#
# 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.
-->

# GLM-Image

## Overview

GLM-Image is an image generation model adopts a hybrid autoregressive + diffusion decoder architecture, effectively pushing the upper bound of visual fidelity and fine-grained details. In general image generation quality, it aligns with industry-standard LDM-based approaches, while demonstrating significant advantages in knowledge-intensive image generation scenarios.

Model architecture: a hybrid autoregressive + diffusion decoder design、

+ Autoregressive generator: a 9B-parameter model initialized from [GLM-4-9B-0414](https://huggingface.co/zai-org/GLM-4-9B-0414), with an expanded vocabulary to incorporate visual tokens. The model first generates a compact encoding of approximately 256 tokens, then expands to 1K–4K tokens, corresponding to 1K–2K high-resolution image outputs. You can check AR model in class `GlmImageForConditionalGeneration` of `transformers` library.
+ Diffusion Decoder: a 7B-parameter decoder based on a single-stream DiT architecture for latent-space image decoding. It is equipped with a Glyph Encoder text module, significantly improving accurate text rendering within images.

Post-training with decoupled reinforcement learning: the model introduces a fine-grained, modular feedback strategy using the GRPO algorithm, substantially enhancing both semantic understanding and visual detail quality.

+ Autoregressive module: provides low-frequency feedback signals focused on aesthetics and semantic alignment, improving instruction following and artistic expressiveness.
+ Decoder module: delivers high-frequency feedback targeting detail fidelity and text accuracy, resulting in highly realistic textures, lighting, and color reproduction, as well as more precise text rendering.

GLM-Image supports both text-to-image and image-to-image generation within a single model

+ Text-to-image: generates high-detail images from textual descriptions, with particularly strong performance in information-dense scenarios.
+ Image-to-image: supports a wide range of tasks, including image editing, style transfer, multi-subject consistency, and identity-preserving generation for people and objects.

This pipeline was contributed by [zRzRzRzRzRzRzR](https://github.com/zRzRzRzRzRzRzR). The codebase can be found [here](https://huggingface.co/zai-org/GLM-Image).

## Usage examples

### Text to Image Generation

```python
import torch
from diffusers.pipelines.glm_image import GlmImagePipeline

pipe = GlmImagePipeline.from_pretrained("zai-org/GLM-Image",dtype=torch.bfloat16,device_map="cuda")
prompt = "A beautifully designed modern food magazine style dessert recipe illustration, themed around a raspberry mousse cake. The overall layout is clean and bright, divided into four main areas: the top left features a bold black title 'Raspberry Mousse Cake Recipe Guide', with a soft-lit close-up photo of the finished cake on the right, showcasing a light pink cake adorned with fresh raspberries and mint leaves; the bottom left contains an ingredient list section, titled 'Ingredients' in a simple font, listing 'Flour 150g', 'Eggs 3', 'Sugar 120g', 'Raspberry puree 200g', 'Gelatin sheets 10g', 'Whipping cream 300ml', and 'Fresh raspberries', each accompanied by minimalist line icons (like a flour bag, eggs, sugar jar, etc.); the bottom right displays four equally sized step boxes, each containing high-definition macro photos and corresponding instructions, arranged from top to bottom as follows: Step 1 shows a whisk whipping white foam (with the instruction 'Whip egg whites to stiff peaks'), Step 2 shows a red-and-white mixture being folded with a spatula (with the instruction 'Gently fold in the puree and batter'), Step 3 shows pink liquid being poured into a round mold (with the instruction 'Pour into mold and chill for 4 hours'), Step 4 shows the finished cake decorated with raspberries and mint leaves (with the instruction 'Decorate with raspberries and mint'); a light brown information bar runs along the bottom edge, with icons on the left representing 'Preparation time: 30 minutes', 'Cooking time: 20 minutes', and 'Servings: 8'. The overall color scheme is dominated by creamy white and light pink, with a subtle paper texture in the background, featuring compact and orderly text and image layout with clear information hierarchy."
image = pipe(
    prompt=prompt,
    height=32 * 32,
    width=36 * 32,
    num_inference_steps=30,
    guidance_scale=1.5,
    generator=torch.Generator(device="cuda").manual_seed(42),
).images[0]

image.save("output_t2i.png")
```

### Image to Image Generation

```python
import torch
from diffusers.pipelines.glm_image import GlmImagePipeline
from PIL import Image

pipe = GlmImagePipeline.from_pretrained("zai-org/GLM-Image",dtype=torch.bfloat16,device_map="cuda")
image_path = "cond.jpg" 
prompt = "Replace the background of the snow forest with an underground station featuring an automatic escalator."
image = Image.open(image_path).convert("RGB")
image = pipe(
    prompt=prompt,
    image=[image], # can input multiple images for multi-image-to-image generation such as [image, image1]
    height=33 * 32,
    width=32 * 32,
    num_inference_steps=30,
    guidance_scale=1.5,
    generator=torch.Generator(device="cuda").manual_seed(42),
).images[0]

image.save("output_i2i.png")
```

+ Since the AR model used in GLM-Image is configured with `do_sample=True` and a temperature of `0.95` by default, the generated images can vary significantly across runs. We do not recommend setting do_sample=False, as this may lead to incorrect or degenerate outputs from the AR model.

## GlmImagePipeline[[diffusers.GlmImagePipeline]]

#### diffusers.GlmImagePipeline[[diffusers.GlmImagePipeline]]

```python
diffusers.GlmImagePipeline(tokenizer: ByT5Tokenizer, processor: GlmImageProcessor, text_encoder: T5EncoderModel, vision_language_encoder: GlmImageForConditionalGeneration, vae: AutoencoderKL, transformer: GlmImageTransformer2DModel, scheduler: FlowMatchEulerDiscreteScheduler)
```

[Source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/glm_image/pipeline_glm_image.py#L161)

**Parameters:**

tokenizer (`PreTrainedTokenizer`) : Tokenizer for the text encoder.

processor (`AutoProcessor`) : Processor for the AR model to handle chat templates and tokenization.

text_encoder (`T5EncoderModel`) : Frozen text-encoder for glyph embeddings.

vision_language_encoder (`GlmImageForConditionalGeneration`) : The AR model that generates image tokens from text prompts.

vae ([AutoencoderKL](/docs/diffusers/main/en/api/models/autoencoderkl#diffusers.AutoencoderKL)) : Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.

transformer ([GlmImageTransformer2DModel](/docs/diffusers/main/en/api/models/glm_image_transformer2d#diffusers.GlmImageTransformer2DModel)) : A text conditioned transformer to denoise the encoded image latents (DiT).

scheduler ([SchedulerMixin](/docs/diffusers/main/en/api/schedulers/overview#diffusers.SchedulerMixin)) : A scheduler to be used in combination with `transformer` to denoise the encoded image latents.

Pipeline for text-to-image generation using GLM-Image.

This pipeline integrates both the AR (autoregressive) model for token generation and the DiT (diffusion
transformer) model for image decoding.

#### __call__[[diffusers.GlmImagePipeline.__call__]]

```python
__call__(prompt: str | list[str] | None = None, image: typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, list[PIL.Image.Image], list[numpy.ndarray], list[torch.Tensor], NoneType] = None, height: int | None = None, width: int | None = None, num_inference_steps: int = 50, timesteps: list[int] | None = None, sigmas: list[float] | None = None, guidance_scale: float = 1.5, num_images_per_prompt: int = 1, generator: typing.Union[torch.Generator, list[torch.Generator], NoneType] = None, latents: typing.Optional[torch.Tensor] = None, prompt_embeds: typing.Optional[torch.Tensor] = None, negative_prompt_embeds: typing.Optional[torch.Tensor] = None, prior_token_ids: typing.Optional[torch.Tensor] = None, prior_token_image_ids: list[torch.Tensor] | None = None, source_image_grid_thw: list[torch.Tensor] | None = None, crops_coords_top_left: tuple = (0, 0), output_type: str = 'pil', return_dict: bool = True, attention_kwargs: dict[str, typing.Any] | None = None, callback_on_step_end: typing.Union[typing.Callable[[int, int, dict], NoneType], diffusers.callbacks.PipelineCallback, diffusers.callbacks.MultiPipelineCallbacks, NoneType] = None, callback_on_step_end_tensor_inputs: list = ['latents'], max_sequence_length: int = 2048)
```

[Source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/glm_image/pipeline_glm_image.py#L719)

**Parameters:**

prompt (`str` or `list[str]`, *optional*) : The prompt or prompts to guide the image generation. Must contain shape info in the format 'H W' where H and W are token dimensions (d32). Example: "A beautiful sunset36 24" generates a 1152x768 image.

image : Optional condition images for image-to-image generation.

height (`int`, *optional*) : The height in pixels. If not provided, derived from prompt shape info.

width (`int`, *optional*) : The width in pixels. If not provided, derived from prompt shape info.

num_inference_steps (`int`, *optional*, defaults to `50`) : The number of denoising steps for DiT.

timesteps (`list[int]`, *optional*) : Custom timesteps to use for the denoising process. If not defined, the scheduler's default schedule for `num_inference_steps` is used.

sigmas (`list[float]`, *optional*) : Custom sigmas to use for the denoising process. If not defined, the scheduler's default schedule is used.

guidance_scale (`float`, *optional*, defaults to `1.5`) : Guidance scale for classifier-free guidance.

num_images_per_prompt (`int`, *optional*, defaults to `1`) : The number of images to generate per prompt.

generator (`torch.Generator`, *optional*) : Random generator for reproducibility.

latents (`torch.Tensor`, *optional*) : Pre-generated noisy latents to be used as inputs for image generation.

prompt_embeds (`torch.Tensor`, *optional*) : Pre-generated text embeddings. If not provided, embeddings are generated from `prompt`.

negative_prompt_embeds (`torch.Tensor`, *optional*) : Pre-generated negative text embeddings. Used when classifier-free guidance is enabled.

prior_token_ids (`torch.Tensor`, *optional*) : Pre-generated prior token ids from `generate_prior_tokens`. If supplied, prior generation is skipped.

prior_token_image_ids (`list[torch.Tensor]`, *optional*) : Image token ids associated with `prior_token_ids`.

source_image_grid_thw (`list[torch.Tensor]`, *optional*) : Per-sample THW grid information for the source image tokens.

crops_coords_top_left (`tuple[int, int]`, *optional*, defaults to `(0, 0)`) : The top-left coordinates of the crop used for conditioning embeddings.

output_type (`str`, *optional*, defaults to `"pil"`) : Output format: "pil", "np", or "latent".

return_dict (`bool`, *optional*, defaults to `True`) : Whether or not to return a `GlmImagePipelineOutput` instead of a plain tuple.

attention_kwargs (`dict`, *optional*) : A kwargs dictionary that if specified is passed along to the `AttentionProcessor`.

callback_on_step_end (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*) : A function called at the end of each denoising step.

callback_on_step_end_tensor_inputs (`list[str]`, *optional*) : Tensor inputs passed to `callback_on_step_end`.

max_sequence_length (`int`, *optional*, defaults to `2048`) : Maximum sequence length for the text encoder.

**Returns:** `GlmImagePipelineOutput` or `tuple`

Generated images.

Function invoked when calling the pipeline for generation.

Examples:
```python
>>> import torch
>>> from diffusers import GlmImagePipeline

>>> pipe = GlmImagePipeline.from_pretrained("zai-org/GLM-Image", 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")
```

#### encode_prompt[[diffusers.GlmImagePipeline.encode_prompt]]

```python
encode_prompt(prompt: str | list[str], do_classifier_free_guidance: bool = True, num_images_per_prompt: int = 1, prompt_embeds: typing.Optional[torch.Tensor] = None, negative_prompt_embeds: typing.Optional[torch.Tensor] = None, device: typing.Optional[torch.device] = None, dtype: typing.Optional[torch.dtype] = None, max_sequence_length: int = 2048)
```

[Source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/glm_image/pipeline_glm_image.py#L545)

**Parameters:**

prompt (`str` or `list[str]`, *optional*) : prompt to be encoded

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.

device : (`torch.device`, *optional*): torch device

dtype : (`torch.dtype`, *optional*): torch dtype

max_sequence_length (`int`, defaults to `2048`) : Maximum sequence length in encoded prompt. Can be set to other values but may lead to poorer results.

Encodes the prompt into text encoder hidden states.

#### generate_prior_tokens[[diffusers.GlmImagePipeline.generate_prior_tokens]]

```python
generate_prior_tokens(prompt: str | list[str], height: int, width: int, image: list[list[PIL.Image.Image]] | None = None, device: typing.Optional[torch.device] = None, generator: typing.Optional[torch.Generator] = None)
```

[Source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/glm_image/pipeline_glm_image.py#L321)

**Parameters:**

prompt : Single prompt or list of prompts

height : Target image height

width : Target image width

image : Normalized image input as List[List[PIL.Image]]. Should be pre-validated using _validate_and_normalize_images() before calling this method.

device : Target device

generator : Random generator for reproducibility

**Returns:** `Tuple of`

- prior_token_ids: Tensor of shape (batch_size, num_tokens) with upsampled prior tokens
- prior_token_image_ids_per_sample: List of tensors, one per sample. Each tensor contains
  the upsampled prior token ids for all condition images in that sample. None for t2i.
- source_image_grid_thw_per_sample: List of tensors, one per sample. Each tensor has shape
  (num_condition_images, 3) with upsampled grid info. None for t2i.

Generate prior tokens for the DiT model using the AR model.

#### get_glyph_texts[[diffusers.GlmImagePipeline.get_glyph_texts]]

```python
get_glyph_texts(prompt)
```

[Source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/glm_image/pipeline_glm_image.py#L476)

Extract glyph texts from prompt(s). Returns a list of lists for batch processing.

## GlmImagePipelineOutput[[diffusers.pipelines.glm_image.pipeline_output.GlmImagePipelineOutput]]

#### diffusers.pipelines.glm_image.pipeline_output.GlmImagePipelineOutput[[diffusers.pipelines.glm_image.pipeline_output.GlmImagePipelineOutput]]

```python
diffusers.pipelines.glm_image.pipeline_output.GlmImagePipelineOutput(images: list[PIL.Image.Image] | numpy.ndarray)
```

[Source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/glm_image/pipeline_output.py#L10)

**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.

Output class for CogView3 pipelines.