[Community] PromptDiffusion Pipeline (huggingface#6752)

* Create promptdiffusioncontrolnet.py

* Update __init__.py

Added PromptDiffusionControlNetModel

* Update __init__.py

Added PromptDiffusionControlNetModel

* Update promptdiffusioncontrolnet.py

* Create pipeline_prompt_diffusion.py

Added Prompt Diffusion pipeline.

* Create convert_original_promptdiffusion_to_diffusers.py

* Update convert_from_ckpt.py

Added download_promptdiffusion_from_original_ckpt, convert_promptdiffusion_checkpoint

* Update promptdiffusioncontrolnet.py

* Update pipeline_prompt_diffusion.py

* Update README.md

* Update pipeline_prompt_diffusion.py

* Delete src/diffusers/models/promptdiffusioncontrolnet.py

* Update __init__.py

* Update __init__.py

* Delete scripts/convert_original_promptdiffusion_to_diffusers.py

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* Update README.md

* Delete examples/community/pipeline_prompt_diffusion.py

* Create README.md

* Create promptdiffusioncontrolnet.py

* Create convert_original_promptdiffusion_to_diffusers.py

* Create pipeline_prompt_diffusion.py

* Update README.md

* Update pipeline_prompt_diffusion.py

* Update README.md

* Update pipeline_prompt_diffusion.py

* Update convert_original_promptdiffusion_to_diffusers.py

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---------

Co-authored-by: Sayak Paul <[email protected]>

Author: iczaw

examples/research_projects/promptdiffusion/README.md

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+# PromptDiffusion Pipeline
+
+From the project [page](https://zhendong-wang.github.io/prompt-diffusion.github.io/)
+
+"With a prompt consisting of a task-specific example pair of images and text guidance, and a new query image, Prompt Diffusion can comprehend the desired task and generate the corresponding output image on both seen (trained) and unseen (new) task types."
+
+For any usage questions, please refer to the [paper](https://arxiv.org/abs/2305.01115).
+
+Prepare models by converting them from the [checkpoint](https://huggingface.co/zhendongw/prompt-diffusion)
+
+To convert the controlnet, use cldm_v15.yaml from the [repository](https://github.com/Zhendong-Wang/Prompt-Diffusion/tree/main/models/):
+
+```bash
+python convert_original_promptdiffusion_to_diffusers.py --checkpoint_path path-to-network-step04999.ckpt --original_config_file path-to-cldm_v15.yaml --dump_path path-to-output-directory
+```
+
+To learn about how to convert the fine-tuned stable diffusion model, see the [Load different Stable Diffusion formats guide](https://huggingface.co/docs/diffusers/main/en/using-diffusers/other-formats).
+
+
+```py
+import torch
+from diffusers import UniPCMultistepScheduler
+from diffusers.utils import load_image
+from promptdiffusioncontrolnet import PromptDiffusionControlNetModel
+from pipeline_prompt_diffusion import PromptDiffusionPipeline
+
+
+from PIL import ImageOps
+
+image_a = ImageOps.invert(load_image("https://github.com/Zhendong-Wang/Prompt-Diffusion/blob/main/images_to_try/house_line.png?raw=true"))
+
+image_b = load_image("https://github.com/Zhendong-Wang/Prompt-Diffusion/blob/main/images_to_try/house.png?raw=true")
+query = ImageOps.invert(load_image("https://github.com/Zhendong-Wang/Prompt-Diffusion/blob/main/images_to_try/new_01.png?raw=true"))
+
+# load prompt diffusion controlnet and prompt diffusion
+
+controlnet = PromptDiffusionControlNetModel.from_pretrained("iczaw/prompt-diffusion-diffusers", subfolder="controlnet", torch_dtype=torch.float16)
+model_id = "path-to-model"
+pipe = PromptDiffusionPipeline.from_pretrained("iczaw/prompt-diffusion-diffusers", subfolder="base", controlnet=controlnet, torch_dtype=torch.float16, variant="fp16")
+
+# speed up diffusion process with faster scheduler and memory optimization
+pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
+# remove following line if xformers is not installed
+pipe.enable_xformers_memory_efficient_attention()
+pipe.enable_model_cpu_offload()
+# generate image
+generator = torch.manual_seed(0)
+image = pipe("a tortoise", num_inference_steps=20, generator=generator, image_pair=[image_a,image_b], image=query).images[0]
+
+```