mirror of
https://github.com/vladmandic/automatic
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CalamitousFelicitousness
2f3d0e719d
Diffusers-native port of the 9.3B flow-matching DiT: dual-transformer asymmetric CFG, a 13-layer Qwen3-VL tap encoder deduped with VQA and prompt-enhance, the Flux.2 VAE, and a logit-normal schedule. Loads a published bf16 repo with SDNQ at load.
46 lines
2.3 KiB
Python
46 lines
2.3 KiB
Python
import diffusers
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from transformers import AutoTokenizer
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from transformers.models.qwen3_vl import Qwen3VLModel
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from modules import shared, devices, sd_models
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from modules.logger import log
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from pipelines import generic
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from pipelines.ideogram4.pipeline_ideogram4 import Ideogram4Pipeline
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from pipelines.ideogram4.scheduler_ideogram4 import Ideogram4Scheduler
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from pipelines.ideogram4.transformer_ideogram4 import Ideogram4Transformer2DModel
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def load_ideogram4(checkpoint_info, diffusers_load_config=None):
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if diffusers_load_config is None:
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diffusers_load_config = {}
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repo_id = sd_models.path_to_repo(checkpoint_info)
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sd_models.hf_auth_check(checkpoint_info)
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log.debug(f'Load model: type=Ideogram4 repo="{repo_id}" offload={shared.opts.diffusers_offload_mode} dtype={devices.dtype}')
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if repo_id is None or repo_id.lower() == 'none':
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return None
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# Each transformer loads independently from its subfolder.
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transformer = generic.load_transformer(repo_id, cls_name=Ideogram4Transformer2DModel, subfolder="transformer", load_config=diffusers_load_config)
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unconditional_transformer = generic.load_transformer(repo_id, cls_name=Ideogram4Transformer2DModel, subfolder="unconditional_transformer", load_config=diffusers_load_config)
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# shared_te_map redirects to the shared Qwen3-VL repo (deduped with VQA + prompt-enhance);
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# the bundled text_encoder is the fallback when sharing is off.
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text_encoder = generic.load_text_encoder(repo_id, cls_name=Qwen3VLModel, load_config=diffusers_load_config)
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tokenizer = AutoTokenizer.from_pretrained(repo_id, subfolder="tokenizer", cache_dir=shared.opts.diffusers_dir)
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vae = diffusers.AutoencoderKLFlux2.from_pretrained(repo_id, subfolder="vae", cache_dir=shared.opts.diffusers_dir, torch_dtype=devices.dtype)
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scheduler = Ideogram4Scheduler()
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pipe = Ideogram4Pipeline(
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transformer=transformer,
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unconditional_transformer=unconditional_transformer,
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text_encoder=text_encoder,
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tokenizer=tokenizer,
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vae=vae,
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scheduler=scheduler,
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)
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# The pipeline decodes the packed latent itself, so keep SD.Next from re-decoding it.
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pipe.task_args = {'output_type': 'pil'}
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del transformer, unconditional_transformer, text_encoder, vae
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devices.torch_gc(force=True, reason='load')
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return pipe
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