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[Community] PromptDiffusion Pipeline (#6752)
* Create promptdiffusioncontrolnet.py * Update __init__.py Added PromptDiffusionControlNetModel * Update __init__.py Added PromptDiffusionControlNetModel * Update promptdiffusioncontrolnet.py * Create pipeline_prompt_diffusion.py Added Prompt Diffusion pipeline. * Create convert_original_promptdiffusion_to_diffusers.py * Update convert_from_ckpt.py Added download_promptdiffusion_from_original_ckpt, convert_promptdiffusion_checkpoint * Update promptdiffusioncontrolnet.py * Update pipeline_prompt_diffusion.py * Update README.md * Update pipeline_prompt_diffusion.py * Delete src/diffusers/models/promptdiffusioncontrolnet.py * Update __init__.py * Update __init__.py * Delete scripts/convert_original_promptdiffusion_to_diffusers.py * Update convert_from_ckpt.py * Update README.md * Delete examples/community/pipeline_prompt_diffusion.py * Create README.md * Create promptdiffusioncontrolnet.py * Create convert_original_promptdiffusion_to_diffusers.py * Create pipeline_prompt_diffusion.py * Update README.md * Update pipeline_prompt_diffusion.py * Update README.md * Update pipeline_prompt_diffusion.py * Update convert_original_promptdiffusion_to_diffusers.py * Update promptdiffusioncontrolnet.py * Update README.md --------- Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
This commit is contained in:
iczaw
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examples/research_projects/promptdiffusion/README.md
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examples/research_projects/promptdiffusion/README.md
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# PromptDiffusion Pipeline
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From the project [page](https://zhendong-wang.github.io/prompt-diffusion.github.io/)
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"With a prompt consisting of a task-specific example pair of images and text guidance, and a new query image, Prompt Diffusion can comprehend the desired task and generate the corresponding output image on both seen (trained) and unseen (new) task types."
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For any usage questions, please refer to the [paper](https://arxiv.org/abs/2305.01115).
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Prepare models by converting them from the [checkpoint](https://huggingface.co/zhendongw/prompt-diffusion)
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To convert the controlnet, use cldm_v15.yaml from the [repository](https://github.com/Zhendong-Wang/Prompt-Diffusion/tree/main/models/):
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```bash
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python convert_original_promptdiffusion_to_diffusers.py --checkpoint_path path-to-network-step04999.ckpt --original_config_file path-to-cldm_v15.yaml --dump_path path-to-output-directory
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```
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To learn about how to convert the fine-tuned stable diffusion model, see the [Load different Stable Diffusion formats guide](https://huggingface.co/docs/diffusers/main/en/using-diffusers/other-formats).
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```py
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import torch
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from diffusers import UniPCMultistepScheduler
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from diffusers.utils import load_image
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from promptdiffusioncontrolnet import PromptDiffusionControlNetModel
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from pipeline_prompt_diffusion import PromptDiffusionPipeline
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from PIL import ImageOps
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image_a = ImageOps.invert(load_image("https://github.com/Zhendong-Wang/Prompt-Diffusion/blob/main/images_to_try/house_line.png?raw=true"))
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image_b = load_image("https://github.com/Zhendong-Wang/Prompt-Diffusion/blob/main/images_to_try/house.png?raw=true")
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query = ImageOps.invert(load_image("https://github.com/Zhendong-Wang/Prompt-Diffusion/blob/main/images_to_try/new_01.png?raw=true"))
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# load prompt diffusion controlnet and prompt diffusion
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controlnet = PromptDiffusionControlNetModel.from_pretrained("iczaw/prompt-diffusion-diffusers", subfolder="controlnet", torch_dtype=torch.float16)
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model_id = "path-to-model"
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pipe = PromptDiffusionPipeline.from_pretrained("iczaw/prompt-diffusion-diffusers", subfolder="base", controlnet=controlnet, torch_dtype=torch.float16, variant="fp16")
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# speed up diffusion process with faster scheduler and memory optimization
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pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
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# remove following line if xformers is not installed
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pipe.enable_xformers_memory_efficient_attention()
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pipe.enable_model_cpu_offload()
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# generate image
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generator = torch.manual_seed(0)
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image = pipe("a tortoise", num_inference_steps=20, generator=generator, image_pair=[image_a,image_b], image=query).images[0]
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```
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# Copyright 2023 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import Any, Dict, Optional, Tuple, Union
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import torch
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from diffusers.configuration_utils import register_to_config
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from diffusers.models.controlnet import (
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ControlNetConditioningEmbedding,
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ControlNetModel,
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ControlNetOutput,
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)
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from diffusers.utils import logging
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logger = logging.get_logger(__name__) # pylint: disable=invalid-name
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class PromptDiffusionControlNetModel(ControlNetModel):
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"""
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A PromptDiffusionControlNet model.
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Args:
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in_channels (`int`, defaults to 4):
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The number of channels in the input sample.
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flip_sin_to_cos (`bool`, defaults to `True`):
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Whether to flip the sin to cos in the time embedding.
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freq_shift (`int`, defaults to 0):
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The frequency shift to apply to the time embedding.
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down_block_types (`tuple[str]`, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`):
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The tuple of downsample blocks to use.
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only_cross_attention (`Union[bool, Tuple[bool]]`, defaults to `False`):
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block_out_channels (`tuple[int]`, defaults to `(320, 640, 1280, 1280)`):
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The tuple of output channels for each block.
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layers_per_block (`int`, defaults to 2):
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The number of layers per block.
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downsample_padding (`int`, defaults to 1):
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The padding to use for the downsampling convolution.
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mid_block_scale_factor (`float`, defaults to 1):
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The scale factor to use for the mid block.
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act_fn (`str`, defaults to "silu"):
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The activation function to use.
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norm_num_groups (`int`, *optional*, defaults to 32):
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The number of groups to use for the normalization. If None, normalization and activation layers is skipped
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in post-processing.
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norm_eps (`float`, defaults to 1e-5):
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The epsilon to use for the normalization.
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cross_attention_dim (`int`, defaults to 1280):
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The dimension of the cross attention features.
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transformer_layers_per_block (`int` or `Tuple[int]`, *optional*, defaults to 1):
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The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`]. Only relevant for
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[`~models.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unet_2d_blocks.CrossAttnUpBlock2D`],
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[`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`].
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encoder_hid_dim (`int`, *optional*, defaults to None):
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If `encoder_hid_dim_type` is defined, `encoder_hidden_states` will be projected from `encoder_hid_dim`
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dimension to `cross_attention_dim`.
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encoder_hid_dim_type (`str`, *optional*, defaults to `None`):
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If given, the `encoder_hidden_states` and potentially other embeddings are down-projected to text
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embeddings of dimension `cross_attention` according to `encoder_hid_dim_type`.
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attention_head_dim (`Union[int, Tuple[int]]`, defaults to 8):
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The dimension of the attention heads.
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use_linear_projection (`bool`, defaults to `False`):
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class_embed_type (`str`, *optional*, defaults to `None`):
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The type of class embedding to use which is ultimately summed with the time embeddings. Choose from None,
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`"timestep"`, `"identity"`, `"projection"`, or `"simple_projection"`.
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addition_embed_type (`str`, *optional*, defaults to `None`):
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Configures an optional embedding which will be summed with the time embeddings. Choose from `None` or
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"text". "text" will use the `TextTimeEmbedding` layer.
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num_class_embeds (`int`, *optional*, defaults to 0):
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Input dimension of the learnable embedding matrix to be projected to `time_embed_dim`, when performing
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class conditioning with `class_embed_type` equal to `None`.
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upcast_attention (`bool`, defaults to `False`):
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resnet_time_scale_shift (`str`, defaults to `"default"`):
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Time scale shift config for ResNet blocks (see `ResnetBlock2D`). Choose from `default` or `scale_shift`.
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projection_class_embeddings_input_dim (`int`, *optional*, defaults to `None`):
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The dimension of the `class_labels` input when `class_embed_type="projection"`. Required when
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`class_embed_type="projection"`.
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controlnet_conditioning_channel_order (`str`, defaults to `"rgb"`):
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The channel order of conditional image. Will convert to `rgb` if it's `bgr`.
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conditioning_embedding_out_channels (`tuple[int]`, *optional*, defaults to `(16, 32, 96, 256)`):
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The tuple of output channel for each block in the `conditioning_embedding` layer.
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global_pool_conditions (`bool`, defaults to `False`):
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TODO(Patrick) - unused parameter.
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addition_embed_type_num_heads (`int`, defaults to 64):
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The number of heads to use for the `TextTimeEmbedding` layer.
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"""
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_supports_gradient_checkpointing = True
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@register_to_config
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def __init__(
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self,
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in_channels: int = 4,
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conditioning_channels: int = 3,
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flip_sin_to_cos: bool = True,
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freq_shift: int = 0,
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down_block_types: Tuple[str, ...] = (
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"CrossAttnDownBlock2D",
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"CrossAttnDownBlock2D",
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"CrossAttnDownBlock2D",
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"DownBlock2D",
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),
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mid_block_type: Optional[str] = "UNetMidBlock2DCrossAttn",
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only_cross_attention: Union[bool, Tuple[bool]] = False,
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block_out_channels: Tuple[int, ...] = (320, 640, 1280, 1280),
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layers_per_block: int = 2,
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downsample_padding: int = 1,
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mid_block_scale_factor: float = 1,
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act_fn: str = "silu",
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norm_num_groups: Optional[int] = 32,
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norm_eps: float = 1e-5,
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cross_attention_dim: int = 1280,
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transformer_layers_per_block: Union[int, Tuple[int, ...]] = 1,
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encoder_hid_dim: Optional[int] = None,
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encoder_hid_dim_type: Optional[str] = None,
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attention_head_dim: Union[int, Tuple[int, ...]] = 8,
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num_attention_heads: Optional[Union[int, Tuple[int, ...]]] = None,
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use_linear_projection: bool = False,
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class_embed_type: Optional[str] = None,
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addition_embed_type: Optional[str] = None,
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addition_time_embed_dim: Optional[int] = None,
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num_class_embeds: Optional[int] = None,
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upcast_attention: bool = False,
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resnet_time_scale_shift: str = "default",
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projection_class_embeddings_input_dim: Optional[int] = None,
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controlnet_conditioning_channel_order: str = "rgb",
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conditioning_embedding_out_channels: Optional[Tuple[int, ...]] = (16, 32, 96, 256),
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global_pool_conditions: bool = False,
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addition_embed_type_num_heads: int = 64,
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):
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super().__init__(
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in_channels,
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conditioning_channels,
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flip_sin_to_cos,
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freq_shift,
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down_block_types,
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mid_block_type,
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only_cross_attention,
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block_out_channels,
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layers_per_block,
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downsample_padding,
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mid_block_scale_factor,
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act_fn,
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norm_num_groups,
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norm_eps,
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cross_attention_dim,
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transformer_layers_per_block,
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encoder_hid_dim,
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encoder_hid_dim_type,
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attention_head_dim,
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num_attention_heads,
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use_linear_projection,
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class_embed_type,
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addition_embed_type,
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addition_time_embed_dim,
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num_class_embeds,
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upcast_attention,
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resnet_time_scale_shift,
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projection_class_embeddings_input_dim,
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controlnet_conditioning_channel_order,
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conditioning_embedding_out_channels,
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global_pool_conditions,
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addition_embed_type_num_heads,
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)
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self.controlnet_query_cond_embedding = ControlNetConditioningEmbedding(
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conditioning_embedding_channels=block_out_channels[0],
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block_out_channels=conditioning_embedding_out_channels,
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conditioning_channels=3,
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)
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def forward(
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self,
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sample: torch.FloatTensor,
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timestep: Union[torch.Tensor, float, int],
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encoder_hidden_states: torch.Tensor,
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controlnet_cond: torch.FloatTensor,
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controlnet_query_cond: torch.FloatTensor,
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conditioning_scale: float = 1.0,
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class_labels: Optional[torch.Tensor] = None,
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timestep_cond: Optional[torch.Tensor] = None,
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attention_mask: Optional[torch.Tensor] = None,
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added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
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cross_attention_kwargs: Optional[Dict[str, Any]] = None,
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guess_mode: bool = False,
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return_dict: bool = True,
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) -> Union[ControlNetOutput, Tuple[Tuple[torch.FloatTensor, ...], torch.FloatTensor]]:
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"""
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The [`~PromptDiffusionControlNetModel`] forward method.
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Args:
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sample (`torch.FloatTensor`):
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The noisy input tensor.
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timestep (`Union[torch.Tensor, float, int]`):
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The number of timesteps to denoise an input.
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encoder_hidden_states (`torch.Tensor`):
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The encoder hidden states.
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controlnet_cond (`torch.FloatTensor`):
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The conditional input tensor of shape `(batch_size, sequence_length, hidden_size)`.
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controlnet_query_cond (`torch.FloatTensor`):
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The conditional input tensor of shape `(batch_size, sequence_length, hidden_size)`.
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conditioning_scale (`float`, defaults to `1.0`):
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The scale factor for ControlNet outputs.
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class_labels (`torch.Tensor`, *optional*, defaults to `None`):
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Optional class labels for conditioning. Their embeddings will be summed with the timestep embeddings.
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timestep_cond (`torch.Tensor`, *optional*, defaults to `None`):
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Additional conditional embeddings for timestep. If provided, the embeddings will be summed with the
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timestep_embedding passed through the `self.time_embedding` layer to obtain the final timestep
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embeddings.
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attention_mask (`torch.Tensor`, *optional*, defaults to `None`):
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An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask
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is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large
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negative values to the attention scores corresponding to "discard" tokens.
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added_cond_kwargs (`dict`):
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Additional conditions for the Stable Diffusion XL UNet.
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cross_attention_kwargs (`dict[str]`, *optional*, defaults to `None`):
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A kwargs dictionary that if specified is passed along to the `AttnProcessor`.
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guess_mode (`bool`, defaults to `False`):
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In this mode, the ControlNet encoder tries its best to recognize the input content of the input even if
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you remove all prompts. A `guidance_scale` between 3.0 and 5.0 is recommended.
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return_dict (`bool`, defaults to `True`):
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Whether or not to return a [`~models.controlnet.ControlNetOutput`] instead of a plain tuple.
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Returns:
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[`~models.controlnet.ControlNetOutput`] **or** `tuple`:
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If `return_dict` is `True`, a [`~models.controlnet.ControlNetOutput`] is returned, otherwise a tuple is
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returned where the first element is the sample tensor.
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"""
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# check channel order
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channel_order = self.config.controlnet_conditioning_channel_order
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if channel_order == "rgb":
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# in rgb order by default
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...
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elif channel_order == "bgr":
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controlnet_cond = torch.flip(controlnet_cond, dims=[1])
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else:
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raise ValueError(f"unknown `controlnet_conditioning_channel_order`: {channel_order}")
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# prepare attention_mask
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if attention_mask is not None:
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attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0
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attention_mask = attention_mask.unsqueeze(1)
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# 1. time
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timesteps = timestep
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if not torch.is_tensor(timesteps):
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# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
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# This would be a good case for the `match` statement (Python 3.10+)
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is_mps = sample.device.type == "mps"
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if isinstance(timestep, float):
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dtype = torch.float32 if is_mps else torch.float64
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else:
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dtype = torch.int32 if is_mps else torch.int64
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timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
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elif len(timesteps.shape) == 0:
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timesteps = timesteps[None].to(sample.device)
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# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
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timesteps = timesteps.expand(sample.shape[0])
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t_emb = self.time_proj(timesteps)
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# timesteps does not contain any weights and will always return f32 tensors
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# but time_embedding might actually be running in fp16. so we need to cast here.
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# there might be better ways to encapsulate this.
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t_emb = t_emb.to(dtype=sample.dtype)
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emb = self.time_embedding(t_emb, timestep_cond)
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aug_emb = None
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if self.class_embedding is not None:
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if class_labels is None:
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raise ValueError("class_labels should be provided when num_class_embeds > 0")
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if self.config.class_embed_type == "timestep":
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class_labels = self.time_proj(class_labels)
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class_emb = self.class_embedding(class_labels).to(dtype=self.dtype)
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emb = emb + class_emb
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if self.config.addition_embed_type is not None:
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if self.config.addition_embed_type == "text":
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aug_emb = self.add_embedding(encoder_hidden_states)
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elif self.config.addition_embed_type == "text_time":
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if "text_embeds" not in added_cond_kwargs:
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raise ValueError(
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f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `text_embeds` to be passed in `added_cond_kwargs`"
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)
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text_embeds = added_cond_kwargs.get("text_embeds")
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if "time_ids" not in added_cond_kwargs:
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raise ValueError(
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f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `time_ids` to be passed in `added_cond_kwargs`"
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)
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time_ids = added_cond_kwargs.get("time_ids")
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time_embeds = self.add_time_proj(time_ids.flatten())
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time_embeds = time_embeds.reshape((text_embeds.shape[0], -1))
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add_embeds = torch.concat([text_embeds, time_embeds], dim=-1)
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add_embeds = add_embeds.to(emb.dtype)
|
||||
aug_emb = self.add_embedding(add_embeds)
|
||||
|
||||
emb = emb + aug_emb if aug_emb is not None else emb
|
||||
|
||||
# 2. pre-process
|
||||
sample = self.conv_in(sample)
|
||||
|
||||
controlnet_cond = self.controlnet_cond_embedding(controlnet_cond)
|
||||
controlnet_query_cond = self.controlnet_query_cond_embedding(controlnet_query_cond)
|
||||
sample = sample + controlnet_cond + controlnet_query_cond
|
||||
|
||||
# 3. down
|
||||
down_block_res_samples = (sample,)
|
||||
for downsample_block in self.down_blocks:
|
||||
if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
|
||||
sample, res_samples = downsample_block(
|
||||
hidden_states=sample,
|
||||
temb=emb,
|
||||
encoder_hidden_states=encoder_hidden_states,
|
||||
attention_mask=attention_mask,
|
||||
cross_attention_kwargs=cross_attention_kwargs,
|
||||
)
|
||||
else:
|
||||
sample, res_samples = downsample_block(hidden_states=sample, temb=emb)
|
||||
|
||||
down_block_res_samples += res_samples
|
||||
|
||||
# 4. mid
|
||||
if self.mid_block is not None:
|
||||
if hasattr(self.mid_block, "has_cross_attention") and self.mid_block.has_cross_attention:
|
||||
sample = self.mid_block(
|
||||
sample,
|
||||
emb,
|
||||
encoder_hidden_states=encoder_hidden_states,
|
||||
attention_mask=attention_mask,
|
||||
cross_attention_kwargs=cross_attention_kwargs,
|
||||
)
|
||||
else:
|
||||
sample = self.mid_block(sample, emb)
|
||||
|
||||
# 5. Control net blocks
|
||||
|
||||
controlnet_down_block_res_samples = ()
|
||||
|
||||
for down_block_res_sample, controlnet_block in zip(down_block_res_samples, self.controlnet_down_blocks):
|
||||
down_block_res_sample = controlnet_block(down_block_res_sample)
|
||||
controlnet_down_block_res_samples = controlnet_down_block_res_samples + (down_block_res_sample,)
|
||||
|
||||
down_block_res_samples = controlnet_down_block_res_samples
|
||||
|
||||
mid_block_res_sample = self.controlnet_mid_block(sample)
|
||||
|
||||
# 6. scaling
|
||||
if guess_mode and not self.config.global_pool_conditions:
|
||||
scales = torch.logspace(-1, 0, len(down_block_res_samples) + 1, device=sample.device) # 0.1 to 1.0
|
||||
scales = scales * conditioning_scale
|
||||
down_block_res_samples = [sample * scale for sample, scale in zip(down_block_res_samples, scales)]
|
||||
mid_block_res_sample = mid_block_res_sample * scales[-1] # last one
|
||||
else:
|
||||
down_block_res_samples = [sample * conditioning_scale for sample in down_block_res_samples]
|
||||
mid_block_res_sample = mid_block_res_sample * conditioning_scale
|
||||
|
||||
if self.config.global_pool_conditions:
|
||||
down_block_res_samples = [
|
||||
torch.mean(sample, dim=(2, 3), keepdim=True) for sample in down_block_res_samples
|
||||
]
|
||||
mid_block_res_sample = torch.mean(mid_block_res_sample, dim=(2, 3), keepdim=True)
|
||||
|
||||
if not return_dict:
|
||||
return (down_block_res_samples, mid_block_res_sample)
|
||||
|
||||
return ControlNetOutput(
|
||||
down_block_res_samples=down_block_res_samples, mid_block_res_sample=mid_block_res_sample
|
||||
)
|
||||
Reference in New Issue
Block a user