mirror of
https://github.com/vladmandic/automatic
synced 2026-09-19 01:04:32 +02:00
add anima and hidream-o1 prequants
Signed-off-by: Vladimir Mandic <mandic00@live.com>
This commit is contained in:
Vladimir Mandic
+5
-1
@@ -21,6 +21,7 @@ For full details, see [ChangeLog](https://github.com/vladmandic/automatic/blob/m
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- [HiDream-O1-Image](https://huggingface.co/HiDream-ai/HiDream-O1-Image) pixel-level unified transformer model support
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HiDream-O1 is based on a single custom *Qwen3-VL* 8.8B 35GB component
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includes both **HiDream-O1-Image** *(base)* and **HiDream-O1-Image-Dev** *(distilled*)* variants
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includes *sdnq-svd-dynamic-int8* pre-quantized variants for both base and dev models
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includes *T2I* and *I2I edit* capabilities and resolutions up to 2048px
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*note*: use steps:50 for base and steps:28 for dev variants
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- [JoyAI Image Edit](https://huggingface.co/jdopensource/JoyAI-Image-Edit-Diffusers) image-editing model support
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@@ -33,10 +34,13 @@ For full details, see [ChangeLog](https://github.com/vladmandic/automatic/blob/m
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primarily image-editing model, but supports t2i as well, uses multi-scale resolution binning up to 2048px
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- [AlphaVLLM Lumina-DiMOO](https://huggingface.co/Alpha-VLLM/Lumina-DiMOO) unified multimodal diffusion model
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includes *T2I*, *I2I edit*, and *MMU* capabilities in a single pipeline
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*note* in addition to normal prompt-based image editing, model also supports special prompts: *dense, canny_pred, control, subject, edit, ref_transfer, multi_view*
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*note* model also supports special prompts: *dense, canny_pred, control, subject, edit, ref_transfer, multi_view*
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*note* as with most multi-modal/unified models, it needs higher step count (recommended is 64 steps) and uses quite a lot of VRAM, so use with caution!
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- [Owen777 UltraFlux-v1](https://huggingface.co/Owen777/UltraFlux-v1) native 4K text-to-image model based on *FLUX.1-dev*
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*note*: UltraFlux is capable of rendering images up to 4K resolution, but it doesnt mean it will do that on any hardware - it will depend on your VRAM!
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- [Anima Preview-v3](https://huggingface.co/circlestone-labs/Anima)
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add *turbo* variant with [turbo-LoRA](https://civitai.com/models/2560840/anima-turbo-lora) pre-merged
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add *sdnq-svd-dynamic-int8* pre-quantized variant
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- **Features**
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- **Multi-image** workflows!
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for models that support multiple images as inputs, you can now add multiple stages in Kanvas
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@@ -242,7 +242,7 @@
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"desc": "ERNIE-Image is a text-to-image diffusion transformer model that combines a Mistral3 text encoder with a FlowMatch transformer and Flux2-style VAE for 1024px image generation.",
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"skip": true,
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"extras": "sampler: Default, cfg_scale: 4.0, steps: 50",
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"size": 23.93,
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"size": 7.52,
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"date": "2026 April"
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},
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"Baidu ERNIE-Image-Turbo sdnq-dynamic-int4": {
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@@ -251,8 +251,44 @@
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"desc": "ERNIE-Image-Turbo is a distilled ERNIE-Image variant optimized for fast generation with fewer denoising steps.",
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"skip": true,
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"extras": "sampler: Default, cfg_scale: 1.0, steps: 8",
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"size": 23.37,
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"size": 7.52,
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"tags": "distilled",
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"date": "2026 April"
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},
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"Anima Preview 3 sdnq-dynamic-int8": {
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"path": "vladmandic/Anima-Preview-3-diffusers-SDNQ-8bit-dynamic",
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"preview": "CalamitousFelicitousness--Anima-Preview-3-sdnext-diffusers.jpg",
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"desc": "Anima Preview V3 with extended 1024-resolution training and expanded dataset coverage for less common artists. A 2B parameter anime-focused text-to-image model based on modified Cosmos-Predict-2B with Qwen3-0.6B text encoder, created by CircleStone Labs and Comfy Org.",
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"tags": "community",
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"date": "2026 April",
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"size": 3.19,
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"skip": true
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},
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"Anima Preview 3 Turbo sdnq-dynamic-int8": {
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"path": "vladmandic/Anima-Preview-3-turbo-diffusers-SDNQ-8bit-dynamic",
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"preview": "CalamitousFelicitousness--Anima-Preview-3-sdnext-diffusers.jpg",
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"desc": "Anima Preview V3 with extended 1024-resolution training and expanded dataset coverage for less common artists. A 2B parameter anime-focused text-to-image model based on modified Cosmos-Predict-2B with Qwen3-0.6B text encoder, created by CircleStone Labs and Comfy Org.",
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"tags": "community",
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"date": "2026 April",
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"size": 3.19,
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"skip": true
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},
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"HiDream-O1 Image sdnq-dynamic-int8": {
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"path": "vladmandic/HiDream-O1-Image-SDNQ-8bit-dynamic",
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"desc": "HiDream-O1-Image is an 8B pixel-level unified transformer model for text-to-image generation, instruction editing, and multi-reference personalization up to 2048x2048.",
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"preview": "HiDream-ai--HiDream-O1-Image.jpg",
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"skip": true,
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"extras": "sampler: Default",
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"size": 10.34,
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"date": "2026 May"
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},
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"HiDream-O1 Image Dev sdnq-dynamic-int8": {
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"path": "vladmandic/HiDream-O1-Image-Dev-SDNQ-8bit-dynamic",
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"desc": "HiDream-O1-Image is an 8B pixel-level unified transformer model for text-to-image generation, instruction editing, and multi-reference personalization up to 2048x2048.",
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"preview": "HiDream-ai--HiDream-O1-Image.jpg",
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"skip": true,
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"extras": "sampler: Default",
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"size": 10.34,
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"date": "2026 May"
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}
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}
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+30
-1
@@ -25,6 +25,31 @@ def unset_config_on_save(quantization_config: SDNQConfig) -> SDNQConfig:
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return quantization_config
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def normalize_tied_weights_keys_for_save(model: ModelMixin, is_pipeline: bool = False) -> list[tuple[torch.nn.Module, object]]:
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normalized_modules = []
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modules_to_walk = []
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if is_pipeline:
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for module_name in get_module_names(model):
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module = getattr(model, module_name, None)
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if isinstance(module, torch.nn.Module):
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modules_to_walk.append(module)
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elif isinstance(model, torch.nn.Module):
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modules_to_walk.append(model)
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for root_module in modules_to_walk:
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for submodule in root_module.modules():
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tied_weights_keys = getattr(submodule, "_tied_weights_keys", None)
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if isinstance(tied_weights_keys, list):
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normalized_modules.append((submodule, tied_weights_keys))
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submodule._tied_weights_keys = {key: key for key in tied_weights_keys} # pylint: disable=protected-access
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return normalized_modules
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def restore_tied_weights_keys_after_save(normalized_modules: list[tuple[torch.nn.Module, object]]) -> None:
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for submodule, tied_weights_keys in normalized_modules:
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submodule._tied_weights_keys = tied_weights_keys # pylint: disable=protected-access
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def save_sdnq_model(model: ModelMixin, model_path: str, max_shard_size: str = "5GB", is_pipeline: bool = False, sdnq_config: SDNQConfig | None = None) -> None:
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if is_pipeline:
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for module_name in get_module_names(model):
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@@ -39,7 +64,11 @@ def save_sdnq_model(model: ModelMixin, model_path: str, max_shard_size: str = "5
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if hasattr(model, "quantization_config") and isinstance(model.quantization_config, SDNQConfig):
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model.quantization_config = unset_config_on_save(model.quantization_config)
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model.save_pretrained(model_path, max_shard_size=max_shard_size) # actual save
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normalized_modules = normalize_tied_weights_keys_for_save(model, is_pipeline=is_pipeline)
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try:
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model.save_pretrained(model_path, max_shard_size=max_shard_size) # actual save
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finally:
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restore_tied_weights_keys_after_save(normalized_modules)
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quantization_config_path = os.path.join(model_path, "quantization_config.json")
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if sdnq_config is not None: # if provided, save global config
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@@ -21,7 +21,7 @@ else:
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from flash_attn_interface import flash_attn_func as _flash_attn_func
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except ImportError:
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try:
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from flash_attn import flash_attn_func as _flash_attn_func
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from flash_attn import flash_attn_func as _flash_attn_func # pylint: disable=ungrouped-imports
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except ImportError:
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_flash_attn_func = None
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@@ -1655,7 +1655,7 @@ class Qwen3VLModel(Qwen3VLPreTrainedModel):
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deepstack_visual_embeds = []
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image_mask_joint = image_mask[visual_pos_masks]
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video_mask_joint = video_mask[visual_pos_masks]
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for img_embed, vid_embed in zip(deepstack_image_embeds, deepstack_video_embeds):
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for img_embed, vid_embed in zip(deepstack_image_embeds, deepstack_video_embeds): # pylint: disable=possibly-used-before-assignment
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embed_joint = img_embed.new_zeros(visual_pos_masks.sum(), img_embed.shape[-1]).to(img_embed.device)
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embed_joint[image_mask_joint, :] = img_embed
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embed_joint[video_mask_joint, :] = vid_embed
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@@ -1766,7 +1766,7 @@ class Qwen3VLCausalLMOutputWithPast(ModelOutput):
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class HiDreamO1Qwen3VLTransformer(Qwen3VLPreTrainedModel, GenerationMixin):
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_checkpoint_conversion_mapping = {}
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_tied_weights_keys = ["lm_head.weight"]
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_tied_weights_keys = {"lm_head.weight": "model.language_model.embed_tokens.weight"}
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# Reference: fix gemma3 grad acc #37208
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accepts_loss_kwargs = False
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config: Qwen3VLConfig
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