pr_13862/en/_app/immutable/nodes/161.f4feb791.js

import{s as Ze,o as qe,n as Ve}from"../chunks/scheduler.53228c21.js";import{S as Le,i as Ne,e as l,s as a,c as u,h as Re,a as d,d as n,b as o,f as O,g,j as M,k as Q,l as i,m as s,n as f,t as h,o as _,p as b}from"../chunks/index.cac5d66a.js";import{C as He}from"../chunks/CopyLLMTxtMenu.956dd022.js";import{D as ge}from"../chunks/Docstring.d64e41fa.js";import{C as Ee}from"../chunks/CodeBlock.606cbaf4.js";import{E as Be}from"../chunks/ExampleCodeBlock.246e9ebe.js";import{H as fe,E as Se}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.798a8f4f.js";function Fe(K){let c,P="Examples:",w,y,v;return y=new Ee({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(){c=l("p"),c.textContent=P,w=a(),u(y.$$.fragment)},l(r){c=d(r,"P",{"data-svelte-h":!0}),M(c)!=="svelte-kvfsh7"&&(c.textContent=P),w=o(r),g(y.$$.fragment,r)},m(r,x){s(r,c,x),s(r,w,x),f(y,r,x),v=!0},p:Ve,i(r){v||(h(y.$$.fragment,r),v=!0)},o(r){_(y.$$.fragment,r),v=!1},d(r){r&&(n(c),n(w)),b(y,r)}}}function ze(K){let c,P,w,y,v,r,x,ee,J,Ie=`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.`,te,k,Te=`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.`,ne,U,je="Key inference-time knobs are exposed via the pipeline call:",ae,C,Pe="<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>",oe,G,se,W,re,E,ie,p,Z,he,H,Je="Text-to-image pipeline for Ideogram4.",_e,B,ke=`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.`,be,$,q,ye,S,Ue="Run text-to-image generation.",ve,j,xe,I,V,Me,F,Ce="Prepare the conditioning for the packed text+image sequence (one entry per prompt).",we,z,Ge=`Returns a flat tuple <code>(prompt_embeds, position_ids, segment_ids, indicator)</code>. The unconditional branch carries
no text, so the pipeline builds its (zeroed) inputs directly rather than encoding a negative prompt.`,le,L,de,T,N,$e,Y,We="Output class for the Ideogram 4 pipeline.",ce,R,pe,X,me;return v=new He({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),x=new fe({props:{title:"Ideogram 4",local:"ideogram-4",headingTag:"h1"}}),G=new fe({props:{title:"Text-to-image",local:"text-to-image",headingTag:"h2"}}),W=new Ee({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}}),E=new fe({props:{title:"Ideogram4Pipeline",local:"diffusers.Ideogram4Pipeline",headingTag:"h2"}}),Z=new ge({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"}],parametersDescription:[{anchor:"diffusers.Ideogram4Pipeline.scheduler",description:`<strong>scheduler</strong> (<a href="/docs/diffusers/pr_13862/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_13862/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_13862/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_13862/src/diffusers/pipelines/ideogram4/pipeline_ideogram4.py#L133"}}),q=new ge({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:"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__.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_13862/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_13862/src/diffusers/pipelines/ideogram4/pipeline_ideogram4.py#L407",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/diffusers/pr_13862/en/api/pipelines/ideogram4#diffusers.pipelines.ideogram4.Ideogram4PipelineOutput"
>Ideogram4PipelineOutput</a> or <code>tuple</code>.</p>
`}}),j=new Be({props:{anchor:"diffusers.Ideogram4Pipeline.__call__.example",$$slots:{default:[Fe]},$$scope:{ctx:K}}}),V=new ge({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_13862/src/diffusers/pipelines/ideogram4/pipeline_ideogram4.py#L273"}}),L=new fe({props:{title:"Ideogram4PipelineOutput",local:"diffusers.pipelines.ideogram4.Ideogram4PipelineOutput",headingTag:"h2"}}),N=new ge({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;
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