# # 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. --> # CogView4 > [!TIP] > Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines. This pipeline was contributed by [zRzRzRzRzRzRzR](https://github.com/zRzRzRzRzRzRzR). The original codebase can be found [here](https://huggingface.co/THUDM). The original weights can be found under [hf.co/THUDM](https://huggingface.co/THUDM). ## CogView4Pipeline[[diffusers.CogView4Pipeline]] #### diffusers.CogView4Pipeline[[diffusers.CogView4Pipeline]] [Source](https://github.com/huggingface/diffusers/blob/v0.37.0/src/diffusers/pipelines/cogview4/pipeline_cogview4.py#L137) Pipeline for text-to-image generation using CogView4. This model inherits from [DiffusionPipeline](/docs/diffusers/v0.37.0/en/api/pipelines/overview#diffusers.DiffusionPipeline). Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) __call__diffusers.CogView4Pipeline.__call__https://github.com/huggingface/diffusers/blob/v0.37.0/src/diffusers/pipelines/cogview4/pipeline_cogview4.py#L402[{"name": "prompt", "val": ": str | list[str] | None = None"}, {"name": "negative_prompt", "val": ": str | list[str] | None = None"}, {"name": "height", "val": ": int | None = None"}, {"name": "width", "val": ": int | None = None"}, {"name": "num_inference_steps", "val": ": int = 50"}, {"name": "timesteps", "val": ": list[int] | None = None"}, {"name": "sigmas", "val": ": list[float] | None = None"}, {"name": "guidance_scale", "val": ": float = 5.0"}, {"name": "num_images_per_prompt", "val": ": int = 1"}, {"name": "generator", "val": ": torch._C.Generator | list[torch._C.Generator] | None = None"}, {"name": "latents", "val": ": torch.FloatTensor | None = None"}, {"name": "prompt_embeds", "val": ": torch.FloatTensor | None = None"}, {"name": "negative_prompt_embeds", "val": ": torch.FloatTensor | None = None"}, {"name": "original_size", "val": ": tuple[int, int] | None = None"}, {"name": "crops_coords_top_left", "val": ": tuple = (0, 0)"}, {"name": "output_type", "val": ": str = 'pil'"}, {"name": "return_dict", "val": ": bool = True"}, {"name": "attention_kwargs", "val": ": dict[str, typing.Any] | None = None"}, {"name": "callback_on_step_end", "val": ": typing.Union[typing.Callable[[int, int], NoneType], diffusers.callbacks.PipelineCallback, diffusers.callbacks.MultiPipelineCallbacks, NoneType] = None"}, {"name": "callback_on_step_end_tensor_inputs", "val": ": list = ['latents']"}, {"name": "max_sequence_length", "val": ": int = 1024"}]- **prompt** (`str` or `list[str]`, *optional*) -- The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. - **negative_prompt** (`str` or `list[str]`, *optional*) -- The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). - **height** (`int`, *optional*, defaults to self.transformer.config.sample_size * self.vae_scale_factor) -- The height in pixels of the generated image. If not provided, it is set to 1024. - **width** (`int`, *optional*, defaults to self.transformer.config.sample_size * self.vae_scale_factor) -- The width in pixels of the generated image. If not provided it is set to 1024. - **num_inference_steps** (`int`, *optional*, defaults to `50`) -- The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. - **timesteps** (`list[int]`, *optional*) -- Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed will be used. Must be in descending order. - **sigmas** (`list[float]`, *optional*) -- Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed will be used. - **guidance_scale** (`float`, *optional*, defaults to `5.0`) -- Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://huggingface.co/papers/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://huggingface.co/papers/2205.11487). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. - **num_images_per_prompt** (`int`, *optional*, defaults to `1`) -- The number of images to generate per prompt. - **generator** (`torch.Generator` or `list[torch.Generator]`, *optional*) -- One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. - **latents** (`torch.FloatTensor`, *optional*) -- Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will be generated by sampling using the supplied random `generator`. - **prompt_embeds** (`torch.FloatTensor`, *optional*) -- Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. - **negative_prompt_embeds** (`torch.FloatTensor`, *optional*) -- Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. - **original_size** (`tuple[int]`, *optional*, defaults to (1024, 1024)) -- If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled. `original_size` defaults to `(height, width)` if not specified. Part of SDXL's micro-conditioning as explained in section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). - **crops_coords_top_left** (`tuple[int]`, *optional*, defaults to (0, 0)) -- `crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). - **output_type** (`str`, *optional*, defaults to `"pil"`) -- The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. - **return_dict** (`bool`, *optional*, defaults to `True`) -- Whether or not to return a `~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput` instead of a plain tuple. - **attention_kwargs** (`dict`, *optional*) -- A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under `self.processor` in [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). - **callback_on_step_end** (`Callable`, *optional*) -- A function that calls at the end of each denoising steps during the inference. The function is called with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by `callback_on_step_end_tensor_inputs`. - **callback_on_step_end_tensor_inputs** (`list`, *optional*) -- The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the `._callback_tensor_inputs` attribute of your pipeline class. - **max_sequence_length** (`int`, defaults to `224`) -- Maximum sequence length in encoded prompt. Can be set to other values but may lead to poorer results.0`~pipelines.cogview4.pipeline_CogView4.CogView4PipelineOutput` or `tuple``~pipelines.cogview4.pipeline_CogView4.CogView4PipelineOutput` if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is a list with the generated images. Function invoked when calling the pipeline for generation. Examples: ```python >>> import torch >>> from diffusers import CogView4Pipeline >>> pipe = CogView4Pipeline.from_pretrained("THUDM/CogView4-6B", torch_dtype=torch.bfloat16) >>> pipe.to("cuda") >>> prompt = "A photo of an astronaut riding a horse on mars" >>> image = pipe(prompt).images[0] >>> image.save("output.png") ``` **Parameters:** vae ([AutoencoderKL](/docs/diffusers/v0.37.0/en/api/models/autoencoderkl#diffusers.AutoencoderKL)) : Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. text_encoder (`GLMModel`) : Frozen text-encoder. CogView4 uses [glm-4-9b-hf](https://huggingface.co/THUDM/glm-4-9b-hf). tokenizer (`PreTrainedTokenizer`) : Tokenizer of class [PreTrainedTokenizer](https://huggingface.co/docs/transformers/main/en/main_classes/tokenizer#transformers.PreTrainedTokenizer). transformer ([CogView4Transformer2DModel](/docs/diffusers/v0.37.0/en/api/models/cogview4_transformer2d#diffusers.CogView4Transformer2DModel)) : A text conditioned `CogView4Transformer2DModel` to denoise the encoded image latents. scheduler ([SchedulerMixin](/docs/diffusers/v0.37.0/en/api/schedulers/overview#diffusers.SchedulerMixin)) : A scheduler to be used in combination with `transformer` to denoise the encoded image latents. **Returns:** ``~pipelines.cogview4.pipeline_CogView4.CogView4PipelineOutput` or `tuple`` `~pipelines.cogview4.pipeline_CogView4.CogView4PipelineOutput` if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is a list with the generated images. #### encode_prompt[[diffusers.CogView4Pipeline.encode_prompt]] [Source](https://github.com/huggingface/diffusers/blob/v0.37.0/src/diffusers/pipelines/cogview4/pipeline_cogview4.py#L221) Encodes the prompt into text encoder hidden states. **Parameters:** prompt (`str` or `list[str]`, *optional*) : prompt to be encoded negative_prompt (`str` or `list[str]`, *optional*) : The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). do_classifier_free_guidance (`bool`, *optional*, defaults to `True`) : Whether to use classifier free guidance or not. num_images_per_prompt (`int`, *optional*, defaults to 1) : Number of images that should be generated per prompt. torch device to place the resulting embeddings on prompt_embeds (`torch.Tensor`, *optional*) : Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.Tensor`, *optional*) : Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. device : (`torch.device`, *optional*): torch device dtype : (`torch.dtype`, *optional*): torch dtype max_sequence_length (`int`, defaults to `1024`) : Maximum sequence length in encoded prompt. Can be set to other values but may lead to poorer results. ## CogView4PipelineOutput[[diffusers.pipelines.cogview4.pipeline_output.CogView4PipelineOutput]] #### diffusers.pipelines.cogview4.pipeline_output.CogView4PipelineOutput[[diffusers.pipelines.cogview4.pipeline_output.CogView4PipelineOutput]] [Source](https://github.com/huggingface/diffusers/blob/v0.37.0/src/diffusers/pipelines/cogview4/pipeline_output.py#L10) Output class for CogView3 pipelines. **Parameters:** images (`list[PIL.Image.Image]` or `np.ndarray`) : list of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width, num_channels)`. PIL images or numpy array present the denoised images of the diffusion pipeline.