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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..modular_pipeline import AutoPipelineBlocks, SequentialPipelineBlocks
from ..modular_pipeline_utils import OutputParam
from .before_denoise import (
AnimaImageInputStep,
AnimaImg2ImgPrepareLatentsStep,
AnimaImg2ImgSetTimestepsStep,
AnimaPrepareLatentsStep,
AnimaSetTimestepsStep,
AnimaTextConditioningStep,
AnimaTextInputStep,
)
from .decoders import AnimaProcessImagesOutputStep, AnimaVaeDecoderStep
from .denoise import AnimaDenoiseStep
from .encoders import AnimaImg2ImgVaeEncoderStep, AnimaTextEncoderStep
# auto_docstring
class AnimaCoreDenoiseStep(SequentialPipelineBlocks):
"""
Denoise block that takes encoded Anima text inputs and runs the denoising process.
Components:
text_conditioner (`AnimaTextConditioner`) transformer (`CosmosTransformer3DModel`) scheduler
(`FlowMatchEulerDiscreteScheduler`) guider (`ClassifierFreeGuidance`)
Inputs:
qwen_prompt_embeds (`Tensor`):
Qwen prompt embeddings generated by the text encoder step.
qwen_attention_mask (`Tensor`):
Qwen prompt attention mask generated by the text encoder step.
t5_input_ids (`Tensor`):
T5 prompt token ids generated by the text encoder step.
t5_attention_mask (`Tensor`):
T5 prompt attention mask generated by the text encoder step.
negative_qwen_prompt_embeds (`Tensor`, *optional*):
Negative Qwen prompt embeddings generated by the text encoder step.
negative_qwen_attention_mask (`Tensor`, *optional*):
Negative Qwen prompt attention mask generated by the text encoder step.
negative_t5_input_ids (`Tensor`, *optional*):
Negative T5 prompt token ids generated by the text encoder step.
negative_t5_attention_mask (`Tensor`, *optional*):
Negative T5 prompt attention mask generated by the text encoder step.
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.
Outputs:
latents (`Tensor`):
Denoised latents.
"""
block_classes = [
AnimaTextConditioningStep,
AnimaTextInputStep,
AnimaPrepareLatentsStep,
AnimaSetTimestepsStep,
AnimaDenoiseStep,
]
block_names = ["text_conditioning", "input", "prepare_latents", "set_timesteps", "denoise"]
@property
def description(self) -> str:
return "Denoise block that takes encoded Anima text inputs and runs the denoising process."
@property
def outputs(self):
return [OutputParam.template("latents")]
# auto_docstring
class AnimaDecodeStep(SequentialPipelineBlocks):
"""
Decode Anima latents into generated images.
Components:
vae (`AutoencoderKLQwenImage`) image_processor (`VaeImageProcessor`)
Inputs:
latents (`Tensor`):
Denoised Anima latents.
output_type (`str`, *optional*, defaults to pil):
Output format: 'pil', 'np', 'pt'.
Outputs:
images (`list`):
Generated images.
"""
block_classes = [AnimaVaeDecoderStep, AnimaProcessImagesOutputStep]
block_names = ["decode", "postprocess"]
@property
def description(self) -> str:
return "Decode Anima latents into generated images."
@property
def outputs(self):
return [OutputParam.template("images")]
# auto_docstring
class AnimaImg2ImgCoreDenoiseStep(SequentialPipelineBlocks):
"""
Denoise block for Anima image-to-image generation. Uses image_latents already in state from
AnimaImg2ImgVaeEncoderStep.
Components:
text_conditioner (`AnimaTextConditioner`) transformer (`CosmosTransformer3DModel`) scheduler
(`FlowMatchEulerDiscreteScheduler`) guider (`ClassifierFreeGuidance`)
Inputs:
qwen_prompt_embeds (`Tensor`):
Qwen prompt embeddings generated by the text encoder step.
qwen_attention_mask (`Tensor`):
Qwen prompt attention mask generated by the text encoder step.
t5_input_ids (`Tensor`):
T5 prompt token ids generated by the text encoder step.
t5_attention_mask (`Tensor`):
T5 prompt attention mask generated by the text encoder step.
negative_qwen_prompt_embeds (`Tensor`, *optional*):
Negative Qwen prompt embeddings generated by the text encoder step.
negative_qwen_attention_mask (`Tensor`, *optional*):
Negative Qwen prompt attention mask generated by the text encoder step.
negative_t5_input_ids (`Tensor`, *optional*):
Negative T5 prompt token ids generated by the text encoder step.
negative_t5_attention_mask (`Tensor`, *optional*):
Negative T5 prompt attention mask generated by the text encoder step.
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
image_latents (`Tensor`):
image latents used to guide the image generation. Can be generated from vae_encoder step.
height (`int`, *optional*):
The height in pixels of the generated image.
width (`int`, *optional*):
The width in pixels of the generated image.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps.
sigmas (`list`, *optional*):
Custom sigmas for the denoising process.
strength (`float`, *optional*, defaults to 0.9):
Strength for img2img/inpainting.
generator (`Generator`, *optional*):
Torch generator for deterministic generation.
latents (`Tensor`, *optional*):
Pre-generated noisy latents for image generation.
**denoiser_input_fields (`None`, *optional*):
The conditional model inputs for the Anima denoiser.
Outputs:
latents (`Tensor`):
Denoised latents.
"""
block_classes = [
AnimaTextConditioningStep,
AnimaTextInputStep,
AnimaImageInputStep,
AnimaImg2ImgSetTimestepsStep,
AnimaImg2ImgPrepareLatentsStep,
AnimaDenoiseStep,
]
block_names = ["text_conditioning", "input", "image_input", "set_timesteps", "prepare_latents", "denoise"]
@property
def description(self) -> str:
return (
"Denoise block for Anima image-to-image generation. "
"Uses image_latents already in state from AnimaImg2ImgVaeEncoderStep."
)
@property
def outputs(self):
return [OutputParam.template("latents")]
# auto_docstring
class AnimaAutoCoreDenoiseStep(AutoPipelineBlocks):
"""
Denoise step that selects between text-to-image and image-to-image denoising based on whether image_latents is
present in state. - `AnimaCoreDenoiseStep` (text2image) is used when no image_latents are present. -
`AnimaImg2ImgCoreDenoiseStep` (img2img) is used when image_latents are present.
Components:
text_conditioner (`AnimaTextConditioner`) transformer (`CosmosTransformer3DModel`) scheduler
(`FlowMatchEulerDiscreteScheduler`) guider (`ClassifierFreeGuidance`)
Inputs:
qwen_prompt_embeds (`Tensor`):
Qwen prompt embeddings generated by the text encoder step.
qwen_attention_mask (`Tensor`):
Qwen prompt attention mask generated by the text encoder step.
t5_input_ids (`Tensor`):
T5 prompt token ids generated by the text encoder step.
t5_attention_mask (`Tensor`):
T5 prompt attention mask generated by the text encoder step.
negative_qwen_prompt_embeds (`Tensor`, *optional*):
Negative Qwen prompt embeddings generated by the text encoder step.
negative_qwen_attention_mask (`Tensor`, *optional*):
Negative Qwen prompt attention mask generated by the text encoder step.
negative_t5_input_ids (`Tensor`, *optional*):
Negative T5 prompt token ids generated by the text encoder step.
negative_t5_attention_mask (`Tensor`, *optional*):
Negative T5 prompt attention mask generated by the text encoder step.
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
image_latents (`Tensor`, *optional*):
image latents used to guide the image generation. Can be generated from vae_encoder step.
height (`int`, *optional*):
The height in pixels of the generated image.
width (`int`, *optional*):
The width in pixels of the generated image.
num_inference_steps (`int`):
The number of denoising steps.
sigmas (`list`, *optional*):
Custom sigmas for the denoising process.
strength (`float`, *optional*, defaults to 0.9):
Strength for img2img/inpainting.
generator (`Generator`, *optional*):
Torch generator for deterministic generation.
latents (`Tensor`):
Pre-generated noisy latents for image generation.
**denoiser_input_fields (`None`, *optional*):
The conditional model inputs for the Anima denoiser.
Outputs:
latents (`Tensor`):
Denoised latents.
"""
block_classes = [AnimaImg2ImgCoreDenoiseStep, AnimaCoreDenoiseStep]
block_names = ["img2img", "text2image"]
block_trigger_inputs = ["image_latents", None]
@property
def description(self) -> str:
return (
"Denoise step that selects between text-to-image and image-to-image denoising based on whether "
"image_latents is present in state."
" - `AnimaCoreDenoiseStep` (text2image) is used when no image_latents are present."
" - `AnimaImg2ImgCoreDenoiseStep` (img2img) is used when image_latents are present."
)
# auto_docstring
class AnimaAutoVaeImageEncoderStep(AutoPipelineBlocks):
"""
VAE Image Encoder step that encodes the input image to produce image_latents. Skipped when no image is provided
(text-to-image workflow).
Components:
vae (`AutoencoderKLQwenImage`) image_processor (`VaeImageProcessor`)
Inputs:
image (`Image | list`, *optional*):
Reference image(s) for denoising. Can be a single image or list of images.
height (`int`, *optional*):
The height in pixels of the generated image.
width (`int`, *optional*):
The width in pixels of the generated image.
generator (`Generator`, *optional*):
Torch generator for deterministic generation.
Outputs:
image_latents (`Tensor`):
Encoded image latents.
height (`int`):
Image height used for generation.
width (`int`):
Image width used for generation.
"""
block_classes = [AnimaImg2ImgVaeEncoderStep]
block_names = ["vae_encoder"]
block_trigger_inputs = ["image"]
@property
def description(self) -> str:
return (
"VAE Image Encoder step that encodes the input image to produce image_latents. "
"Skipped when no image is provided (text-to-image workflow)."
)
# auto_docstring
class AnimaAutoBlocks(SequentialPipelineBlocks):
"""
Auto Modular pipeline for text-to-image and image-to-image generation using Anima.
Supported workflows:
- `text2image`: requires `prompt`
- `img2img`: requires `image`, `prompt`
Components:
text_encoder (`Qwen3Model`) tokenizer (`Qwen2Tokenizer`) t5_tokenizer (`T5Tokenizer`) guider
(`ClassifierFreeGuidance`) vae (`AutoencoderKLQwenImage`) image_processor (`VaeImageProcessor`)
text_conditioner (`AnimaTextConditioner`) transformer (`CosmosTransformer3DModel`) scheduler
(`FlowMatchEulerDiscreteScheduler`)
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.
image (`Image | list`, *optional*):
Reference image(s) for denoising. Can be a single image or list of images.
height (`int`, *optional*):
The height in pixels of the generated image.
width (`int`, *optional*):
The width in pixels of the generated image.
generator (`Generator`, *optional*):
Torch generator for deterministic generation.
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
image_latents (`Tensor`, *optional*):
image latents used to guide the image generation. Can be generated from vae_encoder step.
num_inference_steps (`int`):
The number of denoising steps.
sigmas (`list`, *optional*):
Custom sigmas for the denoising process.
strength (`float`, *optional*, defaults to 0.9):
Strength for img2img/inpainting.
latents (`Tensor`):
Pre-generated noisy latents for image generation.
**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.
"""
block_classes = [
AnimaTextEncoderStep,
AnimaAutoVaeImageEncoderStep,
AnimaAutoCoreDenoiseStep,
AnimaDecodeStep,
]
block_names = ["text_encoder", "vae_encoder", "denoise", "decode"]
_workflow_map = {
"text2image": {"prompt": True},
"img2img": {"image": True, "prompt": True},
}
@property
def description(self) -> str:
return "Auto Modular pipeline for text-to-image and image-to-image generation using Anima."
@property
def outputs(self):
return [OutputParam.template("images")]
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