diffusers/pr_13751/en/api/pipelines/anima.md

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Anima

Anima is a text-to-image model that reuses the CosmosTransformer3DModel with a Qwen3 text encoder, a T5-token text conditioner, and the AutoencoderKLQwenImage VAE.

import torch
from diffusers import ModularPipeline

pipe = ModularPipeline.from_pretrained("circlestone-labs/Anima-Base-v1.0-Diffusers")
pipe.load_components(torch_dtype=torch.bfloat16)
pipe.to("cuda")

image = pipe(prompt="masterpiece, best quality, 1girl, solo, city lights").images[0]

AnimaModularPipeline[[diffusers.AnimaModularPipeline]]

diffusers.AnimaModularPipeline[[diffusers.AnimaModularPipeline]]

Source

A ModularPipeline for Anima.

> This is an experimental feature and is likely to change in the future.

AnimaAutoBlocks[[diffusers.AnimaAutoBlocks]]

diffusers.AnimaAutoBlocks[[diffusers.AnimaAutoBlocks]]

Source

Auto Modular pipeline for text-to-image generation using Anima.

Supported workflows:

• text2image: requires prompt

Components: text_encoder (Qwen3Model) tokenizer (Qwen2Tokenizer) t5_tokenizer (T5TokenizerFast) text_conditioner (AnimaTextConditioner) guider (ClassifierFreeGuidance) transformer (CosmosTransformer3DModel) scheduler (FlowMatchEulerDiscreteScheduler) vae (AutoencoderKLQwenImage) image_processor (VaeImageProcessor)

Inputs: prompt (str): The prompt or prompts to guide image generation. negative_prompt (str, optional): The prompt or prompts not to guide the image generation. max_sequence_length (int, optional, defaults to 512): Maximum sequence length for prompt encoding. num_images_per_prompt (int, optional, defaults to 1): The number of images to generate per prompt. height (int, optional): The height in pixels of the generated image. width (int, optional): The width in pixels of the generated image. latents (Tensor, optional): Pre-generated noisy latents for image generation. generator (Generator, optional): Torch generator for deterministic generation. num_inference_steps (int, optional, defaults to 50): The number of denoising steps. sigmas (list, optional): Custom sigmas for the denoising process. **denoiser_input_fields (None, optional): The conditional model inputs for the Anima denoiser. output_type (str, optional, defaults to pil): Output format: 'pil', 'np', 'pt'.

Outputs: images (list): Generated images.

AnimaTextConditioner[[diffusers.AnimaTextConditioner]]

diffusers.AnimaTextConditioner[[diffusers.AnimaTextConditioner]]

Source

Text conditioner used by Anima to map Qwen3 hidden states and T5 token ids to Cosmos text embeddings.

Anima reuses the Cosmos Predict2 DiT. The only model-specific conditioning module is this LLM adapter, which cross-attends from learned T5 token embeddings to Qwen3 text encoder hidden states before the diffusion loop. target_dim is the conditioner output dimension and must match the transformer's text_embed_dim.

forwarddiffusers.AnimaTextConditioner.forwardhttps://github.com/huggingface/diffusers/blob/vr_13751/src/diffusers/models/condition_embedders/condition_embedder_anima.py#L285[{"name": "source_hidden_states", "val": ": Tensor"}, {"name": "target_input_ids", "val": ": Tensor"}, {"name": "target_attention_mask", "val": ": torch.Tensor | None = None"}, {"name": "source_attention_mask", "val": ": torch.Tensor | None = None"}]- source_hidden_states (torch.Tensor of shape (batch_size, source_sequence_length, source_dim)) -- Qwen3 text encoder hidden states to condition on.

• target_input_ids (torch.Tensor of shape (batch_size, target_sequence_length)) -- T5 token ids used as learned query tokens.
• target_attention_mask (torch.Tensor, optional) -- Attention mask for the target T5 token ids.
• source_attention_mask (torch.Tensor, optional) -- Attention mask for the source Qwen3 hidden states.0torch.TensorText conditioning embeddings for the Cosmos transformer.

Parameters:

source_hidden_states (torch.Tensor of shape (batch_size, source_sequence_length, source_dim)) : Qwen3 text encoder hidden states to condition on.

target_input_ids (torch.Tensor of shape (batch_size, target_sequence_length)) : T5 token ids used as learned query tokens.

target_attention_mask (torch.Tensor, optional) : Attention mask for the target T5 token ids.

source_attention_mask (torch.Tensor, optional) : Attention mask for the source Qwen3 hidden states.

Returns:

torch.Tensor

Text conditioning embeddings for the Cosmos transformer.