TST Use different diffusion model for testing (#2345)

So far, tests are using hf-internal-testing/tiny-stable-diffusion-torch
for testing diffusion models. However, this model has some issues:

- still uses pickle (.bin) instead of safetensors
- there is a FutureWarning because of the config

Now, using hf-internal-testing/tiny-sd-pipe instead which doesn't have
those issues.

tests/test_helpers.py

@@ -179,7 +179,7 @@ def test_scaling_set_to_zero(self, tokenizer):
         assert torch.allclose(logits_base_model, logits_lora_model)
 
     def test_diffusers_pipeline(self):
-        model_id = "hf-internal-testing/tiny-stable-diffusion-torch"
+        model_id = "hf-internal-testing/tiny-sd-pipe"
         pipeline = StableDiffusionPipeline.from_pretrained(model_id)
 
         text_encoder_kwargs = {

tests/test_stablediffusion.py

@@ -36,7 +36,7 @@
 from .testing_utils import temp_seed
 
 
-PEFT_DIFFUSERS_SD_MODELS_TO_TEST = ["hf-internal-testing/tiny-stable-diffusion-torch"]
+PEFT_DIFFUSERS_SD_MODELS_TO_TEST = ["hf-internal-testing/tiny-sd-pipe"]
 CONFIG_TESTING_KWARGS = (
     {
         "text_encoder": {

tests/test_tuners_utils.py

@@ -331,7 +331,7 @@ def test_maybe_include_all_linear_layers_internals(self, initial_target_modules,
             assert new_config.target_modules == expected_target_modules
 
     def test_maybe_include_all_linear_layers_diffusion(self):
-        model_id = "hf-internal-testing/tiny-stable-diffusion-torch"
+        model_id = "hf-internal-testing/tiny-sd-pipe"
         model = StableDiffusionPipeline.from_pretrained(model_id)
         config = LoraConfig(base_model_name_or_path=model_id, target_modules="all-linear")