[PPDiffusers] Fix ppdiffusers bug and support ZH stablediffusion

paddlenlp/transformers/clip/modeling.py

@@ -533,7 +533,7 @@ def quick_gelu(x):
 
 F.quick_gelu = quick_gelu
 
-NEG_INF = float("-inf")  # -1e4 -1e9
+NEG_INF = -1e9  # float("-inf") -1e4 -1e9
 
 
 class VisionTransformer(nn.Layer):

ppdiffusers/README.md

@@ -5,6 +5,7 @@
 
 ## 1. News 📢
 
+* 🔥 **2022.11.04 支持 IDEA-CCNL/Taiyi-Stable-Diffusion-1B-Chinese-v0.1 和 IDEA-CCNL/Taiyi-Stable-Diffusion-1B-Chinese-EN-v0.1 中文权重**
 * 🔥 **2022.10.27 发布 PPDiffusers仓库**
 
 
@@ -39,7 +40,7 @@ python setup.py install
 
 ## 4. 使用PPDiffusers快速体验Stable Diffusion模型!
 
-Stable Diffusion 是一个**文本到图像(text-to-image)**的**潜在扩散模型(latent diffusion model, ldm)**, 该模型是由来自[CompVis](https://github.com/CompVis), [Stability AI](https://stability.ai/), [LAION](https://laion.ai/) 的工程师以及 [RunwayML](https://runwayml.com/)一起开发而完成的。该模型使用了大小为**512x512**的[LAION-5B](https://laion.ai/blog/laion-5b/)数据集子集进行训练。该模型使用了Openai开源的**CLIP ViT-L/14** 文本编码器(text_encoder)来编码提示(prompt)文本，从而作为引导条件（注意该部分权重不进行训练）。该模型使用了Unet模型（860M参数）和text encoder（123M参数），并且可以在具有4GB显存（注：当前paddle版本需要进行优化，无法在4GB的显卡上运行）的GPU进行推理预测。
+Stable Diffusion 是一个**文本到图像(text-to-image)**的**潜在扩散模型(latent diffusion model, ldm)**, 该模型是由来自[CompVis](https://github.com/CompVis), [Stability AI](https://stability.ai/), [LAION](https://laion.ai/) 的工程师以及 [RunwayML](https://runwayml.com/)一起开发而完成的。该模型使用了大小为**512x512**的[LAION-5B](https://laion.ai/blog/laion-5b/)数据集子集进行训练。该模型使用了Openai开源的**CLIP ViT-L/14** 文本编码器(text_encoder)来编码提示(prompt)文本，从而作为引导条件（注意该部分权重不进行训练）。该模型使用了Unet模型（860M参数）和text encoder（123M参数），并且可以在具有4GB显存的GPU上进行推理预测。
 
 ___注意___:
 ___为了方便国内用户下载使用及快速体验Stable Diffusion模型，我们在百度云(BOS)上提供了paddle版本的镜像权重。注意：为了使用该模型与权重，你必须接受该模型所要求的**License**，请访问huggingface的[model card](https://huggingface.co/runwayml/stable-diffusion-v1-5), 仔细阅读里面的**License**，然后签署该协议。___
@@ -61,8 +62,7 @@ image = pipe(prompt).images[0]
 
 image.save("astronaut_rides_horse.png")
 ```
-<center><image src="https://user-images.githubusercontent.com/50394665/197779466-04543823-8b83-41d6-94e8-146a7dac00d7.png" width="600"></center>
-
+<img width="600" alt="image" src="https://user-images.githubusercontent.com/50394665/197779466-04543823-8b83-41d6-94e8-146a7dac00d7.png">
 
 ### 4.2 使用Stable Diffusion进行由文本引导的图片-图片的生成
 
@@ -74,10 +74,10 @@ from io import BytesIO
 
 from ppdiffusers import StableDiffusionImg2ImgPipeline
 
-# load the pipeline
+# 加载pipeline
 pipe = StableDiffusionImg2ImgPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
 
-# let's download an initial image
+# 下载初始图片
 url = "https://paddlenlp.bj.bcebos.com/models/community/CompVis/stable-diffusion-v1-4/sketch-mountains-input.png"
 
 response = requests.get(url)
@@ -92,7 +92,7 @@ with paddle.amp.auto_cast(True):
 image.save("fantasy_landscape.png")
 ```
 
-<center><image src="https://user-images.githubusercontent.com/50394665/197780044-34e6f8ca-6864-4c3d-bb99-28e0aadf867b.png" width="600"></center>
+<img width="600" alt="image" src="https://user-images.githubusercontent.com/50394665/197780044-34e6f8ca-6864-4c3d-bb99-28e0aadf867b.png">
 
 
 ### 4.3 使用Stable Diffusion根据文本补全图片
@@ -125,7 +125,7 @@ with paddle.amp.auto_cast(True):
 
 image.save("cat_on_bench.png")
 ```
-<center><image src="https://user-images.githubusercontent.com/50394665/197783711-ab3caf2e-5a4d-4099-8d01-d6ca80ca8e78.png" width="600"></center>
+<img width="600" alt="image" src="https://user-images.githubusercontent.com/50394665/197783711-ab3caf2e-5a4d-4099-8d01-d6ca80ca8e78.png">
 
 Tips: 下面的使用方法是新版本的代码，也是官方推荐的代码，注意必须配合**runwayml/stable-diffusion-inpainting**才可正常使用。
 ```python
@@ -153,7 +153,7 @@ image = pipe(prompt=prompt, image=init_image, mask_image=mask_image).images[0]
 
 image.save("cat_on_bench_new.png")
 ```
-<center><image src="https://user-images.githubusercontent.com/50394665/198016801-87cec13b-0d89-41c3-aedb-c89a43d76153.png" width="600"></center>
+<img width="600" alt="image" src="https://user-images.githubusercontent.com/50394665/198016801-87cec13b-0d89-41c3-aedb-c89a43d76153.png">
 
 ## 5. Credits
 

ppdiffusers/VERSION

@@ -1 +1 @@
-0.6.0.dev1
+0.6.1

ppdiffusers/examples/community/clip_guided_stable_diffusion.py

@@ -262,7 +262,10 @@ def __call__(
                 "The following part of your input was truncated because CLIP can only handle sequences up to"
                 f" {self.tokenizer.model_max_length} tokens: {removed_text}")
             text_input_ids = text_input_ids[:, :self.tokenizer.model_max_length]
-        text_embeddings = self.text_encoder(text_input_ids)[0]
+
+        attention_mask = paddle.ones_like(text_input_ids)
+        text_embeddings = self.text_encoder(text_input_ids,
+                                            attention_mask=attention_mask)[0]
 
         # duplicate text embeddings for each generation per prompt
         bs_embed, seq_len, _ = text_embeddings.shape
@@ -323,7 +326,9 @@ def __call__(
                 truncation=True,
                 return_tensors="pd",
             )
-            uncond_embeddings = self.text_encoder(uncond_input.input_ids)[0]
+            attention_mask = paddle.ones_like(uncond_input.input_ids)
+            uncond_embeddings = self.text_encoder(
+                uncond_input.input_ids, attention_mask=attention_mask)[0]
 
             # duplicate unconditional embeddings for each generation per prompt
             seq_len = uncond_embeddings.shape[1]

ppdiffusers/examples/community/composable_stable_diffusion.py

@@ -89,7 +89,7 @@ def __init__(
             logger.warn(
                 f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
                 " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
-                " results in services or applications open to the public. Both the diffusers team and Hugging Face"
+                " results in services or applications open to the public. PaddleNLP team, diffusers team and Hugging Face"
                 " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
                 " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
                 " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
@@ -253,7 +253,9 @@ def __call__(
                 "The following part of your input was truncated because CLIP can only handle sequences up to"
                 f" {self.tokenizer.model_max_length} tokens: {removed_text}")
             text_input_ids = text_input_ids[:, :self.tokenizer.model_max_length]
-        text_embeddings = self.text_encoder(text_input_ids)[0]
+        attention_mask = paddle.ones_like(text_input_ids)
+        text_embeddings = self.text_encoder(text_input_ids,
+                                            attention_mask=attention_mask)[0]
 
         # duplicate text embeddings for each generation per prompt, using mps friendly method
         # bs_embed, seq_len, _ = text_embeddings.shape
@@ -318,7 +320,9 @@ def __call__(
                     truncation=True,
                     return_tensors="pd",
                 )
-                uncond_embeddings = self.text_encoder(uncond_input.input_ids)[0]
+                attention_mask = paddle.ones_like(uncond_input.input_ids)
+                uncond_embeddings = self.text_encoder(
+                    uncond_input.input_ids, attention_mask=attention_mask)[0]
 
                 # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
                 # seq_len = uncond_embeddings.shape[1]

ppdiffusers/examples/community/interpolate_stable_diffusion.py

@@ -118,7 +118,7 @@ def __init__(
             logger.warn(
                 f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
                 " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
-                " results in services or applications open to the public. Both the diffusers team and Hugging Face"
+                " results in services or applications open to the public. PaddleNLP team, diffusers team and Hugging Face"
                 " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
                 " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
                 " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
@@ -277,7 +277,9 @@ def __call__(
                 )
                 text_input_ids = text_input_ids[:, :self.tokenizer.
                                                 model_max_length]
-            text_embeddings = self.text_encoder(text_input_ids)[0]
+            attention_mask = paddle.ones_like(text_input_ids)
+            text_embeddings = self.text_encoder(
+                text_input_ids, attention_mask=attention_mask)[0]
         else:
             batch_size = text_embeddings.shape[0]
 
@@ -318,7 +320,9 @@ def __call__(
                 truncation=True,
                 return_tensors="pd",
             )
-            uncond_embeddings = self.text_encoder(uncond_input.input_ids)[0]
+            attention_mask = paddle.ones_like(uncond_input.input_ids)
+            uncond_embeddings = self.text_encoder(
+                uncond_input.input_ids, attention_mask=attention_mask)[0]
 
             # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
             seq_len = uncond_embeddings.shape[1]

ppdiffusers/examples/community/lpw_stable_diffusion.py

@@ -59,7 +59,7 @@
 
 
 def parse_prompt_attention(text):
-    """
+    r"""
     Parses a string with attention tokens and returns a list of pairs: text and its associated weight.
     Accepted tokens are:
       (abc) - increases attention to abc by a multiplier of 1.1
@@ -186,6 +186,7 @@ def pad_tokens_and_weights(tokens,
                            max_length,
                            bos,
                            eos,
+                           pad,
                            no_boseos_middle=True,
                            chunk_length=77):
     r"""
@@ -194,8 +195,9 @@ def pad_tokens_and_weights(tokens,
     max_embeddings_multiples = (max_length - 2) // (chunk_length - 2)
     weights_length = max_length if no_boseos_middle else max_embeddings_multiples * chunk_length
     for i in range(len(tokens)):
-        tokens[i] = [bos
-                     ] + tokens[i] + [eos] * (max_length - 1 - len(tokens[i]))
+        tokens[i] = [bos] + tokens[i] + [
+            eos
+        ] + [pad] * (max_length - 2 - len(tokens[i]))
         if no_boseos_middle:
             weights[i] = [
                 1.0
@@ -238,7 +240,9 @@ def get_unweighted_text_embeddings(
             # cover the head and the tail by the starting and the ending tokens
             text_input_chunk[:, 0] = text_input[0, 0]
             text_input_chunk[:, -1] = text_input[0, -1]
-            text_embedding = pipe.text_encoder(text_input_chunk)[0]
+            attention_mask = paddle.ones_like(text_input_chunk)
+            text_embedding = pipe.text_encoder(text_input_chunk,
+                                               attention_mask=attention_mask)[0]
 
             if no_boseos_middle:
                 if i == 0:
@@ -254,7 +258,9 @@ def get_unweighted_text_embeddings(
             text_embeddings.append(text_embedding)
         text_embeddings = paddle.concat(text_embeddings, axis=1)
     else:
-        text_embeddings = pipe.text_encoder(text_input)[0]
+        attention_mask = paddle.ones_like(text_input)
+        text_embeddings = pipe.text_encoder(text_input,
+                                            attention_mask=attention_mask)[0]
     return text_embeddings
 
 
@@ -336,14 +342,17 @@ def get_weighted_text_embeddings(
                   2) * max_embeddings_multiples + 2
 
     # pad the length of tokens and weights
-    bos = pipe.tokenizer.bos_token_id
-    eos = pipe.tokenizer.eos_token_id
+    # support bert tokenizer
+    bos = pipe.tokenizer.bos_token_id if pipe.tokenizer.bos_token_id is not None else pipe.tokenizer.cls_token_id
+    eos = pipe.tokenizer.eos_token_id if pipe.tokenizer.eos_token_id is not None else pipe.tokenizer.sep_token_id
+    pad = pipe.tokenizer.pad_token_id
     prompt_tokens, prompt_weights = pad_tokens_and_weights(
         prompt_tokens,
         prompt_weights,
         max_length,
         bos,
         eos,
+        pad,
         no_boseos_middle=no_boseos_middle,
         chunk_length=pipe.tokenizer.model_max_length,
     )
@@ -355,6 +364,7 @@ def get_weighted_text_embeddings(
             max_length,
             bos,
             eos,
+            pad,
             no_boseos_middle=no_boseos_middle,
             chunk_length=pipe.tokenizer.model_max_length,
         )
@@ -481,7 +491,7 @@ def __init__(
             logger.warn(
                 f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
                 " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
-                " results in services or applications open to the public. Both the diffusers team and Hugging Face"
+                " results in services or applications open to the public. PaddleNLP team, diffusers team and Hugging Face"
                 " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
                 " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
                 " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
@@ -753,7 +763,8 @@ def __call__(
             timesteps = timesteps.tile([
                 batch_size * num_images_per_prompt,
             ])
-
+            if seed is not None:
+                paddle.seed(seed)
             noise = paddle.randn(
                 init_latents.shape,
                 dtype=latents_dtype,
@@ -926,8 +937,8 @@ def text2img(
 
     def img2img(
         self,
-        init_image: Union[paddle.Tensor, PIL.Image.Image],
         prompt: Union[str, List[str]],
+        init_image: Union[paddle.Tensor, PIL.Image.Image],
         negative_prompt: Optional[Union[str, List[str]]] = None,
         strength: float = 0.8,
         num_inference_steps: Optional[int] = 50,
@@ -1016,9 +1027,9 @@ def img2img(
 
     def inpaint(
         self,
+        prompt: Union[str, List[str]],
         init_image: Union[paddle.Tensor, PIL.Image.Image],
         mask_image: Union[paddle.Tensor, PIL.Image.Image],
-        prompt: Union[str, List[str]],
         negative_prompt: Optional[Union[str, List[str]]] = None,
         strength: float = 0.8,
         num_inference_steps: Optional[int] = 50,

ppdiffusers/examples/community/wildcard_stable_diffusion.py

@@ -153,7 +153,7 @@ def __init__(
             logger.warn(
                 f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
                 " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
-                " results in services or applications open to the public. Both the diffusers team and Hugging Face"
+                " results in services or applications open to the public. PaddleNLP team, diffusers team and Hugging Face"
                 " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
                 " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
                 " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
@@ -298,7 +298,9 @@ def __call__(
                 "The following part of your input was truncated because CLIP can only handle sequences up to"
                 f" {self.tokenizer.model_max_length} tokens: {removed_text}")
             text_input_ids = text_input_ids[:, :self.tokenizer.model_max_length]
-        text_embeddings = self.text_encoder(text_input_ids)[0]
+        attention_mask = paddle.ones_like(text_input_ids)
+        text_embeddings = self.text_encoder(text_input_ids,
+                                            attention_mask=attention_mask)[0]
 
         # duplicate text embeddings for each generation per prompt, using mps friendly method
         bs_embed, seq_len, _ = text_embeddings.shape
@@ -337,7 +339,9 @@ def __call__(
                 truncation=True,
                 return_tensors="pd",
             )
-            uncond_embeddings = self.text_encoder(uncond_input.input_ids)[0]
+            attention_mask = paddle.ones_like(uncond_input.input_ids)
+            uncond_embeddings = self.text_encoder(
+                uncond_input.input_ids, attention_mask=attention_mask)[0]
 
             # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
             seq_len = uncond_embeddings.shape[1]