feature: facilitate references to previous image generation

README.md

@@ -525,7 +525,7 @@ would be uncorrelated to the rest of the surrounding image.  It created terrible
      - https://github.com/LambdaLabsML/lambda-diffusers
      - https://www.reddit.com/r/MachineLearning/comments/x6k5bm/n_stable_diffusion_image_variations_released/
  - Image Editing
-   - outpainting
+   - ✅outpainting
      - https://github.com/parlance-zz/g-diffuser-bot/search?q=noise&type=issues
      - lama cleaner
    - ✅ inpainting
@@ -598,7 +598,9 @@ would be uncorrelated to the rest of the surrounding image.  It created terrible
    - https://github.com/vicgalle/stable-diffusion-aesthetic-gradients
  - Training
    - Finetuning "dreambooth" style
-   - Textual Inversion
+   - [Textual Inversion](https://arxiv.org/abs/2208.01618)
+     - [Fast Textual Inversion](https://github.com/peterwilli/sd-leap-booster) 
+   - [Low-rank Adaptation for Fast Text-to-Image Diffusion Fine-tuning (LORA)](https://github.com/cloneofsimo/lora)
    - Performance Improvements
     - [ColoassalAI](https://github.com/hpcaitech/ColossalAI/tree/main/examples/images/diffusion) - almost got it working but it's not easy enough to install to merit inclusion in imaginairy. We should check back in on this.
     - Xformers

imaginairy/api.py

@@ -6,7 +6,7 @@ import numpy as np
 import torch
 import torch.nn
 from einops import rearrange, repeat
-from PIL import Image, ImageDraw, ImageFilter, ImageOps
+from PIL import Image, ImageDraw, ImageOps
 from pytorch_lightning import seed_everything
 
 from imaginairy.enhancers.clip_masking import get_img_mask
@@ -51,6 +51,9 @@ if IMAGINAIRY_SAFETY_MODE in {"disabled", "classify"}:
 elif IMAGINAIRY_SAFETY_MODE == "filter":
     IMAGINAIRY_SAFETY_MODE = SafetyMode.STRICT
 
+# we put this in the global scope so it can be used in the interactive shell
+_most_recent_result = None
+
 
 def imagine_image_files(
     prompts,
@@ -88,6 +91,9 @@ def imagine_image_files(
         add_caption=print_caption,
     ):
         prompt = result.prompt
+        if prompt.is_intermediate:
+            # we don't save intermediate images
+            continue
         img_str = ""
         if prompt.init_image:
             img_str = f"_img2img-{prompt.init_image_strength}"
@@ -103,7 +109,7 @@ def imagine_image_files(
                 subpath, f"{basefilename}_[{image_type}].{output_file_extension}"
             )
             result.save(filepath, image_type=image_type)
-            logger.info(f"🖼  [{image_type}] saved to: {filepath}")
+            logger.info(f"[{image_type}] saved to: {filepath}")
             if image_type == return_filename_type:
                 result_filenames.append(filepath)
         if make_comparison_gif and prompt.init_image:
@@ -134,6 +140,7 @@ def imagine(
     half_mode=None,
     add_caption=False,
 ):
+    global _most_recent_result  # noqa
     latent_channels = 4
     downsampling_factor = 8
     batch_size = 1
@@ -153,6 +160,18 @@ def imagine(
         precision
     ), fix_torch_nn_layer_norm(), fix_torch_group_norm():
         for i, prompt in enumerate(prompts):
+            # handle prompt pulling in previous values
+            if isinstance(prompt.init_image, str) and prompt.init_image.startswith(
+                "*prev"
+            ):
+                _, img_type = prompt.init_image.strip("*").split(".")
+                prompt.init_image = _most_recent_result.images[img_type]
+            if isinstance(prompt.mask_image, str) and prompt.mask_image.startswith(
+                "*prev"
+            ):
+                _, img_type = prompt.mask_image.strip("*").split(".")
+                prompt.mask_image = _most_recent_result.images[img_type]
+
             logger.info(
                 f"Generating 🖼  {i + 1}/{num_prompts}: {prompt.prompt_description()}"
             )
@@ -419,9 +438,10 @@ def imagine(
                     x_sample_8_orig = x_sample.astype(np.uint8)
                     img = Image.fromarray(x_sample_8_orig)
                     if mask_image_orig and init_image:
-                        mask_final = mask_image_orig.filter(
-                            ImageFilter.GaussianBlur(radius=3)
-                        )
+                        # mask_final = mask_image_orig.filter(
+                        #     ImageFilter.GaussianBlur(radius=3)
+                        # )
+                        mask_final = mask_image_orig.copy()
                         log_img(mask_final, "reconstituting mask")
                         mask_final = ImageOps.invert(mask_final)
                         img = Image.composite(img, init_image, mask_final)
@@ -471,9 +491,9 @@ def imagine(
                                 starting_image.size,
                                 resample=Image.Resampling.LANCZOS,
                             )
-                            mask_for_orig_size = mask_for_orig_size.filter(
-                                ImageFilter.GaussianBlur(radius=5)
-                            )
+                            # mask_for_orig_size = mask_for_orig_size.filter(
+                            #     ImageFilter.GaussianBlur(radius=5)
+                            # )
                             log_img(mask_for_orig_size, "mask for original image size")
 
                             rebuilt_orig_img = Image.composite(
@@ -495,6 +515,7 @@ def imagine(
                         depth_image=depth_image_display,
                         timings=lc.get_timings(),
                     )
+                    _most_recent_result = result
                     logger.info(f"Image Generated. Timings: {result.timings_str()}")
                     yield result
 

imaginairy/schema.py

@@ -115,6 +115,7 @@ class ImaginePrompt:
         tile_mode="",
         model=config.DEFAULT_MODEL,
         model_config_path=None,
+        is_intermediate=False,
     ):
 
         self.prompts = self.process_prompt_input(prompt)
@@ -128,9 +129,11 @@ class ImaginePrompt:
             assert tile_mode in ("", "x", "y", "xy")
 
         if isinstance(init_image, str):
+            if not init_image.startswith("*prev."):
                 init_image = LazyLoadingImage(filepath=init_image)
 
         if isinstance(mask_image, str):
+            if not init_image.startswith("*prev."):
                 mask_image = LazyLoadingImage(filepath=mask_image)
 
         if mask_image is not None and mask_prompt is not None:
@@ -159,6 +162,8 @@ class ImaginePrompt:
         self.tile_mode = tile_mode
         self.model = model
         self.model_config_path = model_config_path
+        # we don't want to save intermediate images
+        self.is_intermediate = is_intermediate
 
         if self.height is None or self.width is None or self.steps is None:
             SamplerCls = SAMPLER_LOOKUP[self.sampler_type]