fix a bug of prompt embeds in sdxl

src/diffusers/pipelines/controlnet/pipeline_controlnet_sd_xl.py

@@ -304,11 +304,6 @@ def encode_prompt(
                 pooled_prompt_embeds = prompt_embeds[0]
                 prompt_embeds = prompt_embeds.hidden_states[-2]
 
-                bs_embed, seq_len, _ = prompt_embeds.shape
-                # duplicate text embeddings for each generation per prompt, using mps friendly method
-                prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
-                prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
-
                 prompt_embeds_list.append(prompt_embeds)
 
             prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)
@@ -361,26 +356,23 @@ def encode_prompt(
                 negative_pooled_prompt_embeds = negative_prompt_embeds[0]
                 negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]
 
-                if do_classifier_free_guidance:
-                    # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
-                    seq_len = negative_prompt_embeds.shape[1]
-
-                    negative_prompt_embeds = negative_prompt_embeds.to(dtype=text_encoder.dtype, device=device)
-
-                    negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
-                    negative_prompt_embeds = negative_prompt_embeds.view(
-                        batch_size * num_images_per_prompt, seq_len, -1
-                    )
-
-                    # For classifier free guidance, we need to do two forward passes.
-                    # Here we concatenate the unconditional and text embeddings into a single batch
-                    # to avoid doing two forward passes
-
                 negative_prompt_embeds_list.append(negative_prompt_embeds)
 
             negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1)
 
-        bs_embed = pooled_prompt_embeds.shape[0]
+        prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
+        bs_embed, seq_len, _ = prompt_embeds.shape
+        # duplicate text embeddings for each generation per prompt, using mps friendly method
+        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
+        prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
+
+        if do_classifier_free_guidance:
+            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+            seq_len = negative_prompt_embeds.shape[1]
+            negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
+            negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
+            negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
+
         pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
             bs_embed * num_images_per_prompt, -1
         )

src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl.py

@@ -319,11 +319,6 @@ def encode_prompt(
                 pooled_prompt_embeds = prompt_embeds[0]
                 prompt_embeds = prompt_embeds.hidden_states[-2]
 
-                bs_embed, seq_len, _ = prompt_embeds.shape
-                # duplicate text embeddings for each generation per prompt, using mps friendly method
-                prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
-                prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
-
                 prompt_embeds_list.append(prompt_embeds)
 
             prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)
@@ -376,26 +371,23 @@ def encode_prompt(
                 negative_pooled_prompt_embeds = negative_prompt_embeds[0]
                 negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]
 
-                if do_classifier_free_guidance:
-                    # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
-                    seq_len = negative_prompt_embeds.shape[1]
-
-                    negative_prompt_embeds = negative_prompt_embeds.to(dtype=text_encoder.dtype, device=device)
-
-                    negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
-                    negative_prompt_embeds = negative_prompt_embeds.view(
-                        batch_size * num_images_per_prompt, seq_len, -1
-                    )
-
-                    # For classifier free guidance, we need to do two forward passes.
-                    # Here we concatenate the unconditional and text embeddings into a single batch
-                    # to avoid doing two forward passes
-
                 negative_prompt_embeds_list.append(negative_prompt_embeds)
 
             negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1)
 
-        bs_embed = pooled_prompt_embeds.shape[0]
+        prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
+        bs_embed, seq_len, _ = prompt_embeds.shape
+        # duplicate text embeddings for each generation per prompt, using mps friendly method
+        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
+        prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
+
+        if do_classifier_free_guidance:
+            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+            seq_len = negative_prompt_embeds.shape[1]
+            negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
+            negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
+            negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
+
         pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
             bs_embed * num_images_per_prompt, -1
         )

src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_img2img.py

@@ -327,11 +327,6 @@ def encode_prompt(
                 pooled_prompt_embeds = prompt_embeds[0]
                 prompt_embeds = prompt_embeds.hidden_states[-2]
 
-                bs_embed, seq_len, _ = prompt_embeds.shape
-                # duplicate text embeddings for each generation per prompt, using mps friendly method
-                prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
-                prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
-
                 prompt_embeds_list.append(prompt_embeds)
 
             prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)
@@ -384,26 +379,23 @@ def encode_prompt(
                 negative_pooled_prompt_embeds = negative_prompt_embeds[0]
                 negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]
 
-                if do_classifier_free_guidance:
-                    # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
-                    seq_len = negative_prompt_embeds.shape[1]
-
-                    negative_prompt_embeds = negative_prompt_embeds.to(dtype=text_encoder.dtype, device=device)
-
-                    negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
-                    negative_prompt_embeds = negative_prompt_embeds.view(
-                        batch_size * num_images_per_prompt, seq_len, -1
-                    )
-
-                    # For classifier free guidance, we need to do two forward passes.
-                    # Here we concatenate the unconditional and text embeddings into a single batch
-                    # to avoid doing two forward passes
-
                 negative_prompt_embeds_list.append(negative_prompt_embeds)
 
             negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1)
 
-        bs_embed = pooled_prompt_embeds.shape[0]
+        prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
+        bs_embed, seq_len, _ = prompt_embeds.shape
+        # duplicate text embeddings for each generation per prompt, using mps friendly method
+        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
+        prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
+
+        if do_classifier_free_guidance:
+            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+            seq_len = negative_prompt_embeds.shape[1]
+            negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
+            negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
+            negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
+
         pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
             bs_embed * num_images_per_prompt, -1
         )

src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py

@@ -433,11 +433,6 @@ def encode_prompt(
                 pooled_prompt_embeds = prompt_embeds[0]
                 prompt_embeds = prompt_embeds.hidden_states[-2]
 
-                bs_embed, seq_len, _ = prompt_embeds.shape
-                # duplicate text embeddings for each generation per prompt, using mps friendly method
-                prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
-                prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
-
                 prompt_embeds_list.append(prompt_embeds)
 
             prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)
@@ -490,26 +485,23 @@ def encode_prompt(
                 negative_pooled_prompt_embeds = negative_prompt_embeds[0]
                 negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]
 
-                if do_classifier_free_guidance:
-                    # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
-                    seq_len = negative_prompt_embeds.shape[1]
-
-                    negative_prompt_embeds = negative_prompt_embeds.to(dtype=text_encoder.dtype, device=device)
-
-                    negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
-                    negative_prompt_embeds = negative_prompt_embeds.view(
-                        batch_size * num_images_per_prompt, seq_len, -1
-                    )
-
-                    # For classifier free guidance, we need to do two forward passes.
-                    # Here we concatenate the unconditional and text embeddings into a single batch
-                    # to avoid doing two forward passes
-
                 negative_prompt_embeds_list.append(negative_prompt_embeds)
 
             negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1)
 
-        bs_embed = pooled_prompt_embeds.shape[0]
+        prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
+        bs_embed, seq_len, _ = prompt_embeds.shape
+        # duplicate text embeddings for each generation per prompt, using mps friendly method
+        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
+        prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
+
+        if do_classifier_free_guidance:
+            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+            seq_len = negative_prompt_embeds.shape[1]
+            negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
+            negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
+            negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
+
         pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
             bs_embed * num_images_per_prompt, -1
         )

tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl.py

@@ -148,6 +148,44 @@ def test_stable_diffusion_xl_euler(self):
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
 
+    def test_stable_diffusion_xl_prompt_embeds(self):
+        components = self.get_dummy_components()
+        sd_pipe = StableDiffusionXLPipeline(**components)
+        sd_pipe = sd_pipe.to(torch_device)
+        sd_pipe = sd_pipe.to(torch_device)
+        sd_pipe.set_progress_bar_config(disable=None)
+
+        # forward without prompt embeds
+        inputs = self.get_dummy_inputs(torch_device)
+        inputs["prompt"] = 2 * [inputs["prompt"]]
+        inputs["num_images_per_prompt"] = 2
+
+        output = sd_pipe(**inputs)
+        image_slice_1 = output.images[0, -3:, -3:, -1]
+
+        # forward with prompt embeds
+        inputs = self.get_dummy_inputs(torch_device)
+        prompt = 2 * [inputs.pop("prompt")]
+
+        (
+            prompt_embeds,
+            negative_prompt_embeds,
+            pooled_prompt_embeds,
+            negative_pooled_prompt_embeds,
+        ) = sd_pipe.encode_prompt(prompt)
+
+        output = sd_pipe(
+            **inputs,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            pooled_prompt_embeds=pooled_prompt_embeds,
+            negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
+        )
+        image_slice_2 = output.images[0, -3:, -3:, -1]
+
+        # make sure that it's equal
+        assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4
+
     def test_stable_diffusion_xl_negative_prompt_embeds(self):
         components = self.get_dummy_components()
         sd_pipe = StableDiffusionXLPipeline(**components)