Delete MODELS/controlnet-canny-sdxl-1.0

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MODELS/controlnet-canny-sdxl-1.0/README.md

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----
-license: openrail++
-base_model: runwayml/stable-diffusion-v1-5
-tags:
-- stable-diffusion-xl
-- stable-diffusion-xl-diffusers
-- text-to-image
-- diffusers
-inference: false
----
-    
-# SDXL-controlnet: Canny
-
-These are controlnet weights trained on stabilityai/stable-diffusion-xl-base-1.0 with canny conditioning. You can find some example images in the following. 
-
-prompt: a couple watching a romantic sunset, 4k photo
-![images_0)](./out_couple.png)
-
-prompt: ultrarealistic shot of a furry blue bird
-![images_1)](./out_bird.png)
-
-prompt: a woman, close up, detailed, beautiful, street photography, photorealistic, detailed, Kodak ektar 100, natural, candid shot
-![images_2)](./out_women.png)
-
-prompt: Cinematic, neoclassical table in the living room, cinematic, contour, lighting, highly detailed, winter, golden hour
-![images_3)](./out_room.png)
-
-prompt: a tornado hitting grass field, 1980's film grain. overcast, muted colors.
-![images_0)](./out_tornado.png)
-
-## Usage
-
-Make sure to first install the libraries:
-
-```bash
-pip install accelerate transformers safetensors opencv-python diffusers
-```
-
-And then we're ready to go:
-
-```python
-from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
-from diffusers.utils import load_image
-from PIL import Image
-import torch
-import numpy as np
-import cv2
-
-prompt = "aerial view, a futuristic research complex in a bright foggy jungle, hard lighting"
-negative_prompt = 'low quality, bad quality, sketches'
-
-image = load_image("https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/hf-logo.png")
-
-controlnet_conditioning_scale = 0.5  # recommended for good generalization
-
-controlnet = ControlNetModel.from_pretrained(
-    "diffusers/controlnet-canny-sdxl-1.0",
-    torch_dtype=torch.float16
-)
-vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
-pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
-    "stabilityai/stable-diffusion-xl-base-1.0",
-    controlnet=controlnet,
-    vae=vae,
-    torch_dtype=torch.float16,
-)
-pipe.enable_model_cpu_offload()
-
-image = np.array(image)
-image = cv2.Canny(image, 100, 200)
-image = image[:, :, None]
-image = np.concatenate([image, image, image], axis=2)
-image = Image.fromarray(image)
-
-images = pipe(
-    prompt, negative_prompt=negative_prompt, image=image, controlnet_conditioning_scale=controlnet_conditioning_scale,
-    ).images
-
-images[0].save(f"hug_lab.png")
-```
-
-![images_10)](./out_hug_lab_7.png)
-
-To more details, check out the official documentation of [`StableDiffusionXLControlNetPipeline`](https://huggingface.co/docs/diffusers/main/en/api/pipelines/controlnet_sdxl).
-
-### Training
-
-Our training script was built on top of the official training script that we provide [here](https://github.com/huggingface/diffusers/blob/main/examples/controlnet/README_sdxl.md). 
-
-#### Training data
-This checkpoint was first trained for 20,000 steps on laion 6a resized to a max minimum dimension of 384. 
-It was then further trained for 20,000 steps on laion 6a resized to a max minimum dimension of 1024 and 
-then filtered to contain only minimum 1024 images. We found the further high resolution finetuning was 
-necessary for image quality.
-
-#### Compute
-one 8xA100 machine
-
-#### Batch size
-Data parallel with a single gpu batch size of 8 for a total batch size of 64.
-
-#### Hyper Parameters
-Constant learning rate of 1e-4 scaled by batch size for total learning rate of 64e-4
-
-#### Mixed precision
-fp16

MODELS/controlnet-canny-sdxl-1.0/config.json

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-{
-  "_class_name": "ControlNetModel",
-  "_diffusers_version": "0.20.0.dev0",
-  "_name_or_path": "../controlnet-1-0-canny/checkpoint-20000/controlnet",
-  "act_fn": "silu",
-  "addition_embed_type": "text_time",
-  "addition_embed_type_num_heads": 64,
-  "addition_time_embed_dim": 256,
-  "attention_head_dim": [
-    5,
-    10,
-    20
-  ],
-  "block_out_channels": [
-    320,
-    640,
-    1280
-  ],
-  "class_embed_type": null,
-  "conditioning_channels": 3,
-  "conditioning_embedding_out_channels": [
-    16,
-    32,
-    96,
-    256
-  ],
-  "controlnet_conditioning_channel_order": "rgb",
-  "cross_attention_dim": 2048,
-  "down_block_types": [
-    "DownBlock2D",
-    "CrossAttnDownBlock2D",
-    "CrossAttnDownBlock2D"
-  ],
-  "downsample_padding": 1,
-  "encoder_hid_dim": null,
-  "encoder_hid_dim_type": null,
-  "flip_sin_to_cos": true,
-  "freq_shift": 0,
-  "global_pool_conditions": false,
-  "in_channels": 4,
-  "layers_per_block": 2,
-  "mid_block_scale_factor": 1,
-  "norm_eps": 1e-05,
-  "norm_num_groups": 32,
-  "num_attention_heads": null,
-  "num_class_embeds": null,
-  "only_cross_attention": false,
-  "projection_class_embeddings_input_dim": 2816,
-  "resnet_time_scale_shift": "default",
-  "transformer_layers_per_block": [
-    1,
-    2,
-    10
-  ],
-  "upcast_attention": null,
-  "use_linear_projection": true
-}

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