Check for latents, before calling prepare_latents - sdxlImg2Img

examples/community/clip_guided_stable_diffusion_img2img.py

@@ -359,9 +359,16 @@ def __call__(
 
         # Preprocess image
         image = preprocess(image, width, height)
-        latents = self.prepare_latents(
-            image, latent_timestep, batch_size, num_images_per_prompt, text_embeddings.dtype, self.device, generator
-        )
+        if latents is None:
+            latents = self.prepare_latents(
+                image,
+                latent_timestep,
+                batch_size,
+                num_images_per_prompt,
+                text_embeddings.dtype,
+                self.device,
+                generator,
+            )
 
         if clip_guidance_scale > 0:
             if clip_prompt is not None:

examples/community/latent_consistency_img2img.py

@@ -335,17 +335,18 @@ def __call__(
 
         # 5. Prepare latent variable
         num_channels_latents = self.unet.config.in_channels
-        latents = self.prepare_latents(
-            image,
-            latent_timestep,
-            batch_size * num_images_per_prompt,
-            num_channels_latents,
-            height,
-            width,
-            prompt_embeds.dtype,
-            device,
-            latents,
-        )
+        if latents is None:
+            latents = self.prepare_latents(
+                image,
+                latent_timestep,
+                batch_size * num_images_per_prompt,
+                num_channels_latents,
+                height,
+                width,
+                prompt_embeds.dtype,
+                device,
+                latents,
+            )
         bs = batch_size * num_images_per_prompt
 
         # 6. Get Guidance Scale Embedding

examples/community/stable_diffusion_controlnet_img2img.py

@@ -802,15 +802,16 @@ def __call__(
         latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
 
         # 6. Prepare latent variables
-        latents = self.prepare_latents(
-            image,
-            latent_timestep,
-            batch_size,
-            num_images_per_prompt,
-            prompt_embeds.dtype,
-            device,
-            generator,
-        )
+        if latents is None:
+            latents = self.prepare_latents(
+                image,
+                latent_timestep,
+                batch_size,
+                num_images_per_prompt,
+                prompt_embeds.dtype,
+                device,
+                generator,
+            )
 
         # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
         extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)

examples/community/stable_diffusion_controlnet_inpaint_img2img.py

@@ -907,15 +907,16 @@ def __call__(
         latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
 
         # 6. Prepare latent variables
-        latents = self.prepare_latents(
-            image,
-            latent_timestep,
-            batch_size,
-            num_images_per_prompt,
-            prompt_embeds.dtype,
-            device,
-            generator,
-        )
+        if latents is None:
+            latents = self.prepare_latents(
+                image,
+                latent_timestep,
+                batch_size,
+                num_images_per_prompt,
+                prompt_embeds.dtype,
+                device,
+                generator,
+            )
 
         mask_image_latents = self.prepare_mask_latents(
             mask_image,

src/diffusers/pipelines/controlnet/pipeline_controlnet_img2img.py

@@ -1169,15 +1169,16 @@ def __call__(
         self._num_timesteps = len(timesteps)
 
         # 6. Prepare latent variables
-        latents = self.prepare_latents(
-            image,
-            latent_timestep,
-            batch_size,
-            num_images_per_prompt,
-            prompt_embeds.dtype,
-            device,
-            generator,
-        )
+        if latents is None:
+            latents = self.prepare_latents(
+                image,
+                latent_timestep,
+                batch_size,
+                num_images_per_prompt,
+                prompt_embeds.dtype,
+                device,
+                generator,
+            )
 
         # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
         extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)

src/diffusers/pipelines/controlnet/pipeline_controlnet_sd_xl_img2img.py

@@ -1429,16 +1429,17 @@ def __call__(
         self._num_timesteps = len(timesteps)
 
         # 6. Prepare latent variables
-        latents = self.prepare_latents(
-            image,
-            latent_timestep,
-            batch_size,
-            num_images_per_prompt,
-            prompt_embeds.dtype,
-            device,
-            generator,
-            True,
-        )
+        if latents is None:
+            latents = self.prepare_latents(
+                image,
+                latent_timestep,
+                batch_size,
+                num_images_per_prompt,
+                prompt_embeds.dtype,
+                device,
+                generator,
+                True,
+            )
 
         # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
         extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)

src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.py

@@ -872,9 +872,10 @@ def __call__(
             else self.scheduler.config.original_inference_steps
         )
         latent_timestep = timesteps[:1]
-        latents = self.prepare_latents(
-            image, latent_timestep, batch_size, num_images_per_prompt, prompt_embeds.dtype, device, generator
-        )
+        if latents is None:
+            latents = self.prepare_latents(
+                image, latent_timestep, batch_size, num_images_per_prompt, prompt_embeds.dtype, device, generator
+            )
         bs = batch_size * num_images_per_prompt
 
         # 6. Get Guidance Scale Embedding

src/diffusers/pipelines/shap_e/pipeline_shap_e_img2img.py

@@ -239,15 +239,15 @@ def __call__(
 
         num_embeddings = self.prior.config.num_embeddings
         embedding_dim = self.prior.config.embedding_dim
-
-        latents = self.prepare_latents(
-            (batch_size, num_embeddings * embedding_dim),
-            image_embeds.dtype,
-            device,
-            generator,
-            latents,
-            self.scheduler,
-        )
+        if latents is None:
+            latents = self.prepare_latents(
+                (batch_size, num_embeddings * embedding_dim),
+                image_embeds.dtype,
+                device,
+                generator,
+                latents,
+                self.scheduler,
+            )
 
         # YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
         latents = latents.reshape(latents.shape[0], num_embeddings, embedding_dim)

src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip_img2img.py

@@ -786,16 +786,17 @@ def __call__(
 
         # 6. Prepare latent variables
         num_channels_latents = self.unet.config.in_channels
-        latents = self.prepare_latents(
-            batch_size=batch_size,
-            num_channels_latents=num_channels_latents,
-            height=height,
-            width=width,
-            dtype=prompt_embeds.dtype,
-            device=device,
-            generator=generator,
-            latents=latents,
-        )
+        if latents is None:
+            latents = self.prepare_latents(
+                batch_size=batch_size,
+                num_channels_latents=num_channels_latents,
+                height=height,
+                width=width,
+                dtype=prompt_embeds.dtype,
+                device=device,
+                generator=generator,
+                latents=latents,
+            )
 
         # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
         extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)

src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_img2img.py

@@ -1247,17 +1247,19 @@ def denoising_value_valid(dnv):
         latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
 
         add_noise = True if self.denoising_start is None else False
+
         # 6. Prepare latent variables
-        latents = self.prepare_latents(
-            image,
-            latent_timestep,
-            batch_size,
-            num_images_per_prompt,
-            prompt_embeds.dtype,
-            device,
-            generator,
-            add_noise,
-        )
+        if latents is None:
+            latents = self.prepare_latents(
+                image,
+                latent_timestep,
+                batch_size,
+                num_images_per_prompt,
+                prompt_embeds.dtype,
+                device,
+                generator,
+                add_noise,
+            )
         # 7. Prepare extra step kwargs.
         extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)