diffusers/pr_14047/en/_app/immutable/nodes/171.5f2c792b.js

import{s as Ve,o as Se,n as He}from"../chunks/scheduler.53228c21.js";import{S as De,i as Ye,e as i,s,c as u,h as Ae,a as l,d as n,b as a,f as P,g as f,j as x,k as J,l as r,m as o,n as _,t as h,o as g,p as b}from"../chunks/index.cac5d66a.js";import{C as Xe}from"../chunks/CopyLLMTxtMenu.0a7e0e29.js";import{D as ne}from"../chunks/Docstring.ef3c6a7f.js";import{C as ze}from"../chunks/CodeBlock.606cbaf4.js";import{E as Re}from"../chunks/ExampleCodeBlock.76765dc9.js";import{H as Me,E as Fe}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.d66c5a2f.js";function Oe(ae){let p,U="Examples:",y,v,M;return v=new ze({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> Krea2Pipeline

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Load from a local directory produced by the Krea 2 conversion (no hub repo yet).</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = Krea2Pipeline.from_pretrained(<span class="hljs-string">&quot;path/to/krea2-diffusers&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 fox in the snow&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Base (midtrain) checkpoint defaults. For the few-step distilled (TDM) checkpoint use</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># \`num_inference_steps=8, guidance_scale=0.0\` instead.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>image = pipe(prompt, num_inference_steps=<span class="hljs-number">28</span>, guidance_scale=<span class="hljs-number">4.5</span>).images[<span class="hljs-number">0</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>image.save(<span class="hljs-string">&quot;krea2.png&quot;</span>)`,lang:"py",wrap:!1}}),{c(){p=i("p"),p.textContent=U,y=s(),u(v.$$.fragment)},l(d){p=l(d,"P",{"data-svelte-h":!0}),x(p)!=="svelte-kvfsh7"&&(p.textContent=U),y=a(d),f(v.$$.fragment,d)},m(d,w){o(d,p,w),o(d,y,w),_(v,d,w),M=!0},p:He,i(d){M||(h(v.$$.fragment,d),M=!0)},o(d){g(v.$$.fragment,d),M=!1},d(d){d&&(n(p),n(y)),b(v,d)}}}function et(ae){let p,U,y,v,M,d,w,oe,C,Ce=`Krea 2 (K2) is a flow-matching text-to-image model built around a single-stream MMDiT with grouped-query attention. A
Qwen3-VL text encoder provides the conditioning: instead of the last hidden state, hidden states from twelve decoder
layers are tapped per token and fused inside the transformer by a small text-fusion stage. Images are decoded with the
Qwen-Image VAE.`,re,I,Ie="Two checkpoints are released, sharing the same architecture but with different recommended sampler settings:",ie,q,qe=`<li><strong>Base (midtrain)</strong> — use the full sampler with classifier-free guidance: <code>num_inference_steps=28</code>,
<code>guidance_scale=4.5</code>.</li> <li><strong>TDM (distilled)</strong> — distilled for few-step sampling, run with <code>num_inference_steps=8</code> and guidance disabled
(<code>guidance_scale=0.0</code>).</li>`,le,Z,Ze=`<code>guidance_scale</code> follows the Krea 2 convention: the velocity is computed as <code>cond + guidance_scale * (cond - uncond)</code>
and guidance is enabled whenever <code>guidance_scale &gt; 0</code> (this equals the usual CFG formulation with scale
<code>1 + guidance_scale</code>).`,de,N,pe,G,ce,Q,me,c,E,we,D,Ne="The Krea 2 pipeline for text-to-image generation.",xe,$,W,ye,Y,Ge="Function invoked when calling the pipeline for generation.",$e,j,ke,A,B,Te,k,L,je,X,Qe="Tokenize <code>prompt</code> into the fixed-length Krea 2 layout and tap the selected encoder hidden states.",Ke,R,Ee="Returns a <code>(hidden_states, attention_mask)</code> tuple of shapes <code>(batch_size, text_seq_len, num_text_layers, text_hidden_dim)</code> and <code>(batch_size, text_seq_len)</code> (bool).",Pe,K,z,Je,F,We=`Build the <code>(text_seq_len + grid_height * grid_width, 3)</code> rotary coordinates for the combined sequence:
text tokens sit at the origin, image tokens carry their <code>(0, h, w)</code> latent-grid coordinates.`,ue,V,fe,T,S,Ue,O,Be="Output class for the Krea 2 pipeline.",_e,H,he,se,ge;return M=new Xe({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),w=new Me({props:{title:"Krea 2",local:"krea-2",headingTag:"h1"}}),N=new Me({props:{title:"Text-to-image",local:"text-to-image",headingTag:"h2"}}),G=new ze({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> Krea2Pipeline

<span class="hljs-comment"># Load from a local directory produced by the Krea 2 conversion (no hub repo yet).</span>
pipe = Krea2Pipeline.from_pretrained(<span class="hljs-string">&quot;path/to/krea2-diffusers&quot;</span>, torch_dtype=torch.bfloat16)
pipe.to(<span class="hljs-string">&quot;cuda&quot;</span>)

prompt = <span class="hljs-string">&quot;a fox in the snow&quot;</span>
image = pipe(
    prompt,
    height=<span class="hljs-number">1024</span>,
    width=<span class="hljs-number">1024</span>,
    num_inference_steps=<span class="hljs-number">28</span>,
    guidance_scale=<span class="hljs-number">4.5</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;krea2.png&quot;</span>)`,lang:"python",wrap:!1}}),Q=new Me({props:{title:"Krea2Pipeline",local:"diffusers.Krea2Pipeline",headingTag:"h2"}}),E=new ne({props:{name:"class diffusers.Krea2Pipeline",anchor:"diffusers.Krea2Pipeline",parameters:[{name:"scheduler",val:": FlowMatchEulerDiscreteScheduler"},{name:"vae",val:": AutoencoderKLQwenImage"},{name:"text_encoder",val:": Qwen3VLModel"},{name:"tokenizer",val:": AutoTokenizer"},{name:"transformer",val:": Krea2Transformer2DModel"},{name:"text_encoder_select_layers",val:": tuple[int, ...] | list[int] | None = None"},{name:"is_distilled",val:": bool = False"},{name:"patch_size",val:": int = 2"}],parametersDescription:[{anchor:"diffusers.Krea2Pipeline.scheduler",description:`<strong>scheduler</strong> (<a href="/docs/diffusers/pr_14047/en/api/schedulers/flow_match_euler_discrete#diffusers.FlowMatchEulerDiscreteScheduler">FlowMatchEulerDiscreteScheduler</a>) &#x2014;
Euler flow-matching scheduler. The Krea 2 sigma schedule is the resolution-aware exponential time shift, so
the scheduler config is expected to set <code>use_dynamic_shifting=True</code> together with the Krea 2 shift
parameters (<code>base_shift=0.5</code>, <code>max_shift=1.15</code>, <code>base_image_seq_len=256</code>, <code>max_image_seq_len=6400</code>).`,name:"scheduler"},{anchor:"diffusers.Krea2Pipeline.vae",description:`<strong>vae</strong> (<a href="/docs/diffusers/pr_14047/en/api/models/autoencoderkl_qwenimage#diffusers.AutoencoderKLQwenImage">AutoencoderKLQwenImage</a>) &#x2014;
The Qwen-Image variational auto-encoder (f8, 16 latent channels) used to decode latents to images.`,name:"vae"},{anchor:"diffusers.Krea2Pipeline.text_encoder",description:`<strong>text_encoder</strong> (<a href="https://huggingface.co/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel" rel="nofollow">PreTrainedModel</a>) &#x2014;
A Qwen3-VL model (e.g. <code>Qwen3VLModel</code> of <code>Qwen/Qwen3-VL-4B-Instruct</code>). The pipeline consumes a stack of
hidden states tapped from several decoder layers rather than the last hidden state.`,name:"text_encoder"},{anchor:"diffusers.Krea2Pipeline.tokenizer",description:`<strong>tokenizer</strong> (<a href="https://huggingface.co/docs/transformers/main/en/model_doc/auto#transformers.AutoTokenizer" rel="nofollow">AutoTokenizer</a>) &#x2014;
The tokenizer paired with the text encoder.`,name:"tokenizer"},{anchor:"diffusers.Krea2Pipeline.transformer",description:`<strong>transformer</strong> (<a href="/docs/diffusers/pr_14047/en/api/models/krea2_transformer2d#diffusers.Krea2Transformer2DModel">Krea2Transformer2DModel</a>) &#x2014;
The Krea 2 single-stream MMDiT that predicts the flow-matching velocity.`,name:"transformer"},{anchor:"diffusers.Krea2Pipeline.text_encoder_select_layers",description:`<strong>text_encoder_select_layers</strong> (<code>tuple[int, ...]</code>, <em>optional</em>) &#x2014;
Indices into the text encoder&#x2019;s <code>hidden_states</code> tuple (0 is the embedding output) whose states are stacked
per token as the transformer&#x2019;s text conditioning. Must have <code>transformer.config.num_text_layers</code> entries.`,name:"text_encoder_select_layers"},{anchor:"diffusers.Krea2Pipeline.is_distilled",description:`<strong>is_distilled</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether the transformer is the few-step distilled (TDM/turbo) checkpoint. When <code>True</code> a fixed timestep
shift <code>mu=1.15</code> is used; otherwise <code>mu</code> is computed from the image resolution.`,name:"is_distilled"},{anchor:"diffusers.Krea2Pipeline.patch_size",description:`<strong>patch_size</strong> (<code>int</code>, <em>optional</em>, defaults to 2) &#x2014;
Side length of the square patches the latents are packed into before entering the transformer. The
effective pixel-to-token downsampling factor is <code>vae_scale_factor * patch_size</code>.`,name:"patch_size"}],source:"https://github.com/huggingface/diffusers/blob/vr_14047/src/diffusers/pipelines/krea2/pipeline_krea2.py#L133"}}),W=new ne({props:{name:"__call__",anchor:"diffusers.Krea2Pipeline.__call__",parameters:[{name:"prompt",val:": str | list[str] | None = None"},{name:"negative_prompt",val:": str | list[str] | None = None"},{name:"height",val:": int = 1024"},{name:"width",val:": int = 1024"},{name:"num_inference_steps",val:": int = 28"},{name:"sigmas",val:": list[float] | None = None"},{name:"guidance_scale",val:": float = 4.5"},{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:"prompt_embeds",val:": torch.Tensor | None = None"},{name:"prompt_embeds_mask",val:": torch.Tensor | None = None"},{name:"negative_prompt_embeds",val:": torch.Tensor | None = None"},{name:"negative_prompt_embeds_mask",val:": torch.Tensor | None = None"},{name:"output_type",val:": str | None = 'pil'"},{name:"return_dict",val:": bool = True"},{name:"callback_on_step_end",val:": typing.Optional[typing.Callable[[int, int, dict], NoneType]] = None"},{name:"callback_on_step_end_tensor_inputs",val:": list = ['latents']"},{name:"max_sequence_length",val:": int = 512"}],parametersDescription:[{anchor:"diffusers.Krea2Pipeline.__call__.prompt",description:`<strong>prompt</strong> (<code>str</code> or <code>list[str]</code>, <em>optional</em>) &#x2014;
The prompt or prompts to guide the image generation. If not defined, one has to pass <code>prompt_embeds</code>.`,name:"prompt"},{anchor:"diffusers.Krea2Pipeline.__call__.negative_prompt",description:`<strong>negative_prompt</strong> (<code>str</code> or <code>list[str]</code>, <em>optional</em>) &#x2014;
The prompt or prompts not to guide the image generation. Ignored when <code>guidance_scale &lt;= 0</code>; defaults
to an empty prompt when guidance is enabled.`,name:"negative_prompt"},{anchor:"diffusers.Krea2Pipeline.__call__.height",description:`<strong>height</strong> (<code>int</code>, defaults to 1024) &#x2014;
The height in pixels of the generated image. Rounded up to a multiple of 16 if needed.`,name:"height"},{anchor:"diffusers.Krea2Pipeline.__call__.width",description:`<strong>width</strong> (<code>int</code>, defaults to 1024) &#x2014;
The width in pixels of the generated image. Rounded up to a multiple of 16 if needed.`,name:"width"},{anchor:"diffusers.Krea2Pipeline.__call__.num_inference_steps",description:`<strong>num_inference_steps</strong> (<code>int</code>, defaults to 28) &#x2014;
The number of denoising steps. Use 28 for the base (midtrain) checkpoint and 8 for the few-step
distilled (TDM) checkpoint.`,name:"num_inference_steps"},{anchor:"diffusers.Krea2Pipeline.__call__.sigmas",description:`<strong>sigmas</strong> (<code>list[float]</code>, <em>optional</em>) &#x2014;
Custom sigmas for the scheduler. If not defined, the default <code>linspace(1.0, 1/num_inference_steps, num_inference_steps)</code> grid is used (the resolution-aware shift is applied inside the scheduler).`,name:"sigmas"},{anchor:"diffusers.Krea2Pipeline.__call__.guidance_scale",description:`<strong>guidance_scale</strong> (<code>float</code>, defaults to 4.5) &#x2014;
Classifier-free guidance scale, following the Krea 2 convention: the velocity is computed as <code>cond + guidance_scale * (cond - uncond)</code> and guidance is enabled whenever <code>guidance_scale &gt; 0</code> (this equals
the usual CFG formulation with scale <code>1 + guidance_scale</code>). Set to <code>0.0</code> to disable (e.g. for the TDM
checkpoint).`,name:"guidance_scale"},{anchor:"diffusers.Krea2Pipeline.__call__.num_images_per_prompt",description:`<strong>num_images_per_prompt</strong> (<code>int</code>, defaults to 1) &#x2014;
The number of images to generate per prompt.`,name:"num_images_per_prompt"},{anchor:"diffusers.Krea2Pipeline.__call__.generator",description:`<strong>generator</strong> (<code>torch.Generator</code> or <code>list[torch.Generator]</code>, <em>optional</em>) &#x2014;
One or more <a href="https://pytorch.org/docs/stable/generated/torch.Generator.html" rel="nofollow">torch generator(s)</a> to
make generation deterministic.`,name:"generator"},{anchor:"diffusers.Krea2Pipeline.__call__.latents",description:`<strong>latents</strong> (<code>torch.Tensor</code>, <em>optional</em>) &#x2014;
Pre-generated noisy latents in packed form <code>(batch_size, image_seq_len, in_channels)</code>, sampled from a
Gaussian distribution, to be used as inputs for image generation.`,name:"latents"},{anchor:"diffusers.Krea2Pipeline.__call__.prompt_embeds",description:`<strong>prompt_embeds</strong> (<code>torch.Tensor</code>, <em>optional</em>) &#x2014;
Pre-generated text embeddings of shape <code>(batch_size, text_seq_len, num_text_layers, text_hidden_dim)</code>.
If not provided, embeddings are generated from <code>prompt</code>.`,name:"prompt_embeds"},{anchor:"diffusers.Krea2Pipeline.__call__.prompt_embeds_mask",description:`<strong>prompt_embeds_mask</strong> (<code>torch.Tensor</code>, <em>optional</em>) &#x2014;
Boolean mask for <code>prompt_embeds</code>; required when <code>prompt_embeds</code> is passed.`,name:"prompt_embeds_mask"},{anchor:"diffusers.Krea2Pipeline.__call__.negative_prompt_embeds",description:`<strong>negative_prompt_embeds</strong> (<code>torch.Tensor</code>, <em>optional</em>) &#x2014;
Pre-generated negative text embeddings; same layout as <code>prompt_embeds</code>.`,name:"negative_prompt_embeds"},{anchor:"diffusers.Krea2Pipeline.__call__.negative_prompt_embeds_mask",description:`<strong>negative_prompt_embeds_mask</strong> (<code>torch.Tensor</code>, <em>optional</em>) &#x2014;
Boolean mask for <code>negative_prompt_embeds</code>; required when <code>negative_prompt_embeds</code> is passed.`,name:"negative_prompt_embeds_mask"},{anchor:"diffusers.Krea2Pipeline.__call__.output_type",description:`<strong>output_type</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;pil&quot;</code>) &#x2014;
The output format of the generated image. Choose between <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.Krea2Pipeline.__call__.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not to return a <a href="/docs/diffusers/pr_14047/en/api/pipelines/krea2#diffusers.pipelines.krea2.Krea2PipelineOutput">Krea2PipelineOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"diffusers.Krea2Pipeline.__call__.callback_on_step_end",description:`<strong>callback_on_step_end</strong> (<code>Callable</code>, <em>optional</em>) &#x2014;
A function that is called at the end of each denoising step with <code>callback_on_step_end(self, step, timestep, callback_kwargs)</code>.`,name:"callback_on_step_end"},{anchor:"diffusers.Krea2Pipeline.__call__.callback_on_step_end_tensor_inputs",description:`<strong>callback_on_step_end_tensor_inputs</strong> (<code>list[str]</code>, <em>optional</em>, defaults to <code>[&quot;latents&quot;]</code>) &#x2014;
The list of tensor inputs for the <code>callback_on_step_end</code> function. Must be a subset of
<code>._callback_tensor_inputs</code>.`,name:"callback_on_step_end_tensor_inputs"},{anchor:"diffusers.Krea2Pipeline.__call__.max_sequence_length",description:`<strong>max_sequence_length</strong> (<code>int</code>, defaults to 512) &#x2014;
Fixed text sequence length consumed by the transformer; prompts are padded or truncated to it.`,name:"max_sequence_length"}],source:"https://github.com/huggingface/diffusers/blob/vr_14047/src/diffusers/pipelines/krea2/pipeline_krea2.py#L440",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/diffusers/pr_14047/en/api/pipelines/krea2#diffusers.pipelines.krea2.Krea2PipelineOutput"
>Krea2PipelineOutput</a> if
<code>return_dict</code> is True, otherwise a <code>tuple</code>, whose first element is a list with the generated images.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/diffusers/pr_14047/en/api/pipelines/krea2#diffusers.pipelines.krea2.Krea2PipelineOutput"
>Krea2PipelineOutput</a> or <code>tuple</code></p>
`}}),j=new Re({props:{anchor:"diffusers.Krea2Pipeline.__call__.example",$$slots:{default:[Oe]},$$scope:{ctx:ae}}}),B=new ne({props:{name:"encode_prompt",anchor:"diffusers.Krea2Pipeline.encode_prompt",parameters:[{name:"prompt",val:": str | list[str]"},{name:"device",val:": torch.device | None = None"},{name:"num_images_per_prompt",val:": int = 1"},{name:"prompt_embeds",val:": torch.Tensor | None = None"},{name:"prompt_embeds_mask",val:": torch.Tensor | None = None"},{name:"max_sequence_length",val:": int = 512"}],parametersDescription:[{anchor:"diffusers.Krea2Pipeline.encode_prompt.prompt",description:`<strong>prompt</strong> (<code>str</code> or <code>list[str]</code>, <em>optional</em>) &#x2014;
prompt to be encoded`,name:"prompt"},{anchor:"diffusers.Krea2Pipeline.encode_prompt.device",description:`<strong>device</strong> &#x2014; (<code>torch.device</code>):
torch device`,name:"device"},{anchor:"diffusers.Krea2Pipeline.encode_prompt.num_images_per_prompt",description:`<strong>num_images_per_prompt</strong> (<code>int</code>) &#x2014;
number of images that should be generated per prompt`,name:"num_images_per_prompt"},{anchor:"diffusers.Krea2Pipeline.encode_prompt.prompt_embeds",description:`<strong>prompt_embeds</strong> (<code>torch.Tensor</code>, <em>optional</em>) &#x2014;
Pre-generated text embeddings of shape <code>(batch_size, text_seq_len, num_text_layers, text_hidden_dim)</code>.
Can be used to easily tweak text inputs, <em>e.g.</em> prompt weighting. If not provided, text embeddings will
be generated from <code>prompt</code> input argument.`,name:"prompt_embeds"},{anchor:"diffusers.Krea2Pipeline.encode_prompt.prompt_embeds_mask",description:`<strong>prompt_embeds_mask</strong> (<code>torch.Tensor</code>, <em>optional</em>) &#x2014;
Pre-generated boolean mask marking valid text tokens, of shape <code>(batch_size, text_seq_len)</code>. Required
when <code>prompt_embeds</code> is passed.`,name:"prompt_embeds_mask"},{anchor:"diffusers.Krea2Pipeline.encode_prompt.max_sequence_length",description:`<strong>max_sequence_length</strong> (<code>int</code>, defaults to 512) &#x2014;
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