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force vqa to use hfcache

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Signed-off-by: Vladimir Mandic <mandic00@live.com>
This commit is contained in:
Vladimir Mandic
2024-11-01 19:39:13 -04:00
parent 26b815941a
commit 76acae595d
2 changed files with 13 additions and 12 deletions
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CHANGELOG.md
+1
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@@ -37,6 +37,7 @@ This release can be considered an LTS release before we kick off the next round
- fix diffusers load from folder
- fix lora enum logging on windows
- fix xyz grid with batch count
- fix vqa models ignoring hfcache folder setting
- move downloads of some auxillary models to hfcache instead of models folder
## Update for 2024-10-29
modules/vqa.py
+12 -12
View File
@@ -30,8 +30,8 @@ MODELS = {
def git(question: str, image: Image.Image, repo: str = None):
global processor, model, loaded # pylint: disable=global-statement
if model is None or loaded != repo:
model = transformers.GitForCausalLM.from_pretrained(repo)
processor = transformers.GitProcessor.from_pretrained(repo)
model = transformers.GitForCausalLM.from_pretrained(repo, cache_dir=shared.opts.hfcache_dir)
processor = transformers.GitProcessor.from_pretrained(repo, cache_dir=shared.opts.hfcache_dir)
loaded = repo
model.to(devices.device, devices.dtype)
shared.log.debug(f'VQA: class={model.__class__.__name__} processor={processor.__class__} model={repo}')
@@ -55,8 +55,8 @@ def git(question: str, image: Image.Image, repo: str = None):
def blip(question: str, image: Image.Image, repo: str = None):
global processor, model, loaded # pylint: disable=global-statement
if model is None or loaded != repo:
model = transformers.BlipForQuestionAnswering.from_pretrained(repo)
processor = transformers.BlipProcessor.from_pretrained(repo)
model = transformers.BlipForQuestionAnswering.from_pretrained(repo, cache_dir=shared.opts.hfcache_dir)
processor = transformers.BlipProcessor.from_pretrained(repo, cache_dir=shared.opts.hfcache_dir)
loaded = repo
model.to(devices.device, devices.dtype)
inputs = processor(image, question, return_tensors="pt")
@@ -73,8 +73,8 @@ def blip(question: str, image: Image.Image, repo: str = None):
def vilt(question: str, image: Image.Image, repo: str = None):
global processor, model, loaded # pylint: disable=global-statement
if model is None or loaded != repo:
model = transformers.ViltForQuestionAnswering.from_pretrained(repo)
processor = transformers.ViltProcessor.from_pretrained(repo)
model = transformers.ViltForQuestionAnswering.from_pretrained(repo, cache_dir=shared.opts.hfcache_dir)
processor = transformers.ViltProcessor.from_pretrained(repo, cache_dir=shared.opts.hfcache_dir)
loaded = repo
model.to(devices.device)
shared.log.debug(f'VQA: class={model.__class__.__name__} processor={processor.__class__} model={repo}')
@@ -94,8 +94,8 @@ def vilt(question: str, image: Image.Image, repo: str = None):
def pix(question: str, image: Image.Image, repo: str = None):
global processor, model, loaded # pylint: disable=global-statement
if model is None or loaded != repo:
model = transformers.Pix2StructForConditionalGeneration.from_pretrained(repo)
processor = transformers.Pix2StructProcessor.from_pretrained(repo)
model = transformers.Pix2StructForConditionalGeneration.from_pretrained(repo, cache_dir=shared.opts.hfcache_dir)
processor = transformers.Pix2StructProcessor.from_pretrained(repo, cache_dir=shared.opts.hfcache_dir)
loaded = repo
model.to(devices.device)
shared.log.debug(f'VQA: class={model.__class__.__name__} processor={processor.__class__} model={repo}')
@@ -115,8 +115,8 @@ def pix(question: str, image: Image.Image, repo: str = None):
def moondream(question: str, image: Image.Image, repo: str = None):
global processor, model, loaded # pylint: disable=global-statement
if model is None or loaded != repo:
model = transformers.AutoModelForCausalLM.from_pretrained(repo, trust_remote_code=True) # revision = "2024-03-05"
processor = transformers.AutoTokenizer.from_pretrained(repo) # revision = "2024-03-05"
model = transformers.AutoModelForCausalLM.from_pretrained(repo, trust_remote_code=True, cache_dir=shared.opts.hfcache_dir) # revision = "2024-03-05"
processor = transformers.AutoTokenizer.from_pretrained(repo, cache_dir=shared.opts.hfcache_dir)
loaded = repo
model.eval()
model.to(devices.device, devices.dtype)
@@ -142,8 +142,8 @@ def florence(question: str, image: Image.Image, repo: str = None, revision: str
return R
if model is None or loaded != repo:
transformers.dynamic_module_utils.get_imports = get_imports
model = transformers.AutoModelForCausalLM.from_pretrained(repo, trust_remote_code=True, revision=revision)
processor = transformers.AutoProcessor.from_pretrained(repo, trust_remote_code=True, revision=revision)
model = transformers.AutoModelForCausalLM.from_pretrained(repo, trust_remote_code=True, revision=revision, cache_dir=shared.opts.hfcache_dir)
processor = transformers.AutoProcessor.from_pretrained(repo, trust_remote_code=True, revision=revision, cache_dir=shared.opts.hfcache_dir)
transformers.dynamic_module_utils.get_imports = _get_imports
loaded = repo
model.eval()