134 lines
5.4 KiB
Markdown
134 lines
5.4 KiB
Markdown
# HuggingFace Diffusers for ComfyUI
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A lot of custom nodes for ComfyUI are really just bindings to HuggingFace
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Diffusers, but overly constrained to use them in very particular ways. This set
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of custom nodes is intended to be a generic set of bindings for HuggingFace
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Diffusers, in theory allowing you to use any pipeline supported by HuggingFace.
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It also supports bitsandbytes quantization, automatic device mapping, and the
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other advantages unique to HuggingFace.
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Because HuggingFace pipelines output PIL images, you'll likely need a node to
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get PIL images into ComfyUI's format. That node isn't provided by this package;
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I recommend
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[ComfyUI_Ib_CustomNodes](https://github.com/Chaoses-Ib/ComfyUI_Ib_CustomNodes/).
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## Philosophy
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I've tried to make everything work fairly generically, as unopinionated as I
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can manage.
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Every node that loads a component has some default class that it loads, but you
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can replace the class simply by its name. For instance, the pipeline loader
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loads `AutoPipelineForText2Image` by default, but you can simply replace
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`AutoPipelineForText2Image` with, e.g., `LongCatImagePipeline` to load LongCat
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Image instead.
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All of the nodes include a generic `kwargs` parameter, which simply takes text.
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Rather than trying to anticipate every possible parameter, I've included only
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the most obvious parameters, and for anything else, use `kwargs`. `kwargs` can
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be empty (for no extra args) or an object in JSON format. Of course, this is
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only sufficient for those arguments that take JSON-compatible values. `kwargs`
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overrides other parameters.
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As in HuggingFace, Pipelines are self-loading and include all components, but
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you can override the VAE and text encoder components if you wish, so they're
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optional inputs. If you want to load *without* these components, use `kwargs` to
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set them to `null` (`None`).
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## Workflows
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These are some example workflows, in simple and exploded forms (where by
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“exploded” I mean “each step done separately”).
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[GLM-Image](workflows/hf_glm_image.json) ([exploded])(workflows/hf_glm_image_exploded.json))
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(Note: As of the writing of this README, requires the git version of both Diffusers and Transformers.)
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[LongCat Image](workflows/hf_longcat_image.json) ([exploded](workflows/hf_longcat_image_exploded.json))
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(Note: Requires [my PR](https://github.com/huggingface/diffusers/pull/12963) to
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use quantization.)
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[SDXL](workflows/hf_sdxl.json) ([exploded](workflows/hf_sdxl_exploded.json))
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## Nodes
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Only the “load pipeline” and “run pipeline” nodes are needed for many use
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cases. Plus “load LoRA” to load LoRAs. Other nodes are provided for low-level
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control.
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### HF Diffusers load pipeline
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Loads a HuggingFace Diffusers pipeline. By default, loads
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`AutoPipelineForText2Image`. Obviously, if the pipeline is supported by
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`AutoPipelineForText2Image`, this is the right choice, but any other Diffusers
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class can be specified.
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The device and dtype can be set, and the model can be set to automatically
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offload to the CPU (`enable_model_cpu_offload`). Quantization is in `dtype`.
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The VAE and text encoder can optionally be overridden, and whether overridden
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or not, the VAE is exported.
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### HF Diffusers load LoRA
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Loads a HuggingFace Diffusers LoRA onto a pipeline. Note that the original,
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un-LoRA'd pipeline *cannot* be used by simply connecting the original “load
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pipeline” node. The non-LoRA version is lost.
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### HF Diffusers run pipeline
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Runs a HuggingFace Diffusers pipeline.
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For image-to-image and inpainting pipelines, supports (optional) image and mask
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inputs.
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The most common/necessary parameters (prompts and image size) are supported
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directly. Other arguments can be provided by `kwargs`. If `num_inference_steps`
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is set to 0, the default number of steps for the pipeline is used.
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Supports outputting latents or PIL images, but not both at the same time (i.e.,
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only one of the two outputs will be activated, based on which you chose).
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### HF Diffusers load AutoencoderKL (VAE)
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Loads a VAE directly. Generally speaking, the pipeline loads its own VAE, but
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you can use this to use a custom VAE, or to, e.g., load the VAE on a different
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device than the rest of the pipeline.
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### HF Transformers load model
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Load a HuggingFace *Transformers* (not Diffusers) model. Generally for the text
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encoder. The pipeline loads its own text encoder, but you can use this to use a
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custom encoder or load it on a different device, etc.
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### HF Diffusers encode prompt
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Use a HuggingFace Diffusers pipeline to encode a prompt into `prompt_embeds`.
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Unfortunately, the way that `encode_prompt` works varies from pipeline to
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pipeline. So, I simply provide four tensors as output, and you need to know how
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the pipeline works to know which you need and where.
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Some examples:
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* SDXL: `(prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, pooled_negative_prompt_embeds)`
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* Flux: `(prompt_embeds, pooled_prompt_embeds, _)`
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* Z Image: `(prompt_embeds, negative_prompt_embeds)`
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* LongCat Image: `(prompt_embeds, _)` (but the pipeline doesn't support `prompt_embeds`)
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### HF Diffusers VAE decode/encode
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Use these to decode/encode HuggingFace latents using HuggingFace VAEs.
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Automatic if pipelines are run in PIL mode.
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