Check for latents, before calling prepare_latents - sdxlImg2Img (#7582)

* Check for latents, before calling prepare_latents - sdxlImg2Img

* Added latents check for all the img2img pipeline

* Fixed silly mistake while checking latents as None

examples/community/clip_guided_stable_diffusion_img2img.py

@@ -359,8 +359,15 @@ class CLIPGuidedStableDiffusion(DiffusionPipeline, StableDiffusionMixin):
 
         # Preprocess image
         image = preprocess(image, width, height)
+        if latents is None:
             latents = self.prepare_latents(
-            image, latent_timestep, batch_size, num_images_per_prompt, text_embeddings.dtype, self.device, generator
+                image,
+                latent_timestep,
+                batch_size,
+                num_images_per_prompt,
+                text_embeddings.dtype,
+                self.device,
+                generator,
             )
 
         if clip_guidance_scale > 0:

examples/community/latent_consistency_img2img.py

@@ -335,6 +335,7 @@ class LatentConsistencyModelImg2ImgPipeline(DiffusionPipeline):
 
         # 5. Prepare latent variable
         num_channels_latents = self.unet.config.in_channels
+        if latents is None:
             latents = self.prepare_latents(
                 image,
                 latent_timestep,

examples/community/stable_diffusion_controlnet_img2img.py

@@ -802,6 +802,7 @@ class StableDiffusionControlNetImg2ImgPipeline(DiffusionPipeline, StableDiffusio
         latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
 
         # 6. Prepare latent variables
+        if latents is None:
             latents = self.prepare_latents(
                 image,
                 latent_timestep,

examples/community/stable_diffusion_controlnet_inpaint_img2img.py

@@ -907,6 +907,7 @@ class StableDiffusionControlNetInpaintImg2ImgPipeline(DiffusionPipeline, StableD
         latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
 
         # 6. Prepare latent variables
+        if latents is None:
             latents = self.prepare_latents(
                 image,
                 latent_timestep,

src/diffusers/pipelines/controlnet/pipeline_controlnet_img2img.py

@@ -1169,6 +1169,7 @@ class StableDiffusionControlNetImg2ImgPipeline(
         self._num_timesteps = len(timesteps)
 
         # 6. Prepare latent variables
+        if latents is None:
             latents = self.prepare_latents(
                 image,
                 latent_timestep,

src/diffusers/pipelines/controlnet/pipeline_controlnet_sd_xl_img2img.py

@@ -1429,6 +1429,7 @@ class StableDiffusionXLControlNetImg2ImgPipeline(
         self._num_timesteps = len(timesteps)
 
         # 6. Prepare latent variables
+        if latents is None:
             latents = self.prepare_latents(
                 image,
                 latent_timestep,

src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.py

@@ -872,6 +872,7 @@ class LatentConsistencyModelImg2ImgPipeline(
             else self.scheduler.config.original_inference_steps
         )
         latent_timestep = timesteps[:1]
+        if latents is None:
             latents = self.prepare_latents(
                 image, latent_timestep, batch_size, num_images_per_prompt, prompt_embeds.dtype, device, generator
             )

src/diffusers/pipelines/shap_e/pipeline_shap_e_img2img.py

@@ -239,7 +239,7 @@ class ShapEImg2ImgPipeline(DiffusionPipeline):
 
         num_embeddings = self.prior.config.num_embeddings
         embedding_dim = self.prior.config.embedding_dim
-
+        if latents is None:
             latents = self.prepare_latents(
                 (batch_size, num_embeddings * embedding_dim),
                 image_embeds.dtype,

src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip_img2img.py

@@ -786,6 +786,7 @@ class StableUnCLIPImg2ImgPipeline(
 
         # 6. Prepare latent variables
         num_channels_latents = self.unet.config.in_channels
+        if latents is None:
             latents = self.prepare_latents(
                 batch_size=batch_size,
                 num_channels_latents=num_channels_latents,

src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_img2img.py

@@ -1247,7 +1247,9 @@ class StableDiffusionXLImg2ImgPipeline(
         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
+        if latents is None:
             latents = self.prepare_latents(
                 image,
                 latent_timestep,