Fix type mismatch error, add tests for negative prompts (#823)

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py

@@ -234,8 +234,8 @@ class StableDiffusionPipeline(DiffusionPipeline):
                 uncond_tokens = [""]
             elif type(prompt) is not type(negative_prompt):
                 raise TypeError(
-                    "`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
-                    " {type(prompt)}."
+                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+                    f" {type(prompt)}."
                 )
             elif isinstance(negative_prompt, str):
                 uncond_tokens = [negative_prompt]

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py

@@ -195,7 +195,6 @@ class StableDiffusionImg2ImgPipeline(DiffusionPipeline):
         """
         if isinstance(prompt, str):
             batch_size = 1
-            prompt = [prompt]
         elif isinstance(prompt, list):
             batch_size = len(prompt)
         else:
@@ -250,8 +249,8 @@ class StableDiffusionImg2ImgPipeline(DiffusionPipeline):
                 uncond_tokens = [""]
             elif type(prompt) is not type(negative_prompt):
                 raise TypeError(
-                    "`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
-                    " {type(prompt)}."
+                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+                    f" {type(prompt)}."
                 )
             elif isinstance(negative_prompt, str):
                 uncond_tokens = [negative_prompt]
@@ -285,6 +284,8 @@ class StableDiffusionImg2ImgPipeline(DiffusionPipeline):
         init_latents = init_latent_dist.sample(generator=generator)
         init_latents = 0.18215 * init_latents
 
+        if isinstance(prompt, str):
+            prompt = [prompt]
         if len(prompt) > init_latents.shape[0] and len(prompt) % init_latents.shape[0] == 0:
             # expand init_latents for batch_size
             deprecation_message = (

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py

@@ -266,8 +266,8 @@ class StableDiffusionInpaintPipeline(DiffusionPipeline):
                 uncond_tokens = [""]
             elif type(prompt) is not type(negative_prompt):
                 raise TypeError(
-                    "`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
-                    " {type(prompt)}."
+                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+                    f" {type(prompt)}."
                 )
             elif isinstance(negative_prompt, str):
                 uncond_tokens = [negative_prompt]

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_onnx.py

@@ -108,8 +108,8 @@ class StableDiffusionOnnxPipeline(DiffusionPipeline):
                 uncond_tokens = [""] * batch_size
             elif type(prompt) is not type(negative_prompt):
                 raise TypeError(
-                    "`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
-                    " {type(prompt)}."
+                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+                    f" {type(prompt)}."
                 )
             elif isinstance(negative_prompt, str):
                 uncond_tokens = [negative_prompt] * batch_size

tests/test_pipelines.py

@@ -575,6 +575,46 @@ class PipelineFastTests(unittest.TestCase):
 
         assert np.abs(output_2.images.flatten() - output_1.images.flatten()).max() < 1e-4
 
+    def test_stable_diffusion_negative_prompt(self):
+        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
+        unet = self.dummy_cond_unet
+        scheduler = PNDMScheduler(skip_prk_steps=True)
+        vae = self.dummy_vae
+        bert = self.dummy_text_encoder
+        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
+
+        # make sure here that pndm scheduler skips prk
+        sd_pipe = StableDiffusionPipeline(
+            unet=unet,
+            scheduler=scheduler,
+            vae=vae,
+            text_encoder=bert,
+            tokenizer=tokenizer,
+            safety_checker=self.dummy_safety_checker,
+            feature_extractor=self.dummy_extractor,
+        )
+        sd_pipe = sd_pipe.to(device)
+        sd_pipe.set_progress_bar_config(disable=None)
+
+        prompt = "A painting of a squirrel eating a burger"
+        negative_prompt = "french fries"
+        generator = torch.Generator(device=device).manual_seed(0)
+        output = sd_pipe(
+            prompt,
+            negative_prompt=negative_prompt,
+            generator=generator,
+            guidance_scale=6.0,
+            num_inference_steps=2,
+            output_type="np",
+        )
+
+        image = output.images
+        image_slice = image[0, -3:, -3:, -1]
+
+        assert image.shape == (1, 128, 128, 3)
+        expected_slice = np.array([0.4851, 0.4617, 0.4765, 0.5127, 0.4845, 0.5153, 0.5141, 0.4886, 0.4719])
+        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
+
     def test_score_sde_ve_pipeline(self):
         unet = self.dummy_uncond_unet
         scheduler = ScoreSdeVeScheduler()
@@ -704,6 +744,48 @@ class PipelineFastTests(unittest.TestCase):
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
 
+    def test_stable_diffusion_img2img_negative_prompt(self):
+        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
+        unet = self.dummy_cond_unet
+        scheduler = PNDMScheduler(skip_prk_steps=True)
+        vae = self.dummy_vae
+        bert = self.dummy_text_encoder
+        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
+
+        init_image = self.dummy_image.to(device)
+
+        # make sure here that pndm scheduler skips prk
+        sd_pipe = StableDiffusionImg2ImgPipeline(
+            unet=unet,
+            scheduler=scheduler,
+            vae=vae,
+            text_encoder=bert,
+            tokenizer=tokenizer,
+            safety_checker=self.dummy_safety_checker,
+            feature_extractor=self.dummy_extractor,
+        )
+        sd_pipe = sd_pipe.to(device)
+        sd_pipe.set_progress_bar_config(disable=None)
+
+        prompt = "A painting of a squirrel eating a burger"
+        negative_prompt = "french fries"
+        generator = torch.Generator(device=device).manual_seed(0)
+        output = sd_pipe(
+            prompt,
+            negative_prompt=negative_prompt,
+            generator=generator,
+            guidance_scale=6.0,
+            num_inference_steps=2,
+            output_type="np",
+            init_image=init_image,
+        )
+        image = output.images
+        image_slice = image[0, -3:, -3:, -1]
+
+        assert image.shape == (1, 32, 32, 3)
+        expected_slice = np.array([0.4065, 0.3783, 0.4050, 0.5266, 0.4781, 0.4252, 0.4203, 0.4692, 0.4365])
+        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
+
     def test_stable_diffusion_img2img_multiple_init_images(self):
         device = "cpu"  # ensure determinism for the device-dependent torch.Generator
         unet = self.dummy_cond_unet
@@ -861,6 +943,52 @@ class PipelineFastTests(unittest.TestCase):
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
 
+    def test_stable_diffusion_inpaint_negative_prompt(self):
+        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
+        unet = self.dummy_cond_unet
+        scheduler = PNDMScheduler(skip_prk_steps=True)
+        vae = self.dummy_vae
+        bert = self.dummy_text_encoder
+        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
+
+        image = self.dummy_image.cpu().permute(0, 2, 3, 1)[0]
+        init_image = Image.fromarray(np.uint8(image)).convert("RGB")
+        mask_image = Image.fromarray(np.uint8(image + 4)).convert("RGB").resize((128, 128))
+
+        # make sure here that pndm scheduler skips prk
+        sd_pipe = StableDiffusionInpaintPipeline(
+            unet=unet,
+            scheduler=scheduler,
+            vae=vae,
+            text_encoder=bert,
+            tokenizer=tokenizer,
+            safety_checker=self.dummy_safety_checker,
+            feature_extractor=self.dummy_extractor,
+        )
+        sd_pipe = sd_pipe.to(device)
+        sd_pipe.set_progress_bar_config(disable=None)
+
+        prompt = "A painting of a squirrel eating a burger"
+        negative_prompt = "french fries"
+        generator = torch.Generator(device=device).manual_seed(0)
+        output = sd_pipe(
+            prompt,
+            negative_prompt=negative_prompt,
+            generator=generator,
+            guidance_scale=6.0,
+            num_inference_steps=2,
+            output_type="np",
+            init_image=init_image,
+            mask_image=mask_image,
+        )
+
+        image = output.images
+        image_slice = image[0, -3:, -3:, -1]
+
+        assert image.shape == (1, 32, 32, 3)
+        expected_slice = np.array([0.4765, 0.5339, 0.4541, 0.6240, 0.5439, 0.4055, 0.5503, 0.5891, 0.5150])
+        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
+
     def test_stable_diffusion_num_images_per_prompt(self):
         device = "cpu"  # ensure determinism for the device-dependent torch.Generator
         unet = self.dummy_cond_unet