diff --git a/CHANGELOG.md b/CHANGELOG.md index 5729e62b3..a112a5c27 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -67,6 +67,7 @@ This release is primary service release with cumulative fixes and several improv - fix control input type video - fix reset pipeline at the end of each iteration - fix faceswap when no faces detected +- fix civitai search - multiple ModernUI fixes ## Update for 2024-06-23 diff --git a/modules/model_kolors.py b/modules/model_kolors.py index dcf7c1f26..a89af487b 100644 --- a/modules/model_kolors.py +++ b/modules/model_kolors.py @@ -1,10 +1,8 @@ import torch -import transformers import diffusers repo_id = 'Kwai-Kolors/Kolors' -encoder_id = 'THUDM/chatglm3-6b' def load_kolors(_checkpoint_info, diffusers_load_config={}): @@ -12,15 +10,16 @@ def load_kolors(_checkpoint_info, diffusers_load_config={}): modelloader.hf_login() diffusers_load_config['variant'] = "fp16" if 'torch_dtype' not in diffusers_load_config: - diffusers_load_config['torch_dtype'] = 'torch.float16' + diffusers_load_config['torch_dtype'] = torch.float16 - text_encoder = transformers.AutoModel.from_pretrained(encoder_id, torch_dtype=torch.float16, trust_remote_code=True, cache_dir=shared.opts.diffusers_dir) + # import torch + # import transformers + # encoder_id = 'THUDM/chatglm3-6b' + # text_encoder = transformers.AutoModel.from_pretrained(encoder_id, torch_dtype=torch.float16, trust_remote_code=True, cache_dir=shared.opts.diffusers_dir) # text_encoder = transformers.AutoModel.from_pretrained("THUDM/chatglm3-6b", torch_dtype=torch.float16, trust_remote_code=True).quantize(4).cuda() - tokenizer = transformers.AutoTokenizer.from_pretrained(encoder_id, trust_remote_code=True, cache_dir=shared.opts.diffusers_dir) - pipe = diffusers.StableDiffusionXLPipeline.from_pretrained( + # tokenizer = transformers.AutoTokenizer.from_pretrained(encoder_id, trust_remote_code=True, cache_dir=shared.opts.diffusers_dir) + pipe = diffusers.KolorsPipeline.from_pretrained( repo_id, - tokenizer=tokenizer, - text_encoder=text_encoder, cache_dir = shared.opts.diffusers_dir, **diffusers_load_config, ) diff --git a/modules/modelloader.py b/modules/modelloader.py index fd9e41edb..02db8b52f 100644 --- a/modules/modelloader.py +++ b/modules/modelloader.py @@ -101,6 +101,9 @@ def download_civit_model_thread(model_name, model_url, model_path, model_type, t 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) + elif model_type == 'VAE': + model_file = os.path.join(shared.opts.vae_dir, model_path, model_name) + temp_file = os.path.join(shared.opts.vae_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) diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 01d42bbfb..ed8acceca 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -121,7 +121,8 @@ def insert_tokens(embeddings: list, tokenizers: list): """ tokens = [] for embedding in embeddings: - tokens += embedding.tokens + if embedding is not None: + tokens += embedding.tokens for tokenizer in tokenizers: tokenizer.add_tokens(tokens) @@ -295,7 +296,8 @@ class EmbeddingDatabase: (len(embedding.vector_sizes) < len(hiddensizes) and len(embedding.vector_sizes) != 2)): # SD3 no T5 embedding.tokens = [] self.skipped_embeddings[embedding.name] = embedding - except Exception: + except Exception as e: + shared.log.error(f'Embedding invalid: name="{embedding.name}" fn="{filename}" {e}') self.skipped_embeddings[embedding.name] = embedding if overwrite: shared.log.info(f"Loading Bundled embeddings: {list(data.keys())}") diff --git a/modules/ui_models.py b/modules/ui_models.py index d29f2f404..a5d3639bf 100644 --- a/modules/ui_models.py +++ b/modules/ui_models.py @@ -418,12 +418,14 @@ 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' - if model_type == 'SD 1.5' or model_type == 'SD XL': + if model_type == 'Model': url += '&types=Checkpoint' elif model_type == 'LoRA': - url += '&types=LORA' + url += '&types=LORA&types=DoRA&types=LoCon' elif model_type == 'Embedding': url += '&types=TextualInversion' + elif model_type == 'VAE': + url += '&types=VAE' if name is not None and len(name) > 0: url += f'&query={name}' if tag is not None and len(tag) > 0: @@ -431,28 +433,26 @@ def create_ui(): r = req(url) log.debug(f'CivitAI search: name="{name}" tag={tag or "none"} url="{url}" status={r.status_code}') if r.status_code != 200: + log.warning(f'CivitAI search: name="{name}" tag={tag} status={r.status_code}') + return [], gr.update(visible=False, value=[]), gr.update(visible=False, value=None), gr.update(visible=False, value=None) + try: + body = r.json() + except Exception as e: + log.error(f'CivitAI search: name="{name}" tag={tag} {e}') return [], gr.update(visible=False, value=[]), gr.update(visible=False, value=None), gr.update(visible=False, value=None) - body = r.json() nonlocal data data = body.get('items', []) data1 = [] for model in data: found = 0 - if model_type == 'LoRA' and model['type'] in ['LORA', 'LoCon']: + if model_type == 'LoRA' and model['type'].lower() in ['lora', 'locon', 'dora', 'lycoris']: found += 1 - elif model_type == 'Embedding' and model['type'] == 'TextualInversion': + elif model_type == 'Embedding' and model['type'].lower() in ['textualinversion', 'embedding']: + found += 1 + elif model_type == 'Model' and model['type'].lower() in ['checkpoint']: + found += 1 + elif model_type == 'VAE' and model['type'].lower() in ['vae']: 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: @@ -464,8 +464,7 @@ def create_ui(): model['stats']['rating'] ]) res = f'Search result: name={name} tag={tag or "none"} type={model_type} models={len(data1)}' - return res, gr.update(visible=len(data1) > 0, value=data1 if len(data1) > 0 else []), gr.update( - visible=False, value=None), gr.update(visible=False, value=None) + return res, gr.update(visible=len(data1) > 0, value=data1 if len(data1) > 0 else []), gr.update(visible=False, value=None), gr.update(visible=False, value=None) def civit_select1(evt: gr.SelectData, in_data): model_id = in_data[evt.index[0]][0] @@ -596,7 +595,7 @@ def create_ui(): gr.HTML('

Search for models

') with gr.Row(): with gr.Column(scale=1): - civit_model_type = gr.Dropdown(label='Model type', choices=['SD 1.5', 'SD XL', 'LoRA', 'Embedding', 'Other'], value='LoRA') + civit_model_type = gr.Dropdown(label='Model type', choices=['Model', 'LoRA', 'Embedding', 'VAE', 'Other'], value='Model') with gr.Column(scale=15): with gr.Row(): civit_search_text = gr.Textbox('', label='Search models', placeholder='keyword')