[ckpt loader] Allow loading the Inpaint and Img2Img pipelines, while loading a ckpt model (#2705)

* [ckpt loader] Allow loading the Inpaint and Img2Img pipelines, while loading a ckpt model

* Address review comment from PR

* PyLint formatting

* Some more pylint fixes, unrelated to our change

* Another pylint fix

* Styling fix

src/diffusers/pipelines/stable_diffusion/convert_from_ckpt.py

@@ -45,6 +45,8 @@ from diffusers import (
     PNDMScheduler,
     PriorTransformer,
     StableDiffusionControlNetPipeline,
+    StableDiffusionImg2ImgPipeline,
+    StableDiffusionInpaintPipeline,
     StableDiffusionPipeline,
     StableUnCLIPImg2ImgPipeline,
     StableUnCLIPPipeline,
@@ -979,6 +981,7 @@ def download_from_original_stable_diffusion_ckpt(
     image_size: int = 512,
     prediction_type: str = None,
     model_type: str = None,
+    is_img2img: bool = False,
     extract_ema: bool = False,
     scheduler_type: str = "pndm",
     num_in_channels: Optional[int] = None,
@@ -1018,6 +1021,8 @@ def download_from_original_stable_diffusion_ckpt(
         model_type (`str`, *optional*, defaults to `None`):
             The pipeline type. `None` to automatically infer, or one of `["FrozenOpenCLIPEmbedder",
             "FrozenCLIPEmbedder", "PaintByExample"]`.
+        is_img2img (`bool`, *optional*, defaults to `False`):
+            Whether the model should be loaded as an img2img pipeline.
         extract_ema (`bool`, *optional*, defaults to `False`): Only relevant for
             checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights or not. Defaults to
             `False`. Pass `True` to extract the EMA weights. EMA weights usually yield higher quality images for
@@ -1193,16 +1198,44 @@ def download_from_original_stable_diffusion_ckpt(
                     requires_safety_checker=False,
                 )
             else:
-                pipe = StableDiffusionPipeline(
-                    vae=vae,
-                    text_encoder=text_model,
-                    tokenizer=tokenizer,
-                    unet=unet,
-                    scheduler=scheduler,
-                    safety_checker=None,
-                    feature_extractor=None,
-                    requires_safety_checker=False,
-                )
+                if (
+                    hasattr(original_config, "model")
+                    and hasattr(original_config.model, "target")
+                    and "LatentInpaintDiffusion" in original_config.model.target
+                ):
+                    pipe = StableDiffusionInpaintPipeline(
+                        vae=vae,
+                        text_encoder=text_model,
+                        tokenizer=tokenizer,
+                        unet=unet,
+                        scheduler=scheduler,
+                        safety_checker=None,
+                        feature_extractor=None,
+                        requires_safety_checker=False,
+                    )
+                else:
+                    if is_img2img:
+                        pipe = StableDiffusionImg2ImgPipeline(
+                            vae=vae,
+                            text_encoder=text_model,
+                            tokenizer=tokenizer,
+                            unet=unet,
+                            scheduler=scheduler,
+                            safety_checker=None,
+                            feature_extractor=None,
+                            requires_safety_checker=False,
+                        )
+                    else:
+                        pipe = StableDiffusionPipeline(
+                            vae=vae,
+                            text_encoder=text_model,
+                            tokenizer=tokenizer,
+                            unet=unet,
+                            scheduler=scheduler,
+                            safety_checker=None,
+                            feature_extractor=None,
+                            requires_safety_checker=False,
+                        )
         else:
             image_normalizer, image_noising_scheduler = stable_unclip_image_noising_components(
                 original_config, clip_stats_path=clip_stats_path, device=device
@@ -1293,15 +1326,41 @@ def download_from_original_stable_diffusion_ckpt(
                 feature_extractor=feature_extractor,
             )
         else:
-            pipe = StableDiffusionPipeline(
-                vae=vae,
-                text_encoder=text_model,
-                tokenizer=tokenizer,
-                unet=unet,
-                scheduler=scheduler,
-                safety_checker=safety_checker,
-                feature_extractor=feature_extractor,
-            )
+            if (
+                hasattr(original_config, "model")
+                and hasattr(original_config.model, "target")
+                and "LatentInpaintDiffusion" in original_config.model.target
+            ):
+                pipe = StableDiffusionInpaintPipeline(
+                    vae=vae,
+                    text_encoder=text_model,
+                    tokenizer=tokenizer,
+                    unet=unet,
+                    scheduler=scheduler,
+                    safety_checker=safety_checker,
+                    feature_extractor=feature_extractor,
+                )
+            else:
+                if is_img2img:
+                    pipe = StableDiffusionImg2ImgPipeline(
+                        vae=vae,
+                        text_encoder=text_model,
+                        tokenizer=tokenizer,
+                        unet=unet,
+                        scheduler=scheduler,
+                        safety_checker=safety_checker,
+                        feature_extractor=feature_extractor,
+                    )
+                else:
+                    pipe = StableDiffusionPipeline(
+                        vae=vae,
+                        text_encoder=text_model,
+                        tokenizer=tokenizer,
+                        unet=unet,
+                        scheduler=scheduler,
+                        safety_checker=safety_checker,
+                        feature_extractor=feature_extractor,
+                    )
     else:
         text_config = create_ldm_bert_config(original_config)
         text_model = convert_ldm_bert_checkpoint(checkpoint, text_config)