修复paddlenlp develop版本适配错误_10-11

ppdiffusers/deploy/controlnet/scripts/export.sh

@@ -14,4 +14,4 @@
 
 export USE_PPXFORMERS=False
 export FLAGS_set_to_1d=1
-python export_model.py --pretrained_model_name_or_path runwayml/stable-diffusion-v1-5 --controlnet_pretrained_model_name_or_path  lllyasviel/sd-controlnet-canny --output_path static_model/stable-diffusion-v1-5-canny
+python export_model.py --pretrained_model_name_or_path runwayml/stable-diffusion-v1-5 --controlnet_pretrained_model_name_or_path  lllyasviel/sd-controlnet-canny --output_path static_model/stable-diffusion-v1-5-canny --width 512 --height 512

ppdiffusers/deploy/controlnet/scripts/tune_and_tensorrt.sh

@@ -19,7 +19,7 @@
 export USE_PPXFORMERS=False
 export FLAGS_set_to_1d=1
 # 1. export the model to static_model.
-python export_model.py --pretrained_model_name_or_path runwayml/stable-diffusion-v1-5 --controlnet_pretrained_model_name_or_path  lllyasviel/sd-controlnet-canny --output_path static_model/stable-diffusion-v1-5-canny
+python export_model.py --pretrained_model_name_or_path runwayml/stable-diffusion-v1-5 --controlnet_pretrained_model_name_or_path  lllyasviel/sd-controlnet-canny --output_path static_model/stable-diffusion-v1-5-canny --width 512 --height 512
 
 # 2. tune the shapes of the model for tensorrt 
 python infer.py --model_dir static_model/stable-diffusion-v1-5-canny/ --scheduler "ddim" --backend paddle --device gpu --task_name all --width 512 --height 512 --inference_steps 50 --tune True --use_fp16 False

ppdiffusers/ppdiffusers/models/embeddings.py

@@ -225,19 +225,20 @@ def forward(self, latent):
             latent = self.norm(latent)
 
         # Interpolate or crop positional embeddings as needed
-        if self.pos_embed_max_size:
-            pos_embed = self.cropped_pos_embed(height, width)
-        else:
-            if self.height != height or self.width != width:
-                pos_embed = get_2d_sincos_pos_embed(
-                    embed_dim=self.pos_embed.shape[-1],
-                    grid_size=(height, width),
-                    base_size=self.base_size,
-                    interpolation_scale=self.interpolation_scale,
-                )
-                pos_embed = paddle.to_tensor(pos_embed).astype(paddle.float32).unsqueeze(0)
+        if self.add_pos_embed:
+            if self.pos_embed_max_size:
+                pos_embed = self.cropped_pos_embed(height, width)
             else:
-                pos_embed = self.pos_embed
+                if self.height != height or self.width != width:
+                    pos_embed = get_2d_sincos_pos_embed(
+                        embed_dim=self.pos_embed.shape[-1],
+                        grid_size=(height, width),
+                        base_size=self.base_size,
+                        interpolation_scale=self.interpolation_scale,
+                    )
+                    pos_embed = paddle.to_tensor(pos_embed).astype(paddle.float32).unsqueeze(0)
+                else:
+                    pos_embed = self.pos_embed
 
         # NOTE, new add for unidiffusers!
         if self.add_pos_embed:

ppdiffusers/ppdiffusers/models/transformer_2d.py

@@ -355,7 +355,7 @@ def forward(
             else:
                 batch, height, width, _ = hidden_states.shape
             residual = hidden_states
-
+            shape = paddle.shape(hidden_states)
             hidden_states = self.norm(hidden_states)
             if not self.use_linear_projection:
                 hidden_states = (
@@ -441,7 +441,10 @@ def custom_forward(*inputs):
         # 3. Output
         if self.is_input_continuous:
             if not self.use_linear_projection:
-                hidden_states = hidden_states.reshape([batch, height, width, self.inner_dim])
+                if self.data_format == "NCHW":
+                    hidden_states = hidden_states.reshape([shape[0], shape[2], shape[3], self.inner_dim])
+                else:
+                    hidden_states = hidden_states.reshape([shape[0], shape[1], shape[2], self.inner_dim])
                 if self.data_format == "NCHW":
                     hidden_states = hidden_states.transpose([0, 3, 1, 2])
                 hidden_states = (
@@ -455,7 +458,10 @@ def custom_forward(*inputs):
                     if not USE_PEFT_BACKEND
                     else self.proj_out(hidden_states)
                 )
-                hidden_states = hidden_states.reshape([batch, height, width, self.inner_dim])
+                if self.data_format == "NCHW":
+                    hidden_states = hidden_states.reshape([shape[0], shape[2], shape[3], self.inner_dim])
+                else:
+                    hidden_states = hidden_states.reshape([shape[0], shape[1], shape[2], self.inner_dim])
                 if self.data_format == "NCHW":
                     hidden_states = hidden_states.transpose([0, 3, 1, 2])
 

ppdiffusers/ppdiffusers/pipelines/unidiffuser/modeling_text_decoder.py

@@ -334,7 +334,8 @@ def generate_beam(
                 logits[is_stopped] = -float(np.inf)
                 logits[is_stopped, 0] = 0
                 scores_sum = scores[:, None] + logits
-                seq_lengths[~is_stopped] += 1
+                is_stopped_tensor_int32 = paddle.cast(~is_stopped, dtype='int32')
+                seq_lengths += is_stopped_tensor_int32
                 scores_sum_average = scores_sum / seq_lengths[:, None].cast(scores_sum.dtype)
                 scores_sum_average, next_tokens = scores_sum_average.reshape([-1]).topk(beam_size, -1)
                 next_tokens_source = next_tokens // scores_sum.shape[1]

ppdiffusers/ppdiffusers/transformers/clip/configuration.py

@@ -448,7 +448,7 @@ def from_text_vision_configs(cls, text_config: CLIPTextConfig, vision_config: CL
 
         return cls(text_config=text_config.to_dict(), vision_config=vision_config.to_dict(), **kwargs)
 
-    def to_dict(self):
+    def to_dict(self, *args, ** kwargs):
         """
         Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].
 

ppdiffusers/tests/pipelines/stable_video_diffusion/test_stable_video_diffusion.py

@@ -479,42 +479,3 @@ def test_xformers_attention_forwardGenerator_pass(self):
         self.assertLess(max_diff, expected_max_diff, "XFormers attention should not affect the inference results")
 
         enable_full_determinism()
-
-
-@slow
-@require_paddle_gpu
-class StableVideoDiffusionPipelineSlowTests(unittest.TestCase):
-    def tearDown(self):
-        # clean up the VRAM after each test
-        super().tearDown()
-        gc.collect()
-        paddle.device.cuda.empty_cache()
-
-    def test_sd_video(self):
-        pipe = StableVideoDiffusionPipeline.from_pretrained(
-            "stabilityai/stable-video-diffusion-img2vid-xt",
-            variant="fp16",
-            paddle_dtype=paddle.float16,
-        )
-        pipe.set_progress_bar_config(disable=None)
-        image = load_image(
-            "https://paddlenlp.bj.bcebos.com/models/community/hf-internal-testing/diffusers-images/cat_6.png"
-        )
-
-        generator = paddle.Generator().manual_seed(0)
-        num_frames = 3
-
-        output = pipe(
-            image=image,
-            num_frames=num_frames,
-            generator=generator,
-            num_inference_steps=3,
-            output_type="np",
-        )
-
-        image = output.frames[0]
-        assert image.shape == (num_frames, 576, 1024, 3)
-
-        image_slice = image[0, -3:, -3:, -1]
-        expected_slice = np.array([0.8592, 0.8645, 0.8499, 0.8722, 0.8769, 0.8421, 0.8557, 0.8528, 0.8285])
-        assert numpy_cosine_similarity_distance(image_slice.flatten(), expected_slice.flatten()) < 1e-3

ppdiffusers/tests/pipelines/stable_video_diffusion/test_stable_video_diffusion_slow.py

@@ -0,0 +1,62 @@
+import gc
+import random
+import tempfile
+import unittest
+
+import numpy as np
+import paddle
+
+import ppdiffusers
+from ppdiffusers import (
+    StableVideoDiffusionPipeline,
+)
+from ppdiffusers.utils import (
+    is_accelerate_available,
+    is_accelerate_version,
+    load_image,
+    logging,
+)
+from ppdiffusers.utils.testing_utils import (
+    paddle_device,
+    slow,
+    require_paddle_gpu,
+    numpy_cosine_similarity_distance,
+)
+
+@slow
+@require_paddle_gpu
+class StableVideoDiffusionPipelineSlowTests(unittest.TestCase):
+    def tearDown(self):
+        # clean up the VRAM after each test
+        super().tearDown()
+        gc.collect()
+        paddle.device.cuda.empty_cache()
+
+    def test_sd_video(self):
+        pipe = StableVideoDiffusionPipeline.from_pretrained(
+            "stabilityai/stable-video-diffusion-img2vid-xt",
+            variant="fp16",
+            paddle_dtype=paddle.float16,
+        )
+        pipe.set_progress_bar_config(disable=None)
+        image = load_image(
+            "https://paddlenlp.bj.bcebos.com/models/community/hf-internal-testing/diffusers-images/cat_6.png"
+        )
+
+        generator = paddle.Generator().manual_seed(0)
+        num_frames = 3
+
+        output = pipe(
+            image=image,
+            num_frames=num_frames,
+            generator=generator,
+            num_inference_steps=25,
+            output_type="np",
+        )
+
+        image = output.frames[0]
+        assert image.shape == (num_frames, 576, 1024, 3)
+
+        image_slice = image[0, -3:, -3:, -1]
+        expected_slice = np.array([0.8592, 0.8645, 0.8499, 0.8722, 0.8769, 0.8421, 0.8557, 0.8528, 0.8285])
+        assert numpy_cosine_similarity_distance(image_slice.flatten(), expected_slice.flatten()) < 1e-3