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civitai enable embedding download

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Vladimir Mandic
2024-01-29 09:55:16 -05:00
parent 682da607a9
commit f45655f077
3 changed files with 31 additions and 16 deletions
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CHANGELOG.md
+1
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@@ -238,6 +238,7 @@ As of this release, default backend is set to **diffusers** as its more feature
- reduce system memory usage after compile
- fix cache loading with multiple models
- **fixes**
- civitai model download: enable downloads of embeddings
- ipadapter: allow changing of model/image on-the-fly
- ipadapter: fix fallback of cross-attention on unload
- rebasin iterations, thanks @AI-Casanova
modules/modelloader.py
+6 -3
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@@ -80,11 +80,14 @@ def download_civit_model_thread(model_name, model_url, model_path, model_type, p
if model_type == 'LoRA':
model_file = os.path.join(shared.opts.lora_dir, model_path, model_name)
temp_file = os.path.join(shared.opts.lora_dir, model_path, temp_file)
elif model_type == 'Embedding':
model_file = os.path.join(shared.opts.embeddings_dir, model_path, model_name)
temp_file = os.path.join(shared.opts.embeddings_dir, model_path, temp_file)
else:
model_file = os.path.join(shared.opts.ckpt_dir, model_path, model_name)
temp_file = os.path.join(shared.opts.ckpt_dir, model_path, temp_file)
res = f'CivitAI download: name={model_name} url={model_url} path={model_path} temp={temp_file}'
res = f'CivitAI download: name="{model_name}" url="{model_url}" path="{model_path}" temp="{temp_file}"'
if os.path.isfile(model_file):
res += ' already exists'
shared.log.warning(res)
@@ -101,7 +104,7 @@ def download_civit_model_thread(model_name, model_url, model_path, model_type, p
r = shared.req(model_url, headers=headers, stream=True)
total_size = int(r.headers.get('content-length', 0))
res += f' size={round((starting_pos + total_size)/1024/1024)}Mb'
res += f' size={round((starting_pos + total_size)/1024/1024, 2)}Mb'
shared.log.info(res)
shared.state.begin('civitai')
block_size = 16384 # 16KB blocks
@@ -117,7 +120,7 @@ def download_civit_model_thread(model_name, model_url, model_path, model_type, p
written = written + len(data)
f.write(data)
download_pbar.update(task, description="Download", completed=written)
if written < 1024 * 1024: # min threshold
if written < 1024: # min threshold
os.remove(temp_file)
raise ValueError(f'removed invalid download: bytes={written}')
if preview is not None:
modules/ui_models.py
+24 -13
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@@ -417,13 +417,19 @@ def create_ui():
def civit_search_model(name, tag, model_type):
# types = 'LORA' if model_type == 'LoRA' else 'Checkpoint'
url = 'https://civitai.com/api/v1/models?limit=25&&Sort=Newest'
url = 'https://civitai.com/api/v1/models?limit=25&Sort=Newest'
if model_type == 'SD 1.5' or model_type == 'SD XL':
url += '&types=Checkpoint'
elif model_type == 'LoRA':
url += '&types=LORA'
elif model_type == 'Embedding':
url += '&types=TextualInversion'
if name is not None and len(name) > 0:
url += f'&query={name}'
if tag is not None and len(tag) > 0:
url += f'&tag={tag}'
r = req(url)
log.debug(f'CivitAI search: name="{name}" tag={tag or "none"} status={r.status_code}')
log.debug(f'CivitAI search: name="{name}" tag={tag or "none"} url="{url}" status={r.status_code}')
if r.status_code != 200:
return [], [], []
body = r.json()
@@ -434,16 +440,21 @@ def create_ui():
found = 0
if model_type == 'LoRA' and model['type'] in ['LORA', 'LoCon']:
found += 1
for variant in model['modelVersions']:
if model_type == 'SD 1.5':
if 'SD 1.' in variant['baseModel']:
found += 1
if model_type == 'SD XL':
if 'SDXL' in variant['baseModel']:
found += 1
else:
if 'SD 1.' not in variant['baseModel'] and 'SDXL' not in variant['baseModel']:
found += 1
elif model_type == 'Embedding' and model['type'] == 'TextualInversion':
found += 1
elif model_type.startswith('SD') and model['type'] == 'Checkpoint':
for variant in model['modelVersions']:
if model_type == 'SD 1.5':
if 'SD 1.' in variant['baseModel']:
found += 1
if model_type == 'SD XL':
if 'SDXL' in variant['baseModel']:
found += 1
else:
if 'SD 1.' not in variant['baseModel'] and 'SDXL' not in variant['baseModel']:
found += 1
elif model_type == 'Other':
found += 1
if found > 0:
data1.append([
model['id'],
@@ -581,7 +592,7 @@ def create_ui():
gr.HTML('<h2>Search for models</h2>')
with gr.Row():
with gr.Column(scale=1):
civit_model_type = gr.Dropdown(label='Model type', choices=['SD 1.5', 'SD XL', 'LoRA', 'Other'], value='LoRA')
civit_model_type = gr.Dropdown(label='Model type', choices=['SD 1.5', 'SD XL', 'LoRA', 'Embedding', 'Other'], value='LoRA')
with gr.Column(scale=15):
with gr.Row():
civit_search_text = gr.Textbox('', label='Search models', placeholder='keyword')