# # 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",torch_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",torch_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]] - **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. - **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.`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") ``` - **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. - **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 reproducibilityTuple 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. Extract glyph texts from prompt(s). Returns a list of lists for batch processing. ## GlmImagePipelineOutput[[diffusers.pipelines.glm_image.pipeline_output.GlmImagePipelineOutput]] - **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.