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Support for Chroma - Flux1 Schnell distilled with CFG (#7355)

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* Upload files for Chroma Implementation

* Remove trailing whitespace

* trim more trailing whitespace..oops

* remove unused imports

* Add supported_inference_dtypes

* Set min_length to 0 and remove attention_mask=True

* Set min_length to 1

* get_mdulations added from blepping and minor changes

* Add lora conversion if statement in lora.py

* Update supported_models.py

* update model_base.py

* add uptream commits

* set modelType.FLOW, will cause beta scheduler to work properly

* Adjust memory usage factor and remove unnecessary code

* fix mistake

* reduce code duplication

* remove unused imports

* refactor for upstream sync

* sync chroma-support with upstream via syncbranch patch

* Update sd.py

* Add Chroma as option for the OptimalStepsScheduler node
This commit is contained in:
Silver
2025-05-01 02:57:00 +02:00
committed by GitHub
parent 39c27a3705
commit 4ca3d84277
11 changed files with 667 additions and 4 deletions
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comfy/sd.py
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@@ -42,6 +42,7 @@ import comfy.text_encoders.cosmos
import comfy.text_encoders.lumina2
import comfy.text_encoders.wan
import comfy.text_encoders.hidream
import comfy.text_encoders.chroma
import comfy.model_patcher
import comfy.lora
@@ -714,6 +715,7 @@ class CLIPType(Enum):
LUMINA2 = 12
WAN = 13
HIDREAM = 14
CHROMA = 15
def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DIFFUSION, model_options={}):
@@ -829,6 +831,9 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
clip_target.clip = comfy.text_encoders.hidream.hidream_clip(**t5xxl_detect(clip_data),
clip_l=False, clip_g=False, t5=True, llama=False, dtype_llama=None, llama_scaled_fp8=None)
clip_target.tokenizer = comfy.text_encoders.hidream.HiDreamTokenizer
elif clip_type == CLIPType.CHROMA:
clip_target.clip = comfy.text_encoders.chroma.chroma_te(**t5xxl_detect(clip_data))
clip_target.tokenizer = comfy.text_encoders.chroma.ChromaT5Tokenizer
else: #CLIPType.MOCHI
clip_target.clip = comfy.text_encoders.genmo.mochi_te(**t5xxl_detect(clip_data))
clip_target.tokenizer = comfy.text_encoders.genmo.MochiT5Tokenizer