Add adm18

examples/AltCLIP-m18/altclip_inference.py

@@ -11,6 +11,7 @@
 )
 
 model = loader.get_model()
+
 tokenizer = loader.get_tokenizer()
 transform = loader.get_transform()
 
@@ -19,7 +20,7 @@
 tokenizer = loader.get_tokenizer()
 
 def inference():
-    image = Image.open("./dog.jpeg")
+    image = Image.open("./examples/AltCLIP-m18//dog.jpeg")
     image = transform(image)
     image = torch.tensor(image["pixel_values"]).to(device)
     tokenizer_out = tokenizer(["a rat", "a dog", "a cat"], 

examples/AltCLIP/altclip_inference.py

@@ -19,7 +19,7 @@
 tokenizer = loader.get_tokenizer()
 
 def inference():
-    image = Image.open("./dog.jpeg")
+    image = Image.open("./examples/AltCLIP/dog.jpeg")
     image = transform(image)
     image = torch.tensor(image["pixel_values"]).to(device)
     tokenizer_out = tokenizer(["a rat", "a dog", "a cat"], 

examples/AltDiffusion-m18/generate.py

@@ -0,0 +1,24 @@
+# Copyright © 2022 BAAI. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License")
+import torch
+from flagai.auto_model.auto_loader import AutoLoader
+from flagai.model.predictor.predictor import Predictor
+
+# Initialize 
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+loader = AutoLoader(task_name="text2img", #contrastive learning
+                    model_name="AltDiffusion-m18",
+                    model_dir="./checkpoints",
+                    use_fp16=False)
+model = loader.get_model()
+model.eval()
+model.to(device)
+predictor = Predictor(model)
+prompt = "Daenerys Targaryen as a mermeid with a piercing gaze wearing an enchanted bikini in an underwater magical forest, highly detailed face, realistic face, beautiful detailed eyes, fantasy art, in the style of artgerm, illustration, epic, fantasy, intricate, hyper detailed, artstation, concept art, smooth, sharp focus, ray tracing, vibrant, photorealistic"
+negative_prompt = "nsfw, lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, extra head, extra legs,fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry"
+seed = 553124
+predictor.predict_generate_images(
+    prompt=prompt,negative_prompt=negative_prompt,seed=seed
+)

examples/AltDiffusion/generate.py

@@ -19,4 +19,4 @@
 predictor = Predictor(model)
 predictor.predict_generate_images(
     "Anime portrait of natalie portman as an anime girl by stanley artgerm lau, wlop, rossdraws, james jean, andrei riabovitchev, marc simonetti, and sakimichan, trending on artstation"
-)
+)

flagai/auto_model/auto_loader.py

@@ -65,6 +65,7 @@ def __getattr__(self, name):
     "cpm3_lm": ("flagai.model.cpm3_model", "CPM3"),
     "cpm3_train": ("flagai.model.cpm3_train_model", "CPM3"),
     "diffusion_text2img": ("flagai.model.mm.AltDiffusion", "LatentDiffusion"),
+    "diffusion_m18_text2img": ("flagai.model.mm.AltDiffusionM18", "LatentDiffusion"),
     "altclip_txt_img_matching": ("flagai.model.mm.AltCLIP", "AltCLIP"),
     "evaclip_txt_img_matching": ("flagai.model.mm.eva_clip_model", "EVA_CLIP"),
 }
@@ -121,7 +122,9 @@ def __getattr__(self, name):
     "altdiffusion":
     ["flagai.model.mm.diffusion", "LatentDiffusion", "diffusion", "mm","flagai.model.mm.AltCLIP", "AltCLIPProcess"],
     "altdiffusion-m9":
-    ["flagai.model.mm.diffusion", "LatentDiffusion", "diffusion", "mm","flagai.model.mm.AltCLIP", "AltCLIPProcess"],
+    ["flagai.model.mm.diffusionM18", "LatentDiffusion", "diffusion", "mm","flagai.model.mm.AltCLIP", "AltCLIPProcess"],
+    "altdiffusion-m18":
+    ["flagai.model.mm.AltdiffusionM18", "LatentDiffusion", "diffusion_m18", "mm","flagai.model.mm.AltCLIP", "AltCLIPProcess"],
     "swinv1-base-patch4-window7-224":
         ["flagai.model.vision.swinv1", "SwinTransformer", "swinv1", "vision"],
     "swinv2-base-patch4-window8-256":
@@ -200,7 +203,6 @@ def __init__(self,
                 f"For the model_name: {model_name}, these tasks are be supported: {tasks}"
             )
             return
-
         download_path = os.path.join(model_dir, raw_model_name)
         print("*" * 20, task_name, model_name)
         model_name_ = self.is_exist_finetuned_model(raw_model_name, task_name)
@@ -213,7 +215,7 @@ def __init__(self,
             **kwargs)
         if kwargs.get("use_fp16", None):
             self.model.half()
-
+        
         if model_type == "nlp":
             if brief_model_name in ["galactica", ]:
                 self.tokenizer = getattr(LazyImport(MODEL_DICT[model_name][4]),
@@ -254,7 +256,6 @@ def is_exist_finetuned_model(self, raw_model_name, task_name):
                 return model_name_
             else :
                 return raw_model_name
-
         except:
             print("Model hub is not reachable.")
             return raw_model_name

flagai/model/mm/AltCLIP.py

@@ -80,8 +80,7 @@ def __init__(self,
         if text_config_dict is None:
             text_config_dict = {}
         # when reload the config from local, we need name to select which class should be instanced.
-        self.text_config = STUDENT_CONFIG_DICT[
-            kwargs['text_config']['model_type']](**kwargs.pop('text_config'))
+        self.text_config = STUDENT_CONFIG_DICT[kwargs['text_config']['model_type']](**kwargs.pop('text_config'))
         self.num_layers = num_layers
         self.text_model_name = text_model_name
         self.vision_model_name = vision_model_name
@@ -98,7 +97,6 @@ def __init__(self, config: AltCLIPConfig, clip_model=None):
             raise ValueError(
                 "config.vision_config is expected to be of type CLIPVisionConfig but is of type"
                 f" {type(config.vision_config)}.")
-
         text_config = config.text_config
         vision_config = config.vision_config
 
@@ -436,7 +434,7 @@ def forward(
 class AltCLIP(BaseModel):
 
     def __init__(self, config, **kwargs):
-        super().__init__(config, **kwargs)
+        super().__init__(config=config, **kwargs)
 
     @classmethod
     def from_pretrain(cls,
@@ -448,4 +446,6 @@ def from_pretrain(cls,
         super().download(download_path, model_name, only_download_config=only_download_config)
         pretrained_model_name_or_path = os.path.join(download_path, model_name)
         print(pretrained_model_name_or_path)
+        print("Downloading AltCLIP")
+
         return CLIPHF.from_pretrained(pretrained_model_name_or_path)

flagai/model/mm/AltDiffusion.py

@@ -1932,4 +1932,4 @@ def normal_kl(mean1, logvar1, mean2, logvar2):
     ]
 
     return 0.5 * (-1.0 + logvar2 - logvar1 + torch.exp(logvar1 - logvar2) +
-                  ((mean1 - mean2)**2) * torch.exp(-logvar2))
+                  ((mean1 - mean2)**2) * torch.exp(-logvar2))